| --- | |
| language: | |
| - en | |
| tags: | |
| - en | |
| - english | |
| - gpt2 | |
| - gpt3 | |
| - text-generation | |
| - lm | |
| - nlp | |
| datasets: | |
| - cnn_dailymail | |
| widget: | |
| - text: "Ever noticed how plane seats appear to be getting smaller and smaller? " | |
| inference: | |
| parameters: | |
| max_length: 120 | |
| do_sample: True | |
| temperature: 0.8 | |
| ---\ | |
| # GPT-3 small | |
| Pretrained GPT-3 small, continuing the development of GPT NEO, with architecture that purposefully mimics that of GPT-3, model was trained on CNN Daily Mail News dataset for text generation. | |
| # How to use the model | |
| ~~~~ | |
| from transformers import GPT2Tokenizer, GPTNeoForCausalLM | |
| tokenizer = GPT2Tokenizer.from_pretrained('minhtoan/gpt3-small-finetune-cnndaily-news') | |
| model = GPTNeoForCausalLM.from_pretrained('minhtoan/gpt3-small-finetune-cnndaily-news') | |
| text = "Ever noticed how plane seats appear to be getting smaller and smaller? " | |
| input_ids = tokenizer.encode(text, return_tensors='pt') | |
| max_length = 150 | |
| sample_outputs = model.generate(input_ids, do_sample=True, max_length=max_length,temperature = 0.8) | |
| for i, sample_output in enumerate(sample_outputs): | |
| print(">> Generated text {}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist()))) | |
| print('\n---') | |
| ~~~~ | |
| ## Author | |
| ` | |
| Phan Minh Toan | |
| ` |