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---
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: logs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# logs
This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0405
- Accuracy: 0.9950
- Precision: 0.9950
- Recall: 0.9950
- F1 Score: 0.9950
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 0.1436 | 1.0 | 907 | 0.0851 | 0.9829 | 0.9829 | 0.9829 | 0.9829 |
| 0.0737 | 2.0 | 1814 | 0.0548 | 0.9915 | 0.9915 | 0.9915 | 0.9915 |
| 0.0216 | 3.0 | 2721 | 0.0469 | 0.9917 | 0.9918 | 0.9917 | 0.9917 |
| 0.0143 | 4.0 | 3628 | 0.0405 | 0.9950 | 0.9950 | 0.9950 | 0.9950 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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