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End of training
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README.md
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---
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license: mit
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library_name: peft
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tags:
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- generated_from_trainer
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base_model: MoritzLaurer/deberta-v3-large-zeroshot-v2.0
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metrics:
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- accuracy
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model-index:
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- name: fine-tuned-MoritzLaurer-deberta-v3-large-zeroshot-v2.0-swag-peft
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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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# fine-tuned-MoritzLaurer-deberta-v3-large-zeroshot-v2.0-swag-peft
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This model is a fine-tuned version of [MoritzLaurer/deberta-v3-large-zeroshot-v2.0](https://huggingface.co/MoritzLaurer/deberta-v3-large-zeroshot-v2.0) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2169
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- Accuracy: 0.9193
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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: 1.5e-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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- num_epochs: 10
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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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| 0.5756 | 1.0 | 4597 | 0.2941 | 0.8993 |
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| 0.5186 | 2.0 | 9194 | 0.2538 | 0.9115 |
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| 0.5139 | 3.0 | 13791 | 0.2399 | 0.9136 |
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| 0.4933 | 4.0 | 18388 | 0.2282 | 0.9158 |
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| 0.4786 | 5.0 | 22985 | 0.2278 | 0.9165 |
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| 0.4657 | 6.0 | 27582 | 0.2215 | 0.9182 |
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| 0.4685 | 7.0 | 32179 | 0.2199 | 0.9189 |
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| 0.4631 | 8.0 | 36776 | 0.2188 | 0.9188 |
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| 0.4629 | 9.0 | 41373 | 0.2186 | 0.9188 |
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| 0.4556 | 10.0 | 45970 | 0.2169 | 0.9193 |
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### Framework versions
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- PEFT 0.11.1
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- Transformers 4.41.2
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- Pytorch 2.3.1+cu121
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- Datasets 2.19.2
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- Tokenizers 0.19.1
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