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train_ner.py
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train_ner.py
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# -*- coding: utf-8 -*-
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
from torch.utils.data import TensorDataset
from torch.utils.data import DataLoader
from utils import load_vocab, load_data, recover_label, get_ner_fmeasure, save_model, load_model
from ner_constant import *
from model_ner import BERT_LSTM_CRF
print('device',device)
# if torch.cuda.is_available():
# device = torch.device("cuda", 2)
# print('device',device)
# use_cuda = True
# else:
# device = torch.device("cpu")
# use_cuda = False
vocab = load_vocab(vocab_file)
vocab_reverse = {v:k for k, v in vocab.items()}
print('max_length',max_length)
train_data = load_data(train_file, max_length=max_length, label_dic=l2i_dic, vocab=vocab)
train_ids = torch.LongTensor([temp.input_id for temp in train_data[1500:]])
train_masks = torch.LongTensor([temp.input_mask for temp in train_data[1500:]])
train_tags = torch.LongTensor([temp.label_id for temp in train_data[1500:]])
train_lenghts = torch.LongTensor([temp.lenght for temp in train_data[1500:]])
train_dataset = TensorDataset(train_ids, train_masks, train_tags,train_lenghts)
train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)
dev_data = load_data(dev_file, max_length=max_length, label_dic=l2i_dic, vocab=vocab)
dev_ids = torch.LongTensor([temp.input_id for temp in dev_data[:1500]])
dev_masks = torch.LongTensor([temp.input_mask for temp in dev_data[:1500]])
dev_tags = torch.LongTensor([temp.label_id for temp in dev_data[:1500]])
dev_lenghts = torch.LongTensor([temp.lenght for temp in dev_data[:1500]])
dev_dataset = TensorDataset(dev_ids, dev_masks, dev_tags,dev_lenghts)
dev_loader = DataLoader(dev_dataset, shuffle=True, batch_size=batch_size)
test_data = load_data(test_file, max_length=max_length, label_dic=l2i_dic, vocab=vocab)
test_ids = torch.LongTensor([temp.input_id for temp in test_data])
test_masks = torch.LongTensor([temp.input_mask for temp in test_data])
test_tags = torch.LongTensor([temp.label_id for temp in test_data])
test_lenghts = torch.LongTensor([temp.lenght for temp in test_data])
test_dataset = TensorDataset(test_ids, test_masks, test_tags,test_lenghts)
test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)
######测试函数
def evaluate(medel, dev_loader):
medel.eval()
pred = []
gold = []
print('evaluate')
with torch.no_grad():
for i, dev_batch in enumerate(dev_loader):
sentence, masks, tags , lengths = dev_batch
sentence, masks, tags, lengths = Variable(sentence), Variable(masks), Variable(tags), Variable(lengths)
if use_cuda:
sentence = sentence.to(device)
masks = masks.to(device)
tags = tags.to(device)
predict_tags = medel(sentence, masks)
loss = model.neg_log_likelihood_loss(sentence, masks, tags)
pred.extend([t for t in predict_tags.tolist()])
gold.extend([t for t in tags.tolist()])
pred_label,gold_label = recover_label(pred, gold, l2i_dic,i2l_dic)
print('dev loss {}'.format(loss.item()))
pred_label_1 = [t[1:] for t in pred_label]
gold_label_1 = [t[1:] for t in gold_label]
acc,p, r, f = get_ner_fmeasure(gold_label_1,pred_label_1)
print('p: {}，r: {}, f: {}'.format(p, r, f))
return p, r, f
# test 函数
def evaluate_test(medel,test_loader,dev_f):
medel.eval()
pred = []
gold = []
print('test')
with torch.no_grad():
for i, dev_batch in enumerate(test_loader):
sentence, masks, tags, lengths = dev_batch
sentence, masks, tags , lengths = Variable(sentence), Variable(masks), Variable(tags),Variable(lengths)
if use_cuda:
sentence = sentence.to(device)
masks = masks.to(device)
tags = tags.to(device)
predict_tags = medel(sentence, masks)
pred.extend([t for t in predict_tags.tolist()])
gold.extend([t for t in tags.tolist()])
pred_label, gold_label = recover_label(pred, gold, l2i_dic,i2l_dic)
pred_label_2 = [t[1:] for t in pred_label]
gold_label_2 = [t[1:] for t in gold_label]
fw = open('data/predict_result'+str(float('%.3f'%dev_f))+'bert.txt','w')
for i in pred_label_2:
for j in range(len(i)-1):
fw.write(i[j])
fw.write(' ')
fw.write(i[len(i)-1])
fw.write('\n')
acc,p, r, f = get_ner_fmeasure(gold_label_2,pred_label_2)
print('p: {}，r: {}, f: {}'.format(p, r, f))
return p, r, f
model = BERT_LSTM_CRF('./data/my_bert', tagset_size, 768, 200, 2,
dropout_ratio=0.5, dropout1=0.5, use_cuda = use_cuda)
if use_cuda:
model.to(device)
optimizer = getattr(optim, 'Adam')
optimizer = optimizer(model.parameters(), lr=0.000005, weight_decay=0.00005)
best_f = -100
model_name = save_model_dir + '0518' + str(float('%.3f' % best_f)) + ".pkl"
print(model_name)
for epoch in range(epochs):
print('epoch: {}，train'.format(epoch))
for i, train_batch in enumerate(tqdm(train_loader)):
sentence, masks, tags , lengths= train_batch
sentence, masks, tags , lengths = Variable(sentence), Variable(masks), Variable(tags), Variable(lengths)
if use_cuda:
sentence = sentence.to(device)
masks = masks.to(device)
tags = tags.to(device)
model.train()
optimizer.zero_grad()
loss = model.neg_log_likelihood_loss(sentence, masks, tags)
loss.backward()
optimizer.step()
print('epoch: {}，train loss: {}'.format(epoch, loss.item()))
p, r, f = evaluate(model,dev_loader)
if f > best_f:
best_f = f
_, _, _ = evaluate_test(model, test_loader, loss.item())
model_name = save_model_dir + 'new' + str(float('%.3f' % best_f)) + ".pkl"
torch.save(model.state_dict(), model_name)