
# CKIP GPT2 Base Chinese

This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).

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）。

## Homepage

- https://github.com/ckiplab/ckip-transformers

## Contributers

- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)

## Usage

Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.

請使用 BertTokenizerFast 而非 AutoTokenizer。

```
from transformers import (
  BertTokenizerFast,
  AutoModel,
)

tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
model = AutoModel.from_pretrained('ckiplab/gpt2-base-chinese')
```

For full usage and more information, please refer to https://github.com/ckiplab/ckip-transformers.

有關完整使用方法及其他資訊，請參見 https://github.com/ckiplab/ckip-transformers 。

{"language": ["zh"], "license": "gpl-3.0", "tags": ["pytorch", "lm-head", "gpt2", "zh"], "thumbnail": "https://ckip.iis.sinica.edu.tw/files/ckip_logo.png"}
transformers pytorch jax gpt2 text-generation lm-head zh license:gpl-3.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 


# CKIP GPT2 Base Chinese

This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).

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）。

## Homepage

- URL

## Contributers

- Mu Yang at CKIP (Author & Maintainer)

## Usage

Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.

請使用 BertTokenizerFast 而非 AutoTokenizer。



For full usage and more information, please refer to URL

有關完整使用方法及其他資訊，請參見 URL 。

# CKIP GPT2 Base Chinese

This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).

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）。 ## Homepage

- URL ## Contributers

- Mu Yang at CKIP (Author & Maintainer) ## Usage

Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.

請使用 BertTokenizerFast 而非 AutoTokenizer。



For full usage and more information, please refer to URL

有關完整使用方法及其他資訊，請參見 URL 。
TAGS
#transformers #pytorch #jax #gpt2 #text-generation #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 # CKIP GPT2 Base Chinese

This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).

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）。 ## Homepage

- URL ## Contributers

- Mu Yang at CKIP (Author & Maintainer) ## Usage

Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.

請使用 BertTokenizerFast 而非 AutoTokenizer。



For full usage and more information, please refer to URL

有關完整使用方法及其他資訊，請參見 URL 。
passage: TAGS
#transformers #pytorch #jax #gpt2 #text-generation #lm-head #zh #license-gpl-3.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
# CKIP GPT2 Base Chinese

This project provides traditional Chinese transformers models (including ALBERT, BERT, GPT2) and NLP tools (including word segmentation, part-of-speech tagging, named entity recognition).

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）。## Homepage

- URL## Contributers

- Mu Yang at CKIP (Author & Maintainer)## Usage

Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.

請使用 BertTokenizerFast 而非 AutoTokenizer。



For full usage and more information, please refer to URL

有關完整使用方法及其他資訊，請參見 URL 。