init
Browse files- README.md +28 -0
- config.json +30 -0
- pytorch_model.bin +3 -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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license:
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- apache-2.0
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---
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```python
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import jieba_fast
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from transformers import BertTokenizer
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from transformers import BigBirdModel
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class JiebaTokenizer(BertTokenizer):
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def __init__(
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self, pre_tokenizer=lambda x: jieba_fast.cut(x, HMM=False), *args, **kwargs
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):
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super().__init__(*args, **kwargs)
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self.pre_tokenizer = pre_tokenizer
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def _tokenize(self, text, *arg, **kwargs):
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split_tokens = []
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for text in self.pre_tokenizer(text):
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if text in self.vocab:
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split_tokens.append(text)
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else:
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split_tokens.extend(super()._tokenize(text))
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return split_tokens
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model = BigBirdModel.from_pretrained('Lowin/chinese-bigbird-base')
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tokenizer = JiebaTokenizer.from_pretrained('Lowin/chinese-bigbird-base')
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```
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https://github.com/LowinLi/chinese-bigbird
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config.json
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{
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"architectures": [
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"BigBirdForMaskedLM"
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],
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"attention_probs_dropout_prob": 0.1,
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"attention_type": "block_sparse",
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"block_size": 64,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"gradient_checkpointing": false,
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"hidden_act": "gelu_fast",
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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": 4096,
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"model_type": "big_bird",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"num_random_blocks": 3,
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"pad_token_id": 0,
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"rescale_embeddings": false,
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"sep_token_id": 66,
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"transformers_version": "4.6.1",
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"type_vocab_size": 2,
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"use_bias": true,
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"use_cache": true,
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"vocab_size": 40000
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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:ecfad0fa352402146a2d62a81c8b2bb13bdaee1b093099b835290e0a5700bd71
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size 480701374
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vocab.txt
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