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--- |
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license: mit |
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base_model: kavg/LiLT-SER-IT |
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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-IT-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: 0.7651331719128329 |
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- name: Recall |
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type: recall |
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value: 0.7783251231527094 |
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- name: F1 |
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type: f1 |
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value: 0.7716727716727716 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8705288259222892 |
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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-IT-SIN |
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This model is a fine-tuned version of [kavg/LiLT-SER-IT](https://huggingface.co/kavg/LiLT-SER-IT) on the xfun dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2031 |
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- Precision: 0.7651 |
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- Recall: 0.7783 |
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- F1: 0.7717 |
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- Accuracy: 0.8705 |
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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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| 0.0301 | 21.74 | 500 | 1.0148 | 0.7146 | 0.7586 | 0.7360 | 0.8470 | |
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| 0.0058 | 43.48 | 1000 | 0.9498 | 0.7121 | 0.7401 | 0.7258 | 0.8566 | |
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| 0.0008 | 65.22 | 1500 | 1.0385 | 0.7310 | 0.7833 | 0.7562 | 0.8559 | |
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| 0.0004 | 86.96 | 2000 | 1.2165 | 0.7484 | 0.7032 | 0.7251 | 0.8512 | |
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| 0.0017 | 108.7 | 2500 | 1.0999 | 0.7252 | 0.7734 | 0.7485 | 0.8726 | |
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| 0.0018 | 130.43 | 3000 | 1.1872 | 0.7293 | 0.7697 | 0.7490 | 0.8564 | |
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| 0.0001 | 152.17 | 3500 | 1.2632 | 0.7386 | 0.7377 | 0.7381 | 0.8457 | |
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| 0.0008 | 173.91 | 4000 | 1.0687 | 0.7337 | 0.7635 | 0.7483 | 0.8691 | |
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| 0.0 | 195.65 | 4500 | 1.0346 | 0.7205 | 0.7746 | 0.7466 | 0.8684 | |
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| 0.0 | 217.39 | 5000 | 1.1440 | 0.7158 | 0.7537 | 0.7343 | 0.8686 | |
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| 0.0 | 239.13 | 5500 | 1.3391 | 0.7690 | 0.7586 | 0.7638 | 0.8578 | |
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| 0.0 | 260.87 | 6000 | 1.0498 | 0.7482 | 0.7722 | 0.7600 | 0.8761 | |
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| 0.0 | 282.61 | 6500 | 1.0602 | 0.7301 | 0.7894 | 0.7586 | 0.8787 | |
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| 0.0 | 304.35 | 7000 | 1.1634 | 0.7355 | 0.7328 | 0.7341 | 0.8613 | |
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| 0.0 | 326.09 | 7500 | 1.1705 | 0.7680 | 0.7746 | 0.7713 | 0.8754 | |
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| 0.0 | 347.83 | 8000 | 1.2455 | 0.7616 | 0.7709 | 0.7662 | 0.8687 | |
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| 0.0 | 369.57 | 8500 | 1.2259 | 0.7327 | 0.7562 | 0.7442 | 0.8665 | |
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| 0.0 | 391.3 | 9000 | 1.1737 | 0.7577 | 0.7857 | 0.7715 | 0.8690 | |
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| 0.0 | 413.04 | 9500 | 1.2174 | 0.7636 | 0.7796 | 0.7715 | 0.8704 | |
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| 0.0 | 434.78 | 10000 | 1.2031 | 0.7651 | 0.7783 | 0.7717 | 0.8705 | |
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### Framework versions |
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- Transformers 4.38.2 |
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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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