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--- |
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license: mit |
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base_model: nielsr/lilt-xlm-roberta-base |
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tags: |
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- generated_from_trainer |
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datasets: |
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- xfun |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: LiLT-SER-DE |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: xfun |
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type: xfun |
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config: xfun.de |
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split: validation |
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args: xfun.de |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7268232385661311 |
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- name: Recall |
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type: recall |
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value: 0.7853962600178095 |
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- name: F1 |
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type: f1 |
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value: 0.7549753905414082 |
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- name: Accuracy |
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type: accuracy |
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value: 0.7816669203063968 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# LiLT-SER-DE |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1833 |
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- Precision: 0.7268 |
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- Recall: 0.7854 |
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- F1: 0.7550 |
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- Accuracy: 0.7817 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 2 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 10000 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | F1 | Validation Loss | Precision | Recall | |
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|:-------------:|:------:|:-----:|:--------:|:------:|:---------------:|:---------:|:------:| |
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| 0.2776 | 10.42 | 500 | 0.7098 | 0.6660 | 1.4820 | 0.6266 | 0.7106 | |
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| 0.0386 | 20.83 | 1000 | 0.7884 | 0.7195 | 1.3364 | 0.6868 | 0.7556 | |
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| 0.002 | 31.25 | 1500 | 0.8102 | 0.7350 | 1.4865 | 0.7000 | 0.7738 | |
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| 0.0043 | 41.67 | 2000 | 0.7965 | 0.7167 | 1.5473 | 0.7050 | 0.7289 | |
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| 0.0009 | 52.08 | 2500 | 0.7797 | 0.7357 | 1.8408 | 0.7371 | 0.7342 | |
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| 0.0003 | 62.5 | 3000 | 0.7841 | 0.7279 | 1.9387 | 0.7021 | 0.7556 | |
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| 0.0044 | 72.92 | 3500 | 0.7900 | 0.7402 | 1.7595 | 0.7292 | 0.7516 | |
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| 0.0005 | 83.33 | 4000 | 0.7677 | 0.7370 | 2.0830 | 0.7084 | 0.7680 | |
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| 0.0001 | 93.75 | 4500 | 0.7746 | 0.7555 | 2.0764 | 0.7301 | 0.7827 | |
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| 0.0001 | 104.17 | 5000 | 0.7716 | 0.7441 | 2.0912 | 0.7158 | 0.7747 | |
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| 0.0 | 114.58 | 5500 | 0.7764 | 0.7572 | 2.1803 | 0.7275 | 0.7894 | |
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| 0.0 | 125.0 | 6000 | 0.7809 | 0.7576 | 2.1028 | 0.7384 | 0.7778 | |
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| 0.0001 | 135.42 | 6500 | 0.7812 | 0.7422 | 2.0825 | 0.7240 | 0.7614 | |
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| 0.0001 | 145.83 | 7000 | 0.7882 | 0.7481 | 2.0649 | 0.7244 | 0.7734 | |
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| 0.0001 | 156.25 | 7500 | 0.7789 | 0.7536 | 2.1535 | 0.7324 | 0.7760 | |
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| 0.0 | 166.67 | 8000 | 0.7760 | 0.7491 | 2.2120 | 0.7307 | 0.7685 | |
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| 0.0 | 177.08 | 8500 | 0.7941 | 0.7615 | 1.9997 | 0.75 | 0.7734 | |
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| 0.0 | 187.5 | 9000 | 0.7854 | 0.7588 | 2.0939 | 0.7355 | 0.7836 | |
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| 0.0 | 197.92 | 9500 | 2.1707 | 0.7262 | 0.7805 | 0.7524 | 0.7825 | |
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| 0.0 | 208.33 | 10000 | 2.1833 | 0.7268 | 0.7854 | 0.7550 | 0.7817 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
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