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---
license: mit
base_model: kavg/LiLT-SER-DE
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-DE-SIN
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xfun
type: xfun
config: xfun.sin
split: validation
args: xfun.sin
metrics:
- name: Precision
type: precision
value: 0.7494033412887828
- name: Recall
type: recall
value: 0.7733990147783252
- name: F1
type: f1
value: 0.7612121212121213
- name: Accuracy
type: accuracy
value: 0.8555197082339739
---
<!-- 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. -->
# LiLT-SER-DE-SIN
This model is a fine-tuned version of [kavg/LiLT-SER-DE](https://huggingface.co/kavg/LiLT-SER-DE) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2864
- Precision: 0.7494
- Recall: 0.7734
- F1: 0.7612
- Accuracy: 0.8555
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0053 | 21.74 | 500 | 0.9346 | 0.6644 | 0.7315 | 0.6964 | 0.8395 |
| 0.0026 | 43.48 | 1000 | 0.9918 | 0.7216 | 0.7278 | 0.7247 | 0.8587 |
| 0.0016 | 65.22 | 1500 | 0.9133 | 0.7098 | 0.7291 | 0.7193 | 0.8603 |
| 0.0001 | 86.96 | 2000 | 1.0041 | 0.7580 | 0.7328 | 0.7451 | 0.8536 |
| 0.0002 | 108.7 | 2500 | 1.1795 | 0.7312 | 0.7438 | 0.7375 | 0.8461 |
| 0.0001 | 130.43 | 3000 | 1.1417 | 0.7169 | 0.7328 | 0.7247 | 0.8642 |
| 0.0 | 152.17 | 3500 | 1.1740 | 0.7336 | 0.7562 | 0.7447 | 0.8453 |
| 0.0 | 173.91 | 4000 | 1.0535 | 0.7350 | 0.7414 | 0.7382 | 0.8635 |
| 0.0 | 195.65 | 4500 | 1.2769 | 0.7443 | 0.7241 | 0.7341 | 0.8502 |
| 0.0 | 217.39 | 5000 | 1.2235 | 0.7148 | 0.7438 | 0.7290 | 0.8363 |
| 0.0004 | 239.13 | 5500 | 1.2500 | 0.7376 | 0.7685 | 0.7527 | 0.8569 |
| 0.0 | 260.87 | 6000 | 1.2864 | 0.7494 | 0.7734 | 0.7612 | 0.8555 |
| 0.0 | 282.61 | 6500 | 1.1766 | 0.7649 | 0.7451 | 0.7548 | 0.8589 |
| 0.0 | 304.35 | 7000 | 1.3060 | 0.7231 | 0.7365 | 0.7297 | 0.8479 |
| 0.0 | 326.09 | 7500 | 1.1780 | 0.7093 | 0.7451 | 0.7267 | 0.8534 |
| 0.0 | 347.83 | 8000 | 1.2882 | 0.7337 | 0.75 | 0.7418 | 0.8614 |
| 0.0 | 369.57 | 8500 | 1.2833 | 0.7436 | 0.75 | 0.7468 | 0.8644 |
| 0.0 | 391.3 | 9000 | 1.4372 | 0.7372 | 0.7463 | 0.7417 | 0.8522 |
| 0.0 | 413.04 | 9500 | 1.4223 | 0.7382 | 0.75 | 0.7440 | 0.8513 |
| 0.0 | 434.78 | 10000 | 1.3686 | 0.7454 | 0.75 | 0.7477 | 0.8554 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1
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