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--- |
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license: mit |
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base_model: microsoft/mdeberta-v3-base |
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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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model-index: |
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- name: scenario-MDBT-TCR_data-cl-cardiff_cl_only |
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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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# scenario-MDBT-TCR_data-cl-cardiff_cl_only |
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This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.7992 |
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- Accuracy: 0.5154 |
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- F1: 0.5146 |
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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: 32 |
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- eval_batch_size: 64 |
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- seed: 66 |
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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: 30 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| No log | 1.09 | 250 | 1.2801 | 0.5069 | 0.5029 | |
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| 0.7278 | 2.17 | 500 | 1.6260 | 0.5077 | 0.5080 | |
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| 0.7278 | 3.26 | 750 | 1.6500 | 0.5193 | 0.5209 | |
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| 0.3512 | 4.35 | 1000 | 2.1813 | 0.5123 | 0.5144 | |
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| 0.3512 | 5.43 | 1250 | 2.5133 | 0.5154 | 0.5167 | |
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| 0.1838 | 6.52 | 1500 | 2.6502 | 0.5093 | 0.5093 | |
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| 0.1838 | 7.61 | 1750 | 3.0408 | 0.5015 | 0.5021 | |
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| 0.118 | 8.7 | 2000 | 3.3486 | 0.4877 | 0.4822 | |
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| 0.118 | 9.78 | 2250 | 3.5117 | 0.4923 | 0.4906 | |
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| 0.072 | 10.87 | 2500 | 3.5966 | 0.5046 | 0.5027 | |
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| 0.072 | 11.96 | 2750 | 3.3823 | 0.5100 | 0.5121 | |
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| 0.0545 | 13.04 | 3000 | 3.7627 | 0.5085 | 0.5053 | |
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| 0.0545 | 14.13 | 3250 | 3.9342 | 0.5108 | 0.5124 | |
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| 0.0336 | 15.22 | 3500 | 4.2215 | 0.5093 | 0.5061 | |
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| 0.0336 | 16.3 | 3750 | 4.2219 | 0.5046 | 0.5021 | |
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| 0.0272 | 17.39 | 4000 | 4.0061 | 0.5208 | 0.5227 | |
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| 0.0272 | 18.48 | 4250 | 4.3214 | 0.5116 | 0.5074 | |
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| 0.0198 | 19.57 | 4500 | 4.5333 | 0.5093 | 0.5075 | |
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| 0.0198 | 20.65 | 4750 | 4.3535 | 0.5247 | 0.5256 | |
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| 0.0161 | 21.74 | 5000 | 4.5169 | 0.5239 | 0.5238 | |
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| 0.0161 | 22.83 | 5250 | 4.4982 | 0.5285 | 0.5298 | |
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| 0.012 | 23.91 | 5500 | 4.5591 | 0.5170 | 0.5186 | |
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| 0.012 | 25.0 | 5750 | 4.7615 | 0.5085 | 0.5069 | |
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| 0.0066 | 26.09 | 6000 | 4.8457 | 0.5100 | 0.5079 | |
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| 0.0066 | 27.17 | 6250 | 4.7872 | 0.5131 | 0.5118 | |
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| 0.0069 | 28.26 | 6500 | 4.6257 | 0.5301 | 0.5303 | |
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| 0.0069 | 29.35 | 6750 | 4.7992 | 0.5154 | 0.5146 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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