Create README.md

README.md

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+---
+language: en
+tags:
+- tapex
+- table-question-answering
+license: apache-2.0
+datasets:
+- wtq
+inference: false
+---
+
+TAPEX-large model fine-tuned on WTQ. This model 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. Original repo can be found [here](https://github.com/microsoft/Table-Pretraining).
+
+To load it and run inference, you can do the following:
+
+```
+from transformers import BartTokenizer, BartForSequenceClassification
+import pandas as pd
+
+tokenizer = BartTokenizer.from_pretrained("nielsr/tapex-large-finetuned-tabfact")
+model = BartForSequenceClassification.from_pretrained("nielsr/tapex-large-finetuned-tabfact")
+
+# create table
+data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]}
+table = pd.DataFrame.from_dict(data)
+
+# turn into dict
+table_dict = {"header": list(table.columns), "rows": [list(row.values) for i,row in table.iterrows()]}
+
+# turn into format TAPEX expects
+# define the linearizer based on this code: https://github.com/microsoft/Table-Pretraining/blob/main/tapex/processor/table_linearize.py
+linearizer = IndexedRowTableLinearize()
+linear_table = linearizer.process_table(table_dict)
+
+# add sentence
+sentence = "George Clooney has 69 movies"
+joint_input = sentence + " " + linear_table
+
+# encode 
+encoding = tokenizer(joint_input, return_tensors="pt")
+
+# forward pass
+outputs = model(**encoding)
+
+# print prediction
+logits = outputs.logits
+print(logits.argmax(-1))
+```