02-toxicity-classification.ipynb

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  {
   "cell_type": "markdown",
   "id": "8d608ac0-a5a0-472f-b3ec-f574d6185501",
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   "source": [
    "# Train a DistilBERT base model (uncase) for toxicity classification\n",
    "\n",
    "In this notebook, we will train a [DistilBERT base model (uncased) ](https://huggingface.co/distilbert-base-uncased) model to classify text with toxic and non-toxic content."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "390f3e78-cf74-40e3-b38c-88c35b8e66d3",
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   "source": [
    "!pip install transformers datasets evaluate\n",
    "!pip install accelerate==0.20.3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1af1ffb1-185b-4b36-9266-745d80ccd599",
   "metadata": {},
   "source": [
    "### Perform inference with the pre-trained model\n",
    "\n",
    "The model available at [tensor-trek/distilbert-toxicity-classifier](https://huggingface.co/tensor-trek/distilbert-toxicity-classifier) was trained exactly described in the rest of this notebook. You can simply use this model directly from HuggingFace Hub using th code below and test it with sample texts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ab7c8d68-f16c-49a3-9586-2059899b2876",
   "metadata": {
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'NEUTRAL', 'score': 0.9995143413543701},\n",
       " {'label': 'TOXIC', 'score': 0.9622979164123535}]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "text = [\"This was a masterpiece. Not completely faithful to the books, but enthralling from beginning to end. Might be my favorite of the three.\", \"I wish i could kill that bird, I hate it\"]\n",
    "\n",
    "classifier = pipeline(\"text-classification\", model=\"tensor-trek/distilbert-toxicity-classifier\")\n",
    "classifier(text)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76659e86-9c5f-4f23-a42e-96d308d678df",
   "metadata": {},
   "source": [
    "If you want to train a `distilbert-base-uncased` model yourself, then follow along the steps below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf503472-be40-4a27-beab-8f24f2728348",
   "metadata": {},
   "source": [
    "### Data preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfaae928-ca6a-4d93-869f-5861e973b72c",
   "metadata": {},
   "source": [
    "Download the [Jigsaw dataset](https://www.kaggle.com/competitions/jigsaw-multilingual-toxic-comment-classification/data) from Kaggle, and place it in a folder named `data`. The `data` folder must contain the `train.csv`, `test.csv`, and `test_labels.csv` files. Once the data is downloaded and ready we will do some slicing and dicing and use `Dataset` to load the data and prepare it for training our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1603626e-a518-4605-b075-1fb81788b76b",
   "metadata": {
    "tags": []
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_train = pd.read_csv('./data/train.csv')\n",
    "df_train.drop(['id'], inplace=True, axis=1)\n",
    "df_train['label'] = df_train[['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']].max(axis=1)\n",
    "\n",
    "new_df_train = df_train[['comment_text', 'label']].rename(columns={'comment_text': 'text'})\n",
    "label_counts = new_df_train['label'].value_counts()\n",
    "print(label_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f201252c-8a6c-494a-8382-3bc8052049ee",
   "metadata": {},
   "source": [
    "This new data frame condenses all the different types of toxic categories into one where `0` signifies `neutral` id no toxic content exists, and `1` signifies if the text contains toxic content. With this we can easily train a binary text classification model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14b88229-fe5a-41a4-bf19-6ab421cfc975",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "new_df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4102ed8-c023-47e9-8c1f-7aabd7da810e",
   "metadata": {},
   "source": [
    "Prepare test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48f799f9-dd9d-48aa-9ccb-0a2727a541cc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_test = pd.read_csv('./data/test.csv')\n",
    "df_test.drop(['id'], inplace=True, axis=1)\n",
    "\n",
    "new_df_test = df_test.rename(columns={'comment_text': 'text'})\n",
    "new_df_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "095bdd52-6368-4c62-a0ee-8bfce33966a2",
   "metadata": {},
   "source": [
    "#### Create a DataSet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "162ae483-0107-4d67-a323-2e84e117da99",
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 520,
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   "source": [
    "from datasets import Dataset, DatasetDict\n",
    "\n",
    "train_dataset = Dataset.from_pandas(new_df_train)\n",
    "test_dataset = Dataset.from_pandas(new_df_test)\n",
