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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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model-index: |
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- name: deberta-v3-base-zeroshot-v2.0-2024-03-21-22-15 |
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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-base-zeroshot-v2.0-2024-03-21-22-15 |
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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: 0.1169 |
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- F1 Macro: 0.5016 |
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- F1 Micro: 0.5474 |
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- Accuracy Balanced: 0.5434 |
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- Accuracy: 0.5474 |
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- Precision Macro: 0.6345 |
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- Recall Macro: 0.5434 |
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- Precision Micro: 0.5474 |
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- Recall Micro: 0.5474 |
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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: 16 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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_ratio: 0.06 |
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- num_epochs: 2 |
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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 | F1 Macro | F1 Micro | Accuracy Balanced | Accuracy | Precision Macro | Recall Macro | Precision Micro | Recall Micro | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|:-----------------:|:--------:|:---------------:|:------------:|:---------------:|:------------:| |
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| 0.2288 | 1.0 | 27331 | 0.6189 | 0.7688 | 0.7881 | 0.7705 | 0.7881 | 0.7673 | 0.7705 | 0.7881 | 0.7881 | |
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| 0.1559 | 2.0 | 54662 | 0.6059 | 0.7896 | 0.8082 | 0.7898 | 0.8082 | 0.7894 | 0.7898 | 0.8082 | 0.8082 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.17.1 |
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- Tokenizers 0.15.2 |
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