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--- |
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language: |
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- zh |
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thumbnail: https://ckip.iis.sinica.edu.tw/files/ckip_logo.png |
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tags: |
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- pytorch |
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- lm-head |
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- albert |
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- zh |
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license: gpl-3.0 |
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--- |
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# CKIP ALBERT Tiny Chinese |
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## Table of Contents |
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- [Model Details](#model-details) |
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- [Uses](#uses) |
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- [Risks, Limitations and Biases](#risks-limitations-and-biases) |
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- [Training](#training) |
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- [Evaluation](#evaluation) |
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- [How to Get Started With the Model](#how-to-get-started-with-the-model) |
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## Model Details |
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- **Model Description:** |
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This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition). |
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這個專案提供了繁體中文的 transformers 模型（包含 ALBERT、BERT、GPT2）及自然語言處理工具（包含斷詞、詞性標記、實體辨識）。 |
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- **Developed by:** [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) |
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- **Model Type:** Fill-Mask |
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- **Language(s):** Chinese |
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- **License:** gpl-3.0 |
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- **Parent Model:** See the [ALBERT base model](https://huggingface.co/albert-base-v2) for more information about the ALBERT base model. |
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- **Resources for more information:** |
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- [GitHub Repo](https://github.com/ckiplab/ckip-transformers) |
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- [CKIP Documentation](https://ckip-transformers.readthedocs.io/en/stable/) |
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## Uses |
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#### Direct Use |
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The model author suggests using BertTokenizerFast as tokenizer instead of AutoTokenizer. |
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請使用 BertTokenizerFast 而非 AutoTokenizer。 |
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For full usage and more information, please refer to [github repository] (https://github.com/ckiplab/ckip-transformers.) |
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有關完整使用方法及其他資訊，請參見 [github repository] (https://github.com/ckiplab/ckip-transformers.) |
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## Risks, Limitations and Biases |
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**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.** |
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Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). |
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## Training |
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#### Training Data |
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The language models are trained on the ZhWiki and CNA datasets; the WS and POS tasks are trained on the ASBC dataset; the NER tasks are trained on the OntoNotes dataset. |
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以上的語言模型訓練於 ZhWiki 與 CNA 資料集上；斷詞（WS）與詞性標記（POS）任務模型訓練於 ASBC 資料集上；實體辨識（NER）任務模型訓練於 OntoNotes 資料集上。 |
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#### Training Procedure |
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* **Parameters:** 4M |
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## Evaluation |
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#### Results |
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* **Perplexity:** 4.40 |
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* **WOS (Word Segmentation) [F1]:** 96.66% |
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* **POS (Part-of-speech) [ACC]:** 94.48% |
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* **NER (Named-entity recognition) [F1]:** 71.17% |
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## How to Get Started With the Model |
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``` |
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from transformers import ( |
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BertTokenizerFast, |
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AutoModel, |
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) |
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tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese') |
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model = AutoModel.from_pretrained('ckiplab/albert-tiny-chinese') |
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``` |
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