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MiniLM-evidence-types

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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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+
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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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+
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+ # MiniLM-evidence-types
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+
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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: 1.8672
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+ - Macro f1: 0.3726
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+ - Weighted f1: 0.7030
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+ - Accuracy: 0.7161
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+ - Balanced accuracy: 0.3616
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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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+
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+ ### Training results
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+
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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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+ | 1.4108 | 1.0 | 250 | 1.2698 | 0.1966 | 0.6084 | 0.6735 | 0.2195 |
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+ | 1.1452 | 2.0 | 500 | 1.0985 | 0.3484 | 0.6914 | 0.7116 | 0.3536 |
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+ | 0.9711 | 3.0 | 750 | 1.0901 | 0.2606 | 0.6413 | 0.6446 | 0.2932 |
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+ | 0.8437 | 4.0 | 1000 | 1.0197 | 0.2764 | 0.7024 | 0.7237 | 0.2783 |
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+ | 0.7186 | 5.0 | 1250 | 1.0895 | 0.2847 | 0.6824 | 0.6963 | 0.2915 |
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+ | 0.6312 | 6.0 | 1500 | 1.1296 | 0.3487 | 0.6888 | 0.6948 | 0.3377 |
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+ | 0.5311 | 7.0 | 1750 | 1.1515 | 0.3591 | 0.6982 | 0.7024 | 0.3496 |
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+ | 0.4737 | 8.0 | 2000 | 1.1962 | 0.3626 | 0.7185 | 0.7314 | 0.3415 |
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+ | 0.4047 | 9.0 | 2250 | 1.3313 | 0.3121 | 0.6920 | 0.7085 | 0.3033 |
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+ | 0.3753 | 10.0 | 2500 | 1.3993 | 0.3628 | 0.6976 | 0.7047 | 0.3495 |
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+ | 0.3217 | 11.0 | 2750 | 1.5078 | 0.3560 | 0.6958 | 0.7055 | 0.3464 |
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+ | 0.3079 | 12.0 | 3000 | 1.5875 | 0.3685 | 0.6968 | 0.7062 | 0.3514 |
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+ | 0.2623 | 13.0 | 3250 | 1.6470 | 0.3606 | 0.6976 | 0.7070 | 0.3490 |
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+ | 0.2393 | 14.0 | 3500 | 1.7164 | 0.3714 | 0.7069 | 0.7207 | 0.3551 |
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+ | 0.2335 | 15.0 | 3750 | 1.8151 | 0.3597 | 0.6975 | 0.7123 | 0.3466 |
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+ | 0.2255 | 16.0 | 4000 | 1.7838 | 0.3940 | 0.7034 | 0.7123 | 0.3869 |
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+ | 0.213 | 17.0 | 4250 | 1.8328 | 0.3725 | 0.6964 | 0.7062 | 0.3704 |
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+ | 0.1908 | 18.0 | 4500 | 1.8788 | 0.3708 | 0.7019 | 0.7154 | 0.3591 |
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+ | 0.1734 | 19.0 | 4750 | 1.8574 | 0.3752 | 0.7031 | 0.7161 | 0.3619 |
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+ | 0.1807 | 20.0 | 5000 | 1.8672 | 0.3726 | 0.7030 | 0.7161 | 0.3616 |
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+
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+
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+ ### Framework versions
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+
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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