| --- |
| 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 |
| --- |
| |
| <!-- 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 |
|
|
| 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 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 4.39.1 |
| - Pytorch 2.1.0+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.1 |
|
|
