gec_bert_data_prep.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "71a8d166",
   "metadata": {},
   "source": [
    "# Data Prep for GEC Model\n",
    "This notebook covers second of GEC specific EDA as well as feature engineering. We export data in formats needed for training various versions of the GEC model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57cd0e25-de95-45e2-834e-1980239e641c",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Imports and Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dfbed645-ead7-4bb6-88cf-7be152699571",
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "import glob\n",
    "import os\n",
    "import random\n",
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import csv\n",
    "import json\n",
    "from IPython.core.display import HTML\n",
    "import matplotlib.pyplot as plt\n",
    "import collections\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "import pickle\n",
    "\n",
    "from string2string.alignment import NeedlemanWunsch\n",
    "from string2string.misc.plotting_functions import plot_pairwise_alignment\n",
    "\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a4a825c1-8b12-44d7-ae29-6061a9ab3908",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Collecting en-core-web-sm==3.7.1\n",
      "  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.7.1/en_core_web_sm-3.7.1-py3-none-any.whl (12.8 MB)\n",
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      "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
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      "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
      "You can now load the package via spacy.load('es_core_news_sm')\n",
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     ]
    }
   ],
   "source": [
    "# Spacy setup. Need to be done once.\n",
    "!pip -q install -U spacy\n",
    "!pip install -U spacy-lookups-data\n",
    "!python -m spacy download en_core_web_sm\n",
    "!python -m spacy download es_core_news_sm\n",
    "!pip install plotly\n",
    "!pip install string2string\n",
    "## !pip3 install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9af4bd3b-89dc-4a4a-9067-eb5471ab88b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.7.2\n",
      "1.4.2\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "from spacy.lang.es.examples import sentences\n",
    "from spacy import displacy\n",
    "\n",
    "print(spacy.__version__)\n",
    "print(pd.__version__)\n",
    "\n",
    "# from nltk.parse import stanford, corenlp\n",
    "NLP_ES = spacy.load(\"es_core_news_sm\")\n",
    "NLP_EN = spacy.load(\"en_core_web_sm\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "46f984c1-97fc-4ce7-b455-619e9bcc1add",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Helper methods\n",
    "def add_neighbors_of(idx, cur_order, words, id_to_head, head_to_ids):\n",
    "    if idx is None:\n",
    "        # Handle edge case where we do not have an index to work with.\n",
    "        return\n",
    "    if cur_order > 3:\n",
    "        # We are only interesting in the first three orders of dependency:\n",
    "        #  1 - self\n",
    "        #  2 - immediate neighbors and\n",
    "        #  3 - 1-skip neighbors\n",
    "        return\n",
    "    if f'{idx}' in words:\n",
    "        # Node has been processed already\n",
    "        return\n",
    "    if idx not in id_to_head:\n",
    "        # The current index does not identify a word in the corrected sentence.\n",
    "        # This can happen as the correction index holds indices into string with both\n",
    "        # versions (corrected and uncorrected) appended.\n",
    "        return\n",
    "\n",
    "    # Add current index and its order to the dict\n",
    "    words[f'{idx}'] = cur_order\n",
    "\n",
    "    # Process head of current index\n",
    "    upstream = id_to_head[idx]\n",
    "    add_neighbors_of(upstream, cur_order + 1, words, id_to_head, head_to_ids)\n",
    "\n",
    "    # Process words that are immediately downstream of current index\n",
    "    if idx in head_to_ids:\n",
    "        downstream = head_to_ids[idx]\n",
    "        [add_neighbors_of(down_idx, cur_order + 1, words, id_to_head, head_to_ids) for down_idx in downstream]\n",
    "\n",
    "\n",
    "def dep_parsing_tree(nlp, text, correction_index, verbosity=0):\n",
    "    if len(correction_index) == 0:\n",
    "        return dict()\n",
    "\n",
    "    doc = nlp(text)\n",
    "    js_str = doc.to_json()\n",
    "    id_to_head = dict()\n",
    "    head_to_ids = dict()\n",
    "    for token in js_str[\"tokens\"]:\n",
    "        id = token[\"id\"]\n",
    "        head = token[\"head\"]\n",
    "        if token['pos'] != 'PUNCT':\n",
    "            id_to_head[id] = head\n",
    "            if head in head_to_ids:\n",
    "                head_to_ids[head].append(id)\n",
    "            else:\n",
    "                head_to_ids[head] = [id]\n",
    "    if verbosity > 0:\n",
    "        print(f'id -> head: {id_to_head}')\n",
    "        print(f'head -> ids: {head_to_ids}')\n",
    "\n",
    "    pred_parse_order = dict()\n",
    "    for cor_idx in correction_index:\n",
    "        add_neighbors_of(cor_idx, 1, pred_parse_order, id_to_head, head_to_ids)\n",
    "\n",
    "    pred_parse_order = dict(sorted(pred_parse_order.items()))\n",
    "    if verbosity > 0:\n",
    "        df_dep_tree = pd.DataFrame()\n",
    "        df_dep_tree['text'] = [token.text for token in doc]\n",
    "        df_dep_tree['lemma'] = [token.lemma_ for token in doc]\n",
    "        df_dep_tree['is_punctuation'] = [token.is_punct for token in doc]\n",
    "        df_dep_tree['is_space'] = [token.is_space for token in doc]\n",
    "        df_dep_tree['shape'] = [token.shape_ for token in doc]\n",
    "        df_dep_tree['part_of_speech'] = [token.pos_ for token in doc]\n",
    "        df_dep_tree['pos_tag'] = [token.tag_ for token in doc]\n",
    "        df_dep_tree['head'] = [token.head.text for token in doc]\n",
    "        df_dep_tree['dep'] = [token.dep_ for token in doc]\n",
    "        html = displacy.render(doc, style=\"dep\")\n",
    "        HTML(html)\n",
    "    return pred_parse_order\n",
    "\n",
    "def to_json_df(in_df):\n",
    "    df_proj = in_df[in_df.predicted_parsing_order.notnull()][['target', 'source', 'evidence_index', 'correction_index', 'error_type', 'predicted_parsing_order', 'origin']]\n",
    "    df_proj = df_proj.dropna()\n",
    "    print(f'Selected {df_proj.shape[0]} non-null rows from {in_df.shape[0]} rows. {in_df.shape[0] - df_proj.shape[0]} rows dropped.')\n",
    "    df_json = df_proj.copy(deep=True)\n",
    "    df_json['json'] = df_proj.apply(lambda x: x.to_json(), axis=1)\n",
    "    return df_json[['json']]\n",
    "\n",
    "def export_as_json(df, train_file, test_file, dev_file):\n",
    "    def write_out(df, filename):\n",
    "        with open(f'{args.PROCESSED_DATA_FOLDER}/{filename}', \"w+\") as file1:\n",
    "            for i, row in df.iterrows():\n",
    "                file1.write(f'{json.dumps(eval(row.json))}\\n')\n",
    "\n",
    "    json_df = to_json_df(df)\n",
    "\n",
    "    train_df, test_dev_df = train_test_split(json_df, test_size=0.2)\n",
    "    test_df, dev_df = train_test_split(test_dev_df, test_size=0.5)\n",
    "    # tt.to_csv(args.PROCESSED_DATA_FOLDER + \"/rows_as_json.csv\", index=False, header=False, quoting=csv.QUOTE_NONE, escapechar=\" \")\n",
    "    write_out(train_df, train_file)\n",
    "    write_out(test_df, test_file)\n",
    "    write_out(dev_df, dev_file)\n",
    "\n",
    "# Helper methods to convert to NER format\n",
    "def idx2vec(idx, length):\n",
    "    if isinstance(idx, list):\n",
    "        if len(idx) != 0:\n",
    "            idx_vec = [1 if i in idx else 0 for i in range(length)]\n",
    "        else:\n",
    "            idx_vec = [0] * length\n",
    "    elif isinstance(idx, dict):\n",
    "        idx_vec = [0] * length\n",
    "        for k, v in idx.items():\n",
    "            if int(k) < len(idx_vec):\n",
    "                idx_vec[int(k)] = v\n",
    "    return idx_vec\n",
    "\n",
    "def to_ner(csv_filename, pkl_filename):\n",
    "    sep_token = ['[MOD]']\n",
    "    data = []\n",
    "\n",
    "    with open(f'{args.PROCESSED_DATA_FOLDER}/{csv_filename}', 'r') as f:\n",
    "        for line in f:\n",
    "            data.append(json.loads(line))\n",
    "\n",
    "    # convert into ner data structure\n",
    "    def sent_to_ner_data(sentence, evidence_index, correction_index, error_type, parsing_order, ids=0, prefix=True):\n",
    "        sent_data = []\n",
    "        for j, token in enumerate(sentence):\n",
    "            new_tokens = token\n",
    "            if evidence_index:  # for dev and train data\n",
    "                if evidence_index[j]:\n",
    "                    type = args.CLASS_IDS[error_type]\n",
    "                    if prefix:\n",
    "                        if j == 0 or evidence_index[j - 1] == 0:\n",
    "                            cls = 'B-' + type\n",
    "                        else:\n",
    "                            cls = 'I-' + type\n",
    "                    else:\n",
    "                        cls = type\n",
    "                else:\n",
    "                    cls = 'O'\n",
    "                sent_data.append([\n",
    "                    ids,\n",
    "                    new_tokens,\n",
    "                    cls,\n",
    "                    args.CLASS_IDS[error_type],\n",
    "                    correction_index[j],\n",
    "                    parsing_order[j]\n",
    "                ])\n",
    "        return sent_data\n",
    "\n",
    "    new_data = []\n",
    "    for i, d in tqdm(enumerate(data)):\n",
    "        sentence = d['target'] + sep_token + d['source']\n",
    "        correction_index = idx2vec(d['correction_index'], len(sentence))\n",
    "        evidence_index = idx2vec(d['evidence_index'], len(sentence))\n",
    "        predicted_parsing_order = idx2vec(d['predicted_parsing_order'], len(sentence))\n",
    "        ner_d = sent_to_ner_data(sentence, evidence_index, correction_index, d['error_type'], predicted_parsing_order, ids=i)\n",
    "        new_data.extend(ner_d)\n",
    "\n",
    "    with open(f'{args.PROCESSED_DATA_FOLDER}/{pkl_filename}', 'wb') as f:\n",
    "        pickle.dump(new_data, f)\n",
    "    pd.DataFrame(new_data).to_csv(f'{args.PROCESSED_DATA_FOLDER}/tt.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "03002a95-85e1-421b-9af3-e5fe2387b17a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'article': 'a',\n",
       " 'gender agreement': 'ga',\n",
       " 'gender and number agreement': 'gna',\n",
       " 'number agreement': 'na'}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from utils import Config\n",
    "args = Config()\n",
    "args.CLASS_IDS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eacac58b-46de-4e07-8dc1-404ca0b125ec",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Load Data and Do Sanity Tests"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322d34f5-788c-4bf7-adfa-59b3301f9172",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Sanity on Full Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f7359767-b51a-4cef-99c1-5a06fb2acafb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load():\n",
    "    # Converters not working\n",
    "    filename = \"final.csv\"\n",
    "    filename = \"dataset_with_evident_word.csv\"\n",
    "    df = pd.read_csv(f'{args.PROCESSED_DATA_FOLDER}/{filename}', index_col=None, header=0)\n",
    "    if filename == \"dataset_with_evident_word.csv\":\n",
    "        df[\"error_type\"] = df.new_error_type\n",
    "        df['evidence_index'] = df.apply(lambda row: eval(row.final_evident_index), axis=1)\n",
    "    else:\n",
    "        df['evidence_index'] = np.empty((len(df), 0)).tolist()\n",
    "\n",
    "\n",
    "    # The source, target and correction_index cols are loaded as string version of lists. Convert them to actual lists.\n",
    "    df.source = df.apply(lambda row: eval(row.source), axis=1)\n",
    "    df.target = df.apply(lambda row: eval(row.target), axis=1)\n",
    "    df.correction_index = df.apply(lambda row: eval(row.correction_index), axis=1)\n",
    "\n",
    "    df['predicted_parsing_order'] = \"\"\n",
    "    df['origin'] = 'A'\n",
    "    return df\n",
    "\n",
    "df_raw = load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5f206ea4-a21e-4c63-9325-746c038e3847",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'paragraph_id', 'sentence_id', 'course', 'essay_new',\n",
       "       'annotated_sentence', 'full_error_tag', 'error_tag', 'correction',\n",
       "       'error', 'target', 'source', 'correction_index', 'new_error_type',\n",
       "       'final_evident_index', 'target_sentence', 'source_sentence',\n",
       "       'error_type', 'evidence_index', 'predicted_parsing_order', 'origin'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the columns\n",
    "df_raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ceae3d57-5b3c-4de2-a646-3613be1cc60f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>paragraph_id</th>\n",
       "      <th>sentence_id</th>\n",
       "      <th>course</th>\n",
       "      <th>essay_new</th>\n",
       "      <th>annotated_sentence</th>\n",
       "      <th>full_error_tag</th>\n",
       "      <th>error_tag</th>\n",
       "      <th>correction</th>\n",
       "      <th>error</th>\n",
       "      <th>...</th>\n",
       "      <th>source</th>\n",
       "      <th>correction_index</th>\n",
       "      <th>new_error_type</th>\n",
       "      <th>final_evident_index</th>\n",
       "      <th>target_sentence</th>\n",
       "      <th>source_sentence</th>\n",
       "      <th>error_type</th>\n",
       "      <th>evidence_index</th>\n",
       "      <th>predicted_parsing_order</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, paragraph_id, sentence_id, course, essay_new, annotated_sentence, full_error_tag, error_tag, correction, error, target, source, correction_index, new_error_type, final_evident_index, target_sentence, source_sentence, error_type, evidence_index, predicted_parsing_order, origin]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 21 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All rows should have an error_type\n",
    "df_raw[df_raw.error_type.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d7bfbfb8-ff8d-4266-ba4a-fa24f23072a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                 5212\n",
       "paragraph_id               5212\n",
       "sentence_id                5212\n",
       "course                     5212\n",
       "essay_new                  5212\n",
       "annotated_sentence         5212\n",
       "full_error_tag             5212\n",
       "error_tag                  5212\n",
       "correction                 5165\n",
       "error                      4798\n",
       "target                     5212\n",
       "source                     5212\n",
       "correction_index           5212\n",
       "new_error_type             5212\n",
       "final_evident_index        5212\n",
       "target_sentence            5212\n",
       "source_sentence            5212\n",
       "error_type                 5212\n",
       "evidence_index             5212\n",
       "predicted_parsing_order    5212\n",
       "origin                     5212\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for null values\n",
    "df_raw[df_raw.notnull()].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a103de41-5ebc-469e-b1d0-19f6a50b7ff2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0                   0\n",
       "paragraph_id                 0\n",
       "sentence_id                  0\n",
       "course                       0\n",
       "essay_new                    0\n",
       "annotated_sentence           0\n",
       "full_error_tag               0\n",
       "error_tag                    0\n",
       "correction                  47\n",
       "error                      414\n",
       "target                       0\n",
       "source                       0\n",
       "correction_index             0\n",
       "new_error_type               0\n",
       "final_evident_index          0\n",
       "target_sentence              0\n",
       "source_sentence              0\n",
       "error_type                   0\n",
       "evidence_index               0\n",
       "predicted_parsing_order      0\n",
       "origin                       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_raw.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9ee0f4fc-0100-4961-8455-06460e03f286",
   "metadata": {},
   "outputs": [],
   "source": [
    "# with pd.option_context('display.max_colwidth', None):\n",
    "#     print(df_raw[['source', 'target']].sample(n=50))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43c3534a-5baf-4968-b2a3-382ce2bc1e64",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Split Dataset into With Errors and Without Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de320c30-8573-4d83-936f-8e609401dfeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split dataset into two dataframes one with errors and one without\n",
    "def split_on_error(df):\n",
    "    df_no_errors = df.query('error_type == \"None\"').copy()\n",
    "    df_no_errors.reset_index(inplace=True)\n",
    "    df_no_errors.rename(columns={'index': \"orig_index\"}, inplace=True)\n",
    "    df_errors = df.query('error_type != \"None\"').copy()\n",
    "    df_errors['orig_error_type'] = df_errors['error_type']\n",
    "    df_errors['error_type'] = df_errors['new_error_type']\n",
    "\n",
    "    df_errors.reset_index(inplace=True)\n",
    "    df_errors.rename(columns={'index': \"orig_index\"}, inplace=True)\n",
    "    return df_errors, df_no_errors\n",
    "\n",
    "df_errors, df_no_errors = split_on_error(df_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2f0ba05d-fd71-49d2-b6b8-be72853f02af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([5166, 5167], dtype='int64')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_errors.index[(df_errors.essay_new == 'Entonces será certificado como “perfecto\".')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "63138408-b25e-4833-ba47-01bce11e0b6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "orig_index                 5212\n",
       "Unnamed: 0                 5212\n",
       "paragraph_id               5212\n",
       "sentence_id                5212\n",
       "course                     5212\n",
       "essay_new                  5212\n",
       "annotated_sentence         5212\n",
       "full_error_tag             5212\n",
       "error_tag                  5212\n",
       "correction                 5165\n",
       "error                      4798\n",
       "target                     5212\n",
       "source                     5212\n",
       "correction_index           5212\n",
       "new_error_type             5212\n",
       "final_evident_index        5212\n",
       "target_sentence            5212\n",
       "source_sentence            5212\n",
       "error_type                 5212\n",
       "evidence_index             5212\n",
       "predicted_parsing_order    5212\n",
       "origin                     5212\n",
       "orig_error_type            5212\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_errors[df_errors.notnull()].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f6d9e1e2-8efb-43e0-893f-ee90c03090ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "orig_index                 0\n",
       "Unnamed: 0                 0\n",
       "paragraph_id               0\n",
       "sentence_id                0\n",
       "course                     0\n",
       "essay_new                  0\n",
       "annotated_sentence         0\n",
       "full_error_tag             0\n",
       "error_tag                  0\n",
       "correction                 0\n",
       "error                      0\n",
       "target                     0\n",
       "source                     0\n",
       "correction_index           0\n",
       "new_error_type             0\n",
       "final_evident_index        0\n",
       "target_sentence            0\n",
       "source_sentence            0\n",
       "error_type                 0\n",
       "evidence_index             0\n",
       "predicted_parsing_order    0\n",
       "origin                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_no_errors[df_no_errors.notnull()].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f24a1320-5708-4d6e-b361-24d431c9b7a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['[]{a}<az:do:an>', '[a]{}<za:do:inan>', '[el]{la}<ga:fm:det:an>',\n",
       "       ..., '[palido]{pálida}<ga:fm:adj:an>',\n",
       "       '[muchos]{muchos}<na:sp:det:inan>',\n",
       "       '[muchas]{muchas}<na:sp:det:inan>'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_errors.full_error_tag.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9ff047ad-d778-4bb3-8dd3-1f5a7f5bf7ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['article', 'gender agreement', 'number agreement',\n",
       "       'gender and number agreement'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_errors.error_type.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "02b4ab6f-3232-4b51-9614-38e0c7ff780a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Reduce the number of error classes to 4 - ga, na, ga-na and az.\n",
    "# error_map = {\n",
    "# 'aa:do': 'az',\n",
    "# 'az:do': 'az',\n",
    "# 'za:do': 'az',\n",
    "# 'aa:io': 'az',\n",
    "# 'az:io': 'az',\n",
    "# 'na:ps': 'na',\n",
    "# 'ga:fm': 'ga',\n",
    "# 'ga:mf:det:aty': 'ga',\n",
    "# 'ga:mf': 'ga',\n",
    "# 'ig:f': 'na',\n",
    "# 'na:sp': 'na',\n",
    "# 'in:s': 'na',\n",
    "# 'ga:na:fm:ps': 'ga-na',\n",
    "# 'ga:fm:det:aty': 'ga',\n",
    "# 'ga:ps': 'ga',\n",
    "# 'ig:e': 'na',\n",
    "# 'ig:m': 'na',\n",
    "# 'ga:na:mf:ps': 'ga-na',\n",
    "# 'ga:mf:noun:aty': 'ga',\n",
    "# 'ga:mf:adj:aty': 'ga',\n",
    "# 'ga:na:fm:sp': 'ga-na',\n",
    "# 'ga:na:mf:sp': 'ga-na',\n",
    "# 'in:p': 'na',\n",
    "# 'ga:fm:adj:aty': 'ga',\n",
    "# 'ga:fm:noun:aty': 'ga',\n",
    "# 'ig:in:f:s': 'na',\n",
    "# 'ga:fm:gat:noun': 'ga',\n",
    "# 'in:p:gat:noun': 'na',\n",
    "# 'na:sp:gat:noun': 'na',\n",
    "# 'ga:mf:pron:aty': 'ga',\n",
    "# 'in:s:gat:noun': 'na',\n",
    "# 'ga': 'ga'}\n",
    "\n",
    "# df_errors['error_type'] = df_errors.apply(lambda row: error_map[row.orig_error_type], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2dff89c9-dcb5-4263-a392-beb17fcc6510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender agreement               2993\n",
       "number agreement               1460\n",
       "article                         468\n",
       "gender and number agreement     291\n",
       "Name: error_type, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_errors.error_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4da8395e-85c2-4f72-aa68-b35a0b8a6c19",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Fix Data Issues"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ae37542-251e-474b-8bdc-0844557454fa",
   "metadata": {},
   "source": [
    "### Handle Non-breaking Space Character"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7031e516-3343-4ea0-9261-aa994979e151",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "273 rows have non-breaking spaces before cleanup.\n",
      "0 rows have non-breaking spaces after cleanup.\n"
     ]
    }
   ],
   "source": [
    "# Some of the sentences have non-breaking space ('\\xa0') which breaks dependency tree logic.\n",
    "# Count number of lines with non-breaking space in them\n",
    "def cleanup_nbsp(df):\n",
    "    rows_with_nbsp = df[(df.essay_new.str.contains('\\xa0'))]\n",
    "    print(f\"{rows_with_nbsp.shape[0]} rows have non-breaking spaces before cleanup.\")\n",
    "    df.essay_new = df.essay_new.str.replace('\\xa0', ' ')\n",
    "    rows_with_nbsp = df[(df.essay_new.str.contains('\\xa0'))]\n",
    "    print(f\"{rows_with_nbsp.shape[0]} rows have non-breaking spaces after cleanup.\")\n",
    "    return df\n",
    "\n",
    "df_errors = cleanup_nbsp(df_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "587ffc6d-3169-4ba0-8e4c-053bf24f4fff",
   "metadata": {},
   "source": [
    "### Handle Newline Characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d2017f0d-b125-4f4d-9ad2-8e9097d17899",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71 rows have new line before cleanup.\n",
      "Few samples before cleanup:\n",
      "4927    \\n\\nEn el ultimo día mis novia y yo tendríamos...\n",
      "5162    Llevaria mi familia al Museo Nacional para mir...\n",
      "4854    Una Vacación Perfecta\\n\\nUn mes antes de comen...\n",
      "5165    Llevaria mi familia al Museo Nacional para mir...\n",
      "3841    A la fin de película, ella lastimó la protagon...\n",
      "1534    \\n\\nLos canciones más populares de Rhianna en ...\n",
      "2900    Hay dos lugares que me gustaría ir para postre...\n",
      "3840    A la fin de película, ella lastimó la protagon...\n",
      "2026    \\n\\nQuiero ver fashion shows de diseñadoras fa...\n",
      "5129    \\n\\nEn la primera día, vamos al aeropuerto de ...\n",
      "Name: essay_new, dtype: object\n",
      "\n",
      "Same entries after cleanup:\n",
      "4927      En el ultimo día mis novia y yo tendríamos u...\n",
      "5162    Llevaria mi familia al Museo Nacional para mir...\n",
      "4854    Una Vacación Perfecta  Un mes antes de comenza...\n",
      "5165    Llevaria mi familia al Museo Nacional para mir...\n",
      "3841    A la fin de película, ella lastimó la protagon...\n",
      "1534      Los canciones más populares de Rhianna en el...\n",
      "2900    Hay dos lugares que me gustaría ir para postre...\n",
      "3840    A la fin de película, ella lastimó la protagon...\n",
      "2026      Quiero ver fashion shows de diseñadoras famo...\n",
      "5129      En la primera día, vamos al aeropuerto de Lo...\n",
      "Name: essay_new, dtype: object\n",
      "0 rows have double spaces after cleanup.\n"
     ]
    }
   ],
   "source": [
    "# Some of the sentences have new line characters which breaks dependency tree logic.\n",
    "def cleanup_new_line(df):\n",
    "    rows = df[(df.essay_new.str.contains(r'\\n+'))]\n",
    "    print(f\"{rows.shape[0]} rows have new line before cleanup.\")\n",
    "    sampled = None\n",
    "    if rows.shape[0] > 0:\n",
    "        sampled = rows.sample(10).index\n",
    "        print(f'Few samples before cleanup:')\n",
    "        print(df.iloc[sampled].essay_new)\n",
    "    df.essay_new = df.essay_new.str.replace('\\n', ' ')\n",
    "    if sampled is not None:\n",
    "        print(f'\\nSame entries after cleanup:')\n",
    "        print(df.iloc[sampled].essay_new)\n",
    "\n",
    "    rows = df[(df.essay_new.str.contains('\\n'))]\n",
    "    print(f\"{rows.shape[0]} rows have double spaces after cleanup.\")\n",
    "    return df\n",
    "\n",
    "df_errors, df_no_errors = split_on_error(df_raw)\n",
    "df_errors = cleanup_new_line(df_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0098ca3-9d74-43d5-b5ad-c47214dd1b75",
   "metadata": {},
   "source": [
    "### Handle Double Space Characters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "00fbb20f-37a5-41b9-be98-48b206c0df7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "139 rows have double spaces before cleanup.\n",
      "Few samples before cleanup:\n",
      "2184    No deberia haber ruido  Cuando vuelvo, voy al ...\n",
      "3567    Su muerte fue muy trágico y tejanos, mexicanos...\n",
      "3484         No se sobre muchos personas famosos, porq...\n",
      "1456    Es muy especial  para tener este característic...\n",
      "1718    Bruno Mars tiene el pelo negra y los ojos marr...\n",
      "88      Yo voy a estudiar matemática y biología  a en ...\n",
      "2545         Si yo fuera a cualquier lugar para el via...\n",
      "4163       Mi perfecto vacaciones comienzo cuando me d...\n",
      "2873    A perfect vacation  Me gusta ir a una vacación...\n",
      "5160         Mi comida favorita es “腌笃鲜” (los bambús l...\n",
      "Name: essay_new, dtype: object\n",
      "\n",
      "Same entries after cleanup:\n",
      "2184    No deberia haber ruido Cuando vuelvo, voy al z...\n",
      "3567    Su muerte fue muy trágico y tejanos, mexicanos...\n",
      "3484    No se sobre muchos personas famosos, porque no...\n",
      "1456    Es muy especial para tener este característica...\n",
      "1718    Bruno Mars tiene el pelo negra y los ojos marr...\n",
      "88      Yo voy a estudiar matemática y biología a en l...\n",
      "2545    Si yo fuera a cualquier lugar para el viaje pe...\n",
      "4163    Mi perfecto vacaciones comienzo cuando me desp...\n",
      "2873    A perfect vacation Me gusta ir a una vacación ...\n",
      "5160    Mi comida favorita es “腌笃鲜” (los bambús la sop...\n",
      "Name: essay_new, dtype: object\n",
      "0 rows have double spaces after cleanup.\n"
     ]
    }
   ],
   "source": [
    "# Some of the sentences have multiple spaces which breaks dependency tree logic.\n",
    "# Count number of lines with multiple spaces in them\n",
    "def cleanup_double_spaces(df):\n",
    "    rows = df[(df.essay_new.str.contains(r'\\s\\s+'))]\n",
    "    print(f\"{rows.shape[0]} rows have double spaces before cleanup.\")\n",
    "    sampled = None\n",
    "    if rows.shape[0] > 0:\n",
    "        sampled = rows.sample(10).index\n",
    "        print(f'Few samples before cleanup:')\n",
    "        print(df.iloc[sampled].essay_new)\n",
    "    # df.essay_new = df.essay_new.str.replace('  ', ' ')\n",
    "\n",
    "    df.essay_new = df.essay_new.str.replace(r'\\s+', ' ', regex=True).str.strip()\n",
    "    if sampled is not None:\n",
    "        print(f'\\nSame entries after cleanup:')\n",
    "        print(df.iloc[sampled].essay_new)\n",
    "\n",
    "    print(f\"{df[(df.essay_new.str.contains('  '))].shape[0]} rows have double spaces after cleanup.\")\n",
    "    return df\n",
    "\n",
    "# df_errors, df_no_errors = split_on_error(df_raw)\n",
    "df_errors = cleanup_double_spaces(df_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ab68e362-30e0-4ec1-8320-beb6849c2e49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1078                          Mickey tiene orejas grande.\n",
       "1472    Sus ninas estan muy buenas chicas y su esposo ...\n",
       "Name: essay_new, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_errors.iloc[[1078, 1472]].essay_new"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b11f3ea5-4110-4e5a-b624-b272863726d1",
   "metadata": {},
   "source": [
    "### Sanity Check Lines with Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f1c77926-5280-416b-9438-f6933a464f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_status(row):\n",
    "    num_len_mismatch = 0\n",
    "    num_extra_corrections = 0\n",
    "    source = row.source\n",
    "    target = row.target\n",
    "    correction_index = set(row.correction_index)\n",
    "    row_status = 'GOOD'\n",
    "    if len(source) != len(target):\n",
    "        num_len_mismatch = num_len_mismatch + 1\n",
    "        row_status = 'LEN_MISMATCH'\n",
    "    else:\n",
    "        mismatches = map(lambda x: x[0] == x[1], zip(source, target))\n",
    "        for (i, flag) in enumerate(mismatches):\n",
    "            if not flag and i not in correction_index:\n",
    "                # print('Extra corrections')\n",
    "                num_extra_corrections = num_extra_corrections + 1\n",
    "                row_status = 'EXTRA_CORRECTIONS'\n",
    "                break\n",
    "    return row_status\n",
    "\n",
    "def check_errors():\n",
    "    df_status = df_errors[['orig_index', 'sentence_id', 'essay_new', 'source', 'target', 'correction_index']].copy(deep=True)\n",
    "    df_status['status'] = df_status.apply(compute_status, axis=1)\n",
    "    df_status['has_annotation'] = df_status.apply(lambda row: '<' in row.source or '<' in row.target, axis=1)\n",
    "    return df_status\n",
    "\n",
    "df_error_status = check_errors()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "bf34d765-6f10-45aa-85fa-04085df74737",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Should have just GOOD rows\n",
      "GOOD    5212\n",
      "Name: status, dtype: int64\n",
      "\n",
      "Should not have annotations\n",
      "False    5212\n",
      "Name: has_annotation, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orig_index</th>\n",
       "      <th>sentence_id</th>\n",
       "      <th>essay_new</th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>correction_index</th>\n",
       "      <th>status</th>\n",
       "      <th>has_annotation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [orig_index, sentence_id, essay_new, source, target, correction_index, status, has_annotation]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orig_index</th>\n",
       "      <th>sentence_id</th>\n",
       "      <th>essay_new</th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>correction_index</th>\n",
       "      <th>status</th>\n",
       "      <th>has_annotation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [orig_index, sentence_id, essay_new, source, target, correction_index, status, has_annotation]\n",
       "Index: []"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('Should have just GOOD rows')\n",
    "print(df_error_status.status.value_counts())\n",
    "\n",
    "print()\n",
    "print('Should not have annotations')\n",
    "print(df_error_status.has_annotation.value_counts())\n",
    "\n",
    "with pd.option_context('display.max_colwidth', None):\n",
    "    display(df_error_status[df_error_status['status'] == 'EXTRA_CORRECTIONS'])\n",
    "    print(\"----------------------\")\n",
    "    display(df_error_status[df_error_status.has_annotation == True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "678b7efd-359f-4953-bf6c-8b5f2bc6db45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lower number of mismatches is better with 0 being ideal\n",
      "----------------------------------------------\n",
      "Number of mismatches in COWS-L2H dataset: 0/5212\n"
     ]
    }
   ],
   "source": [
    "# Check on alignment logic to ensure that all lines in the error dataset can be aligned\n",
    "def is_same(list1, list2):\n",
    "    if len(list1) != len(list2):\n",
    "        return False\n",
    "\n",
    "    for v1, v2 in zip(list1, list2):\n",
    "        if v1.strip() != v2.strip():\n",
    "            return False\n",
    "\n",
    "    return True\n",
    "\n",
    "def print_context(row):\n",
    "    print(f'error_type: {row.error_type} => {row.new_error_type}')\n",
    "\n",
    "def alignment_sanity(df, context_printer=None, verbosity=0):\n",
    "    mismatches = 0\n",
    "    for i, row in df.iterrows():\n",
    "        nw = NeedlemanWunsch()\n",
    "        aligned_s1, aligned_s2, score_matrix = nw.get_alignment(row.source, row.target, return_score_matrix=True)\n",
    "        path, s1_pieces, s2_pieces = nw.get_alignment_strings_and_indices(aligned_s1, aligned_s2)\n",
    "        if not (is_same(row.source, s1_pieces) and is_same(row.target, s2_pieces)):\n",
    "            mismatches = mismatches + 1\n",
    "            if verbosity > 0:\n",
    "                print('----------------------------------------------')\n",
    "                if context_printer:\n",
    "                    context_printer(row)\n",
    "                print(f\"Original: \\n=>{row.source}\\n=>{row.target}\")\n",
    "                print(f'Correction index: {row.correction_index}')\n",
    "                print(f'Path:\\n{path}\\n===>{s1_pieces}, \\n===>{s2_pieces}')\n",
    "\n",
    "    print('----------------------------------------------')\n",
    "    return mismatches\n",
    "\n",
    "print(f'Lower number of mismatches is better with 0 being ideal')\n",
    "print(f\"Number of mismatches in COWS-L2H dataset: {alignment_sanity(df_errors)}/{df_errors.shape[0]}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3b989c89-abd4-4707-ae9a-53caa518da55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distribution of number of elements in correction count\n",
      "2    5212\n",
      "Name: correction_index, dtype: int64\n",
      "\n",
      "Pretty much every row has 2 indices for correction count which means none of the sentences have more than one word changing. Alignment is easier.\n"
     ]
    }
   ],
   "source": [
    "print(f'Distribution of number of elements in correction count')\n",
    "print(df_errors.correction_index.map(lambda v: len(v)).value_counts())\n",
    "print('')\n",
    "print(f'Pretty much every row has 2 indices for correction count which means none of the sentences have more than one word changing. Alignment is easier.')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61b5fed2-5b90-42b5-88eb-6c2b563c972e",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "## Explore EXPECT dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04f83a62-2c2c-40be-83c9-008a4ca051eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_expect_data(num_lines=None):\n",
    "    expect_train_file = f\"{args.EXPLAINABLE_GEC_DATA_FOLDER}/train.json\"\n",
    "    with open(expect_train_file, \"r\") as file1:\n",
    "        count = 0\n",
    "        rows = file1.readlines()\n",
    "        df = pd.DataFrame([eval(row) for row in rows])\n",
    "        df['source_org'] = df.source.map(lambda v: ' '.join(v).replace(\" .\", \".\"))\n",
    "        df['target_org'] = df.target.map(lambda v: ' '.join(v).replace(\" .\", \".\"))\n",
