Я пробую это руководство по «Обнимающему лицу»
Я пытаюсь использовать трейнер для тренировки своего режима. На этом этапе возникают ошибки кода:
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification, DataCollatorWithPadding, TrainingArguments, Trainer
checkpoint = "bert-base-uncased"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
raw_datasets = load_dataset("glue", "mrpc")
def tokenize_function(example):
return tokenizer(example["sentence1"], example["sentence2"], truncation=True)
tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
training_args = TrainingArguments("test-trainer")
model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
#The above code works upto here
#The following line fails
trainer = Trainer(
model,
training_args,
train_dataset=tokenized_datasets["train"],
eval_dataset=tokenized_datasets["validation"],
tokenizer=tokenizer,
)
Ошибка отображается как:
File "tutorial.py", line 21, in
trainer = Trainer(
^^^^^^^^
File "/opt/miniconda3/envs/py3env/lib/python3.12/site-packages/transformers/trainer.py", line 388, in __init__
self.create_accelerator_and_postprocess()
File "/opt/miniconda3/envs/py3env/lib/python3.12/site-packages/transformers/trainer.py", line 4364, in create_accelerator_and_postprocess
self.accelerator = Accelerator(**args)
^^^^^^^^^^^^^^^^^^^
TypeError: Accelerator.__init__() got an unexpected keyword argument 'use_seedable_sampler'
Версии:
Python: 3.12.3
Transformer: 4.40.2
Datasets: 2.19.1
Accelerate: 0.21.0
Подробнее здесь: https://stackoverflow.com/questions/784 ... ansformers
