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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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<!-- 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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# BERT-evidence-types |
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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: 2.4683 |
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- Macro f1: 0.3854 |
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- Weighted f1: 0.6985 |
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- Accuracy: 0.7116 |
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- Balanced accuracy: 0.3720 |
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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: 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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| 1.1736 | 1.0 | 125 | 1.0715 | 0.2535 | 0.6525 | 0.6667 | 0.2721 | |
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| 0.8182 | 2.0 | 250 | 0.9876 | 0.3068 | 0.6769 | 0.6804 | 0.3152 | |
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| 0.6024 | 3.0 | 375 | 1.0436 | 0.4067 | 0.7024 | 0.7047 | 0.4089 | |
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| 0.4004 | 4.0 | 500 | 1.1912 | 0.4045 | 0.6988 | 0.7078 | 0.4050 | |
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| 0.2531 | 5.0 | 625 | 1.3408 | 0.3882 | 0.6861 | 0.6903 | 0.3967 | |
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| 0.1625 | 6.0 | 750 | 1.5378 | 0.3866 | 0.6951 | 0.7040 | 0.3807 | |
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| 0.0985 | 7.0 | 875 | 1.7579 | 0.3850 | 0.6990 | 0.7161 | 0.3824 | |
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| 0.0664 | 8.0 | 1000 | 1.9837 | 0.3609 | 0.6849 | 0.7032 | 0.3529 | |
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| 0.0411 | 9.0 | 1125 | 2.0033 | 0.3807 | 0.6929 | 0.7024 | 0.3618 | |
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| 0.0262 | 10.0 | 1250 | 2.1714 | 0.3771 | 0.6924 | 0.7085 | 0.3585 | |
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| 0.0204 | 11.0 | 1375 | 2.2539 | 0.3734 | 0.6832 | 0.6933 | 0.3658 | |
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| 0.0141 | 12.0 | 1500 | 2.3033 | 0.3654 | 0.6830 | 0.6979 | 0.3556 | |
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| 0.0118 | 13.0 | 1625 | 2.3853 | 0.3679 | 0.6912 | 0.7108 | 0.3520 | |
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| 0.0109 | 14.0 | 1750 | 2.3749 | 0.3810 | 0.6952 | 0.7100 | 0.3665 | |
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| 0.0078 | 15.0 | 1875 | 2.4042 | 0.3777 | 0.6942 | 0.7078 | 0.3645 | |
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| 0.0079 | 16.0 | 2000 | 2.5097 | 0.3790 | 0.6938 | 0.7123 | 0.3632 | |
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| 0.0073 | 17.0 | 2125 | 2.4305 | 0.3844 | 0.6957 | 0.7070 | 0.3725 | |
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| 0.0046 | 18.0 | 2250 | 2.4700 | 0.3762 | 0.6941 | 0.7093 | 0.3638 | |
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| 0.0064 | 19.0 | 2375 | 2.4566 | 0.3844 | 0.6974 | 0.7100 | 0.3713 | |
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| 0.0057 | 20.0 | 2500 | 2.4683 | 0.3854 | 0.6985 | 0.7116 | 0.3720 | |
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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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