    "        return df\n",
    "\n",
    "df_expect = load_expect_data()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e83e1bd9-dad1-4018-b34f-d8aa093adefa",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pd.option_context('display.max_colwidth', None):\n",
    "    display(df_expect.sample(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "057e8a95-8fd7-4bd3-b4c6-9ed833e7a6ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_expect.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "993a825c-70e5-4c29-8035-e607605208c9",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Dependency parsing tree on EXPECT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f73d4803-cb37-44e5-8687-308f9cd01256",
   "metadata": {},
   "outputs": [],
   "source": [
    "with pd.option_context('display.max_colwidth', None):\n",
    "    display(df_expect.iloc[11872])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84217b82-0560-410a-b48f-4d13b7a861a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_one_EXPECT(nlp, row_idx):\n",
    "    row = df_expect.iloc[row_idx]\n",
    "    parsing_order = dep_parsing_tree(nlp, row.target_org, row.correction_index, verbosity=1)\n",
    "    print()\n",
    "    print(f'Orig: {row.source_org}')\n",
    "    print(f'Corr: {row.target_org}')\n",
    "    concacted = [*row.source, 'SEP', *row.target]\n",
    "    print(f'Concatenated: {concacted}')\n",
    "    print(f'Corr: {row.target_org}')\n",
    "    print()\n",
    "    print(f'Correction index: {row.correction_index}')\n",
    "    print()\n",
    "    print(f'Corrected offset: {len(row.target) + 1}')\n",
    "    print(f'Original pred_parse_order: {row.predicted_parsing_order}')\n",
    "    print()\n",
    "    print(f'computed pred_parse_order: {parsing_order}')\n",
    "\n",
    "# show_one_EXPECT(11872)\n",
    "show_one_EXPECT(NLP_EN, 1314)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "914c1baa-6aae-43b3-b2b8-4f701fc1eba9",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Evidence words are important for GEC\")\n",
    "print(f'computed pred_parse_order: {dep_parsing_tree(spacy.load(\"en_core_web_sm\"), \"Evidence words are important for GEC\", [2])}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b837674-84a7-4e81-ba3c-45f52518b35d",
   "metadata": {},
   "outputs": [],
   "source": [
    "show_one_EXPECT(NLP_EN, 11872)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47270056-6e3a-49b3-b1a5-8cfead06a5e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "show_one_EXPECT(NLP_EN, 1289)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bba35328-e09a-4e9f-b98d-ac18109d986c",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Number of mismatches in EXPECT dataset: {alignment_sanity(df_expect)}/{df_expect.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c057766-20d7-4eb0-9bf6-d4183c30988a",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Explore EXPECT's pickle files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ec88329-0f2d-4f5c-abf0-35a44262b09d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# open a file, where you stored the pickled data\n",
    "def tt():\n",
    "    with open('/Users/rams/personal/MIDS/w210/Explainable_GEC/data/ner/train.pkl', 'rb') as file:\n",
    "        # dump information to that file\n",
    "        data = pickle.load(file)\n",
    "        df = pd.DataFrame(data)\n",
    "        df.to_csv('/Users/rams/personal/MIDS/w210/Explainable_GEC/data/ner/train.csv', index=False)\n",
    "        return df\n",
    "tt()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64873aab-3f5b-4e85-bd8d-b3b81cd2ee4f",
   "metadata": {
    "tags": []
   },
   "source": [
    "## COWS-L2H - Dependency Parsing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe75ef0-8466-491c-a71a-da4e37f26270",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Understand Issues With Dependency Parse Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "01174b13-48a7-469a-8a10-56b8692f7c81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def check_dep_parsing_tree(nlp, text, correction_index, verbosity=0):\n",
    "#     if len(correction_index) == 0:\n",
    "#         return dict()\n",
    "#     ignored = set(['PUNCT'])\n",
    "#     doc = nlp(text)\n",
    "#     js_str = doc.to_json()\n",
    "#     id_to_head = dict()\n",
    "#     head_to_ids = dict()\n",
    "#     for token in js_str[\"tokens\"]:\n",
    "#         # print(token)\n",
    "#         id = token[\"id\"]\n",
    "#         head = token[\"head\"]\n",
    "#         if token['pos'] not in ignored:\n",
    "#             id_to_head[id] = head\n",
    "#             if head in head_to_ids:\n",
    "#                 head_to_ids[head].append(id)\n",
    "#             else:\n",
    "#                 head_to_ids[head] = [id]\n",
    "#     if verbosity > 0:\n",
    "#         print(f'id -> head: {id_to_head}')\n",
    "#         print(f'head -> ids: {head_to_ids}')\n",
    "\n",
    "#     pred_parse_order = dict()\n",
    "#     for cor_idx in correction_index:\n",
    "#         add_neighbors_of(cor_idx, 1, pred_parse_order, id_to_head, head_to_ids)\n",
    "\n",
    "#     pred_parse_order = dict(sorted(pred_parse_order.items()))\n",
    "#     if verbosity > 0:\n",
    "#         df_dep_tree = pd.DataFrame()\n",
    "#         df_dep_tree['text'] = [token.text for token in doc]\n",
    "#         df_dep_tree['lemma'] = [token.lemma_ for token in doc]\n",
    "#         df_dep_tree['is_punctuation'] = [token.is_punct for token in doc]\n",
    "#         df_dep_tree['is_space'] = [token.is_space for token in doc]\n",
    "#         df_dep_tree['shape'] = [token.shape_ for token in doc]\n",
    "#         df_dep_tree['part_of_speech'] = [token.pos_ for token in doc]\n",
    "#         df_dep_tree['pos_tag'] = [token.tag_ for token in doc]\n",
    "#         df_dep_tree['head'] = [token.head.text for token in doc]\n",
    "#         df_dep_tree['dep'] = [token.dep_ for token in doc]\n",
    "#         html = displacy.render(doc, style=\"dep\")\n",
    "#         HTML(html)\n",
    "#     return pred_parse_order\n",
    "\n",
    "# def check(df, idx):\n",
    "#     row = df.iloc[idx]\n",
    "#     print(f'{idx}: {row.essay_new}')\n",
    "#     return check_dep_parsing_tree(NLP_ES, row.essay_new, row.correction_index, verbosity=2)\n",
    "\n",
    "# # [print(check(df_errors, idx)) for idx in [2238, 4360, 4361]]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4165f29-9db0-4d6e-aeda-33e15d71a283",
   "metadata": {
    "tags": []
   },
   "source": [
    "### First and Second Order Dependencies for Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "36da98c9-779a-4e47-9d0b-b95626b5d7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def COWSL2H_parsing_order(nlp, df, row_idx):\n",
    "    row = df.iloc[row_idx]\n",
    "    parsing_order = dep_parsing_tree(nlp, row.essay_new, row.correction_index, verbosity=2)\n",
    "    print('-----------------------------------------------')\n",
    "    print\n",
    "    print()\n",
    "    print(f'Orig: {\" \".join(row.source)}')\n",
    "    print(f'Corr: {row.essay_new}')\n",
    "    print(f'row.source: {row.source}')\n",
    "    print(f'row.target: {row.target}')\n",
    "    concacted = [*row.source, 'SEP', *row.target]\n",
    "    print(f'Concatenated: {concacted}')\n",
    "    print()\n",
    "    print(f'Correction index: {row.correction_index}')\n",
    "    print()\n",
    "    print(f'Corrected offset: {len(row.target) + 1}')\n",
    "    print()\n",
    "    print(f'computed pred_parse_order: {parsing_order}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e059da6b-f090-4910-b442-aa24107fe687",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20.4 s, sys: 78.4 ms, total: 20.5 s\n",
      "Wall time: 20.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "def count_by_order(row, order_req):\n",
    "    count = 0\n",
    "    if row.predicted_parsing_order:\n",
    "        for (idx, cur_order) in row.predicted_parsing_order.items():\n",
    "            if cur_order == order_req:\n",
    "                count = count + 1\n",
    "    return count\n",
    "\n",
    "def add_target_dependencies(df):\n",
    "    def add_parsing_order(row):\n",
    "        dependencies = dep_parsing_tree(NLP_ES, row.essay_new, row.correction_index, verbosity=0)\n",
    "        if len(dependencies) == 0:\n",
    "            return None\n",
    "        else:\n",
    "            return dependencies\n",
    "    df['predicted_parsing_order_src'] = df.apply(add_parsing_order, axis=1)\n",
    "    # df['parsing_order_len'] = df_errors.apply(lambda row: len(row.predicted_parsing_order), axis=1)\n",
    "    # df['num_first_order_dependencies'] = df_errors.apply(lambda row: count_by_order(row, 2), axis=1)\n",
    "    # df['num_second_order_dependencies'] = df_errors.apply(lambda row: count_by_order(row, 3), axis=1)\n",
    "\n",
    "add_target_dependencies(df_errors)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "290b06d8-c206-4264-b2a9-62c9c483a4d4",
   "metadata": {
    "tags": []
   },
   "source": [
    "### First and Second Order Dependencies for Source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1dec03d2-6b8e-47be-82ea-a86c5b34347a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logic is failing for row orig_index                                                                  2337\n",
      "Unnamed: 0                                                                   148\n",
      "paragraph_id                                                                1241\n",
      "sentence_id                    7d41323560f6726aeaa1667d58a3f6a2e06b6c62214761...\n",
      "course                                                                     SPA 3\n",
      "essay_new                         Mi lugar favorito es *CITY* en *STATE* Mexico.\n",
      "annotated_sentence             Mi lugar favorito es [Puerta]{puerto}<ig:f:nou...\n",
      "full_error_tag                                  [Puerta]{puerto}<ig:f:noun:inan>\n",
      "error_tag                                                         ig:f:noun:inan\n",
      "correction                                                                puerto\n",
      "error                                                                     Puerta\n",
      "target                         [Mi, lugar, favorito, es, puerto, Vallarta, en...\n",
      "source                         [Mi, lugar, favorito, es, Puerta, Vallarta, en...\n",
      "correction_index                                                         [4, 15]\n",
      "new_error_type                                                  gender agreement\n",
      "final_evident_index                                                      [5, 16]\n",
      "target_sentence                Mi lugar favorito es puerto Vallarta en Jalisc...\n",
      "source_sentence                Mi lugar favorito es Puerta Vallarta en Jalisc...\n",
      "error_type                                                      gender agreement\n",
      "evidence_index                                                           [5, 16]\n",
      "predicted_parsing_order                                                         \n",
      "origin                                                                         A\n",
      "orig_error_type                                                 gender agreement\n",
      "predicted_parsing_order_src    {'1': 3, '10': 3, '11': 3, '3': 3, '4': 1, '5'...\n",
      "Name: 2337, dtype: object\n"
     ]
    }
   ],
   "source": [
    "def add_source_parsing_order(row, verbosity=0):\n",
    "    if row.predicted_parsing_order_src is None:\n",
    "        return None\n",
    "\n",
    "    nw = NeedlemanWunsch()\n",
    "    aligned_s1, aligned_s2, score_matrix = nw.get_alignment(row.source, row.target, return_score_matrix=True)\n",
    "    path, s1_pieces, s2_pieces = nw.get_alignment_strings_and_indices(aligned_s1, aligned_s2)\n",
    "    path_as_dict = dict(path)\n",
    "    if verbosity > 0:\n",
    "        print(row.sentence_id, path_as_dict, s1_pieces, s2_pieces)\n",
    "    pred_parsing_order = row.predicted_parsing_order_src.copy()\n",
    "\n",
    "    if verbosity > 0:\n",
    "        print(row.predicted_parsing_order_src)\n",
    "    src_offset = len(row.target) + 1\n",
    "    for idx, dependency_num in row.predicted_parsing_order_src.items():\n",
    "        if dependency_num == 1:\n",
    "            pred_parsing_order[f'{row.correction_index[1]}'] = 1\n",
    "        elif dependency_num == 2 or dependency_num == 3:\n",
    "            if verbosity > 0:\n",
    "                print(int(idx), dependency_num)\n",
    "            if int(idx) not in path_as_dict:\n",
    "                print(f'Logic is failing for row {row}')\n",
    "                return None\n",
    "            pred_parsing_order[f'{path_as_dict[int(idx)] + src_offset}'] = dependency_num\n",
    "    return pred_parsing_order\n",
    "\n",
    "def add_source_dependencies(df):\n",
    "    df['predicted_parsing_order'] = df.apply(add_source_parsing_order, axis=1)\n",
    "\n",
    "add_source_dependencies(df_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f9a8a6da-64f3-4d3b-a65d-5df4b932f402",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_errors.predicted_parsing_order.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "135c114c-18b4-4809-b32c-0dc74f7e3d66",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for item in df_errors[df_errors.essay_new.str.contains('\\*')].essay_new.items():\n",
    "#     print(f'{item[0]}:{item[1]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1c1d85f5-1d2b-4511-9b46-9aca6f91fedb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orig_index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>paragraph_id</th>\n",
       "      <th>sentence_id</th>\n",
       "      <th>course</th>\n",
       "      <th>essay_new</th>\n",
       "      <th>annotated_sentence</th>\n",
       "      <th>full_error_tag</th>\n",
       "      <th>error_tag</th>\n",
       "      <th>correction</th>\n",
       "      <th>...</th>\n",
       "      <th>new_error_type</th>\n",
       "      <th>final_evident_index</th>\n",
       "      <th>target_sentence</th>\n",
       "      <th>source_sentence</th>\n",
       "      <th>error_type</th>\n",
       "      <th>evidence_index</th>\n",
       "      <th>predicted_parsing_order</th>\n",
       "      <th>origin</th>\n",
       "      <th>orig_error_type</th>\n",
       "      <th>predicted_parsing_order_src</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>65</td>\n",
       "      <td>72</td>\n",
       "      <td>412</td>\n",
       "      <td>6004f54d6469a368868a7377b03f3e9eb8d7a780202ab1...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Después Harry Potter, J. K. Rowling ha estado ...</td>\n",
       "      <td>Después Harry Potter, J. K. Rowling ha estado ...</td>\n",
       "      <td>[ocupado]{ocupada}&lt;ga:fm:adj:an&gt;</td>\n",
       "      <td>ga:fm:adj:an</td>\n",
       "      <td>ocupada</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[]</td>\n",
       "      <td>Después Harry Potter, J. K. Rowling ha estado ...</td>\n",
       "      <td>Después Harry Potter, J. K. Rowling ha estado ...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>68</td>\n",
       "      <td>111</td>\n",
       "      <td>436</td>\n",
       "      <td>340344b3cfed0317bbb5c95dafdae2033cf7c6732f5ea3...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>En Shawshank Redemption -- un libro de Steven ...</td>\n",
       "      <td>En Shawshank Redemption -- un libro de Steven ...</td>\n",
       "      <td>[encarcelado]{encarcelada}&lt;ga:fm:adj:an&gt;</td>\n",
       "      <td>ga:fm:adj:an</td>\n",
       "      <td>encarcelada</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[]</td>\n",
       "      <td>En Shawshank Redemption- - un libro de Steven ...</td>\n",
       "      <td>En Shawshank Redemption- - un libro de Steven ...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>421</td>\n",
       "      <td>211</td>\n",
       "      <td>775</td>\n",
       "      <td>0c66030cc2c5487d1736df818425e524874d29ada3c571...</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>No tiene \"oscars\" pero Leonardo Dicaprio solom...</td>\n",
       "      <td>No tiene \"oscars\" pero Leonardo Dicaprio solom...</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[31, 33, 34, 71, 73, 74]</td>\n",
       "      <td>No tiene \"oscars\" pero Leonardo Dicaprio solom...</td>\n",
       "      <td>No tiene \"oscars\" pero Leonardo Dicaprio solom...</td>\n",
       "      <td>article</td>\n",
       "      <td>[31, 33, 34, 71, 73, 74]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>624</td>\n",
       "      <td>440</td>\n",
       "      <td>29</td>\n",
       "      <td>daeec1fa32f04db8a029f6cfe42d541e117b4e7621f61a...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Bill and Ted es de los dos Bill and Ted y els ...</td>\n",
       "      <td>Bill and Ted es de los dos Bill and Ted y els ...</td>\n",
       "      <td>[la]{el}&lt;ga:mf:det:inan&gt;</td>\n",
       "      <td>ga:mf:det:inan</td>\n",
       "      <td>el</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[31, 32, 63, 64]</td>\n",
       "      <td>Bill and Ted es de los dos Bill and Ted y els ...</td>\n",
       "      <td>Bill and Ted es de los dos Bill and Ted y els ...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[31, 32, 63, 64]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>658</td>\n",
       "      <td>475</td>\n",
       "      <td>61</td>\n",
       "      <td>77581a46b29c5a9e1e74abbffea7e7a3c03b59fc345764...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Todavía me gusta escuchar \"Destiny's Child\" y ...</td>\n",
       "      <td>Todavía me gusta escuchar \"Destiny's Child\" y ...</td>\n",
       "      <td>[estupendo]{estupenda}&lt;ga:fm:adj:inan&gt;</td>\n",
       "      <td>ga:fm:adj:inan</td>\n",
       "      <td>estupenda</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[16, 33]</td>\n",
       "      <td>Todavía me gusta escuchar \"Destiny' s Child\" y...</td>\n",
       "      <td>Todavía me gusta escuchar \"Destiny' s Child\" y...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[16, 33]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>663</td>\n",
       "      <td>480</td>\n",
       "      <td>65</td>\n",
       "      <td>139d6a79730caee9d68ad994e3fb047e5c0b6b31d42450...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Empezó con el grupo se llama Destiny's Child a...</td>\n",
       "      <td>Empezó con el grupo se llama Destiny's Child a...</td>\n",
       "      <td>[solo]{sola}&lt;ga:fm:adj:an&gt;</td>\n",
       "      <td>ga:fm:adj:an</td>\n",
       "      <td>sola</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[12, 28]</td>\n",
       "      <td>Empezó con el grupo se llama Destiny' s Child ...</td>\n",
       "      <td>Empezó con el grupo se llama Destiny' s Child ...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[12, 28]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1034</th>\n",
       "      <td>1034</td>\n",
       "      <td>874</td>\n",
       "      <td>298</td>\n",
       "      <td>d539be0f37ba2351c41b5ce478dfc7a23b8bb61a7256f2...</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Hugh Jackman es un buen actor pero también es...</td>\n",
       "      <td>Hugh Jackman es un buen actor pero también es...</td>\n",
       "      <td>[bailarina]{bailarín}&lt;ga:mf:noun:an&gt;</td>\n",
       "      <td>ga:mf:noun:an</td>\n",
       "      <td>bailarín</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[2, 3, 4, 17, 20, 21, 22, 35]</td>\n",
       "      <td>Hugh Jackman es un buen actor pero tambie ́ n ...</td>\n",
       "      <td>Hugh Jackman es un buen actor pero tambie ́ n ...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[2, 3, 4, 17, 20, 21, 22, 35]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>1104</td>\n",
       "      <td>946</td>\n",
       "      <td>336</td>\n",
       "      <td>52d24c89b5a3209f034532a43e7693abc52d49d3f25a2a...</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Me gustan muchos tipos de musica (rap, r&amp;b,pop...</td>\n",
       "      <td>Me gustan muchos tipos de musica (rap, r&amp;b,pop...</td>\n",
       "      <td>[muchas]{muchos}&lt;ga:mf:det:an&gt;</td>\n",
       "      <td>ga:mf:det:an</td>\n",
       "      <td>muchos</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[22, 23, 43, 44]</td>\n",
       "      <td>Me gustan muchos tipos de musica( rap, r&amp; b, p...</td>\n",
       "      <td>Me gustan muchos tipos de musica( rap, r&amp; b, p...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[22, 23, 43, 44]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>1270</td>\n",
       "      <td>74</td>\n",
       "      <td>412</td>\n",
       "      <td>a5eb6bff54851d9f5b95e8eec421ccb1e1bb16093f1ca3...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>J. K. Rowling no escribía la obra, pero lo apr...</td>\n",
       "      <td>J. K. Rowling no escribía la obra, pero [lo]{l...</td>\n",
       "      <td>[lo]{la}&lt;ga:fm:pron:inan&gt;</td>\n",
       "      <td>ga:fm:pron:inan</td>\n",
       "      <td>la</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[12, 7, 10, 27, 22, 25]</td>\n",
       "      <td>J. K. Rowling no escribía la obra, pero la apr...</td>\n",
       "      <td>J. K. Rowling no escribía la obra, pero lo apr...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[12, 7, 10, 27, 22, 25]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>1280</td>\n",
       "      <td>84</td>\n",
       "      <td>419</td>\n",
       "      <td>1899da74fd3c342ae0359985a667606be211be7e7c7c0c...</td>\n",
       "      <td>SPA 1</td>\n",
       "      <td>Oprah Winfrey es African- Americano.</td>\n",
       "      <td>Oprah Winfrey es African- [Americano]{american...</td>\n",
       "      <td>[Americano]{americana}&lt;ga:fm:adj:an&gt;</td>\n",
       "      <td>ga:fm:adj:an</td>\n",
       "      <td>americana</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[0, 1, 2, 3, 4, 8, 9, 10, 11, 12]</td>\n",
       "      <td>Oprah Winfrey es African- americana.</td>\n",
       "      <td>Oprah Winfrey es African- Americano.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[0, 1, 2, 3, 4, 8, 9, 10, 11, 12]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>1291</td>\n",
       "      <td>95</td>\n",
       "      <td>427</td>\n",
       "      <td>8169da7c36d4bc5f4c27f5c24158fdddb88c8531dce841...</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Por ejemplo, los invitó a Taylor's casa para c...</td>\n",
       "      <td>Por ejemplo, los invitó a Taylor's casa para c...</td>\n",
       "      <td>[especial]{especiales}&lt;na:ps:adj:inan&gt;</td>\n",
       "      <td>na:ps:adj:inan</td>\n",
       "      <td>especiales</td>\n",
       "      <td>...</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[15, 16, 17, 31, 32, 33]</td>\n",
       "      <td>Por ejemplo, los invitó a Taylor' s casa para ...</td>\n",
       "      <td>Por ejemplo, los invitó a Taylor' s casa para ...</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[15, 16, 17, 31, 32, 33]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2337</th>\n",
       "      <td>2337</td>\n",
       "      <td>148</td>\n",
       "      <td>1241</td>\n",
       "      <td>7d41323560f6726aeaa1667d58a3f6a2e06b6c62214761...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Mi lugar favorito es *CITY* en *STATE* Mexico.</td>\n",
       "      <td>Mi lugar favorito es [Puerta]{puerto}&lt;ig:f:nou...</td>\n",
       "      <td>[Puerta]{puerto}&lt;ig:f:noun:inan&gt;</td>\n",
       "      <td>ig:f:noun:inan</td>\n",
       "      <td>puerto</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[5, 16]</td>\n",
       "      <td>Mi lugar favorito es puerto Vallarta en Jalisc...</td>\n",
       "      <td>Mi lugar favorito es Puerta Vallarta en Jalisc...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[5, 16]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>{'1': 3, '10': 3, '11': 3, '3': 3, '4': 1, '5'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401</th>\n",
       "      <td>2401</td>\n",
       "      <td>215</td>\n",
       "      <td>1264</td>\n",
       "      <td>79975f6953bc2ae73e7cfbd2a311400b945505bf8482b2...</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Nosotros acabaremos la noche con cocinando los...</td>\n",
       "      <td>Nosotros acabaremos la noche con cocinando los...</td>\n",
       "      <td>[embarazoso]{embarazosos}&lt;na:ps:adj:inan&gt;</td>\n",
       "      <td>na:ps:adj:inan</td>\n",
       "      <td>embarazosos</td>\n",
       "      <td>...</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[31, 32, 33, 61, 62, 63]</td>\n",
       "      <td>Nosotros acabaremos la noche con cocinando los...</td>\n",
       "      <td>Nosotros acabaremos la noche con cocinando los...</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[31, 32, 33, 61, 62, 63]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920</th>\n",
       "      <td>2920</td>\n",
       "      <td>755</td>\n",
       "      <td>131</td>\n",
       "      <td>260c8efe2b960b3810b6cb6bb65eb33301c608b9109d53...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Beyoncé Knowles-Carter es una cantante, actriz...</td>\n",
       "      <td>Beyoncé Knowles-Carter es una cantante, actriz...</td>\n",
       "      <td>[americano]{americana}&lt;ga:fm:adj:an&gt;</td>\n",
       "      <td>ga:fm:adj:an</td>\n",
       "      <td>americana</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[3, 5, 16, 20, 22, 33]</td>\n",
       "      <td>Beyoncé Knowles- Carter es una cantante, actri...</td>\n",
       "      <td>Beyoncé Knowles- Carter es una cantante, actri...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[3, 5, 16, 20, 22, 33]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3050</th>\n",
       "      <td>3050</td>\n",
       "      <td>896</td>\n",
       "      <td>350</td>\n",
       "      <td>69ee9c42605d18dd2342d1528adb040030fdd411254510...</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Cuando era un bebe, Señor Voldemort asesinó lo...</td>\n",
       "      <td>Cuando era un bebe, Señor Voldemort asesinó []...</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[15, 18, 35, 38]</td>\n",
       "      <td>Cuando era un bebe, Señor Voldemort asesinó a ...</td>\n",
       "      <td>Cuando era un bebe, Señor Voldemort asesinó a ...</td>\n",
       "      <td>article</td>\n",
       "      <td>[15, 18, 35, 38]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3054</th>\n",
       "      <td>3054</td>\n",
       "      <td>900</td>\n",
       "      <td>649</td>\n",
       "      <td>9772575305dfa7f2672e3c00cd1d447ab83d9e84608dd4...</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Jimmy Fallon tiene otras personas famosas en s...</td>\n",
       "      <td>Jimmy Fallon tiene []{a}&lt;az:do:an&gt; otras perso...</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[20, 21, 22, 45, 46, 47]</td>\n",
       "      <td>Jimmy Fallon tiene a otras personas famosas en...</td>\n",
       "      <td>Jimmy Fallon tiene a otras personas famosas en...</td>\n",
       "      <td>article</td>\n",
       "      <td>[20, 21, 22, 45, 46, 47]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068</th>\n",
       "      <td>3068</td>\n",
       "      <td>916</td>\n",
       "      <td>937</td>\n",
       "      <td>644a985a157ba246a333f122b6aab4fd56ab32ee361c33...</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Traería a mis mejores amigos, mi hermana y mi ...</td>\n",
       "      <td>Traería  mis mejores amigos, []{a}&lt;az:do:an&gt; m...</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[7, 8, 9, 10, 11, 12, 13, 21, 22, 23, 24, 25, ...</td>\n",
       "      <td>Traería mis mejores amigos, a mi hermana y a m...</td>\n",
       "      <td>Traería mis mejores amigos, [NONE] mi hermana ...</td>\n",
       "      <td>article</td>\n",
       "      <td>[7, 8, 9, 10, 11, 12, 13, 21, 22, 23, 24, 25, ...</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>3080</td>\n",
       "      <td>929</td>\n",
       "      <td>1081</td>\n",
       "      <td>a344d375871772b542d319cd517c718348d88f2e225917...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Luego voy a llamar mi mamá, mi hermana, mi abu...</td>\n",
       "      <td>Luego voy a llamar []{a}&lt;az:do:an&gt; mi mamá, []...</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14,...</td>\n",
       "      <td>Luego voy a llamar a mi mamá, a mi hermana, a ...</td>\n",
       "      <td>Luego voy a llamar a mi mamá, a mi hermana, [N...</td>\n",
       "      <td>article</td>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14,...</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3085</th>\n",
       "      <td>3085</td>\n",
       "      <td>935</td>\n",
       "      <td>1198</td>\n",
       "      <td>94acf628361ec20601a3028d3ad9b4777f8e20540e66f9...</td>\n",
       "      <td>SPA 1</td>\n",
       "      <td>Yo traigo mi novio, los padres, la hermana may...</td>\n",
       "      <td>Yo traigo []{a}&lt;az:do:an&gt; mi novio, []{a}&lt;az:d...</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>Yo traigo a mi novio, a los padres, a la herma...</td>\n",
       "      <td>Yo traigo a mi novio, a los padres, a la herma...</td>\n",
       "      <td>article</td>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3089</th>\n",
       "      <td>3089</td>\n",
       "      <td>940</td>\n",
       "      <td>1241</td>\n",
       "      <td>1d7d5a1c69639976fa360b8d54b3579121bf53f7499e14...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Primero, planaria mi vacacion por un mes y tra...</td>\n",
       "      <td>Primero, planaria mi vacacion por un mes y tra...</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[20, 21, 44, 45]</td>\n",
       "      <td>Primero, planaria mi vacacion por un mes y tra...</td>\n",
       "      <td>Primero, planaria mi vacacion por un mes y tra...</td>\n",
       "      <td>article</td>\n",
       "      <td>[20, 21, 44, 45]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3097</th>\n",
       "      <td>3097</td>\n",
       "      <td>949</td>\n",
       "      <td>1363</td>\n",
       "      <td>7fb0c6b9e1bb965cf21ca24cd62110527336fa456aff77...</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Una hermosa vista ayuda, pero me encantaría te...</td>\n",
       "      <td>Una hermosa vista ayuda, pero me encantaría te...</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[23, 24, 53, 54]</td>\n",
       "      <td>Una hermosa vista ayuda, pero me encantaría te...</td>\n",
       "      <td>Una hermosa vista ayuda, pero me encantaría te...</td>\n",
       "      <td>article</td>\n",
       "      <td>[23, 24, 53, 54]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3139</th>\n",
       "      <td>3139</td>\n",
       "      <td>992</td>\n",
       "      <td>46</td>\n",
       "      <td>76833841bbbdfd09a73d434bc78ad99cb572eb5ef46ee5...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Kim Kardashian-West es una modela quién tiene ...</td>\n",
       "      <td>Kim Kardashian-West es una [modela]{modelo}&lt;ig...</td>\n",
       "      <td>[bonita]{bonito}&lt;ga:mf:adj:an&gt;</td>\n",
       "      <td>ga:mf:adj:an</td>\n",
       "      <td>bonito</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>Kim Kardashian- West es una modelo quién tiene...</td>\n",
       "      <td>Kim Kardashian- West es una modelo quién tiene...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3470</th>\n",
       "      <td>3470</td>\n",
       "      <td>291</td>\n",
       "      <td>316</td>\n",
       "      <td>ccfb36e3dfbe363bb7b44a6952daf7c70b24472a9dc8da...</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Si un animale le gusta comida grande, necesita...</td>\n",
       "      <td>Si un animale le gusta comida grande, necesita...</td>\n",
       "      <td>[pequeño]{pequeña}&lt;ga:fm:adj:inan&gt;</td>\n",
       "      <td>ga:fm:adj:inan</td>\n",
       "      <td>pequeña</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[2, 4, 6, 31, 33, 34, 33, 35, 37, 62, 64, 65]</td>\n",
       "      <td>Si un animale le gusta comida grande, necesita...</td>\n",
       "      <td>Si un animale le gusta comida grande, necesita...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[2, 4, 6, 31, 33, 34, 33, 35, 37, 62, 64, 65]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3642</th>\n",
       "      <td>3642</td>\n",
       "      <td>480</td>\n",
       "      <td>445</td>\n",
       "      <td>f1386f14918b3bde3c65c0b820188c47218a0ebad39d3f...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Espero que tenga las últimas dos años es incre...</td>\n",
       "      <td>Espero que tenga [las]{los}&lt;ga:mf:det:inan&gt; [ú...</td>\n",
       "      <td>[verlas]{verlos}&lt;ga:mf:pron:inan&gt;</td>\n",
       "      <td>ga:mf:pron:inan</td>\n",
       "      <td>verlos</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[4, 5, 6, 7, 27, 26, 27, 28, 29, 49]</td>\n",
       "      <td>Espero que tenga los últimos dos años es incre...</td>\n",
       "      <td>Espero que tenga los últimos dos años es incre...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[4, 5, 6, 7, 27, 26, 27, 28, 29, 49]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3660</th>\n",
       "      <td>3660</td>\n",
       "      <td>499</td>\n",
       "      <td>468</td>\n",
       "      <td>759acb091d84eed0c49bf2fc424002e5939d710f42d3ad...</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Pero no se termino aye, porque tantos personas...</td>\n",
       "      <td>Pero no se termino aye, porque [tantos]{tantas...</td>\n",
       "      <td>[muchas]{mucha}&lt;na:sp:det:inan&gt;</td>\n",
       "      <td>na:sp:det:inan</td>\n",
       "      <td>mucha</td>\n",
       "      <td>...</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[46, 48, 94, 96]</td>\n",
       "      <td>Pero no se termino aye, porque tantas personas...</td>\n",
       "      <td>Pero no se termino aye, porque tantas personas...</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[46, 48, 94, 96]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3661</th>\n",
       "      <td>3661</td>\n",
       "      <td>500</td>\n",
       "      <td>468</td>\n",
       "      <td>759acb091d84eed0c49bf2fc424002e5939d710f42d3ad...</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Pero no se termino aye, porque tantos personas...</td>\n",
       "      <td>Pero no se termino aye, porque [tantos]{tantas...</td>\n",
       "      <td>[musicas]{música}&lt;na:sp:noun:inan&gt;</td>\n",
       "      <td>na:sp:noun:inan</td>\n",
       "      <td>música</td>\n",
       "      <td>...</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[50, 98]</td>\n",
       "      <td>Pero no se termino aye, porque tantas personas...</td>\n",
       "      <td>Pero no se termino aye, porque tantas personas...</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[50, 98]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4008</th>\n",
       "      <td>4008</td>\n",
       "      <td>868</td>\n",
       "      <td>876</td>\n",
       "      <td>374c0876305edbef3c42709975db308f3e6ae592efe6f8...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Joanne Rowling, cuyos seudónimos son J. K. Row...</td>\n",
       "      <td>Joanne Rowling, cuyos seudónimos son J. K. Row...</td>\n",
       "      <td>[filántropo]{filántropa}&lt;ga:fm:noun:an&gt;</td>\n",
       "      <td>ga:fm:noun:an</td>\n",
       "      <td>filántropa</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[20, 31, 53, 64]</td>\n",
       "      <td>Joanne Rowling, cuyos seudónimos son J. K. Row...</td>\n",
       "      <td>Joanne Rowling, cuyos seudónimos son J. K. Row...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[20, 31, 53, 64]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4401</th>\n",
       "      <td>4401</td>\n",
       "      <td>238</td>\n",
       "      <td>1159</td>\n",
       "      <td>ec502ac7d84891a318b52a8f7f6f5ff765d1da51eb150a...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Me encanta todas las islas diferentes y me gus...</td>\n",
       "      <td>Me encanta todas las islas diferentes y me gus...</td>\n",
       "      <td>[mucho]{muchas}&lt;ga:na:fm:ps:pron:inan&gt;</td>\n",
       "      <td>ga:na:fm:ps:pron:inan</td>\n",
       "      <td>muchas</td>\n",
       "      <td>...</td>\n",
       "      <td>gender and number agreement</td>\n",
       "      <td>[4, 24, 31, 51]</td>\n",
       "      <td>Me encanta todas las islas diferentes y me gus...</td>\n",
       "      <td>Me encanta todas las islas diferentes y me gus...</td>\n",
       "      <td>gender and number agreement</td>\n",
       "      <td>[4, 24, 31, 51]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender and number agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4547</th>\n",
       "      <td>4547</td>\n",
       "      <td>396</td>\n",
       "      <td>1241</td>\n",
       "      <td>3b65f88da754cd2666ba1c0d5a169660e9e5385d24c643...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>En el tercer semana, mis padres, mis amigos, y...</td>\n",
       "      <td>En [el]{la}&lt;ga:fm:det:inan&gt; [tercer]{tercera}&lt;...</td>\n",
       "      <td>[Puerta]{puerto}&lt;ig:f:noun:inan&gt;</td>\n",
       "      <td>ig:f:noun:inan</td>\n",
       "      <td>puerto</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[15, 16, 18, 40, 41, 43]</td>\n",
       "      <td>En la tercera semana, mis padres, mis amigos, ...</td>\n",
       "      <td>En la tercera semana, mis padres, mis amigos, ...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[15, 16, 18, 40, 41, 43]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5074</th>\n",
