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--- |
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license: mit |
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base_model: kavg/LiLT-SER-JA |
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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-JA-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: 0.7378410438908659 |
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- name: Recall |
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type: recall |
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value: 0.7660098522167488 |
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- name: F1 |
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type: f1 |
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value: 0.7516616314199396 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8793659699817646 |
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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-JA-SIN |
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This model is a fine-tuned version of [kavg/LiLT-SER-JA](https://huggingface.co/kavg/LiLT-SER-JA) on the xfun dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1113 |
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- Precision: 0.7378 |
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- Recall: 0.7660 |
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- F1: 0.7517 |
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- Accuracy: 0.8794 |
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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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| 0.0009 | 21.74 | 500 | 0.8785 | 0.6584 | 0.7217 | 0.6886 | 0.8505 | |
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| 0.0031 | 43.48 | 1000 | 1.0637 | 0.7309 | 0.7291 | 0.7300 | 0.8533 | |
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| 0.0046 | 65.22 | 1500 | 0.9166 | 0.7219 | 0.7512 | 0.7363 | 0.8729 | |
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| 0.0002 | 86.96 | 2000 | 1.0366 | 0.7212 | 0.7389 | 0.7299 | 0.8721 | |
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| 0.0 | 108.7 | 2500 | 1.0535 | 0.7191 | 0.7377 | 0.7283 | 0.8662 | |
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| 0.0006 | 130.43 | 3000 | 1.1869 | 0.7409 | 0.7291 | 0.7349 | 0.8495 | |
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| 0.005 | 152.17 | 3500 | 1.2062 | 0.7356 | 0.7401 | 0.7379 | 0.8627 | |
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| 0.0002 | 173.91 | 4000 | 1.2067 | 0.7011 | 0.7192 | 0.7100 | 0.8451 | |
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| 0.0002 | 195.65 | 4500 | 1.1819 | 0.7290 | 0.7389 | 0.7339 | 0.8578 | |
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| 0.0 | 217.39 | 5000 | 1.1699 | 0.7463 | 0.75 | 0.7482 | 0.8632 | |
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| 0.0 | 239.13 | 5500 | 1.1548 | 0.7267 | 0.7599 | 0.7429 | 0.8637 | |
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| 0.0 | 260.87 | 6000 | 1.1867 | 0.7227 | 0.7574 | 0.7396 | 0.8651 | |
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| 0.0 | 282.61 | 6500 | 1.1614 | 0.7222 | 0.7525 | 0.7370 | 0.8721 | |
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| 0.0 | 304.35 | 7000 | 1.1884 | 0.7146 | 0.7648 | 0.7388 | 0.8681 | |
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| 0.0 | 326.09 | 7500 | 1.2186 | 0.6975 | 0.7438 | 0.7199 | 0.8582 | |
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| 0.0001 | 347.83 | 8000 | 1.0423 | 0.7313 | 0.7709 | 0.7506 | 0.8754 | |
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| 0.0 | 369.57 | 8500 | 1.1254 | 0.7278 | 0.7574 | 0.7423 | 0.8705 | |
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| 0.0 | 391.3 | 9000 | 1.1113 | 0.7378 | 0.7660 | 0.7517 | 0.8794 | |
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| 0.0 | 413.04 | 9500 | 1.1517 | 0.7424 | 0.7562 | 0.7492 | 0.8732 | |
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| 0.0 | 434.78 | 10000 | 1.1568 | 0.7413 | 0.7586 | 0.7498 | 0.8726 | |
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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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