---
library_name: transformers
license: mit
base_model: TomasFAV/LiLTInvoiceCzechV0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLTInvoiceCzechV03
  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. -->

# LiLTInvoiceCzechV03

This model is a fine-tuned version of [TomasFAV/LiLTInvoiceCzechV0](https://huggingface.co/TomasFAV/LiLTInvoiceCzechV0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0473
- Precision: 0.8752
- Recall: 0.8976
- F1: 0.8863
- Accuracy: 0.9899

## 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   | 0.0974          | 0.7395    | 0.7218 | 0.7306 | 0.9775   |
| No log        | 2.0   | 24   | 0.0776          | 0.6830    | 0.7867 | 0.7312 | 0.9764   |
| No log        | 3.0   | 36   | 0.0662          | 0.7488    | 0.7884 | 0.7681 | 0.9809   |
| No log        | 4.0   | 48   | 0.0576          | 0.7648    | 0.8823 | 0.8193 | 0.9836   |
| No log        | 5.0   | 60   | 0.0498          | 0.825     | 0.8447 | 0.8347 | 0.9862   |
| No log        | 6.0   | 72   | 0.0495          | 0.8102    | 0.8379 | 0.8238 | 0.9861   |
| No log        | 7.0   | 84   | 0.0500          | 0.8078    | 0.9181 | 0.8594 | 0.9871   |
| No log        | 8.0   | 96   | 0.0454          | 0.8629    | 0.8805 | 0.8716 | 0.9890   |
| No log        | 9.0   | 108  | 0.0444          | 0.8479    | 0.8942 | 0.8704 | 0.9892   |
| No log        | 10.0  | 120  | 0.0467          | 0.8344    | 0.9113 | 0.8711 | 0.9887   |
| No log        | 11.0  | 132  | 0.0457          | 0.8509    | 0.8959 | 0.8728 | 0.9892   |
| No log        | 12.0  | 144  | 0.0450          | 0.8553    | 0.8976 | 0.8759 | 0.9893   |
| No log        | 13.0  | 156  | 0.0463          | 0.8719    | 0.8942 | 0.8829 | 0.9897   |
| No log        | 14.0  | 168  | 0.0474          | 0.8555    | 0.8993 | 0.8769 | 0.9894   |
| No log        | 15.0  | 180  | 0.0468          | 0.8765    | 0.8959 | 0.8861 | 0.9897   |
| No log        | 16.0  | 192  | 0.0473          | 0.8752    | 0.8976 | 0.8863 | 0.9899   |
| No log        | 17.0  | 204  | 0.0467          | 0.8731    | 0.8925 | 0.8827 | 0.9896   |
| No log        | 18.0  | 216  | 0.0473          | 0.8709    | 0.8976 | 0.8840 | 0.9897   |
| No log        | 19.0  | 228  | 0.0474          | 0.8746    | 0.8925 | 0.8834 | 0.9897   |
| No log        | 20.0  | 240  | 0.0473          | 0.8763    | 0.8942 | 0.8851 | 0.9897   |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2