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license: mit |
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base_model: MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7 |
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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: cyber_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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# cyber_deberta |
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This model is a fine-tuned version of [MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4424 |
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- Accuracy: 0.8383 |
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- F1: 0.8222 |
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- Precision: 0.8189 |
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- Recall: 0.8260 |
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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: 1e-05 |
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- train_batch_size: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 500 |
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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 | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.5757 | 1.0 | 105 | 0.5760 | 0.6948 | 0.5676 | 0.6774 | 0.5811 | |
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| 0.4861 | 2.0 | 210 | 0.4837 | 0.7663 | 0.7328 | 0.7410 | 0.7272 | |
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| 0.4106 | 3.0 | 315 | 0.4200 | 0.8033 | 0.7800 | 0.7817 | 0.7785 | |
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| 0.3777 | 4.0 | 420 | 0.3928 | 0.8200 | 0.7953 | 0.8033 | 0.7893 | |
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| 0.2995 | 5.0 | 525 | 0.3835 | 0.8331 | 0.8187 | 0.8129 | 0.8272 | |
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| 0.3012 | 6.0 | 630 | 0.3786 | 0.8404 | 0.8222 | 0.8227 | 0.8217 | |
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| 0.26 | 7.0 | 735 | 0.3827 | 0.8399 | 0.8265 | 0.8202 | 0.8361 | |
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| 0.2388 | 8.0 | 840 | 0.4340 | 0.8346 | 0.8139 | 0.8180 | 0.8104 | |
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| 0.2092 | 9.0 | 945 | 0.4377 | 0.8388 | 0.8237 | 0.8192 | 0.8294 | |
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| 0.1957 | 10.0 | 1050 | 0.4424 | 0.8383 | 0.8222 | 0.8189 | 0.8260 | |
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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 2.20.0 |
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- Tokenizers 0.19.1 |
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