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license: mit |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: MiniLM-evidence-types |
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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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# MiniLM-evidence-types |
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This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8388 |
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- Macro f1: 0.4307 |
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- Weighted f1: 0.6983 |
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- Accuracy: 0.7032 |
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- Balanced accuracy: 0.4139 |
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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: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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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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- num_epochs: 20 |
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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 | Macro f1 | Weighted f1 | Accuracy | Balanced accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:-----------------:| |
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| 1.3124 | 1.0 | 250 | 1.1166 | 0.2582 | 0.6393 | 0.6788 | 0.2758 | |
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| 0.9939 | 2.0 | 500 | 0.9671 | 0.3859 | 0.6988 | 0.7093 | 0.3799 | |
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| 0.8486 | 3.0 | 750 | 1.0263 | 0.3519 | 0.6632 | 0.6606 | 0.3642 | |
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| 0.7396 | 4.0 | 1000 | 1.0125 | 0.4195 | 0.7092 | 0.7192 | 0.4186 | |
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| 0.6425 | 5.0 | 1250 | 1.0983 | 0.3910 | 0.6746 | 0.6826 | 0.3925 | |
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| 0.5648 | 6.0 | 1500 | 1.0948 | 0.4184 | 0.7145 | 0.7222 | 0.4089 | |
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| 0.4858 | 7.0 | 1750 | 1.1658 | 0.4242 | 0.7058 | 0.7184 | 0.4279 | |
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| 0.4329 | 8.0 | 2000 | 1.3020 | 0.4178 | 0.6806 | 0.6849 | 0.4081 | |
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| 0.3799 | 9.0 | 2250 | 1.2622 | 0.4466 | 0.7004 | 0.7055 | 0.4419 | |
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| 0.326 | 10.0 | 2500 | 1.3822 | 0.4162 | 0.6971 | 0.7032 | 0.4048 | |
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| 0.2849 | 11.0 | 2750 | 1.4716 | 0.3933 | 0.6941 | 0.6971 | 0.3826 | |
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| 0.251 | 12.0 | 3000 | 1.5651 | 0.4259 | 0.6928 | 0.6956 | 0.4231 | |
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| 0.2205 | 13.0 | 3250 | 1.6920 | 0.4257 | 0.6942 | 0.7032 | 0.4112 | |
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| 0.205 | 14.0 | 3500 | 1.7016 | 0.4269 | 0.6899 | 0.6872 | 0.4260 | |
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| 0.1946 | 15.0 | 3750 | 1.7647 | 0.4312 | 0.6891 | 0.6910 | 0.4232 | |
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| 0.1661 | 16.0 | 4000 | 1.8255 | 0.4168 | 0.6886 | 0.6933 | 0.4003 | |
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| 0.1502 | 17.0 | 4250 | 1.8261 | 0.4190 | 0.6950 | 0.7040 | 0.3996 | |
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| 0.1625 | 18.0 | 4500 | 1.8163 | 0.4260 | 0.7001 | 0.7047 | 0.4079 | |
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| 0.1329 | 19.0 | 4750 | 1.8274 | 0.4368 | 0.7023 | 0.7055 | 0.4218 | |
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| 0.1248 | 20.0 | 5000 | 1.8388 | 0.4307 | 0.6983 | 0.7032 | 0.4139 | |
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### Framework versions |
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- Transformers 4.19.2 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.2.2 |
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- Tokenizers 0.12.1 |
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