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--- |
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license: apache-2.0 |
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base_model: distilbert-base-uncased |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: trueparagraph.ai-DistilBERT |
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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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# trueparagraph.ai-DistilBERT |
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This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co./distilbert-base-uncased) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.9427 |
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- F1: 0.9429 |
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- Precision: 0.9352 |
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- Recall: 0.9506 |
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- Mcc: 0.8854 |
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- Roc Auc: 0.9427 |
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- Pr Auc: 0.9136 |
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- Log Loss: 0.9232 |
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- Loss: 0.3017 |
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## Model description |
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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. |
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Key characteristics: |
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- **Architecture**: Transformer-based model |
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- **Pre-training objective**: Masked Language Modeling (MLM) |
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- **Fine-tuning objective**: Binary classification (Human-written vs AI-generated) |
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## Intended uses & limitations |
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### Intended uses |
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- **AI Text Detection**: Identifying paragraphs in the STEM domain that are generated by AI versus those written by humans. |
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- **Educational Tools**: Assisting educators in detecting AI-generated content in academic submissions. |
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- **Research**: Analyzing the effectiveness of AI-generated content detection in STEM-related texts. |
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### Limitations |
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- **Domain Specificity**: The model is fine-tuned specifically on STEM paragraphs and may not perform as well on texts from other domains. |
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- **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. |
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- **Biases**: The model may inherit biases present in the training data, which could affect its performance and fairness. |
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## Training and evaluation data |
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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. |
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### Dataset Details |
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- **Size**: 16,000 paragraphs |
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- **Sources**: Academic papers, research articles, and other STEM-related documents. |
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- **Balance**: Approximately 50% human-written paragraphs and 50% AI-generated paragraphs. |
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## Training procedure |
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### Preprocessing |
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- **Tokenization**: Texts were tokenized using the DistilBERT tokenizer. |
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- **Truncation/Padding**: All inputs were truncated or padded to a maximum length of 512 tokens. |
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### Hyperparameters |
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- **Optimizer**: AdamW |
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- **Learning Rate**: 5e-5 |
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- **Batch Size**: 16 |
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- **Number of Epochs**: 3 |
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### Training |
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- **Loss Function**: Binary Cross-Entropy Loss |
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- **Evaluation Metrics**: Accuracy, Precision, Recall, F1-Score, ROC-AUC |
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### Hardware |
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- **Environment**: Training was conducted on a single NVIDIA Tesla V100 GPU. |
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- **Training Time**: Approximately 4 hours. |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Accuracy | F1 | Precision | Recall | Mcc | Roc Auc | Pr Auc | Log Loss | Validation Loss | |
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|:-------------:|:------:|:----:|:--------:|:------:|:---------:|:------:|:------:|:-------:|:------:|:--------:|:---------------:| |
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| 0.5806 | 0.6297 | 500 | 0.8207 | 0.8349 | 0.7708 | 0.9108 | 0.6525 | 0.8211 | 0.7464 | 3.1049 | 0.4137 | |
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| 0.3015 | 1.2594 | 1000 | 0.8919 | 0.8885 | 0.9137 | 0.8646 | 0.7849 | 0.8918 | 0.8574 | 1.7818 | 0.3298 | |
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| 0.2287 | 1.8892 | 1500 | 0.9175 | 0.9155 | 0.9330 | 0.8987 | 0.8354 | 0.9174 | 0.8889 | 1.3631 | 0.2585 | |
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| 0.1444 | 2.5189 | 2000 | 0.9310 | 0.9312 | 0.9240 | 0.9386 | 0.8621 | 0.9310 | 0.8978 | 1.1225 | 0.2439 | |
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| 0.1149 | 3.1486 | 2500 | 0.9272 | 0.9304 | 0.8874 | 0.9778 | 0.8589 | 0.9274 | 0.8788 | 1.1773 | 0.3574 | |
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| 0.0716 | 3.7783 | 3000 | 0.9401 | 0.9405 | 0.9311 | 0.95 | 0.8805 | 0.9402 | 0.9095 | 0.9662 | 0.2655 | |
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| 0.0411 | 4.4081 | 3500 | 0.9427 | 0.9429 | 0.9352 | 0.9506 | 0.8854 | 0.9427 | 0.9136 | 0.9232 | 0.3017 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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