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--- |
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license: mit |
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language: ja |
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tags: |
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- luke |
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- pytorch |
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- transformers |
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- ner |
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- 固有表現抽出 |
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- named entity recognition |
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- named-entity-recognition |
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--- |
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# このモデルはluke-japanese-baseをファインチューニングして、固有表現抽出（NER）に用いれるようにしたものです。 |
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このモデルはluke-japanese-baseを |
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Wikipediaを用いた日本語の固有表現抽出データセット(ストックマーク社、https://github.com/stockmarkteam/ner-wikipedia-dataset )を用いてファインチューニングしたものです。 |
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固有表現抽出（NER）タスクに用いることができます。 |
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# This model is fine-tuned model for Named-Entity-Recognition(NER) which is based on luke-japanese-base |
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This model is fine-tuned by using Wikipedia dataset. |
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You could use this model for NER tasks. |
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# モデルの精度 accuracy of model |
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|| precision |recall | f1-score | support| |
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|---|----|----|----|----| |
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|その他の組織名 | 0.76 | 0.77 | 0.77 | 238| |
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|イベント名 |0.83 |0.90 | 0.87 |215| |
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|人名 |0.88 |0.91 | 0.90 | 546| |
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|地名 | 0.84 | 0.83 |0.83 | 440| |
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|政治的組織名 | 0.80 |0.84 | 0.82 | 263| |
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|施設名 | 0.78 | 0.83 | 0.80 | 241| |
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|法人名 | 0.88 | 0.90 | 0.89 | 487| |
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|製品名 | 0.74 | 0.80 |0.77 | 252| |
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|micro avg |0.83 |0.86 | 0.84 | 2682| |
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|macro avg | 0.81 | 0.85 | 0.83 | 2682| |
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|weighted avg |0.83 | 0.86 | 0.84 | 2682| |
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# How to use 使い方 |
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sentencepieceとtransformersをインストールして (pip install sentencepiece , pip install transformers) |
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以下のコードを実行することで、NERタスクを解かせることができます。 |
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please execute this code. |
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```python |
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from transformers import MLukeTokenizer,pipeline, LukeForTokenClassification |
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tokenizer = MLukeTokenizer.from_pretrained('Mizuiro-sakura/luke-japanese-base-finetuned-ner') |
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model=LukeForTokenClassification.from_pretrained('Mizuiro-sakura/luke-japanese-base-finetuned-ner') # 学習済みモデルの読み込み |
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text=('昨日は東京で買い物をした') |
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ner=pipeline('ner', model=model, tokenizer=tokenizer) |
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result=ner(text) |
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print(result) |
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``` |
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# what is Luke? Lukeとは？[1] |
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LUKE (Language Understanding with Knowledge-based Embeddings) is a new pre-trained contextualized representation of words and entities based on transformer. LUKE treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. LUKE adopts an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the transformer, and considers the types of tokens (words or entities) when computing attention scores. |
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LUKE achieves state-of-the-art results on five popular NLP benchmarks including SQuAD v1.1 (extractive question answering), CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), TACRED (relation classification), and Open Entity (entity typing). luke-japaneseは、単語とエンティティの知識拡張型訓練済み Transformer モデルLUKEの日本語版です。LUKE は単語とエンティティを独立したトークンとして扱い、これらの文脈を考慮した表現を出力します。 |
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# Acknowledgments 謝辞 |
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Lukeの開発者である山田先生とStudio ousiaさんには感謝いたします。 I would like to thank Mr.Yamada @ikuyamada and Studio ousia @StudioOusia. |
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# Citation |
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[1]@inproceedings{yamada2020luke, title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention}, author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto}, booktitle={EMNLP}, year={2020} } |
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