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library_name: transformers
license: apache-2.0
base_model: TomasFAV/BERTInvoiceCzechV01
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERTInvoiceCzechV013
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. -->
# BERTInvoiceCzechV013
This model is a fine-tuned version of [TomasFAV/BERTInvoiceCzechV01](https://huggingface.co/TomasFAV/BERTInvoiceCzechV01) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0656
- Precision: 0.8707
- Recall: 0.8816
- F1: 0.8761
- Accuracy: 0.9835
## 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: 1e-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 | 20 | 0.1306 | 0.7273 | 0.7581 | 0.7423 | 0.9645 |
| No log | 2.0 | 40 | 0.1137 | 0.7402 | 0.8054 | 0.7714 | 0.9678 |
| No log | 3.0 | 60 | 0.0909 | 0.7828 | 0.8369 | 0.8089 | 0.9740 |
| No log | 4.0 | 80 | 0.0766 | 0.8162 | 0.8711 | 0.8428 | 0.9785 |
| No log | 5.0 | 100 | 0.0761 | 0.8141 | 0.8858 | 0.8484 | 0.9784 |
| No log | 6.0 | 120 | 0.0785 | 0.7960 | 0.8850 | 0.8382 | 0.9769 |
| No log | 7.0 | 140 | 0.0662 | 0.8520 | 0.8722 | 0.8620 | 0.9817 |
| No log | 8.0 | 160 | 0.0722 | 0.8378 | 0.8765 | 0.8567 | 0.9810 |
| No log | 9.0 | 180 | 0.0712 | 0.8250 | 0.8827 | 0.8529 | 0.9801 |
| No log | 10.0 | 200 | 0.0670 | 0.8544 | 0.8819 | 0.8680 | 0.9823 |
| No log | 11.0 | 220 | 0.0663 | 0.8518 | 0.8909 | 0.8709 | 0.9828 |
| No log | 12.0 | 240 | 0.0680 | 0.8341 | 0.8843 | 0.8584 | 0.9809 |
| No log | 13.0 | 260 | 0.0656 | 0.8704 | 0.8816 | 0.8759 | 0.9835 |
| No log | 14.0 | 280 | 0.0655 | 0.8566 | 0.8885 | 0.8723 | 0.9827 |
| No log | 15.0 | 300 | 0.0659 | 0.8466 | 0.8831 | 0.8645 | 0.9822 |
| No log | 16.0 | 320 | 0.0662 | 0.8483 | 0.8862 | 0.8669 | 0.9821 |
| No log | 17.0 | 340 | 0.0689 | 0.8402 | 0.8885 | 0.8637 | 0.9815 |
| No log | 18.0 | 360 | 0.0662 | 0.8566 | 0.8905 | 0.8732 | 0.9829 |
| No log | 19.0 | 380 | 0.0670 | 0.8519 | 0.8893 | 0.8702 | 0.9824 |
| No log | 20.0 | 400 | 0.0663 | 0.8541 | 0.8885 | 0.8710 | 0.9825 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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