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devhem
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LLMGUARD-roberta

Text Classification
Transformers
Safetensors
roberta
Generated from Trainer
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  1. README.md +13 -9
  2. model.safetensors +1 -1
README.md CHANGED
@@ -18,8 +18,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.6480
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- - Accuracy: 0.7775
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  ## Model description
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@@ -45,17 +45,21 @@ The following hyperparameters were used during training:
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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- - num_epochs: 4
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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 |
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- |:-------------:|:-----:|:----:|:---------------:|:--------:|
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- | 1.1863 | 1.0 | 1332 | 0.7541 | 0.7598 |
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- | 0.7092 | 2.0 | 2664 | 0.6746 | 0.7690 |
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- | 0.6626 | 3.0 | 3996 | 0.6527 | 0.7760 |
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- | 0.6266 | 4.0 | 5328 | 0.6480 | 0.7775 |
 
 
 
 
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  ### Framework versions
 
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  This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6566
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+ - Accuracy: 0.7760
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  ## Model description
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  - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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  - lr_scheduler_type: linear
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  - lr_scheduler_warmup_ratio: 0.1
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+ - num_epochs: 8
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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 |
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+ |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 1.6725 | 1.0 | 1332 | 0.8128 | 0.7566 |
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+ | 0.7216 | 2.0 | 2664 | 0.6906 | 0.7707 |
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+ | 0.6441 | 3.0 | 3996 | 0.6679 | 0.7743 |
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+ | 0.5968 | 4.0 | 5328 | 0.6599 | 0.7790 |
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+ | 0.562 | 5.0 | 6660 | 0.6604 | 0.7777 |
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+ | 0.5516 | 6.0 | 7992 | 0.6527 | 0.7763 |
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+ | 0.5497 | 7.0 | 9324 | 0.6550 | 0.7767 |
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+ | 0.513 | 8.0 | 10656 | 0.6566 | 0.7760 |
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  ### Framework versions
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