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from transformers import GTP2Tokenizer, TrainingArguments, Trainer, GPT2LMHeadModel
from datasets import load_dataset
dataset = load_dataset("sst2")
for row in dataset['train']:
print(row)
for i, row in enumerate(dataset):
prep_text = f"<|startoftext|> {rwo['sentence']}<|pad|>Sentiment: {rwo['label']}<|endoftext|>"
encodings_dict = tokenizer(prep_txt)
tokenizer = GTP2Tokenizer.from_pretrained('gpt2', bos_token='<|startoftext|>', eos_token='<|endoftext|>', pad_token='<|pad|>')
model = GPT2LMHeadModel.from_pretrained('gpt2')
train_args = TrainingArguments(output_dir='results', num_train_epochs = 1, warmup_steps =100, weight_decay = 0.01)
Trainer(model='gpt2', args=train_args,train_dataset=train_dataset)
model.eval()
prompt = f'<|startoftext|>Tweet: {text}\nSentiment:'
tokenizer_text = tokenizer(prompt, return_tensors="pt").input_ids
output = model.generate(tokenized_text)
predicted_text = tokenizer.decode(output)
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