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Niels Rogge
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README.md
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README.md
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---
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language: en
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tags:
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- tapex
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license: mit
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---
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# TAPEX (large-sized model)
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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).
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## Model description
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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.
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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.
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This model is the `tapex-base` model fine-tuned on the [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) dataset.
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## Intended Uses
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You can use the model for table question answering on *complex* questions. Some **solveable** questions are shown below (corresponding tables now shown):
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| Question | Answer |
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| according to the table, what is the last title that spicy horse produced? | Akaneiro: Demon Hunters |
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| what is the difference in runners-up from coleraine academical institution and royal school dungannon? | 20 |
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| what were the first and last movies greenstreet acted in? | The Maltese Falcon, Malaya |
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| in which olympic games did arasay thondike not finish in the top 20? | 2012 |
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| which broadcaster hosted 3 titles but they had only 1 episode? | Channel 4 |
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### How to Use
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Here is how to use this model in transformers:
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```python
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from transformers import TapexTokenizer, BartForConditionalGeneration
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import pandas as pd
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tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")
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model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-finetuned-wtq")
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data = {
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"year": [1896, 1900, 1904, 2004, 2008, 2012],
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"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
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}
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table = pd.DataFrame.from_dict(data)
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# tapex accepts uncased input since it is pre-trained on the uncased corpus
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query = "In which year did beijing host the Olympic Games?"
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encoding = tokenizer(table=table, query=query, return_tensors="pt")
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outputs = model.generate(**encoding)
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print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
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# [' 2008.0']
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```
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### How to Eval
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Please find the eval script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex).
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### BibTeX entry and citation info
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```bibtex
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@inproceedings{
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liu2022tapex,
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title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor},
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author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou},
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booktitle={International Conference on Learning Representations},
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year={2022},
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url={https://openreview.net/forum?id=O50443AsCP}
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}
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---
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language: en
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tags:
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- tapex
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- table-question-answering
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license: mit
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---
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# TAPEX (large-sized model)
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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).
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## Model description
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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.
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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.
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This model is the `tapex-base` model fine-tuned on the [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) dataset.
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## Intended Uses
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You can use the model for table question answering on *complex* questions. Some **solveable** questions are shown below (corresponding tables now shown):
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| Question | Answer |
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|:---: |:---:|
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| according to the table, what is the last title that spicy horse produced? | Akaneiro: Demon Hunters |
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| what is the difference in runners-up from coleraine academical institution and royal school dungannon? | 20 |
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| what were the first and last movies greenstreet acted in? | The Maltese Falcon, Malaya |
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| in which olympic games did arasay thondike not finish in the top 20? | 2012 |
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| which broadcaster hosted 3 titles but they had only 1 episode? | Channel 4 |
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### How to Use
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Here is how to use this model in transformers:
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```python
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from transformers import TapexTokenizer, BartForConditionalGeneration
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import pandas as pd
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tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")
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model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-finetuned-wtq")
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data = {
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"year": [1896, 1900, 1904, 2004, 2008, 2012],
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"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
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}
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table = pd.DataFrame.from_dict(data)
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# tapex accepts uncased input since it is pre-trained on the uncased corpus
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query = "In which year did beijing host the Olympic Games?"
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encoding = tokenizer(table=table, query=query, return_tensors="pt")
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outputs = model.generate(**encoding)
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print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
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# [' 2008.0']
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```
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### How to Eval
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Please find the eval script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex).
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### BibTeX entry and citation info
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```bibtex
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@inproceedings{
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liu2022tapex,
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title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor},
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author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou},
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booktitle={International Conference on Learning Representations},
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year={2022},
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url={https://openreview.net/forum?id=O50443AsCP}
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}
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```
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