Scottish_Fold007andJulien Chaumond
6b410bedfc
* Create README.md * Update README.md * tweak Co-authored-by: Julien Chaumond <chaumond@gmail.com>
67 lines
1.6 KiB
Markdown
67 lines
1.6 KiB
Markdown
## Generating Chinese poetry by topic.
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```python
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from transformers import *
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tokenizer = BertTokenizer.from_pretrained("gaochangkuan/model_dir")
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model = AutoModelWithLMHead.from_pretrained("gaochangkuan/model_dir")
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prompt= '''<s>田园躬耕'''
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length= 84
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stop_token='</s>'
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temperature = 1.2
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repetition_penalty=1.3
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k= 30
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p= 0.95
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device ='cuda'
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seed=2020
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no_cuda=False
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prompt_text = prompt if prompt else input("Model prompt >>> ")
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encoded_prompt = tokenizer.encode(
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'<s>'+prompt_text+'<sep>',
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add_special_tokens=False,
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return_tensors="pt"
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)
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encoded_prompt = encoded_prompt.to(device)
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output_sequences = model.generate(
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input_ids=encoded_prompt,
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max_length=length,
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min_length=10,
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do_sample=True,
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early_stopping=True,
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num_beams=10,
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temperature=temperature,
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top_k=k,
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top_p=p,
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repetition_penalty=repetition_penalty,
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bad_words_ids=None,
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bos_token_id=tokenizer.bos_token_id,
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pad_token_id=tokenizer.pad_token_id,
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eos_token_id=tokenizer.eos_token_id,
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length_penalty=1.2,
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no_repeat_ngram_size=2,
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num_return_sequences=1,
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attention_mask=None,
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decoder_start_token_id=tokenizer.bos_token_id,)
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generated_sequence = output_sequences[0].tolist()
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text = tokenizer.decode(generated_sequence)
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text = text[: text.find(stop_token) if stop_token else None]
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print(''.join(text).replace(' ','').replace('<pad>','').replace('<s>',''))
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```
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