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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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- f1
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model-index:
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- name: codebert-code-clone-detector
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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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# codebert-code-clone-detector
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This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3539
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- Accuracy: 0.9333
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- Precision: 0.9508
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- Recall: 0.9206
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- F1: 0.9355
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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: 16
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- eval_batch_size: 16
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- seed: 42
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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: 15
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| 0.3416 | 0.49 | 33 | 0.1724 | 0.9417 | 0.9828 | 0.9048 | 0.9421 |
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| 0.221 | 0.97 | 66 | 0.2768 | 0.925 | 1.0 | 0.8571 | 0.9231 |
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| 0.0929 | 1.46 | 99 | 0.2469 | 0.9583 | 1.0 | 0.9206 | 0.9587 |
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| 0.1696 | 1.94 | 132 | 0.2142 | 0.95 | 0.9524 | 0.9524 | 0.9524 |
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| 0.0818 | 2.43 | 165 | 0.4142 | 0.925 | 1.0 | 0.8571 | 0.9231 |
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| 0.0676 | 2.91 | 198 | 0.3539 | 0.9333 | 0.9508 | 0.9206 | 0.9355 |
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### Framework versions
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- Transformers 4.39.3
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- Pytorch 2.1.2
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 498612824
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version https://git-lfs.github.com/spec/v1
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size 498612824
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