tapex-base-finetuned-wikisql EU AI Act Compliance Profile

TAPEX (base-sized model)

TAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here.

Model description

TAPEX (Table Pre-training via Execution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with table reasoning skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries.

TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder.

This model is the tapex-base model fine-tuned on the WikiSQL dataset.

Intended Uses

You can use the model for table question answering on relatively simple questions. Some solveable questions are shown below (corresponding tables now shown):

| Question | Answer | |:---: |:---:| | tell me what the notes are for south australia | no slogan on current series | | what position does the player who played for butler cc (ks) play? | guard-forward | | how many schools did player number 3 play at? | 1.0 | | how many winning drivers in the kraco twin 125 (r2) race were there? | 1.0 | | for the episode(s) aired in the u.s. on 4 april 2008, what were the names? | "bust a move" part one, "bust a move" part two |

How to Use

Here is how to use this model in transformers:

from transformers import TapexTokenizer, BartForConditionalGeneration
import pandas as pd

tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-base-finetuned-wikisql")
model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-base-finetuned-wikisql")

data = {
    "year"

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