---
library_name: transformers
license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLTInvoiceCzechV3
  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. -->

# LiLTInvoiceCzechV3

This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0643
- Precision: 0.8479
- Recall: 0.8464
- F1: 0.8471
- Accuracy: 0.9875

## 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: 40
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 12   | 2.4543          | 0.0       | 0.0    | 0.0    | 0.9376   |
| No log        | 2.0   | 24   | 0.3935          | 0.0       | 0.0    | 0.0    | 0.9473   |
| No log        | 3.0   | 36   | 0.3048          | 0.0       | 0.0    | 0.0    | 0.9473   |
| No log        | 4.0   | 48   | 0.2729          | 0.0       | 0.0    | 0.0    | 0.9473   |
| No log        | 5.0   | 60   | 0.2406          | 0.2267    | 0.1536 | 0.1831 | 0.9501   |
| No log        | 6.0   | 72   | 0.2033          | 0.2516    | 0.2713 | 0.2611 | 0.9477   |
| No log        | 7.0   | 84   | 0.1644          | 0.4503    | 0.4403 | 0.4452 | 0.9609   |
| No log        | 8.0   | 96   | 0.1413          | 0.5264    | 0.4420 | 0.4805 | 0.9643   |
| No log        | 9.0   | 108  | 0.1255          | 0.5960    | 0.5563 | 0.5755 | 0.9683   |
| No log        | 10.0  | 120  | 0.1264          | 0.5579    | 0.5751 | 0.5664 | 0.9669   |
| No log        | 11.0  | 132  | 0.1059          | 0.6198    | 0.6092 | 0.6145 | 0.9700   |
| No log        | 12.0  | 144  | 0.0942          | 0.6203    | 0.6689 | 0.6437 | 0.9733   |
| No log        | 13.0  | 156  | 0.0820          | 0.6545    | 0.6758 | 0.6650 | 0.9757   |
| No log        | 14.0  | 168  | 0.0759          | 0.6805    | 0.6980 | 0.6891 | 0.9790   |
| No log        | 15.0  | 180  | 0.0732          | 0.6328    | 0.6911 | 0.6607 | 0.9782   |
| No log        | 16.0  | 192  | 0.0766          | 0.6141    | 0.6980 | 0.6534 | 0.9774   |
| No log        | 17.0  | 204  | 0.0653          | 0.7132    | 0.7765 | 0.7435 | 0.9828   |
| No log        | 18.0  | 216  | 0.0639          | 0.7039    | 0.7747 | 0.7376 | 0.9824   |
| No log        | 19.0  | 228  | 0.0673          | 0.7537    | 0.7833 | 0.7682 | 0.9842   |
| No log        | 20.0  | 240  | 0.0687          | 0.7684    | 0.8208 | 0.7937 | 0.9839   |
| No log        | 21.0  | 252  | 0.0591          | 0.7689    | 0.8003 | 0.7843 | 0.9846   |
| No log        | 22.0  | 264  | 0.0602          | 0.8083    | 0.8345 | 0.8212 | 0.9862   |
| No log        | 23.0  | 276  | 0.0625          | 0.8162    | 0.8259 | 0.8210 | 0.9860   |
| No log        | 24.0  | 288  | 0.0685          | 0.7772    | 0.7918 | 0.7844 | 0.9844   |
| No log        | 25.0  | 300  | 0.0610          | 0.8194    | 0.8515 | 0.8351 | 0.9867   |
| No log        | 26.0  | 312  | 0.0642          | 0.7885    | 0.8652 | 0.8251 | 0.9852   |
| No log        | 27.0  | 324  | 0.0667          | 0.7961    | 0.8328 | 0.8140 | 0.9851   |
| No log        | 28.0  | 336  | 0.0650          | 0.8112    | 0.8430 | 0.8268 | 0.9858   |
| No log        | 29.0  | 348  | 0.0639          | 0.8249    | 0.8362 | 0.8305 | 0.9866   |
| No log        | 30.0  | 360  | 0.0643          | 0.8479    | 0.8464 | 0.8471 | 0.9875   |
| No log        | 31.0  | 372  | 0.0631          | 0.8345    | 0.8345 | 0.8345 | 0.9868   |
| No log        | 32.0  | 384  | 0.0630          | 0.7978    | 0.8618 | 0.8285 | 0.9858   |
| No log        | 33.0  | 396  | 0.0605          | 0.8331    | 0.8515 | 0.8422 | 0.9873   |
| No log        | 34.0  | 408  | 0.0638          | 0.8230    | 0.8413 | 0.8321 | 0.9866   |
| No log        | 35.0  | 420  | 0.0602          | 0.8367    | 0.8567 | 0.8465 | 0.9877   |
| No log        | 36.0  | 432  | 0.0603          | 0.8439    | 0.8396 | 0.8417 | 0.9876   |
| No log        | 37.0  | 444  | 0.0615          | 0.8339    | 0.8481 | 0.8409 | 0.9873   |
| No log        | 38.0  | 456  | 0.0604          | 0.8344    | 0.8515 | 0.8429 | 0.9874   |
| No log        | 39.0  | 468  | 0.0603          | 0.8401    | 0.8515 | 0.8458 | 0.9876   |
| No log        | 40.0  | 480  | 0.0602          | 0.8375    | 0.8532 | 0.8453 | 0.9876   |


### Framework versions

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