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--- |
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license: mit |
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base_model: microsoft/deberta-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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- precision |
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- recall |
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model-index: |
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- name: fine_tuned_deberta |
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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_deberta |
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This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2513 |
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- Accuracy: 0.9388 |
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- F1: 0.9313 |
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- Precision: 0.9839 |
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- Recall: 0.8841 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 64 |
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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_steps: 10 |
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- num_epochs: 5 |
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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 | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.1234 | 0.97 | 9 | 0.2398 | 0.9184 | 0.9155 | 0.8904 | 0.9420 | |
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| 0.1959 | 1.95 | 18 | 0.4097 | 0.8435 | 0.8535 | 0.7614 | 0.9710 | |
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| 0.1138 | 2.92 | 27 | 0.4617 | 0.8639 | 0.8305 | 1.0 | 0.7101 | |
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| 0.1014 | 4.0 | 37 | 0.2190 | 0.9388 | 0.9323 | 0.9688 | 0.8986 | |
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| 0.0477 | 4.86 | 45 | 0.2513 | 0.9388 | 0.9313 | 0.9839 | 0.8841 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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