在下一个句子预测任务上，我正在尝试使用Huggingface库对Bert进行微调。我看了教程，并尝试使用DataCollatorForNextSentencePrediction和TextDatasetForNextSentencePrediction。当我使用它时，出现以下错误。我已经提供了下面的代码。
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-18-7678758b2c9c> in <module>()
56 train(bert_model,bert_tokenizer,train_data_set_path)
57 #prepare_data_set(bert_tokenizer)
---> 58 main()
9 frames
<ipython-input-18-7678758b2c9c> in main()
54 bert_model = BertForNextSentencePrediction.from_pretrained("bert-base-cased")
55 train_data_set_path = "/content/drive/My Drive/next_sentence/line_data_set_file.txt"
---> 56 train(bert_model,bert_tokenizer,train_data_set_path)
57 #prepare_data_set(bert_tokenizer)
58 main()
<ipython-input-18-7678758b2c9c> in train(bert_model, bert_tokenizer, path, eval_path)
47
48 )
---> 49 trainer.train()
50 trainer.save_model(out_dir)
51 def main():
/usr/local/lib/python3.6/dist-packages/transformers/trainer.py in train(self, model_path, trial)
697
698 epoch_pbar = tqdm(epoch_iterator, desc="Iteration", disable=disable_tqdm)
--> 699 for step, inputs in enumerate(epoch_iterator):
700
701 # Skip past any already trained steps if resuming training
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in __next__(self)
361
362 def __next__(self):
--> 363 data = self._next_data()
364 self._num_yielded += 1
365 if self._dataset_kind == _DatasetKind.Iterable and \
/usr/local/lib/python3.6/dist-packages/torch/utils/data/dataloader.py in _next_data(self)
401 def _next_data(self):
402 index = self._next_index() # may raise StopIteration
--> 403 data = self._dataset_fetcher.fetch(index) # may raise StopIteration
404 if self._pin_memory:
405 data = _utils.pin_memory.pin_memory(data)
/usr/local/lib/python3.6/dist-packages/torch/utils/data/_utils/fetch.py in fetch(self, possibly_batched_index)
45 else:
46 data = self.dataset[possibly_batched_index]
---> 47 return self.collate_fn(data)
/usr/local/lib/python3.6/dist-packages/transformers/data/data_collator.py in __call__(self, examples)
356 for i, doc in enumerate(examples):
357 input_id, segment_id, attention_mask, label = self.create_examples_from_document(doc, i, examples)
--> 358 input_ids.extend(input_id)
359 segment_ids.extend(segment_id)
360 attention_masks.extend(attention_mask)
/usr/local/lib/python3.6/dist-packages/transformers/data/data_collator.py in create_examples_from_document(self, document, doc_index, examples)
444 random_document = examples[random_document_index]
445 random_start = random.randint(0, len(random_document) - 1)
--> 446 for j in range(random_start, len(random_document)):
447 tokens_b.extend(random_document[j])
448 if len(tokens_b) >= target_b_length:
/usr/lib/python3.6/random.py in randint(self, a, b)
219 """
220
--> 221 return self.randrange(a, b+1)
222
223 def _randbelow(self, n, int=int, maxsize=1<<BPF, type=type,
/usr/lib/python3.6/random.py in randrange(self, start, stop, step, _int)
197 return istart + self._randbelow(width)
198 if step == 1:
--> 199 raise ValueError("empty range for randrange() (%d,%d, %d)" % (istart, istop, width))
200
201 # Non-unit step argument supplied.
ValueError: empty range for randrange() (0,0, 0)
def train(bert_model,bert_tokenizer,path,eval_path=None):
out_dir = "/content/drive/My Drive/next_sentence/"
training_args = TrainingArguments(
output_dir=out_dir,
overwrite_output_dir=True,
num_train_epochs=1,
per_device_train_batch_size=30,
save_steps=10000,
save_total_limit=2,
)
data_collator = DataCollatorForNextSentencePrediction(
tokenizer=bert_tokenizer,mlm=False,block_size=512,nsp_probability =0.5
)
dataset = TextDatasetForNextSentencePrediction(
tokenizer = bert_tokenizer,
file_path=path,
block_size=512,
)
trainer = Trainer(
model=bert_model,
args=training_args,
train_dataset=dataset,
data_collator=data_collator,
)
trainer.train()
trainer.save_model(out_dir)
def main():
print("Running main")
bert_tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
bert_model = BertForNextSentencePrediction.from_pretrained("bert-base-cased")
train_data_set_path = "/content/drive/My Drive/next_sentence/line_data_set_file.txt"
train(bert_model,bert_tokenizer,train_data_set_path)
#prepare_data_set(bert_tokenizer)
main()