Upload model files.
Browse files- README.md +45 -0
- config.json +32 -0
- pytorch_model.bin +3 -0
- special_tokens_map.json +7 -0
- tokenizer_config.json +14 -0
- vocab.txt +0 -0
README.md
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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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- question-answering
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- bert
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- zh
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license: gpl-3.0
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---
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# CKIP BERT Base Chinese
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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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## Homepage
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- https://github.com/ckiplab/ckip-transformers
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## Contributers
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- [Mu Yang](https://muyang.pro) at [CKIP](https://ckip.iis.sinica.edu.tw) (Author & Maintainer)
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## Usage
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Please use BertTokenizerFast as tokenizer instead of AutoTokenizer.
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請使用 BertTokenizerFast 而非 AutoTokenizer。
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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/bert-base-chinese-qa')
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```
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For full usage and more information, please refer to 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 https://github.com/ckiplab/ckip-transformers 。
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config.json
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{
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"architectures": [
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"BertForQuestionAnswering"
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],
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"attention_probs_dropout_prob": 0.1,
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"classifier_dropout": null,
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"directionality": "bidi",
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"gradient_checkpointing": false,
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 768,
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"initializer_range": 0.02,
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"intermediate_size": 3072,
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"pooler_fc_size": 768,
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"pooler_num_attention_heads": 12,
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"pooler_num_fc_layers": 3,
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"pooler_size_per_head": 128,
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"pooler_type": "first_token_transform",
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"position_embedding_type": "absolute",
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"tokenizer_class": "BertTokenizerFast",
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"torch_dtype": "float32",
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"transformers_version": "4.21.3",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 21128
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}
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:b97fee98374da90086cf6dc7e49ffa6c35057e5900bec2bbb7125ea81a807088
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size 406784817
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special_tokens_map.json
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{
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"cls_token": "[CLS]",
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"mask_token": "[MASK]",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"unk_token": "[UNK]"
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}
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tokenizer_config.json
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{
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"cls_token": "[CLS]",
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"do_lower_case": false,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"name_or_path": "bert-base-chinese",
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"special_tokens_map_file": null,
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.txt
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