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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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widget: |
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- text: "On September 1st George won 1 dollar while watching Game of Thrones." |
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--- |
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# (NER) roberta-base : conll2012_ontonotesv5-english-v4 |
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This `roberta-base` NER model was finetuned on `conll2012_ontonotesv5` version `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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## Dataset |
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- conll2012_ontonotesv5 |
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- Language : English |
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- Version : v4 |
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| Dataset | Examples | |
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| --- | --- | |
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| Training | 75187 | |
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| Testing | 9479 | |
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## Evaluation |
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- Precision: 88.88 |
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- Recall: 90.69 |
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- F1-Score: 89.78 |
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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 0.85 0.85 935 |
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DATE 0.85 0.90 0.87 1602 |
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EVENT 0.67 0.76 0.71 63 |
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FAC 0.74 0.72 0.73 135 |
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GPE 0.97 0.96 0.96 2240 |
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LANGUAGE 0.83 0.68 0.75 22 |
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LAW 0.66 0.62 0.64 40 |
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LOC 0.74 0.80 0.77 179 |
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MONEY 0.85 0.89 0.87 314 |
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NORP 0.93 0.96 0.95 841 |
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ORDINAL 0.81 0.89 0.85 195 |
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ORG 0.90 0.91 0.91 1795 |
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PERCENT 0.90 0.92 0.91 349 |
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PERSON 0.95 0.95 0.95 1988 |
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PRODUCT 0.74 0.83 0.78 76 |
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QUANTITY 0.76 0.80 0.78 105 |
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TIME 0.62 0.67 0.65 212 |
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WORK_OF_ART 0.58 0.69 0.63 166 |
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micro avg 0.89 0.91 0.90 11257 |
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macro avg 0.80 0.82 0.81 11257 |
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weighted avg 0.89 0.91 0.90 11257 |
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``` |
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## Usage |
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``` |
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from transformers import pipeline |
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ner_pipeline = pipeline( |
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'token-classification', |
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model=r'djagatiya/ner-roberta-base-ontonotesv5-englishv4', |
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aggregation_strategy='simple' |
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) |
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``` |
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TEST 1 |
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``` |
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ner_pipeline("India is a beautiful country") |
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``` |
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``` |
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# Output |
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[{'entity_group': 'GPE', |
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'score': 0.99186057, |
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'word': ' India', |
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'start': 0, |
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'end': 5}] |
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``` |
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TEST 2 |
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``` |
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ner_pipeline("On September 1st George won 1 dollar while watching Game of Thrones.") |
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``` |
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``` |
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# Output |
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[{'entity_group': 'DATE', |
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'score': 0.99720246, |
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'word': ' September 1st', |
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'start': 3, |
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'end': 16}, |
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{'entity_group': 'PERSON', |
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'score': 0.99071586, |
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'word': ' George', |
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'start': 17, |
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'end': 23}, |
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{'entity_group': 'MONEY', |
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'score': 0.9872978, |
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'word': ' 1 dollar', |
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'start': 28, |
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'end': 36}, |
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{'entity_group': 'WORK_OF_ART', |
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'score': 0.9946732, |
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'word': ' Game of Thrones', |
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'start': 52, |
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'end': 67}] |
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``` |
