---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: trueparagraph.ai-DistilBERT
  results: []
---

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![image/png](https://cdn-uploads.huggingface.co/production/uploads/659ee7cec0c53b7cb5c0afea/2itkREYfuCrPNFw28efRe.png)

# trueparagraph.ai-DistilBERT

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.9427
- F1: 0.9429
- Precision: 0.9352
- Recall: 0.9506
- Mcc: 0.8854
- Roc Auc: 0.9427
- Pr Auc: 0.9136
- Log Loss: 0.9232
- Loss: 0.3017


## Model description

DistilBERT is a smaller, faster, cheaper version of BERT, achieved through knowledge distillation. It retains 97% of BERT’s language understanding while being 60% faster and smaller. This fine-tuned version of DistilBERT is trained to detect AI-generated text in paragraphs from the STEM domain.

Key characteristics:
- **Architecture**: Transformer-based model
- **Pre-training objective**: Masked Language Modeling (MLM)
- **Fine-tuning objective**: Binary classification (Human-written vs AI-generated)

## Intended uses & limitations

### Intended uses
- **AI Text Detection**: Identifying paragraphs in the STEM domain that are generated by AI versus those written by humans.
- **Educational Tools**: Assisting educators in detecting AI-generated content in academic submissions.
- **Research**: Analyzing the effectiveness of AI-generated content detection in STEM-related texts.

### Limitations
- **Domain Specificity**: The model is fine-tuned specifically on STEM paragraphs and may not perform as well on texts from other domains.
- **Generalization**: While the model is effective at detecting AI-generated text in STEM, it may not generalize well to other types of AI-generated content outside of its training data.
- **Biases**: The model may inherit biases present in the training data, which could affect its performance and fairness.

## Training and evaluation data

The model was fine-tuned on the "16K-trueparagraph-STEM" dataset, which consists of 16,000 paragraphs from various STEM domains. The dataset includes both human-written and AI-generated paragraphs to provide a balanced training set for the model.

### Dataset Details
- **Size**: 16,000 paragraphs
- **Sources**: Academic papers, research articles, and other STEM-related documents.
- **Balance**: Approximately 50% human-written paragraphs and 50% AI-generated paragraphs.

## Training procedure

### Preprocessing
- **Tokenization**: Texts were tokenized using the DistilBERT tokenizer.
- **Truncation/Padding**: All inputs were truncated or padded to a maximum length of 512 tokens.

### Hyperparameters
- **Optimizer**: AdamW
- **Learning Rate**: 5e-5
- **Batch Size**: 16
- **Number of Epochs**: 3

### Training
- **Loss Function**: Binary Cross-Entropy Loss
- **Evaluation Metrics**: Accuracy, Precision, Recall, F1-Score, ROC-AUC

### Hardware
- **Environment**: Training was conducted on a single NVIDIA Tesla V100 GPU.
- **Training Time**: Approximately 4 hours.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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 | Accuracy | F1     | Precision | Recall | Mcc    | Roc Auc | Pr Auc | Log Loss | Validation Loss |
|:-------------:|:------:|:----:|:--------:|:------:|:---------:|:------:|:------:|:-------:|:------:|:--------:|:---------------:|
| 0.5806        | 0.6297 | 500  | 0.8207   | 0.8349 | 0.7708    | 0.9108 | 0.6525 | 0.8211  | 0.7464 | 3.1049   | 0.4137          |
| 0.3015        | 1.2594 | 1000 | 0.8919   | 0.8885 | 0.9137    | 0.8646 | 0.7849 | 0.8918  | 0.8574 | 1.7818   | 0.3298          |
| 0.2287        | 1.8892 | 1500 | 0.9175   | 0.9155 | 0.9330    | 0.8987 | 0.8354 | 0.9174  | 0.8889 | 1.3631   | 0.2585          |
| 0.1444        | 2.5189 | 2000 | 0.9310   | 0.9312 | 0.9240    | 0.9386 | 0.8621 | 0.9310  | 0.8978 | 1.1225   | 0.2439          |
| 0.1149        | 3.1486 | 2500 | 0.9272   | 0.9304 | 0.8874    | 0.9778 | 0.8589 | 0.9274  | 0.8788 | 1.1773   | 0.3574          |
| 0.0716        | 3.7783 | 3000 | 0.9401   | 0.9405 | 0.9311    | 0.95   | 0.8805 | 0.9402  | 0.9095 | 0.9662   | 0.2655          |
| 0.0411        | 4.4081 | 3500 | 0.9427   | 0.9429 | 0.9352    | 0.9506 | 0.8854 | 0.9427  | 0.9136 | 0.9232   | 0.3017          |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1