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mcanoglu
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Salesforce-codet5p-770m-finetuned-defect-detection

Text Classification
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t5
Generated from Trainer
text-generation-inference
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mcanoglu commited on
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End of training

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  1. README.md +19 -18
  2. model.safetensors +1 -1
README.md CHANGED
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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:
@@ -20,10 +19,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Salesforce/codet5p-770m](https://huggingface.co/Salesforce/codet5p-770m) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4831
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- - Accuracy: 0.7337
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- - F1: 0.7377
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- - Precision: 0.7108
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  - Recall: 0.7667
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  ## Model description
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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: 2
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- - eval_batch_size: 2
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  - seed: 4711
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- - gradient_accumulation_steps: 16
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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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- - num_epochs: 3
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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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- | 0.6813 | 1.0 | 996 | 0.5630 | 0.6898 | 0.6580 | 0.7128 | 0.6110 |
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- | 0.5483 | 2.0 | 1992 | 0.5040 | 0.7103 | 0.7071 | 0.6986 | 0.7158 |
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- | 0.4502 | 3.0 | 2988 | 0.4831 | 0.7337 | 0.7377 | 0.7108 | 0.7667 |
 
 
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  ### Framework versions
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- - Transformers 4.36.2
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- - Pytorch 2.1.2+cu121
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- - Datasets 2.16.1
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- - Tokenizers 0.15.0
 
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  - generated_from_trainer
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  metrics:
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  - accuracy
 
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  - precision
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  - recall
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  model-index:
 
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  This model is a fine-tuned version of [Salesforce/codet5p-770m](https://huggingface.co/Salesforce/codet5p-770m) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5699
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+ - Accuracy: 0.7505
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+ - Roc Auc: 0.7509
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+ - Precision: 0.7343
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  - Recall: 0.7667
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  ## Model description
 
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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: 8
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+ - eval_batch_size: 8
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  - seed: 4711
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+ - gradient_accumulation_steps: 4
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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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+ - 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 | Roc Auc | Precision | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|:---------:|:------:|
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+ | 0.6826 | 1.0 | 996 | 0.5735 | 0.6923 | 0.6925 | 0.6791 | 0.7014 |
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+ | 0.528 | 2.0 | 1993 | 0.4960 | 0.7191 | 0.7211 | 0.6785 | 0.8078 |
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+ | 0.4308 | 3.0 | 2989 | 0.4821 | 0.7415 | 0.7419 | 0.7234 | 0.7621 |
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+ | 0.3495 | 4.0 | 3986 | 0.5010 | 0.7455 | 0.7463 | 0.7217 | 0.7795 |
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+ | 0.2731 | 5.0 | 4980 | 0.5699 | 0.7505 | 0.7509 | 0.7343 | 0.7667 |
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  ### Framework versions
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+ - Transformers 4.37.2
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+ - Pytorch 2.2.0+cu121
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+ - Datasets 2.17.1
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+ - Tokenizers 0.15.2
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