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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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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: 1.6603 |
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- Macro f1: 0.4329 |
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- Weighted f1: 0.7053 |
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- Accuracy: 0.7154 |
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- Balanced accuracy: 0.4114 |
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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: 3e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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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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| 1.3633 | 1.0 | 125 | 1.1325 | 0.3442 | 0.6470 | 0.6872 | 0.3862 | |
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| 1.0162 | 2.0 | 250 | 0.9858 | 0.3062 | 0.6889 | 0.7131 | 0.3135 | |
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| 0.868 | 3.0 | 375 | 0.9587 | 0.4091 | 0.7071 | 0.7207 | 0.3993 | |
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| 0.75 | 4.0 | 500 | 0.9983 | 0.4105 | 0.7080 | 0.7192 | 0.4039 | |
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| 0.6317 | 5.0 | 625 | 1.0197 | 0.4095 | 0.6941 | 0.6994 | 0.4093 | |
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| 0.5253 | 6.0 | 750 | 1.0760 | 0.4303 | 0.7073 | 0.7123 | 0.4223 | |
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| 0.4615 | 7.0 | 875 | 1.1371 | 0.4328 | 0.7040 | 0.7169 | 0.4096 | |
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| 0.3984 | 8.0 | 1000 | 1.1649 | 0.4516 | 0.6997 | 0.7002 | 0.4678 | |
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| 0.3332 | 9.0 | 1125 | 1.2009 | 0.4364 | 0.6994 | 0.7040 | 0.4243 | |
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| 0.2996 | 10.0 | 1250 | 1.2760 | 0.4336 | 0.7095 | 0.7192 | 0.4162 | |
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| 0.255 | 11.0 | 1375 | 1.3266 | 0.4353 | 0.6914 | 0.6918 | 0.4402 | |
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| 0.2318 | 12.0 | 1500 | 1.3591 | 0.4322 | 0.7011 | 0.7116 | 0.4101 | |
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| 0.2163 | 13.0 | 1625 | 1.4554 | 0.4226 | 0.7080 | 0.7237 | 0.4029 | |
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| 0.1837 | 14.0 | 1750 | 1.4363 | 0.4385 | 0.6938 | 0.6963 | 0.4250 | |
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| 0.1735 | 15.0 | 1875 | 1.5356 | 0.4363 | 0.7118 | 0.7230 | 0.4098 | |
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| 0.1526 | 16.0 | 2000 | 1.5731 | 0.4370 | 0.7073 | 0.7169 | 0.4181 | |
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| 0.1288 | 17.0 | 2125 | 1.6258 | 0.4406 | 0.7123 | 0.7245 | 0.4151 | |
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| 0.1321 | 18.0 | 2250 | 1.6590 | 0.4364 | 0.7081 | 0.7184 | 0.4148 | |
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| 0.114 | 19.0 | 2375 | 1.6598 | 0.4324 | 0.7074 | 0.7192 | 0.4081 | |
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| 0.1063 | 20.0 | 2500 | 1.6603 | 0.4329 | 0.7053 | 0.7154 | 0.4114 | |
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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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