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--- |
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language: ko |
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tags: |
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- korean |
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mask_token: "[MASK]" |
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widget: |
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- text: 대한민국의 수도는 [MASK] 입니다. |
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--- |
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# KoBigBird |
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<img src="https://user-images.githubusercontent.com/28896432/140442206-e34b02d5-e279-47e5-9c2a-db1278b1c14d.png" width="200"/> |
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Pretrained BigBird Model for Korean (**kobigbird-bert-base**) |
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## About |
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BigBird, is a sparse-attention based transformer which extends Transformer based models, such as BERT to much longer sequences. |
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BigBird relies on **block sparse attention** instead of normal attention (i.e. BERT's attention) and can handle sequences up to a length of 4096 at a much lower compute cost compared to BERT. |
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Model is warm started from Korean BERT’s checkpoint. |
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## How to use |
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*NOTE:* Use `BertTokenizer` instead of BigBirdTokenizer. (`AutoTokenizer` will load `BertTokenizer`) |
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```python |
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from transformers import AutoModel, AutoTokenizer |
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# by default its in `block_sparse` mode with num_random_blocks=3, block_size=64 |
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model = AutoModel.from_pretrained("monologg/kobigbird-bert-base") |
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# you can change `attention_type` to full attention like this: |
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model = AutoModel.from_pretrained("monologg/kobigbird-bert-base", attention_type="original_full") |
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# you can change `block_size` & `num_random_blocks` like this: |
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model = AutoModel.from_pretrained("monologg/kobigbird-bert-base", block_size=16, num_random_blocks=2) |
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tokenizer = AutoTokenizer.from_pretrained("monologg/kobigbird-bert-base") |
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text = "한국어 BigBird 모델을 공개합니다!" |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
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