RST Word Sense Disambiguation Model
| Property | Value |
|---|---|
| Parameter Count | 11 Billion |
| Model Type | Text2Text Generation |
| Framework | PyTorch, T5-based |
| License | AFL-3.0 |
| Paper | Research Paper |
What is rst-word-sense-disambiguation-11b?
The rst-word-sense-disambiguation-11b is a specialized language model that's part of the reStructured Pre-training (RST) framework. This particular model focuses on word sense disambiguation and related linguistic tasks, trained specifically on WordNet signals including word meanings, part-of-speech information, synonyms, and antonyms. It represents one of 13 specialized models in the RST family, each containing 11 billion parameters.
Implementation Details
The model leverages the T5 architecture and is implemented using PyTorch. It's trained on four key signal types from WordNet:
- Word meanings and their contextual usage
- Part-of-speech information
- Synonym relationships
- Antonym relationships
Core Capabilities
- Word sense disambiguation in context
- Part-of-speech tagging
- Information extraction tasks
- Common sense reasoning
- Linguistic relationship understanding
Frequently Asked Questions
Q: What makes this model unique?
This model is unique in its specialized focus on word sense disambiguation and linguistic understanding, being part of the larger RST framework that unifies 26 different types of signals from 10 data sources. It's specifically optimized for understanding word meanings and relationships through comprehensive WordNet training data.
Q: What are the recommended use cases?
The model is particularly well-suited for: Natural language processing tasks requiring word sense disambiguation, applications needing accurate part-of-speech tagging, information extraction systems, and projects involving common sense reasoning about word relationships and meanings.
