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README.md
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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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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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