Update README.md

README.md

@@ -1,64 +1,65 @@
----
+---
-language: en
+language: en
-tags:
+tags:
-- tapex
+- tapex
-license: mit
+- table-question-answering
----
+license: mit
-
+---
-# TAPEX (base-sized model) 
+
-
+# 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).
+
-
+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.
-## Intended Uses
+
-
+## Intended Uses
-You can use the raw model for simulating neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table. However, the model is mostly meant to be fine-tuned on a supervised dataset. Currently TAPEX can be fine-tuned to tackle table question answering tasks and table fact verification tasks. See the [model hub](https://huggingface.co/models?search=tapex) to look for fine-tuned versions on a task that interests you.
+
-
+You can use the raw model for simulating neural SQL execution, i.e., employ TAPEX to execute a SQL query on a given table. However, the model is mostly meant to be fine-tuned on a supervised dataset. Currently TAPEX can be fine-tuned to tackle table question answering tasks and table fact verification tasks. See the [model hub](https://huggingface.co/models?search=tapex) to look for fine-tuned versions on a task that interests you.
-### 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-base")
+
-model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-base")
+tokenizer = TapexTokenizer.from_pretrained("microsoft/tapex-base")
-
+model = BartForConditionalGeneration.from_pretrained("microsoft/tapex-base")
-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 = "select year where city = beijing"
+# tapex accepts uncased input since it is pre-trained on the uncased corpus
-encoding = tokenizer(table=table, query=query, return_tensors="pt")
+query = "select year where city = beijing"
-
+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']
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
-```
+# ['2008']
-
+```
-### How to Fine-tuning
+
-
+### How to Fine-tuning
-Please find the fine-tuning script [here](https://github.com/SivilTaram/transformers/tree/add_tapex_bis/examples/research_projects/tapex).
+
-
+Please find the fine-tuning 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}
+}
 ```