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--- |
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language: |
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- zh |
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license: apache-2.0 |
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tags: |
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- bert |
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- deberta |
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inference: true |
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widget: |
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- text: "桂林是世界闻名的旅游城市,它有[MASK]江。" |
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--- |
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# Erlangshen-DeBERTa-v2-320M-Chinese，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM) |
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The 320 million parameter deberta-V2 base model, using 180G Chinese data, 8 A100(80G) training for 7 days，which is a encoder-only transformer structure. Consumed totally 250M samples. |
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**our model is still training. And we will update our model once a week!** |
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## Task Description |
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Erlangshen-Deberta-97M-Chinese is pre-trained by bert like mask task from Deberta [paper](https://readpaper.com/paper/3033187248) |
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## Usage |
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```python |
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from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline |
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import torch |
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tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese', use_fast=False) |
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model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese') |
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text = '桂林是世界闻名的旅游城市,它有[MASK]江。' |
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fillmask_pipe = FillMaskPipeline(model, tokenizer, device=0) |
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print(fillmask_pipe(text, top_k=10)) |
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``` |
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## Finetune |
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We present the dev results on some tasks. |
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| Model | AFQMC | TNEWS1.1 | IFLYTEK | OCNLI | CMNLI | |
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| -------------------------------------------------------------------------------------------------------------------------------- | ------ | -------- | |
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| RoBERTa-base | 0.7406 | 0.575 | 0.6036 | 0.743 | 0.7973 | |
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| RoBERTa-large | 0.7488 | 0.5879 | 0.6152 | 0.777 | 0.814 | |
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| [IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-DeBERTa-v2-186M-Chinese-SentencePiece) | 0.7405 | 0.571 | 0.5977 | 0.752 | 0.7568 | 0.807 | |
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| **[IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-DeBERTa-v2-320M-Chinese)** | 0.7498 | 0.5817 | 0.6042 | 0.8022 | 0.8301| |
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| [Erlangshen-Deberta-XLarge-710M-Chinese](https://huggingface.co/IDEA-CCNL/Erlangshen-DeBERTa-v2-710M-Chinese) | 0.7549|0.5873|0.6177|0.8012|0.8389| |
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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={2022}, |
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howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, |
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} |
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``` |
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