---
library_name: transformers
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: ai-code-detector-finetuned
  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. -->

# ai-code-detector-finetuned

This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0
- F1: 1.0
- Tpr: 1.0
- Tnr: 1.0

## 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Tpr    | Tnr |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---:|
| 0.0004        | 1.0   | 479  | 0.0136          | 0.9941   | 0.9942 | 0.9884 | 1.0 |
| 0.0001        | 2.0   | 958  | 0.0001          | 1.0      | 1.0    | 1.0    | 1.0 |
| 0.0001        | 3.0   | 1437 | 0.0000          | 1.0      | 1.0    | 1.0    | 1.0 |
| 0.0000        | 4.0   | 1916 | 0.0000          | 1.0      | 1.0    | 1.0    | 1.0 |
| 0.0000        | 5.0   | 2395 | 0.0000          | 1.0      | 1.0    | 1.0    | 1.0 |


### Framework versions

- Transformers 5.9.0
- Pytorch 2.12.0+cu130
- Datasets 4.8.5
- Tokenizers 0.22.2