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--- |
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tags: |
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- spacy |
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- token-classification |
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language: |
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- en |
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model-index: |
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- name: en_spacy_ner_finetuned_news_article |
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results: |
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- task: |
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name: NER |
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type: token-classification |
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metrics: |
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- name: NER Precision |
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type: precision |
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value: 0.9865652034 |
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- name: NER Recall |
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type: recall |
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value: 0.9823041611 |
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- name: NER F Score |
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type: f_score |
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value: 0.9844300714 |
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--- |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `en_spacy_ner_finetuned_news_article` | |
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| **Version** | `0.0.0` | |
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| **spaCy** | `>=3.5.2,<3.6.0` | |
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| **Default Pipeline** | `transformer`, `ner` | |
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| **Components** | `transformer`, `ner` | |
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| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) | |
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| **Sources** | n/a | |
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| **License** | n/a | |
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| **Author** | [n/a]() | |
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### Label Scheme |
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<details> |
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<summary>View label scheme (15 labels for 1 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`ner`** | `EVENT`, `FAC`, `GPE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` | |
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</details> |
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### Accuracy |
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| Type | Score | |
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| --- | --- | |
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| `ENTS_F` | 98.44 | |
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| `ENTS_P` | 98.66 | |
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| `ENTS_R` | 98.23 | |
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| `TRANSFORMER_LOSS` | 18923.26 | |
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| `NER_LOSS` | 21258.62 | |
