📊 Showing 5 results | 📏 Metric: Accuracy
| Rank | Model | Paper | Accuracy | Date | Code |
|---|---|---|---|---|---|
| 1 | TAPEX-Large (weak supervision) | TAPEX: Table Pre-training via Learning a Neural SQL Executor | 89.50 | 2021-07-16 | 📦 microsoft/Table-Pretraining 📦 sohanpatnaik106/cabinet_qa 📦 MindCode-4/code-5 📦 pwc-1/Paper-9 |
| 2 | CABINET | CABINET: Content Relevance based Noise Reduction for Table Question Answering | 89.50 | 2024-02-02 | 📦 sohanpatnaik106/cabinet_qa |
| 3 | ReasTAP-Large (weak supervision) | ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples | 89.20 | 2022-10-22 | 📦 yale-lily/reastap |
| 4 | NL2SQL-BERT | Content Enhanced BERT-based Text-to-SQL Generation | 89.00 | 2019-10-16 | 📦 guotong1988/NL2SQL-RULE 📦 guotong1988/NL2SQL-BERT 📦 shivam017arora/Conversational-BI 📦 yangyucheng000/University 📦 realsonalkumar/Mish-Mash-Hackathon |
| 5 | TAPAS-Large (weak supervision) 📚 | TAPAS: Weakly Supervised Table Parsing via Pre-training | 83.60 | 2020-04-05 | 📦 huggingface/transformers 📦 google-research/tapas 📦 kamalkraj/TAPAS-TF2 |