train.py

from datasets import load_dataset
from transformers import BertForSequenceClassification, Trainer, TrainingArguments
from transformers import BertTokenizer

# Load the dataset
dataset = load_dataset('csv', data_files='dataset.csv')

# Load the tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

# Tokenize the dataset
def tokenize_function(examples):
    return tokenizer(examples['question'], padding="max_length", truncation=True)

tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Load the model
model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=1)

# Define training arguments
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-3,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=8,
    num_train_epochs=10,
    weight_decay=0.01,
)

# Create Trainer instance
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets['train'],
    eval_dataset=tokenized_datasets['test']
)

# Train the model
trainer.train()