LiLT-SER-DE / README.md
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---
license: mit
base_model: nielsr/lilt-xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xfun
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: LiLT-SER-DE
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xfun
type: xfun
config: xfun.de
split: validation
args: xfun.de
metrics:
- name: Precision
type: precision
value: 0.7268232385661311
- name: Recall
type: recall
value: 0.7853962600178095
- name: F1
type: f1
value: 0.7549753905414082
- name: Accuracy
type: accuracy
value: 0.7816669203063968
---
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# LiLT-SER-DE
This model is a fine-tuned version of [nielsr/lilt-xlm-roberta-base](https://huggingface.co/nielsr/lilt-xlm-roberta-base) on the xfun dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1833
- Precision: 0.7268
- Recall: 0.7854
- F1: 0.7550
- Accuracy: 0.7817
## 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 | Accuracy | F1 | Validation Loss | Precision | Recall |
|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:|
| 0.2776 | 10.42 | 500 | 0.7098 | 0.6660 | 1.4820 | 0.6266 | 0.7106 |
| 0.0386 | 20.83 | 1000 | 0.7884 | 0.7195 | 1.3364 | 0.6868 | 0.7556 |
| 0.002 | 31.25 | 1500 | 0.8102 | 0.7350 | 1.4865 | 0.7000 | 0.7738 |
| 0.0043 | 41.67 | 2000 | 0.7965 | 0.7167 | 1.5473 | 0.7050 | 0.7289 |
| 0.0009 | 52.08 | 2500 | 0.7797 | 0.7357 | 1.8408 | 0.7371 | 0.7342 |
| 0.0003 | 62.5 | 3000 | 0.7841 | 0.7279 | 1.9387 | 0.7021 | 0.7556 |
| 0.0044 | 72.92 | 3500 | 0.7900 | 0.7402 | 1.7595 | 0.7292 | 0.7516 |
| 0.0005 | 83.33 | 4000 | 0.7677 | 0.7370 | 2.0830 | 0.7084 | 0.7680 |
| 0.0001 | 93.75 | 4500 | 0.7746 | 0.7555 | 2.0764 | 0.7301 | 0.7827 |
| 0.0001 | 104.17 | 5000 | 0.7716 | 0.7441 | 2.0912 | 0.7158 | 0.7747 |
| 0.0 | 114.58 | 5500 | 0.7764 | 0.7572 | 2.1803 | 0.7275 | 0.7894 |
| 0.0 | 125.0 | 6000 | 0.7809 | 0.7576 | 2.1028 | 0.7384 | 0.7778 |
| 0.0001 | 135.42 | 6500 | 0.7812 | 0.7422 | 2.0825 | 0.7240 | 0.7614 |
| 0.0001 | 145.83 | 7000 | 0.7882 | 0.7481 | 2.0649 | 0.7244 | 0.7734 |
| 0.0001 | 156.25 | 7500 | 0.7789 | 0.7536 | 2.1535 | 0.7324 | 0.7760 |
| 0.0 | 166.67 | 8000 | 0.7760 | 0.7491 | 2.2120 | 0.7307 | 0.7685 |
| 0.0 | 177.08 | 8500 | 0.7941 | 0.7615 | 1.9997 | 0.75 | 0.7734 |
| 0.0 | 187.5 | 9000 | 0.7854 | 0.7588 | 2.0939 | 0.7355 | 0.7836 |
| 0.0 | 197.92 | 9500 | 2.1707 | 0.7262 | 0.7805 | 0.7524 | 0.7825 |
| 0.0 | 208.33 | 10000 | 2.1833 | 0.7268 | 0.7854 | 0.7550 | 0.7817 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.1