| | --- |
| | license: mit |
| | datasets: |
| | - daily_dialog |
| | - multi_woz_v22 |
| | language: |
| | - en |
| | --- |
| | ### Useless ChitChat Language Model |
| |
|
| | Basic Dialog Model from DialoGPT-small. |
| | Finetuned on Dialog dataset. (Daily Dialog, MultiWoz) |
| |
|
| | ### How to use |
| |
|
| | Use it as any torch python Language Model |
| |
|
| | ```python |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | import torch |
| | |
| | tokenizer = AutoTokenizer.from_pretrained("gpt") |
| | model = AutoModelForCausalLM.from_pretrained("jinymusim/dialogmodel") |
| | |
| | # Take user Input |
| | user_utterance = input('USER> ') |
| | user_utterance = user_utterance.strip() |
| | tokenized_context = tokenizer.encode(user_utterance + tokenizer.eos_token, return_tensors='pt') |
| | |
| | # generated a response, limit max_lenght to resonable size |
| | out_response = model.generate(tokenized_context, |
| | max_length=100, |
| | num_beams=2, |
| | no_repeat_ngram_size=2, |
| | early_stopping=True, |
| | pad_token_id=self.tokenizer.eos_token_id) |
| | |
| | # Truncate User Input |
| | decoded_response = self.tokenizer.decode(out_response[0], skip_special_tokens=True)[len(user_utterance):] |
| | |
| | print(f'SYSTEM> {decoded_response}') |
| | ``` |