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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: 2.3471 |
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- Macro f1: 0.4351 |
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- Weighted f1: 0.7056 |
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- Accuracy: 0.7207 |
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- Balanced accuracy: 0.4063 |
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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: 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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| 1.249 | 1.0 | 250 | 1.1782 | 0.2143 | 0.5844 | 0.6385 | 0.2417 | |
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| 1.0481 | 2.0 | 500 | 1.0009 | 0.3079 | 0.6757 | 0.6865 | 0.3192 | |
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| 0.903 | 3.0 | 750 | 1.0094 | 0.3105 | 0.6840 | 0.6986 | 0.3179 | |
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| 0.7604 | 4.0 | 1000 | 1.0636 | 0.3817 | 0.6834 | 0.6994 | 0.3751 | |
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| 0.6367 | 5.0 | 1250 | 1.0813 | 0.3999 | 0.6963 | 0.7108 | 0.3945 | |
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| 0.5293 | 6.0 | 1500 | 1.1597 | 0.3909 | 0.6920 | 0.6986 | 0.3895 | |
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| 0.4097 | 7.0 | 1750 | 1.3520 | 0.3517 | 0.6739 | 0.6865 | 0.3757 | |
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| 0.3442 | 8.0 | 2000 | 1.5343 | 0.4012 | 0.6684 | 0.6743 | 0.4028 | |
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| 0.2663 | 9.0 | 2250 | 1.5623 | 0.4241 | 0.7007 | 0.7154 | 0.4052 | |
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| 0.2383 | 10.0 | 2500 | 1.6971 | 0.4327 | 0.7080 | 0.7169 | 0.4179 | |
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| 0.2053 | 11.0 | 2750 | 1.7675 | 0.4331 | 0.7073 | 0.7177 | 0.4199 | |
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| 0.1698 | 12.0 | 3000 | 1.8678 | 0.4381 | 0.7103 | 0.7298 | 0.4097 | |
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| 0.1467 | 13.0 | 3250 | 2.0007 | 0.4343 | 0.7113 | 0.7268 | 0.4082 | |
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| 0.1098 | 14.0 | 3500 | 2.0797 | 0.4267 | 0.7004 | 0.7131 | 0.3986 | |
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| 0.1049 | 15.0 | 3750 | 2.2048 | 0.4190 | 0.7037 | 0.7192 | 0.3939 | |
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| 0.0912 | 16.0 | 4000 | 2.2582 | 0.4263 | 0.6903 | 0.7024 | 0.4003 | |
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| 0.0678 | 17.0 | 4250 | 2.2735 | 0.4276 | 0.7052 | 0.7222 | 0.4019 | |
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| 0.0623 | 18.0 | 4500 | 2.3478 | 0.4317 | 0.7048 | 0.7207 | 0.4030 | |
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| 0.0546 | 19.0 | 4750 | 2.3598 | 0.4298 | 0.7043 | 0.7207 | 0.4003 | |
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| 0.0415 | 20.0 | 5000 | 2.3471 | 0.4351 | 0.7056 | 0.7207 | 0.4063 | |
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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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