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我正在使用 HuggingFace Trainer 类训练模型。以下代码做得不错：

!pip install datasets
!pip install transformers

from datasets import load_dataset
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer, AutoTokenizer

dataset = load_dataset('glue', 'mnli')
model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=3)
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', use_fast=True)

def preprocess_function(examples):
  return tokenizer(examples["premise"], examples["hypothesis"], truncation=True, padding=True)
encoded_dataset = dataset.map(preprocess_function, batched=True)

args = TrainingArguments(
    "test-glue",
    learning_rate=3e-5,
    per_device_train_batch_size=8,
    num_train_epochs=3,
    remove_unused_columns=True
  )

trainer = Trainer(
    model,
    args,
    train_dataset=encoded_dataset["train"],
    tokenizer=tokenizer
)
trainer.train()

但是，设置remove_unused_columns=False会导致以下错误：

ValueError                                Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py in convert_to_tensors(self, tensor_type, prepend_batch_axis)
    704                 if not is_tensor(value):
--> 705                     tensor = as_tensor(value)
    706 

ValueError: too many dimensions 'str'

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
8 frames
/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py in convert_to_tensors(self, tensor_type, prepend_batch_axis)
    720                     )
    721                 raise ValueError(
--> 722                     "Unable to create tensor, you should probably activate truncation and/or padding "
    723                     "with 'padding=True' 'truncation=True' to have batched tensors with the same length."
    724                 )

ValueError: Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.

任何建议都受到高度赞赏。

4

1 回答 1

1

它失败了，因为valuein 行705是 str 的列表，它指向hypothesis. 并且hypothesis是其中ignored_columns之一trainer.py。

/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py in convert_to_tensors(self, tensor_type, prepend_batch_axis)
    704                 if not is_tensor(value):
--> 705                     tensor = as_tensor(value)

trainer.py请参阅以下remove_unused_columns标志片段：

def _remove_unused_columns(self, dataset: "datasets.Dataset", description: Optional[str] = None):
    if not self.args.remove_unused_columns:
        return dataset
    if self._signature_columns is None:
        # Inspect model forward signature to keep only the arguments it accepts.
        signature = inspect.signature(self.model.forward)
        self._signature_columns = list(signature.parameters.keys())
        # Labels may be named label or label_ids, the default data collator handles that.
        self._signature_columns += ["label", "label_ids"]
    columns = [k for k in self._signature_columns if k in dataset.column_names]
    ignored_columns = list(set(dataset.column_names) - set(self._signature_columns))

如果标志是False. 但总的来说，标志实现看起来并不完整，例如它不能与 Tensorflow 一起使用。

相反，保留它并没有什么坏处True，除非有特殊需要。

于 2021-07-28T15:58:36.350 回答