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library_name: transformers
license: mit
base_model: TomasFAV/LiLTInvoiceCzechV01
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLTInvoiceCzechV013
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. -->
# LiLTInvoiceCzechV013
This model is a fine-tuned version of [TomasFAV/LiLTInvoiceCzechV01](https://huggingface.co/TomasFAV/LiLTInvoiceCzechV01) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0467
- Precision: 0.8824
- Recall: 0.8959
- F1: 0.8891
- Accuracy: 0.9907
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 12 | 0.0851 | 0.7551 | 0.7577 | 0.7564 | 0.9790 |
| No log | 2.0 | 24 | 0.0668 | 0.7643 | 0.7526 | 0.7584 | 0.9801 |
| No log | 3.0 | 36 | 0.0664 | 0.8217 | 0.7867 | 0.8038 | 0.9833 |
| No log | 4.0 | 48 | 0.0564 | 0.7759 | 0.8567 | 0.8143 | 0.9842 |
| No log | 5.0 | 60 | 0.0501 | 0.8368 | 0.8140 | 0.8253 | 0.9866 |
| No log | 6.0 | 72 | 0.0444 | 0.8571 | 0.8601 | 0.8586 | 0.9886 |
| No log | 7.0 | 84 | 0.0435 | 0.8503 | 0.9113 | 0.8797 | 0.9896 |
| No log | 8.0 | 96 | 0.0444 | 0.8610 | 0.8771 | 0.8690 | 0.9893 |
| No log | 9.0 | 108 | 0.0431 | 0.8756 | 0.8891 | 0.8823 | 0.9904 |
| No log | 10.0 | 120 | 0.0441 | 0.8669 | 0.9113 | 0.8885 | 0.9906 |
| No log | 11.0 | 132 | 0.0450 | 0.8501 | 0.9096 | 0.8788 | 0.9897 |
| No log | 12.0 | 144 | 0.0436 | 0.8588 | 0.9027 | 0.8802 | 0.9902 |
| No log | 13.0 | 156 | 0.0434 | 0.8733 | 0.8942 | 0.8836 | 0.9905 |
| No log | 14.0 | 168 | 0.0456 | 0.8564 | 0.8959 | 0.8757 | 0.9900 |
| No log | 15.0 | 180 | 0.0451 | 0.8725 | 0.8993 | 0.8857 | 0.9907 |
| No log | 16.0 | 192 | 0.0444 | 0.8842 | 0.8857 | 0.8849 | 0.9908 |
| No log | 17.0 | 204 | 0.0451 | 0.8807 | 0.8942 | 0.8874 | 0.9908 |
| No log | 18.0 | 216 | 0.0466 | 0.87 | 0.8908 | 0.8803 | 0.9904 |
| No log | 19.0 | 228 | 0.0468 | 0.8807 | 0.8942 | 0.8874 | 0.9906 |
| No log | 20.0 | 240 | 0.0467 | 0.8824 | 0.8959 | 0.8891 | 0.9907 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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