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--- |
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license: mit |
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base_model: microsoft/deberta-v3-small |
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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: DeBERTaV3_model |
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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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# DeBERTaV3_model |
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This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1419 |
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- Accuracy: 0.9615 |
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- F1: 0.8400 |
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- Precision: 0.875 |
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- Recall: 0.8077 |
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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: 5 |
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- eval_batch_size: 5 |
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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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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| No log | 1.0 | 26 | 0.3810 | 0.875 | 0.0 | 0.0 | 0.0 | |
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| No log | 2.0 | 52 | 0.3740 | 0.875 | 0.0 | 0.0 | 0.0 | |
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| No log | 3.0 | 78 | 0.3303 | 0.875 | 0.0 | 0.0 | 0.0 | |
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| No log | 4.0 | 104 | 0.2997 | 0.875 | 0.0 | 0.0 | 0.0 | |
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| No log | 5.0 | 130 | 0.2484 | 0.8894 | 0.2581 | 0.8 | 0.1538 | |
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| No log | 6.0 | 156 | 0.1951 | 0.9375 | 0.6977 | 0.8824 | 0.5769 | |
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| No log | 7.0 | 182 | 0.1752 | 0.9423 | 0.7273 | 0.8889 | 0.6154 | |
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| No log | 8.0 | 208 | 0.1582 | 0.9519 | 0.7917 | 0.8636 | 0.7308 | |
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| No log | 9.0 | 234 | 0.1449 | 0.9615 | 0.8400 | 0.875 | 0.8077 | |
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| No log | 10.0 | 260 | 0.1419 | 0.9615 | 0.8400 | 0.875 | 0.8077 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
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