README.md

---
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