| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import torch | |
| def generate_rti_draft(complaint_text: str, model_id: str = "CaseLoop/rti-formatter") -> str: | |
| """Loads the fine-tuned T5 formatter from Hugging Face and generates a structured RTI draft.""" | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = AutoModelForSeq2SeqLM.from_pretrained(model_id) | |
| prompt = f"Format as RTI: {complaint_text}" | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=256, | |
| num_beams=4, | |
| no_repeat_ngram_size=3, # Prevents repeated loops | |
| repetition_penalty=2.0, # Penalizes word/phrase repetition | |
| early_stopping=True | |
| ) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) |