| # What does this model do? | |
| This model generates a subject line for the email, given the whole email as input. It is fine-tuned T5-Base | |
| Here is how to use this model | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| import torch | |
| model = AutoModelForSeq2SeqLM.from_pretrained("Chirayu/subject-generator-t5-base") | |
| tokenizer = AutoTokenizer.from_pretrained("Chirayu/subject-generator-t5-base") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model = model.to(device) | |
| def get_subject(content, num_beams=5,max_length=512, repetition_penalty=2.5, length_penalty=1, early_stopping=True,top_p=.95, top_k=50, num_return_sequences=3): | |
| text = "title: " + content + " </s>" | |
| input_ids = tokenizer.encode( | |
| text, return_tensors="pt", add_special_tokens=True | |
| ) | |
| input_ids = input_ids.to(device) | |
| generated_ids = model.generate( | |
| input_ids=input_ids, | |
| num_beams=num_beams, | |
| max_length=max_length, | |
| repetition_penalty=repetition_penalty, | |
| length_penalty=length_penalty, | |
| early_stopping=early_stopping, | |
| top_p=top_p, | |
| top_k=top_k, | |
| num_return_sequences=num_return_sequences, | |
| ) | |
| subjects = [tokenizer.decode(generated_id,skip_special_tokens=True,clean_up_tokenization_spaces=True,) for generated_id in generated_ids] | |
| return subjects | |
| ``` | |