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--- |
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tags: |
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- token-classification |
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datasets: |
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- djagatiya/ner-ontonotes-v5-eng-v4 |
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--- |
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# (NER) distilbert-base-uncased : conll2012_ontonotesv5-english-v4 |
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This **distilbert-base-uncased** NER model was finetuned on **conll2012_ontonotesv5-english-v4** dataset. <br> |
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Check out [NER-System Repository](https://github.com/djagatiya/NER-System) for more information. |
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## Evaluation |
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- Precision: 84.60 |
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- Recall: 86.47 |
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- F1-Score: 85.53 |
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> check out this [eval.log](eval.log) file for evaluation metrics and classification report. |
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``` |
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precision recall f1-score support |
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CARDINAL 0.84 0.86 0.85 935 |
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DATE 0.83 0.88 0.85 1602 |
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EVENT 0.57 0.57 0.57 63 |
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FAC 0.55 0.62 0.58 135 |
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GPE 0.95 0.92 0.94 2240 |
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LANGUAGE 0.82 0.64 0.72 22 |
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LAW 0.50 0.50 0.50 40 |
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LOC 0.55 0.72 0.62 179 |
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MONEY 0.87 0.89 0.88 314 |
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NORP 0.85 0.89 0.87 841 |
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ORDINAL 0.81 0.88 0.84 195 |
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ORG 0.81 0.83 0.82 1795 |
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PERCENT 0.87 0.89 0.88 349 |
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PERSON 0.93 0.93 0.93 1988 |
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PRODUCT 0.55 0.55 0.55 76 |
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QUANTITY 0.71 0.80 0.75 105 |
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TIME 0.59 0.66 0.62 212 |
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WORK_OF_ART 0.42 0.44 0.43 166 |
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micro avg 0.85 0.86 0.86 11257 |
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macro avg 0.72 0.75 0.73 11257 |
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weighted avg 0.85 0.86 0.86 11257 |
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``` |
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