def fine_tune(pos_action, neg_action, tokenizer, model):
nlg_usr = TemplateNLG(is_user=True)
nlg_sys = TemplateNLG(is_user=False)
pos_train_usr_utter = []
pos_train_sys_utter = []
neg_train_usr_utter = []
neg_train_sys_utter = []
for turn in pos_action:
if turn[0] != [] and turn[1] != []:
s_u = nlg_usr.generate(turn[0])
s_a = nlg_sys.generate(turn[1])
pos_train_usr_utter.append(s_u)
pos_train_sys_utter.append(s_a)
for turn in neg_action:
if turn[0] != [] and turn[1] != []:
s_u = nlg_usr.generate(turn[0])
s_a = nlg_sys.generate(turn[1])
neg_train_usr_utter.append(s_u)
neg_train_sys_utter.append(s_a)
train_usr_utter = pos_train_usr_utter + neg_train_usr_utter
train_sys_utter = pos_train_sys_utter + neg_train_sys_utter
train_encoding = tokenizer(train_usr_utter,
train_sys_utter,
padding=True,
truncation=True,
max_length=80)
train_encoding['label'] = [1] * len(pos_train_usr_utter) + [0] * len(
neg_train_usr_utter)
train_dataset = Dataset.from_dict(train_encoding)
train_dataset.set_format('torch',
columns=['input_ids', 'attention_mask', 'label'])
save_dir = os.path.join(root_dir,
'convlab2/policy/dqn/NLE/save/script_fine_tune')
log_dir = os.path.join(
root_dir, 'convlab2/policy/dqn/NLE/save/script_fine_tune/logs')
training_args = TrainingArguments(
output_dir=save_dir,
num_train_epochs=2,
per_device_train_batch_size=32,
per_device_eval_batch_size=128,
warmup_steps=500,
weight_decay=0.01,
evaluate_during_training=False,
logging_dir=log_dir,
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
)
trainer.train()
trainer.save_model(os.path.join(save_dir, 'fine_tune_checkpoint'))
def __call__(self, examples):
ds = Dataset.from_dict({'text':examples})
ds = ds.map(lambda batch: self.tokenizer(batch['text'], truncation=True, padding='max_length'), batched=True, batch_size=512)
ds.set_format('torch', columns=['input_ids','token_type_ids', 'attention_mask'])
dataloader = torch.utils.data.DataLoader(ds, batch_size=16)
res = []
for batch in tqdm(dataloader):
batch = {k: v.to(self.device) for k, v in batch.items()}
outputs = self.model(**batch)
res.append(outputs[0].softmax(1).detach().cpu())
return torch.cat(res,dim=0).numpy()
train_usr_utter, train_sys_utter = generate_data(multiwoz_train)
val_usr_utter, val_sys_utter = generate_data(multiwoz_val)
test_usr_utter, test_sys_utter = generate_data(multiwoz_test)
tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
model = RobertaForSequenceClassification.from_pretrained('roberta-base')
train_encoding = tokenizer(train_usr_utter,
train_sys_utter,
padding=True,
truncation=True,
max_length=80)
train_encoding['label'] = [1] * (len(train_usr_utter) //
2) + [0] * (len(train_usr_utter) // 2)
train_dataset = Dataset.from_dict(train_encoding)
train_dataset.set_format('torch',
columns=['input_ids', 'attention_mask', 'label'])
val_encoding = tokenizer(val_usr_utter,
val_sys_utter,
padding=True,
truncation=True,
max_length=80)
val_encoding['label'] = [1] * (len(val_usr_utter) //
2) + [0] * (len(val_usr_utter) // 2)
val_dataset = Dataset.from_dict(val_encoding)
val_dataset.set_format('torch',
columns=['input_ids', 'attention_mask', 'label'])
test_encoding = tokenizer(test_usr_utter,
test_sys_utter,
padding=True,
one_token, one_tag = line_split
# 去掉右侧的换行
one_tag = one_tag.rstrip('\n')
sentence.append(one_token)
sentence_tag.append(one_tag)
else:
print(f"这一行出现问题{line}，不是2个字段")
else:
#如果是空行，那么说明是下一句了,需要把sentence和 sentence_tag 加入到总的tokens和ner_tags中，然后重置
if sentence and sentence_tag:
tokens.append(sentence)
ner_tags.append(sentence_tag)
sentence = []
sentence_tag = []
if len(tokens) != len(ner_tags):
print(f'tokens 和ner_tags的长度不相等，读取的文件有问题,请检查')
result = {'tokens': [], 'ner_tags': []}
else:
result = {'tokens': tokens, 'ner_tags': ner_tags}
return result
if __name__ == '__main__':
train_file = "msra/msra_train_bio.txt"
test_file = "msra/msra_test_bio.txt"
mini_file = "msra/mini.txt"
# test_dict = read_ner_txt(test_file)
# dataset = Dataset.from_dict(test_dict)
mini_dict = read_ner_txt(mini_file)
dataset = Dataset.from_dict(mini_dict)
print(dataset)
line_split = line.split('\t')
if len(line_split) == 2:
one_token, one_tag = line_split
# 去掉右侧的换行
one_tag = one_tag.rstrip('\n')
sentence.append(one_token)
sentence_tag.append(one_tag)
else:
print(f"这一行出现问题{line}，不是2个字段")
else:
#如果是空行，那么说明是下一句了,需要把sentence和 sentence_tag 加入到总的tokens和ner_tags中，然后重置
if sentence and sentence_tag:
tokens.append(sentence)
ner_tags.append(sentence_tag)
sentence = []
sentence_tag = []
if len(tokens) != len(ner_tags):
print(f'tokens 和ner_tags的长度不相等，读取的文件有问题,请检查')
result = {'tokens': [], 'ner_tags': []}
else:
result = {'tokens': tokens, 'ner_tags': ner_tags}
return result
if __name__ == '__main__':
dev_file = "dataset/cosmetics/dev.txt"
train_file = "dataset/cosmetics/train.txt"
test_file = "dataset/cosmetics/test.txt"
test_dict = read_ner_txt(test_file)
dataset = Dataset.from_dict(test_dict)
print(dataset)