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BERT-evidence-types

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+ ---
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+ license: apache-2.0
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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: BERT-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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+ # BERT-evidence-types
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+
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+ This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 2.9735
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+ - Macro f1: 0.3791
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+ - Weighted f1: 0.6925
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+ - Accuracy: 0.7070
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+ - Balanced accuracy: 0.3625
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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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+ | 1.098 | 1.0 | 250 | 1.0176 | 0.2666 | 0.6861 | 0.7070 | 0.2775 |
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+ | 0.7656 | 2.0 | 500 | 1.0072 | 0.4124 | 0.7126 | 0.7215 | 0.3876 |
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+ | 0.5045 | 3.0 | 750 | 1.1791 | 0.3759 | 0.6843 | 0.6910 | 0.3799 |
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+ | 0.2874 | 4.0 | 1000 | 1.4338 | 0.3738 | 0.6888 | 0.6986 | 0.3705 |
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+ | 0.1599 | 5.0 | 1250 | 1.8058 | 0.3839 | 0.6947 | 0.7070 | 0.3682 |
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+ | 0.0991 | 6.0 | 1500 | 2.0263 | 0.3777 | 0.6793 | 0.6903 | 0.3627 |
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+ | 0.0529 | 7.0 | 1750 | 2.2380 | 0.4046 | 0.6932 | 0.7047 | 0.3877 |
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+ | 0.0311 | 8.0 | 2000 | 2.4153 | 0.4185 | 0.6999 | 0.7131 | 0.3899 |
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+ | 0.0129 | 9.0 | 2250 | 2.7230 | 0.3702 | 0.6852 | 0.7123 | 0.3331 |
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+ | 0.0102 | 10.0 | 2500 | 2.6453 | 0.4115 | 0.6934 | 0.7070 | 0.3880 |
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+ | 0.0141 | 11.0 | 2750 | 2.7078 | 0.4054 | 0.6859 | 0.6979 | 0.3863 |
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+ | 0.0088 | 12.0 | 3000 | 2.7182 | 0.3724 | 0.6904 | 0.7062 | 0.3559 |
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+ | 0.0061 | 13.0 | 3250 | 2.7814 | 0.4091 | 0.6917 | 0.7055 | 0.3839 |
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+ | 0.0069 | 14.0 | 3500 | 2.8035 | 0.3836 | 0.6986 | 0.7108 | 0.3688 |
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+ | 0.0067 | 15.0 | 3750 | 2.9326 | 0.4119 | 0.6952 | 0.7139 | 0.3793 |
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+ | 0.0049 | 16.0 | 4000 | 2.9338 | 0.4133 | 0.6885 | 0.7040 | 0.3794 |
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+ | 0.0065 | 17.0 | 4250 | 2.9380 | 0.3820 | 0.6964 | 0.7100 | 0.3650 |
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+ | 0.0045 | 18.0 | 4500 | 2.9439 | 0.3802 | 0.6925 | 0.7055 | 0.3646 |
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+ | 0.0044 | 19.0 | 4750 | 2.9731 | 0.3796 | 0.6932 | 0.7078 | 0.3626 |
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+ | 0.0056 | 20.0 | 5000 | 2.9735 | 0.3791 | 0.6925 | 0.7070 | 0.3625 |
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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