text_class.py

!pip install datasets
pip install transformers[torch]

from datasets import load_dataset, load_metric
raw_datasets = load_dataset("wiki_qa")

dataset = raw_datasets['test'].train_test_split(train_size=0.67, seed=42)
raw_datasets["validation"]=dataset.pop("test")
raw_datasets['test']= dataset['train']


print(raw_datasets)

raw_datasets.set_format('pandas')

print('n\n\n\ntraining_labels:\n', raw_datasets['train']['label'].value_counts(),'\n\n',
     'validation_labels:\n', raw_datasets['validation']['label'].value_counts(),'\n\n',
     'testing_labels:\n',raw_datasets['test']['label'].value_counts())
raw_datasets.reset_format()

from transformers import GPT2Config, GPT2ForSequenceClassification, GPT2Tokenizer

# Load the GPT-2 tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")

# Load the GPT-2 configuration
config = GPT2Config.from_pretrained("gpt2")

# Modify the configuration for sequence classification
config.num_labels = 2  # Specify the number of classes for your classification task
config.pad_token_id = tokenizer.eos_token_id

# Initialize the GPT-2 model for sequence classification
model = GPT2ForSequenceClassification.from_pretrained("gpt2", config=config)


tokenizer.pad_token = tokenizer.eos_token

def tokenize_function(examples):
    # Tokenize the question and answer text
    question_inputs = tokenizer(examples['question'], padding='max_length', truncation=True, return_tensors='pt', max_length=800)
    answer_inputs = tokenizer(examples['answer'], padding='max_length', truncation=True, return_tensors='pt', max_length=800)

    # Combine question and answer inputs
    inputs = {
        'input_ids': question_inputs['input_ids'],
        'attention_mask': question_inputs['attention_mask'],
        'answer_input_ids': answer_inputs['input_ids'],
        'answer_attention_mask': answer_inputs['attention_mask'],
    }

    return inputs

# Tokenize the train, test, and validation datasets
tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)


from transformers import Trainer, TrainingArguments


# Training arguments
training_args = TrainingArguments(
    output_dir="./output",
    num_train_epochs=3,
    evaluation_strategy="steps",
    save_total_limit=2,
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    save_steps=200,
    eval_steps=200,
    logging_steps=200,
    fp16=True,
)

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

# Train the model
trainer.train()

# Evaluate on the test dataset
results = trainer.evaluate(tokenized_datasets['test'])
print(results)