| # from transformers import DistilBertTokenizer, DistilBertForSequenceClassification | |
| # import torch | |
| # tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english") | |
| # model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english") | |
| # inputs = tokenizer("Hello, my dog is sad", return_tensors="pt") | |
| # inputs = tokenizer("Hello, my dog is sad", return_tensors="pt") | |
| # with torch.no_grad(): | |
| # logits = model(**inputs).logits | |
| # predicted_class_id = logits.argmax().item() | |
| # model.config.id2label[predicted_class_id] | |
| # outputs = model(**inputs) | |
| # print(predicted_class_id) | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # Load the tokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2") | |
| tokenizer.pad_token = tokenizer.eos_token | |
| # Load the model | |
| model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2") | |
| # Example input prompt | |
| input_text = "Ann wants to buy a new car" | |
| # Tokenize input | |
| inputs = tokenizer(input_text, return_tensors="pt",padding=True, truncation=True) | |
| # Generate text | |
| outputs = model.generate(inputs.input_ids, max_length=100, num_return_sequences=1, top_k=50, top_p=0.9, temperature=0.7,do_sample=True,eos_token_id=None, attention_mask=inputs.attention_mask) | |
| print(model.config) | |
| # Decode the generated text | |
| generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| print("Generated Text:\n", generated_text) | |