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--- |
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language: |
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- ja |
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- zh |
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- ko |
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license: cc-by-sa-4.0 |
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datasets: |
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- wikipedia |
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mask_token: "[MASK]" |
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widget: |
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- text: "早稲田大学で自然言語処理を[MASK]ぶ。" |
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- text: "李白是[MASK]朝人。" |
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- text: "불고기[MASK] 먹겠습니다." |
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--- |
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### Model description |
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- This model was trained on **ZH, JA, KO**'s Wikipedia (5 epochs). |
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### How to use |
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```python |
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from transformers import AutoTokenizer, AutoModelForMaskedLM |
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tokenizer = AutoTokenizer.from_pretrained("conan1024hao/cjkbert-small") |
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model = AutoModelForMaskedLM.from_pretrained("conan1024hao/cjkbert-small") |
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``` |
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- Before you fine-tune downstream tasks, you don't need any text segmentation. |
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- (Though you may obtain better results if you applied morphological analysis to the data before fine-tuning.) |
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### Morphological analysis tools |
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- ZH: For Chinese, we use [LTP](https://github.com/HIT-SCIR/ltp). |
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- JA: For Japanese, we use [Juman++](https://github.com/ku-nlp/jumanpp). |
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- KO: For Korean, we use [KoNLPy](https://github.com/konlpy/konlpy)(Kkma class). |
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### Tokenization |
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- We use character-based tokenization with **whole-word-masking** strategy. |
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### Model size |
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- vocab_size: 15015 |
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- num_hidden_layers: 4 |
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- hidden_size: 512 |
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- num_attention_heads: 8 |
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- param_num: 25M |
