
# ReasTAP

ReasTAP is a table reasoning model proposed in EMNLP 2022 paper [ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples](https://arxiv.org/pdf/2210.12374.pdf). The original Github repository is [https://github.com/Yale-LILY/ReasTAP](https://github.com/Yale-LILY/ReasTAP).

## Description

`Yale-LILY/reastap-large` (based on BART architecture) is initialized with `facebook/bart-large` and continuously pretrained on synthetic Table QA data to learn table structure understanding and table reasoning skills.

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import pandas as pd

tokenizer = AutoTokenizer.from_pretrained("Yale-LILY/reastap-large")
model = AutoModelForSeq2SeqLM.from_pretrained("Yale-LILY/reastap-large")

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

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']
```

## Reference

```bibtex
@inproceedings{zhao-etal-2022-reastap,
    title = "{R}eas{TAP}: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples",
    author = "Zhao, Yilun  and
      Nan, Linyong  and
      Qi, Zhenting  and
      Zhang, Rui  and
      Radev, Dragomir",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.emnlp-main.615",
    pages = "9006--9018",
    abstract = "Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills. Current models with table-specific architectures and pre-training methods perform well on understanding table structures, but they still struggle with tasks that require various table reasoning skills. In this work, we develop ReasTAP to show that high-level table reasoning skills can be injected into models during pre-training without a complex table-specific architecture design. We define 7 table reasoning skills, such as numerical operation, temporal comparison, and conjunction. Each reasoning skill is associated with one example generator, which synthesizes questions over semi-structured tables according to the sampled templates. We model the table pre-training task as a sequence generation task and pre-train ReasTAP to generate precise answers of the synthetic examples. ReasTAP is evaluated on four benchmarks covering three downstream tasks including 1) WikiSQL-Weak and WikiTQ for Table Question Answering, 2) TabFact for Table Fact Verification, and 3) LogicNLG for Faithful Table-to-Text Generation. Experimental results demonstrate that ReasTAP achieves new state-of-the-art results on all of them and delivers a significant improvement under low-resource setting. Our code is publicly available at https://github.com/Yale-LILY/ReasTAP.",
}
```
{"language": "en", "tags": ["table-question-answering", "table-fact-checking", "table-to-text"], "datasets": ["wikitablequestions", "wikisql", "tabfact", "logicnlg"]}
transformers pytorch bart text2text-generation table-question-answering table-fact-checking table-to-text en dataset:wikitablequestions dataset:wikisql dataset:tabfact dataset:logicnlg arxiv:2210.12374 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #bart #text2text-generation #table-question-answering #table-fact-checking #table-to-text #en #dataset-wikitablequestions #dataset-wikisql #dataset-tabfact #dataset-logicnlg #arxiv-2210.12374 #autotrain_compatible #endpoints_compatible #has_space #region-us 


# ReasTAP

ReasTAP is a table reasoning model proposed in EMNLP 2022 paper ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples. The original Github repository is URL

## Description

'Yale-LILY/reastap-large' (based on BART architecture) is initialized with 'facebook/bart-large' and continuously pretrained on synthetic Table QA data to learn table structure understanding and table reasoning skills.

## Usage



## Reference


# ReasTAP

ReasTAP is a table reasoning model proposed in EMNLP 2022 paper ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples. The original Github repository is URL ## Description

'Yale-LILY/reastap-large' (based on BART architecture) is initialized with 'facebook/bart-large' and continuously pretrained on synthetic Table QA data to learn table structure understanding and table reasoning skills. ## Usage ## Reference
TAGS
#transformers #pytorch #bart #text2text-generation #table-question-answering #table-fact-checking #table-to-text #en #dataset-wikitablequestions #dataset-wikisql #dataset-tabfact #dataset-logicnlg #arxiv-2210.12374 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # ReasTAP

ReasTAP is a table reasoning model proposed in EMNLP 2022 paper ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples. The original Github repository is URL ## Description

'Yale-LILY/reastap-large' (based on BART architecture) is initialized with 'facebook/bart-large' and continuously pretrained on synthetic Table QA data to learn table structure understanding and table reasoning skills. ## Usage ## Reference
TAGS
#transformers #pytorch #bart #text2text-generation #table-question-answering #table-fact-checking #table-to-text #en #dataset-wikitablequestions #dataset-wikisql #dataset-tabfact #dataset-logicnlg #arxiv-2210.12374 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# ReasTAP

ReasTAP is a table reasoning model proposed in EMNLP 2022 paper ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples. The original Github repository is URL## Description

'Yale-LILY/reastap-large' (based on BART architecture) is initialized with 'facebook/bart-large' and continuously pretrained on synthetic Table QA data to learn table structure understanding and table reasoning skills.## Usage## Reference