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task_sentence_embedding_sup_concat_CrossEntropyLoss.py
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task_sentence_embedding_sup_concat_CrossEntropyLoss.py
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#! -*- coding:utf-8 -*-
# loss: 句向量concat后 (u, v, u-v, u*v) 走CrossEntropyLoss
from bert4torch.tokenizers import Tokenizer
from bert4torch.models import build_transformer_model, BaseModel
from bert4torch.callbacks import Callback
from bert4torch.snippets import sequence_padding, ListDataset, get_pool_emb, seed_everything
import torch.nn as nn
import torch
import torch.optim as optim
from torch.utils.data import DataLoader
from sklearn.metrics.pairwise import paired_cosine_distances
from scipy.stats import spearmanr
from tqdm import tqdm
import argparse
import numpy as np
# =============================基本参数=============================
parser = argparse.ArgumentParser()
parser.add_argument('--pooling', default='cls', choices=['first-last-avg', 'last-avg', 'cls', 'pooler'])
parser.add_argument('--task_name', default='ATEC', choices=['ATEC', 'BQ', 'LCQMC', 'PAWSX', 'STS-B'])
args = parser.parse_args()
pooling = args.pooling
task_name = args.task_name
maxlen = 64 if task_name != 'PAWSX' else 128
batch_size = 32
config_path = 'E:/data/pretrain_ckpt/bert/google@chinese_L-12_H-768_A-12/bert4torch_config.json'
checkpoint_path = 'E:/data/pretrain_ckpt/bert/google@chinese_L-12_H-768_A-12/pytorch_model.bin'
dict_path = 'E:/data/pretrain_ckpt/bert/google@chinese_L-12_H-768_A-12/vocab.txt'
device = 'cuda' if torch.cuda.is_available() else 'cpu'
seed_everything(42)
# 建立分词器
tokenizer = Tokenizer(dict_path, do_lower_case=True)
class MyDataset(ListDataset):
@staticmethod
def load_data(filename):
"""加载数据
单条格式：(文本1, 文本2, 标签id)
"""
D = []
with open(filename, encoding='utf-8') as f:
for l in f:
l = l.strip().split('\t')
if len(l) == 3:
D.append((l[0], l[1], int(l[2])))
return D
def collate_fn(batch):
batch_token1_ids, batch_token2_ids, batch_labels = [], [], []
for text1, text2, label in batch:
label = int(label > 2.5) if task_name == 'STS-B' else label
token1_ids, _ = tokenizer.encode(text1, maxlen=maxlen)
batch_token1_ids.append(token1_ids)
token2_ids, _ = tokenizer.encode(text2, maxlen=maxlen)
batch_token2_ids.append(token2_ids)
batch_labels.append([label])
batch_token1_ids = torch.tensor(sequence_padding(batch_token1_ids), dtype=torch.long, device=device)
batch_token2_ids = torch.tensor(sequence_padding(batch_token2_ids), dtype=torch.long, device=device)
batch_labels = torch.tensor(batch_labels, dtype=torch.long, device=device)
return (batch_token1_ids, batch_token2_ids), batch_labels.flatten()
# 加载数据集
train_dataloader = DataLoader(MyDataset(f'F:/data/corpus/sentence_embedding/{task_name}/{task_name}.train.data'), batch_size=batch_size, shuffle=True, collate_fn=collate_fn)
valid_dataloader = DataLoader(MyDataset(f'F:/data/corpus/sentence_embedding/{task_name}/{task_name}.valid.data'), batch_size=batch_size, collate_fn=collate_fn)
test_dataloader = DataLoader(MyDataset(f'F:/data/corpus/sentence_embedding/{task_name}/{task_name}.test.data'), batch_size=batch_size, collate_fn=collate_fn)
# 定义bert上的模型结构
class Model(BaseModel):
def __init__(self, pool_method='cls', concatenation_sent_rep=True, concatenation_sent_difference=True, concatenation_sent_multiplication=False):
super().__init__()
self.pool_method = pool_method
with_pool = 'linear' if pool_method == 'pooler' else True
output_all_encoded_layers = True if pool_method == 'first-last-avg' else False
self.bert = build_transformer_model(config_path, checkpoint_path, segment_vocab_size=0,
with_pool=with_pool, output_all_encoded_layers=output_all_encoded_layers)
self.concatenation_sent_rep = concatenation_sent_rep
self.concatenation_sent_difference = concatenation_sent_difference
self.concatenation_sent_multiplication = concatenation_sent_multiplication
hidden_unit = 0
hidden_unit += 768*2 if self.concatenation_sent_rep else 0
hidden_unit += 768 if self.concatenation_sent_difference else 0
hidden_unit += 768 if self.concatenation_sent_multiplication else 0
self.fc = nn.Linear(hidden_unit, 2)
def forward(self, token1_ids, token2_ids):
hidden_state1, pooler1 = self.bert([token1_ids])
rep_a = get_pool_emb(hidden_state1, pooler1, token1_ids.gt(0).long(), self.pool_method)
hidden_state2, pooler2 = self.bert([token2_ids])
rep_b = get_pool_emb(hidden_state2, pooler2, token2_ids.gt(0).long(), self.pool_method)
vectors_concat = []
if self.concatenation_sent_rep:
vectors_concat.append(rep_a)
vectors_concat.append(rep_b)
if self.concatenation_sent_difference:
vectors_concat.append(torch.abs(rep_a - rep_b))
if self.concatenation_sent_multiplication:
vectors_concat.append(rep_a * rep_b)
vectors_concat = torch.cat(vectors_concat, dim=1)
return self.fc(vectors_concat)
def predict(self, token_ids):
self.eval()
with torch.no_grad():
hidden_state, pooler = self.bert([token_ids])
attention_mask = token_ids.gt(0).long()
output = get_pool_emb(hidden_state, pooler, attention_mask, self.pool_method)
return output
model = Model().to(device)
# 定义使用的loss和optimizer，这里支持自定义
model.compile(
loss=nn.CrossEntropyLoss(),
optimizer=optim.Adam(model.parameters(), lr=2e-5),
metrics=['accuracy']
)
class Evaluator(Callback):
"""评估与保存
"""
def __init__(self):
self.best_val_consine = 0.
def on_epoch_end(self, global_step, epoch, logs=None):
val_consine = self.evaluate(valid_dataloader)
test_consine = self.evaluate(test_dataloader)
if val_consine > self.best_val_consine:
self.best_val_consine = val_consine
# model.save_weights('best_model.pt')
print(f'valid_consine: {val_consine:.5f}, test_consine: {test_consine:.5f}, best_val_consine: {self.best_val_consine:.5f}\n')
# 定义评价函数
def evaluate(self, data):
cosine_scores, labels = [], []
for (batch_token1_ids, batch_token2_ids), batch_labels in tqdm(data, desc='Evaluate'):
embeddings1 = model.predict(batch_token1_ids).cpu().numpy()
embeddings2 = model.predict(batch_token2_ids).cpu().numpy()
cosine_score = 1 - paired_cosine_distances(embeddings1, embeddings2)
cosine_scores.append(cosine_score)
labels.append(batch_labels.cpu().numpy())
labels = np.concatenate(labels)
cosine_scores = np.concatenate(cosine_scores)
eval_pearson_cosine, _ = spearmanr(labels, cosine_scores)
return eval_pearson_cosine
if __name__ == '__main__':
evaluator = Evaluator()
model.fit(train_dataloader,
epochs=5,
steps_per_epoch=None,
callbacks=[evaluator]
)
else:
model.load_weights('best_model.pt')