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cyber_deberta

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bert
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cyber_deberta

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  1. README.md +16 -11
  2. model.safetensors +1 -1
  3. training_args.bin +1 -1
README.md CHANGED
@@ -20,11 +20,11 @@ should probably proofread and complete it, then remove this comment. -->
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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: 0.3811
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- - Accuracy: 0.8357
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- - F1: 0.8180
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- - Precision: 0.8167
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- - Recall: 0.8193
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  ## Model description
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@@ -52,18 +52,23 @@ The following hyperparameters were used during training:
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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: 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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- | 0.6162 | 1.0 | 105 | 0.6158 | 0.6573 | 0.3966 | 0.3292 | 0.4988 |
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- | 0.4929 | 2.0 | 210 | 0.4845 | 0.7621 | 0.7338 | 0.7353 | 0.7325 |
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- | 0.4092 | 3.0 | 315 | 0.4153 | 0.8044 | 0.7827 | 0.7824 | 0.7830 |
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- | 0.3707 | 4.0 | 420 | 0.3846 | 0.8206 | 0.7986 | 0.8015 | 0.7960 |
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- | 0.3046 | 5.0 | 525 | 0.3811 | 0.8357 | 0.8180 | 0.8167 | 0.8193 |
 
 
 
 
 
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  ### Framework versions
 
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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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  - 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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