       "      <td>5074</td>\n",
       "      <td>947</td>\n",
       "      <td>385</td>\n",
       "      <td>4a04df071e67e06800a4f4b84b4268e3a65ef4a3398824...</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Perro a mi me gusta los personas famosas de Co...</td>\n",
       "      <td>Perro a mi me gusta [los]{las}&lt;ga:fm:det:an&gt; p...</td>\n",
       "      <td>[favorita]{favorito}&lt;ga:mf:adj:an&gt;</td>\n",
       "      <td>ga:mf:adj:an</td>\n",
       "      <td>favorito</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[24, 26, 28, 58, 60, 62]</td>\n",
       "      <td>Perro a mi me gusta las personas famosas de Co...</td>\n",
       "      <td>Perro a mi me gusta las personas famosas de Co...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[24, 26, 28, 58, 60, 62]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5092</th>\n",
       "      <td>5092</td>\n",
       "      <td>965</td>\n",
       "      <td>729</td>\n",
       "      <td>8321d7a209cd3fcd5a0f4e71bb24d4b367d705ae087d2f...</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>E’l es un actor y está en muchas películas de ...</td>\n",
       "      <td>E’l es un actor y está en muchas películas de ...</td>\n",
       "      <td>[dramático]{dramáticas}&lt;ga:na:fm:ps:adj:inan&gt;</td>\n",
       "      <td>ga:na:fm:ps:adj:inan</td>\n",
       "      <td>dramáticas</td>\n",
       "      <td>...</td>\n",
       "      <td>gender and number agreement</td>\n",
       "      <td>[8, 9, 23, 28, 29, 43]</td>\n",
       "      <td>E ’ l es un actor y está en muchas películas d...</td>\n",
       "      <td>E ’ l es un actor y está en muchas películas d...</td>\n",
       "      <td>gender and number agreement</td>\n",
       "      <td>[8, 9, 23, 28, 29, 43]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender and number agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      orig_index  Unnamed: 0  paragraph_id  \\\n",
       "65            65          72           412   \n",
       "68            68         111           436   \n",
       "421          421         211           775   \n",
       "624          624         440            29   \n",
       "658          658         475            61   \n",
       "663          663         480            65   \n",
       "1034        1034         874           298   \n",
       "1104        1104         946           336   \n",
       "1270        1270          74           412   \n",
       "1280        1280          84           419   \n",
       "1291        1291          95           427   \n",
       "2337        2337         148          1241   \n",
       "2401        2401         215          1264   \n",
       "2920        2920         755           131   \n",
       "3050        3050         896           350   \n",
       "3054        3054         900           649   \n",
       "3068        3068         916           937   \n",
       "3080        3080         929          1081   \n",
       "3085        3085         935          1198   \n",
       "3089        3089         940          1241   \n",
       "3097        3097         949          1363   \n",
       "3139        3139         992            46   \n",
       "3470        3470         291           316   \n",
       "3642        3642         480           445   \n",
       "3660        3660         499           468   \n",
       "3661        3661         500           468   \n",
       "4008        4008         868           876   \n",
       "4401        4401         238          1159   \n",
       "4547        4547         396          1241   \n",
       "5074        5074         947           385   \n",
       "5092        5092         965           729   \n",
       "\n",
       "                                            sentence_id course  \\\n",
       "65    6004f54d6469a368868a7377b03f3e9eb8d7a780202ab1...  SPA 3   \n",
       "68    340344b3cfed0317bbb5c95dafdae2033cf7c6732f5ea3...  SPA 3   \n",
       "421   0c66030cc2c5487d1736df818425e524874d29ada3c571...  SPA 2   \n",
       "624   daeec1fa32f04db8a029f6cfe42d541e117b4e7621f61a...  SPA 3   \n",
       "658   77581a46b29c5a9e1e74abbffea7e7a3c03b59fc345764...  SPA 3   \n",
       "663   139d6a79730caee9d68ad994e3fb047e5c0b6b31d42450...  SPA 3   \n",
       "1034  d539be0f37ba2351c41b5ce478dfc7a23b8bb61a7256f2...  SPA 2   \n",
       "1104  52d24c89b5a3209f034532a43e7693abc52d49d3f25a2a...  SPA 2   \n",
       "1270  a5eb6bff54851d9f5b95e8eec421ccb1e1bb16093f1ca3...  SPA 3   \n",
       "1280  1899da74fd3c342ae0359985a667606be211be7e7c7c0c...  SPA 1   \n",
       "1291  8169da7c36d4bc5f4c27f5c24158fdddb88c8531dce841...  SPA 2   \n",
       "2337  7d41323560f6726aeaa1667d58a3f6a2e06b6c62214761...  SPA 3   \n",
       "2401  79975f6953bc2ae73e7cfbd2a311400b945505bf8482b2...  SPA 2   \n",
       "2920  260c8efe2b960b3810b6cb6bb65eb33301c608b9109d53...  SPA 3   \n",
       "3050  69ee9c42605d18dd2342d1528adb040030fdd411254510...  SPA 2   \n",
       "3054  9772575305dfa7f2672e3c00cd1d447ab83d9e84608dd4...  SPA 2   \n",
       "3068  644a985a157ba246a333f122b6aab4fd56ab32ee361c33...  SPA 2   \n",
       "3080  a344d375871772b542d319cd517c718348d88f2e225917...  SPA 3   \n",
       "3085  94acf628361ec20601a3028d3ad9b4777f8e20540e66f9...  SPA 1   \n",
       "3089  1d7d5a1c69639976fa360b8d54b3579121bf53f7499e14...  SPA 3   \n",
       "3097  7fb0c6b9e1bb965cf21ca24cd62110527336fa456aff77...  SPA 2   \n",
       "3139  76833841bbbdfd09a73d434bc78ad99cb572eb5ef46ee5...  SPA 3   \n",
       "3470  ccfb36e3dfbe363bb7b44a6952daf7c70b24472a9dc8da...  SPA 2   \n",
       "3642  f1386f14918b3bde3c65c0b820188c47218a0ebad39d3f...  SPA 3   \n",
       "3660  759acb091d84eed0c49bf2fc424002e5939d710f42d3ad...  SPA 2   \n",
       "3661  759acb091d84eed0c49bf2fc424002e5939d710f42d3ad...  SPA 2   \n",
       "4008  374c0876305edbef3c42709975db308f3e6ae592efe6f8...  SPA 3   \n",
       "4401  ec502ac7d84891a318b52a8f7f6f5ff765d1da51eb150a...  SPA 3   \n",
       "4547  3b65f88da754cd2666ba1c0d5a169660e9e5385d24c643...  SPA 3   \n",
       "5074  4a04df071e67e06800a4f4b84b4268e3a65ef4a3398824...  SPA 2   \n",
       "5092  8321d7a209cd3fcd5a0f4e71bb24d4b367d705ae087d2f...  SPA 3   \n",
       "\n",
       "                                              essay_new  \\\n",
       "65    Después Harry Potter, J. K. Rowling ha estado ...   \n",
       "68    En Shawshank Redemption -- un libro de Steven ...   \n",
       "421   No tiene \"oscars\" pero Leonardo Dicaprio solom...   \n",
       "624   Bill and Ted es de los dos Bill and Ted y els ...   \n",
       "658   Todavía me gusta escuchar \"Destiny's Child\" y ...   \n",
       "663   Empezó con el grupo se llama Destiny's Child a...   \n",
       "1034  Hugh Jackman es un buen actor pero también es...   \n",
       "1104  Me gustan muchos tipos de musica (rap, r&b,pop...   \n",
       "1270  J. K. Rowling no escribía la obra, pero lo apr...   \n",
       "1280               Oprah Winfrey es African- Americano.   \n",
       "1291  Por ejemplo, los invitó a Taylor's casa para c...   \n",
       "2337     Mi lugar favorito es *CITY* en *STATE* Mexico.   \n",
       "2401  Nosotros acabaremos la noche con cocinando los...   \n",
       "2920  Beyoncé Knowles-Carter es una cantante, actriz...   \n",
       "3050  Cuando era un bebe, Señor Voldemort asesinó lo...   \n",
       "3054  Jimmy Fallon tiene otras personas famosas en s...   \n",
       "3068  Traería a mis mejores amigos, mi hermana y mi ...   \n",
       "3080  Luego voy a llamar mi mamá, mi hermana, mi abu...   \n",
       "3085  Yo traigo mi novio, los padres, la hermana may...   \n",
       "3089  Primero, planaria mi vacacion por un mes y tra...   \n",
       "3097  Una hermosa vista ayuda, pero me encantaría te...   \n",
       "3139  Kim Kardashian-West es una modela quién tiene ...   \n",
       "3470  Si un animale le gusta comida grande, necesita...   \n",
       "3642  Espero que tenga las últimas dos años es incre...   \n",
       "3660  Pero no se termino aye, porque tantos personas...   \n",
       "3661  Pero no se termino aye, porque tantos personas...   \n",
       "4008  Joanne Rowling, cuyos seudónimos son J. K. Row...   \n",
       "4401  Me encanta todas las islas diferentes y me gus...   \n",
       "4547  En el tercer semana, mis padres, mis amigos, y...   \n",
       "5074  Perro a mi me gusta los personas famosas de Co...   \n",
       "5092  E’l es un actor y está en muchas películas de ...   \n",
       "\n",
       "                                     annotated_sentence  \\\n",
       "65    Después Harry Potter, J. K. Rowling ha estado ...   \n",
       "68    En Shawshank Redemption -- un libro de Steven ...   \n",
       "421   No tiene \"oscars\" pero Leonardo Dicaprio solom...   \n",
       "624   Bill and Ted es de los dos Bill and Ted y els ...   \n",
       "658   Todavía me gusta escuchar \"Destiny's Child\" y ...   \n",
       "663   Empezó con el grupo se llama Destiny's Child a...   \n",
       "1034  Hugh Jackman es un buen actor pero también es...   \n",
       "1104  Me gustan muchos tipos de musica (rap, r&b,pop...   \n",
       "1270  J. K. Rowling no escribía la obra, pero [lo]{l...   \n",
       "1280  Oprah Winfrey es African- [Americano]{american...   \n",
       "1291  Por ejemplo, los invitó a Taylor's casa para c...   \n",
       "2337  Mi lugar favorito es [Puerta]{puerto}<ig:f:nou...   \n",
       "2401  Nosotros acabaremos la noche con cocinando los...   \n",
       "2920  Beyoncé Knowles-Carter es una cantante, actriz...   \n",
       "3050  Cuando era un bebe, Señor Voldemort asesinó []...   \n",
       "3054  Jimmy Fallon tiene []{a}<az:do:an> otras perso...   \n",
       "3068  Traería  mis mejores amigos, []{a}<az:do:an> m...   \n",
       "3080  Luego voy a llamar []{a}<az:do:an> mi mamá, []...   \n",
       "3085  Yo traigo []{a}<az:do:an> mi novio, []{a}<az:d...   \n",
       "3089  Primero, planaria mi vacacion por un mes y tra...   \n",
       "3097  Una hermosa vista ayuda, pero me encantaría te...   \n",
       "3139  Kim Kardashian-West es una [modela]{modelo}<ig...   \n",
       "3470  Si un animale le gusta comida grande, necesita...   \n",
       "3642  Espero que tenga [las]{los}<ga:mf:det:inan> [ú...   \n",
       "3660  Pero no se termino aye, porque [tantos]{tantas...   \n",
       "3661  Pero no se termino aye, porque [tantos]{tantas...   \n",
       "4008  Joanne Rowling, cuyos seudónimos son J. K. Row...   \n",
       "4401  Me encanta todas las islas diferentes y me gus...   \n",
       "4547  En [el]{la}<ga:fm:det:inan> [tercer]{tercera}<...   \n",
       "5074  Perro a mi me gusta [los]{las}<ga:fm:det:an> p...   \n",
       "5092  E’l es un actor y está en muchas películas de ...   \n",
       "\n",
       "                                     full_error_tag              error_tag  \\\n",
       "65                 [ocupado]{ocupada}<ga:fm:adj:an>           ga:fm:adj:an   \n",
       "68         [encarcelado]{encarcelada}<ga:fm:adj:an>           ga:fm:adj:an   \n",
       "421                                 []{a}<az:do:an>               az:do:an   \n",
       "624                        [la]{el}<ga:mf:det:inan>         ga:mf:det:inan   \n",
       "658          [estupendo]{estupenda}<ga:fm:adj:inan>         ga:fm:adj:inan   \n",
       "663                      [solo]{sola}<ga:fm:adj:an>           ga:fm:adj:an   \n",
       "1034           [bailarina]{bailarín}<ga:mf:noun:an>          ga:mf:noun:an   \n",
       "1104                 [muchas]{muchos}<ga:mf:det:an>           ga:mf:det:an   \n",
       "1270                      [lo]{la}<ga:fm:pron:inan>        ga:fm:pron:inan   \n",
       "1280           [Americano]{americana}<ga:fm:adj:an>           ga:fm:adj:an   \n",
       "1291         [especial]{especiales}<na:ps:adj:inan>         na:ps:adj:inan   \n",
       "2337               [Puerta]{puerto}<ig:f:noun:inan>         ig:f:noun:inan   \n",
       "2401      [embarazoso]{embarazosos}<na:ps:adj:inan>         na:ps:adj:inan   \n",
       "2920           [americano]{americana}<ga:fm:adj:an>           ga:fm:adj:an   \n",
       "3050                                []{a}<az:do:an>               az:do:an   \n",
       "3054                                []{a}<az:do:an>               az:do:an   \n",
       "3068                                []{a}<az:do:an>               az:do:an   \n",
       "3080                                []{a}<az:do:an>               az:do:an   \n",
       "3085                                []{a}<az:do:an>               az:do:an   \n",
       "3089                                []{a}<az:do:an>               az:do:an   \n",
       "3097                                []{a}<az:do:an>               az:do:an   \n",
       "3139                 [bonita]{bonito}<ga:mf:adj:an>           ga:mf:adj:an   \n",
       "3470             [pequeño]{pequeña}<ga:fm:adj:inan>         ga:fm:adj:inan   \n",
       "3642              [verlas]{verlos}<ga:mf:pron:inan>        ga:mf:pron:inan   \n",
       "3660                [muchas]{mucha}<na:sp:det:inan>         na:sp:det:inan   \n",
       "3661             [musicas]{música}<na:sp:noun:inan>        na:sp:noun:inan   \n",
       "4008        [filántropo]{filántropa}<ga:fm:noun:an>          ga:fm:noun:an   \n",
       "4401         [mucho]{muchas}<ga:na:fm:ps:pron:inan>  ga:na:fm:ps:pron:inan   \n",
       "4547               [Puerta]{puerto}<ig:f:noun:inan>         ig:f:noun:inan   \n",
       "5074             [favorita]{favorito}<ga:mf:adj:an>           ga:mf:adj:an   \n",
       "5092  [dramático]{dramáticas}<ga:na:fm:ps:adj:inan>   ga:na:fm:ps:adj:inan   \n",
       "\n",
       "       correction  ...               new_error_type  \\\n",
       "65        ocupada  ...             gender agreement   \n",
       "68    encarcelada  ...             gender agreement   \n",
       "421             a  ...                      article   \n",
       "624            el  ...             gender agreement   \n",
       "658     estupenda  ...             gender agreement   \n",
       "663          sola  ...             gender agreement   \n",
       "1034     bailarín  ...             gender agreement   \n",
       "1104       muchos  ...             gender agreement   \n",
       "1270           la  ...             gender agreement   \n",
       "1280    americana  ...             gender agreement   \n",
       "1291   especiales  ...             number agreement   \n",
       "2337       puerto  ...             gender agreement   \n",
       "2401  embarazosos  ...             number agreement   \n",
       "2920    americana  ...             gender agreement   \n",
       "3050            a  ...                      article   \n",
       "3054            a  ...                      article   \n",
       "3068            a  ...                      article   \n",
       "3080            a  ...                      article   \n",
       "3085            a  ...                      article   \n",
       "3089            a  ...                      article   \n",
       "3097            a  ...                      article   \n",
       "3139       bonito  ...             gender agreement   \n",
       "3470      pequeña  ...             gender agreement   \n",
       "3642       verlos  ...             gender agreement   \n",
       "3660        mucha  ...             number agreement   \n",
       "3661       música  ...             number agreement   \n",
       "4008   filántropa  ...             gender agreement   \n",
       "4401       muchas  ...  gender and number agreement   \n",
       "4547       puerto  ...             gender agreement   \n",
       "5074     favorito  ...             gender agreement   \n",
       "5092   dramáticas  ...  gender and number agreement   \n",
       "\n",
       "                                    final_evident_index  \\\n",
       "65                                                   []   \n",
       "68                                                   []   \n",
       "421                            [31, 33, 34, 71, 73, 74]   \n",
       "624                                    [31, 32, 63, 64]   \n",
       "658                                            [16, 33]   \n",
       "663                                            [12, 28]   \n",
       "1034                      [2, 3, 4, 17, 20, 21, 22, 35]   \n",
       "1104                                   [22, 23, 43, 44]   \n",
       "1270                            [12, 7, 10, 27, 22, 25]   \n",
       "1280                  [0, 1, 2, 3, 4, 8, 9, 10, 11, 12]   \n",
       "1291                           [15, 16, 17, 31, 32, 33]   \n",
       "2337                                            [5, 16]   \n",
       "2401                           [31, 32, 33, 61, 62, 63]   \n",
       "2920                             [3, 5, 16, 20, 22, 33]   \n",
       "3050                                   [15, 18, 35, 38]   \n",
       "3054                           [20, 21, 22, 45, 46, 47]   \n",
       "3068  [7, 8, 9, 10, 11, 12, 13, 21, 22, 23, 24, 25, ...   \n",
       "3080  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14,...   \n",
       "3085  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...   \n",
       "3089                                   [20, 21, 44, 45]   \n",
       "3097                                   [23, 24, 53, 54]   \n",
       "3139  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...   \n",
       "3470      [2, 4, 6, 31, 33, 34, 33, 35, 37, 62, 64, 65]   \n",
       "3642               [4, 5, 6, 7, 27, 26, 27, 28, 29, 49]   \n",
       "3660                                   [46, 48, 94, 96]   \n",
       "3661                                           [50, 98]   \n",
       "4008                                   [20, 31, 53, 64]   \n",
       "4401                                    [4, 24, 31, 51]   \n",
       "4547                           [15, 16, 18, 40, 41, 43]   \n",
       "5074                           [24, 26, 28, 58, 60, 62]   \n",
       "5092                             [8, 9, 23, 28, 29, 43]   \n",
       "\n",
       "                                        target_sentence  \\\n",
       "65    Después Harry Potter, J. K. Rowling ha estado ...   \n",
       "68    En Shawshank Redemption- - un libro de Steven ...   \n",
       "421   No tiene \"oscars\" pero Leonardo Dicaprio solom...   \n",
       "624   Bill and Ted es de los dos Bill and Ted y els ...   \n",
       "658   Todavía me gusta escuchar \"Destiny' s Child\" y...   \n",
       "663   Empezó con el grupo se llama Destiny' s Child ...   \n",
       "1034  Hugh Jackman es un buen actor pero tambie ́ n ...   \n",
       "1104  Me gustan muchos tipos de musica( rap, r& b, p...   \n",
       "1270  J. K. Rowling no escribía la obra, pero la apr...   \n",
       "1280               Oprah Winfrey es African- americana.   \n",
       "1291  Por ejemplo, los invitó a Taylor' s casa para ...   \n",
       "2337  Mi lugar favorito es puerto Vallarta en Jalisc...   \n",
       "2401  Nosotros acabaremos la noche con cocinando los...   \n",
       "2920  Beyoncé Knowles- Carter es una cantante, actri...   \n",
       "3050  Cuando era un bebe, Señor Voldemort asesinó a ...   \n",
       "3054  Jimmy Fallon tiene a otras personas famosas en...   \n",
       "3068  Traería mis mejores amigos, a mi hermana y a m...   \n",
       "3080  Luego voy a llamar a mi mamá, a mi hermana, a ...   \n",
       "3085  Yo traigo a mi novio, a los padres, a la herma...   \n",
       "3089  Primero, planaria mi vacacion por un mes y tra...   \n",
       "3097  Una hermosa vista ayuda, pero me encantaría te...   \n",
       "3139  Kim Kardashian- West es una modelo quién tiene...   \n",
       "3470  Si un animale le gusta comida grande, necesita...   \n",
       "3642  Espero que tenga los últimos dos años es incre...   \n",
       "3660  Pero no se termino aye, porque tantas personas...   \n",
       "3661  Pero no se termino aye, porque tantas personas...   \n",
       "4008  Joanne Rowling, cuyos seudónimos son J. K. Row...   \n",
       "4401  Me encanta todas las islas diferentes y me gus...   \n",
       "4547  En la tercera semana, mis padres, mis amigos, ...   \n",
       "5074  Perro a mi me gusta las personas famosas de Co...   \n",
       "5092  E ’ l es un actor y está en muchas películas d...   \n",
       "\n",
       "                                        source_sentence  \\\n",
       "65    Después Harry Potter, J. K. Rowling ha estado ...   \n",
       "68    En Shawshank Redemption- - un libro de Steven ...   \n",
       "421   No tiene \"oscars\" pero Leonardo Dicaprio solom...   \n",
       "624   Bill and Ted es de los dos Bill and Ted y els ...   \n",
       "658   Todavía me gusta escuchar \"Destiny' s Child\" y...   \n",
       "663   Empezó con el grupo se llama Destiny' s Child ...   \n",
       "1034  Hugh Jackman es un buen actor pero tambie ́ n ...   \n",
       "1104  Me gustan muchos tipos de musica( rap, r& b, p...   \n",
       "1270  J. K. Rowling no escribía la obra, pero lo apr...   \n",
       "1280               Oprah Winfrey es African- Americano.   \n",
       "1291  Por ejemplo, los invitó a Taylor' s casa para ...   \n",
       "2337  Mi lugar favorito es Puerta Vallarta en Jalisc...   \n",
       "2401  Nosotros acabaremos la noche con cocinando los...   \n",
       "2920  Beyoncé Knowles- Carter es una cantante, actri...   \n",
       "3050  Cuando era un bebe, Señor Voldemort asesinó a ...   \n",
       "3054  Jimmy Fallon tiene a otras personas famosas en...   \n",
       "3068  Traería mis mejores amigos, [NONE] mi hermana ...   \n",
       "3080  Luego voy a llamar a mi mamá, a mi hermana, [N...   \n",
       "3085  Yo traigo a mi novio, a los padres, a la herma...   \n",
       "3089  Primero, planaria mi vacacion por un mes y tra...   \n",
       "3097  Una hermosa vista ayuda, pero me encantaría te...   \n",
       "3139  Kim Kardashian- West es una modelo quién tiene...   \n",
       "3470  Si un animale le gusta comida grande, necesita...   \n",
       "3642  Espero que tenga los últimos dos años es incre...   \n",
       "3660  Pero no se termino aye, porque tantas personas...   \n",
       "3661  Pero no se termino aye, porque tantas personas...   \n",
       "4008  Joanne Rowling, cuyos seudónimos son J. K. Row...   \n",
       "4401  Me encanta todas las islas diferentes y me gus...   \n",
       "4547  En la tercera semana, mis padres, mis amigos, ...   \n",
       "5074  Perro a mi me gusta las personas famosas de Co...   \n",
       "5092  E ’ l es un actor y está en muchas películas d...   \n",
       "\n",
       "                       error_type  \\\n",
       "65               gender agreement   \n",
       "68               gender agreement   \n",
       "421                       article   \n",
       "624              gender agreement   \n",
       "658              gender agreement   \n",
       "663              gender agreement   \n",
       "1034             gender agreement   \n",
       "1104             gender agreement   \n",
       "1270             gender agreement   \n",
       "1280             gender agreement   \n",
       "1291             number agreement   \n",
       "2337             gender agreement   \n",
       "2401             number agreement   \n",
       "2920             gender agreement   \n",
       "3050                      article   \n",
       "3054                      article   \n",
       "3068                      article   \n",
       "3080                      article   \n",
       "3085                      article   \n",
       "3089                      article   \n",
       "3097                      article   \n",
       "3139             gender agreement   \n",
       "3470             gender agreement   \n",
       "3642             gender agreement   \n",
       "3660             number agreement   \n",
       "3661             number agreement   \n",
       "4008             gender agreement   \n",
       "4401  gender and number agreement   \n",
       "4547             gender agreement   \n",
       "5074             gender agreement   \n",
       "5092  gender and number agreement   \n",
       "\n",
       "                                         evidence_index  \\\n",
       "65                                                   []   \n",
       "68                                                   []   \n",
       "421                            [31, 33, 34, 71, 73, 74]   \n",
       "624                                    [31, 32, 63, 64]   \n",
       "658                                            [16, 33]   \n",
       "663                                            [12, 28]   \n",
       "1034                      [2, 3, 4, 17, 20, 21, 22, 35]   \n",
       "1104                                   [22, 23, 43, 44]   \n",
       "1270                            [12, 7, 10, 27, 22, 25]   \n",
       "1280                  [0, 1, 2, 3, 4, 8, 9, 10, 11, 12]   \n",
       "1291                           [15, 16, 17, 31, 32, 33]   \n",
       "2337                                            [5, 16]   \n",
       "2401                           [31, 32, 33, 61, 62, 63]   \n",
       "2920                             [3, 5, 16, 20, 22, 33]   \n",
       "3050                                   [15, 18, 35, 38]   \n",
       "3054                           [20, 21, 22, 45, 46, 47]   \n",
       "3068  [7, 8, 9, 10, 11, 12, 13, 21, 22, 23, 24, 25, ...   \n",
       "3080  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14,...   \n",
       "3085  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...   \n",
       "3089                                   [20, 21, 44, 45]   \n",
       "3097                                   [23, 24, 53, 54]   \n",
       "3139  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,...   \n",
       "3470      [2, 4, 6, 31, 33, 34, 33, 35, 37, 62, 64, 65]   \n",
       "3642               [4, 5, 6, 7, 27, 26, 27, 28, 29, 49]   \n",
       "3660                                   [46, 48, 94, 96]   \n",
       "3661                                           [50, 98]   \n",
       "4008                                   [20, 31, 53, 64]   \n",
       "4401                                    [4, 24, 31, 51]   \n",
       "4547                           [15, 16, 18, 40, 41, 43]   \n",
       "5074                           [24, 26, 28, 58, 60, 62]   \n",
       "5092                             [8, 9, 23, 28, 29, 43]   \n",
       "\n",
       "     predicted_parsing_order origin              orig_error_type  \\\n",
       "65                      None      A             gender agreement   \n",
       "68                      None      A             gender agreement   \n",
       "421                     None      A                      article   \n",
       "624                     None      A             gender agreement   \n",
       "658                     None      A             gender agreement   \n",
       "663                     None      A             gender agreement   \n",
       "1034                    None      A             gender agreement   \n",
       "1104                    None      A             gender agreement   \n",
       "1270                    None      A             gender agreement   \n",
       "1280                    None      A             gender agreement   \n",
       "1291                    None      A             number agreement   \n",
       "2337                    None      A             gender agreement   \n",
       "2401                    None      A             number agreement   \n",
       "2920                    None      A             gender agreement   \n",
       "3050                    None      A                      article   \n",
       "3054                    None      A                      article   \n",
       "3068                    None      A                      article   \n",
       "3080                    None      A                      article   \n",
       "3085                    None      A                      article   \n",
       "3089                    None      A                      article   \n",
       "3097                    None      A                      article   \n",
       "3139                    None      A             gender agreement   \n",
       "3470                    None      A             gender agreement   \n",
       "3642                    None      A             gender agreement   \n",
       "3660                    None      A             number agreement   \n",
       "3661                    None      A             number agreement   \n",
       "4008                    None      A             gender agreement   \n",
       "4401                    None      A  gender and number agreement   \n",
       "4547                    None      A             gender agreement   \n",
       "5074                    None      A             gender agreement   \n",
       "5092                    None      A  gender and number agreement   \n",
       "\n",
       "                            predicted_parsing_order_src  \n",
       "65                                                 None  \n",
       "68                                                 None  \n",
       "421                                                None  \n",
       "624                                                None  \n",
       "658                                                None  \n",
       "663                                                None  \n",
       "1034                                               None  \n",
       "1104                                               None  \n",
       "1270                                               None  \n",
       "1280                                               None  \n",
       "1291                                               None  \n",
       "2337  {'1': 3, '10': 3, '11': 3, '3': 3, '4': 1, '5'...  \n",
       "2401                                               None  \n",
       "2920                                               None  \n",
       "3050                                               None  \n",
       "3054                                               None  \n",
       "3068                                               None  \n",
       "3080                                               None  \n",
       "3085                                               None  \n",
       "3089                                               None  \n",
       "3097                                               None  \n",
       "3139                                               None  \n",
       "3470                                               None  \n",
       "3642                                               None  \n",
       "3660                                               None  \n",
       "3661                                               None  \n",
       "4008                                               None  \n",
       "4401                                               None  \n",
       "4547                                               None  \n",
       "5074                                               None  \n",
       "5092                                               None  \n",
       "\n",
       "[31 rows x 24 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_errors[df_errors.predicted_parsing_order.isna()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2883b66b-9712-4da8-b5e6-2f527189264d",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Visualize Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8607013d-1fe3-4d26-8125-fa2cbf2eb8dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.27 s, sys: 1.1 s, total: 4.37 s\n",
      "Wall time: 808 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Plot the data\n",
    "def build_df_dependencies(df):\n",
    "    df_dependencies = pd.DataFrame({'Num Dependencies':list(range(0, 14))})\n",
    "    df_dependencies['1st Order Deps'] = 0\n",
    "    df_dependencies['2nd Order Deps'] = 0\n",
    "    df_first_order =  df['First Order Dependencies'].value_counts().sort_index(axis=0)\n",
    "    df_second_order =  df['Second Order Dependencies'].value_counts().sort_index(axis=0)\n",
    "    for num_dependencies, count in df_first_order.items():\n",
    "        df_dependencies.loc[df_dependencies['Num Dependencies'] == num_dependencies, '1st Order Deps'] = count\n",
    "    for num_dependencies, count in df_second_order.items():\n",
    "        df_dependencies.loc[df_dependencies['Num Dependencies'] == num_dependencies, '2nd Order Deps'] = count\n",
    "    return df_dependencies\n",
    "\n",
    "def build_dfs(df):\n",
    "    df_plot = pd.DataFrame()\n",
    "    df_plot['Sentence Len'] = df.apply(lambda row: len(row.target), axis=1)\n",
    "    df_plot['Parsing Order Len'] = df.apply(lambda row: len(row.predicted_parsing_order) if row.predicted_parsing_order else 0, axis=1)\n",
    "    df_plot['First Order Dependencies'] = df.apply(lambda row: count_by_order(row, 2), axis=1)\n",
    "    df_plot['Second Order Dependencies'] = df.apply(lambda row: count_by_order(row, 3), axis=1)\n",
    "    return df_plot, build_df_dependencies(df_plot)\n",
    "\n",
    "def plots(df):\n",
    "    df_plot, df_dependencies = build_dfs(df)\n",
    "\n",
    "    df_plot['Sentence Len'].value_counts().sort_index(axis=0).plot(\n",
    "        kind='bar',\n",
    "        fontsize=10,\n",
    "        figsize=(9, 6),\n",
    "        xlabel=\"Num Words\",\n",
    "        ylabel=\"Count of Lines\",\n",
    "        xticks = None,\n",
    "        title=\"Distribution of Sentence Length\",\n",
    "    )\n",
    "    plt.savefig('training_report/sentence_len_distr.png', dpi=300, transparent=True)\n",
    "    plt.show()\n",
    "\n",
    "    plot_combo = df_dependencies.plot(\n",
    "        kind='bar',\n",
    "        x='Num Dependencies',\n",
    "        fontsize=10,\n",
    "        figsize=(8, 5),\n",
    "        ylabel='Count of Lines',\n",
    "        title='Distribution of First and Second Order Dependencies',\n",
    "        grid=True,\n",
    "        table=True\n",
    "    )\n",
    "    plot_combo.axes.get_xaxis().set_visible(False)\n",
    "    # Getting the table created by pandas and matplotlib\n",
    "    table = plot_combo.tables[0]\n",
    "    # Setting the font size\n",
    "    table.set_fontsize(10)\n",
    "    # Rescaling the rows to be more readable\n",
    "    table.scale(1,2)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    df_class_dist = df_errors.error_type.value_counts()\n",
    "    plot_classes = df_class_dist.plot(\n",
    "        kind='bar',\n",
    "        fontsize=12,\n",
    "        figsize=(10, 5),\n",
    "        xlabel=\"Number of samples\",\n",
    "        ylabel=\"Error Class\",\n",
    "        title=\"Distribution of Error Class\",\n",
    "        table=True)\n",
    "    plot_classes.axes.get_xaxis().set_visible(False)\n",
    "    # Getting the table created by pandas and matplotlib\n",
    "    table = plot_classes.tables[0]\n",
    "    # Setting the font size\n",
    "    table.set_fontsize(10)\n",
    "    # Rescaling the rows to be more readable\n",
    "    table.scale(1,1)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "plots(df_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8fc213d0-4bb8-4bda-99e0-9a9e02436d52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(31, 24)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orig_index</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>paragraph_id</th>\n",
       "      <th>sentence_id</th>\n",
       "      <th>course</th>\n",
       "      <th>essay_new</th>\n",
       "      <th>annotated_sentence</th>\n",
       "      <th>full_error_tag</th>\n",
       "      <th>error_tag</th>\n",
       "      <th>correction</th>\n",
       "      <th>...</th>\n",
       "      <th>new_error_type</th>\n",
       "      <th>final_evident_index</th>\n",
       "      <th>target_sentence</th>\n",
       "      <th>source_sentence</th>\n",
       "      <th>error_type</th>\n",
       "      <th>evidence_index</th>\n",
       "      <th>predicted_parsing_order</th>\n",
       "      <th>origin</th>\n",
       "      <th>orig_error_type</th>\n",
       "      <th>predicted_parsing_order_src</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>65</td>\n",
       "      <td>72</td>\n",
       "      <td>412</td>\n",
       "      <td>6004f54d6469a368868a7377b03f3e9eb8d7a780202ab1862ad151095ab6e16b</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Después Harry Potter, J. K. Rowling ha estado muy ocupado.</td>\n",
       "      <td>Después Harry Potter, J. K. Rowling ha estado muy [ocupado]{ocupada}&lt;ga:fm:adj:an&gt;.</td>\n",
       "      <td>[ocupado]{ocupada}&lt;ga:fm:adj:an&gt;</td>\n",
       "      <td>ga:fm:adj:an</td>\n",
       "      <td>ocupada</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[]</td>\n",
       "      <td>Después Harry Potter, J. K. Rowling ha estado muy ocupada.</td>\n",
       "      <td>Después Harry Potter, J. K. Rowling ha estado muy ocupado.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>68</td>\n",
       "      <td>111</td>\n",
       "      <td>436</td>\n",
       "      <td>340344b3cfed0317bbb5c95dafdae2033cf7c6732f5ea383f2e64ffe93297fd5</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>En Shawshank Redemption -- un libro de Steven King que hicieron en pelicula -- el es un prisonero serviendo tiempo y ayuda otro prisonero sobrevivir los malos que una persona sufre quando esta encarcelado.</td>\n",
       "      <td>En Shawshank Redemption -- un libro de Steven King que hicieron en pelicula -- el es un prisonero serviendo tiempo y ayuda otro prisonero sobrevivir los malos que una persona sufre quando esta [encarcelado]{encarcelada}&lt;ga:fm:adj:an&gt;.</td>\n",
