
# TAPEX (base-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 [Tabfact](https://huggingface.co/datasets/tab_fact) dataset.

## Intended Uses

You can use the model for table fact verficiation.

### How to Use

Here is how to use this model in transformers:

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

tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-base-finetuned-tabfact")
model = BartForSequenceClassification.from_pretrained("microsoft/tapex-base-finetuned-tabfact")

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 = "beijing hosts the olympic games in 2012"
encoding = tokenizer(table=table, query=query, return_tensors="pt")

outputs = model(**encoding)
output_id = int(outputs.logits[0].argmax(dim=0))
print(model.config.id2label[output_id])
# Refused
```

### 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"], "datasets": ["tab_fact"]}
transformers pytorch bart text-classification tapex en dataset:tab_fact arxiv:2107.07653 license:mit autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bart #text-classification #tapex #en #dataset-tab_fact #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 


# 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 Tabfact dataset.

## Intended Uses

You can use the model for table fact verficiation.

### 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


# 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 Tabfact dataset. ## Intended Uses

You can use the model for table fact verficiation. ### 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 #bart #text-classification #tapex #en #dataset-tab_fact #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # 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 Tabfact dataset. ## Intended Uses

You can use the model for table fact verficiation. ### 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 #bart #text-classification #tapex #en #dataset-tab_fact #arxiv-2107.07653 #license-mit #autotrain_compatible #endpoints_compatible #has_space #region-us 
# 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 Tabfact dataset.## Intended Uses

You can use the model for table fact verficiation.### 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