BERT for Patents
| Property | Value |
|---|---|
| Parameter Count | 346M |
| License | Apache 2.0 |
| Framework | PyTorch, TensorFlow |
| Task Type | Fill-Mask (Masked Language Modeling) |
What is bert-for-patents?
BERT for Patents is a specialized language model developed by Google, based on the BERT-Large architecture and trained on an extensive dataset of over 100 million patents. This model represents a significant advancement in patent-specific natural language processing, designed to understand and process the unique linguistic patterns and technical terminology found in patent documents.
Implementation Details
The model implements a masked language modeling approach, utilizing the BERT-Large architecture with 346M parameters. It supports both PyTorch and TensorFlow frameworks and uses the [MASK] token for predictions. The model can process both F32 and I64 tensor types, making it versatile for different computational requirements.
- Built on BERT-Large architecture
- Trained on 100M+ patent documents
- Supports multiple deep learning frameworks
- Implements masked language modeling
Core Capabilities
- Patent-specific text analysis and understanding
- Technical terminology comprehension
- Masked word prediction in patent contexts
- High-efficiency patent document processing
- Support for inference endpoints
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its specialized training on patent documents, making it particularly effective for patent-related NLP tasks. The extensive training dataset of over 100 million patents ensures comprehensive coverage of technical and legal patent terminology.
Q: What are the recommended use cases?
The model is ideal for patent analysis, technical document processing, and research applications involving patent documents. It's particularly useful for tasks such as patent classification, technical term prediction, and patent document analysis, as demonstrated in projects like Patents4IPPC.