       "      <td>[encarcelado]{encarcelada}&lt;ga:fm:adj:an&gt;</td>\n",
       "      <td>ga:fm:adj:an</td>\n",
       "      <td>encarcelada</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[]</td>\n",
       "      <td>En Shawshank Redemption- - un libro de Steven King que hicieron en pelicula- - el es un prisonero serviendo tiempo y ayuda otro prisonero sobrevivir los malos que una persona sufre quando esta encarcelada.</td>\n",
       "      <td>En Shawshank Redemption- - un libro de Steven King que hicieron en pelicula- - el es un prisonero serviendo tiempo y ayuda otro prisonero sobrevivir los malos que una persona sufre quando esta encarcelado.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>421</td>\n",
       "      <td>211</td>\n",
       "      <td>775</td>\n",
       "      <td>0c66030cc2c5487d1736df818425e524874d29ada3c571b76adb1731e1015055</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>No tiene \"oscars\" pero Leonardo Dicaprio solomente recibió un oscar el año pasado, así que....Pues, quiero ver una película con él este año y quiero conocer Walken también.</td>\n",
       "      <td>No tiene \"oscars\" pero Leonardo Dicaprio solomente recibió un oscar el año pasado, así que....Pues, quiero ver una película con él este año y quiero conocer []{a}&lt;az:do:an&gt; Walken también.</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[31, 33, 34, 71, 73, 74]</td>\n",
       "      <td>No tiene \"oscars\" pero Leonardo Dicaprio solomente recibió un oscar el año pasado, así que. . . . Pues, quiero ver una película con él este año y quiero conocer a Walken también.</td>\n",
       "      <td>No tiene \"oscars\" pero Leonardo Dicaprio solomente recibió un oscar el año pasado, así que. . . . Pues, quiero ver una película con él este año y quiero conocer [NONE] Walken también.</td>\n",
       "      <td>article</td>\n",
       "      <td>[31, 33, 34, 71, 73, 74]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>624</td>\n",
       "      <td>440</td>\n",
       "      <td>29</td>\n",
       "      <td>daeec1fa32f04db8a029f6cfe42d541e117b4e7621f61ae9fddbc7b5016766fc</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Bill and Ted es de los dos Bill and Ted y els van en diferentes tiempos como los 1800’s o más patras.También van en la futuro.</td>\n",
       "      <td>Bill and Ted es de los dos Bill and Ted y els van en diferentes tiempos como los 1800’s o más patras.También van en [la]{el}&lt;ga:mf:det:inan&gt; futuro.</td>\n",
       "      <td>[la]{el}&lt;ga:mf:det:inan&gt;</td>\n",
       "      <td>ga:mf:det:inan</td>\n",
       "      <td>el</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[31, 32, 63, 64]</td>\n",
       "      <td>Bill and Ted es de los dos Bill and Ted y els van en diferentes tiempos como los 1800 ’ s o más patras. También van en el futuro.</td>\n",
       "      <td>Bill and Ted es de los dos Bill and Ted y els van en diferentes tiempos como los 1800 ’ s o más patras. También van en la futuro.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[31, 32, 63, 64]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>658</td>\n",
       "      <td>475</td>\n",
       "      <td>61</td>\n",
       "      <td>77581a46b29c5a9e1e74abbffea7e7a3c03b59fc3457647f571a1ba1ddbacce3</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Todavía me gusta escuchar \"Destiny's Child\" y su música es estupendo.</td>\n",
       "      <td>Todavía me gusta escuchar \"Destiny's Child\" y su música es [estupendo]{estupenda}&lt;ga:fm:adj:inan&gt;.</td>\n",
       "      <td>[estupendo]{estupenda}&lt;ga:fm:adj:inan&gt;</td>\n",
       "      <td>ga:fm:adj:inan</td>\n",
       "      <td>estupenda</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[16, 33]</td>\n",
       "      <td>Todavía me gusta escuchar \"Destiny' s Child\" y su música es estupenda.</td>\n",
       "      <td>Todavía me gusta escuchar \"Destiny' s Child\" y su música es estupendo.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[16, 33]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>663</td>\n",
       "      <td>480</td>\n",
       "      <td>65</td>\n",
       "      <td>139d6a79730caee9d68ad994e3fb047e5c0b6b31d42450bcf838d3943b5e33dc</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Empezó con el grupo se llama Destiny's Child antes de realizar solo.</td>\n",
       "      <td>Empezó con el grupo se llama Destiny's Child antes de realizar [solo]{sola}&lt;ga:fm:adj:an&gt;.</td>\n",
       "      <td>[solo]{sola}&lt;ga:fm:adj:an&gt;</td>\n",
       "      <td>ga:fm:adj:an</td>\n",
       "      <td>sola</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[12, 28]</td>\n",
       "      <td>Empezó con el grupo se llama Destiny' s Child antes de realizar sola.</td>\n",
       "      <td>Empezó con el grupo se llama Destiny' s Child antes de realizar solo.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[12, 28]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1034</th>\n",
       "      <td>1034</td>\n",
       "      <td>874</td>\n",
       "      <td>298</td>\n",
       "      <td>d539be0f37ba2351c41b5ce478dfc7a23b8bb61a7256f29cb0bef8e2e203e25f</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Hugh Jackman es un buen actor pero también es un buen cantante y bailarina.</td>\n",
       "      <td>Hugh Jackman es un buen actor pero también es un buen cantante y [bailarina]{bailarín}&lt;ga:mf:noun:an&gt;.</td>\n",
       "      <td>[bailarina]{bailarín}&lt;ga:mf:noun:an&gt;</td>\n",
       "      <td>ga:mf:noun:an</td>\n",
       "      <td>bailarín</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[2, 3, 4, 17, 20, 21, 22, 35]</td>\n",
       "      <td>Hugh Jackman es un buen actor pero tambie ́ n es un buen cantante y bailarín.</td>\n",
       "      <td>Hugh Jackman es un buen actor pero tambie ́ n es un buen cantante y bailarina.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[2, 3, 4, 17, 20, 21, 22, 35]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>1104</td>\n",
       "      <td>946</td>\n",
       "      <td>336</td>\n",
       "      <td>52d24c89b5a3209f034532a43e7693abc52d49d3f25a2ae4aeaf0e32de8a6a45</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Me gustan muchos tipos de musica (rap, r&amp;b,pop) , y muchas artistas.</td>\n",
       "      <td>Me gustan muchos tipos de musica (rap, r&amp;b,pop) , y [muchas]{muchos}&lt;ga:mf:det:an&gt; artistas.</td>\n",
       "      <td>[muchas]{muchos}&lt;ga:mf:det:an&gt;</td>\n",
       "      <td>ga:mf:det:an</td>\n",
       "      <td>muchos</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[22, 23, 43, 44]</td>\n",
       "      <td>Me gustan muchos tipos de musica( rap, r&amp; b, pop) , y muchos artistas.</td>\n",
       "      <td>Me gustan muchos tipos de musica( rap, r&amp; b, pop) , y muchas artistas.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[22, 23, 43, 44]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1270</th>\n",
       "      <td>1270</td>\n",
       "      <td>74</td>\n",
       "      <td>412</td>\n",
       "      <td>a5eb6bff54851d9f5b95e8eec421ccb1e1bb16093f1ca30f076f18b93ed55080</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>J. K. Rowling no escribía la obra, pero lo aprobó.</td>\n",
       "      <td>J. K. Rowling no escribía la obra, pero [lo]{la}&lt;ga:fm:pron:inan&gt; aprobó.</td>\n",
       "      <td>[lo]{la}&lt;ga:fm:pron:inan&gt;</td>\n",
       "      <td>ga:fm:pron:inan</td>\n",
       "      <td>la</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[12, 7, 10, 27, 22, 25]</td>\n",
       "      <td>J. K. Rowling no escribía la obra, pero la aprobó.</td>\n",
       "      <td>J. K. Rowling no escribía la obra, pero lo aprobó.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[12, 7, 10, 27, 22, 25]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1280</th>\n",
       "      <td>1280</td>\n",
       "      <td>84</td>\n",
       "      <td>419</td>\n",
       "      <td>1899da74fd3c342ae0359985a667606be211be7e7c7c0ca2ee7f1e6bdaa0574b</td>\n",
       "      <td>SPA 1</td>\n",
       "      <td>Oprah Winfrey es African- Americano.</td>\n",
       "      <td>Oprah Winfrey es African- [Americano]{americana}&lt;ga:fm:adj:an&gt;.</td>\n",
       "      <td>[Americano]{americana}&lt;ga:fm:adj:an&gt;</td>\n",
       "      <td>ga:fm:adj:an</td>\n",
       "      <td>americana</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[0, 1, 2, 3, 4, 8, 9, 10, 11, 12]</td>\n",
       "      <td>Oprah Winfrey es African- americana.</td>\n",
       "      <td>Oprah Winfrey es African- Americano.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[0, 1, 2, 3, 4, 8, 9, 10, 11, 12]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>1291</td>\n",
       "      <td>95</td>\n",
       "      <td>427</td>\n",
       "      <td>8169da7c36d4bc5f4c27f5c24158fdddb88c8531dce841dbe0025b4bbaf53fc2</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Por ejemplo, los invitó a Taylor's casa para cantar especial canciones.</td>\n",
       "      <td>Por ejemplo, los invitó a Taylor's casa para cantar [especial]{especiales}&lt;na:ps:adj:inan&gt; canciones.</td>\n",
       "      <td>[especial]{especiales}&lt;na:ps:adj:inan&gt;</td>\n",
       "      <td>na:ps:adj:inan</td>\n",
       "      <td>especiales</td>\n",
       "      <td>...</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[15, 16, 17, 31, 32, 33]</td>\n",
       "      <td>Por ejemplo, los invitó a Taylor' s casa para cantar especiales canciones.</td>\n",
       "      <td>Por ejemplo, los invitó a Taylor' s casa para cantar especial canciones.</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[15, 16, 17, 31, 32, 33]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2337</th>\n",
       "      <td>2337</td>\n",
       "      <td>148</td>\n",
       "      <td>1241</td>\n",
       "      <td>7d41323560f6726aeaa1667d58a3f6a2e06b6c62214761f7368fc0ab39ed763b</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Mi lugar favorito es *CITY* en *STATE* Mexico.</td>\n",
       "      <td>Mi lugar favorito es [Puerta]{puerto}&lt;ig:f:noun:inan&gt; Vallarta en Jalisco Mexico.</td>\n",
       "      <td>[Puerta]{puerto}&lt;ig:f:noun:inan&gt;</td>\n",
       "      <td>ig:f:noun:inan</td>\n",
       "      <td>puerto</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[5, 16]</td>\n",
       "      <td>Mi lugar favorito es puerto Vallarta en Jalisco Mexico.</td>\n",
       "      <td>Mi lugar favorito es Puerta Vallarta en Jalisco Mexico.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[5, 16]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>{'1': 3, '10': 3, '11': 3, '3': 3, '4': 1, '5': 2, '6': 3, '9': 3}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2401</th>\n",
       "      <td>2401</td>\n",
       "      <td>215</td>\n",
       "      <td>1264</td>\n",
       "      <td>79975f6953bc2ae73e7cfbd2a311400b945505bf8482b2ec0b02d5b66fdf3807</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Nosotros acabaremos la noche con cocinando los perros calientes, los perros tofu, y s’mores encima de la hoguera mientras contando cuentos cómicos y embarazoso.</td>\n",
       "      <td>Nosotros acabaremos la noche con cocinando los perros calientes, los perros tofu, y s’mores encima de la hoguera mientras contando cuentos cómicos y [embarazoso]{embarazosos}&lt;na:ps:adj:inan&gt;.</td>\n",
       "      <td>[embarazoso]{embarazosos}&lt;na:ps:adj:inan&gt;</td>\n",
       "      <td>na:ps:adj:inan</td>\n",
       "      <td>embarazosos</td>\n",
       "      <td>...</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[31, 32, 33, 61, 62, 63]</td>\n",
       "      <td>Nosotros acabaremos la noche con cocinando los perros calientes, los perros tofu, y s ’ mores encima de la hoguera mientras contando cuentos cómicos y embarazosos.</td>\n",
       "      <td>Nosotros acabaremos la noche con cocinando los perros calientes, los perros tofu, y s ’ mores encima de la hoguera mientras contando cuentos cómicos y embarazoso.</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[31, 32, 33, 61, 62, 63]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2920</th>\n",
       "      <td>2920</td>\n",
       "      <td>755</td>\n",
       "      <td>131</td>\n",
       "      <td>260c8efe2b960b3810b6cb6bb65eb33301c608b9109d5352b43710c83fec8ea5</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Beyoncé Knowles-Carter es una cantante, actriz, y compositora de canciones americano.</td>\n",
       "      <td>Beyoncé Knowles-Carter es una cantante, actriz, y compositora de canciones [americano]{americana}&lt;ga:fm:adj:an&gt;.</td>\n",
       "      <td>[americano]{americana}&lt;ga:fm:adj:an&gt;</td>\n",
       "      <td>ga:fm:adj:an</td>\n",
       "      <td>americana</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[3, 5, 16, 20, 22, 33]</td>\n",
       "      <td>Beyoncé Knowles- Carter es una cantante, actriz, y compositora de canciones americana.</td>\n",
       "      <td>Beyoncé Knowles- Carter es una cantante, actriz, y compositora de canciones americano.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[3, 5, 16, 20, 22, 33]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3050</th>\n",
       "      <td>3050</td>\n",
       "      <td>896</td>\n",
       "      <td>350</td>\n",
       "      <td>69ee9c42605d18dd2342d1528adb040030fdd41125451085bf22b91751c0036e</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Cuando era un bebe, Señor Voldemort asesinó los padres de Harry y intentó asesinar Harry.</td>\n",
       "      <td>Cuando era un bebe, Señor Voldemort asesinó []{a}&lt;az:do:an&gt; los padres de Harry y intentó asesinar []{a}&lt;az:do:an&gt;  Harry.</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[15, 18, 35, 38]</td>\n",
       "      <td>Cuando era un bebe, Señor Voldemort asesinó a los padres de Harry y intentó asesinar a Harry.</td>\n",
       "      <td>Cuando era un bebe, Señor Voldemort asesinó a los padres de Harry y intentó asesinar [NONE] Harry.</td>\n",
       "      <td>article</td>\n",
       "      <td>[15, 18, 35, 38]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3054</th>\n",
       "      <td>3054</td>\n",
       "      <td>900</td>\n",
       "      <td>649</td>\n",
       "      <td>9772575305dfa7f2672e3c00cd1d447ab83d9e84608dd4f64b06d1cdd5e8719c</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Jimmy Fallon tiene otras personas famosas en su show como P!NK, Katy Perry, y muchos otras personas.</td>\n",
       "      <td>Jimmy Fallon tiene []{a}&lt;az:do:an&gt; otras personas famosas en su show como P!NK, Katy Perry, y []{a}&lt;az:do:an&gt;  muchos otras personas.</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[20, 21, 22, 45, 46, 47]</td>\n",
       "      <td>Jimmy Fallon tiene a otras personas famosas en su show como P! NK, Katy Perry, y a muchos otras personas.</td>\n",
       "      <td>Jimmy Fallon tiene a otras personas famosas en su show como P! NK, Katy Perry, y [NONE] muchos otras personas.</td>\n",
       "      <td>article</td>\n",
       "      <td>[20, 21, 22, 45, 46, 47]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3068</th>\n",
       "      <td>3068</td>\n",
       "      <td>916</td>\n",
       "      <td>937</td>\n",
       "      <td>644a985a157ba246a333f122b6aab4fd56ab32ee361c3381a5d697dcc7247cd7</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Traería a mis mejores amigos, mi hermana y mi novio.</td>\n",
       "      <td>Traería  mis mejores amigos, []{a}&lt;az:do:an&gt; mi hermana y []{a}&lt;az:do:an&gt; mi novio.</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[7, 8, 9, 10, 11, 12, 13, 21, 22, 23, 24, 25, 26, 27]</td>\n",
       "      <td>Traería mis mejores amigos, a mi hermana y a mi novio.</td>\n",
       "      <td>Traería mis mejores amigos, [NONE] mi hermana y a mi novio.</td>\n",
       "      <td>article</td>\n",
       "      <td>[7, 8, 9, 10, 11, 12, 13, 21, 22, 23, 24, 25, 26, 27]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3080</th>\n",
       "      <td>3080</td>\n",
       "      <td>929</td>\n",
       "      <td>1081</td>\n",
       "      <td>a344d375871772b542d319cd517c718348d88f2e2259175c4cc0888d0a0cccae</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Luego voy a llamar mi mamá, mi hermana, mi abuela.</td>\n",
       "      <td>Luego voy a llamar []{a}&lt;az:do:an&gt; mi mamá, []{a}&lt;az:do:an&gt; mi hermana, []{a}&lt;az:do:an&gt; mi abuela.</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32]</td>\n",
       "      <td>Luego voy a llamar a mi mamá, a mi hermana, a mi abuela.</td>\n",
       "      <td>Luego voy a llamar a mi mamá, a mi hermana, [NONE] mi abuela.</td>\n",
       "      <td>article</td>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3085</th>\n",
       "      <td>3085</td>\n",
       "      <td>935</td>\n",
       "      <td>1198</td>\n",
       "      <td>94acf628361ec20601a3028d3ad9b4777f8e20540e66f96f61aaa858eee03c52</td>\n",
       "      <td>SPA 1</td>\n",
       "      <td>Yo traigo mi novio, los padres, la hermana mayor, y perro pequeno.</td>\n",
       "      <td>Yo traigo []{a}&lt;az:do:an&gt; mi novio, []{a}&lt;az:do:an&gt; los padres, []{a}&lt;az:do:an&gt; la hermana mayor, y []{a}&lt;az:do:an&gt; perro pequeno.</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40]</td>\n",
       "      <td>Yo traigo a mi novio, a los padres, a la hermana mayor, y a perro pequeno.</td>\n",
       "      <td>Yo traigo a mi novio, a los padres, a la hermana mayor, y [NONE] perro pequeno.</td>\n",
       "      <td>article</td>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3089</th>\n",
       "      <td>3089</td>\n",
       "      <td>940</td>\n",
       "      <td>1241</td>\n",
       "      <td>1d7d5a1c69639976fa360b8d54b3579121bf53f7499e14919c58da6bf02a363f</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Primero, planaria mi vacacion por un mes y traeria mi madre, mi padre, y mis amigos.</td>\n",
       "      <td>Primero, planaria mi vacacion por un mes y traeria []{a}&lt;az:do:an&gt; mi madre, []{a}&lt;az:do:an&gt; mi padre, y []{a}&lt;az:do:an&gt; mis amigos.</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[20, 21, 44, 45]</td>\n",
       "      <td>Primero, planaria mi vacacion por un mes y traeria a mi madre, a mi padre, y a mis amigos.</td>\n",
       "      <td>Primero, planaria mi vacacion por un mes y traeria a mi madre, a mi padre, y [NONE] mis amigos.</td>\n",
       "      <td>article</td>\n",
       "      <td>[20, 21, 44, 45]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3097</th>\n",
       "      <td>3097</td>\n",
       "      <td>949</td>\n",
       "      <td>1363</td>\n",
       "      <td>7fb0c6b9e1bb965cf21ca24cd62110527336fa456aff774bd15c44f4de030811</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Una hermosa vista ayuda, pero me encantaría tener a mis sobrinos y sobrinas allí, mis dos hermanos, mi hermana y mis padres.</td>\n",
       "      <td>Una hermosa vista ayuda, pero me encantaría tener   mis sobrinos y []{a}&lt;az:do:an&gt; sobrinas allí, []{a}&lt;az:do:an&gt; mis dos hermanos, []{a}&lt;az:do:an&gt; mi hermana y []{a}&lt;az:do:an&gt; mis padres.</td>\n",
       "      <td>[]{a}&lt;az:do:an&gt;</td>\n",
       "      <td>az:do:an</td>\n",
       "      <td>a</td>\n",
       "      <td>...</td>\n",
       "      <td>article</td>\n",
       "      <td>[23, 24, 53, 54]</td>\n",
       "      <td>Una hermosa vista ayuda, pero me encantaría tener mis sobrinos y a sobrinas allí, a mis dos hermanos, a mi hermana y a mis padres.</td>\n",
       "      <td>Una hermosa vista ayuda, pero me encantaría tener mis sobrinos y a sobrinas allí, a mis dos hermanos, a mi hermana y [NONE] mis padres.</td>\n",
       "      <td>article</td>\n",
       "      <td>[23, 24, 53, 54]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>article</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3139</th>\n",
       "      <td>3139</td>\n",
       "      <td>992</td>\n",
       "      <td>46</td>\n",
       "      <td>76833841bbbdfd09a73d434bc78ad99cb572eb5ef46ee5e8e5ce5d7a7e0bd011</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Kim Kardashian-West es una modela quién tiene un cuerpo muy famosa y bonita.</td>\n",
       "      <td>Kim Kardashian-West es una [modela]{modelo}&lt;ig:f:noun:an&gt; quién tiene un cuerpo muy [famosa]{famoso}&lt;ga:mf:adj:an&gt; y [bonita]{bonito}&lt;ga:mf:adj:an&gt;.</td>\n",
       "      <td>[bonita]{bonito}&lt;ga:mf:adj:an&gt;</td>\n",
       "      <td>ga:mf:adj:an</td>\n",
       "      <td>bonito</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]</td>\n",
       "      <td>Kim Kardashian- West es una modelo quién tiene un cuerpo muy famoso y bonito.</td>\n",
       "      <td>Kim Kardashian- West es una modelo quién tiene un cuerpo muy famoso y bonita.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3470</th>\n",
       "      <td>3470</td>\n",
       "      <td>291</td>\n",
       "      <td>316</td>\n",
       "      <td>ccfb36e3dfbe363bb7b44a6952daf7c70b24472a9dc8daa1b588ca86fd6194a0</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Si un animale le gusta comida grande, necesita una cabeza grade y si un animale le gusta comida pequeño tiene una cabeza pequeño- un idea muy importante.</td>\n",
       "      <td>Si un animale le gusta comida grande, necesita una cabeza grade y si un animale le gusta comida [pequeño]{pequeña}&lt;ga:fm:adj:inan&gt; tiene una cabeza [pequeño]{pequeña}&lt;ga:fm:adj:inan&gt; - [un]{una}&lt;ga:fm:det:inan&gt; idea muy importante.</td>\n",
       "      <td>[pequeño]{pequeña}&lt;ga:fm:adj:inan&gt;</td>\n",
       "      <td>ga:fm:adj:inan</td>\n",
       "      <td>pequeña</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[2, 4, 6, 31, 33, 34, 33, 35, 37, 62, 64, 65]</td>\n",
       "      <td>Si un animale le gusta comida grande, necesita una cabeza grade y si un animale le gusta comida pequeña tiene una cabeza pequeña- una idea muy importante.</td>\n",
       "      <td>Si un animale le gusta comida grande, necesita una cabeza grade y si un animale le gusta comida pequeña tiene una cabeza pequeño- una idea muy importante.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[2, 4, 6, 31, 33, 34, 33, 35, 37, 62, 64, 65]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3642</th>\n",
       "      <td>3642</td>\n",
       "      <td>480</td>\n",
       "      <td>445</td>\n",
       "      <td>f1386f14918b3bde3c65c0b820188c47218a0ebad39d3f6446368d634c065d4c</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Espero que tenga las últimas dos años es increíbles de Grey's Anatomy y estoy muy emocionada de verlas.</td>\n",
       "      <td>Espero que tenga [las]{los}&lt;ga:mf:det:inan&gt; [últimas]{últimos}&lt;ga:mf:det:inan&gt; dos años es increíbles de Grey's Anatomy y estoy muy emocionada de [verlas]{verlos}&lt;ga:mf:pron:inan&gt;.</td>\n",
       "      <td>[verlas]{verlos}&lt;ga:mf:pron:inan&gt;</td>\n",
       "      <td>ga:mf:pron:inan</td>\n",
       "      <td>verlos</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[4, 5, 6, 7, 27, 26, 27, 28, 29, 49]</td>\n",
       "      <td>Espero que tenga los últimos dos años es increíbles de Grey' s Anatomy y estoy muy emocionada de verlos.</td>\n",
       "      <td>Espero que tenga los últimos dos años es increíbles de Grey' s Anatomy y estoy muy emocionada de verlas.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[4, 5, 6, 7, 27, 26, 27, 28, 29, 49]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3660</th>\n",
       "      <td>3660</td>\n",
       "      <td>499</td>\n",
       "      <td>468</td>\n",
       "      <td>759acb091d84eed0c49bf2fc424002e5939d710f42d3ad8827d25270c6c36a45</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Pero no se termino aye, porque tantos personas gustan el libro tambien salio en television para ocho anos (tenia tresciento sesenta y seis episodos), viendo CD's, tenia cuatro peliculas, un monton de video juegos, y muchas musicas.</td>\n",
       "      <td>Pero no se termino aye, porque [tantos]{tantas}&lt;ga:fm:det:an&gt; personas gustan el libro tambien salio en television para ocho anos (tenia [tresciento]{trescientos}&lt;in:s:adj:inan&gt; sesenta y seis episodos), viendo CD's, tenia cuatro peliculas, un monton de video juegos, y [muchas]{mucha}&lt;na:sp:det:inan&gt; [musicas]{música}&lt;na:sp:noun:inan&gt;.</td>\n",
       "      <td>[muchas]{mucha}&lt;na:sp:det:inan&gt;</td>\n",
       "      <td>na:sp:det:inan</td>\n",
       "      <td>mucha</td>\n",
       "      <td>...</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[46, 48, 94, 96]</td>\n",
       "      <td>Pero no se termino aye, porque tantas personas gustan el libro tambien salio en television para ocho anos( tenia trescientos sesenta y seis episodos) , viendo CD' s, tenia cuatro peliculas, un monton de video juegos, y mucha música.</td>\n",
       "      <td>Pero no se termino aye, porque tantas personas gustan el libro tambien salio en television para ocho anos( tenia trescientos sesenta y seis episodos) , viendo CD' s, tenia cuatro peliculas, un monton de video juegos, y muchas música.</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[46, 48, 94, 96]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3661</th>\n",
       "      <td>3661</td>\n",
       "      <td>500</td>\n",
       "      <td>468</td>\n",
       "      <td>759acb091d84eed0c49bf2fc424002e5939d710f42d3ad8827d25270c6c36a45</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Pero no se termino aye, porque tantos personas gustan el libro tambien salio en television para ocho anos (tenia tresciento sesenta y seis episodos), viendo CD's, tenia cuatro peliculas, un monton de video juegos, y muchas musicas.</td>\n",
       "      <td>Pero no se termino aye, porque [tantos]{tantas}&lt;ga:fm:det:an&gt; personas gustan el libro tambien salio en television para ocho anos (tenia [tresciento]{trescientos}&lt;in:s:adj:inan&gt; sesenta y seis episodos), viendo CD's, tenia cuatro peliculas, un monton de video juegos, y [muchas]{mucha}&lt;na:sp:det:inan&gt; [musicas]{música}&lt;na:sp:noun:inan&gt;.</td>\n",
       "      <td>[musicas]{música}&lt;na:sp:noun:inan&gt;</td>\n",
       "      <td>na:sp:noun:inan</td>\n",
       "      <td>música</td>\n",
       "      <td>...</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[50, 98]</td>\n",
       "      <td>Pero no se termino aye, porque tantas personas gustan el libro tambien salio en television para ocho anos( tenia trescientos sesenta y seis episodos) , viendo CD' s, tenia cuatro peliculas, un monton de video juegos, y mucha música.</td>\n",
       "      <td>Pero no se termino aye, porque tantas personas gustan el libro tambien salio en television para ocho anos( tenia trescientos sesenta y seis episodos) , viendo CD' s, tenia cuatro peliculas, un monton de video juegos, y mucha musicas.</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>[50, 98]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4008</th>\n",
       "      <td>4008</td>\n",
       "      <td>868</td>\n",
       "      <td>876</td>\n",
       "      <td>374c0876305edbef3c42709975db308f3e6ae592efe6f8ecf1b20876140e5be7</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Joanne Rowling, cuyos seudónimos son J. K. Rowling y Robert Galbraith, es un novelista inglés, productor de cine, productor de televisión, guionista y filántropo.</td>\n",
       "      <td>Joanne Rowling, cuyos seudónimos son J. K. Rowling y Robert Galbraith, es [un]{una}&lt;ga:fm:det:an&gt; novelista [inglés]{inglesa}&lt;ga:fm:adj:an&gt;, [productor]{productora}&lt;ga:fm:noun:an&gt; de cine, [productor]{productora}&lt;ga:fm:noun:an&gt; de televisión, guionista y [filántropo]{filántropa}&lt;ga:fm:noun:an&gt;.</td>\n",
       "      <td>[filántropo]{filántropa}&lt;ga:fm:noun:an&gt;</td>\n",
       "      <td>ga:fm:noun:an</td>\n",
       "      <td>filántropa</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[20, 31, 53, 64]</td>\n",
       "      <td>Joanne Rowling, cuyos seudónimos son J. K. Rowling y Robert Galbraith, es una novelista inglesa, productora de cine, productora de televisión, guionista y filántropa.</td>\n",
       "      <td>Joanne Rowling, cuyos seudónimos son J. K. Rowling y Robert Galbraith, es una novelista inglesa, productora de cine, productora de televisión, guionista y filántropo.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[20, 31, 53, 64]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4401</th>\n",
       "      <td>4401</td>\n",
       "      <td>238</td>\n",
       "      <td>1159</td>\n",
       "      <td>ec502ac7d84891a318b52a8f7f6f5ff765d1da51eb150a3521f32842afe61806</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>Me encanta todas las islas diferentes y me gustaría visitar todos ellos, por ejemplo ... Maui, Honolulu, y mucho más.</td>\n",
       "      <td>Me encanta todas las islas diferentes y me gustaría visitar [todos]{todas}&lt;ga:fm:det:inan&gt; [ellos]{ellas}&lt;ga:fm:pron:inan&gt;, por ejemplo ... Maui, Honolulu, y [mucho]{muchas}&lt;ga:na:fm:ps:pron:inan&gt; más.</td>\n",
       "      <td>[mucho]{muchas}&lt;ga:na:fm:ps:pron:inan&gt;</td>\n",
       "      <td>ga:na:fm:ps:pron:inan</td>\n",
       "      <td>muchas</td>\n",
       "      <td>...</td>\n",
       "      <td>gender and number agreement</td>\n",
       "      <td>[4, 24, 31, 51]</td>\n",
       "      <td>Me encanta todas las islas diferentes y me gustaría visitar todas ellas, por ejemplo. . . Maui, Honolulu, y muchas más.</td>\n",
       "      <td>Me encanta todas las islas diferentes y me gustaría visitar todas ellas, por ejemplo. . . Maui, Honolulu, y mucho más.</td>\n",
       "      <td>gender and number agreement</td>\n",
       "      <td>[4, 24, 31, 51]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender and number agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4547</th>\n",
       "      <td>4547</td>\n",
       "      <td>396</td>\n",
       "      <td>1241</td>\n",
       "      <td>3b65f88da754cd2666ba1c0d5a169660e9e5385d24c643058149ccf30c2cc396</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>En el tercer semana, mis padres, mis amigos, y yo iriamos al mercado de *CITY* y comprariamos muchas cosas.</td>\n",
       "      <td>En [el]{la}&lt;ga:fm:det:inan&gt; [tercer]{tercera}&lt;ga:fm:det:inan&gt; semana, mis padres, mis amigos, y yo iriamos al mercado de [Puerta]{puerto}&lt;ig:f:noun:inan&gt; Vallarta y comprariamos muchas cosas.</td>\n",
       "      <td>[Puerta]{puerto}&lt;ig:f:noun:inan&gt;</td>\n",
       "      <td>ig:f:noun:inan</td>\n",
       "      <td>puerto</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[15, 16, 18, 40, 41, 43]</td>\n",
       "      <td>En la tercera semana, mis padres, mis amigos, y yo iriamos al mercado de puerto Vallarta y comprariamos muchas cosas.</td>\n",
       "      <td>En la tercera semana, mis padres, mis amigos, y yo iriamos al mercado de Puerta Vallarta y comprariamos muchas cosas.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[15, 16, 18, 40, 41, 43]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5074</th>\n",
       "      <td>5074</td>\n",
       "      <td>947</td>\n",
       "      <td>385</td>\n",
       "      <td>4a04df071e67e06800a4f4b84b4268e3a65ef4a339882425afae275fb895bfca</td>\n",
       "      <td>SPA 2</td>\n",
       "      <td>Perro a mi me gusta los personas famosas de Corea como Kpop(pop de corea), uno groupo de Kpop esta much famosa es BTS y es mi groupo favorita.</td>\n",
       "      <td>Perro a mi me gusta [los]{las}&lt;ga:fm:det:an&gt; personas famosas de Corea como Kpop(pop de corea), uno groupo de Kpop esta much [famosa]{famoso}&lt;ga:mf:adj:an&gt; es BTS y es mi groupo [favorita]{favorito}&lt;ga:mf:adj:an&gt;.</td>\n",
       "      <td>[favorita]{favorito}&lt;ga:mf:adj:an&gt;</td>\n",
       "      <td>ga:mf:adj:an</td>\n",
       "      <td>favorito</td>\n",
       "      <td>...</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[24, 26, 28, 58, 60, 62]</td>\n",
       "      <td>Perro a mi me gusta las personas famosas de Corea como Kpop( pop de corea) , uno groupo de Kpop esta much famoso es BTS y es mi groupo favorito.</td>\n",
       "      <td>Perro a mi me gusta las personas famosas de Corea como Kpop( pop de corea) , uno groupo de Kpop esta much famoso es BTS y es mi groupo favorita.</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>[24, 26, 28, 58, 60, 62]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5092</th>\n",
       "      <td>5092</td>\n",
       "      <td>965</td>\n",
       "      <td>729</td>\n",
       "      <td>8321d7a209cd3fcd5a0f4e71bb24d4b367d705ae087d2f6e2ac87bddc044877e</td>\n",
       "      <td>SPA 3</td>\n",
       "      <td>E’l es un actor y está en muchas películas de comedias, musicales, y dramático.</td>\n",
       "      <td>E’l es un actor y está en muchas películas de [comedias]{comedia}&lt;in:s:noun:inan&gt;, musicales, y [dramático]{dramáticas}&lt;ga:na:fm:ps:adj:inan&gt;.</td>\n",
       "      <td>[dramático]{dramáticas}&lt;ga:na:fm:ps:adj:inan&gt;</td>\n",
       "      <td>ga:na:fm:ps:adj:inan</td>\n",
       "      <td>dramáticas</td>\n",
       "      <td>...</td>\n",
       "      <td>gender and number agreement</td>\n",
       "      <td>[8, 9, 23, 28, 29, 43]</td>\n",
       "      <td>E ’ l es un actor y está en muchas películas de comedia, musicales, y dramáticas.</td>\n",
       "      <td>E ’ l es un actor y está en muchas películas de comedia, musicales, y dramático.</td>\n",
       "      <td>gender and number agreement</td>\n",
       "      <td>[8, 9, 23, 28, 29, 43]</td>\n",
       "      <td>None</td>\n",
       "      <td>A</td>\n",
       "      <td>gender and number agreement</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      orig_index  Unnamed: 0  paragraph_id  \\\n",
       "65            65          72           412   \n",
       "68            68         111           436   \n",
       "421          421         211           775   \n",
       "624          624         440            29   \n",
       "658          658         475            61   \n",
       "663          663         480            65   \n",
       "1034        1034         874           298   \n",
       "1104        1104         946           336   \n",
       "1270        1270          74           412   \n",
       "1280        1280          84           419   \n",
       "1291        1291          95           427   \n",
       "2337        2337         148          1241   \n",
       "2401        2401         215          1264   \n",
       "2920        2920         755           131   \n",
       "3050        3050         896           350   \n",
       "3054        3054         900           649   \n",
       "3068        3068         916           937   \n",
       "3080        3080         929          1081   \n",
       "3085        3085         935          1198   \n",
       "3089        3089         940          1241   \n",
       "3097        3097         949          1363   \n",
       "3139        3139         992            46   \n",
       "3470        3470         291           316   \n",
       "3642        3642         480           445   \n",
       "3660        3660         499           468   \n",
       "3661        3661         500           468   \n",
       "4008        4008         868           876   \n",
       "4401        4401         238          1159   \n",
       "4547        4547         396          1241   \n",
       "5074        5074         947           385   \n",
       "5092        5092         965           729   \n",
       "\n",
       "                                                           sentence_id course  \\\n",
       "65    6004f54d6469a368868a7377b03f3e9eb8d7a780202ab1862ad151095ab6e16b  SPA 3   \n",
       "68    340344b3cfed0317bbb5c95dafdae2033cf7c6732f5ea383f2e64ffe93297fd5  SPA 3   \n",
       "421   0c66030cc2c5487d1736df818425e524874d29ada3c571b76adb1731e1015055  SPA 2   \n",
       "624   daeec1fa32f04db8a029f6cfe42d541e117b4e7621f61ae9fddbc7b5016766fc  SPA 3   \n",
       "658   77581a46b29c5a9e1e74abbffea7e7a3c03b59fc3457647f571a1ba1ddbacce3  SPA 3   \n",
       "663   139d6a79730caee9d68ad994e3fb047e5c0b6b31d42450bcf838d3943b5e33dc  SPA 3   \n",
       "1034  d539be0f37ba2351c41b5ce478dfc7a23b8bb61a7256f29cb0bef8e2e203e25f  SPA 2   \n",
       "1104  52d24c89b5a3209f034532a43e7693abc52d49d3f25a2ae4aeaf0e32de8a6a45  SPA 2   \n",
       "1270  a5eb6bff54851d9f5b95e8eec421ccb1e1bb16093f1ca30f076f18b93ed55080  SPA 3   \n",
       "1280  1899da74fd3c342ae0359985a667606be211be7e7c7c0ca2ee7f1e6bdaa0574b  SPA 1   \n",
       "1291  8169da7c36d4bc5f4c27f5c24158fdddb88c8531dce841dbe0025b4bbaf53fc2  SPA 2   \n",
       "2337  7d41323560f6726aeaa1667d58a3f6a2e06b6c62214761f7368fc0ab39ed763b  SPA 3   \n",
       "2401  79975f6953bc2ae73e7cfbd2a311400b945505bf8482b2ec0b02d5b66fdf3807  SPA 2   \n",
       "2920  260c8efe2b960b3810b6cb6bb65eb33301c608b9109d5352b43710c83fec8ea5  SPA 3   \n",
       "3050  69ee9c42605d18dd2342d1528adb040030fdd41125451085bf22b91751c0036e  SPA 2   \n",
       "3054  9772575305dfa7f2672e3c00cd1d447ab83d9e84608dd4f64b06d1cdd5e8719c  SPA 2   \n",
       "3068  644a985a157ba246a333f122b6aab4fd56ab32ee361c3381a5d697dcc7247cd7  SPA 2   \n",
       "3080  a344d375871772b542d319cd517c718348d88f2e2259175c4cc0888d0a0cccae  SPA 3   \n",
       "3085  94acf628361ec20601a3028d3ad9b4777f8e20540e66f96f61aaa858eee03c52  SPA 1   \n",
       "3089  1d7d5a1c69639976fa360b8d54b3579121bf53f7499e14919c58da6bf02a363f  SPA 3   \n",
       "3097  7fb0c6b9e1bb965cf21ca24cd62110527336fa456aff774bd15c44f4de030811  SPA 2   \n",
       "3139  76833841bbbdfd09a73d434bc78ad99cb572eb5ef46ee5e8e5ce5d7a7e0bd011  SPA 3   \n",
       "3470  ccfb36e3dfbe363bb7b44a6952daf7c70b24472a9dc8daa1b588ca86fd6194a0  SPA 2   \n",
