bert-base-uncased-mrpc
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | BERT Paper |
| Accuracy | 86.03% |
| F1 Score | 90.42% |
| Framework | PyTorch |
What is bert-base-uncased-mrpc?
bert-base-uncased-mrpc is a fine-tuned version of BERT base model specifically optimized for paraphrase detection using the Microsoft Research Paraphrase Corpus (MRPC). This model excels at determining whether two sentences are semantically equivalent, making it particularly valuable for tasks involving text similarity and paraphrase identification.
Implementation Details
The model is built upon the bert-base-uncased architecture and trained using masked language modeling (MLM) and next sentence prediction (NSP) objectives. It was fine-tuned using specific hyperparameters including a learning rate of 2e-05, batch sizes of 16 for training and 8 for evaluation, and trained for 5 epochs using the Adam optimizer.
- Utilizes bidirectional context understanding
- Case-insensitive tokenization
- Trained on GLUE MRPC dataset
- Implements PyTorch framework version 1.10.0
Core Capabilities
- Paraphrase Detection: Achieves 86.03% accuracy in identifying semantic equivalence
- Sentence Pair Classification: Specialized in comparing and analyzing sentence pairs
- Contextual Understanding: Leverages bidirectional attention mechanisms
- Production Ready: Includes quantization support for deployment optimization
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized fine-tuning on the MRPC dataset, achieving a high F1 score of 90.42%. It also offers quantization options through Intel Neural Compressor for deployment optimization, making it particularly suitable for production environments.
Q: What are the recommended use cases?
The model is ideal for: Paraphrase detection in content analysis, Semantic similarity assessment in search systems, Plagiarism detection, and Content deduplication in document processing systems.
