| --- |
| license: mit |
| base_model: roberta-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - recall |
| - precision |
| - f1 |
| model-index: |
| - name: roberta-large-AI-detection |
| 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. --> |
|
|
| # roberta-large-AI-detection |
|
|
| This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.6875 |
| - Accuracy: 0.5533 |
| - Recall: 1.0 |
| - Precision: 0.5533 |
| - F1: 0.7124 |
|
|
| ## 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: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 500 |
| - num_epochs: 5 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
| | 0.7352 | 1.0 | 197 | 0.6915 | 0.5533 | 1.0 | 0.5533 | 0.7124 | |
| | 0.6731 | 2.0 | 394 | 0.7159 | 0.5533 | 1.0 | 0.5533 | 0.7124 | |
| | 0.7003 | 3.0 | 591 | 0.6945 | 0.5533 | 1.0 | 0.5533 | 0.7124 | |
| | 0.7474 | 4.0 | 788 | 0.6890 | 0.5533 | 1.0 | 0.5533 | 0.7124 | |
| | 0.6842 | 5.0 | 985 | 0.6875 | 0.5533 | 1.0 | 0.5533 | 0.7124 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.38.2 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
| |
