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--- |
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license: mit |
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base_model: kavg/LiLT-SER-ES |
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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-ES-SIN |
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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.sin |
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split: validation |
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args: xfun.sin |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.7538829151732378 |
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- name: Recall |
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type: recall |
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value: 0.7770935960591133 |
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- name: F1 |
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type: f1 |
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value: 0.7653123104912068 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8560807967456866 |
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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-ES-SIN |
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This model is a fine-tuned version of [kavg/LiLT-SER-ES](https://huggingface.co/kavg/LiLT-SER-ES) on the xfun dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.4009 |
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- Precision: 0.7539 |
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- Recall: 0.7771 |
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- F1: 0.7653 |
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- Accuracy: 0.8561 |
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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 | Validation Loss | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:------:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| 0.0045 | 21.74 | 500 | 0.8773 | 0.7107 | 0.7352 | 0.7228 | 0.8582 | |
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| 0.0044 | 43.48 | 1000 | 1.1262 | 0.7030 | 0.7463 | 0.7240 | 0.8495 | |
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| 0.0021 | 65.22 | 1500 | 1.1512 | 0.6938 | 0.7254 | 0.7092 | 0.8419 | |
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| 0.0 | 86.96 | 2000 | 1.2416 | 0.7043 | 0.7537 | 0.7281 | 0.8390 | |
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| 0.0002 | 108.7 | 2500 | 1.2400 | 0.7036 | 0.7426 | 0.7226 | 0.8492 | |
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| 0.0001 | 130.43 | 3000 | 1.2076 | 0.7095 | 0.7488 | 0.7286 | 0.8432 | |
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| 0.0001 | 152.17 | 3500 | 1.1215 | 0.7174 | 0.7315 | 0.7244 | 0.8552 | |
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| 0.0008 | 173.91 | 4000 | 1.1580 | 0.7188 | 0.7303 | 0.7245 | 0.8534 | |
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| 0.0 | 195.65 | 4500 | 1.2805 | 0.7256 | 0.7328 | 0.7292 | 0.8596 | |
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| 0.0001 | 217.39 | 5000 | 1.1563 | 0.7110 | 0.7635 | 0.7363 | 0.8526 | |
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| 0.0 | 239.13 | 5500 | 1.1503 | 0.7585 | 0.7734 | 0.7659 | 0.8645 | |
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| 0.0 | 260.87 | 6000 | 1.3623 | 0.7419 | 0.7648 | 0.7532 | 0.8557 | |
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| 0.001 | 282.61 | 6500 | 1.1415 | 0.7405 | 0.7660 | 0.7530 | 0.8707 | |
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| 0.0 | 304.35 | 7000 | 1.2738 | 0.7390 | 0.7635 | 0.7511 | 0.8644 | |
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| 0.0 | 326.09 | 7500 | 1.3134 | 0.7682 | 0.7672 | 0.7677 | 0.8683 | |
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| 0.0 | 347.83 | 8000 | 1.4709 | 0.7608 | 0.7599 | 0.7603 | 0.8475 | |
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| 0.0 | 369.57 | 8500 | 1.4720 | 0.7509 | 0.75 | 0.7505 | 0.8499 | |
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| 0.0 | 391.3 | 9000 | 1.4492 | 0.7617 | 0.7635 | 0.7626 | 0.8530 | |
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| 0.0 | 413.04 | 9500 | 1.4251 | 0.7458 | 0.7734 | 0.7594 | 0.8550 | |
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| 0.0 | 434.78 | 10000 | 1.4009 | 0.7539 | 0.7771 | 0.7653 | 0.8561 | |
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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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