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--- |
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license: mit |
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base_model: kavg/LiLT-SER-FR |
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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-FR-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: 0.7617924528301887 |
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- name: Recall |
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type: recall |
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value: 0.7955665024630542 |
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- name: F1 |
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type: f1 |
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value: 0.7783132530120481 |
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- name: Accuracy |
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type: accuracy |
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value: 0.8647776686772338 |
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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-FR-SIN |
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This model is a fine-tuned version of [kavg/LiLT-SER-FR](https://huggingface.co/kavg/LiLT-SER-FR) on the xfun dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2426 |
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- Precision: 0.7618 |
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- Recall: 0.7956 |
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- F1: 0.7783 |
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- Accuracy: 0.8648 |
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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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| 0.0057 | 21.74 | 500 | 0.8019 | 0.6884 | 0.7020 | 0.6951 | 0.8582 | |
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| 0.008 | 43.48 | 1000 | 1.0139 | 0.6963 | 0.7623 | 0.7278 | 0.8648 | |
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| 0.0006 | 65.22 | 1500 | 0.9878 | 0.7090 | 0.7562 | 0.7318 | 0.8592 | |
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| 0.0038 | 86.96 | 2000 | 1.2269 | 0.7104 | 0.7401 | 0.7250 | 0.8373 | |
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| 0.001 | 108.7 | 2500 | 0.9751 | 0.7276 | 0.7697 | 0.7481 | 0.8707 | |
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| 0.0004 | 130.43 | 3000 | 1.0918 | 0.7479 | 0.7672 | 0.7574 | 0.8538 | |
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| 0.0003 | 152.17 | 3500 | 1.0782 | 0.7102 | 0.7635 | 0.7359 | 0.8604 | |
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| 0.0 | 173.91 | 4000 | 1.0515 | 0.7402 | 0.7894 | 0.7640 | 0.8704 | |
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| 0.0001 | 195.65 | 4500 | 1.2154 | 0.7373 | 0.7709 | 0.7538 | 0.8419 | |
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| 0.0 | 217.39 | 5000 | 1.1026 | 0.7411 | 0.7722 | 0.7563 | 0.8642 | |
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| 0.0001 | 239.13 | 5500 | 1.0594 | 0.7262 | 0.7512 | 0.7385 | 0.8576 | |
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| 0.0 | 260.87 | 6000 | 1.1103 | 0.7377 | 0.7759 | 0.7563 | 0.8609 | |
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| 0.0 | 282.61 | 6500 | 1.1591 | 0.7267 | 0.7599 | 0.7429 | 0.8610 | |
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| 0.0 | 304.35 | 7000 | 1.2382 | 0.7574 | 0.7537 | 0.7556 | 0.8562 | |
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| 0.0 | 326.09 | 7500 | 1.2027 | 0.7485 | 0.7882 | 0.7678 | 0.8578 | |
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| 0.0001 | 347.83 | 8000 | 1.1492 | 0.7433 | 0.7808 | 0.7616 | 0.8659 | |
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| 0.0002 | 369.57 | 8500 | 1.1924 | 0.7570 | 0.7980 | 0.7770 | 0.8655 | |
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| 0.0 | 391.3 | 9000 | 1.2426 | 0.7618 | 0.7956 | 0.7783 | 0.8648 | |
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| 0.0 | 413.04 | 9500 | 1.3078 | 0.7620 | 0.7808 | 0.7713 | 0.8597 | |
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| 0.0 | 434.78 | 10000 | 1.3219 | 0.7639 | 0.7771 | 0.7705 | 0.8579 | |
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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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