我得到“RuntimeError:输入、输出和索引必须在当前设备上。” 当我跑这条线的时候。 加注口罩(“汽车”)
我在Colab上运行它。 我的代码:
from transformers import BertTokenizer, BertForMaskedLM
from pathlib import Path
from tokenizers import ByteLevelBPETokenizer
from transformers import BertTokenizer, BertForMaskedLM
paths = [str(x) for x in Path(".").glob("**/*.txt")]
print(paths)
bert_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
from transformers import BertModel, BertConfig
configuration = BertConfig()
model = BertModel(configuration)
configuration = model.config
print(configuration)
model = BertForMaskedLM.from_pretrained("bert-base-uncased")
from transformers import LineByLineTextDataset
dataset = LineByLineTextDataset(
tokenizer=bert_tokenizer,
file_path="./kant.txt",
block_size=128,
)
from transformers import DataCollatorForLanguageModeling
data_collator = DataCollatorForLanguageModeling(
tokenizer=bert_tokenizer, mlm=True, mlm_probability=0.15
)
from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(
output_dir="./KantaiBERT",
overwrite_output_dir=True,
num_train_epochs=1,
per_device_train_batch_size=64,
save_steps=10_000,
save_total_limit=2,
)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=dataset,
)
trainer.train()
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model=model,
tokenizer=bert_tokenizer
)
fill_mask("Auto Car <mask>.")
最后一行告诉我上面提到的错误。请让我知道我做错了什么,或者为了消除这个错误我必须做什么
培训师在GPU(default value no_cuda=False)上自动培训您的模型。您可以通过运行以下命令来验证这一点:
训练后。管道没有这样做,这会导致您看到的错误(即,您的模型在您的GPU上,但您的示例语句在您的CPU上)。您也可以通过运行支持GPU的管道来修复此问题:
或者在初始化管道之前将模型传输到CPU:
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