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Mizuiro-sakura
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luke-japanese-base-finetuned-ner

Token Classification
Transformers
PyTorch
Safetensors
Japanese
luke
ner
固有表現抽出
named entity recognition
named-entity-recognition
Model card Files
xet
Community
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Update README.md

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@@ -25,20 +25,19 @@ 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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-
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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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  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