| tags: | |
| - spacy | |
| - token-classification | |
| - named-entity-recognition | |
| - text-to-sql | |
| - schema-linking | |
| language: | |
| - en | |
| license: mit | |
| # Text-to-SQL spaCy NER Model | |
| This is a custom spaCy NER model for Text-to-SQL query understanding. | |
| It detects: | |
| - AGGREGATION | |
| - METRIC | |
| - FILTER_LOCATION | |
| - FILTER_YEAR | |
| - FILTER_DATE | |
| - FILTER_CATEGORY | |
| - FILTER_VALUE | |
| - GROUP_BY | |
| - ORDER_BY | |
| - LIMIT | |
| - COMPARISON | |
| Example: | |
| Input: | |
| `In 2015, how many complaints about Billing disputes were sent by clients in Portland?` | |
| Expected entities: | |
| - 2015 -> FILTER_YEAR | |
| - how many -> AGGREGATION | |
| - complaints -> METRIC | |
| - Billing disputes -> FILTER_CATEGORY | |
| - Portland -> FILTER_LOCATION | |
| Current baseline: | |
| - Overall NER F1: 0.77 | |