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---
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
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