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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_SkillExtraction |
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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: 0.9513964454 |
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- name: NER Recall |
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type: recall |
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value: 0.973283859 |
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- name: NER F Score |
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type: f_score |
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value: 0.9622157007 |
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--- |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `en_SkillExtraction` | |
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| **Version** | `0.0.0` | |
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| **spaCy** | `>=3.5.3,<3.6.0` | |
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| **Default Pipeline** | `tok2vec`, `ner` | |
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| **Components** | `tok2vec`, `ner` | |
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| **Vectors** | 514157 keys, 514157 unique vectors (300 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 (8 labels for 1 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`ner`** | `DESIGNATION`, `EDUCATION`, `EMAIL`, `LANGUAGE`, `NAME`, `PHONE`, `PLACE`, `SKILL` | |
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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` | 96.22 | |
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| `ENTS_P` | 95.14 | |
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| `ENTS_R` | 97.33 | |
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| `TOK2VEC_LOSS` | 15547.71 | |
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| `NER_LOSS` | 105573.97 | |
