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from transformers import GPT2Tokenizer, GPT2LMHeadModel
def generate_response(input_text):
inputs = tokenizer(input_text, return_tensors="pt")
output_sequences = model.generate(
input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask'],
max_length=100, # Adjusted max_length
temperature=0.3,
top_k=40,
top_p=0.85,
num_return_sequences=1,
no_repeat_ngram_size=2,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
early_stopping=True,
do_sample=True,
use_cache=True,
)
full_generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
bot_response_start = full_generated_text.find('[Bot]') + len('[Bot]')
bot_response = full_generated_text[bot_response_start:]
return bot_response
model_name = 'KhantKyaw/Chat_GPT-2'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
response = generate_response(user_input)
print("Chatbot:", response)
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