This is a fine-tuned version of MPNet-base for paraphrase detection, trained on four benchmark datasets: MRPC, QQP, PAWS-X, and PIT. The model is optimized for fast inference speed while maintaining high accuracy, making it suitable for applications like duplicate content detection, question answering, and semantic similarity analysis.

Developed by: Viswadarshan R R
Model Type: Transformer-based Sentence Pair Classifier
Language: English
Finetuned from: microsoft/mpnet-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.
Enhancing semantic search in information retrieval systems.
Improving 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:

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification

# Load the tokenizer and model
model_path = "viswadarshan06/pd-mpnet"
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: MPNetTokenizer
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

MPNet Model Evaluation Metrics

Model	Dataset	Accuracy (%)	Precision (%)	Recall (%)	F1-Score (%)	Runtime (sec)
MPNet	MRPC Validation	87.01	86.45	96.06	91.00	5.79
MPNet	MRPC Test	86.03	85.56	95.03	90.05	24.75
MPNet	QQP Validation	89.04	82.34	89.26	85.66	7.30
MPNet	QQP Test	88.98	82.95	88.65	85.70	17.77
MPNet	PAWS-X Validation	95.15	92.94	96.06	94.47	7.66
MPNet	PAWS-X Test	95.35	93.39	96.58	94.96	7.75
MPNet	PIT Validation	83.92	81.70	70.48	75.68	7.50
MPNet	PIT Test	89.50	75.74	73.14	74.42	1.57

Summary

This MPNet-based Paraphrase Detection Model has been fine-tuned on four benchmark datasets: MRPC, QQP, PAWS-X, and PIT, enabling fast and efficient paraphrase detection across diverse linguistic structures. The model offers superior inference speed while maintaining high accuracy, making it ideal for applications requiring real-time semantic similarity analysis and duplicate detection.

Citation

If you use this model, please cite:

bibtex

@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: viswadarshanrramiya@gmail.com

🔗 GitHub: Viswadarshan R R