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--- |
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license: apache-2.0 |
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base_model: google-bert/bert-base-multilingual-cased |
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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 [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4669 |
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- Accuracy: 0.8315 |
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- F1: 0.8135 |
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- Precision: 0.8121 |
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- Recall: 0.8150 |
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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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| 0.5788 | 1.0 | 105 | 0.5623 | 0.6755 | 0.4813 | 0.6766 | 0.5352 | |
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| 0.478 | 2.0 | 210 | 0.4430 | 0.7746 | 0.7444 | 0.7501 | 0.7401 | |
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| 0.4087 | 3.0 | 315 | 0.3948 | 0.8096 | 0.7835 | 0.7911 | 0.7777 | |
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| 0.4004 | 4.0 | 420 | 0.3868 | 0.8080 | 0.7917 | 0.7864 | 0.7998 | |
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| 0.3216 | 5.0 | 525 | 0.4005 | 0.8106 | 0.7928 | 0.7888 | 0.7980 | |
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| 0.3144 | 6.0 | 630 | 0.3878 | 0.8299 | 0.8062 | 0.8153 | 0.7994 | |
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| 0.2598 | 7.0 | 735 | 0.4040 | 0.8258 | 0.8084 | 0.8053 | 0.8121 | |
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| 0.2234 | 8.0 | 840 | 0.4280 | 0.8284 | 0.8108 | 0.8083 | 0.8137 | |
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| 0.2088 | 9.0 | 945 | 0.4580 | 0.8320 | 0.8154 | 0.8121 | 0.8194 | |
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| 0.1775 | 10.0 | 1050 | 0.4669 | 0.8315 | 0.8135 | 0.8121 | 0.8150 | |
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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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