Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?
Paper
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2010.12725
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Published
Model Card for T5-3B/SynQL-KaggleDBQA-Train-Run-01
- Developed by: Semiotic Labs
- Model type: [Text to SQL]
- License: [Apache-2.0]
- Finetuned from model: google-t5/t5-3b
- Dataset used for finetuning: semiotic/SynQL-KaggleDBQA-Train
Model Context
Example metadata can be found below, context represents the prompt that is presented to the model. Database schemas follow the encoding method proposed by Shaw et al (2020).
"query": "SELECT count(*) FROM singer",
"question": "How many singers do we have?",
"context": "How many singers do we have? | concert_singer | stadium : stadium_id, location, name, capacity, highest, lowest, average | singer : singer_id, name, country, song_name, song_release_year, age, is_male | concert : concert_id, concert_name, theme, stadium_id, year | singer_in_concert : concert_id, singer_id",
"db_id": "concert_singer",
Model Results
Evaluation set: KaggleDBQA/test
Evaluation metrics: [Execution Accuracy]
| Model | Data | Run | Execution Accuracy |
|---|---|---|---|
| T5-3B | semiotic/SynQL-KaggleDBQA | 00 | 0.3514 |
| T5-3B | semiotic/SynQL-KaggleDBQA | 01 | 0.3514 |
| T5-3B | semiotic/SynQL-KaggleDBQA | 02 | 0.3514 |
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Model tree for semiotic/T5-3B-SynQL-KaggleDBQA-Train-Run-01
Base model
google-t5/t5-3b