contradiction-psb-lds
| Property | Value |
|---|---|
| Model Type | Sentence Transformer |
| Embedding Dimensions | 768 |
| Base Architecture | PatentSBERTa (MPNet) |
| Author | nategro |
What is contradiction-psb-lds?
contradiction-psb-lds is a specialized sentence transformer model built on PatentSBERTa architecture, designed specifically for identifying contradictions in patent documents. The model maps sentences and paragraphs to a 768-dimensional dense vector space, making it particularly effective for tasks like semantic similarity analysis and clustering in patent-related applications.
Implementation Details
The model utilizes a sophisticated architecture combining MPNet with custom pooling strategies. It was trained using CosineSimilarityLoss with carefully tuned hyperparameters, including a learning rate of 2e-05 and AdamW optimizer. The training process involved 1,128 steps per epoch with warmup steps of 113.
- Maximum sequence length: 512 tokens
- Custom pooling configuration with CLS token emphasis
- Integrated with both sentence-transformers and HuggingFace Transformers frameworks
- Optimized batch size of 16 for training
Core Capabilities
- Patent-specific contradiction detection
- Dense vector representation generation
- Semantic similarity analysis
- Efficient text clustering for patent documents
- Cross-sentence relationship identification
Frequently Asked Questions
Q: What makes this model unique?
The model's uniqueness lies in its specialization for patent document analysis, particularly in identifying contradictions. It combines PatentSBERTa's domain-specific knowledge with optimized pooling strategies for improved performance on patent-related tasks.
Q: What are the recommended use cases?
This model is particularly well-suited for patent analysis tasks such as identifying conflicting claims, semantic searching across patent databases, and automated patent similarity assessment. It's optimized for both sentence-level and paragraph-level analysis in patent documents.
