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--- |
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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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base_model: microsoft/deberta-v3-large |
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model-index: |
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- name: deberta-v3-large__sst2__train-16-0 |
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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-v3-large__sst2__train-16-0 |
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This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9917 |
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- Accuracy: 0.7705 |
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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: 50 |
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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 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.7001 | 1.0 | 7 | 0.7327 | 0.2857 | |
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| 0.6326 | 2.0 | 14 | 0.6479 | 0.5714 | |
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| 0.5232 | 3.0 | 21 | 0.5714 | 0.5714 | |
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| 0.3313 | 4.0 | 28 | 0.6340 | 0.7143 | |
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| 0.3161 | 5.0 | 35 | 0.6304 | 0.7143 | |
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| 0.0943 | 6.0 | 42 | 0.4719 | 0.8571 | |
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| 0.0593 | 7.0 | 49 | 0.5000 | 0.7143 | |
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| 0.0402 | 8.0 | 56 | 0.3530 | 0.8571 | |
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| 0.0307 | 9.0 | 63 | 0.3499 | 0.8571 | |
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| 0.0033 | 10.0 | 70 | 0.3258 | 0.8571 | |
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| 0.0021 | 11.0 | 77 | 0.3362 | 0.8571 | |
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| 0.0012 | 12.0 | 84 | 0.4591 | 0.8571 | |
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| 0.0036 | 13.0 | 91 | 0.4661 | 0.8571 | |
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| 0.001 | 14.0 | 98 | 0.5084 | 0.8571 | |
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| 0.0017 | 15.0 | 105 | 0.5844 | 0.8571 | |
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| 0.0005 | 16.0 | 112 | 0.6645 | 0.8571 | |
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| 0.002 | 17.0 | 119 | 0.7422 | 0.8571 | |
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| 0.0006 | 18.0 | 126 | 0.7354 | 0.8571 | |
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| 0.0005 | 19.0 | 133 | 0.7265 | 0.8571 | |
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| 0.0005 | 20.0 | 140 | 0.7207 | 0.8571 | |
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### Framework versions |
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- Transformers 4.15.0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.2 |
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- Tokenizers 0.10.3 |
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