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mcanoglu
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salesforce-codet5p-220m-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 +20 -17
  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:
@@ -20,11 +19,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [Salesforce/codet5p-220m](https://huggingface.co/Salesforce/codet5p-220m) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5107
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- - Accuracy: 0.7108
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- - F1: 0.7079
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- - Precision: 0.6987
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- - Recall: 0.7174
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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: 32
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  - eval_batch_size: 8
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  - seed: 4711
 
 
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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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- | 0.6934 | 1.0 | 997 | 0.5894 | 0.6632 | 0.5788 | 0.7435 | 0.4738 |
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- | 0.572 | 2.0 | 1994 | 0.5250 | 0.7063 | 0.7057 | 0.6911 | 0.7210 |
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- | 0.4947 | 3.0 | 2991 | 0.5107 | 0.7108 | 0.7079 | 0.6987 | 0.7174 |
 
 
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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-220m](https://huggingface.co/Salesforce/codet5p-220m) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.5331
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+ - Accuracy: 0.7289
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+ - Roc Auc: 0.7292
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+ - Precision: 0.7152
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+ - Recall: 0.7395
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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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+ | 0.6935 | 1.0 | 996 | 0.5783 | 0.6689 | 0.6657 | 0.7197 | 0.5277 |
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+ | 0.5694 | 2.0 | 1993 | 0.5279 | 0.7013 | 0.7026 | 0.6723 | 0.7580 |
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+ | 0.4812 | 3.0 | 2989 | 0.5058 | 0.7181 | 0.7179 | 0.7129 | 0.7081 |
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+ | 0.4235 | 4.0 | 3986 | 0.5088 | 0.7292 | 0.7291 | 0.7213 | 0.7261 |
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+ | 0.3658 | 5.0 | 4980 | 0.5331 | 0.7289 | 0.7292 | 0.7152 | 0.7395 |
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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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