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library_name: transformers
license: apache-2.0
base_model: TomasFAV/BERTInvoiceCzechV012
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: BERTInvoiceCzechV0123Test
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. -->
# BERTInvoiceCzechV0123Test
This model is a fine-tuned version of [TomasFAV/BERTInvoiceCzechV012](https://huggingface.co/TomasFAV/BERTInvoiceCzechV012) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0612
- Precision: 0.8944
- Recall: 0.9177
- F1: 0.9059
- Accuracy: 0.9856
## 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 | 0.1056 | 0.7803 | 0.84 | 0.8091 | 0.9716 |
| No log | 2.0 | 40 | 0.0831 | 0.8105 | 0.8901 | 0.8484 | 0.9764 |
| No log | 3.0 | 60 | 0.0704 | 0.8410 | 0.8994 | 0.8692 | 0.9804 |
| No log | 4.0 | 80 | 0.0675 | 0.8403 | 0.9095 | 0.8736 | 0.9808 |
| No log | 5.0 | 100 | 0.0632 | 0.8630 | 0.8932 | 0.8779 | 0.9821 |
| No log | 6.0 | 120 | 0.0706 | 0.8319 | 0.9111 | 0.8697 | 0.9800 |
| No log | 7.0 | 140 | 0.0611 | 0.8729 | 0.8932 | 0.8829 | 0.9834 |
| No log | 8.0 | 160 | 0.0608 | 0.8754 | 0.9056 | 0.8902 | 0.9835 |
| No log | 9.0 | 180 | 0.0595 | 0.8769 | 0.9243 | 0.9000 | 0.9848 |
| No log | 10.0 | 200 | 0.0606 | 0.8759 | 0.9153 | 0.8952 | 0.9842 |
| No log | 11.0 | 220 | 0.0610 | 0.8855 | 0.9192 | 0.9021 | 0.9850 |
| No log | 12.0 | 240 | 0.0632 | 0.8720 | 0.9258 | 0.8981 | 0.9844 |
| No log | 13.0 | 260 | 0.0608 | 0.8961 | 0.9115 | 0.9037 | 0.9853 |
| No log | 14.0 | 280 | 0.0610 | 0.8953 | 0.9165 | 0.9058 | 0.9855 |
| No log | 15.0 | 300 | 0.0615 | 0.8874 | 0.9181 | 0.9025 | 0.9853 |
| No log | 16.0 | 320 | 0.0627 | 0.8841 | 0.9216 | 0.9025 | 0.9851 |
| No log | 17.0 | 340 | 0.0625 | 0.8807 | 0.92 | 0.8999 | 0.9847 |
| No log | 18.0 | 360 | 0.0612 | 0.8944 | 0.9177 | 0.9059 | 0.9856 |
| No log | 19.0 | 380 | 0.0619 | 0.8893 | 0.92 | 0.9044 | 0.9854 |
| No log | 20.0 | 400 | 0.0618 | 0.8901 | 0.9212 | 0.9053 | 0.9856 |
### Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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