
# TAPEX (large-sized model) 

TAPEX was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found [here](https://github.com/microsoft/Table-Pretraining).

## Model description

TAPEX (**Ta**ble **P**re-training via **Ex**ecution) 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](https://huggingface.co/datasets/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:

```python
from transformers import TapexTokenizer, BartForConditionalGeneration
import pandas as pd

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

data = {
    "year": [1896, 1900, 1904, 2004, 2008, 2012],
    "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
}
table = pd.DataFrame.from_dict(data)

# tapex accepts uncased input since it is pre-trained on the uncased corpus
query = "In which year did beijing host the Olympic Games?"
encoding = tokenizer(table=table, query=query, return_tensors="pt")

outputs = model.generate(**encoding)

print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
# [' 2008.0']
```

### How to Eval

Please find the eval script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex).

### BibTeX entry and citation info

```bibtex
@inproceedings{
    liu2022tapex,
    title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor},
    author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou},
    booktitle={International Conference on Learning Representations},
    year={2022},
    url={https://openreview.net/forum?id=O50443AsCP}
}
```
{"language": "en", "license": "mit", "tags": ["tapex", "table-question-answering"], "datasets": ["wikisql"]}
transformers pytorch safetensors bart text2text-generation tapex table-question-answering en dataset:wikisql arxiv:2107.07653 license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #dataset-wikisql #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 

TAPEX (large-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):



### How to Use


Here is how to use this model in transformers:


### How to Eval


Please find the eval script here.


### BibTeX entry and citation info



### How to Use


Here is how to use this model in transformers: ### How to Eval


Please find the eval script here. ### BibTeX entry and citation info
TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #dataset-wikisql #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### How to Use


Here is how to use this model in transformers: ### How to Eval


Please find the eval script here. ### BibTeX entry and citation info
passage: TAGS
#transformers #pytorch #safetensors #bart #text2text-generation #tapex #table-question-answering #en #dataset-wikisql #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
### How to Use


Here is how to use this model in transformers:### How to Eval


Please find the eval script here.### BibTeX entry and citation info