Update README.md
This commit is contained in:
Niels Rogge
huggingface-web
parent
03c7da25f9
commit
59fa639a30
149
README.md
149
README.md
|
|
@ -1,75 +1,76 @@
|
||||||
---
|
---
|
||||||
language: en
|
language: en
|
||||||
tags:
|
tags:
|
||||||
- tapex
|
- tapex
|
||||||
license: mit
|
- table-question-answering
|
||||||
---
|
license: mit
|
||||||
|
---
|
||||||
# TAPEX (large-sized model)
|
|
||||||
|
# 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).
|
|
||||||
|
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
|
|
||||||
|
## 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 (**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.
|
|
||||||
|
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 [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) dataset.
|
|
||||||
|
This model is the `tapex-base` model fine-tuned on the [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) dataset.
|
||||||
## Intended Uses
|
|
||||||
|
## Intended Uses
|
||||||
You can use the model for table question answering on *complex* questions. Some **solveable** questions are shown below (corresponding tables now shown):
|
|
||||||
|
You can use the model for table question answering on *complex* questions. Some **solveable** questions are shown below (corresponding tables now shown):
|
||||||
| Question | Answer |
|
|
||||||
|:---: |:---:|
|
| Question | Answer |
|
||||||
| according to the table, what is the last title that spicy horse produced? | Akaneiro: Demon Hunters |
|
|:---: |:---:|
|
||||||
| what is the difference in runners-up from coleraine academical institution and royal school dungannon? | 20 |
|
| according to the table, what is the last title that spicy horse produced? | Akaneiro: Demon Hunters |
|
||||||
| what were the first and last movies greenstreet acted in? | The Maltese Falcon, Malaya |
|
| what is the difference in runners-up from coleraine academical institution and royal school dungannon? | 20 |
|
||||||
| in which olympic games did arasay thondike not finish in the top 20? | 2012 |
|
| what were the first and last movies greenstreet acted in? | The Maltese Falcon, Malaya |
|
||||||
| which broadcaster hosted 3 titles but they had only 1 episode? | Channel 4 |
|
| in which olympic games did arasay thondike not finish in the top 20? | 2012 |
|
||||||
|
| which broadcaster hosted 3 titles but they had only 1 episode? | Channel 4 |
|
||||||
|
|
||||||
### How to Use
|
|
||||||
|
### How to Use
|
||||||
Here is how to use this model in transformers:
|
|
||||||
|
Here is how to use this model in transformers:
|
||||||
```python
|
|
||||||
from transformers import TapexTokenizer, BartForConditionalGeneration
|
```python
|
||||||
import pandas as pd
|
from transformers import TapexTokenizer, BartForConditionalGeneration
|
||||||
|
import pandas as pd
|
||||||
tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")
|
|
||||||
model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-finetuned-wtq")
|
tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-large-finetuned-wtq")
|
||||||
|
model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-large-finetuned-wtq")
|
||||||
data = {
|
|
||||||
"year": [1896, 1900, 1904, 2004, 2008, 2012],
|
data = {
|
||||||
"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
|
"year": [1896, 1900, 1904, 2004, 2008, 2012],
|
||||||
}
|
"city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
|
||||||
table = pd.DataFrame.from_dict(data)
|
}
|
||||||
|
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?"
|
# tapex accepts uncased input since it is pre-trained on the uncased corpus
|
||||||
encoding = tokenizer(table=table, query=query, return_tensors="pt")
|
query = "In which year did beijing host the Olympic Games?"
|
||||||
|
encoding = tokenizer(table=table, query=query, return_tensors="pt")
|
||||||
outputs = model.generate(**encoding)
|
|
||||||
|
outputs = model.generate(**encoding)
|
||||||
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
|
|
||||||
# [' 2008.0']
|
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
|
||||||
```
|
# [' 2008.0']
|
||||||
|
```
|
||||||
### How to Eval
|
|
||||||
|
### How to Eval
|
||||||
Please find the eval script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex).
|
|
||||||
|
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 entry and citation info
|
||||||
```bibtex
|
|
||||||
@inproceedings{
|
```bibtex
|
||||||
liu2022tapex,
|
@inproceedings{
|
||||||
title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor},
|
liu2022tapex,
|
||||||
author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou},
|
title={{TAPEX}: Table Pre-training via Learning a Neural {SQL} Executor},
|
||||||
booktitle={International Conference on Learning Representations},
|
author={Qian Liu and Bei Chen and Jiaqi Guo and Morteza Ziyadi and Zeqi Lin and Weizhu Chen and Jian-Guang Lou},
|
||||||
year={2022},
|
booktitle={International Conference on Learning Representations},
|
||||||
url={https://openreview.net/forum?id=O50443AsCP}
|
year={2022},
|
||||||
}
|
url={https://openreview.net/forum?id=O50443AsCP}
|
||||||
|
}
|
||||||
```
|
```
|
||||||
Loading…
Reference in New Issue