       "3642  f1386f14918b3bde3c65c0b820188c47218a0ebad39d3f6446368d634c065d4c  SPA 3   \n",
       "3660  759acb091d84eed0c49bf2fc424002e5939d710f42d3ad8827d25270c6c36a45  SPA 2   \n",
       "3661  759acb091d84eed0c49bf2fc424002e5939d710f42d3ad8827d25270c6c36a45  SPA 2   \n",
       "4008  374c0876305edbef3c42709975db308f3e6ae592efe6f8ecf1b20876140e5be7  SPA 3   \n",
       "4401  ec502ac7d84891a318b52a8f7f6f5ff765d1da51eb150a3521f32842afe61806  SPA 3   \n",
       "4547  3b65f88da754cd2666ba1c0d5a169660e9e5385d24c643058149ccf30c2cc396  SPA 3   \n",
       "5074  4a04df071e67e06800a4f4b84b4268e3a65ef4a339882425afae275fb895bfca  SPA 2   \n",
       "5092  8321d7a209cd3fcd5a0f4e71bb24d4b367d705ae087d2f6e2ac87bddc044877e  SPA 3   \n",
       "\n",
       "                                                                                                                                                                                                                                    essay_new  \\\n",
       "65                                                                                                                                                                                 Después Harry Potter, J. K. Rowling ha estado muy ocupado.   \n",
       "68                              En Shawshank Redemption -- un libro de Steven King que hicieron en pelicula -- el es un prisonero serviendo tiempo y ayuda otro prisonero sobrevivir los malos que una persona sufre quando esta encarcelado.   \n",
       "421                                                              No tiene \"oscars\" pero Leonardo Dicaprio solomente recibió un oscar el año pasado, así que....Pues, quiero ver una película con él este año y quiero conocer Walken también.   \n",
       "624                                                                                                            Bill and Ted es de los dos Bill and Ted y els van en diferentes tiempos como los 1800’s o más patras.También van en la futuro.   \n",
       "658                                                                                                                                                                     Todavía me gusta escuchar \"Destiny's Child\" y su música es estupendo.   \n",
       "663                                                                                                                                                                      Empezó con el grupo se llama Destiny's Child antes de realizar solo.   \n",
       "1034                                                                                                                                                             Hugh Jackman es un buen actor pero también es un buen cantante y bailarina.   \n",
       "1104                                                                                                                                                                     Me gustan muchos tipos de musica (rap, r&b,pop) , y muchas artistas.   \n",
       "1270                                                                                                                                                                                       J. K. Rowling no escribía la obra, pero lo aprobó.   \n",
       "1280                                                                                                                                                                                                     Oprah Winfrey es African- Americano.   \n",
       "1291                                                                                                                                                                  Por ejemplo, los invitó a Taylor's casa para cantar especial canciones.   \n",
       "2337                                                                                                                                                                                           Mi lugar favorito es *CITY* en *STATE* Mexico.   \n",
       "2401                                                                         Nosotros acabaremos la noche con cocinando los perros calientes, los perros tofu, y s’mores encima de la hoguera mientras contando cuentos cómicos y embarazoso.   \n",
       "2920                                                                                                                                                    Beyoncé Knowles-Carter es una cantante, actriz, y compositora de canciones americano.   \n",
       "3050                                                                                                                                                Cuando era un bebe, Señor Voldemort asesinó los padres de Harry y intentó asesinar Harry.   \n",
       "3054                                                                                                                                     Jimmy Fallon tiene otras personas famosas en su show como P!NK, Katy Perry, y muchos otras personas.   \n",
       "3068                                                                                                                                                                                     Traería a mis mejores amigos, mi hermana y mi novio.   \n",
       "3080                                                                                                                                                                                       Luego voy a llamar mi mamá, mi hermana, mi abuela.   \n",
       "3085                                                                                                                                                                       Yo traigo mi novio, los padres, la hermana mayor, y perro pequeno.   \n",
       "3089                                                                                                                                                     Primero, planaria mi vacacion por un mes y traeria mi madre, mi padre, y mis amigos.   \n",
       "3097                                                                                                             Una hermosa vista ayuda, pero me encantaría tener a mis sobrinos y sobrinas allí, mis dos hermanos, mi hermana y mis padres.   \n",
       "3139                                                                                                                                                             Kim Kardashian-West es una modela quién tiene un cuerpo muy famosa y bonita.   \n",
       "3470                                                                                Si un animale le gusta comida grande, necesita una cabeza grade y si un animale le gusta comida pequeño tiene una cabeza pequeño- un idea muy importante.   \n",
       "3642                                                                                                                                  Espero que tenga las últimas dos años es increíbles de Grey's Anatomy y estoy muy emocionada de verlas.   \n",
       "3660  Pero no se termino aye, porque tantos personas gustan el libro tambien salio en television para ocho anos (tenia tresciento sesenta y seis episodos), viendo CD's, tenia cuatro peliculas, un monton de video juegos, y muchas musicas.   \n",
       "3661  Pero no se termino aye, porque tantos personas gustan el libro tambien salio en television para ocho anos (tenia tresciento sesenta y seis episodos), viendo CD's, tenia cuatro peliculas, un monton de video juegos, y muchas musicas.   \n",
       "4008                                                                       Joanne Rowling, cuyos seudónimos son J. K. Rowling y Robert Galbraith, es un novelista inglés, productor de cine, productor de televisión, guionista y filántropo.   \n",
       "4401                                                                                                                    Me encanta todas las islas diferentes y me gustaría visitar todos ellos, por ejemplo ... Maui, Honolulu, y mucho más.   \n",
       "4547                                                                                                                              En el tercer semana, mis padres, mis amigos, y yo iriamos al mercado de *CITY* y comprariamos muchas cosas.   \n",
       "5074                                                                                           Perro a mi me gusta los personas famosas de Corea como Kpop(pop de corea), uno groupo de Kpop esta much famosa es BTS y es mi groupo favorita.   \n",
       "5092                                                                                                                                                          E’l es un actor y está en muchas películas de comedias, musicales, y dramático.   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                     annotated_sentence  \\\n",
       "65                                                                                                                                                                                                                                                                  Después Harry Potter, J. K. Rowling ha estado muy [ocupado]{ocupada}<ga:fm:adj:an>.   \n",
       "68                                                                                                           En Shawshank Redemption -- un libro de Steven King que hicieron en pelicula -- el es un prisonero serviendo tiempo y ayuda otro prisonero sobrevivir los malos que una persona sufre quando esta [encarcelado]{encarcelada}<ga:fm:adj:an>.   \n",
       "421                                                                                                                                                        No tiene \"oscars\" pero Leonardo Dicaprio solomente recibió un oscar el año pasado, así que....Pues, quiero ver una película con él este año y quiero conocer []{a}<az:do:an> Walken también.   \n",
       "624                                                                                                                                                                                                Bill and Ted es de los dos Bill and Ted y els van en diferentes tiempos como los 1800’s o más patras.También van en [la]{el}<ga:mf:det:inan> futuro.   \n",
       "658                                                                                                                                                                                                                                                  Todavía me gusta escuchar \"Destiny's Child\" y su música es [estupendo]{estupenda}<ga:fm:adj:inan>.   \n",
       "663                                                                                                                                                                                                                                                          Empezó con el grupo se llama Destiny's Child antes de realizar [solo]{sola}<ga:fm:adj:an>.   \n",
       "1034                                                                                                                                                                                                                                            Hugh Jackman es un buen actor pero también es un buen cantante y [bailarina]{bailarín}<ga:mf:noun:an>.   \n",
       "1104                                                                                                                                                                                                                                                       Me gustan muchos tipos de musica (rap, r&b,pop) , y [muchas]{muchos}<ga:mf:det:an> artistas.   \n",
       "1270                                                                                                                                                                                                                                                                          J. K. Rowling no escribía la obra, pero [lo]{la}<ga:fm:pron:inan> aprobó.   \n",
       "1280                                                                                                                                                                                                                                                                                    Oprah Winfrey es African- [Americano]{americana}<ga:fm:adj:an>.   \n",
       "1291                                                                                                                                                                                                                                              Por ejemplo, los invitó a Taylor's casa para cantar [especial]{especiales}<na:ps:adj:inan> canciones.   \n",
       "2337                                                                                                                                                                                                                                                                  Mi lugar favorito es [Puerta]{puerto}<ig:f:noun:inan> Vallarta en Jalisco Mexico.   \n",
       "2401                                                                                                                                                    Nosotros acabaremos la noche con cocinando los perros calientes, los perros tofu, y s’mores encima de la hoguera mientras contando cuentos cómicos y [embarazoso]{embarazosos}<na:ps:adj:inan>.   \n",
       "2920                                                                                                                                                                                                                                   Beyoncé Knowles-Carter es una cantante, actriz, y compositora de canciones [americano]{americana}<ga:fm:adj:an>.   \n",
       "3050                                                                                                                                                                                                                         Cuando era un bebe, Señor Voldemort asesinó []{a}<az:do:an> los padres de Harry y intentó asesinar []{a}<az:do:an>  Harry.   \n",
       "3054                                                                                                                                                                                                              Jimmy Fallon tiene []{a}<az:do:an> otras personas famosas en su show como P!NK, Katy Perry, y []{a}<az:do:an>  muchos otras personas.   \n",
       "3068                                                                                                                                                                                                                                                                Traería  mis mejores amigos, []{a}<az:do:an> mi hermana y []{a}<az:do:an> mi novio.   \n",
       "3080                                                                                                                                                                                                                                                 Luego voy a llamar []{a}<az:do:an> mi mamá, []{a}<az:do:an> mi hermana, []{a}<az:do:an> mi abuela.   \n",
       "3085                                                                                                                                                                                                                 Yo traigo []{a}<az:do:an> mi novio, []{a}<az:do:an> los padres, []{a}<az:do:an> la hermana mayor, y []{a}<az:do:an> perro pequeno.   \n",
       "3089                                                                                                                                                                                                               Primero, planaria mi vacacion por un mes y traeria []{a}<az:do:an> mi madre, []{a}<az:do:an> mi padre, y []{a}<az:do:an> mis amigos.   \n",
       "3097                                                                                                                                                       Una hermosa vista ayuda, pero me encantaría tener   mis sobrinos y []{a}<az:do:an> sobrinas allí, []{a}<az:do:an> mis dos hermanos, []{a}<az:do:an> mi hermana y []{a}<az:do:an> mis padres.   \n",
       "3139                                                                                                                                                                                               Kim Kardashian-West es una [modela]{modelo}<ig:f:noun:an> quién tiene un cuerpo muy [famosa]{famoso}<ga:mf:adj:an> y [bonita]{bonito}<ga:mf:adj:an>.   \n",
       "3470                                                                                                            Si un animale le gusta comida grande, necesita una cabeza grade y si un animale le gusta comida [pequeño]{pequeña}<ga:fm:adj:inan> tiene una cabeza [pequeño]{pequeña}<ga:fm:adj:inan> - [un]{una}<ga:fm:det:inan> idea muy importante.   \n",
       "3642                                                                                                                                                               Espero que tenga [las]{los}<ga:mf:det:inan> [últimas]{últimos}<ga:mf:det:inan> dos años es increíbles de Grey's Anatomy y estoy muy emocionada de [verlas]{verlos}<ga:mf:pron:inan>.   \n",
       "3660  Pero no se termino aye, porque [tantos]{tantas}<ga:fm:det:an> personas gustan el libro tambien salio en television para ocho anos (tenia [tresciento]{trescientos}<in:s:adj:inan> sesenta y seis episodos), viendo CD's, tenia cuatro peliculas, un monton de video juegos, y [muchas]{mucha}<na:sp:det:inan> [musicas]{música}<na:sp:noun:inan>.   \n",
       "3661  Pero no se termino aye, porque [tantos]{tantas}<ga:fm:det:an> personas gustan el libro tambien salio en television para ocho anos (tenia [tresciento]{trescientos}<in:s:adj:inan> sesenta y seis episodos), viendo CD's, tenia cuatro peliculas, un monton de video juegos, y [muchas]{mucha}<na:sp:det:inan> [musicas]{música}<na:sp:noun:inan>.   \n",
       "4008                                            Joanne Rowling, cuyos seudónimos son J. K. Rowling y Robert Galbraith, es [un]{una}<ga:fm:det:an> novelista [inglés]{inglesa}<ga:fm:adj:an>, [productor]{productora}<ga:fm:noun:an> de cine, [productor]{productora}<ga:fm:noun:an> de televisión, guionista y [filántropo]{filántropa}<ga:fm:noun:an>.   \n",
       "4401                                                                                                                                          Me encanta todas las islas diferentes y me gustaría visitar [todos]{todas}<ga:fm:det:inan> [ellos]{ellas}<ga:fm:pron:inan>, por ejemplo ... Maui, Honolulu, y [mucho]{muchas}<ga:na:fm:ps:pron:inan> más.   \n",
       "4547                                                                                                                                                    En [el]{la}<ga:fm:det:inan> [tercer]{tercera}<ga:fm:det:inan> semana, mis padres, mis amigos, y yo iriamos al mercado de [Puerta]{puerto}<ig:f:noun:inan> Vallarta y comprariamos muchas cosas.   \n",
       "5074                                                                                                                              Perro a mi me gusta [los]{las}<ga:fm:det:an> personas famosas de Corea como Kpop(pop de corea), uno groupo de Kpop esta much [famosa]{famoso}<ga:mf:adj:an> es BTS y es mi groupo [favorita]{favorito}<ga:mf:adj:an>.   \n",
       "5092                                                                                                                                                                                                     E’l es un actor y está en muchas películas de [comedias]{comedia}<in:s:noun:inan>, musicales, y [dramático]{dramáticas}<ga:na:fm:ps:adj:inan>.   \n",
       "\n",
       "                                     full_error_tag              error_tag  \\\n",
       "65                 [ocupado]{ocupada}<ga:fm:adj:an>           ga:fm:adj:an   \n",
       "68         [encarcelado]{encarcelada}<ga:fm:adj:an>           ga:fm:adj:an   \n",
       "421                                 []{a}<az:do:an>               az:do:an   \n",
       "624                        [la]{el}<ga:mf:det:inan>         ga:mf:det:inan   \n",
       "658          [estupendo]{estupenda}<ga:fm:adj:inan>         ga:fm:adj:inan   \n",
       "663                      [solo]{sola}<ga:fm:adj:an>           ga:fm:adj:an   \n",
       "1034           [bailarina]{bailarín}<ga:mf:noun:an>          ga:mf:noun:an   \n",
       "1104                 [muchas]{muchos}<ga:mf:det:an>           ga:mf:det:an   \n",
       "1270                      [lo]{la}<ga:fm:pron:inan>        ga:fm:pron:inan   \n",
       "1280           [Americano]{americana}<ga:fm:adj:an>           ga:fm:adj:an   \n",
       "1291         [especial]{especiales}<na:ps:adj:inan>         na:ps:adj:inan   \n",
       "2337               [Puerta]{puerto}<ig:f:noun:inan>         ig:f:noun:inan   \n",
       "2401      [embarazoso]{embarazosos}<na:ps:adj:inan>         na:ps:adj:inan   \n",
       "2920           [americano]{americana}<ga:fm:adj:an>           ga:fm:adj:an   \n",
       "3050                                []{a}<az:do:an>               az:do:an   \n",
       "3054                                []{a}<az:do:an>               az:do:an   \n",
       "3068                                []{a}<az:do:an>               az:do:an   \n",
       "3080                                []{a}<az:do:an>               az:do:an   \n",
       "3085                                []{a}<az:do:an>               az:do:an   \n",
       "3089                                []{a}<az:do:an>               az:do:an   \n",
       "3097                                []{a}<az:do:an>               az:do:an   \n",
       "3139                 [bonita]{bonito}<ga:mf:adj:an>           ga:mf:adj:an   \n",
       "3470             [pequeño]{pequeña}<ga:fm:adj:inan>         ga:fm:adj:inan   \n",
       "3642              [verlas]{verlos}<ga:mf:pron:inan>        ga:mf:pron:inan   \n",
       "3660                [muchas]{mucha}<na:sp:det:inan>         na:sp:det:inan   \n",
       "3661             [musicas]{música}<na:sp:noun:inan>        na:sp:noun:inan   \n",
       "4008        [filántropo]{filántropa}<ga:fm:noun:an>          ga:fm:noun:an   \n",
       "4401         [mucho]{muchas}<ga:na:fm:ps:pron:inan>  ga:na:fm:ps:pron:inan   \n",
       "4547               [Puerta]{puerto}<ig:f:noun:inan>         ig:f:noun:inan   \n",
       "5074             [favorita]{favorito}<ga:mf:adj:an>           ga:mf:adj:an   \n",
       "5092  [dramático]{dramáticas}<ga:na:fm:ps:adj:inan>   ga:na:fm:ps:adj:inan   \n",
       "\n",
       "       correction  ...               new_error_type  \\\n",
       "65        ocupada  ...             gender agreement   \n",
       "68    encarcelada  ...             gender agreement   \n",
       "421             a  ...                      article   \n",
       "624            el  ...             gender agreement   \n",
       "658     estupenda  ...             gender agreement   \n",
       "663          sola  ...             gender agreement   \n",
       "1034     bailarín  ...             gender agreement   \n",
       "1104       muchos  ...             gender agreement   \n",
       "1270           la  ...             gender agreement   \n",
       "1280    americana  ...             gender agreement   \n",
       "1291   especiales  ...             number agreement   \n",
       "2337       puerto  ...             gender agreement   \n",
       "2401  embarazosos  ...             number agreement   \n",
       "2920    americana  ...             gender agreement   \n",
       "3050            a  ...                      article   \n",
       "3054            a  ...                      article   \n",
       "3068            a  ...                      article   \n",
       "3080            a  ...                      article   \n",
       "3085            a  ...                      article   \n",
       "3089            a  ...                      article   \n",
       "3097            a  ...                      article   \n",
       "3139       bonito  ...             gender agreement   \n",
       "3470      pequeña  ...             gender agreement   \n",
       "3642       verlos  ...             gender agreement   \n",
       "3660        mucha  ...             number agreement   \n",
       "3661       música  ...             number agreement   \n",
       "4008   filántropa  ...             gender agreement   \n",
       "4401       muchas  ...  gender and number agreement   \n",
       "4547       puerto  ...             gender agreement   \n",
       "5074     favorito  ...             gender agreement   \n",
       "5092   dramáticas  ...  gender and number agreement   \n",
       "\n",
       "                                                                                                                                 final_evident_index  \\\n",
       "65                                                                                                                                                []   \n",
       "68                                                                                                                                                []   \n",
       "421                                                                                                                         [31, 33, 34, 71, 73, 74]   \n",
       "624                                                                                                                                 [31, 32, 63, 64]   \n",
       "658                                                                                                                                         [16, 33]   \n",
       "663                                                                                                                                         [12, 28]   \n",
       "1034                                                                                                                   [2, 3, 4, 17, 20, 21, 22, 35]   \n",
       "1104                                                                                                                                [22, 23, 43, 44]   \n",
       "1270                                                                                                                         [12, 7, 10, 27, 22, 25]   \n",
       "1280                                                                                                               [0, 1, 2, 3, 4, 8, 9, 10, 11, 12]   \n",
       "1291                                                                                                                        [15, 16, 17, 31, 32, 33]   \n",
       "2337                                                                                                                                         [5, 16]   \n",
       "2401                                                                                                                        [31, 32, 33, 61, 62, 63]   \n",
       "2920                                                                                                                          [3, 5, 16, 20, 22, 33]   \n",
       "3050                                                                                                                                [15, 18, 35, 38]   \n",
       "3054                                                                                                                        [20, 21, 22, 45, 46, 47]   \n",
       "3068                                                                                           [7, 8, 9, 10, 11, 12, 13, 21, 22, 23, 24, 25, 26, 27]   \n",
       "3080                                  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32]   \n",
       "3085  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40]   \n",
       "3089                                                                                                                                [20, 21, 44, 45]   \n",
       "3097                                                                                                                                [23, 24, 53, 54]   \n",
       "3139                                          [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]   \n",
       "3470                                                                                                   [2, 4, 6, 31, 33, 34, 33, 35, 37, 62, 64, 65]   \n",
       "3642                                                                                                            [4, 5, 6, 7, 27, 26, 27, 28, 29, 49]   \n",
       "3660                                                                                                                                [46, 48, 94, 96]   \n",
       "3661                                                                                                                                        [50, 98]   \n",
       "4008                                                                                                                                [20, 31, 53, 64]   \n",
       "4401                                                                                                                                 [4, 24, 31, 51]   \n",
       "4547                                                                                                                        [15, 16, 18, 40, 41, 43]   \n",
       "5074                                                                                                                        [24, 26, 28, 58, 60, 62]   \n",
       "5092                                                                                                                          [8, 9, 23, 28, 29, 43]   \n",
       "\n",
       "                                                                                                                                                                                                                               target_sentence  \\\n",
       "65                                                                                                                                                                                  Después Harry Potter, J. K. Rowling ha estado muy ocupada.   \n",
       "68                               En Shawshank Redemption- - un libro de Steven King que hicieron en pelicula- - el es un prisonero serviendo tiempo y ayuda otro prisonero sobrevivir los malos que una persona sufre quando esta encarcelada.   \n",
       "421                                                         No tiene \"oscars\" pero Leonardo Dicaprio solomente recibió un oscar el año pasado, así que. . . . Pues, quiero ver una película con él este año y quiero conocer a Walken también.   \n",
       "624                                                                                                          Bill and Ted es de los dos Bill and Ted y els van en diferentes tiempos como los 1800 ’ s o más patras. También van en el futuro.   \n",
       "658                                                                                                                                                                     Todavía me gusta escuchar \"Destiny' s Child\" y su música es estupenda.   \n",
       "663                                                                                                                                                                      Empezó con el grupo se llama Destiny' s Child antes de realizar sola.   \n",
       "1034                                                                                                                                                             Hugh Jackman es un buen actor pero tambie ́ n es un buen cantante y bailarín.   \n",
       "1104                                                                                                                                                                    Me gustan muchos tipos de musica( rap, r& b, pop) , y muchos artistas.   \n",
       "1270                                                                                                                                                                                        J. K. Rowling no escribía la obra, pero la aprobó.   \n",
       "1280                                                                                                                                                                                                      Oprah Winfrey es African- americana.   \n",
       "1291                                                                                                                                                                Por ejemplo, los invitó a Taylor' s casa para cantar especiales canciones.   \n",
       "2337                                                                                                                                                                                   Mi lugar favorito es puerto Vallarta en Jalisco Mexico.   \n",
       "2401                                                                       Nosotros acabaremos la noche con cocinando los perros calientes, los perros tofu, y s ’ mores encima de la hoguera mientras contando cuentos cómicos y embarazosos.   \n",
       "2920                                                                                                                                                    Beyoncé Knowles- Carter es una cantante, actriz, y compositora de canciones americana.   \n",
       "3050                                                                                                                                             Cuando era un bebe, Señor Voldemort asesinó a los padres de Harry y intentó asesinar a Harry.   \n",
       "3054                                                                                                                                 Jimmy Fallon tiene a otras personas famosas en su show como P! NK, Katy Perry, y a muchos otras personas.   \n",
       "3068                                                                                                                                                                                    Traería mis mejores amigos, a mi hermana y a mi novio.   \n",
       "3080                                                                                                                                                                                  Luego voy a llamar a mi mamá, a mi hermana, a mi abuela.   \n",
       "3085                                                                                                                                                                Yo traigo a mi novio, a los padres, a la hermana mayor, y a perro pequeno.   \n",
       "3089                                                                                                                                                Primero, planaria mi vacacion por un mes y traeria a mi madre, a mi padre, y a mis amigos.   \n",
       "3097                                                                                                        Una hermosa vista ayuda, pero me encantaría tener mis sobrinos y a sobrinas allí, a mis dos hermanos, a mi hermana y a mis padres.   \n",
       "3139                                                                                                                                                             Kim Kardashian- West es una modelo quién tiene un cuerpo muy famoso y bonito.   \n",
       "3470                                                                                Si un animale le gusta comida grande, necesita una cabeza grade y si un animale le gusta comida pequeña tiene una cabeza pequeña- una idea muy importante.   \n",
       "3642                                                                                                                                  Espero que tenga los últimos dos años es increíbles de Grey' s Anatomy y estoy muy emocionada de verlos.   \n",
       "3660  Pero no se termino aye, porque tantas personas gustan el libro tambien salio en television para ocho anos( tenia trescientos sesenta y seis episodos) , viendo CD' s, tenia cuatro peliculas, un monton de video juegos, y mucha música.   \n",
       "3661  Pero no se termino aye, porque tantas personas gustan el libro tambien salio en television para ocho anos( tenia trescientos sesenta y seis episodos) , viendo CD' s, tenia cuatro peliculas, un monton de video juegos, y mucha música.   \n",
       "4008                                                                    Joanne Rowling, cuyos seudónimos son J. K. Rowling y Robert Galbraith, es una novelista inglesa, productora de cine, productora de televisión, guionista y filántropa.   \n",
       "4401                                                                                                                   Me encanta todas las islas diferentes y me gustaría visitar todas ellas, por ejemplo. . . Maui, Honolulu, y muchas más.   \n",
       "4547                                                                                                                     En la tercera semana, mis padres, mis amigos, y yo iriamos al mercado de puerto Vallarta y comprariamos muchas cosas.   \n",
       "5074                                                                                          Perro a mi me gusta las personas famosas de Corea como Kpop( pop de corea) , uno groupo de Kpop esta much famoso es BTS y es mi groupo favorito.   \n",
