Update README.md

README.md

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 ---
+language: en
+tags:
+- tapex
 license: mit
 ---
+
+# 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 [WikiTableQuestions](https://huggingface.co/datasets/wikitablequestions) dataset.
+
+## Intended Uses
+
+You can use the model for table question answering on *complex* questions. Some **solveable** questions are shown below (corresponding tables now shown):
+
+| 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 |
+| what were the first and last movies greenstreet acted in? | The Maltese Falcon, Malaya |
+| 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
+
+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-base-finetuned-wtq")
+model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-base-finetuned-wtq")
+
+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}
+}
+```