我复制的代码是微调域适应。这是主要链接到后更多的细节：
（https://towardsdatascience.com/fine-tuning-for-domain-adaptation-in-nlp-c47def356fd6）
代码如下所示：

!pip install -q transformers
!pip install -q datasets

import multiprocessing
import pandas as pd
import numpy as np
import torch
import matplotlib.pyplot as plt
import transformers
from datasets import Dataset
from sklearn.model_selection import train_test_split

from transformers import AutoModelForMaskedLM
from transformers import AutoTokenizer, AutoConfig
from transformers import BertForMaskedLM, DistilBertForMaskedLM
from transformers import BertTokenizer, DistilBertTokenizer
from transformers import RobertaTokenizer, RobertaForMaskedLM
from transformers import Trainer, TrainingArguments
from transformers import DataCollatorForLanguageModeling
from tokenizers import BertWordPieceTokenizer

# HYPERPARAMS
SEED_SPLIT = 0
SEED_TRAIN = 0

MAX_SEQ_LEN = 128
TRAIN_BATCH_SIZE = 16
EVAL_BATCH_SIZE = 16
LEARNING_RATE = 2e-5 
LR_WARMUP_STEPS = 100
WEIGHT_DECAY = 0.01

# load data
dtf_mlm = pd.read_csv('data/jigsaw_train.csv', nrows=1000)
dtf_mlm = dtf_mlm[dtf_mlm["target"] < 0.5]
dtf_mlm = dtf_mlm.rename(columns={"comment_text": "text"})

# Train/Valid Split
df_train, df_valid = train_test_split(
    dtf_mlm, test_size=0.15, random_state=SEED_SPLIT
)

len(df_train), len(df_valid)

# Convert to Dataset object
train_dataset = Dataset.from_pandas(df_train[['text']].dropna())
valid_dataset = Dataset.from_pandas(df_valid[['text']].dropna())

#Model Selection Part
MODEL = 'bert'
bert_type = 'bert-base-cased'

TokenizerClass = BertTokenizer
ModelClass = BertForMaskedLM 

#Tokenization Part
tokenizer = TokenizerClass.from_pretrained(
            bert_type, use_fast=True, do_lower_case=False, max_len=MAX_SEQ_LEN
            )
model = ModelClass.from_pretrained(bert_type)

def tokenize_function(row):
    return tokenizer(
        row['text'],
        padding='max_length',
        truncation=True,
        max_length=MAX_SEQ_LEN,
        return_special_tokens_mask=True)
  
column_names = train_dataset.column_names

train_dataset = train_dataset.map(
    tokenize_function,
    batched=True,
    num_proc=multiprocessing.cpu_count(),
    remove_columns=column_names,
)

valid_dataset = valid_dataset.map(
    tokenize_function,
    batched=True,
    num_proc=multiprocessing.cpu_count(),
    remove_columns=column_names,
)

#Training and Model Saving Part
data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer, mlm=True, mlm_probability=0.15
)

steps_per_epoch = int(len(train_dataset) / TRAIN_BATCH_SIZE)

training_args = TrainingArguments(
    output_dir='./bert-news',
    logging_dir='./LMlogs',             
    num_train_epochs=2,
    do_train=True,
    do_eval=True,
    per_device_train_batch_size=TRAIN_BATCH_SIZE,
    per_device_eval_batch_size=EVAL_BATCH_SIZE,
    warmup_steps=LR_WARMUP_STEPS,
    save_steps=steps_per_epoch,
    save_total_limit=3,
    weight_decay=WEIGHT_DECAY,
    learning_rate=LEARNING_RATE, 
    evaluation_strategy='epoch',
    save_strategy='epoch',
    load_best_model_at_end=True,
    metric_for_best_model='loss', 
    greater_is_better=False,
    seed=SEED_TRAIN
)

trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=train_dataset,
    eval_dataset=valid_dataset,
    tokenizer=tokenizer,
)

trainer.train()
trainer.save_model("SavedModel/TestModel") #save your custom model

这是我正在使用的GPU：

我想使用GPU来训练模型，大约有150万条评论。
我试着这么做：

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

#Setting the tokenizer and the model
tokenizer = TokenizerClass.from_pretrained(
            bert_type, use_fast=True, do_lower_case=False, max_len=MAX_SEQ_LEN
            )
model = ModelClass.from_pretrained(bert_type).to(device)

但我不确定如何将输入和令牌发送到GPU。
请随意给出你的建议，* 我不欠这个代码 *，大声喊出马塞洛波利蒂。谢谢!