| 52 | return sents |
| 53 | |
| 54 | def generate_sample(model, tokenizer, input_txt): |
| 55 | input_ids = tokenizer([input_txt], padding=False)["input_ids"] |
| 56 | context_enc = torch.tensor(input_ids, device=model.device) |
| 57 | raw_text_len = len(input_ids[0]) |
| 58 | print(f"Input text: {input_txt}\n") |
| 59 | outputs = model.generate(context_enc, pad_token_id=tokenizer.pad_token_id) |
| 60 | output_text = decode(outputs, tokenizer, raw_text_len)[0] |
| 61 | print(f"\nOutput text: {output_text}\n") |
| 62 | return output_text |
| 63 | |
| 64 | |
| 65 | def extract_answer_hf(completion): |