       "5092                                                                                                                                                         E ’ l es un actor y está en muchas películas de comedia, musicales, y dramáticas.   \n",
       "\n",
       "                                                                                                                                                                                                                                source_sentence  \\\n",
       "65                                                                                                                                                                                   Después Harry Potter, J. K. Rowling ha estado muy ocupado.   \n",
       "68                                En Shawshank Redemption- - un libro de Steven King que hicieron en pelicula- - el es un prisonero serviendo tiempo y ayuda otro prisonero sobrevivir los malos que una persona sufre quando esta encarcelado.   \n",
       "421                                                     No tiene \"oscars\" pero Leonardo Dicaprio solomente recibió un oscar el año pasado, así que. . . . Pues, quiero ver una película con él este año y quiero conocer [NONE] Walken también.   \n",
       "624                                                                                                           Bill and Ted es de los dos Bill and Ted y els van en diferentes tiempos como los 1800 ’ s o más patras. También van en la futuro.   \n",
       "658                                                                                                                                                                      Todavía me gusta escuchar \"Destiny' s Child\" y su música es estupendo.   \n",
       "663                                                                                                                                                                       Empezó con el grupo se llama Destiny' s Child antes de realizar solo.   \n",
       "1034                                                                                                                                                             Hugh Jackman es un buen actor pero tambie ́ n es un buen cantante y bailarina.   \n",
       "1104                                                                                                                                                                     Me gustan muchos tipos de musica( rap, r& b, pop) , y muchas artistas.   \n",
       "1270                                                                                                                                                                                         J. K. Rowling no escribía la obra, pero lo aprobó.   \n",
       "1280                                                                                                                                                                                                       Oprah Winfrey es African- Americano.   \n",
       "1291                                                                                                                                                                   Por ejemplo, los invitó a Taylor' s casa para cantar especial canciones.   \n",
       "2337                                                                                                                                                                                    Mi lugar favorito es Puerta Vallarta en Jalisco Mexico.   \n",
       "2401                                                                         Nosotros acabaremos la noche con cocinando los perros calientes, los perros tofu, y s ’ mores encima de la hoguera mientras contando cuentos cómicos y embarazoso.   \n",
       "2920                                                                                                                                                     Beyoncé Knowles- Carter es una cantante, actriz, y compositora de canciones americano.   \n",
       "3050                                                                                                                                         Cuando era un bebe, Señor Voldemort asesinó a los padres de Harry y intentó asesinar [NONE] Harry.   \n",
       "3054                                                                                                                             Jimmy Fallon tiene a otras personas famosas en su show como P! NK, Katy Perry, y [NONE] muchos otras personas.   \n",
       "3068                                                                                                                                                                                Traería mis mejores amigos, [NONE] mi hermana y a mi novio.   \n",
       "3080                                                                                                                                                                              Luego voy a llamar a mi mamá, a mi hermana, [NONE] mi abuela.   \n",
       "3085                                                                                                                                                            Yo traigo a mi novio, a los padres, a la hermana mayor, y [NONE] perro pequeno.   \n",
       "3089                                                                                                                                            Primero, planaria mi vacacion por un mes y traeria a mi madre, a mi padre, y [NONE] mis amigos.   \n",
       "3097                                                                                                    Una hermosa vista ayuda, pero me encantaría tener mis sobrinos y a sobrinas allí, a mis dos hermanos, a mi hermana y [NONE] mis padres.   \n",
       "3139                                                                                                                                                              Kim Kardashian- West es una modelo quién tiene un cuerpo muy famoso y bonita.   \n",
       "3470                                                                                 Si un animale le gusta comida grande, necesita una cabeza grade y si un animale le gusta comida pequeña tiene una cabeza pequeño- una idea muy importante.   \n",
       "3642                                                                                                                                   Espero que tenga los últimos dos años es increíbles de Grey' s Anatomy y estoy muy emocionada de verlas.   \n",
       "3660  Pero no se termino aye, porque tantas personas gustan el libro tambien salio en television para ocho anos( tenia trescientos sesenta y seis episodos) , viendo CD' s, tenia cuatro peliculas, un monton de video juegos, y muchas música.   \n",
       "3661  Pero no se termino aye, porque tantas personas gustan el libro tambien salio en television para ocho anos( tenia trescientos sesenta y seis episodos) , viendo CD' s, tenia cuatro peliculas, un monton de video juegos, y mucha musicas.   \n",
       "4008                                                                     Joanne Rowling, cuyos seudónimos son J. K. Rowling y Robert Galbraith, es una novelista inglesa, productora de cine, productora de televisión, guionista y filántropo.   \n",
       "4401                                                                                                                     Me encanta todas las islas diferentes y me gustaría visitar todas ellas, por ejemplo. . . Maui, Honolulu, y mucho más.   \n",
       "4547                                                                                                                      En la tercera semana, mis padres, mis amigos, y yo iriamos al mercado de Puerta Vallarta y comprariamos muchas cosas.   \n",
       "5074                                                                                           Perro a mi me gusta las personas famosas de Corea como Kpop( pop de corea) , uno groupo de Kpop esta much famoso es BTS y es mi groupo favorita.   \n",
       "5092                                                                                                                                                           E ’ l es un actor y está en muchas películas de comedia, musicales, y dramático.   \n",
       "\n",
       "                       error_type  \\\n",
       "65               gender agreement   \n",
       "68               gender agreement   \n",
       "421                       article   \n",
       "624              gender agreement   \n",
       "658              gender agreement   \n",
       "663              gender agreement   \n",
       "1034             gender agreement   \n",
       "1104             gender agreement   \n",
       "1270             gender agreement   \n",
       "1280             gender agreement   \n",
       "1291             number agreement   \n",
       "2337             gender agreement   \n",
       "2401             number agreement   \n",
       "2920             gender agreement   \n",
       "3050                      article   \n",
       "3054                      article   \n",
       "3068                      article   \n",
       "3080                      article   \n",
       "3085                      article   \n",
       "3089                      article   \n",
       "3097                      article   \n",
       "3139             gender agreement   \n",
       "3470             gender agreement   \n",
       "3642             gender agreement   \n",
       "3660             number agreement   \n",
       "3661             number agreement   \n",
       "4008             gender agreement   \n",
       "4401  gender and number agreement   \n",
       "4547             gender agreement   \n",
       "5074             gender agreement   \n",
       "5092  gender and number agreement   \n",
       "\n",
       "                                                                                                                                      evidence_index  \\\n",
       "65                                                                                                                                                []   \n",
       "68                                                                                                                                                []   \n",
       "421                                                                                                                         [31, 33, 34, 71, 73, 74]   \n",
       "624                                                                                                                                 [31, 32, 63, 64]   \n",
       "658                                                                                                                                         [16, 33]   \n",
       "663                                                                                                                                         [12, 28]   \n",
       "1034                                                                                                                   [2, 3, 4, 17, 20, 21, 22, 35]   \n",
       "1104                                                                                                                                [22, 23, 43, 44]   \n",
       "1270                                                                                                                         [12, 7, 10, 27, 22, 25]   \n",
       "1280                                                                                                               [0, 1, 2, 3, 4, 8, 9, 10, 11, 12]   \n",
       "1291                                                                                                                        [15, 16, 17, 31, 32, 33]   \n",
       "2337                                                                                                                                         [5, 16]   \n",
       "2401                                                                                                                        [31, 32, 33, 61, 62, 63]   \n",
       "2920                                                                                                                          [3, 5, 16, 20, 22, 33]   \n",
       "3050                                                                                                                                [15, 18, 35, 38]   \n",
       "3054                                                                                                                        [20, 21, 22, 45, 46, 47]   \n",
       "3068                                                                                           [7, 8, 9, 10, 11, 12, 13, 21, 22, 23, 24, 25, 26, 27]   \n",
       "3080                                  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32]   \n",
       "3085  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 38, 39, 40]   \n",
       "3089                                                                                                                                [20, 21, 44, 45]   \n",
       "3097                                                                                                                                [23, 24, 53, 54]   \n",
       "3139                                          [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30]   \n",
       "3470                                                                                                   [2, 4, 6, 31, 33, 34, 33, 35, 37, 62, 64, 65]   \n",
       "3642                                                                                                            [4, 5, 6, 7, 27, 26, 27, 28, 29, 49]   \n",
       "3660                                                                                                                                [46, 48, 94, 96]   \n",
       "3661                                                                                                                                        [50, 98]   \n",
       "4008                                                                                                                                [20, 31, 53, 64]   \n",
       "4401                                                                                                                                 [4, 24, 31, 51]   \n",
       "4547                                                                                                                        [15, 16, 18, 40, 41, 43]   \n",
       "5074                                                                                                                        [24, 26, 28, 58, 60, 62]   \n",
       "5092                                                                                                                          [8, 9, 23, 28, 29, 43]   \n",
       "\n",
       "     predicted_parsing_order origin              orig_error_type  \\\n",
       "65                      None      A             gender agreement   \n",
       "68                      None      A             gender agreement   \n",
       "421                     None      A                      article   \n",
       "624                     None      A             gender agreement   \n",
       "658                     None      A             gender agreement   \n",
       "663                     None      A             gender agreement   \n",
       "1034                    None      A             gender agreement   \n",
       "1104                    None      A             gender agreement   \n",
       "1270                    None      A             gender agreement   \n",
       "1280                    None      A             gender agreement   \n",
       "1291                    None      A             number agreement   \n",
       "2337                    None      A             gender agreement   \n",
       "2401                    None      A             number agreement   \n",
       "2920                    None      A             gender agreement   \n",
       "3050                    None      A                      article   \n",
       "3054                    None      A                      article   \n",
       "3068                    None      A                      article   \n",
       "3080                    None      A                      article   \n",
       "3085                    None      A                      article   \n",
       "3089                    None      A                      article   \n",
       "3097                    None      A                      article   \n",
       "3139                    None      A             gender agreement   \n",
       "3470                    None      A             gender agreement   \n",
       "3642                    None      A             gender agreement   \n",
       "3660                    None      A             number agreement   \n",
       "3661                    None      A             number agreement   \n",
       "4008                    None      A             gender agreement   \n",
       "4401                    None      A  gender and number agreement   \n",
       "4547                    None      A             gender agreement   \n",
       "5074                    None      A             gender agreement   \n",
       "5092                    None      A  gender and number agreement   \n",
       "\n",
       "                                             predicted_parsing_order_src  \n",
       "65                                                                  None  \n",
       "68                                                                  None  \n",
       "421                                                                 None  \n",
       "624                                                                 None  \n",
       "658                                                                 None  \n",
       "663                                                                 None  \n",
       "1034                                                                None  \n",
       "1104                                                                None  \n",
       "1270                                                                None  \n",
       "1280                                                                None  \n",
       "1291                                                                None  \n",
       "2337  {'1': 3, '10': 3, '11': 3, '3': 3, '4': 1, '5': 2, '6': 3, '9': 3}  \n",
       "2401                                                                None  \n",
       "2920                                                                None  \n",
       "3050                                                                None  \n",
       "3054                                                                None  \n",
       "3068                                                                None  \n",
       "3080                                                                None  \n",
       "3085                                                                None  \n",
       "3089                                                                None  \n",
       "3097                                                                None  \n",
       "3139                                                                None  \n",
       "3470                                                                None  \n",
       "3642                                                                None  \n",
       "3660                                                                None  \n",
       "3661                                                                None  \n",
       "4008                                                                None  \n",
       "4401                                                                None  \n",
       "4547                                                                None  \n",
       "5074                                                                None  \n",
       "5092                                                                None  \n",
       "\n",
       "[31 rows x 24 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Explore the rows with empty parsing order\n",
    "# TODO: explore why about 30 rows have empty parsing orders and 38 rows are missing first order dependencies.\n",
    "print(f'{df_errors[(df_errors.predicted_parsing_order.isna())].shape}')\n",
    "with pd.option_context('display.max_colwidth', None):\n",
    "    display(df_errors[df_errors.predicted_parsing_order.isna()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "70c93103-9463-4a3d-bba3-637df523589e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "      <th>error_tag</th>\n",
       "      <th>correction</th>\n",
       "      <th>...</th>\n",
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       "      <td>Me gustan muchos tipos de musica (rap, r&amp;b,pop...</td>\n",
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      ],
      "text/plain": [
       "      orig_index  Unnamed: 0  paragraph_id  \\\n",
       "1104        1104         946           336   \n",
       "\n",
       "                                            sentence_id course  \\\n",
       "1104  52d24c89b5a3209f034532a43e7693abc52d49d3f25a2a...  SPA 2   \n",
       "\n",
       "                                              essay_new  \\\n",
       "1104  Me gustan muchos tipos de musica (rap, r&b,pop...   \n",
       "\n",
       "                                     annotated_sentence  \\\n",
       "1104  Me gustan muchos tipos de musica (rap, r&b,pop...   \n",
       "\n",
       "                      full_error_tag     error_tag correction  ...  \\\n",
       "1104  [muchas]{muchos}<ga:mf:det:an>  ga:mf:det:an     muchos  ...   \n",
       "\n",
       "        new_error_type final_evident_index  \\\n",
       "1104  gender agreement    [22, 23, 43, 44]   \n",
       "\n",
       "                                        target_sentence  \\\n",
       "1104  Me gustan muchos tipos de musica( rap, r& b, p...   \n",
       "\n",
       "                                        source_sentence        error_type  \\\n",
       "1104  Me gustan muchos tipos de musica( rap, r& b, p...  gender agreement   \n",
       "\n",
       "        evidence_index predicted_parsing_order origin   orig_error_type  \\\n",
       "1104  [22, 23, 43, 44]                    None      A  gender agreement   \n",
       "\n",
       "     predicted_parsing_order_src  \n",
       "1104                        None  \n",
       "\n",
       "[1 rows x 24 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_errors[df_errors.essay_new == 'Me gustan muchos tipos de musica (rap, r&b,pop) , y muchas artistas.']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aa86fa51-cadb-4599-964a-f57ad839de33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id -> head: {0: 1, 1: 1, 2: 4, 3: 4, 4: 1}\n",
      "head -> ids: {1: [0, 1, 4], 4: [2, 3]}\n"
     ]
    },
    {
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    {
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      "text/plain": [
       "{'1': 3, '2': 3, '3': 1, '4': 2}"
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     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dep_parsing_tree(NLP_ES, 'Puedes visitar a sus amigos.', df_errors.iloc[363].correction_index, verbosity=2)\n",
    "\n",
    "\n",
    "# COWSL2H_parsing_order(NLP_ES, df_errors, 363)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8e1a5b7d-9978-4237-8b05-47021c1809d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id -> head: {0: 1, 1: 1, 2: 3, 3: 1, 4: 1, 5: 7, 6: 7, 7: 4}\n",
      "head -> ids: {1: [0, 1, 3, 4], 3: [2], 7: [5, 6], 4: [7]}\n"
     ]
    },
    {
     "data": {
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       "</text>\n",
       "\n",
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      "-----------------------------------------------\n",
      "\n",
      "Orig: Le dice su amigo luchar con otras hombres .\n",
      "Corr: Le dice su amigo luchar con otras hombres.\n",
      "row.source: ['Le', 'dice', 'su', 'amigo', 'luchar', 'con', 'otras', 'hombres', '.']\n",
      "row.target: ['Le', 'dice', 'su', 'amigo', 'luchar', 'con', 'otros', 'hombres', '.']\n",
      "Concatenated: ['Le', 'dice', 'su', 'amigo', 'luchar', 'con', 'otras', 'hombres', '.', 'SEP', 'Le', 'dice', 'su', 'amigo', 'luchar', 'con', 'otros', 'hombres', '.']\n",
      "\n",
      "Correction index: [6, 16]\n",
      "\n",
      "Corrected offset: 10\n",
      "\n",
      "computed pred_parse_order: {'4': 3, '5': 3, '6': 1, '7': 2}\n",
      "id -> head: {0: 2, 1: 2, 2: 7, 3: 5, 4: 5, 5: 7, 6: 7, 7: 7, 8: 7, 9: 10, 10: 8, 11: 12, 12: 8, 14: 8}\n",
      "head -> ids: {2: [0, 1], 7: [2, 5, 6, 7, 8], 5: [3, 4], 10: [9], 8: [10, 12, 14], 12: [11]}\n"
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     "text": [
      "-----------------------------------------------\n",
      "\n",
      "Orig: En el vacaciones mi favoritas cosas hacer es comer muchas comidas para differente ( places ) .\n",
      "Corr: En el vacacion mi favorito cosas hacer es comer muchas comida para differente (places).\n",
      "row.source: ['En', 'el', 'vacaciones', 'mi', 'favoritas', 'cosas', 'hacer', 'es', 'comer', 'muchas', 'comidas', 'para', 'differente', '(', 'places', ')', '.']\n",
      "row.target: ['En', 'la', 'vacaciones', 'mi', 'favoritas', 'cosas', 'hacer', 'es', 'comer', 'muchas', 'comidas', 'para', 'differente', '(', 'places', ')', '.']\n",
      "Concatenated: ['En', 'el', 'vacaciones', 'mi', 'favoritas', 'cosas', 'hacer', 'es', 'comer', 'muchas', 'comidas', 'para', 'differente', '(', 'places', ')', '.', 'SEP', 'En', 'la', 'vacaciones', 'mi', 'favoritas', 'cosas', 'hacer', 'es', 'comer', 'muchas', 'comidas', 'para', 'differente', '(', 'places', ')', '.']\n",
      "\n",
      "Correction index: [1, 19]\n",
      "\n",
      "Corrected offset: 18\n",
      "\n",
      "computed pred_parse_order: {'0': 3, '1': 1, '2': 2, '7': 3}\n"
     ]
    }
   ],
   "source": [
    "ids_to_check = [946, 4833]\n",
    "# print(df_errors.iloc[id_to_check])\n",
    "# print(type(df_errors.iloc[id_to_check].source))\n",
    "# print(type(df_errors.iloc[id_to_check].source))\n",
    "# print(type(df_errors.iloc[id_to_check].essay_new))\n",
    "for idx in ids_to_check:\n",
    "    COWSL2H_parsing_order(NLP_ES, df_errors, idx)\n",
    "# parsing_order = dep_parsing_tree(NLP_ES, df_errors.iloc[id_to_check].essay_new, eval(df_errors.iloc[id_to_check].correction_index), verbosity=2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "0ef7efad-4458-4412-8047-cd533e6e9d88",
   "metadata": {},
   "outputs": [
    {
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       "      <td>394</td>\n",
       "      <td>fad9adecf29b415e78858b7357702fcf0559ce28449e57...</td>\n",
       "      <td>[triste]{tristes}&lt;na:ps:adj:inan&gt;</td>\n",
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       "      <td>1081</td>\n",
       "      <td>4b9c8c086254d300366203704f8c7161382b203681e764...</td>\n",
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       "      <td>aca8763b560527f6ac1726cf393c2fa535f27d8a6a5c42...</td>\n",
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       "      <th>3845</th>\n",
       "      <td>719</td>\n",
       "      <td>31d15d3db856eaf5489b381a604c91c384c1c066baee48...</td>\n",
       "      <td>[muchos]{muchas}&lt;ga:fm:det:an&gt;</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>Están muchas famosas personas en el globo y es...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4442</th>\n",
       "      <td>1190</td>\n",
       "      <td>9b0ff2471aa244948f722cb02e4bdbff8572f9f0d1b3f2...</td>\n",
       "      <td>[difícil]{difíciles}&lt;na:ps:adj:inan&gt;</td>\n",
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       "      <td>Estoy muy contenta terminar mis clases porque ...</td>\n",
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       "      <th>3783</th>\n",
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       "      <td>[el]{la}&lt;ga:fm:det:an&gt;</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>Mi madre dijo que William fue la más amable, g...</td>\n",
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       "      <th>4252</th>\n",
       "      <td>1061</td>\n",
       "      <td>463983a6e4c69d404626400041efc366fa0645fbf350b3...</td>\n",
       "      <td>[vacacion]{vacaciones}&lt;in:s:gat:noun:inan&gt;</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>la última vacaciones fui en era año pasado via...</td>\n",
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       "      <th>1507</th>\n",
       "      <td>653</td>\n",
       "      <td>afc8d488a02094d44ffd70b7a2517adfbc31335aeefc53...</td>\n",
       "      <td>[la]{el}&lt;ga:mf:det:an&gt;</td>\n",
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       "      <td>Es el presidente de corea norte.</td>\n",
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       "      <td>gender agreement</td>\n",
       "      <td>En junio, nosotros vamos a el aeropuerto.</td>\n",
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       "      <th>1394</th>\n",
       "      <td>565</td>\n",
       "      <td>b7746fb67865f01df80617af302a0812b4564022cabea1...</td>\n",
       "      <td>[Muchos]{muchas}&lt;ga:fm:pron:an&gt;</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>muchas de mis amigas les gusta David Beckham m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>335</td>\n",
       "      <td>f63ee8ec1b2b7838ec02b1eafd71b376e9b973c81f9057...</td>\n",
       "      <td>[único]{única}&lt;ga:fm:adj:inan&gt;</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>También, su música es muy única y ningún otro ...</td>\n",
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       "      <th>3291</th>\n",
       "      <td>186</td>\n",
       "      <td>abd7b4ebec53d8c8843ca3efe17f125607eaad503d7a31...</td>\n",
       "      <td>[delgado]{delgados}&lt;na:ps:adj:an&gt;</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>Ellos estan delgados y altos.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3946</th>\n",
       "      <td>826</td>\n",
       "      <td>0f350bac5ea793d625e163361007e364f080793cb94d18...</td>\n",
       "      <td>[actriz]{actor}&lt;ig:f:noun:inan&gt;</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>Él es también un actor de All American.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1322</th>\n",
       "      <td>451</td>\n",
       "      <td>85f0fefd50f349bf5179cae0fa4571b1412b58ac271be7...</td>\n",
       "      <td>[su]{sus}&lt;na:ps:det:an&gt;</td>\n",
       "      <td>number agreement</td>\n",
       "      <td>Los meyores amigos de Hayley son sus otros mie...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1325</th>\n",
       "      <td>461</td>\n",
       "      <td>77966e1e9a8fcd030579ce89ceb18ae9bc4538d4e2c29e...</td>\n",
       "      <td>[simpatico]{simpática}&lt;ga:fm:adj:an&gt;</td>\n",
       "      <td>gender agreement</td>\n",
       "      <td>Meryl Streep es mi favorita persona más famosa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "      paragraph_id                                        sentence_id  \\\n",
       "3587           394  fad9adecf29b415e78858b7357702fcf0559ce28449e57...   \n",
       "2587          1446  35d463ad5f397f2dcda7f56f6d9af043aab74d5b54961e...   \n",
       "543           1346  3ea9bbf117ac043c10f1f799eb2b82995049e1c8035810...   \n",
       "3421           292  8c6aecee654e455d182eca477c470c4a82e543896ecd46...   \n",
       "4293          1081  4b9c8c086254d300366203704f8c7161382b203681e764...   \n",
       "4647          1380  aca8763b560527f6ac1726cf393c2fa535f27d8a6a5c42...   \n",
       "3021          1360  086b4910eb1cbf7b7bd1d6c21e1bf84a5163161420de6c...   \n",
       "3845           719  31d15d3db856eaf5489b381a604c91c384c1c066baee48...   \n",
       "4442          1190  9b0ff2471aa244948f722cb02e4bdbff8572f9f0d1b3f2...   \n",
       "3783           652  1d697e4dc2e39862e42cca95ae435e381dd35ac93d4aef...   \n",
       "3481           321  dda051c6e6ce66b4d7301c67bf80e9403295b3c290c9dc...   \n",
       "4252          1061  463983a6e4c69d404626400041efc366fa0645fbf350b3...   \n",
       "1507           653  afc8d488a02094d44ffd70b7a2517adfbc31335aeefc53...   \n",
       "2155          1154  d593128c171610b684d850c793ed0ce3b97166983a64e9...   \n",
       "1394           565  b7746fb67865f01df80617af302a0812b4564022cabea1...   \n",
       "1101           335  f63ee8ec1b2b7838ec02b1eafd71b376e9b973c81f9057...   \n",
       "3291           186  abd7b4ebec53d8c8843ca3efe17f125607eaad503d7a31...   \n",
       "3946           826  0f350bac5ea793d625e163361007e364f080793cb94d18...   \n",
       "1322           451  85f0fefd50f349bf5179cae0fa4571b1412b58ac271be7...   \n",
       "1325           461  77966e1e9a8fcd030579ce89ceb18ae9bc4538d4e2c29e...   \n",
       "\n",
       "                                  full_error_tag        error_type  \\\n",
       "3587           [triste]{tristes}<na:ps:adj:inan>  number agreement   \n",
       "2587        [divertida]{divertido}<ga:mf:adj:an>  gender agreement   \n",
       "543                              []{a}<az:do:an>           article   \n",
       "3421          [persona]{personas}<na:ps:noun:an>  number agreement   \n",
       "4293  [vacacion]{vacaciones}<in:s:gat:noun:inan>  number agreement   \n",
       "4647       [favorita]{favoritas}<na:ps:adj:inan>  number agreement   \n",
       "3021                  [al]{a la}<ga:fm:det:inan>  gender agreement   \n",
       "3845              [muchos]{muchas}<ga:fm:det:an>  gender agreement   \n",
       "4442        [difícil]{difíciles}<na:ps:adj:inan>  number agreement   \n",
       "3783                 [al]{a los}<na:ps:det:inan>  number agreement   \n",
       "3481                      [el]{la}<ga:fm:det:an>  gender agreement   \n",
       "4252  [vacacion]{vacaciones}<in:s:gat:noun:inan>  number agreement   \n",
       "1507                      [la]{el}<ga:mf:det:an>  gender agreement   \n",
       "2155                    [la]{el}<ga:mf:det:inan>  gender agreement   \n",
       "1394             [Muchos]{muchas}<ga:fm:pron:an>  gender agreement   \n",
       "1101              [único]{única}<ga:fm:adj:inan>  gender agreement   \n",
       "3291           [delgado]{delgados}<na:ps:adj:an>  number agreement   \n",
       "3946             [actriz]{actor}<ig:f:noun:inan>  gender agreement   \n",
       "1322                     [su]{sus}<na:ps:det:an>  number agreement   \n",
       "1325        [simpatico]{simpática}<ga:fm:adj:an>  gender agreement   \n",
       "\n",
       "                                        target_sentence  \n",
       "3587  La mayoría de las veces las letras son tristes...  \n",
       "2587      Quiero viajer con mi amigo que sea divertido.  \n",
       "543   También, cada noche iríamos a un club diferent...  \n",
       "3421  Hay muchas personas famosa en el mundo y cada ...  \n",
       "4293                        Es una vacaciones perfecta.  \n",
       "4647  mis favoritas vacaciones es cuando yo vaya a I...  \n",
       "3021  Voy a estar con mi mejor amiga y por la tarde ...  \n",
       "3845  Están muchas famosas personas en el globo y es...  \n",
       "4442  Estoy muy contenta terminar mis clases porque ...  \n",
       "3783  llegó a los Estados Unidos cuatro anos pasado ...  \n",
       "3481  Mi madre dijo que William fue la más amable, g...  \n",
       "4252  la última vacaciones fui en era año pasado via...  \n",
       "1507                   Es el presidente de corea norte.  \n",
       "2155          En junio, nosotros vamos a el aeropuerto.  \n",
       "1394  muchas de mis amigas les gusta David Beckham m...  \n",
       "1101  También, su música es muy única y ningún otro ...  \n",
       "3291                      Ellos estan delgados y altos.  \n",
       "3946            Él es también un actor de All American.  \n",
       "1322  Los meyores amigos de Hayley son sus otros mie...  \n",
       "1325  Meryl Streep es mi favorita persona más famosa...  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_errors.sample(20)[['paragraph_id', 'sentence_id', 'full_error_tag', 'error_type', 'target_sentence']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06c1cc85-ae7b-42a6-b800-1a139a075bdf",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Create Training Datasets for GEC V1 Model\n",
    "In this section we export training dataset for training a token classification model to predict evidence words in addition to errors, covered in the [gec_bert_train.ipynb](gec_bert_train.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c2fceb8-7c7b-421d-92b9-31e4a29d9b35",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Export Full Dataset\n",
    "In this section we export the full dataset including samples for all 4 error classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5b97279a-6553-435f-88cf-194e581ffdc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_errors.to_csv(args.PROCESSED_DATA_FOLDER + \"/final_with_errors.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "be21523f-8879-4f57-be47-73ed2eebe5b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected 5181 non-null rows from 5212 rows. 31 rows dropped.\n"
     ]
    }
   ],
   "source": [
    "# Create the JSON version\n",
    "# Export after remove lines with empty predicted parsing order until the underlying data is fixed.\n",
