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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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- f1 |
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base_model: microsoft/deberta-base |
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model-index: |
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- name: deberta-base-CoLA |
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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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# deberta-base-CoLA |
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This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.1655 |
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- Accuracy: 0.8482 |
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- F1: 0.8961 |
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- Roc Auc: 0.8987 |
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- Mcc: 0.6288 |
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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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- lr_scheduler_warmup_ratio: 0.05 |
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- num_epochs: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Roc Auc | Mcc | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|:------:| |
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| 0.5266 | 1.0 | 535 | 0.4138 | 0.8159 | 0.8698 | 0.8627 | 0.5576 | |
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| 0.3523 | 2.0 | 1070 | 0.3852 | 0.8387 | 0.8880 | 0.9041 | 0.6070 | |
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| 0.2479 | 3.0 | 1605 | 0.3981 | 0.8482 | 0.8901 | 0.9120 | 0.6447 | |
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| 0.1712 | 4.0 | 2140 | 0.4732 | 0.8558 | 0.9008 | 0.9160 | 0.6486 | |
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| 0.1354 | 5.0 | 2675 | 0.7181 | 0.8463 | 0.8938 | 0.9024 | 0.6250 | |
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| 0.0876 | 6.0 | 3210 | 0.8453 | 0.8520 | 0.8992 | 0.9123 | 0.6385 | |
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| 0.0682 | 7.0 | 3745 | 1.0282 | 0.8444 | 0.8938 | 0.9061 | 0.6189 | |
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| 0.0431 | 8.0 | 4280 | 1.1114 | 0.8463 | 0.8960 | 0.9010 | 0.6239 | |
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| 0.0323 | 9.0 | 4815 | 1.1663 | 0.8501 | 0.8970 | 0.8967 | 0.6340 | |
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| 0.0163 | 10.0 | 5350 | 1.1655 | 0.8482 | 0.8961 | 0.8987 | 0.6288 | |
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### Framework versions |
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- Transformers 4.11.0 |
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- Pytorch 1.9.0+cu102 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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