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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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inference: true |
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widget: |
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- text: "生活的真谛是[MASK]。" |
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--- |
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# Erlangshen-Deberta-97M-Chinese，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM). |
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The 97 million parameter deberta-V2 base model, using 180G Chinese data, 24 A100(40G) training for 7 days，which is a encoder-only transformer structure. Consumed totally 1B samples. |
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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-97M-Chinese', use_fast=false) |
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model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-Deberta-97M-Chinese') |
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text = '生活的真谛是[MASK]。' |
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fillmask_pipe = FillMaskPipeline(model, tokenizer, device=7) |
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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 | OCNLI | CMNLI | |
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| ---------------------------------- | ----- | ------ | |
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| RoBERTa-base | 0.743 | 0.7973 | |
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| **Erlangshen-Deberta-97M-Chinese** | 0.752 | 0.807 | |
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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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``` |
