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!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) |