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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: Regression_BERT_NOaug_MSEloss |
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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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# Regression_BERT_NOaug_MSEloss |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4928 |
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- Mse: 0.4928 |
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- Mae: 0.6337 |
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- R2: 0.0926 |
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- Accuracy: 0.4737 |
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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: 4 |
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- eval_batch_size: 4 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 33 | 0.3184 | 0.3184 | 0.5205 | 0.0487 | 0.5946 | |
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| No log | 2.0 | 66 | 0.2439 | 0.2439 | 0.3571 | 0.2712 | 0.7027 | |
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| No log | 3.0 | 99 | 0.2950 | 0.2950 | 0.3792 | 0.1185 | 0.6757 | |
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| No log | 4.0 | 132 | 0.3179 | 0.3179 | 0.4267 | 0.0503 | 0.6757 | |
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| No log | 5.0 | 165 | 0.2869 | 0.2869 | 0.3984 | 0.1426 | 0.6757 | |
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| No log | 6.0 | 198 | 0.2967 | 0.2967 | 0.3688 | 0.1134 | 0.7027 | |
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| No log | 7.0 | 231 | 0.2797 | 0.2797 | 0.3599 | 0.1643 | 0.7027 | |
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| No log | 8.0 | 264 | 0.2730 | 0.2730 | 0.3438 | 0.1844 | 0.7027 | |
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| No log | 9.0 | 297 | 0.2813 | 0.2813 | 0.3623 | 0.1596 | 0.7027 | |
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| No log | 10.0 | 330 | 0.2733 | 0.2733 | 0.3296 | 0.1835 | 0.7027 | |
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| No log | 11.0 | 363 | 0.2770 | 0.2770 | 0.3432 | 0.1725 | 0.7027 | |
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| No log | 12.0 | 396 | 0.3009 | 0.3009 | 0.3574 | 0.1010 | 0.6757 | |
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| No log | 13.0 | 429 | 0.2735 | 0.2735 | 0.3318 | 0.1827 | 0.7027 | |
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| No log | 14.0 | 462 | 0.2787 | 0.2787 | 0.3341 | 0.1672 | 0.7027 | |
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| No log | 15.0 | 495 | 0.2790 | 0.2790 | 0.3312 | 0.1663 | 0.7297 | |
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| 0.0804 | 16.0 | 528 | 0.2683 | 0.2683 | 0.3229 | 0.1984 | 0.7027 | |
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| 0.0804 | 17.0 | 561 | 0.2749 | 0.2749 | 0.3273 | 0.1785 | 0.7297 | |
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| 0.0804 | 18.0 | 594 | 0.2709 | 0.2709 | 0.3202 | 0.1906 | 0.7297 | |
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| 0.0804 | 19.0 | 627 | 0.2711 | 0.2711 | 0.3205 | 0.1901 | 0.7297 | |
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| 0.0804 | 20.0 | 660 | 0.2694 | 0.2694 | 0.3197 | 0.1950 | 0.7297 | |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu118 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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