README.md
| 1 | --- |
| 2 | license: mit |
| 3 | widget: |
| 4 | - src: https://documentation.tricentis.com/tosca/1420/en/content/tbox/images/table.png |
| 5 | example_title: Table |
| 6 | --- |
| 7 | |
| 8 | # Table Transformer (fine-tuned for Table Structure Recognition) |
| 9 | |
| 10 | Table Transformer (DETR) model trained on PubTables1M. It was introduced in the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Smock et al. and first released in [this repository](https://github.com/microsoft/table-transformer). |
| 11 | |
| 12 | Disclaimer: The team releasing Table Transformer did not write a model card for this model so this model card has been written by the Hugging Face team. |
| 13 | |
| 14 | ## Model description |
| 15 | |
| 16 | The Table Transformer is equivalent to [DETR](https://huggingface.co/docs/transformers/model_doc/detr), a Transformer-based object detection model. Note that the authors decided to use the "normalize before" setting of DETR, which means that layernorm is applied before self- and cross-attention. |
| 17 | |
| 18 | ## Usage |
| 19 | |
| 20 | You can use the raw model for detecting the structure (like rows, columns) in tables. See the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/table-transformer) for more info. |