End of training
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README.md
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---
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base_model: microsoft/codebert-base
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tags:
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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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- name: microsoft-codebert-base-finetuned-defect-cwe-group-detection
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# microsoft-codebert-base-finetuned-defect-cwe-group-detection
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This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6195
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- Accuracy: 0.7490
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- Precision: 0.5725
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- Recall: 0.5159
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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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 | 0.6077 | 0.7288 | 0.6350 | 0.4460 |
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| 0.7284 | 2.0 | 925 | 0.5435 | 0.7485 | 0.6418 | 0.4633 |
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| 0.5295 | 3.0 | 1387 | 0.5937 | 0.7209 | 0.5285 | 0.5098 |
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| 0.4242 | 4.0 | 1850 | 0.6071 | 0.7400 | 0.5543 | 0.5354 |
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| 0.3509 | 4.99 | 2310 | 0.6195 | 0.7490 | 0.5725 | 0.5159 |
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### Framework versions
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- Transformers 4.38.1
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- Pytorch 2.2.1+cu121
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- Datasets 2.17.1
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- Tokenizers 0.15.2
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model.safetensors
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