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--- |
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library_name: transformers |
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--- |
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Microsoft Table Transformer Table Structure Recognition trained on Pubtables and Fintabnet |
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If you do not have the deepdoctection Profile of the model, please add: |
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```python |
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import deepdoctection as dd |
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dd.ModelCatalog.register("deepdoctection/tatr_tab_struct_v2/pytorch_model.bin", dd.ModelProfile( |
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name="deepdoctection/tatr_tab_struct_v2/pytorch_model.bin", |
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description="Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper " |
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"Aligning benchmark datasets for table structure recognition by Smock et " |
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"al. This model is devoted to table structure recognition and assumes to receive a slightly cropped" |
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"table as input. It will predict rows, column and spanning cells. Use a padding of around 5 pixels", |
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size=[115511753], |
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tp_model=False, |
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config="deepdoctection/tatr_tab_struct_v2/config.json", |
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preprocessor_config="deepdoctection/tatr_tab_struct_v2/preprocessor_config.json", |
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hf_repo_id="deepdoctection/tatr_tab_struct_v2", |
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hf_model_name="pytorch_model.bin", |
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hf_config_file=["config.json", "preprocessor_config.json"], |
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categories={ |
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"1": dd.LayoutType.table, |
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"2": dd.LayoutType.column, |
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"3": dd.LayoutType.row, |
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"4": dd.CellType.column_header, |
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"5": dd.CellType.projected_row_header, |
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"6": dd.CellType.spanning, |
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}, |
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dl_library="PT", |
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model_wrapper="HFDetrDerivedDetector", |
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)) |
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``` |
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When running the model within the deepdoctection analyzer, adjust the segmentation parameters in order to get better predictions. |
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```python |
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import deepdoctection as dd |
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analyzer = dd.get_dd_analyzer(reset_config_file=True, config_overwrite=["PT.ITEM.WEIGHTS=deepdoctection/tatr_tab_struct_v2/pytorch_model.bin", |
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"PT.ITEM.FILTER=['table']", |
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"PT.ITEM.PAD.TOP=5", |
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"PT.ITEM.PAD.RIGHT=5", |
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"PT.ITEM.PAD.BOTTOM=5", |
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"PT.ITEM.PAD.LEFT=5", |
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"SEGMENTATION.THRESHOLD_ROWS=0.9", |
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"SEGMENTATION.THRESHOLD_COLS=0.9", |
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"SEGMENTATION.REMOVE_IOU_THRESHOLD_ROWS=0.3", |
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"SEGMENTATION.REMOVE_IOU_THRESHOLD_COLS=0.3", |
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"WORD_MATCHING.MAX_PARENT_ONLY=True"]) |
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``` |
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