    "\n",
    "dataset = DatasetDict({\n",
    "    'train': train_dataset,\n",
    "    'test': test_dataset\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e42b334-e7b2-415a-95be-7a92d1c8e464",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87766f4c-7307-467b-9bcc-0f8b904ff90e",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Perform tokenization\n",
    "\n",
    "Load a DistilBERT tokenizer to preprocess the text field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b278112-b984-45fd-88b6-8b78477d6697",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoTokenizer\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")\n",
    "\n",
    "def preprocess_function(examples):\n",
    "    return tokenizer(examples[\"text\"], truncation=True)\n",
    "\n",
    "tokenized_data = dataset.map(preprocess_function, batched=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7965e16-a51e-40ec-8218-3acfd1a908ac",
   "metadata": {},
   "source": [
    "Create a batch of examples using DataCollatorWithPadding. It’s more efficient to dynamically pad the sentences to the longest length in a batch during collation, instead of padding the whole dataset to the maximum length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "23767bad-4fc4-455e-b9a7-8e8f12bbaecf",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import DataCollatorWithPadding\n",
    "\n",
    "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8070ecb4-d468-4ac9-9ca4-9e39f85db6d9",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Define evaluation function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e615ffd-c202-4342-9806-5094869d49d9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import evaluate\n",
    "import numpy as np\n",
    "\n",
    "accuracy = evaluate.load(\"accuracy\")\n",
    "def compute_metrics(eval_pred):\n",
    "    predictions, labels = eval_pred\n",
    "    predictions = np.argmax(predictions, axis=1)\n",
    "    return accuracy.compute(predictions=predictions, references=labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84de70a4-e83e-4544-9f32-6addb6014667",
   "metadata": {},
   "source": [
    "We will create a map of the Labels to their respective IDs and vice versa. Remember `0` is neutral and `1` is toxic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "db0a28a0-b487-4f14-a004-7f79f2fe916b",
   "metadata": {},
   "outputs": [],
   "source": [
    "id2label = {1: \"TOXIC\", 0: \"NEUTRAL\"}\n",
    "label2id = {\"TOXIC\":1, \"NEUTRAL\": 0}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe8ebf56-9b55-49f3-a512-4dc25b70c005",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Train the model\n",
    "\n",
    "In this step we train the model and save it locally. Remember to change the `output_dir` argument to give it a proper name for the directory where the model artifacts are going to be saved. This model is best trained with GPU instances (faster), but CPU instances should work as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17c73dc1-947d-4624-b2fc-4b384ed788fa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
    "\n",
    "model = AutoModelForSequenceClassification.from_pretrained(\n",
    "    \"distilbert-base-uncased\", num_labels=2, id2label=id2label, label2id=label2id\n",
    ")\n",
    "\n",
    "training_args = TrainingArguments(\n",
    "    output_dir=\"my-toxicity-classifier\",\n",
    "    label_names=[\"label\"],\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=16,\n",
    "    per_device_eval_batch_size=16,\n",
    "    num_train_epochs=1,\n",
    "    weight_decay=0.01,\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    save_strategy=\"epoch\",\n",
    "    load_best_model_at_end=True,\n",
    "    push_to_hub=False,\n",
    ")\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    args=training_args,\n",
    "    train_dataset=tokenized_data[\"train\"],\n",
    "    eval_dataset=tokenized_data[\"test\"],\n",
    "    tokenizer=tokenizer,\n",
    "    data_collator=data_collator,\n",
    "    compute_metrics=compute_metrics,\n",
    ")\n",
    "\n",
    "trainer.train()\n",
    "trainer.save_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c596c177-f46e-4800-8086-1b3c474d8e57",
   "metadata": {},
   "source": [
    "### Perform inference with the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6222d872-3d60-4adb-8319-c10749ac043e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import pipeline\n",
    "\n",
    "toxicity = pipeline('text-classification', model='./my-toxicity-classifier/<replace_your_checkpoint_dir>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3f79bff5-cd8e-46d1-8a7b-d32bccbfc895",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'NEUTRAL', 'score': 0.9966611862182617}]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "toxicity(\"Hey There, how are you?\")"
   ]
  }
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