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--- |
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tags: |
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- spacy |
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- token-classification |
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- text-classification |
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language: |
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- en |
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model-index: |
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- name: en_tako_query_analyzer |
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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: 0.7170177384 |
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- name: NER Recall |
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type: recall |
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value: 0.7172165234 |
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- name: NER F Score |
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type: f_score |
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value: 0.7171171171 |
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--- |
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| Feature | Description | |
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| --- | --- | |
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| **Name** | `en_tako_query_analyzer` | |
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| **Version** | `0.0.1` | |
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| **spaCy** | `>=3.7.5,<3.8.0` | |
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| **Default Pipeline** | `tok2vec`, `ner`, `textcat` | |
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| **Components** | `tok2vec`, `ner`, `textcat` | |
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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 (33 labels for 2 components)</summary> |
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| Component | Labels | |
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| --- | --- | |
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| **`ner`** | `CARDINAL`, `CUSTOM_ATTRIBUTE`, `CUSTOM_SEMANTIC_FUNCTION`, `CUSTOM_SPORTS_CONFERENCE`, `CUSTOM_SPORTS_LEAGUE`, `CUSTOM_SPORTS_ROLE`, `CUSTOM_STOCK_TICKER`, `CUSTOM_TEAM`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` | |
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| **`textcat`** | `Business and Finance`, `Arts, Culture, and Entertainment`, `Crime`, `Sports`, `Politics`, `Science and Technology`, `Health and Wellness`, `Lifestyle and Fashion` | |
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|
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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` | 71.71 | |
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| `ENTS_P` | 71.70 | |
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| `ENTS_R` | 71.72 | |
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| `CATS_SCORE` | 70.53 | |
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| `CATS_MICRO_P` | 85.89 | |
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| `CATS_MICRO_R` | 85.89 | |
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| `CATS_MICRO_F` | 85.89 | |
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| `CATS_MACRO_P` | 74.89 | |
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| `CATS_MACRO_R` | 67.56 | |
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| `CATS_MACRO_F` | 70.53 | |
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| `CATS_MACRO_AUC` | 93.04 | |
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| `TOK2VEC_LOSS` | 61786.52 | |
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| `NER_LOSS` | 46852.50 | |
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| `TEXTCAT_LOSS` | 1.09 | |
