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--- |
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license: mit |
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base_model: kavg/LiLT-SER-EN |
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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-EN-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: 0.7420494699646644 |
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- name: Recall |
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type: recall |
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value: 0.7758620689655172 |
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- name: F1 |
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type: f1 |
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value: 0.7585791691751957 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8473839248141394 |
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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-EN-SIN |
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This model is a fine-tuned version of [kavg/LiLT-SER-EN](https://huggingface.co/kavg/LiLT-SER-EN) on the xfun dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.3790 |
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- Precision: 0.7420 |
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- Recall: 0.7759 |
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- F1: 0.7586 |
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- Accuracy: 0.8474 |
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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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| 0.0089 | 21.74 | 500 | 0.8362 | 0.6606 | 0.7118 | 0.6852 | 0.8545 | |
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| 0.0013 | 43.48 | 1000 | 1.3605 | 0.7269 | 0.7081 | 0.7174 | 0.8230 | |
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| 0.0051 | 65.22 | 1500 | 0.9220 | 0.7113 | 0.7525 | 0.7313 | 0.8725 | |
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| 0.0054 | 86.96 | 2000 | 1.2086 | 0.6965 | 0.7291 | 0.7124 | 0.8467 | |
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| 0.0001 | 108.7 | 2500 | 1.1308 | 0.6843 | 0.7315 | 0.7071 | 0.8449 | |
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| 0.0001 | 130.43 | 3000 | 1.0934 | 0.7362 | 0.7044 | 0.7199 | 0.8606 | |
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| 0.0 | 152.17 | 3500 | 1.0390 | 0.7297 | 0.7512 | 0.7403 | 0.8590 | |
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| 0.0001 | 173.91 | 4000 | 1.1448 | 0.7128 | 0.7672 | 0.7390 | 0.8599 | |
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| 0.0 | 195.65 | 4500 | 1.1902 | 0.7393 | 0.7229 | 0.7310 | 0.8551 | |
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| 0.0001 | 217.39 | 5000 | 1.1164 | 0.7141 | 0.7783 | 0.7448 | 0.8555 | |
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| 0.0001 | 239.13 | 5500 | 1.4359 | 0.7197 | 0.7241 | 0.7219 | 0.8313 | |
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| 0.0 | 260.87 | 6000 | 1.4027 | 0.7256 | 0.7426 | 0.7340 | 0.8376 | |
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| 0.0 | 282.61 | 6500 | 1.4112 | 0.7085 | 0.7574 | 0.7321 | 0.8524 | |
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| 0.0 | 304.35 | 7000 | 1.5045 | 0.7627 | 0.7599 | 0.7613 | 0.8432 | |
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| 0.0 | 326.09 | 7500 | 1.4482 | 0.7390 | 0.7672 | 0.7529 | 0.8398 | |
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| 0.0 | 347.83 | 8000 | 1.5717 | 0.7155 | 0.7525 | 0.7335 | 0.8360 | |
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| 0.0 | 369.57 | 8500 | 1.3845 | 0.7348 | 0.7746 | 0.7542 | 0.8422 | |
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| 0.0 | 391.3 | 9000 | 1.3238 | 0.7283 | 0.7660 | 0.7467 | 0.8499 | |
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| 0.0 | 413.04 | 9500 | 1.3677 | 0.7321 | 0.7672 | 0.7492 | 0.8492 | |
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| 0.0001 | 434.78 | 10000 | 1.3790 | 0.7420 | 0.7759 | 0.7586 | 0.8474 | |
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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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