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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: new_model |
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results: [] |
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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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# new_model |
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This model is a fine-tuned version of [SCUT-DLVCLab/lilt-roberta-en-base](https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0582 |
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- Answer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} |
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- Header: {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13} |
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- Question: {'precision': 0.1, 'recall': 0.15384615384615385, 'f1': 0.12121212121212123, 'number': 13} |
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- Overall Precision: 0.0682 |
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- Overall Recall: 0.0882 |
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- Overall F1: 0.0769 |
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- Overall Accuracy: 0.6434 |
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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: 4 |
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- eval_batch_size: 4 |
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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: 2500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:-------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 0.1677 | 3.08 | 200 | 0.0239 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | 0.0 | 0.0 | 0.0 | 0.7295 | |
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| 0.0578 | 6.15 | 400 | 0.0251 | {'precision': 0.4, 'recall': 0.25, 'f1': 0.3076923076923077, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | 0.1333 | 0.0588 | 0.0816 | 0.7295 | |
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| 0.0275 | 9.23 | 600 | 0.0291 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.05555555555555555, 'recall': 0.07692307692307693, 'f1': 0.06451612903225808, 'number': 13} | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13} | 0.0526 | 0.0588 | 0.0556 | 0.7008 | |
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| 0.0124 | 12.31 | 800 | 0.0401 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.0625, 'recall': 0.07692307692307693, 'f1': 0.06896551724137931, 'number': 13} | 0.0303 | 0.0294 | 0.0299 | 0.6352 | |
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| 0.0086 | 15.38 | 1000 | 0.0416 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13} | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13} | 0.0513 | 0.0588 | 0.0548 | 0.6311 | |
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| 0.0045 | 18.46 | 1200 | 0.0447 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 13} | 0.0 | 0.0 | 0.0 | 0.6639 | |
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| 0.0027 | 21.54 | 1400 | 0.0467 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.05, 'recall': 0.07692307692307693, 'f1': 0.060606060606060615, 'number': 13} | {'precision': 0.09523809523809523, 'recall': 0.15384615384615385, 'f1': 0.11764705882352941, 'number': 13} | 0.0667 | 0.0882 | 0.0759 | 0.6639 | |
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| 0.0013 | 24.62 | 1600 | 0.0494 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.045454545454545456, 'recall': 0.07692307692307693, 'f1': 0.05714285714285715, 'number': 13} | {'precision': 0.08695652173913043, 'recall': 0.15384615384615385, 'f1': 0.1111111111111111, 'number': 13} | 0.0612 | 0.0882 | 0.0723 | 0.6434 | |
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| 0.0009 | 27.69 | 1800 | 0.0559 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.045454545454545456, 'recall': 0.07692307692307693, 'f1': 0.05714285714285715, 'number': 13} | {'precision': 0.08695652173913043, 'recall': 0.15384615384615385, 'f1': 0.1111111111111111, 'number': 13} | 0.06 | 0.0882 | 0.0714 | 0.6475 | |
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| 0.0006 | 30.77 | 2000 | 0.0522 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0625, 'recall': 0.07692307692307693, 'f1': 0.06896551724137931, 'number': 13} | {'precision': 0.05555555555555555, 'recall': 0.07692307692307693, 'f1': 0.06451612903225808, 'number': 13} | 0.0526 | 0.0588 | 0.0556 | 0.6393 | |
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| 0.0004 | 33.85 | 2200 | 0.0557 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13} | {'precision': 0.1, 'recall': 0.15384615384615385, 'f1': 0.12121212121212123, 'number': 13} | 0.0682 | 0.0882 | 0.0769 | 0.6516 | |
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| 0.0005 | 36.92 | 2400 | 0.0582 | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.05263157894736842, 'recall': 0.07692307692307693, 'f1': 0.0625, 'number': 13} | {'precision': 0.1, 'recall': 0.15384615384615385, 'f1': 0.12121212121212123, 'number': 13} | 0.0682 | 0.0882 | 0.0769 | 0.6434 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.1.0.dev20230810 |
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- Datasets 2.14.4 |
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- Tokenizers 0.11.0 |
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