---
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