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Add Table Transformer #18920
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Add Table Transformer #18920
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8848ead
Add conversion script, normalize_before to encoder
a3f6764
Make encoder work
82f2682
Make forward pass work
acb907d
Clean up
5970e75
Add cs_loss_coefficient attribute
5f2c9d9
Add id2label
aaf0507
Convert table structure recognition as well
a6099bf
Add docs, integration test
83eb689
Fix integration test
d34f7e6
Add model to test_fetcher
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<!--Copyright 2022 The HuggingFace Team. All rights reserved. | ||||||
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with | ||||||
the License. You may obtain a copy of the License at | ||||||
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http://www.apache.org/licenses/LICENSE-2.0 | ||||||
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on | ||||||
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the | ||||||
specific language governing permissions and limitations under the License. | ||||||
--> | ||||||
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# Table Transformer | ||||||
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## Overview | ||||||
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The Table Transformer model was proposed in [PubTables-1M: Towards comprehensive table extraction from unstructured documents](https://arxiv.org/abs/2110.00061) by | ||||||
Brandon Smock, Rohith Pesala, Robin Abraham. The authors introduce a new dataset, PubTables-1M, to benchmark progress in table extraction from unstructured documents, | ||||||
as well as table structure recognition and functional analysis. The authors train 2 [DETR](detr) models, one for table detection and one for table structure recognition, dubbed Table Transformers. | ||||||
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The abstract from the paper is the following: | ||||||
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*Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents. | ||||||
However, one of the greatest challenges remains the creation of datasets with complete, unambiguous ground truth at scale. To address this, we develop a new, more | ||||||
comprehensive dataset for table extraction, called PubTables-1M. PubTables-1M contains nearly one million tables from scientific articles, supports multiple input | ||||||
modalities, and contains detailed header and location information for table structures, making it useful for a wide variety of modeling approaches. It also addresses a significant | ||||||
source of ground truth inconsistency observed in prior datasets called oversegmentation, using a novel canonicalization procedure. We demonstrate that these improvements lead to a | ||||||
significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition. Further, we show that transformer-based | ||||||
object detection models trained on PubTables-1M produce excellent results for all three tasks of detection, structure recognition, and functional analysis without the need for any | ||||||
special customization for these tasks.* | ||||||
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Tips: | ||||||
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- The authors released 2 models, one for table detection in documents, one for table structure recognition (the task of recognizing the individual rows, columns etc. in a table). | ||||||
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Suggested change
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- Both models can be directly plugged into [`DetrForObjectDetection`]. We therefore refer to DETR's [documentation](detr) regarding usage. | ||||||
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This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be | ||||||
found [here](https://github.com/microsoft/table-transformer). |
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swin, | ||
swinv2, | ||
t5, | ||
table_transformer, | ||
tapas, | ||
tapex, | ||
trajectory_transformer, | ||
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