t5-large-word-sense-disambiguation
| Property | Value |
|---|---|
| Parameter Count | 738M |
| Model Type | Text2Text Generation |
| Base Architecture | T5-large |
| Training Dataset | SemCor 3.0 |
| Paper | Incorporating Word Sense Disambiguation in Neural Language Models |
What is t5-large-word-sense-disambiguation?
This model is a specialized version of T5-large, fine-tuned specifically for word sense disambiguation tasks. It's designed to accurately determine the intended meaning of words based on their context, leveraging the powerful T5 architecture trained on the SemCor 3.0 dataset.
Implementation Details
Built on the T5-large architecture, this model implements a text-to-text approach for word sense disambiguation. It uses F32 tensor types and employs the transformers library for inference. The model can be easily integrated into existing NLP pipelines using the HuggingFace transformers library.
- Built on T5-large architecture with 738M parameters
- Trained on SemCor 3.0 dataset for optimal word sense disambiguation
- Implements few-shot classification capabilities
- Uses PyTorch backend with Safetensors support
Core Capabilities
- Accurate word sense disambiguation in context
- Few-shot classification for flexible deployment
- Support for multiple word meanings analysis
- Context-aware meaning extraction
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its specialized training for word sense disambiguation using the SemCor 3.0 dataset, combined with the powerful T5-large architecture. It can effectively distinguish between different meanings of words based on context, making it valuable for various NLP applications.
Q: What are the recommended use cases?
The model is ideal for applications requiring precise word meaning disambiguation, such as semantic analysis, content understanding, and automated text processing systems. It's particularly useful in scenarios where understanding the exact context-dependent meaning of words is crucial.
