viswadarshan06/pd-robert

Model Card for Fine-Tuned RoBERTa for Paraphrase Detection

Model Description

This is a fine-tuned version of RoBERTa-base for paraphrase detection, trained on four benchmark datasets: MRPC, QQP, PAWS-X, and PIT. The model is designed for applications like duplicate content detection, question answering, and semantic similarity analysis. It demonstrates high performance across varied linguistic structures.

• Developed by: Viswadarshan R R
• Model Type: Transformer-based Sentence Pair Classifier
• Language: English
• Finetuned from: FacebookAI/roberta-base

Model Sources

• Repository: Hugging Face Model Hub
• Research Paper: Comparative Insights into Modern Architectures for Paraphrase Detection (Accepted at ICCIDS 2025)
• Demo: (To be added upon deployment)

Uses

Direct Use

• Identifying duplicate questions in FAQs and customer support.
• Improving semantic search in information retrieval systems.
• Enhancing document deduplication and content moderation.

Downstream Use

The model can be further fine-tuned on domain-specific paraphrase datasets (e.g., medical, legal, or finance).

Out-of-Scope Use

• The model is not designed for multilingual paraphrase detection since it is trained only on English datasets.
• May not perform well on low-resource languages without additional fine-tuning.

Bias, Risks, and Limitations

Known Limitations

• Struggles with idiomatic expressions: The model finds it difficult to detect paraphrases in figurative language.
• Contextual ambiguity: May fail when sentences require deep contextual reasoning.

Recommendations

Users should fine-tune the model with additional cultural and idiomatic datasets for improved generalization in real-world applications.

How to Get Started with the Model

To use the model, install transformers and load the fine-tuned model as follows:

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load the tokenizer and model
model_path = "viswadarshan06/pd-robert"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSequenceClassification.from_pretrained(model_path)

# Encode sentence pairs
inputs = tokenizer("The car is fast.", "The vehicle moves quickly.", return_tensors="pt", padding=True, truncation=True)

# Get predictions
outputs = model(**inputs)
logits = outputs.logits
predicted_class = logits.argmax().item()

print("Paraphrase" if predicted_class == 1 else "Not a Paraphrase")

Training Details

This model was trained using a combination of four datasets:

• MRPC: News-based paraphrases.
• QQP: Duplicate question detection.
• PAWS-X: Adversarial paraphrases for robustness testing.
• PIT: Short-text paraphrase dataset.

Training Procedure

• Tokenizer: RobertaTokenizer
• Batch Size: 16
• Optimizer: AdamW
• Loss Function: Cross-entropy

Training Hyperparameters

• Learning Rate: 2e-5
• Sequence Length:
• MRPC: 256
• QQP: 336
• PIT: 64
• PAWS-X: 256

Speeds, Sizes, Times

• GPU Used: NVIDIA A100
• Total Training Time: ~6 hours
• Compute Units Used: 80

Testing Data, Factors & Metrics

Testing Data

The model was tested on combined test sets and evaluated on:

• Accuracy
• Precision
• Recall
• F1-Score
• Runtime

Results

RoBERTa Model Evaluation Metrics

Model	Dataset	Accuracy (%)	Precision (%)	Recall (%)	F1-Score (%)	Runtime (sec)
RoBERTa	MRPC Validation	89.22	89.56	95.34	92.36	5.08
RoBERTa	MRPC Test	87.65	88.53	93.55	90.97	21.98
RoBERTa	QQP Validation	89.17	84.38	86.48	85.42	8.32
RoBERTa	QQP Test	89.36	85.14	86.56	85.84	19.44
RoBERTa	PAWS-X Validation	94.75	92.58	95.48	94.01	7.78
RoBERTa	PAWS-X Test	94.60	92.82	95.48	94.13	7.88
RoBERTa	PIT Validation	82.28	82.57	63.47	71.77	7.01
RoBERTa	PIT Test	90.45	84.67	66.29	74.35	1.47

Summary

This RoBERTa-based Paraphrase Detection Model has been fine-tuned on four benchmark datasets: MRPC, QQP, PAWS-X, and PIT, enabling robust performance across diverse paraphrase structures. The model effectively identifies semantic similarity between sentence pairs, making it suitable for applications like semantic search, duplicate content detection, and question answering systems.

Citation

If you use this model, please cite:

@inproceedings{viswadarshan2025paraphrase,
   title={Comparative Insights into Modern Architectures for Paraphrase Detection},
   author={Viswadarshan R R, Viswaa Selvam S, Felcia Lilian J, Mahalakshmi S},
   booktitle={International Conference on Computational Intelligence, Data Science, and Security (ICCIDS)},
   year={2025},
   publisher={IFIP AICT Series by Springer}
}

Model Card Contact

📧 Email: [email protected]

🔗 GitHub: Viswadarshan R R