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---
tags:
- spacy
- token-classification
- text-classification
language:
- en
model-index:
- name: en_tako_query_analyzer
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.7170177384
- name: NER Recall
type: recall
value: 0.7172165234
- name: NER F Score
type: f_score
value: 0.7171171171
---
| Feature | Description |
| --- | --- |
| **Name** | `en_tako_query_analyzer` |
| **Version** | `0.0.1` |
| **spaCy** | `>=3.7.5,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `ner`, `textcat` |
| **Components** | `tok2vec`, `ner`, `textcat` |
| **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (33 labels for 2 components)</summary>
| Component | Labels |
| --- | --- |
| **`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` |
| **`textcat`** | `Business and Finance`, `Arts, Culture, and Entertainment`, `Crime`, `Sports`, `Politics`, `Science and Technology`, `Health and Wellness`, `Lifestyle and Fashion` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `ENTS_F` | 71.71 |
| `ENTS_P` | 71.70 |
| `ENTS_R` | 71.72 |
| `CATS_SCORE` | 70.53 |
| `CATS_MICRO_P` | 85.89 |
| `CATS_MICRO_R` | 85.89 |
| `CATS_MICRO_F` | 85.89 |
| `CATS_MACRO_P` | 74.89 |
| `CATS_MACRO_R` | 67.56 |
| `CATS_MACRO_F` | 70.53 |
| `CATS_MACRO_AUC` | 93.04 |
| `TOK2VEC_LOSS` | 61786.52 |
| `NER_LOSS` | 46852.50 |
| `TEXTCAT_LOSS` | 1.09 | |