bert-base-uncased-qqp
| Property | Value |
|---|---|
| Author | JeremiahZ |
| Base Model | bert-base-uncased |
| Task | Question Pair Classification |
| Accuracy | 91.00% |
| F1 Score | 0.8788 |
What is bert-base-uncased-qqp?
bert-base-uncased-qqp is a fine-tuned version of BERT base uncased specifically optimized for the GLUE QQP (Quora Question Pairs) dataset. This model excels at determining semantic equivalence between question pairs, achieving an impressive accuracy of 91% and F1 score of 0.8788.
Implementation Details
The model was trained using the Adam optimizer with carefully tuned hyperparameters, including a learning rate of 2e-05 and linear scheduler. Training was conducted over 3 epochs with a batch size of 32 for training and 8 for evaluation.
- Training Loss: 0.1221 (final epoch)
- Validation Loss: 0.2829
- Combined Score: 0.8944
- Framework: Transformers 4.20.0.dev0 with PyTorch 1.11.0
Core Capabilities
- Question similarity detection
- Semantic equivalence analysis
- High-accuracy classification of question pairs
- Robust performance with uncased text
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized fine-tuning on the QQP dataset, achieving state-of-the-art performance metrics with a combined score of 0.8944, making it particularly effective for question similarity tasks.
Q: What are the recommended use cases?
The model is ideal for applications requiring question pair similarity detection, such as duplicate question detection in Q&A platforms, semantic search systems, and content matching applications.
