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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/table-transformer-detection |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: DertInvoiceCzech |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# DertInvoiceCzech |
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This model is a fine-tuned version of [microsoft/table-transformer-detection](https://huggingface.co/microsoft/table-transformer-detection) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 6.0877 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 40 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| No log | 1.0 | 38 | 8.9100 | |
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| No log | 2.0 | 76 | 8.6387 | |
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| No log | 3.0 | 114 | 8.2508 | |
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| No log | 4.0 | 152 | 7.7641 | |
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| No log | 5.0 | 190 | 7.2348 | |
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| No log | 6.0 | 228 | 7.0283 | |
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| No log | 7.0 | 266 | 6.8791 | |
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| No log | 8.0 | 304 | 6.7655 | |
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| No log | 9.0 | 342 | 6.6748 | |
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| No log | 10.0 | 380 | 6.6063 | |
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| No log | 11.0 | 418 | 6.5481 | |
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| No log | 12.0 | 456 | 6.4950 | |
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| No log | 13.0 | 494 | 6.4460 | |
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| 7.372 | 14.0 | 532 | 6.4030 | |
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| 7.372 | 15.0 | 570 | 6.3710 | |
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| 7.372 | 16.0 | 608 | 6.3404 | |
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| 7.372 | 17.0 | 646 | 6.3093 | |
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| 7.372 | 18.0 | 684 | 6.2916 | |
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| 7.372 | 19.0 | 722 | 6.2786 | |
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| 7.372 | 20.0 | 760 | 6.2660 | |
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| 7.372 | 21.0 | 798 | 6.2440 | |
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| 7.372 | 22.0 | 836 | 6.2129 | |
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| 7.372 | 23.0 | 874 | 6.2026 | |
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| 7.372 | 24.0 | 912 | 6.1897 | |
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| 7.372 | 25.0 | 950 | 6.1778 | |
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| 7.372 | 26.0 | 988 | 6.1668 | |
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| 6.3365 | 27.0 | 1026 | 6.1562 | |
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| 6.3365 | 28.0 | 1064 | 6.1451 | |
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| 6.3365 | 29.0 | 1102 | 6.1313 | |
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| 6.3365 | 30.0 | 1140 | 6.1220 | |
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| 6.3365 | 31.0 | 1178 | 6.1164 | |
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| 6.3365 | 32.0 | 1216 | 6.1105 | |
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| 6.3365 | 33.0 | 1254 | 6.1038 | |
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| 6.3365 | 34.0 | 1292 | 6.1002 | |
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| 6.3365 | 35.0 | 1330 | 6.0966 | |
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| 6.3365 | 36.0 | 1368 | 6.0935 | |
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| 6.3365 | 37.0 | 1406 | 6.0926 | |
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| 6.3365 | 38.0 | 1444 | 6.0905 | |
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| 6.3365 | 39.0 | 1482 | 6.0892 | |
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| 6.1779 | 40.0 | 1520 | 6.0877 | |
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### Framework versions |
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- Transformers 4.57.6 |
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- Pytorch 2.9.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.2 |
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