Create README.md

README.md

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+---
+library_name: transformers
+---
+
+Microsoft Table Transformer Table Structure Recognition trained on Pubtables and Fintabnet
+
+If you do not have the deepdoctection Profile of the model, please add:
+
+
+```python
+import deepdoctection as dd
+
+dd.ModelCatalog.register("deepdoctection/tatr_tab_struct_v2/pytorch_model.bin", dd.ModelProfile(
+    name="deepdoctection/tatr_tab_struct_v2/pytorch_model.bin",
+    description="Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper "
+                "Aligning benchmark datasets for table structure recognition by Smock et "
+                "al. This model is devoted to table structure recognition and assumes to receive a slightly cropped"
+                "table as input. It will predict rows, column and spanning cells. Use a padding of around 5 pixels",
+    size=[115511753],
+    tp_model=False,
+    config="deepdoctection/tatr_tab_struct_v2/config.json",
+    preprocessor_config="deepdoctection/tatr_tab_struct_v2/preprocessor_config.json",
+    hf_repo_id="deepdoctection/tatr_tab_struct_v2",
+    hf_model_name="pytorch_model.bin",
+    hf_config_file=["config.json", "preprocessor_config.json"],
+    categories={
+        "1": dd.LayoutType.table,
+        "2": dd.LayoutType.column,
+        "3": dd.LayoutType.row,
+        "4": dd.CellType.column_header,
+        "5": dd.CellType.projected_row_header,
+        "6": dd.CellType.spanning,
+    },
+    dl_library="PT",
+    model_wrapper="HFDetrDerivedDetector",
+))
+```
+
+When running the model within the deepdoctection analyzer, adjust the segmentation parameters in order to get better predictions.
+
+```python
+    import deepdoctection as dd
+
+    analyzer = dd.get_dd_analyzer(reset_config_file=True, config_overwrite=["PT.ITEM.WEIGHTS=microsoft/tatr_v1/pytorch_model.bin",
+                                                                            "PT.ITEM.FILTER=['table']",
+                                                                            "PT.ITEM.PAD.TOP=5",
+                                                                            "PT.ITEM.PAD.RIGHT=5",
+                                                                            "PT.ITEM.PAD.BOTTOM=5",
+                                                                            "PT.ITEM.PAD.LEFT=5",
+                                                                            "SEGMENTATION.THRESHOLD_ROWS=0.9",
+                                                                            "SEGMENTATION.THRESHOLD_COLS=0.9",
+                                                                            "SEGMENTATION.REMOVE_IOU_THRESHOLD_ROWS=0.3",
+                                                                            "SEGMENTATION.REMOVE_IOU_THRESHOLD_COLS=0.3",
+                                                                            "WORD_MATCHING.MAX_PARENT_ONLY=True"])
+```
+