| import gradio as gr | |
| import transformers | |
| import tokenizers | |
| # https://huggingface.co/docs/hub/spaces-sdks-gradio | |
| tokenizer_bert = BertTokenizerFast.from_pretrained('bert-base-chinese', | |
| additional_special_tokens=["<s>","<pad>","</s>","<unk>","<mask>"], | |
| pad_token='<pad>' ,max_len=512) | |
| configuration = GPT2Config(vocab_size=25000, n_layer=8) | |
| model = GPT2LMHeadModel(config=configuration) | |
| #%% | |
| path2pytorch_model = "pytorch_model.bin" | |
| model.load_state_dict(torch.load(path2pytorch_model)) | |
| generator = pipeline('text-generation', model=model, tokenizer=tokenizer_bert) | |
| def generate(prompt): | |
| outputs = generator(prompt, max_length=30, num_return_sequences=5, num_beams=10, top_p=0.999, repetition_penalty=1.5) | |
| return outputs[0]['generated_text'] | |
| iface = gr.Interface(fn=generate, inputs="text", outputs="text") | |
| iface.launch() | |