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README.md
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## Pretrained model
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This model is trained on Japanese Wikipedia data and relased under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) .
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## Training data
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## Model description
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`SentenceTransformer` model from the [sentence-transformers](https://github.com/UKPLab/sentence-transformers)
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The model detail is as below.
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```py
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>>> sentence_transformers
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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## Pretrained model
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This model utilizes a Japanese BERT model [colorfulscoop/bert-base-ja](https://huggingface.co/colorfulscoop/bert-base-ja) v1.0 released under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) as a pretrained model.
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## Training data
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## Model description
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This model utilizes `SentenceTransformer` model from the [sentence-transformers](https://github.com/UKPLab/sentence-transformers) .
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The model detail is as below.
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```py
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>>> from sentence_transformers import SentenceTransformer
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>>> SentenceTransformer("colorfulscoop/sbert-base-ja")
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SentenceTransformer(
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
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