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--- |
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license: apache-2.0 |
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base_model: OuteAI/Lite-Oute-2-Mamba2Attn-Base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: mambaformer |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<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) |
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# mambaformer |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1639 |
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- Accuracy: 0.9607 |
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- Precision: 0.9628 |
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- Recall: 0.9607 |
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- F1: 0.9613 |
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- Auroc: 0.9925 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 4 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 1 |
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- label_smoothing_factor: 0.03 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Auroc | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:| |
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| 0.8973 | 0.0988 | 128 | 0.6661 | 0.6897 | 0.6807 | 0.6897 | 0.6850 | 0.5552 | |
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| 0.5525 | 0.1976 | 256 | 0.4682 | 0.7898 | 0.7526 | 0.7898 | 0.7413 | 0.7643 | |
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| 0.4086 | 0.2965 | 384 | 0.3500 | 0.8523 | 0.8452 | 0.8523 | 0.8472 | 0.9024 | |
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| 0.3067 | 0.3953 | 512 | 0.2573 | 0.9107 | 0.9085 | 0.9107 | 0.9091 | 0.9620 | |
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| 0.2477 | 0.4941 | 640 | 0.2234 | 0.9309 | 0.9298 | 0.9309 | 0.9288 | 0.9761 | |
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| 0.2283 | 0.5929 | 768 | 0.2074 | 0.9404 | 0.9396 | 0.9404 | 0.9398 | 0.9804 | |
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| 0.2035 | 0.6918 | 896 | 0.1875 | 0.9529 | 0.9530 | 0.9529 | 0.9530 | 0.9853 | |
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| 0.1963 | 0.7906 | 1024 | 0.1809 | 0.9464 | 0.9458 | 0.9464 | 0.9460 | 0.9867 | |
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| 0.1798 | 0.8894 | 1152 | 0.1638 | 0.9601 | 0.9610 | 0.9601 | 0.9604 | 0.9900 | |
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| 0.1749 | 0.9882 | 1280 | 0.1652 | 0.9583 | 0.9579 | 0.9583 | 0.9581 | 0.9894 | |
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### Framework versions |
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- Transformers 4.43.0.dev0 |
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- Pytorch 2.4.0+cu124 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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