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--- |
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language: |
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- zh |
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inference: |
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parameters: |
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max_new_tokens: 250 |
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repetition_penalty: 1.1 |
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top_p: 0.9 |
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do_sample: True |
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license: apache-2.0 |
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--- |
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# Wenzhong2.0-GPT2-3.5B model (chinese),one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM). |
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As we all know, the single direction language model based on decoder structure has strong generation ability, such as GPT model. The 3.5 billion parameter Wenzhong-GPT2-3.5B large model, using 100G chinese common data, 32 A100 training for 28 hours, is the largest open source **GPT2 large model of chinese**. **Our model performs well in Chinese continuation generation.** **Wenzhong2.0-GPT2-3.5B-Chinese is a Chinese gpt2 model trained with cleaner data on the basis of Wenzhong-GPT2-3.5B.** |
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## Usage |
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### load model |
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```python |
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from transformers import GPT2Tokenizer, GPT2Model |
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tokenizer = GPT2Tokenizer.from_pretrained('IDEA-CCNL/Wenzhong2.0-GPT2-3.5B-chinese') |
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model = GPT2Model.from_pretrained('IDEA-CCNL/Wenzhong2.0-GPT2-3.5B-chinese') |
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text = "Replace me by any text you'd like." |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
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### generation |
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```python |
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from transformers import pipeline, set_seed |
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set_seed(55) |
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generator = pipeline('text-generation', model='IDEA-CCNL/Wenzhong2.0-GPT2-3.5B-chinese') |
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generator("北京位于", max_length=30, num_return_sequences=1) |
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``` |
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## Citation |
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If you find the resource is useful, please cite the following website in your paper. |
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``` |
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@misc{Fengshenbang-LM, |
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title={Fengshenbang-LM}, |
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author={IDEA-CCNL}, |
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year={2021}, |
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howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, |
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} |
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``` |