---
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