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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: lilt-ruroberta |
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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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# lilt-ruroberta |
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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: 0.2043 |
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- Comment: {'precision': 1.0, 'recall': 0.9444444444444444, 'f1': 0.9714285714285714, 'number': 18} |
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- Date: {'precision': 0.8571428571428571, 'recall': 0.75, 'f1': 0.7999999999999999, 'number': 8} |
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- Labname: {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 5} |
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- Laboratory: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} |
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- Measure: {'precision': 1.0, 'recall': 0.9230769230769231, 'f1': 0.9600000000000001, 'number': 13} |
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- Ref Value: {'precision': 0.875, 'recall': 1.0, 'f1': 0.9333333333333333, 'number': 14} |
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- Result: {'precision': 1.0, 'recall': 0.9285714285714286, 'f1': 0.962962962962963, 'number': 14} |
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- Overall Precision: 0.9296 |
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- Overall Recall: 0.8919 |
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- Overall F1: 0.9103 |
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- Overall Accuracy: 0.9563 |
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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: 8 |
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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: 25 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Comment | Date | Labname | Laboratory | Measure | Ref Value | Result | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------:|:---------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:| |
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| 1.2584 | 5.0 | 5 | 0.9810 | {'precision': 1.0, 'recall': 0.05555555555555555, 'f1': 0.10526315789473684, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.6666666666666666, 'recall': 0.3076923076923077, 'f1': 0.42105263157894735, 'number': 13} | {'precision': 0.5714285714285714, 'recall': 0.2857142857142857, 'f1': 0.38095238095238093, 'number': 14} | {'precision': 0.4482758620689655, 'recall': 0.9285714285714286, 'f1': 0.6046511627906977, 'number': 14} | 0.44 | 0.2973 | 0.3548 | 0.7125 | |
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| 0.6614 | 10.0 | 10 | 0.5382 | {'precision': 0.8947368421052632, 'recall': 0.9444444444444444, 'f1': 0.918918918918919, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8333333333333334, 'recall': 0.38461538461538464, 'f1': 0.5263157894736842, 'number': 13} | {'precision': 0.8125, 'recall': 0.9285714285714286, 'f1': 0.8666666666666666, 'number': 14} | {'precision': 1.0, 'recall': 0.7857142857142857, 'f1': 0.88, 'number': 14} | 0.8475 | 0.6757 | 0.7519 | 0.9 | |
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| 0.3955 | 15.0 | 15 | 0.3360 | {'precision': 0.8947368421052632, 'recall': 0.9444444444444444, 'f1': 0.918918918918919, 'number': 18} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 8} | {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 0.8333333333333334, 'recall': 0.38461538461538464, 'f1': 0.5263157894736842, 'number': 13} | {'precision': 0.8125, 'recall': 0.9285714285714286, 'f1': 0.8666666666666666, 'number': 14} | {'precision': 1.0, 'recall': 0.7857142857142857, 'f1': 0.88, 'number': 14} | 0.8475 | 0.6757 | 0.7519 | 0.9 | |
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| 0.2654 | 20.0 | 20 | 0.2405 | {'precision': 1.0, 'recall': 0.8888888888888888, 'f1': 0.9411764705882353, 'number': 18} | {'precision': 0.8571428571428571, 'recall': 0.75, 'f1': 0.7999999999999999, 'number': 8} | {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 1.0, 'recall': 0.9230769230769231, 'f1': 0.9600000000000001, 'number': 13} | {'precision': 0.875, 'recall': 1.0, 'f1': 0.9333333333333333, 'number': 14} | {'precision': 0.9285714285714286, 'recall': 0.9285714285714286, 'f1': 0.9285714285714286, 'number': 14} | 0.9155 | 0.8784 | 0.8966 | 0.95 | |
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| 0.2125 | 25.0 | 25 | 0.2043 | {'precision': 1.0, 'recall': 0.9444444444444444, 'f1': 0.9714285714285714, 'number': 18} | {'precision': 0.8571428571428571, 'recall': 0.75, 'f1': 0.7999999999999999, 'number': 8} | {'precision': 0.6666666666666666, 'recall': 0.8, 'f1': 0.7272727272727272, 'number': 5} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2} | {'precision': 1.0, 'recall': 0.9230769230769231, 'f1': 0.9600000000000001, 'number': 13} | {'precision': 0.875, 'recall': 1.0, 'f1': 0.9333333333333333, 'number': 14} | {'precision': 1.0, 'recall': 0.9285714285714286, 'f1': 0.962962962962963, 'number': 14} | 0.9296 | 0.8919 | 0.9103 | 0.9563 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.8.0 |
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- Tokenizers 0.13.2 |
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