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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: apache-2.0 |
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inference: false |
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--- |
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TAPEX-large model pre-trained-only model. 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). |
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To load it and run inference, you can do the following: |
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``` |
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from transformers import BartTokenizer, BartForConditionalGeneration |
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import pandas as pd |
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tokenizer = BartTokenizer.from_pretrained("nielsr/tapex-large") |
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model = BartForConditionalGeneration.from_pretrained("nielsr/tapex-large") |
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# create table |
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data = {'Actors': ["Brad Pitt", "Leonardo Di Caprio", "George Clooney"], 'Number of movies': ["87", "53", "69"]} |
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table = pd.DataFrame.from_dict(data) |
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# turn into dict |
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table_dict = {"header": list(table.columns), "rows": [list(row.values) for i,row in table.iterrows()]} |
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# turn into format TAPEX expects |
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# define the linearizer based on this code: https://github.com/microsoft/Table-Pretraining/blob/main/tapex/processor/table_linearize.py |
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linearizer = IndexedRowTableLinearize() |
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linear_table = linearizer.process_table(table_dict) |
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# add query |
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query = "SELECT ... FROM ..." |
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joint_input = query + " " + linear_table |
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# encode |
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encoding = tokenizer(joint_input, return_tensors="pt") |
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# forward pass |
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outputs = model.generate(**encoding) |
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# decode |
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tokenizer.batch_decode(outputs, skip_special_tokens=True) |
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``` |
