---
license: apache-2.0
base_model: OuteAI/Lite-Oute-2-Mamba2Attn-Base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: mambaformer
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/truonggiabjnh2003-fpt-university/Detect%20AI%20Generated%20Text/runs/vbdymxf4)
# mambaformer

This model is a fine-tuned version of [OuteAI/Lite-Oute-2-Mamba2Attn-Base](https://huggingface.co./OuteAI/Lite-Oute-2-Mamba2Attn-Base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1639
- Accuracy: 0.9607
- Precision: 0.9628
- Recall: 0.9607
- F1: 0.9613
- Auroc: 0.9925

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- label_smoothing_factor: 0.03

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Auroc  |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.8973        | 0.0988 | 128  | 0.6661          | 0.6897   | 0.6807    | 0.6897 | 0.6850 | 0.5552 |
| 0.5525        | 0.1976 | 256  | 0.4682          | 0.7898   | 0.7526    | 0.7898 | 0.7413 | 0.7643 |
| 0.4086        | 0.2965 | 384  | 0.3500          | 0.8523   | 0.8452    | 0.8523 | 0.8472 | 0.9024 |
| 0.3067        | 0.3953 | 512  | 0.2573          | 0.9107   | 0.9085    | 0.9107 | 0.9091 | 0.9620 |
| 0.2477        | 0.4941 | 640  | 0.2234          | 0.9309   | 0.9298    | 0.9309 | 0.9288 | 0.9761 |
| 0.2283        | 0.5929 | 768  | 0.2074          | 0.9404   | 0.9396    | 0.9404 | 0.9398 | 0.9804 |
| 0.2035        | 0.6918 | 896  | 0.1875          | 0.9529   | 0.9530    | 0.9529 | 0.9530 | 0.9853 |
| 0.1963        | 0.7906 | 1024 | 0.1809          | 0.9464   | 0.9458    | 0.9464 | 0.9460 | 0.9867 |
| 0.1798        | 0.8894 | 1152 | 0.1638          | 0.9601   | 0.9610    | 0.9601 | 0.9604 | 0.9900 |
| 0.1749        | 0.9882 | 1280 | 0.1652          | 0.9583   | 0.9579    | 0.9583 | 0.9581 | 0.9894 |


### Framework versions

- Transformers 4.43.0.dev0
- Pytorch 2.4.0+cu124
- Datasets 2.19.1
- Tokenizers 0.19.1