    "export_as_json(df_errors, args.full_dataset.GEC_TRAIN_CSV, args.full_dataset.GEC_TEST_CSV, args.full_dataset.GEC_DEV_CSV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "dad6cfbe-3b30-4bcf-a099-c05a07a9e315",
   "metadata": {},
   "outputs": [
    {
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       "0it [00:00, ?it/s]"
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   ],
   "source": [
    "# Create the NER version\n",
    "# Convert training dataset to NER\n",
    "to_ner(args.full_dataset.GEC_TRAIN_CSV, args.full_dataset.GEC_TRAIN_NER)\n",
    "\n",
    "# Convert test dataset to NER\n",
    "to_ner(args.full_dataset.GEC_TEST_CSV, args.full_dataset.GEC_TEST_NER)\n",
    "\n",
    "# Convert dev dataset to NER\n",
    "to_ner(args.full_dataset.GEC_DEV_CSV, args.full_dataset.GEC_DEV_NER)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cbd89c7-58ce-4900-9152-b7543e59e814",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Create Small Handcrafted Dataset\n",
    "This section exports a much smaller dataset with manually annotated evidence words. This is used for debugging and continuing model development while we work on feature engineering of evidence words in parallel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5876a765-5879-4019-9f8e-4595e4c4cdc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample 100 rows with 5 to 15 words\n",
    "def sampled(df, min_len, max_len, num_per_class):\n",
    "    df_filtered = df[(df.target.map(len) > min_len) & (df.target.map(len) < max_len)]\n",
    "    classes = df_filtered.error_type.unique()\n",
    "    df_selected = pd.concat([df_filtered[df_filtered.error_type == cur].sample(num_per_class) for cur in classes])\n",
    "    return df_selected\n",
    "\n",
    "def create_small_dataset(df):\n",
    "    df_small = sampled(df_errors, 5, 10, 25)\n",
    "    # df_small.evidence_index = df_small.apply(evidence_index_for, axis=1)\n",
    "    df_smaller = df_small #.sample(1)\n",
    "    # df_smaller.evidence_index = df_smaller.apply(evidence_index_for, axis=1)\n",
    "    df_smaller.to_csv(f'{args.PROCESSED_DATA_FOLDER}/bert_small_evidence.csv', index=False)\n",
    "    return df_smaller\n",
    "    # return df_small.sample(2).apply(evidence_index_for, axis=1)\n",
    "    # .to_csv(f'{args.PROCESSED_DATA_FOLDER}/bert_small_evidence.csv', index=False)\n",
    "\n",
    "df_errors_smaller = create_small_dataset(df_errors)\n",
    "\n",
    "# Manually add evidence words\n",
    "# Convert to JSON\n",
    "# Create train-dev-test split\n",
    "# Create NER output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b2fc5b7-1c9c-47ab-b85b-09e46b87a0e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def evidence_index_for(row, force_overwrite):\n",
    "#     print(f'-------------------{row.orig_index}-------------------')\n",
    "#     if len(row.evidence_index) > 0 and not force_overwrite:\n",
    "#         # Already processed\n",
    "#         print(f'-------------------using existing-------------------')\n",
    "#         return row.evidence_index\n",
    "#     print(f'Error Type: {row.error_type}')\n",
    "#     print(f'Correction: {row.correction_index}')\n",
    "#     print(f'X: {row.source}')\n",
    "#     print(f'Y: {row.target}')\n",
    "#     user_opt = None\n",
    "#     msg = f'Enter evidence indices (csv with each value between {0} and {len(row.source) - 1} ) or -1 to quit'\n",
    "#     quit = False\n",
    "#     evidence_indices = []\n",
    "#     try:\n",
    "#         user_input = input(f'{msg}: ')\n",
    "#         if user_input == '-1':\n",
    "#             quit = True\n",
    "#         evidence_indices = eval(user_input)\n",
    "#         print(f'Evidence indices: {evidence_indices}')\n",
    "#     except ValueError:\n",
    "#         print(msg)\n",
    "#         user_opt = None\n",
    "\n",
    "#     print(f'------------------------------------------------------')\n",
    "#     if quit:\n",
    "#         return []\n",
    "#     else:\n",
    "#         return evidence_indices\n",
    "\n",
    "# def update_evidence_index(df, force_overwrite=False, num_rows=10):\n",
    "#     count = 0\n",
    "#     for i, row in df.iterrows():\n",
    "#         if count >= num_rows:\n",
    "#             break\n",
    "#         if len(row.evidence_index) == 0 or force_overwrite:\n",
    "#             df.at[i,'evidence_index'] = evidence_index_for(row, force_overwrite)\n",
    "#             count = count + 1\n",
    "#         # else:\n",
    "#         #     print(f'-------------------{row.orig_index}-------------------')\n",
    "#         #     # Already processed\n",
    "#         #     print(f'-------------------using existing-------------------')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e47454c-6e49-4450-a25a-a3040357fbac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do this only if you want to do manual annotation of evidence index again\n",
    "# update_evidence_index(df_errors_smaller, num_rows=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a92a40ce-4b26-459b-80aa-3ffcc8eaa386",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_errors_smaller[df_errors_smaller.evidence_index.map(len) > 0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "568b8665-5a1f-45c4-8918-58a162985a89",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Do this only if you want to do manual annotation of evidence index again\n",
    "# Commenting it out to avoid accidental overwriting of manually annotated data\n",
    "# df_errors_smaller.to_csv(f'{args.PROCESSED_DATA_FOLDER}/bert_small.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c53c232a-f42d-410c-95be-085ab906c992",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reload the smaller dataset\n",
    "def load_smaller():\n",
    "    # Converters not working\n",
    "    df = pd.read_csv(args.PROCESSED_DATA_FOLDER + \"/bert_small_evidence.csv\", index_col=None, header=0)\n",
    "    # The source, target and correction_index cols are loaded as string version of lists. Convert them to actual lists.\n",
    "    df.source = df.apply(lambda row: eval(row.source), axis=1)\n",
    "    df.target = df.apply(lambda row: eval(row.target), axis=1)\n",
    "    df.correction_index = df.apply(lambda row: eval(row.correction_index), axis=1)\n",
    "    df.evidence_index = df.apply(lambda row: eval(row.evidence_index), axis=1)\n",
    "    df.predicted_parsing_order = df.apply(lambda row: eval(row.predicted_parsing_order), axis=1)\n",
    "    return df\n",
    "\n",
    "df_errors_smaller = load_smaller()\n",
    "df_errors_smaller"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f697f3f-539e-46db-acf8-6cf88d2955c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "export_as_json(df_errors_smaller, args.small_dataset.GEC_TRAIN_CSV, args.small_dataset.GEC_TEST_CSV, args.small_dataset.GEC_DEV_CSV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7db15dcd-19a6-434d-9ee4-ab15d3ea06d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Play with the teeny dataset to check logic\n",
    "def logic_check(df, num_rows=10):\n",
    "    df_teeny = df[0:num_rows]\n",
    "\n",
    "    # export_as_json(df_teeny, args.tiny_dataset.GEC_TRAIN_CSV, args.tiny_dataset.GEC_TEST_CSV, args.tiny_dataset.GEC_DEV_CSV)\n",
    "    # to_ner(args.tiny_dataset.GEC_TEST_CSV, args.small_dataset.GEC_TEST_NER)\n",
    "    to_ner(\"expect_tiny.csv\", \"expect_tiny.pkl\")\n",
    "\n",
    "logic_check(df_errors_smaller)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "462af8b5-3e7b-460b-bd2e-36719bf6e923",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert training dataset to NER\n",
    "to_ner(args.small_dataset.GEC_TRAIN_CSV, args.small_dataset.GEC_TRAIN_NER)\n",
    "\n",
    "# Convert test dataset to NER\n",
    "to_ner(args.small_dataset.GEC_TEST_CSV, args.small_dataset.GEC_TEST_NER)\n",
    "\n",
    "# Convert dev dataset to NER\n",
    "to_ner(args.small_dataset.GEC_DEV_CSV, args.small_dataset.GEC_DEV_NER)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e115d4f-d91c-4ce4-b414-a2acd4d609db",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true,
    "tags": []
   },
   "source": [
    "### Export With Two Classes (Gender and Number)\n",
    "Here we limit the samples to just two error classes - gender mismatch and number mismatch. We select just these two classes as the other two classes do not have enough samples to train the model effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c1fa4e28-b41f-42d1-8bb1-ac2b1d19c7a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected 4453 rows from 5212\n",
      "Classes in filtered dataframe: ['gender agreement' 'number agreement']\n",
      "Selected 4432 non-null rows from 4453 rows. 21 rows dropped.\n"
     ]
    }
   ],
   "source": [
    "def export_with_classes(df, classes, verbosity=1):\n",
    "    as_set = set(classes)\n",
    "    df_filtered = df[(df.error_type.map(lambda v: v in classes))]\n",
    "\n",
    "    if verbosity > 0:\n",
    "        print(f'Selected {df_filtered.shape[0]} rows from {df.shape[0]}')\n",
    "        print(f'Classes in filtered dataframe: {df_filtered.error_type.unique()}')\n",
    "    # Export JSON version\n",
    "    export_as_json(df_filtered, args.two_classed_dataset.GEC_TRAIN_CSV, args.two_classed_dataset.GEC_TEST_CSV, args.two_classed_dataset.GEC_DEV_CSV)\n",
    "\n",
    "    # Convert training dataset to NER\n",
    "    to_ner(args.two_classed_dataset.GEC_TRAIN_CSV, args.two_classed_dataset.GEC_TRAIN_NER)\n",
    "\n",
    "    # Convert test dataset to NER\n",
    "    to_ner(args.two_classed_dataset.GEC_TEST_CSV, args.two_classed_dataset.GEC_TEST_NER)\n",
    "\n",
    "    # Convert dev dataset to NER\n",
    "    to_ner(args.two_classed_dataset.GEC_DEV_CSV, args.two_classed_dataset.GEC_DEV_NER)\n",
    "\n",
    "\n",
    "export_with_classes(df_errors, ['gender agreement', 'number agreement'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7f65311-802e-402b-a5ed-28425872ae60",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Export Two Classes (GA and NA) for Plain Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e39fd5b0-aa2f-4dac-8a24-114da38ff924",
   "metadata": {},
   "source": [
    "In this section, we will prepare the data for two plain vanilla NER models:\n",
    "* Token classification approach using a classification layer on top of one or more of the BERT variants\n",
    "* Translation approach using mT5 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1f68e9a4-ed63-4c70-8733-8ab41b780284",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected 4453 rows from 5212\n",
      "Classes in filtered dataframe: ['gender agreement' 'number agreement']\n",
      "Number of rows with duplicate orig_index: 0\n"
     ]
    }
   ],
   "source": [
    "def export_for_plain(df, verbosity=1):\n",
    "    def process_row(i, row):\n",
    "        final_data = []\n",
    "        previous_class = 'O'\n",
    "        first = True\n",
    "        set_corrections = set(row.correction_index)\n",
    "        for i, word in enumerate(row.source):\n",
    "            if i in set_corrections:\n",
    "                if previous_class == 'O':\n",
    "                    cls = f'B-{args.CLASS_IDS[row.error_type]}'\n",
    "                else:\n",
    "                    cls = f'I-{args.CLASS_IDS[row.error_type]}'\n",
    "            else:\n",
    "                cls = 'O'\n",
    "            previous_class = cls\n",
    "            if first:\n",
    "                first = False\n",
    "                final_data.append([f'Sentence: {row.orig_index}', word, '', cls])\n",
    "            else:\n",
    "                final_data.append([f'', word, '', cls])\n",
    "        return final_data\n",
    "\n",
    "    def write_out(df, pkl_filename, csv_filename):\n",
    "        final_data = []\n",
    "        for i, row in df.iterrows():\n",
    "            final_data.extend(process_row(i, row))\n",
    "        cols = ['Sentence #','Word','POS','Tag']\n",
    "        with open(f'{args.PROCESSED_DATA_FOLDER}/{pkl_filename}', 'wb') as f:\n",
    "            pickle.dump([cols, *final_data], f)\n",
    "        df_ner = pd.DataFrame(final_data, columns=cols)\n",
    "        df_ner.to_csv(f'{args.PROCESSED_DATA_FOLDER}/{csv_filename}', index=False)\n",
    "\n",
    "    df_filtered = df[(df.error_type == 'gender agreement') | (df.error_type == 'number agreement')]\n",
    "    df_dups = df_filtered[df_filtered[['orig_index']].duplicated()]\n",
    "    if df_dups.shape[0] > 1:\n",
    "        raise Exception(\"Rows with duplicated orig_index found. Aborting.\")\n",
    "    if verbosity > 0:\n",
    "        print(f'Selected {df_filtered.shape[0]} rows from {df.shape[0]}')\n",
    "        print(f'Classes in filtered dataframe: {df_filtered.error_type.unique()}')\n",
    "        print(f'Number of rows with duplicate orig_index: {df_dups.shape[0]}')\n",
    "\n",
    "    train_df, test_dev_df = train_test_split(df_filtered, test_size=0.2)\n",
    "    test_df, dev_df = train_test_split(test_dev_df, test_size=0.5)\n",
    "\n",
    "    file_names = args.two_classed_plain_dataset\n",
    "    write_out(train_df, file_names.GEC_TRAIN_NER, file_names.GEC_TRAIN_CSV)\n",
    "    write_out(test_df, file_names.GEC_TEST_NER, file_names.GEC_TEST_CSV)\n",
    "    write_out(dev_df, file_names.GEC_DEV_NER, file_names.GEC_DEV_CSV)\n",
    "\n",
    "export_for_plain(df_errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6fdc7e46-3ec3-424b-826a-d34216f6a7dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import BertTokenizer, TFBertModel, T5Tokenizer, TFT5ForConditionalGeneration, AutoTokenizer, TFMT5ForConditionalGeneration\n",
    "import io"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "812f7df3-773f-4083-a1df-06ea8c7b2790",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "def addWord(tokenizer, word, ner):\n",
    "    \"\"\"\n",
    "    Convert a word into a word token and add supplied NER labels. Note that the word can be\n",
    "    tokenized to two or more tokens. Correspondingly, we add - for now - custom 'X' tokens to the labels in order to\n",
    "    maintain the 1:1 mappings between word tokens and labels.\n",
    "\n",
    "    arguments: word, ner label\n",
    "    returns: dictionary with tokens and labels\n",
    "    \"\"\"\n",
    "    # the dataset contains various '\"\"\"' combinations which we choose to truncate to '\"', etc.\n",
    "    if word == '\"\"\"\"':\n",
    "        word = '\"'\n",
    "    elif word == '``':\n",
    "        word = '`'\n",
    "\n",
    "    tokens = tokenizer.tokenize(word)\n",
    "    tokenLength = len(tokens)      # find number of tokens corresponfing to word to later add 'X' tokens to labels\n",
    "\n",
    "    addDict = dict()\n",
    "\n",
    "    addDict['wordToken'] = tokens\n",
    "    addDict['nerToken'] = [ner] + ['nerX'] * (tokenLength - 1)\n",
    "    addDict['tokenLength'] = tokenLength\n",
    "    return addDict\n",
    "\n",
    "def analyze_generated(config, state):\n",
    "    sentLengthList = state['sentLengthList']\n",
    "    bertSentenceIDs = state['bertSentenceIDs']\n",
    "    nerTokenList = state['nerTokenList']\n",
    "    bertMasks = state['bertMasks']\n",
    "    sentLengthList = state['sentLengthList']\n",
    "    max_length = state['max_length']\n",
    "    sentenceLengths= [l for l in sentLengthList]\n",
    "\n",
    "    # let us check what was generated\n",
    "    print(f'sentenceTokenList({len(state[\"sentenceTokenList\"][2])}): {state[\"sentenceTokenList\"][2]}')\n",
    "    print()\n",
    "    print(f'nerTokenList({len(state[\"nerTokenList\"][2])}): {state[\"nerTokenList\"][2]}')\n",
    "    print()\n",
    "    print(f'bertMasks({len(state[\"bertMasks\"][2])}): {state[\"bertMasks\"][2]}')\n",
    "\n",
    "    plt.hist(np.array(sentenceLengths), bins=(max_length-2))\n",
    "    plt.title(f'{config[\"name\"]} - Sentence Lengths')\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    numSentences = len(bertSentenceIDs)\n",
    "    state['numSentences'] = numSentences\n",
    "    nerClasses = pd.DataFrame(np.array(nerTokenList).reshape(-1))\n",
    "    nerClasses.columns = ['tag']\n",
    "\n",
    "    nerClasses.tag = pd.Categorical(nerClasses.tag)\n",
    "    nerClasses['cat'] = nerClasses.tag.cat.codes\n",
    "    nerClasses['sym'] = nerClasses.tag.cat.codes\n",
    "    nerLabels = np.array(nerClasses.cat).reshape(numSentences, -1)\n",
    "\n",
    "    state['NUM_ORIG_CLASSES'] = 3\n",
    "    state['NUM_NER_CLASSES'] = 4\n",
    "    state['NUM_TOTAL_CLASSES'] = state['NUM_ORIG_CLASSES'] + state['NUM_NER_CLASSES']\n",
    "    state['nerClassesTag'] = pd.Categorical(nerClasses.tag)\n",
    "    state['nerLabels'] = nerLabels\n",
    "\n",
    "    nerClasses[['cat']].hist(bins=state['NUM_TOTAL_CLASSES'])\n",
    "    plt.title(f'{config[\"name\"]} - Class Distribution')\n",
    "    plt.show()\n",
    "\n",
    "    nerDistribution = (nerClasses.groupby(['tag', 'cat']).agg({'sym':'count'}).reset_index()\n",
    "                   .rename(columns={'sym':'occurences'}))\n",
    "\n",
    "    print(nerDistribution.to_markdown())\n",
    "\n",
    "    O_occurences = int(nerDistribution.loc[nerDistribution.tag == 'O','occurences']\\\n",
    "                                    .reset_index().drop(['index'], axis=1).loc[2])   # Some gymnasics to get the count..\n",
    "    ga_occurences = nerDistribution[nerDistribution.cat == 0]['occurences'].sum()\n",
    "    na_occurences = nerDistribution[nerDistribution.cat == 1]['occurences'].sum()\n",
    "    All_occurences = nerDistribution[nerDistribution.cat < 3]['occurences'].sum()\n",
    "\n",
    "    fig1, ax1 = plt.subplots()\n",
    "\n",
    "    pie_sizes = [ga_occurences, na_occurences, O_occurences]\n",
    "    pie_labels = ['Gender', 'Number', 'No Error']\n",
    "    legend_labels = ['{} ({:.0f})'.format(l, s) for l, s in zip(pie_labels, pie_sizes)]\n",
    "\n",
    "    ax1.pie(\n",
    "        np.array([ga_occurences, na_occurences, O_occurences]),\n",
    "        # labels = ['Gender', 'Number', 'No Error'],\n",
    "        # autopct=lambda p: '{:.2f}% \\n({:.0f})'.format(p, p * All_occurences / 100),\n",
    "        autopct=lambda p: '{:.2f}%'.format(p),\n",
    "        explode=[0, 0.12, 0.12],\n",
    "        colors=sns.color_palette('Set2')\n",
    "    )\n",
    "    ax1.axis('equal')\n",
    "    # plt.legend(bbox_to_anchor=(0.85, 1), loc='upper left', labels=legend_labels)\n",
    "    plt.legend(labels=legend_labels)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('training_report/class_imbalance.png', dpi=300, transparent=True)\n",
    "    plt.title('Class Imbalance')\n",
    "    plt.show()\n",
    "\n",
    "    print()\n",
    "    print(f'\\'O\\' class percentage: {O_occurences}/{All_occurences} = {round((O_occurences * 100)/All_occurences, 2)}%')\n",
    "    print(f'\\'B-ga\\' class percentage: {ga_occurences}/{All_occurences} = {round((ga_occurences * 100)/All_occurences, 2)}%')\n",
    "    print(f'\\'B-na\\' class percentage: {na_occurences}/{All_occurences} = {round((na_occurences * 100)/All_occurences, 2)}%')\n",
    "\n",
    "    return state\n",
    "\n",
    "def split_and_export(state, filename, seed=42):\n",
    "    trainSentence_ids = []\n",
    "    trainMasks = []\n",
    "    trainSequence_ids = []\n",
    "\n",
    "    testSentence_ids = []\n",
    "    testMasks = []\n",
    "    testSequence_ids = []\n",
    "\n",
    "    nerLabels_train =[]\n",
    "    nerLabels_test = []\n",
    "\n",
    "    bertSentenceIDs = state['bertSentenceIDs']\n",
    "    bertSequenceIDs = state['bertSequenceIDs']\n",
    "    bertMasks = state['bertMasks']\n",
    "    nerLabels = state['nerLabels']\n",
    "    numSentences = state['numSentences']\n",
    "\n",
    "    bert_inputs = np.array([bertSentenceIDs, bertMasks, bertSequenceIDs])\n",
    "    numSentences = len(bert_inputs[0])\n",
    "    state['numSentences'] = numSentences\n",
    "    np.random.seed(seed)\n",
    "    training_examples = np.random.binomial(1, 0.7, numSentences)\n",
    "\n",
    "    for example in range(numSentences):\n",
    "        if training_examples[example] == 1:\n",
    "            trainSentence_ids.append(bert_inputs[0][example])\n",
    "            trainMasks.append(bert_inputs[1][example])\n",
    "            trainSequence_ids.append(bert_inputs[2][example])\n",
    "            nerLabels_train.append(nerLabels[example])\n",
    "        else:\n",
    "            testSentence_ids.append(bert_inputs[0][example])\n",
    "            testMasks.append(bert_inputs[1][example])\n",
    "            testSequence_ids.append(bert_inputs[2][example])\n",
    "            nerLabels_test.append(nerLabels[example])\n",
    "\n",
    "    X_train = np.array([trainSentence_ids,trainMasks,trainSequence_ids])\n",
    "    X_test = np.array([testSentence_ids,testMasks,testSequence_ids])\n",
    "\n",
    "    nerLabels_train = np.array(nerLabels_train)\n",
    "    nerLabels_test = np.array(nerLabels_test)\n",
    "\n",
    "    print(f'X_train[0,0]: {X_train[0,0]}')\n",
    "    print(f'nerLabels_train[0]:{nerLabels_train[0]}')\n",
    "    print(f'sentenceTokenList[0]: {state[\"sentenceTokenList\"][0]}')\n",
    "    print(f'training_examples[:10]: {training_examples[:10]}')\n",
    "\n",
    "    k_start = 0\n",
    "\n",
    "    k_end_train = X_train[0].shape[0]\n",
    "    k_end_test = X_test[0].shape[0]\n",
    "\n",
    "    bert_inputs_train_k = [X_train[0][k_start:k_end_train], X_train[1][k_start:k_end_train],\n",
    "                           X_train[2][k_start:k_end_train]]\n",
    "    bert_inputs_test_k = [X_test[0][k_start:k_end_test], X_test[1][k_start:k_end_test],\n",
    "                          X_test[2][k_start:k_end_test]]\n",
    "\n",
    "\n",
    "    labels_train_k = nerLabels_train[k_start:k_end_train]\n",
    "    labels_test_k = nerLabels_test[k_start:k_end_test]\n",
    "\n",
    "    train_all = [bert_inputs_train_k, labels_train_k]\n",
    "    test_all = [bert_inputs_test_k, labels_test_k]\n",
    "\n",
    "    training_data = {\n",
    "        'nerClassesTag': state['nerClassesTag'],\n",
    "        'bertModelName':state['model_name'],\n",
    "        'numSentences': state['numSentences'],\n",
    "        'NUM_ORIG_CLASSES': state['NUM_ORIG_CLASSES'],\n",
    "        'NUM_NER_CLASSES': state['NUM_NER_CLASSES'],\n",
    "        'NUM_TOTAL_CLASSES': state['NUM_TOTAL_CLASSES'],\n",
    "        'train_all': train_all,\n",
    "        'test_all': test_all\n",
    "    }\n",
    "\n",
    "    with open(f'{args.PROCESSED_DATA_FOLDER}/{filename}', \"wb\") as output_file:\n",
    "        pickle.dump(training_data, output_file)\n",
    "\n",
    "def prep_for(config):\n",
    "    config_name = config['name']\n",
    "    model_name = config['model_name']\n",
    "    dev_filename = config['dev_intermediate_file']\n",
    "    train_filename = config['train_intermediate_file']\n",
    "    test_filename = config['test_intermediate_file']\n",
    "    max_length = config['max_length']\n",
    "\n",
    "    text = []\n",
    "    with io.open(f'{args.PROCESSED_DATA_FOLDER}/{train_filename}', 'r', encoding='utf-8', errors='ignore') as train:\n",
    "        text = train.readlines()[1:-1] # Read ignoring the header line\n",
    "\n",
    "    with io.open(f'{args.PROCESSED_DATA_FOLDER}/{dev_filename}', 'r', encoding='utf-8', errors='ignore') as eval:\n",
    "        text.extend(eval.readlines()[1: -1]) # Read ignoring the header line\n",
    "\n",
    "    with io.open(f'{args.PROCESSED_DATA_FOLDER}/{test_filename}', 'r', encoding='utf-8', errors='ignore') as test:\n",
    "        text.extend(test.readlines()[1: -1]) # Read ignoring the header line\n",
    "\n",
    "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "    # lists for sentences, tokens, labels, etc.\n",
    "    sentenceList = []\n",
    "    sentenceTokenList = []\n",
    "    nerTokenList = []\n",
    "    sentLengthList = []\n",
    "\n",
    "    # lists for BERT input\n",
    "    bertSentenceIDs = []\n",
    "    bertMasks = []\n",
    "    bertSequenceIDs = []\n",
    "\n",
    "    sentence = ''\n",
    "\n",
    "    # always start with [CLS] tokens\n",
    "    sentenceTokens = ['[CLS]']\n",
    "    nerTokens = ['[nerCLS]']\n",
    "\n",
    "    for line in text:\n",
    "        line = line.replace('\\n', '')\n",
    "        cleanLine = re.sub(r'(?!(([^\"]*\"){2})*[^\"]*$),', '', line)  # deal with '\"10,000\"' and convert them to '10000'\n",
    "        sent, word, pos, ner = cleanLine.split(',')\n",
    "\n",
    "        # if new sentence starts\n",
    "        if (sent[:8] == 'Sentence'):\n",
    "            sentenceLength = min(max_length -1, len(sentenceTokens))\n",
    "            sentLengthList.append(sentenceLength)\n",
    "\n",
    "            # Create space for at least a final '[SEP]' token\n",
    "            if sentenceLength >= max_length - 1:\n",
    "                sentenceTokens = sentenceTokens[:max_length - 2]\n",
    "                nerTokens = nerTokens[:max_length - 2]\n",
    "\n",
    "            # add a ['SEP'] token and padding\n",
    "            sentenceTokens += ['[SEP]'] + ['[PAD]'] * (max_length -1 - len(sentenceTokens))\n",
    "            nerTokens += ['[nerSEP]'] + ['[nerPAD]'] * (max_length - 1 - len(nerTokens) )\n",
    "            sentenceList.append(sentence)\n",
    "            sentenceTokenList.append(sentenceTokens)\n",
    "\n",
    "            bertSentenceIDs.append(tokenizer.convert_tokens_to_ids(sentenceTokens))\n",
    "            bertMasks.append([1] * (sentenceLength + 1) + [0] * (max_length -1 - sentenceLength ))\n",
    "            bertSequenceIDs.append([0] * (max_length))\n",
    "\n",
    "            nerTokenList.append(nerTokens)\n",
    "            sentence = ''\n",
    "            sentenceTokens = ['[CLS]']\n",
    "            posTokens = ['[posCLS]']\n",
    "            nerTokens = ['[nerCLS]']\n",
    "\n",
    "            sentence += ' ' + word\n",
    "\n",
    "        addDict = addWord(tokenizer, word, ner)\n",
    "        sentenceTokens += addDict['wordToken']\n",
    "        nerTokens += addDict['nerToken']\n",
    "\n",
    "    # The first list element need to be removed as it does not a sentence.\n",
    "    sentLengthList = sentLengthList[1:]\n",
    "    sentenceTokenList = sentenceTokenList[1:]\n",
    "    bertSentenceIDs = bertSentenceIDs[1:]\n",
    "    bertMasks = bertMasks[1:]\n",
    "    bertSequenceIDs = bertSequenceIDs[1:]\n",
    "    nerTokenList = nerTokenList[1:]\n",
    "\n",
    "    state = {\n",
    "        'model_name': model_name,\n",
    "        'sentLengthList': sentLengthList,\n",
    "        'sentenceTokenList': sentenceTokenList,\n",
    "        'bertSentenceIDs': bertSentenceIDs,\n",
    "        'bertMasks': bertMasks,\n",
    "        'bertSequenceIDs': bertSequenceIDs,\n",
    "        'nerTokenList': nerTokenList,\n",
    "        'max_length': max_length\n",
    "    }\n",
    "\n",
    "    state = analyze_generated(config, state)\n",
    "    split_and_export(state, config['train_data_file'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "8b41cdf2-abba-4d48-a415-482a7ec58250",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sentenceTokenList(40): ['[CLS]', 'a', 'vacaciones', 'perfecto', 'es', 'cuando', 'todo', 'es', 'gratis', '.', '[SEP]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "\n",
      "nerTokenList(40): ['[nerCLS]', 'O', 'O', 'B-ga', 'O', 'O', 'O', 'O', 'O', 'O', '[nerSEP]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]']\n",
      "\n",
      "bertMasks(40): [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    | tag      |   cat |   occurences |\n",
      "|---:|:---------|------:|-------------:|\n",
      "|  0 | B-ga     |     0 |         2965 |\n",
      "|  1 | B-ga     |     1 |            0 |\n",
      "|  2 | B-ga     |     2 |            0 |\n",
      "|  3 | B-ga     |     3 |            0 |\n",
      "|  4 | B-ga     |     4 |            0 |\n",
      "|  5 | B-ga     |     5 |            0 |\n",
      "|  6 | B-ga     |     6 |            0 |\n",
      "|  7 | B-na     |     0 |            0 |\n",
      "|  8 | B-na     |     1 |         1448 |\n",
      "|  9 | B-na     |     2 |            0 |\n",
      "| 10 | B-na     |     3 |            0 |\n",
      "| 11 | B-na     |     4 |            0 |\n",
      "| 12 | B-na     |     5 |            0 |\n",
      "| 13 | B-na     |     6 |            0 |\n",
      "| 14 | O        |     0 |            0 |\n",
      "| 15 | O        |     1 |            0 |\n",
      "| 16 | O        |     2 |        61271 |\n",
      "| 17 | O        |     3 |            0 |\n",
      "| 18 | O        |     4 |            0 |\n",
      "| 19 | O        |     5 |            0 |\n",
      "| 20 | O        |     6 |            0 |\n",
      "| 21 | [nerCLS] |     0 |            0 |\n",
      "| 22 | [nerCLS] |     1 |            0 |\n",
      "| 23 | [nerCLS] |     2 |            0 |\n",
      "| 24 | [nerCLS] |     3 |         4452 |\n",
      "| 25 | [nerCLS] |     4 |            0 |\n",
      "| 26 | [nerCLS] |     5 |            0 |\n",
      "| 27 | [nerCLS] |     6 |            0 |\n",
      "| 28 | [nerPAD] |     0 |            0 |\n",
      "| 29 | [nerPAD] |     1 |            0 |\n",
      "| 30 | [nerPAD] |     2 |            0 |\n",
      "| 31 | [nerPAD] |     3 |            0 |\n",
      "| 32 | [nerPAD] |     4 |        94280 |\n",
      "| 33 | [nerPAD] |     5 |            0 |\n",
      "| 34 | [nerPAD] |     6 |            0 |\n",
      "| 35 | [nerSEP] |     0 |            0 |\n",
      "| 36 | [nerSEP] |     1 |            0 |\n",
      "| 37 | [nerSEP] |     2 |            0 |\n",
      "| 38 | [nerSEP] |     3 |            0 |\n",
      "| 39 | [nerSEP] |     4 |            0 |\n",
      "| 40 | [nerSEP] |     5 |         4452 |\n",
      "| 41 | [nerSEP] |     6 |            0 |\n",
      "| 42 | nerX     |     0 |            0 |\n",
      "| 43 | nerX     |     1 |            0 |\n",
      "| 44 | nerX     |     2 |            0 |\n",
      "| 45 | nerX     |     3 |            0 |\n",
      "| 46 | nerX     |     4 |            0 |\n",
      "| 47 | nerX     |     5 |            0 |\n",
      "| 48 | nerX     |     6 |         9212 |\n"
     ]
    },
    {
     "data": {
      "image/png": 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ymQwKhQJ79uwB0NUb7uDBgwgKCsKECROgVqsxZcoU0+O/tGzZMvj7+yM1NRWvv/46Ojs7uz2uUCgwcuRI7N69+5J1ion32BHRoOnQ6tHWoUdHpx56vRE605cBOr2x2za9vuvyzC9n/J//oDq/TQJAKpNC7vLLLxkUcikUchlcFTK4ymVwdXWB0pVvb+Q48vLyIAhCt3vLACAgIAAdHR0AgAULFmDZsmXYvn07MjMzUV1dbRqRevvtt7F+/XqsXbvWNDnBaDTis88+g5eXFwDgzjvvxNatW/H666+jpaUFK1aswOeff46ZM2cCAFatWoXw8HDTc+fn52P16tUoLS1FaGgoAODJJ5/Ehg0b8Omnn2LJkiUAuvqyffDBBxg5cmSvr6+4uBiCIJjOczGCIODxxx/HVVddhWHDhgEAhg4diqioKDz77LP46KOP4OHhgXfffReVlZWoqKgA0HUJG+gapXz77beRmpqKzz//HNOnT0dWVpbpXrxHHnkEaWlp8PX1xaFDh/Dss8+isLAQ//rXv7rVERYWhqKiol7rFBvf+Yio31rbdWhs1qKxRYvWdh3a2nVo69CjrUOHtvau/23v0MNgFESrUSaVwF0ph7ubCzzdFXBXusBDKe/2pfJ0hbtSLlqNRP31615nhw4dgtFoxO9+9zvT6FV6ejpaWlp6rKzR3t6O/Px80/fR0dGmUAd0LWV1flmr/Px8dHZ2Yvz48abH/fz8ugXLjIwMCIKAhISEbs+j1Wq7PbdCocCIESMu+bra29sBdB95+7WFCxfixIkT3Uba5HI5vv76a9x7773w8/ODTCbDjBkzcO2115r2OX9P35/+9Cfcc889AIBRo0Zh69atWLlyJZYuXQqg63LteSNGjICvry9uvvlm0yjeeUqlEm1tbZd8PWJisCOiHoxGAQ3NWjQ0d6CxuRONLVo0NmvR0KxFU4sWOv3Fb362JQajgObWTjS3dqKqrvc3YVe5DCovV/h6u8LHyw0+3q7w9XaDjxdDH9mOuLg4SCQSnD59utv2IUOGALiwJBXQFWRCQkK63et2no+Pj+n/y+Xdf78lEokpBAnC5f8oMxqNkMlkSE9P77EE1i/vTVMqlZdtvhsQEACg65JsYGBgj8cfeughfPfdd9i1a1e3UUMAGD16NI4dO4bGxkZ0dnYiMDAQ48aNM13WPb/2anJycrfjkpKSUFJS0mtNV155JYCu0dJfBjuNRoPY2NhLvh4xMdgRObn2Dh1q6ttRo2lDTX07auvbUdfQLupomzVpdQZUa9pQrekZ/hRyGfxUbgj0UyLIzx2Bfu4I9FVC7mL+Oo5E5vD398fMmTPxz3/+Ew899FCv99kBQFpaGiorK+Hi4tJthml/xMXFQS6X48CBA4iMjATQFbpycnIwZcoUAF2jXgaDAdXV1Zg0aZJZz3NebGwsvL29kZ2d3W0EUBAEPPTQQ1i3bh127NiBmJiYXs+hUqkAdE2oOHLkCF577TUAXSOToaGhOHPmTLf9c3Jyuo3s/drRo0cBXAiG52VlZeHmm2/u3wu0IgY7IifS0taJ8uoWVNa1oba+DTWadrS2D2xdQkfWqTOgsrYVlbUX2iZIJICvtxsC/dwRdC7wBfl5QOnGt1MaXB988AEmTpyIMWPG4OWXX8aIESMglUpx+PBhnD59GqNHjwYAzJgxA+PHj8f8+fNNPd/Ky8vx008/Yf78+ZedoAB0jbjde++9WLRoEfz9/aFWq7F48eJu7UESEhLwu9/9DnfddRfeeecdjBo1CrW1tdi2bRuGDx+O6667rs+vTSqVYsaMGdizZw/mz59v2r5gwQJ8+eWX+Pbbb+Hl5YXKykoAXSHu/Cjl//73PwQGBiIyMhKZmZl45JFHMH/+fMyaNQtA10jkokWL8NJLL2HkyJFITU3FqlWrcPr0aaxduxZAVzuUAwcO4Oqrr4ZKpcLhw4fx2GOPYd68eaZgCwBFRUUoKyvDjBkz+vzarI3vREQOymgUUFvfjvKaFpRVt6CiugVNrZ2XP5AuSRAATWMHNI0dOFN4Ybufyg1hQZ4IU3siLMgLKi/X3k9CZIbY2FgcPXoUS5YswbPPPovS0lK4uroiOTkZTz75JB588EEAXUHmp59+wuLFi/HHP/4RNTU1CA4OxuTJk6FWq/v8fG+99RZaWlowb948eHl54YknnjDNSj3v008/xV/+8hc88cQTKCsrg7+/P8aPH9+vUHfe/fffj3vvvRdvvvmmKUAuX74cQNeKF79+3rvvvhsAUFFRgccffxxVVVUICQnBXXfdhRdeeKHb/o8++ig6Ojrw2GOPQaPRYOTIkdi8ebPpkqqrqyv++9//4pVXXoFWq0VUVBTuu+8+PPXUU93Os3r1asyaNQtRUVH9fn3WIhH6ciGdiGye3mBEeXULSquau0blalvRqbP9e+Eclae7/FzQ80JYkCcCfC9/nxENvo6ODhQWFiImJuaSN+qT9QmCgCuvvBKPPvoobr/9drHL6UGr1SI+Ph6rV6/GxIkTLX5+S/1ucsSOyI5pGttRVNaEovJGlFa1mFqGkPha2nQ4U1SPM0X1AABXhQxRId6IDlMhOswbnu4KkSsksi0SiQQff/xxtyXFbElxcTEWL148KKHOkjhiR2RHtJ16FFc0o7isEcXlTby0aseC/NwRHeaNmDAVQgI9IZVyNM8aOGJHtoojdkROorFZi9ySeuSXNKC8pgX8U8wxnJ+Jeyiz0jSaFxOuwpAIHzZXJiKz8d2DyAbVNbQjt7geuSX1qNG0i10ODTJtpwE5xfXIKa6HVCpBZIgXEqL8EBflAzcF36aJqO/4jkFkIzSNHcgp0uBMUT3qGhjmnJXRKHTdN1nWhC0HJIgK8UZCtC/iIn3gypBHRJfBdwkiEbV36HCqQIOT+bUcmaMejEYBhWWNKCxrxJb9EkSFeiMh2g/xUT5skkxEF8VgR2RlXSMyjcjKq0VBaSOMTrLCAw2MwSigoLQRBaWN2HZQhqExfhgWH4DggN5XICAi58NgR2QldQ3tOJlXi1MFGq72QAPSqTPgRE4NTuTUIMBXiWHxAUga4s9JF0TEYEc0mHR6I04X1iEzp7bbslREllJb344dh85i95FSxEX6YFh8ICJDvNgMmchJSS+/CxH1V3NrJ3anl+KTtSeweV8xQx0NOoNRwJmieny9OQcrv8lC+slKaDv1YpdFDqSoqAgSiQTHjh2z2nN2dnYiLi4Oe/futdpz9seTTz6Jhx9+WOwyuuGIHZEFlVU34+ipauQVN8DIhnMkksYWLXYeKcW+Y+VIjvXHqCQ1/FRsxns5hnfvterzyR5f0a/97777bqxatQpLly7FM888Y9q+fv163HDDDXDE9QY+/vhjREVFdVvt4fXXX8ePP/6IY8eOQaFQoKGhodfj6+rqMHLkSJSVlaG+vh4+Pj499snLy8OoUaMgk8l6nOuLL77Am2++idzcXKhUKlxzzTV4++234e/vDwB46qmnEBsbi8ceewwxMTGWeMkDxhE7ogEyGIzIzq/DFz9k478/n0FOUT1DHdkEnd6I42dq8Nn6LKzbkouSiiaxS6IBcnNzw7Jly1BfXy92KRbT2dn7Cjrvvfce/u///q/H/rfccgseeOCBy5773nvvxYgRI3p9XKfT4fbbb8ekSZN6PLZnzx7cdddduPfee3Hy5En873//w+HDh7vVExQUhFmzZuHDDz+8bC3WwmBHZKZOnQGHMivwr68zsWFPIarq2sQuiahXhWWNWLspB//5PhunCuo4G9tOzZgxA8HBwVi6dGmv+7z88stITU3ttu1vf/sboqOjTd/ffffdmD9/PpYsWQK1Wg0fHx+88sor0Ov1WLRoEfz8/BAeHo6VK1f2OP/p06cxYcIEuLm5ISUlBTt27Oj2eHZ2Nq677jp4enpCrVbjzjvvRG1trenxqVOnYuHChXj88ccREBCAmTNnXvR1ZGRkIC8vD3PmzOm2/ZVXXsFjjz2G4cOH9/ozAIDly5ejoaEBTz75ZK/7PP/88xg6dChuvfXWHo8dOHAA0dHRePjhhxETE4OrrroKf/rTn3DkyJFu+82bNw+rV6++ZC3WxGBH1E8dnXrsP16Of319AnsyyjjDlexKtaYNP+8uxKfrMpGZUwOD0Sh2SdQPMpkMS5YswXvvvYfS0tIBnWvbtm0oLy/Hrl278O677+Lll1/G9ddfD19fXxw8eBB//vOf8ec//xlnz57tdtyiRYvwxBNP4OjRo5gwYQLmzZuHuro6AEBFRQWmTJmC1NRUHDlyBBs2bEBVVVWP4LRq1Sq4uLhg7969+Oijjy5a365du5CQkABvb+9+v7bs7Gy8+uqr+PzzzyGVXjzqbNu2Df/73//w/vvvX/TxCRMmoLS0FD/99BMEQUBVVRXWrl3bI2iOHTsWZ8+eRXFxcb/rHAwMdkR91N6hx96MMqxYm4n9x8rRoTWIXRKR2RpbOrF5fzE+XZeFEwx4duWGG25AamoqXnrppQGdx8/PD//4xz+QmJiIP/7xj0hMTERbWxuee+45xMfH49lnn4VCoegxcWHhwoW46aabkJSUhOXLl0OlUmHFiq77BZcvX460tDQsWbIEQ4cOxahRo7By5Ups374dOTk5pnPExcXhzTffRGJiIoYOHXrR+oqKihAaGtrv16XVanH77bfjrbfeQmRk5EX3qaurw913343PPvus1+A4YcIEfPHFF/jtb38LhUKB4OBg+Pj44L333uu2X1hYmKleW8BgR3QZre067DpyFv/6+gQOZlZAq2OgI8fR1NKJLfuL8ek3DHj2ZNmyZVi1ahWys7PNPkdKSkq30Sy1Wt3t8qZMJoO/vz+qq6u7HTd+/HjT/3dxccGYMWNw6tQpAEB6ejq2b98OT09P09f54Jafn286bsyYMZetr729HW5u/Z/08+yzzyIpKQm///3ve93nvvvuwx133IHJkyf3uk92djYefvhhvPjii0hPT8eGDRtQWFiIP//5z932UyqVAIC2Ntu4HYezYol60d6hx8HMCpw4UwO9gR925NiaWrsC3qETFRg7PAQpcf6Qyfi3v62aPHkyZs+ejeeeew533313t8ekUmmPGbI6Xc9bRuRyebfvJRLJRbcZ+xD2z/dNNBqNmDt3LpYtW9Zjn5CQENP/9/C4/IopAQEByMzMvOx+v7Zt2zZkZmZi7dq1AGD6WQQEBGDx4sV45ZVXsG3bNnz33Xd4++23TfsYjUa4uLjg448/xh//+EcsXboUEydOxKJFiwAAI0aMgIeHByZNmoS//OUvptej0WgAAIGBgf2udTAw2BH9ik5vxNFTVTicWcnROXI6Ta2d2HKgGAczKzBxVBiShvix2bGNeuONN5CamoqEhIRu2wMDA1FZWQlBEEz/7SzZe+7AgQOmkS69Xo/09HQsXLgQAJCWloavv/4a0dHRcHEZWMQYNWoUli9f3u119MXXX3+N9vYLa28fPnwYf/zjH7F7927ExsYCAPbv3w+D4cL7+7fffotly5Zh3759pkurbW1tPV6DTNa1RvMvg3NWVhbkcjlSUlL6/yIHAYMd0TmCIOBkXh32HStDSxsnRJBza27txIY9hTh6qgqTx0QgIthL7JLoV4YPH47f/e53Pe75mjp1KmpqavDmm2/i5ptvxoYNG/Dzzz+bNQnhYt5//33Ex8cjKSkJf/3rX1FfX48//vGPAIAFCxbgk08+we23345FixYhICAAeXl5WLNmDT755BNTMOqLq6++Gq2trTh58iSGDRtm2l5SUgKNRoOSkhIYDAZTaI2Li4Onp6cpvJ13fkZuUlKSqY9dUlJSt32OHDkCqVTa7Xnmzp2L++67D8uXL8fs2bNRUVGBRx99FGPHju1279/u3bsxadIk0yVZsTHYEQEoKG3Anowy1Na3X35nIidSVdeG/208g9gIH0waHe7QjY772zDYFrz22mv46quvum1LSkrCBx98gCVLluC1117DTTfdhCeffBIff/yxRZ7zjTfewLJly3D06FHExsbi22+/RUBAAAAgNDQUe/fuxdNPP43Zs2dDq9UiKioK11xzTa+zU3vj7++PG2+8EV988UW39i4vvvgiVq1aZfp+1KhRAIDt27dj6tSpA3+B59x9991obm7GP//5TzzxxBPw8fHBtGnTelxmXr16NV555RWLPe9ASQRHbFVN1EdVda3YdaQUZyubxS6FyOZJJRIMTwjAhNRQKN3klz/ABnV0dKCwsBAxMTFm3ZhP1pWZmYkZM2YgLy8PXl62N2r8448/YtGiRThx4sSALz1b6neTI3bklNo79NidUYqs3NrL70xEAACjIOD4mRqcKtBg7PBgpCWr4cIJFjSIhg8fjjfffBNFRUWXbUgshtbWVnz66acDDnWWxBE7ciqCICArtxa7M8rQoeUC6UQD4evthulXRiIyxDL3blkDR+zIVnHEjqifquvasPVgMSpqWsUuhcgh1Dd1YO2mHAwd4oepYyLgrrTPy7NEjoTBjhyetlOPvUfLcfxMNTg+TWR5pws0KCxtxKS0cAxPCGB7FCIRMdiRQztVUIddR0q5nivRINN2GrDlQDFO5tdixpVRCPRzF7skIqfEYEcOqaWtE5v3FaOwrFHsUoicSkVNK7744RRGJQVhwqhQyF363reMiAaOwY4cTnZ+LbYfOgttJ1eNIBKDURCQnl2F/NIGXDMxBqFBnmKXROQ0GOzIYbS267B5XxEKSjlKR2QLGpq0+O+G0xiTEowJqaFce5bIChjsyCGcKqjD9kMl6NBylI7IlggCcDirEoVljbj2qhjee0c0yPjnE9m1tnYdvtueh593FzLUEdmw2vp2fPHjKRw8UQ6jkdPTqX9WrFiBWbNmiV2G2TIzMxEeHo7W1sFvt8UGxWS3CkobsHFPEdrZaJjIrgQHeOCaq2JEWXf2Uk1g3111xKq1PP6HMf3a/+6778aqVauwdOlSPPPMM6bt69evxw033ICBfJx/9tlnuOeee3psd3V1RUdHh9nntQStVoshQ4ZgzZo1mDRpkml7Q0MDFi9ejG+++Qb19fWIiYnBO++8g+uuuw4AsGvXLrz11ltIT09HRUUF1q1bh/nz55uO1+l0eP755/HTTz+hoKAAKpUKM2bMwBtvvIHQ0FAAQFFREWJiYi5a11dffYVbbrkFAPD666/jxx9/xLFjx6BQKNDQ0NBj/xtvvBFpaWl4/vnnL3o+SzUo5ogd2R2D0YidR85i/dY8hjoiO1RZ24r/fJ/NJf3M4ObmhmXLlqG+vt7i5/b29kZFRUW3r+Li4l737+zs7LFNEATo9f1/X77UcV9//TU8PT27hbrOzk7MnDkTRUVFWLt2Lc6cOYNPPvkEYWFhpn1aW1sxcuRI/POf/7zoedva2pCRkYEXXngBGRkZ+Oabb5CTk4N58+aZ9omIiOjxM3nllVfg4eGBa6+9tls9t9xyCx544IFeX+M999yD5cuXw2AY3KtLDHZkVxpbtPjvz2eQfrJK7FKIaAD0BiM27SvChj2F0Ol5G0VfzZgxA8HBwVi6dOkl9/v666+RkpICV1dXREdH45133rnsuSUSCYKDg7t9qdVq0+NTp07FwoUL8fjjjyMgIAAzZ87Ejh07IJFIsHHjRowZMwaurq7YvXs3tFotHn74YQQFBcHNzQ1XXXUVDh8+bDpXb8ddzJo1a7qFLQBYuXIlNBoN1q9fj4kTJyIqKgpXXXUVRo4cadrn2muvxV/+8hfceOONFz2vSqXC5s2bceuttyIxMRFXXnkl3nvvPaSnp6OkpAQAIJPJevxM1q1bh9/+9rfw9Lww2/uVV17BY489dsn1bGfPno26ujrs3LnzEv8VBo7BjuxGTnE9/vN9NipruSQYkaPIzq/Dlz+cQl1Du9il2AWZTIYlS5bgvffeQ2lp6UX3SU9Px6233orbbrsNmZmZePnll/HCCy/gs88+G/Dzr1q1Ci4uLti7dy8++ugj0/annnoKS5cuxalTpzBixAg89dRT+Prrr7Fq1SpkZGQgLi4Os2fPhkaj6Xa+Xx93Mbt378aYMd0vW3/33XcYP348FixYALVajWHDhmHJkiUDHg1rbGyERCKBj4/PRR9PT0/HsWPHcO+99/b73AqFAiNHjuw1wFoKgx3ZPL3BiK0HivHDjnz2piNyQHWNHfjix1M4mcdLs31xww03IDU1FS+99NJFH3/33Xcxffp0vPDCC0hISMDdd9+NhQsX4q233rrkeRsbG+Hp6dnt69cTFuLi4vDmm28iMTERQ4cONW1/9dVXMXPmTMTGxsLNzQ3Lly/HW2+9hWuvvRbJycn45JNPoFQqsWLFim7n++Vx/v7+PWpqaGhAQ0OD6Z638woKCrB27VoYDAb89NNPeP755/HOO+/g9ddfv+RrvJSOjg4888wzuOOOO+Dt7X3RfVasWIGkpCRMmDDBrOcICwtDUVGR2TX2BdudkE2rb+zADzvzUVPPv+aJHJleb8TGvUUorWzGtCsjuWLFZSxbtgzTpk3DE0880eOxU6dO4Te/+U23bRMnTsTf/vY3GAwGyGQX/9l6eXkhIyOj2zalUtnt+1+PnF1se35+PnQ6HSZOnGjaJpfLMXbsWJw6dapP5zuvvb3rvf/XkwmMRiOCgoLw8ccfQyaTYfTo0SgvL8dbb72FF1988ZLnvBidTofbbrsNRqMRH3zwQa+1fPnll3jhhRf6ff7zlEol2trazD6+LxjsyGYVnG3AT7sL0anjKB2RsziZX4fKulbMnRoLP5Xy8gc4qcmTJ2P27Nl47rnncPfdd3d7TBAESCSSHtsuRyqVIi4u7pL7eHh4XHb7+ee6WA2/3tbb+c7z9/eHRCLpMVkkJCQEcrm8W0hNSkpCZWUlOjs7oVAoLnneX9LpdLj11ltRWFiIbdu29Tpat3btWrS1teGuu+7q87l/TaPRIDY21uzj+4KXYsnmCIKA/cfLsX5bHkMdkROqa+jAlz+eRiFXkbmkN954A99//z327dvXbXtycjL27NnTbdu+ffuQkJDQ62idJcXFxUGhUHSrQafT4ciRI0hKSurXuRQKBZKTk5Gdnd1t+8SJE5GXlwej0WjalpOTg5CQELNCXW5uLrZs2XLRy8HnrVixAvPmzUNgYGC/XsMvZWVlYdSoUWYf3xccsSOb0qkz4Ofdhcg/2yB2KUQkok6dAeu35WLS6HCMSQkWuxybNHz4cPzud7/De++91237E088gSuuuAKvvfYafvvb32L//v345z//2eslxvMEQUBlZWWP7UFBQZBK+z4O5OHhgQceeACLFi2Cn58fIiMj8eabb6Ktrc2sSQezZ8/Gnj178Oijj5q2PfDAA3jvvffwyCOP4KGHHkJubi6WLFmChx9+2LRPS0sL8vLyTN8XFhbi2LFjppr0ej1uvvlmZGRk4IcffoDBYDC9fj8/v24BMS8vD7t27cJPP/100RpLSkqg0WhQUlICg8GAY8eOAegKuednzxYVFaGsrAwzZszo98+gPxjsyGY0NmuxflseZ8cREYCu5ch2HSlFXX07ZoyPGvS1ZvvbMNgWvPbaa/jqq6+6bUtLS8NXX32FF198Ea+99hpCQkLw6quv9rhk+2tNTU0ICQnpsb2iogLBwf0L12+88QaMRiPuvPNONDc3Y8yYMdi4cSN8fX37dR4AuO+++5CWlobGxkaoVCoAXf3lNm3ahMceewwjRoxAWFgYHnnkETz99NOm444cOYKrr77a9P3jjz8OAPjDH/6Azz77DKWlpfjuu+8AAKmpqd2ec/v27Zg6darp+5UrVyIsLKzX1S9efPFFrFq1yvT9+VG5X55n9erVmDVrFqKiovr9M+gPrjxBNqG0shnf78hnw2EiuqjQIE/MmxoLd6V8QOexVHd/sq5bb70Vo0aNwrPPPit2KWbRarWIj4/H6tWru00q+SWuPEEO40RODdZuzmGoI6JelVe34IsfT6FaM7gzCsk2vfXWW90aAtub4uJiLF68uNdQZ0kcsSNR7c0ow8HMCrHLICI7IXeR4pqrYhAf1f9LegBH7Mh2ccSO7JrRKGDjnkKGOiLqF53eiB925uPY6WqxSyGySZw8QVan0xnw/c58FJU1iV0KEdkhQQC2HSxBa7sOE0eFXf4AIifCYEdW1dahw7otuaiq430yRDQwB09UoK1dh+lXRkEqlVz+gF/gXUhkayz1O8lLsWQ1DU0dWP3TaYY6IrKYzNxafL8jH3qD8fI7o2tpKwCDvqwTUX+d/508/ztqLk6eIKuorG3F+q25aOvgzFcisrywIE/8Znoc3BSXvxBVUVGBhoYGBAUFwd3dvccyV0TWJAgC2traUF1dDR8fn4v2EuwPBjsadKVVzVi3JRc6fd/+oiYiMkeArxI3zoiHp/ull5Q6v8JCQ0ODdQoj6gMfHx8EBwcP+A8NBjsaVCUVTVi/LQ96hjoisgJvTwVumZ0IlafrZfc1GAzQ6XRWqIro0uRyucXW8WWwo0FTVNaI77b3/d4XIiJL8PJQ4JZZCfDxZp86cj4MdjQoCkob8P32fBiM/PUiIuvzdJfjllmJ8FUx3JFzYbAji8svacAPOxnqiEhcHko5bpmdAD+VUuxSiKyGwY4sKqe4Hj/tKoCRoY6IbEBXuEuEH0fuyEmwjx1ZzJkiDX7ayVBHRLajtV2H/208g/qmDrFLIbIKBjuyiILSBvy8qxBGDgATkY1huCNnwmBHA1Za2YwfdhQw1BGRzWpp0+HrTTloaesUuxSiQcVgRwNSVdfa1aeOLU2IyMY1tXbi68056NByBRxyXAx2ZDZNYwe+2ZKLTp1B7FKIiPqkrqED67bmQqfn+xY5JgY7MktTixZfb85BO9d+JSI7U1HTiu935MNg5JUGcjwMdtRvbe06fL05B82tvFeFiOxTUVkTNu4pAjt+kaNhsKN+6dQZ8M2WXNQ3acUuhYhoQE4XarDj8FmxyyCyKAY76jOjUcCPOwtQrWkTuxQiIos4eqoaB46Xi10GkcUw2FGf7Th8FoVljWKXQURkUfuOleN0QZ3YZRBZBIMd9cnRU9U4drpa7DKIiAbFxn1FqKhpEbsMogFjsKPLKixtxI7DJWKXQUQ0aAwGAd9tz+ekMLJ7DHZ0STX1bfhxVz44cYyIHF1ruw7rt+VCx96cZMcY7KhXre06rN+ah04dez0RkXOo0bTjp92FbINCdovBji5KbzDi2215vCxBRE4n/2wD9mSUiV0GkVkY7Oiith4oRmVtq9hlEBGJ4nBWJbLza8Uug6jfGOyohxM5NTiZx6n/ROTcNu8rRlUd/8Al+8JgR91U1bVi+0HOgCUiMhgF/LAjHx2dXBOb7AeDHZm0a/XnFsbmTcNERADQ2NKJjXuKxC6DqM8Y7AgAIAgCft5dgKYWTpYgIvql/LMNOJJVKXYZRH3CYEcAgAMnKlBU1iR2GURENmlPRhnKqprFLoPoshjsCEVljVwEm4joEoyCgB93FaCtQyd2KUSXxGDn5FraOvHz7kKuLEFEdBktbTr8tKuAzYvJpjHYOTFBELBxbxHatZzxRUTUFyUVzThwvELsMoh6xWDnxI6erkZxOe+rIyLqjwMnylFR0yJ2GUQXxWDnpGrr27E7vVTsMoiI7I4gAD/vKYRObxC7FKIeGOyckMFgxM+7C2Aw8D4RIiJzNDRpsesI/zgm28Ng54T2HC1DTX272GUQEdm142dqUFjWKHYZRN0w2DmZkoomZGRXiV0GEZFD2MQJaGRjGOycSEenHhv3FrG1CRGRhbS267D1QLHYZRCZMNg5kZ2Hz6K5lUuGERFZUk5RPU4V1IldBhEABjunUVLRhJN5fOMhIhoM2w6WoKWNfziT+BjsnIDeYMSW/bxUQEQ0WLSdBuw4fFbsMogY7JzB/mPlaGjWil0GEZFDyymqR0Fpg9hlkJNjsHNwNZo2pJ/kLFgiImvYdrCEjYtJVAx2DsxoFLBpXxGMnAZLRGQVTS2d2H+sXOwyyIkx2Dmwo6eqUFXXJnYZREROJSO7GjUavveSOBjsHFRTixb7+FcjEZHVGQUBm/cXQ+DVEhIBg52D2n7oLHR6o9hlEBE5pcraVhw/UyN2GeSEGOwcUFFZI/LPNohdBhGRU9uTUYa2dp3YZZCTYbBzMAajkb2UiIhsQKfOgL3HysQug5wMg52DOXaqGprGDrHLICIiAFm5taip50QKsh4GOwfS1qHDgeMVYpdBRETnCAKw63Cp2GWQE2GwcyD7j5VDq2NjTCIiW1Jc0cQVKchqGOwcRF1DO07kcAYWEZEt2nWkFEYj25/Q4GOwcxA7j5wFWyYREdkmTWMH25+QVTDYOYCSiiYUlTWJXQYREV3C/uPl6OjUi12GxQntLRA0vL/bVjDYOYC9RzmdnojI1nVo9TjoQBPchPoqGLf+B8ZPFsG45d9il0PnuIhdAA1MQWkDKmpaxS6DiIj64PiZGowZFgwPpVzsUswmlOXCeGQjUHAMpnuASs9AqC6BJChS1NqIwc6uCYKAfUe5HiwRkb3QG4w4lFmBq8faVwASjEYIuekQ0jcClYUX3ydjMyTX3GvlyujXGOzsWG5JA6o1bHxJRGRPTuTU4IphwfB0V4hdymUJnR0QsnZDyNgCNNVeet8zhyBMvgUSd28rVUcXw2BnpwRBwH4uVUNEZHcMBgGHMisxbZztjtoJzRoIR7dCyNwJaNv7dpBBD+H4dkjG/2Zwi6NLYrCzU6cLNahr4NJhRET2KPPcqJ2Xh22N2gnVxRCObIKQcxgw9r/hvXB8B4SxcyCRMV6IhT95O2Q0Cth/nPfWERHZK4NRwKHMCky/MkrsUiAIAlB4Asb0TcDZ0wM7WVsThNx0SIaOs0xx1G8MdnYoO78ODU1ascsgIqIByMqtxRXDguHt6SrK8wt6HYTsfRAyNgMW7EMnnNwDMNiJhsHOzgiCgCMnK8Uug4iIBqhr1K4SM8Zbd9ROaGuGcHwbhOM7gLZBaG5fcgpCUx0k3v6WPzddFoOdnck/2wBNI++tIyJyBCfzanHlyBCrzJAVNJUQ0jdByN4HGHSD+EQChOx9kFw5d/Ceg3rFYGdnjpysErsEIiKyEINRQEZ2FSaPiRi05xDOnu66f67gBADrLCounNwLYdz1kEgkVnk+uoDBzo6UV7egvLpF7DKIiMiCTuTUYtyIULgqZBY7p2A0QDhzGEL6JqC62GLn7bPGGqD0DBAx1PrP7eQY7OzI4SzeW0dE5Gg6dQZT0+KBErRtEDJ3QTi6FWjWWKC6AdSStQcSBjurY7CzE/WNHSgobRC7DCIisjClmwtcZAO7ZCk01ULI2AIhazfQaRv3YQt5GRB0Wkjk4sz6dVYMdnbiSHalaa1lIiKyf77ebhidokZyrD9cZFKzziFUFkJI3wghJx0QjBaucIB0Wgh5RyFJulLsSpwKg50daG3XITu/TuwyiIjIAsKDvTAmWY2YcJVZkwsEwQjkH+uaEFGWOwgVWo5w+gDAYGdVDHZ24MSZGhgMHK4jIrJXUokECdG+GJ2ihtrfw6xzCDpt12zTjC1Ag510SCjOhtDWDIm7l9iVOA0GOxtnNArIzK0RuwwiIjKDq1yGYQkBSEtSm70urNDaCOHYVgjHdwIddtYZwWiAkHMYktRpYlfiNBjsbFxBaQNa2gaxkSQREVmct4cCo5KCMDwhEAq5eW1MhNqyrvvnTh8EDHoLV2g9wqkDAIOd1TDY2bgTORytIyKyF2p/d4xJCUZ8lC+kUvNmugrFJ2E8sgkozrJwdSKpyIfQWAOJKlDsSpwCg50Na2zRorh8ENbxIyIii5FIgCHhPhidoka42rx7yQSDHsLpg10NhWtLLVyh+IT8Y5CkzRS7DKfAYGfDMnNq2OKEiMhGubhIkRLrj7RkNXy93cw6h9DRCuHEDghHtwGtDZYt0IYI+ccABjurYLCzUQajEVm5tWKXQUREv+KhlCN1aCBGJARB6Wbex6jQUAMhYxOEk3sBndbCFdqgslwIHW2QuLmLXYnDY7CzUfklDWjrsN+bZYmIHI2/jxtGJwdj6BA/8xsKl+fBeGQjkH8UTnVJxmiAUHiCzYqtgMHORnHSBBGRbYgK8cboFDWiw1RmHS8YjUBeBozpG4GKAgtXZ0cKjrNZsRUw2NmgphYtSiqaxS6DiMhpyaQSJMb4YXSyGoF+5l0+FDo7IGTtgXB0M9DIW2uEoiwIBj0kMkaPwcSfrg06XagRuwQiIqfkqpBhZGIgUocGwdPdzIbCzfVdDYVP7AS0bRau0E4pvSCJGQ50tgNKrkIxmBjsbBCDHRGRdak8XZGWrMawOH/IzW0oXHMWwpGNEM4cAowGC1dobySAOhKSmBGQxIwAgqMhkZh3XyL1D4Odjamtb0dtfbvYZRAROYXQQA+MTglGXKQPJJL+NxQWBAEoyuq6f67k1CBUaEcUSiAq+VyYGw6Jh3n3JNLAMNjZmNOFdWKXQETk0CQSIC7SF6NT1AgN9DTrHIJeB+HUAQgZm4C6cgtXaEf8Qi6MyoXF8f45G8D/AjbmDC/DEhENCrmLFMPiA5CWpIbKy9WscwjtLRCOb4dwbBvQ5oQrA8nkQMRQSIacG5XjMmE2h8HOhpRXt6CxpVPsMoiIHIqnuxypQ4MwIjEQbgozGwrXV0JI3wwhex+gd7L3aW//rhAXMwKISIJEbt6kErIOBjsbwkkTRESWE+irxOiUYCTG+EImNbOhcOkZGNM3AfnHAThJQ2GpDAiNM4U5SUCY2BVRPzDY2QijUUBOEYMdEdFARYd5Y0xKMCJDvM06XjAaIOQcgZC+CagqsmxxtsrdG5LoYZAMGQFEpUDiyqW/7BWDnY0or27hEmJERGaSSSVIivXH6GQ1/H2UZp1D0LZDyNoFIWML0Ozof2hLAHVU14jckBGAOtqsWcFkexjsbER+aYPYJRAR2R2lqwtGnGso7KGUm3UOoVkDIWMzhMzdXQ10HZWrEpKoFCBmOCTRF9qRdBr0OFNfjkxNObI05Xhy5Az4uXqIXCyZi8HORhScbRC7BCIiu+Hr3dVQODk2AHKX7vfPLV26FN988w1Onz4NpVKJCRMmYNmyZUhMTOy2n1BVBOHIJgi5R7DuWD4+3Hcax8s10OoNSA72wYuzRmH20PBux3xzoggvbchAfm0TYgO88dq1aZg/PNr0+Jfp+XjuxyNo7dThnnEJeHPuWNNjRZpmXPvRRhx8bB683aw0AcE/1NRXDmHxkEi7mi/XdrQgszwHWZoynGmshu4XDZWz6ytwVXCcdeoji2OwswH1jR2ob9KKXQYRkc0LU3tiTHIwhkSoer10uHPnTixYsABXXHEF9Ho9Fi9ejFmzZiE7Oxvu7u5AwfGuCRGlZ0zH7C6oxIyEUPzlutHwUSrw2eFczF+5BfsenotR4f4AgP1F1bj939vxyjVpmD8sCuuzinHb59uxc+EcjIsKQm1LB+7/ag9W3jYJMf5emLdiM6bEhmBOcgQAYMHX+7BkzpjBDXUuCiAi0dRbTqIKAAAYjEbkNdWcG5UrQ0V7761aTmoY7OwZg50N4GVYIqLeSSUSxEd1NRQODrj8JcINGzZ0+/7TTz9FUFAQDn/1L0xCNVBf2eOYd+df2e37168bg++zSvBDdokp2P1j10nMSAjFM9NHAgCeUftgV34l/rHrJL64MwgFmmaolArcOmoIAGBqbAhOVTVgTnIEVmfkQyGT4YYR0eb8CC7NO+AX7UiGmtqRNHW2I7MyH1n15ciur0SHQden051urIRRMELKJcDsEoOdDeBlWCKinhRyGYbHB2BUUhC8Pc1sKNzWhPrtawEAvtk7gRDfPh1nNApo1urg537heQ8UV+ORySnd9puVGIZ/7D4JAIgP8EZbpx5HS+sQ5eeJI2drcPfYeGjatHh5Qwa2PHCtWa+hB1M7kq6JDxL/UABdy5sVtdQhs7zrXrmSFo1ZDVra9DoUNtch1pvNh+0Rg53IOrR6lFe3il0GEZHN8PJQIC0pCMPiA+GqkJl1DqGuHEL6Jhiz9+OJT37GxBg1hvUx1AHAuzuz0Nqpxy0jY0zbKpvbofbqPuNW7aVEZVPXhAtfd1d8evsk3LN6F9p1evx+TBxmDw3H/63ZjQVXJaNQ04IbVm6BzmjEi7NG4aZfnPuy3L0vjMpFJZvakbTrO3GyphiZmnKcrK9As66j7+e8hJOaCgY7O8VgJ7LCskYYBSdpeklEdAlqf3eMTglGQpQvpFLzWm8IJadgTN8IFGYBEPDw1/uQWVGPnQvn9PkcazLy8eqmo/jmnukI+lWQ+3VVgoBu9/rNHx7dbTLFjrwKZFXW4x83jkfi0v/hP7+fimAvd4z/+3eYNCS4x/m7PZM6GpIhwyGJGdnVmuTc85S1NiCrugiZmnLkN9cMymfIyYYKzMMIi5+XBh+DncgKzjaKXQIRkahiI3wwOlmN8GAvs44XDHoIZw51NRSuOWva/sg3+/H9ybPYvuA6hPv0rX3HV0cLcN9Xe7DmrmmYkdB9xYVgLyUqm7u3Q6luaYfay+2i59LqDXjom/1Ydcdk5NU2QW8UMCU2BACQEKjCwZIazE2JvHCAqxKSqGFd7UhihkPi3tVgudOgx2lNOTI1ZThZX4E67eBf5Slu1qDDoIObzLwWMiQeBjsRCYKA4gonXESaiJyei0yK5HMNhX1VFw9GlyN0tEE4sQPCsW1AS/2F7YKAR9YdwPrMYmx98FrE+PctMK7JyMf//XcPvvj9VNNM1l+6MioIW3LK8eiUYaZtm3PKMD4q6KLn+8vmY5g9NAxp4QE4WloHvcFoekxnMMJoFH7RjmQEEBb3q3YkZ5CpKUfOr9qRWIMAAcXNGiT6qK36vDRwDHYiqq1vR4eWq00QkfNwd3NB6tAgjEwMhNLNzIbCjTVdDYWz9gC6nq2iHvpmP1ZnFOCbP06Hl6sclU1tAACVUgGlvOtj77kfj6C8sRWf3TEFQFeou3v1Lvx1/pUYFxVoOkYpd4FK2TXL9KFJybj6g5/w5rYTmJcSie9OlmBrTvlFL/OerKzH/44VIv3x3wAAhqpVkEokWHk4H+ohCThd24yxT/8VsqHDAXS1I8ltqkaWpqtRcOUl2pFYS2FzLYOdHWKwE9HZqmaxSyAisgp/lRvSUtRIGuIPF5l5bTSE8vyu++fyjgKCsdf9Ptx3GgAw/YOfu21f8dtJ+MPYeABAZVMbShouXNL8+MAZ6I0CHvpmPx76Zr9p+11j4rDy9skAgAkxanz5+6l48ecMvLQhA7H+Xlh959UY96sRO0EQ8Of/7cXbvxkLD1c5oAqAe/QIfPr3K7Dwtbeg3X4a/3z/A3gNicPeynxkacqR3dD3diTWUthcJ3YJZAaJIPDOfbF8uy0P+Wx1QkQOLDLEC6OTgxEd5m3WWqSCYATyjnY1FC7PG4QKB4FU1rXKw7lZrOfbkRgFAcXNdcjUlCOzvgxnW+rNakdiLSqFEm+Ou0HsMqifOGInEkEQUMoROyJyQFKpBInRfhidokaQn7tZ5xB0WghZeyBkbAEaqy1c4SDwUEESPexcO5IUSFy7Zru2nWtHknVu4kPzRS4d26rGznZoOlrh58Z1Y+0Jg51Iaurboe207s2wRESDyVUhw4iEQKQODYKXh3nLZgktDRCObYVwfAegbbNsgZYkkQDqmK5RuSEjgKDu7UgyqwuQqSlHQVMtjDY9Lndphc11DHZ2hsFOJGc5G5aIHITKU4FRyWoMiwuAQm5mQ+GaUgjpGyGcOQQYbHRSmas7JNEpQMwISKKHQ+LeNdu206DHKU0ZsjTlyKovh8aWA2k/FTbXYnRg5OV3JJvBYCeS0qoWsUsgIhqQkEAPjE5WIy5yAA2Fi7K6JkQUZ1u4OgvxD+tatitmeNcyXufakdS0tyCz7Awy68uR01AF/SUmc9izAk6gsDsMdiLg/XVEZK8kkq6GwmNSghEa5GnWOQSDHsKpA10NhevKLFzhALkogMikc73lhkPi7Q+gqx1JTmM1suq72pFU2UA7EmsoadHAIBghk5g3k5msj8FOBLUNvL+OiOyL3EWKlLgApCUFwcfbzIbC7S0XGgq32tCqO6rAC+uwRgyFxKWrv15jZzsyK/ORqSnD6YZKdNjqJeJBpDMaUNbagEhPP7FLoT5isBNBVa3j3H9BRI7NQynHqKQgDE8IhNLVvI8Mob6qq6Hwyb2AvtPCFZrB1I5kRNdlVr+uZb6MgoCi5jpkasqQqSlHaatttyOxloKmWgY7O8JgJ4KqusFf54+IaCACfJUYnazG0Bg/yMxtKFyWC+ORjUDBMUDslqkeqq4JD0NGAFHJkCi62pG06jpxsroIWfXlOKmpQIveftqRWEthcx2mil0E9RmDnQgqaxnsiMg2RYV6Y0yKGlGhKrOOF4xGCLnpENI3ApWFFq6uHyQSIDjmwjqsQZGmdiSlrfXIrCpAlgO0I7EGrkBhXxjsrMxgNKK2vl3sMoiITGRSCYYO8cPo5GAE+CrNOofQ2X6hoXBTrYUr7CNXd0iihwExw7u1I9Ea9Dh97vJqlqYc9Z28HaY/qtub0K7XQeli3tq+ZF0MdlZWW98Og5F/HRKR+NxcZRiZEITUpCB4KM370BaaNRCOboWQuRPQivBHa0C4aQZrVzuSrsvGNe3NXe1INGXIaax22HYk1iAAqGpvQrSXv9ilUB8w2FlZVR3/UiQicfl4uSItWY2UOH/IXcxsKFxdDOHIJgg5hwGjFWf5y127Zq4O6brEKvHquqlfbzQgt7EamZoyZNWXo6qdLaUsqbKNwc5eMNhZGe+vIyKxhAV5YnSKGrERPqb7zfpDEASg8ASM6ZuAs6cHocJeqIIuLN0VnmhqR9KgbUNWZR4yNeU41VAJrRO2I7GWSifp2+cIGOysjDNiiciaJBIgPsoXo5PVCAk0s6GwXgchex+EjM2ApsLCFV6EVAaEJ5gmPkj8ggF0tSMpbK413St3trV+8GshAEBVG4OdvWCwsyKDwYi6hg6xyyAiJ6CQSzEsLgCjktVQebqadQ6hrRnC8W0Qju8ABvuD3UN1YQZrVNIv2pFocbK6CJmacpysr0Ar25GIgiN29oPBzorqm7QwcuIEEQ0iT3c5RiWpMSIhAK4KMxsKayohpG+CkL0PMOgsXOE5EgkQPOTCJdbAC+1IzrbUI7OyAFn1ZShsqmM7EhtQ3d4Mo2CElEuL2TwGOyuqa2SbEyIaHEF+7hidokZCtC9kUjMbCp893XX/XMEJYDDClKs7JNHDu9qRxAyDRHmhHckpTVnXxAdNORo6+V5pa/SCEXUdrQg899+MbBeDnRXVNfDNiogsKyZchdHJakSGeJt1vGA0QDhzGEL6JqC62MLVoasdybkZrAiJNbUjqWpvQmbZaWRpypHLdiR2oaajhcHODjDYWZGG99cRkQXIZBIkD/HH6BQ1/FRmNhTWtkHI3AXh6FagWWO54uSuQGSSqbfcL9uR5DRWdU18qC9HNduR2J3ajhaxS6A+YLCzIk0Tgx0RmU/p5oLUxCCMHBoIdzczGwo31ULI2AIhazfQaaH3JJ9z7Uhiurcjqde2IasiD5n15TjNdiR2r7aDXR3sAYOdlQiCgHoGOyIyg5/KDWnJaiTH+sNFZub9c5WFENI3QshJBwZ62VPmAoQlmCY+SHzPtyMxoqCpDln15cjUlKG0tWFgz0M2hSN29oHBzkqaWzthMHBmFxH1XUSwF0anqBETpjKzobARyD/WNSGiLHdgxXj4XJjBGpkMicINANCi0+JkdSGyTO1IOgf2PGSzGOzsA4OdlXC0joj6QiqRICGmq6Gw2t/DrHMIOi2Ek3shZGwBGqrMK0Qi6ZrscO4SqyQo0vRQSYvGtOJDYXMdBLYjcQq8FGsfGOyspL6JTTWJqHeuchmGJwRgVJIaXh4Ks84htDZCOLYVwvGdgDmjK24ekEQPA2JGQBI9DBJl10oVHQYdTtWeRVZ9udXbkRz9z7c4/PFXGHbzNZjw8J0X3af8aDZ+eOT1Httv/fdb8IkKBQBoCktxZMVa1OYUoqWyFuMX/h7Db7222/65m/bi0EdroO/QInHOVFz54B2mx5oravDTE2/ghk9eg8LD3YKv0H606rVo1+ugdDHv/k6yDgY7K2lqYbAjop68PRRIS1ZjWHwAFHKZWecQasu67p87fRDo7wSFwAjTDNZu7UjaxG9HUn0qH6e/2w6/2MjL7wzg1i/ehsL9wixhN58LLWD0HVp4hwZhyNXjsP+9//Q4tqOhGbve/ARTn/0TvEKDsOHptxE6KgmR40cBAPa8uxJj/3Sb04a685o62xnsbByDnZU0tw1S93YiskvBAR4YnaJGfKQvpNL+3z8HAELxSRiPbAKKs/p+ULd2JCMg8fIFAOiMBuQ2VpmaBFeLfD+Vrq0D21/7AJOe+j8c/Xx9n45R+njD1evil6+DkmIRlBQLADj00ZoejzeVV0Ph6Y7Y6eMBAKGjklBfVIbI8aOQt3kvpC4uiJlyhXkvxoHwHkrbx2BnJS2t/MdA5OwkEmBIuA/GpKgRpjav0atg0EM4fbCroXBtad8O8lFfmPgQngiJrOutv17bhsyKPGRpynC6oQpao+20I9nz188QMT4V4WOG9TnYfXPvYug7dfCNDkPaXb9BaFpKn59PFREMfYcWtTlF8AwOQM3pAiTOmYqOphYcWfk1rv/bYjNfiWPhWr22j8HOSlraGOyInJWLixQpsf5IS1bD19vNrHMIHa0QTuyAcHQbcLk2IjIXIDzhwqicrxrA+XYktaYmwbbajiRv637U5hTiho9f69P+7v4+mLToXgQmxMCg0yN30x788NhSzP37YoSkJvXpHK5eHpj63J+x/fXlMHTqED97EiLGjsCONz5Gyo2z0FxRjY3PvQOj3oDR99yIIVPHDeQl2i2O2Nk+BjsrEAQBLbwUS+R0PJRypA4NxIiEICjdzHu7FRpqIGRsgnByL6C7xGiJp++FJsGRSb9oR9KBrHPtSLLtoB1JS1Ud9v/jc1z3zjNwce3bJBKfyFD4RIaavlcPi0dLdR2Or/mpz8EOAGImX4GYyRcut5YfzUZ9wVlc9egfsOb2xzHtpYVw91Nh3Z9eRMjIoVD6qvr+whxEm862f3+Iwc4q2jv0MBjZDoDIWfj7KDE6WY2hQ/zMbyhcngfjkY1A/lFAuMj7h0QKhAzpGpEbMgKSwIiu4wQBZ1vrkVmZh0xNGYqaNXbVjqQ2pxDt9U345r7nTdsEgxEVx0/j5LpNuHfLKkj78DMNSo5D3qa9Ztdh6NRhz7ufYtrzD6KxrApGgxGh50KiT0QIqrPzETUxzezz26sWXoq1eQx2VtDMy7BETiEqxBujU9SIDjNvJEcwGoG8DBjTNwIVBT13cPM8145kePd2JHodsmvPIuvcJdZGK7YjsbTQ0Sm4+bM3um3b+cbHUEWGIPWOuX0KdQBQl1sEd38fs+vIWLUOEeNGIiAxBrU5RRAMBtNjRr2+67+VE2rliJ3NY7CzgpZWXoYlclQyqQSJMX4YnaxGoJ95rTCEzg4IWXsgHN0MNNZ2fzAwomtELmZE1widpCvYVLY1IbP0FDI15chvqhGlHclgULgr4Tckots2FzdXuHl7mbYf+mgNWmvrcfXiBwAAmV/9DK+QQPhGh8Og1yNv0x4U7jyMma89ajqHQadHfVHXZBOjTo/W2nrU5hZBrnSDKjy42/NpCkuRv+0Ablq5BAC6euFJJTj9ww64+6vQUFKBwKFDButHYNPaOGJn8xjsrIAjdkSOx1Uhw8jEQKQODYKnu5kNhZvruxoKn9gJaNu6Nspdu5bsGtLVW07ieaEdSU5DZdfEB005apx4eae2uga0VNWZvjfo9TjwwZdordHAxVUB35hwXLNsESLHp144prYe39x7YWbriTU/4sSaHxGSmoS5//jFZV9BwO63/oXxD/0ecmXXfYourgpMffZP2PvXz2DQ6THx0T/AI9Bv8F+oDbL1ezQJkAjCxW7eIEvad6wMB45XiF0GEVmAyssVaUlqDIvzh9zchsI1ZyEc2QjhzCHAaAB81aYZrAhPMLUj0WhbTUHudEMlOo2Gy5yZaHBFe/nj2dTZYpdBl8AROyvo0PLNmMjehQZ6YHRKMOIifSCR9L+hsCAIQFFW1/1zZbld/eQm39o1KveLdiT559uRaMpR1tZg4VdBNDBtl5qZTTaBwc4KtJ220/STiPpOIgHiIn0xOkWN0EBPs84h6HUQTh2AkHMEEm9/SEfNAH7zECRyVwDn2pFUFSJTU4bshkq08VIX2TBeirV9DHZWoO3kiB2RPZG7SDEsPgBpSWqovFzNOofQ0QZUl0BoqoUkOBrS4ZO6tgsCSlrqkVWRg0xNud21IyHn1qbXwSgIkJoxak3WwWBnBQx2RPbB012OUUlBGJ4QCDfFAN8eXeSQRA6FBEC7XodTtSXI1JTjZH2FXbcjIecmQEC7vhMecvP+4KHBx2BnBR28FEtk0wL9lBidHIzEGF/IpOY1FP61qs42ZFaWI1NTjrymGhgcpB0JUZtex2BnwxjsrIAjdkS2KSZMhdEpakSGeA/4XDqjAWcaqkzrsNY6cTsScmxG/pFi0xjsrIDBjsh2yKQSJMX6Y3SyGv4+ygGdS9PR1Y4ks74MZxqq2I6EnIKR94TaNAa7QWY0CtDp+dcNkdiUri4YOTQQqYlBcFfKzTqHwdSOpAxZmnKUtzVauEoi22dk+1ubxmA3yDhaRyQuX29XjE4ORlKsP+Qu/b9/rrmzA1n1XffKnWqoQJueSwSSc2Ows20MdoPM4KQLRROJLVztidEpwRgSrupXQ2FBEFDcokGWphyZ9eUoZjsSom4Y7Gwbg90gMxr5D4DIWqQSCeKjfTEmRQ21v0efj2vX65BdX4HM+nKc1JSjSdcxiFUS2TcjOGBhyxjsBhmDHdHgU8hlGB4fgFFJQfD27FsbBoPRiNymamRqypHfVAP9udF1lUIJlWJgkyqIHJlcat4ayWQdDHaDjEPWRIPHy0OBtKQgDIsPhKuifx82MqkUQ32CMdQneJCqIyKyPga7QWbgiB2Rxan93TE6JRgJUb6QSrm0ERHReQx2g4x9HIksJzbCB6OT1QgP9hK7FCIim8RgN8g4K5ZoYFxkUiSfayjsq3ITuxwiIpvGYDfIeIsdkXnc3VyQOjQIIxMDoXQzr6EwEZGzkQgCo8dgOlvZjP9tPCN2GURE5ASkEgkevWu02GWQiPrfhp36hbd1ExGRtfSjFzc5KAa7QSaT8V8ZERFZCT9ynB6D3SCTyfgjJiIi6+jP8nnkmJg6BpmMPbaIiMhK+JFDDHaDzIUjdkREZCX8zCH+Bgwy3mNHRETWInfhx7qz42/AIJNJ+SMmIiLrkMv7t2YyOR6mjkHGETsiIrIWjtgRfwMGGYMdERFZC4Md8TdgkMmkUjaMJCIiq5C78FKss2OwswI3BZfkJSKiwaeQ82Pd2fE3wArcXBnsiIho8HHEjhjsrEDpyn9oREQ0+HiPHfE3wAo4YkdERNbgquBAgrNjsLMCJYMdERFZgbtSLnYJJDIGOyvgiB0REVmDB4Od02OwswIGOyIisgZ3N37eODsGOyvgpVgiIrIGjtgRg50VuHFWLBERWQHvsSMOJVmBp7tC7BKsrqO9FRu++RBZGTvQ3FSPsKgEzL/jCUQOSQEAbFz3MY4e3IRGTRVkLnKERw/FtTc9iKjYYb2e88COdTiy7ydUluYDAMKjh+K6mxeYzgkABoMem9Z/goz9G9DUWAdvH39ccdX1mDH3XkilXX/HbP/539jx838AANPm/AFTZt9hOr44PwvffL4Mj7z0GaRSBnIish+uChlcZByvcXYMdlbg5eF8we6rT/+CytJ83H7/K1D5BCJ938/46K0FeGrJV1D5BiEwOBI33rkI/oFh0Om02LlxNT5+eyGeXbYOnt6+Fz1n3ul0jBo3C9G/GwEXuSu2//w5PnprIZ5a8l+ofIMAANt//Bz7tn+N2//vZQSHDcHZolP474pX4ab0xORZt6PibB42rvsI9z76VwgQsOKvjyMhZSxCwuNg0OuxdtVS3HL3cwx1RGR3eBmWAF6KtQpPdzmkUudZMFbX2YHMI9tx/a0PIzYxDQHqCMy+4X74BYRi37avAQBp469BQso4+AeFIzgsFr+5/VF0tLeivDS31/P+/s9/wcTptyAsKhHq0Gjces9iCIKA3OzDpn2K8jMxbNQUJKdeBb/AUIy8YjoSUsahtPAUAKCqohAh4fGIT74CCcljERoRh+ryIgBdI3mxiaO6jQASEdkLTpwggMHOKiQSCbzcnecvKYPBAKPRABdF95FKucINhTnHeuyv1+uwf8c6uCk9ERqR0Ofn6dR2wGDQw93D27QtJn4kcrMPo6ayGABQXpKDwtzjGDpyIgAgJDwONVUlqK+rhKa2AjWVJQgOj0Vt1Vkc3vMDrrnxATNeMRGR+DyUznd1iHpivLcSL09XNLZ0il2GVbgpPRAVNxxbvl0BdUgMvFR+OHpgI0oKshCgjjDtl31sN/69fDF0nR3wUgXgT4v+CU8vnz4/z4//+ydUvoGITx5r2jZtzh/Q0d6CZc/eAolUCsFoxLU3PYC0K2cDANShMbjupgfx0VsLAADX3bwA6tAYfPjmg7j+1odwJusANq3/GFKZC+b/7gnEJqZZ5odCRDTIVF4MdgRIBEEQxC7CGWzYU4js/Dqxy7Ca2upS/HfFqyg4cxRSqQxhUYkIDI5EWfEZPLXkKwCAVtuO5oZatDY34MDO9cg7dQQPv/gpvLz9Lnv+bT99jm0/rsKDz3yI0Ih40/ajBzbh+6/+jrm3PoLgsCEoK8nBt1++i3m3P4orrrr+ouc6tPt7nDy6Czf/4Rm88czNePSlVWisr8YXH72AxW99Cxc53yyJyPbNnBCF4fGBYpdBIuOInZU42wSKgKBwLHj2Y2i17dC2t8LbJwCff/As/AJCTfu4uirhqo5AgDoCUXHDsfTpG3Fo17eYfv09lzz39p//ja3ff4o/P/V+t1AHAN9/9XdMu+4PGHXlLABASEQc6usqsPWHzy4a7FqaG7D5u39hwbMfo7ggC4HBkaYvg0GPmsoShETEWeAnQkQ0uHy83MQugWwA77GzEm9P5wp257m6KuHtE4C21iacyTyAlLTJve4rCAL0Ot0lz7f9p39jy3crcP8T/0BETHKPx3VaramtyXlSqRS9DUx/++U7mDzrDvj4qSEYjTAY9KbHjOfuFSQisgc+Xq5il0A2gCN2VuLtZCN2pzP3A4KAwJAo1FaV4of//h1BIVEYe9U8aLXt2Pr9SqSkToaXTwDaWhqxd9taNGqqMXLsdNM5vvz4Jah8AzHnloUAui6/bvjmQ/z+T3+Bb0AImhpqAQCubu5wdXMHACSnXoUt338KH7/gc5diz2Dnxi8xdtK8HjWeyTqI2qqzuP2+VwAAkUNSUF1RjFMn9qJBUwWJVIqgkKjB/lEREQ2YTCaBpxNN0qPeMdhZicrTuf6S6mhvwU//ex8N9dVw9/DGiDHTcO1ND0Lm4gKj0YDqiiIc3vMjWlsa4OGpQkRMMhY89zGCw2JN52ioq4REcqFNzL6ta2HQ67Dq/ae7Pdes39yH2TfcDwC44feLsOGbD/HNv5ehuakeKp8AjJ96I2b+5v+6HaPr7MC6/7yJOx9YYhrhU/kG4YbfP4n//utVuMgVuP3/XoZcwUsbRGT7fDxdu71fkvPi5AkrEQQB731xFHqDUexSiIjIwQwJV2H+9PjL70gOj/fYWYlEIoGfiqM/RERkeby/js5jsLMifx8GOyIisjwfb36+UBcGOyvy91GKXQIRETkgXhGi8xjsrMhfxWBHRESWF+jnLnYJZCMY7KyIl2KJiMjSPN3lULqyyQV1YbCzIpWXK1xk/JETEZHlBHG0jn6BKcOKODOWiIgsLdCXwY4uYLCzMk6gICIiSwr04+cKXcBgZ2Vqf/5lRURElsOJE/RLDHZWpg7gP0AiIrIMuYuUzYmpGwY7KwvycweX8yMiIksI8FVyjVjqhsHOyuQuMt5nR0REFqH29xC7BLIxDHYiCA7gP0QiIhq40CBPsUsgG8NgJ4KQQAY7IiIauDAGO/oVBjsRhAbyHyIREQ2Ml4cCXh4KscsgG8NgJwI/lRtcFTKxyyAiIjvG0Tq6GAY7EUgkEt5nR0REA8JgRxfDYCeSiGAvsUsgIiI7xokTdDEMdiKJDPEWuwQiIrJTrgoZAnzZOot6YrATidrfnffZERGRWUICPdiYmC6KwU4kEokE4WpejiUiov7j5wf1hsFORLwcS0RE5ogK5ecHXRyDnYiiQvkXFxER9Y+7mwuC/NzFLoNsFIOdiPxUSngo5WKXQUREdiQqVMX766hXDHYi4+VYIiLqj+gwfm5Q7xjsRBYZwsuxRETUNxIJEM376+gSGOxEFh2mAkfUiYioL4L83KF04y081DsGO5F5KOVcXoyIiPokOkwldglk4xjsbEBcpI/YJRARkR2IYbCjy2CwswFxEb5il0BERDbOzVXGKzx0WQx2NsBX5QY/lZvYZRARkQ2LjfCBVMqbsunSGOxsRGyEj9glEBGRDUuI8hO7BLIDDHY2gvfZERFRb1wVMkRytSLqAwY7GxEc4MFVKIiI6KLiInwgk/Ijmy6PvyU2QiKR8HIsERFdVEI0L8NS3zDY2ZCEaM6OJSKi7ngZlvqDwc6GRAR7wctDIXYZRERkQ3gZlvqDvyk2RCKRYGgMh9uJiOiCeF7NoX5gsLMxybH+YpdAREQ2wlUhQ1SIt9hlkB1hsLMx/j5KBPm5i10GERHZgMRoP8hk/KimvuNviw3iqB0REQFAShw/D6h/GOxsUGKMHyRcNYaIyKn5qdwQEugpdhlkZxjsbJCHUo6oUN5TQUTkzHj1hszBYGejkofwHzQRkbOSSiQMdmQWBjsbFRflCzdXF7HLICIiEcSEq+Dpzr6m1H8MdjbKRSblTbNERE5qeHyA2CWQnWKws2EjE4M4iYKIyMl4ussRHaYSuwyyUwx2NszHy5WTKIiInMyw+ABIpfyrnszDYGfjUocGiV0CERFZiUwqwchEvu+T+RjsbFxMmAoqT95AS0TkDBKi/eChlItdBtkxBjsbJ5FIMCIxUOwyiIjICtKSOVpHA8NgZweGxQVCJuP9FkREjiwsyBNqfw+xyyA7x2BnB5RuLkiM9hO7DCIiGkRpyWqxSyAHwGBnJ8akBItdAhERDRKVpwJxkT5il0EOgMHOTgT4KjEknH2NiIgcUerQIEjYuJQsgMHOjowdHiJ2CUREZGEKuRTD4jlJjiyDwc6OhAZ5IizIU+wyiIjIgobHB8JVIRO7DHIQDHZ25orhvNeOiMhRuMikGDOM7+tkOQx2dmZIuA8CfZVil0FERBYwPCGADYnJohjs7NAV/OuOiMjuyWQSvp+TxTHY2aGEaD+oPF3FLoOIiAZgWFwAPN25ZCRZFoOdHZJKJRjLe+2IiOyWTCphpwMaFAx2diolLgC+3hy1IyKyRylxAfDy4GgdWR6DnZ2SSiWYMCpM7DKIiKifeNWFBhODnR1LiPJFkJ+72GUQEVE/pMT6w5v3SdMgYbCzYxKJBBPTOGpHRGQvXFykGJ8aKnYZ5MAY7OxcTJgKYWquRkFEZA9GJ6s5E5YGFYOdA5iUFi52CUREdBnubi7sW0eDjsHOAYQGeWJIuErsMoiI6BLGp4ZCIeeasDS4GOwcxMS0MEgkYldBREQX46dyw/D4QLHLICfAYOcgAn3d+aZBRGSjJo0Oh1TKv75p8DHYOZCJo8Lg5sphfiIiWxKu9kRshI/YZZCTYLBzIEo3F0xIZfsTIiJbMnlMhNglkBNhsHMwIxICEeirFLsMIiICMCw+AMEBHmKXQU6Ewc7BSKUSXD0uUuwyiIicnpurC9tRkdUx2DmgcLUXEmP8xC6DiMipTR4dDqWbi9hlkJNhsHNQk0eHQ+7C/7xERGIIDfJESpy/2GWQE+Inv4Py8lBg3IgQscsgInI6UokE06+MhITNRUkEDHYObHSKGv4+nEhBRGRNo5KCEOjrLnYZ5KQY7ByYTCrFrAlRXJGCiMhKPN3lGJ8aKnYZ5MQY7BxcSKAnUocGiV0GEZFTuHpsJNeDJVEx2DmBq0aFwdtDIXYZREQOLT7KF/FRvmKXQU6Owc4JyOUyzJwQJXYZREQOy93NBdOvZA9REh+DnZOIClVhWHyA2GUQETmk6VdGwd1NLnYZRAx2zmTKmAh48ZIsEZFFDR3ix0uwZDMY7JyIq0KGGeN5SZaIyFI8lHJMG8tLsGQ7GOycTEyYCiMTA8Uug4jIIcyaEA03Vy4bRraDwc4JTbkigo2LiYgGaFh8AGLCVWKXQdQNg50TcpFJMWfyEMhk7FxMRGQObw8FpoyJELsMoh4Y7JxUgK8Sk0fzTYmIqL+kEgmumzwErgo2Iibbw2DnxEYlBWEILyMQEfXLxLQwhAZ5il0G0UUx2Dm52ROj4aFk7yUior6ICVNhTIpa7DKIesVg5+SUbnJcc1WM2GUQEdk8T/eu90uJhPcnk+1isCNEhXrjimHBYpdBRGSzpBIJ5kweAqUbW5uQbWOwIwDAxFFhiAzxErsMIiKbNGFUKMLUfI8k28dgRwAAqbTrr1FvLjlGRNQNr2qQPWGwIxOlmxxzr45lfzsionO8PBS4dhLvqyP7wWBH3aj9PTDjSq4nS0Tk4iLFb6bFwd2NnQPIfjDYUQ8pcQFcT5aInN41E6MR5OcudhlE/cJgRxc1dWwEQgM9xC6DiEgU40aEICHaT+wyiPqNwY4uSiaV4vqpsWxeTEROJy7SBxNSQ8Uug8gsDHbUK093BeZPj4Pchb8mROQcAnyVbEJMdo2f2HRJan8PXDd5CPgeR0SOTunqgt9Mi4NCLhO7FCKzMdjRZcVG+GDqFZFil0FENGikUgmunzoEKk9XsUshGhAGO+qTUUlBSEvmwtdE5JhmT4xGRLC32GUQDRiDHfXZlDHhiIv0EbsMIiKLuiotDElD/MUug8giGOyozyQSCa6dNATBAWyDQkSOYWRiIMYODxG7DCKLYbCjfpG7SDF/ehzvQyEiuxcf5Ytp43j/MDkWBjvqN3c3OW6aGc8ed0RktyKCvbgGLDkkBjsyi4+3G26elQClq4vYpRAR9UuQnzt+My0OLjJ+BJLj4W81mc3fR4kbZ8bDlT2fiMhO+Hi74sYZ8exVRw6LwY4GRO3vgfkzuDoFEdk+lZcrbpmVCHfeRkIOjJ/GNGBhQV74zbQ4yKS8V4WIbJPKU4FbZyfCy0MhdilEg4rBjiwiMsQb10+NhZQ3IhORjfH2UOAWhjpyEgx2ZDGxET7nZpmJXQkRURevc6HOmy2ayElwSiNZVGKMHwDg592FMAqCyNUQkTPzdJfjltmJUHkx1JHzkAgCP33J8vJLGvDDznwYjPz1IiLr81DKces1ifD1dhO7FCKrYrCjQVNc3ohvt+dDrzeKXQoRORFPdzlumZUIXxVDHTkfBjsaVKVVzVi/NRedOoY7Ihp8vt5uuGlmPO+pI6fFYEeDrqKmBd9syYW20yB2KUTkwIL83HHjzHi4u7FPHTkvBjuyihpNG9ZuzkF7h17sUojIAYWrPfGbafFwVXBFCXJuDHZkNXUN7fhmSy6aWzvFLoWIHEhshA/mTBnCtV+JwGBHVtbS1on1W/NQrWkTuxQicgDJsf6YNSEaUq58QwSAwY5E0Kkz4Ied+SgqaxK7FCKyY2lJQZhyRQQk7IpOZMJgR6IwGgVsPVCMzNxasUshIjsjkQBTrohAWpJa7FKIbA6DHYnq4IkK7D1aJnYZRGQnFHIZ5kwZgpgwldilENkkBjsS3amCOmzaW8RVKojokrw9FZg/LR4BvkqxSyGyWQx2ZBPOVjbh+x356NCy1x0R9RQa5Il5V8eyRx3RZTDYkc1obNbiu+15qKlvF7sUIrIhSUP8MHNCNNuZEPUBgx3ZFJ3egE37inGmUCN2KURkAyakhuLKkaFil0FkNxjsyCaln6zErvRS8LeTyDm5KmS49qoYDInwEbsUIrvCYEc2q6SiCT/uLEC7lsuQETkTtb87rp8SC5WXq9ilENkdBjuyaU0tWny3PZ8rVRA5ieEJAbh6bCTvpyMyE4Md2Ty9wYitB4pxMq9O7FKIaJC4uEgx48pIJMcGiF0KkV1jsCO7kZ1fh60HiqHTG8UuhYgsyNfbFddPjUWgr7vYpRDZPQY7siv1TR34cWcBL80SOYiEKF/MnBANV4VM7FKIHAKDHdkdvcGIPemlyDhVLXYpRGQmhVyGq8dGICWOl16JLInBjuxWYVkjNu0tQmu7TuxSiKgfwoI8cc2kGKg8OeuVyNIY7MiutXXosGlvEQpKG8UuhYguQyaVYHxqKK4YFgyJRCJ2OUQOicGOHEJmbg12HS6FVse1Zolskb+PG66dNARBfpwgQTSYGOzIYTS3dmLrgWKO3hHZmLSkIFw1Opy96YisgMGOHM6pgjpsP3QWHVyxgkhUvt5umDE+ChHBXmKXQuQ0GOzIIbW167D1YAlyi+vFLoXI6cikEowZFoxxI0I4SkdkZQx25NByijTYdrAEbR0cvSOyhrAgT8wYHwV/H6XYpRA5JQY7cnjtWj32pJciK68W/G0nGhyuChkmpYVjeEIAZ7wSiYjBjpxGZW0rth0sQWVtq9ilEDmUhChfXD0uEh5KudilEDk9BjtyKoIg4GReHfZklPLyLNEA+ancMOWKCMSEqcQuhYjOYbAjp9TRqce+o+U4fqaal2eJ+snN1QXjR4ZiZGIgpFJediWyJQx25NRq6tuw/WAJSqtaxC6FyOZJpRKMTAzE+JGhcHN1EbscIroIBjsiALnF9dh7tAyaxg6xSyGySTHhKkwZEwE/lZvYpRDRJTDYEZ1jNAo4mV+L/cfK0dKmE7scIpvg76PE1CvCERXK++iI7AGDHdGv6A1GHDtVjUNZFejQcu1Zck4qL1eMHxmKpCF+bF9CZEcY7Ih6oe3U43BWJTJOVUOvN4pdDpFVeLrLceXIUAyLC+DECCI7xGBHdBmt7TocPFGBzNwaGAz850KOyUMpx9jhwRieEMhlwIjsGIMdUR+1tuuQkV2F42dq0KnjJVpyDB5KOa4YHowRDHREDoHBjqiftJ16HDtdjYxT1Whnk2OyU77erkhLViMlLoCBjsiBMNgRmUmnNyAzpxZHTlZyFi3ZjdAgT4xJUSM2woeTIogcEIMd0QAZjEacytcgPbsKdQ3tYpdD1INEAsRF+mJ0ihqhgZ5il0NEg4jBjsiCzlY249jpauSXNMDIf1okMhcXKVJi/TE6WQ0fbzYWJnIGDHZEg6C5tRMncmqQmVODNt6HR1bmr3LD8IRAJMX6Q8mlv4icCoMd0SAyGIzIKa7H8dPVKK9pFbsccmAymQQJUb4YkRCIMLWX2OUQkUgY7IispFrThpN5tThdqOFsWrKY86NzybH+cOPoHJHTY7AjsjKjUUBhWSOy8+tQcLYBBiP/CVL/KOQyxEX6YHh8AEfniKgbBjsiEXVo9ThTpEF2fh0qeKmWLkEmk2BIuA+GxvghJlzF3nNEdFEMdkQ2or6pA6cK6pBb3MC2KQQAkEokiAz1wtAYf8RF+kAhl4ldEhHZOAY7IhtU39SBvJJ65JU0cCTPyUglEoSpPZEQ7YuEKF8o3eRil0REdoTBjsjGtbR1Ir+kAXklDThb1Qwj78lzOG6uLogJ88aQcB9Eh3nDVcFJEERkHgY7IjvS0alHYWkjisoaUVzexB55dizAV4kh4SrEhKsQGujJ5b2IyCIY7IjslCAIqKlvR3F5V8grr26F3mAUuyzqhYdSjnC1JyKCvREd5g1vT1exSyIiB8RgR+QgDAYjymtacbayCWcrmlFZ28pWKiLydJcjXO2F8GAvRKi94Kvikl5ENPgY7IgclMFgRLWmDZW1raio6fpqbNGKXZZDkkgAX283qP09EK72RHiwF3y5NisRiYDBjsiJtHXozoW8FlTUtKKmvg0dWoPYZdmVCyHOHWp/DwT5uyPIz52tSIjIJjDYETm5lrZO1DW0o7a+HXWNHaitb4emsR2dOt6v5+7mAl9vN/iq3BDgo2SIIyKbx2BHRD0IgoDm1k7UNrRD09iBxmYtmls70djSiaYWLXR6xwl9MpkEPl6u8FMp4evtCj+VmynMubHtCBHZGQY7Iuq39g49mlq1aDoX9JpaO9HWrke7Vod2rR7tHXq0a/UwGMR7e5FKJXBTyOCulMNDKYenUg4Pdzk83RXwclfAy0MBLw85GwATkUNhsCOiQaPTGbqC3rmw16kzQG8QoDcYYTAYoTcYoTcI5/7/hf+VSLpWYJBIugKaVCKBRCqBVAJIJF3fu7hI4aqQdX3JZVAoZHBTyKCQd22Tu/ByKRE5HwY7IiIiIgchFbsAIiIiIrIMBjsiIiIiB8FgR0REROQgGOyIiIiIHASDHREREZGDYLAjIiIichAMdkREREQOgsGOiIiIyEEw2BERERE5CAY7IiIiIgfBYEdERETkIBjsiIiIiBwEgx0RERGRg2CwIyIiInIQDHZEREREDoLBjoiIiMhBMNgREREROQgGOyIiIiIHwWBHRERE5CAY7IiIiIgcBIMdERERkYNgsCMiIiJyEAx2RERERA6CwY6IiIjIQTDYERERETkIBjsiIiIiB8FgR0REROQg/h80n4vDGBByfAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "'O' class percentage: 61271/65684 = 93.28%\n",
      "'B-ga' class percentage: 2965/65684 = 4.51%\n",
      "'B-na' class percentage: 1448/65684 = 2.2%\n",
      "X_train[0,0]: [    4  1035  1136  4030  1032  3255  1028  1355 21442  1100  1100 18355\n",
      "  1040  1417  5099  2415  1492  1008     5     1     1     1     1     1\n",
      "     1     1     1     1     1     1     1     1     1     1     1     1\n",
      "     1     1     1     1]\n",
      "nerLabels_train[0]:[3 2 2 2 2 2 2 2 0 2 6 2 2 2 6 6 6 2 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4\n",
      " 4 4 4]\n",
      "sentenceTokenList[0]: ['[CLS]', 'en', 'mi', 'opinión', 'la', 'película', 'es', 'muy', 'cómico', '\"', '\"', 'romántica', 'y', 'ins', '##tres', '##ts', '##ante', '.', '[SEP]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "training_examples[:10]: [1 0 0 1 1 1 1 0 1 0]\n",
      "sentenceTokenList(30): ['[CLS]', 'A', 'va', '##caciones', 'perfect', '##o', 'es', 'cuando', 'todo', 'es', 'gratis', '.', '[SEP]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "\n",
      "nerTokenList(30): ['[nerCLS]', 'O', 'O', 'nerX', 'B-ga', 'nerX', 'O', 'O', 'O', 'O', 'O', 'O', '[nerSEP]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]', '[nerPAD]']\n",
      "\n",
      "bertMasks(30): [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|    | tag      |   cat |   occurences |\n",
      "|---:|:---------|------:|-------------:|\n",
      "|  0 | B-ga     |     0 |         2884 |\n",
      "|  1 | B-ga     |     1 |            0 |\n",
      "|  2 | B-ga     |     2 |            0 |\n",
      "|  3 | B-ga     |     3 |            0 |\n",
      "|  4 | B-ga     |     4 |            0 |\n",
      "|  5 | B-ga     |     5 |            0 |\n",
      "|  6 | B-ga     |     6 |            0 |\n",
      "|  7 | B-na     |     0 |            0 |\n",
      "|  8 | B-na     |     1 |         1399 |\n",
      "|  9 | B-na     |     2 |            0 |\n",
      "| 10 | B-na     |     3 |            0 |\n",
      "| 11 | B-na     |     4 |            0 |\n",
      "| 12 | B-na     |     5 |            0 |\n",
      "| 13 | B-na     |     6 |            0 |\n",
      "| 14 | O        |     0 |            0 |\n",
      "| 15 | O        |     1 |            0 |\n",
      "| 16 | O        |     2 |        57214 |\n",
      "| 17 | O        |     3 |            0 |\n",
      "| 18 | O        |     4 |            0 |\n",
      "| 19 | O        |     5 |            0 |\n",
      "| 20 | O        |     6 |            0 |\n",
      "| 21 | [nerCLS] |     0 |            0 |\n",
      "| 22 | [nerCLS] |     1 |            0 |\n",
      "| 23 | [nerCLS] |     2 |            0 |\n",
      "| 24 | [nerCLS] |     3 |         4452 |\n",
      "| 25 | [nerCLS] |     4 |            0 |\n",
      "| 26 | [nerCLS] |     5 |            0 |\n",
      "| 27 | [nerCLS] |     6 |            0 |\n",
      "| 28 | [nerPAD] |     0 |            0 |\n",
      "| 29 | [nerPAD] |     1 |            0 |\n",
      "| 30 | [nerPAD] |     2 |            0 |\n",
      "| 31 | [nerPAD] |     3 |            0 |\n",
      "| 32 | [nerPAD] |     4 |        46630 |\n",
      "| 33 | [nerPAD] |     5 |            0 |\n",
      "| 34 | [nerPAD] |     6 |            0 |\n",
      "| 35 | [nerSEP] |     0 |            0 |\n",
      "| 36 | [nerSEP] |     1 |            0 |\n",
      "| 37 | [nerSEP] |     2 |            0 |\n",
      "| 38 | [nerSEP] |     3 |            0 |\n",
      "| 39 | [nerSEP] |     4 |            0 |\n",
      "| 40 | [nerSEP] |     5 |         4452 |\n",
      "| 41 | [nerSEP] |     6 |            0 |\n",
      "| 42 | nerX     |     0 |            0 |\n",
      "| 43 | nerX     |     1 |            0 |\n",
      "| 44 | nerX     |     2 |            0 |\n",
      "| 45 | nerX     |     3 |            0 |\n",
      "| 46 | nerX     |     4 |            0 |\n",
      "| 47 | nerX     |     5 |            0 |\n",
      "| 48 | nerX     |     6 |        16529 |\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "'O' class percentage: 57214/61497 = 93.04%\n",
      "'B-ga' class percentage: 2884/61497 = 4.69%\n",
      "'B-na' class percentage: 1399/61497 = 2.27%\n",
      "X_train[0,0]: [   101  10243  12132  62276  10109  14970  10196  13436  10601  31657\n",
      "    107    107  32106  13640    193  15498  30555 103148  11227    119\n",
      "    102      0      0      0      0      0      0      0      0      0]\n",
      "nerLabels_train[0]:[3 2 2 2 2 2 2 2 0 6 2 6 2 6 2 2 6 6 6 2 5 4 4 4 4 4 4 4 4 4]\n",
      "sentenceTokenList[0]: ['[CLS]', 'En', 'mi', 'opinión', 'la', 'película', 'es', 'muy', 'có', '##mico', '\"', '\"', 'román', '##tica', 'y', 'ins', '##tres', '##tsa', '##nte', '.', '[SEP]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]', '[PAD]']\n",
      "training_examples[:10]: [1 0 0 1 1 1 1 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "for config in args.bert_plain_models:\n",
    "    prep_for(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "id": "8342a2f2-e4db-4cd2-a709-0cfe4350e12c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # let us check what was generated\n",
    "# print(f'sentenceTokenList({len(state[\"sentenceTokenList\"][2])}): {state[\"sentenceTokenList\"][2]}')\n",
    "# print()\n",
    "# print(f'nerTokenList({len(state[\"nerTokenList\"][2])}): {state[\"nerTokenList\"][2]}')\n",
    "# print()\n",
    "# print(f'bertMasks({len(state[\"bertMasks\"][2])}): {state[\"bertMasks\"][2]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "9716785f-aad5-4305-8c1c-2c602ac86423",
   "metadata": {},
   "outputs": [],
   "source": [
    "# state = analyze_generated(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "453556f8-799e-41b4-8c7b-ac42731d55ee",
   "metadata": {},
   "source": [
    "## Export GA and NA for Plain Model in WNUT17 Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e082de6f-dfde-456e-a489-412a49c48065",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Export data as\n",
    "# {'id': '0',\n",
    "# 'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],\n",
    "# 'tokens': ['@paulwalk', 'It', \"'s\", 'the', 'view', 'from', 'where', 'I', \"'m\", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.']\n",
    "#}\n",
    "\n",
    "def export_wnut17(df, args, verbosity=0):\n",
    "    id2label = {0: 'B-ga', 1: 'B-na', 2: 'O'}\n",
    "    label2id = {'B-ga': 0, 'B-na': 1, 'O': 2}\n",
    "\n",
    "    def process_row(i, row):\n",
    "        final_data = []\n",
    "        previous_class = 'O'\n",
    "        first = True\n",
    "        set_corrections = set(row.correction_index)\n",
    "        classes = []\n",
    "        for i, word in enumerate(row.source):\n",
    "            if i in set_corrections:\n",
    "                if previous_class == 'O':\n",
    "                    cls = f'B-{args.CLASS_IDS[row.error_type]}'\n",
    "                else:\n",
    "                    cls = f'I-{args.CLASS_IDS[row.error_type]}'\n",
    "            else:\n",
    "                cls = 'O'\n",
    "            previous_class = cls\n",
    "            cls_id = label2id[cls]\n",
    "            classes.append(cls_id)\n",
    "\n",
    "        return [row.orig_index, classes, row.source]\n",
    "\n",
    "    def write_out(df, csv_filename):\n",
    "        final_data = []\n",
    "        for i, row in df.iterrows():\n",
    "            final_data.append(process_row(i, row))\n",
    "        print(final_data[0:2])\n",
    "        cols = ['id', 'ner_tags', 'tokens']\n",
    "        df_ner = pd.DataFrame(final_data, columns=cols)\n",
    "        df_ner.to_csv(f'{args.PROCESSED_DATA_FOLDER}/{csv_filename}', index=False)\n",
    "\n",
    "    df_filtered = df[(df.error_type == 'gender agreement') | (df.error_type == 'number agreement')]\n",
    "    # Create a separate test dataset for final evaluation.\n",
    "\n",
    "    df_train, df_test = train_test_split(df_filtered, test_size=0.05)\n",
    "    write_out(df_train, 'two_class_wnut17_train.csv')\n",
    "    write_out(df_test, 'two_class_wnut17_val.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "7ff68026-aebf-482a-9066-c16c40895ef7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1882, [2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2], ['Después', 'de', 'una', 'inmersión', 'en', 'el', 'océano', 'tiempo', 'para', 'un', 'ducha', '.']], [2358, [2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], ['He', 'oído', 'que', 'los', 'montanas', 'en', 'Costa', 'Rica', 'son', 'hermosas', 'con', 'muchas', 'flores', ',', 'plantas', ',', 'árboles', ',', 'y', 'animales', '.']]]\n",
      "[[2735, [2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2], ['Oahu', 'tiene', 'un', 'gran', 'ciudad', 'con', 'muchos', 'restuarantes', ',', 'centros', 'commerciales', ',', 'y', 'muchos', 'turistas', '.']], [3995, [2, 2, 2, 2, 2, 2, 0, 2, 2, 2], ['Ella', 'es', 'on', 'television', 'y', 'tiene', 'las', 'programas', 'muchos', '.']]]\n"
     ]
    }
   ],
   "source": [
    "export_wnut17(df_errors, args, verbosity=1)"
   ]
  },
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   "id": "3d01bd65-7102-4953-a66d-d971e7a7db68",
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       "      <td>3d4d6382c49275261b7cb519383da33ca9174dcc6c170f...</td>\n",
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       "      <td>Me encanta [todos]{toda}&lt;ga:na:fm:sp:det:inan&gt;...</td>\n",
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       "      <td>gender and number agreement</td>\n",
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       "      <td>A</td>\n",
       "      <td>gender and number agreement</td>\n",
       "      <td>{'2': 1, '3': 2, '5': 3}</td>\n",
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       "      orig_index  Unnamed: 0  paragraph_id  \\\n",
       "967          967         801           259   \n",
       "4064        4064         927           906   \n",
       "\n",
       "                                            sentence_id course  \\\n",
       "967   3d4d6382c49275261b7cb519383da33ca9174dcc6c170f...  SPA 1   \n",
       "4064  82ac13ff4a24d9b022d92152fe5a25915fed6ac281fe61...  SPA 1   \n",
       "\n",
       "                                              essay_new  \\\n",
       "967                   El maestro para un anos en India.   \n",
       "4064  Me encanta todos el japonesa comidas, especial...   \n",
       "\n",
       "                                     annotated_sentence  \\\n",
       "967   El maestro para un [anos]{año}<na:sp:noun:inan...   \n",
       "4064  Me encanta [todos]{toda}<ga:na:fm:sp:det:inan>...   \n",
       "\n",
       "                           full_error_tag             error_tag correction  \\\n",
       "967          [anos]{año}<na:sp:noun:inan>       na:sp:noun:inan        año   \n",
       "4064  [todos]{toda}<ga:na:fm:sp:det:inan>  ga:na:fm:sp:det:inan       toda   \n",
       "\n",
       "      ...               new_error_type    final_evident_index  \\\n",
       "967   ...             number agreement         [2, 3, 11, 12]   \n",
       "4064  ...  gender and number agreement  [3, 4, 5, 16, 17, 18]   \n",
       "\n",
       "                                        target_sentence  \\\n",
       "967                    El maestro para un año en India.   \n",
       "4064  Me encanta toda la japonesa comida, especialme...   \n",
       "\n",
       "                                        source_sentence  \\\n",
       "967                   El maestro para un anos en India.   \n",
       "4064  Me encanta todos la japonesa comida, especialm...   \n",
       "\n",
       "                       error_type         evidence_index  \\\n",
       "967              number agreement         [2, 3, 11, 12]   \n",
       "4064  gender and number agreement  [3, 4, 5, 16, 17, 18]   \n",
       "\n",
       "                                predicted_parsing_order origin  \\\n",
       "967   {'0': 3, '1': 2, '2': 2, '3': 2, '4': 1, '5': ...      A   \n",
       "4064  {'2': 1, '3': 2, '5': 3, '15': 1, '16': 2, '18...      A   \n",
       "\n",
       "                  orig_error_type  \\\n",
       "967              number agreement   \n",
       "4064  gender and number agreement   \n",
       "\n",
       "                            predicted_parsing_order_src  \n",
       "967   {'0': 3, '1': 2, '2': 2, '3': 2, '4': 1, '5': ...  \n",
       "4064                           {'2': 1, '3': 2, '5': 3}  \n",
       "\n",
       "[2 rows x 24 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_errors.sample(2)"
   ]
  },
  {
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   "execution_count": null,
   "id": "9eeb996c-b2e3-4c01-aaf6-a9dc94b287da",
   "metadata": {},
   "outputs": [],
   "source": []
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