---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: CodeDetect-BERT-T1
  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. -->

# CodeDetect-BERT-T1

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5563
- Accuracy: 0.9806
- F1: 0.9806
- Precision: 0.9804
- Recall: 0.9808

## 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: 128
- eval_batch_size: 128
- 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: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.5845        | 1.2798 | 5000  | 0.5880          | 0.9755   | 0.9755 | 0.9752    | 0.9760 |
| 0.4368        | 2.5595 | 10000 | 0.5183          | 0.9790   | 0.9790 | 0.9788    | 0.9792 |
| 0.3441        | 3.8393 | 15000 | 0.5152          | 0.9804   | 0.9804 | 0.9802    | 0.9805 |
| 0.2844        | 5.0    | 19535 | 0.5563          | 0.9806   | 0.9806 | 0.9804    | 0.9808 |


### Framework versions

- Transformers 5.13.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2