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---
library_name: transformers
license: mit
base_model: sumitD/table-transformer-structure-recognition-v1.1-all-finetuned
tags:
- generated_from_trainer
model-index:
- name: table-transformer-structure-recognition-v1.1-all-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# table-transformer-structure-recognition-v1.1-all-finetuned
This model is a fine-tuned version of [sumitD/table-transformer-structure-recognition-v1.1-all-finetuned](https://huggingface.co/sumitD/table-transformer-structure-recognition-v1.1-all-finetuned) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1419
- Map: 0.9219
- Map 50: 0.966
- Map 75: 0.9496
- Map Small: -1.0
- Map Medium: 0.8782
- Map Large: 0.921
- Mar 1: 0.5537
- Mar 10: 0.9407
- Mar 100: 0.9694
- Mar Small: -1.0
- Mar Medium: 0.9079
- Mar Large: 0.9693
- Map Table: 0.9882
- Mar 100 Table: 0.9964
- Map Table column: 0.9732
- Mar 100 Table column: 0.9892
- Map Table column header: 0.9543
- Mar 100 Table column header: 0.9847
- Map Table projected row header: 0.8673
- Mar 100 Table projected row header: 0.964
- Map Table row: 0.9584
- Mar 100 Table row: 0.9838
- Map Table spanning cell: 0.7903
- Mar 100 Table spanning cell: 0.8983
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Table | Mar 100 Table | Map Table column | Mar 100 Table column | Map Table column header | Mar 100 Table column header | Map Table projected row header | Mar 100 Table projected row header | Map Table row | Mar 100 Table row | Map Table spanning cell | Mar 100 Table spanning cell |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:---------:|:-------------:|:----------------:|:--------------------:|:-----------------------:|:---------------------------:|:------------------------------:|:----------------------------------:|:-------------:|:-----------------:|:-----------------------:|:---------------------------:|
| 0.2338 | 1.0 | 23715 | 0.1991 | 0.8756 | 0.9505 | 0.9307 | -1.0 | 0.7912 | 0.8748 | 0.5395 | 0.9175 | 0.9471 | -1.0 | 0.8518 | 0.947 | 0.9844 | 0.9935 | 0.9582 | 0.981 | 0.9111 | 0.9647 | 0.7701 | 0.9364 | 0.9147 | 0.9577 | 0.7149 | 0.8496 |
| 0.2048 | 2.0 | 47430 | 0.1915 | 0.8827 | 0.9567 | 0.9384 | -1.0 | 0.8103 | 0.8823 | 0.54 | 0.9197 | 0.9498 | -1.0 | 0.8538 | 0.9499 | 0.9855 | 0.9944 | 0.9564 | 0.9819 | 0.9047 | 0.9527 | 0.7905 | 0.9437 | 0.9222 | 0.9651 | 0.7371 | 0.8607 |
| 0.1841 | 3.0 | 71145 | 0.1605 | 0.9087 | 0.9636 | 0.9467 | -1.0 | 0.8373 | 0.9077 | 0.548 | 0.933 | 0.9616 | -1.0 | 0.8868 | 0.9613 | 0.9836 | 0.9935 | 0.9703 | 0.9888 | 0.94 | 0.9771 | 0.8468 | 0.9545 | 0.9466 | 0.9781 | 0.765 | 0.8778 |
| 0.1914 | 4.0 | 94860 | 0.1496 | 0.9181 | 0.9652 | 0.9496 | -1.0 | 0.8741 | 0.917 | 0.552 | 0.9387 | 0.9678 | -1.0 | 0.9024 | 0.9676 | 0.9886 | 0.9968 | 0.9724 | 0.9886 | 0.9508 | 0.9824 | 0.8561 | 0.9628 | 0.9574 | 0.9829 | 0.7836 | 0.8934 |
| 0.1739 | 5.0 | 118575 | 0.1419 | 0.9219 | 0.966 | 0.9496 | -1.0 | 0.8782 | 0.921 | 0.5537 | 0.9407 | 0.9694 | -1.0 | 0.9079 | 0.9693 | 0.9882 | 0.9964 | 0.9732 | 0.9892 | 0.9543 | 0.9847 | 0.8673 | 0.964 | 0.9584 | 0.9838 | 0.7903 | 0.8983 |
### Framework versions
- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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