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deepseek-ai-deepseek-coder-1.3b-base-finetuned-defect-cwe-group-detection

Text Classification
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Generated from Trainer
text-generation-inference
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End of training

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  1. README.md +19 -19
  2. model-00001-of-00002.safetensors +1 -1
  3. model-00002-of-00002.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 +19,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.5777
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- - Accuracy: 0.7586
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- - F1: 0.7499
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- - Precision: 0.7513
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- - Recall: 0.7586
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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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- | No log | 1.0 | 462 | 0.4832 | 0.7743 | 0.7594 | 0.7720 | 0.7743 |
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- | 0.5829 | 2.0 | 924 | 0.4705 | 0.7788 | 0.7700 | 0.7737 | 0.7788 |
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- | 0.3078 | 3.0 | 1386 | 0.5777 | 0.7586 | 0.7499 | 0.7513 | 0.7586 |
 
 
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  ### Framework versions
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- - Transformers 4.37.0
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- - Pytorch 2.1.2+cu121
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- - Datasets 2.16.1
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- - Tokenizers 0.15.1
 
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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 [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.6902
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+ - Accuracy: 0.7715
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+ - Precision: 0.8036
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+ - Recall: 0.5867
 
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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 | Precision | Recall |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
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+ | No log | 1.0 | 462 | 0.4904 | 0.7800 | 0.6028 | 0.5178 |
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+ | 0.5739 | 2.0 | 925 | 0.4917 | 0.7985 | 0.8159 | 0.5552 |
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+ | 0.3111 | 3.0 | 1387 | 0.6582 | 0.7918 | 0.7907 | 0.5901 |
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+ | 0.2395 | 4.0 | 1850 | 0.6238 | 0.7800 | 0.8018 | 0.6132 |
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+ | 0.2047 | 4.99 | 2310 | 0.6902 | 0.7715 | 0.8036 | 0.5867 |
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  ### Framework versions
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+ - Transformers 4.38.1
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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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