| --- |
| library_name: transformers |
| license: mit |
| base_model: SCUT-DLVCLab/lilt-roberta-en-base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: LiLTInvoiceCzechV3 |
| 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. --> |
|
|
| # LiLTInvoiceCzechV3 |
|
|
| This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0643 |
| - Precision: 0.8479 |
| - Recall: 0.8464 |
| - F1: 0.8471 |
| - Accuracy: 0.9875 |
|
|
| ## 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: 3e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 2 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 0.1 |
| - num_epochs: 40 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | No log | 1.0 | 12 | 2.4543 | 0.0 | 0.0 | 0.0 | 0.9376 | |
| | No log | 2.0 | 24 | 0.3935 | 0.0 | 0.0 | 0.0 | 0.9473 | |
| | No log | 3.0 | 36 | 0.3048 | 0.0 | 0.0 | 0.0 | 0.9473 | |
| | No log | 4.0 | 48 | 0.2729 | 0.0 | 0.0 | 0.0 | 0.9473 | |
| | No log | 5.0 | 60 | 0.2406 | 0.2267 | 0.1536 | 0.1831 | 0.9501 | |
| | No log | 6.0 | 72 | 0.2033 | 0.2516 | 0.2713 | 0.2611 | 0.9477 | |
| | No log | 7.0 | 84 | 0.1644 | 0.4503 | 0.4403 | 0.4452 | 0.9609 | |
| | No log | 8.0 | 96 | 0.1413 | 0.5264 | 0.4420 | 0.4805 | 0.9643 | |
| | No log | 9.0 | 108 | 0.1255 | 0.5960 | 0.5563 | 0.5755 | 0.9683 | |
| | No log | 10.0 | 120 | 0.1264 | 0.5579 | 0.5751 | 0.5664 | 0.9669 | |
| | No log | 11.0 | 132 | 0.1059 | 0.6198 | 0.6092 | 0.6145 | 0.9700 | |
| | No log | 12.0 | 144 | 0.0942 | 0.6203 | 0.6689 | 0.6437 | 0.9733 | |
| | No log | 13.0 | 156 | 0.0820 | 0.6545 | 0.6758 | 0.6650 | 0.9757 | |
| | No log | 14.0 | 168 | 0.0759 | 0.6805 | 0.6980 | 0.6891 | 0.9790 | |
| | No log | 15.0 | 180 | 0.0732 | 0.6328 | 0.6911 | 0.6607 | 0.9782 | |
| | No log | 16.0 | 192 | 0.0766 | 0.6141 | 0.6980 | 0.6534 | 0.9774 | |
| | No log | 17.0 | 204 | 0.0653 | 0.7132 | 0.7765 | 0.7435 | 0.9828 | |
| | No log | 18.0 | 216 | 0.0639 | 0.7039 | 0.7747 | 0.7376 | 0.9824 | |
| | No log | 19.0 | 228 | 0.0673 | 0.7537 | 0.7833 | 0.7682 | 0.9842 | |
| | No log | 20.0 | 240 | 0.0687 | 0.7684 | 0.8208 | 0.7937 | 0.9839 | |
| | No log | 21.0 | 252 | 0.0591 | 0.7689 | 0.8003 | 0.7843 | 0.9846 | |
| | No log | 22.0 | 264 | 0.0602 | 0.8083 | 0.8345 | 0.8212 | 0.9862 | |
| | No log | 23.0 | 276 | 0.0625 | 0.8162 | 0.8259 | 0.8210 | 0.9860 | |
| | No log | 24.0 | 288 | 0.0685 | 0.7772 | 0.7918 | 0.7844 | 0.9844 | |
| | No log | 25.0 | 300 | 0.0610 | 0.8194 | 0.8515 | 0.8351 | 0.9867 | |
| | No log | 26.0 | 312 | 0.0642 | 0.7885 | 0.8652 | 0.8251 | 0.9852 | |
| | No log | 27.0 | 324 | 0.0667 | 0.7961 | 0.8328 | 0.8140 | 0.9851 | |
| | No log | 28.0 | 336 | 0.0650 | 0.8112 | 0.8430 | 0.8268 | 0.9858 | |
| | No log | 29.0 | 348 | 0.0639 | 0.8249 | 0.8362 | 0.8305 | 0.9866 | |
| | No log | 30.0 | 360 | 0.0643 | 0.8479 | 0.8464 | 0.8471 | 0.9875 | |
| | No log | 31.0 | 372 | 0.0631 | 0.8345 | 0.8345 | 0.8345 | 0.9868 | |
| | No log | 32.0 | 384 | 0.0630 | 0.7978 | 0.8618 | 0.8285 | 0.9858 | |
| | No log | 33.0 | 396 | 0.0605 | 0.8331 | 0.8515 | 0.8422 | 0.9873 | |
| | No log | 34.0 | 408 | 0.0638 | 0.8230 | 0.8413 | 0.8321 | 0.9866 | |
| | No log | 35.0 | 420 | 0.0602 | 0.8367 | 0.8567 | 0.8465 | 0.9877 | |
| | No log | 36.0 | 432 | 0.0603 | 0.8439 | 0.8396 | 0.8417 | 0.9876 | |
| | No log | 37.0 | 444 | 0.0615 | 0.8339 | 0.8481 | 0.8409 | 0.9873 | |
| | No log | 38.0 | 456 | 0.0604 | 0.8344 | 0.8515 | 0.8429 | 0.9874 | |
| | No log | 39.0 | 468 | 0.0603 | 0.8401 | 0.8515 | 0.8458 | 0.9876 | |
| | No log | 40.0 | 480 | 0.0602 | 0.8375 | 0.8532 | 0.8453 | 0.9876 | |
|
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|
| ### Framework versions |
|
|
| - Transformers 5.0.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
|
|
