Instructor-large
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | Research Paper |
| Framework | PyTorch, Sentence-Transformers |
What is instructor-large?
Instructor-large is a groundbreaking instruction-finetuned text embedding model that can generate customized embeddings for various tasks and domains without requiring additional finetuning. The model leverages natural language instructions to produce task-specific embeddings, making it highly versatile for applications ranging from classification and retrieval to clustering and text evaluation.
Implementation Details
The model is built on the sentence-transformers framework and utilizes a T5-based architecture. It has been trained with hard negatives to enhance performance and can be easily integrated using the InstructorEmbedding library.
- Supports customizable domain and task instructions
- Achieves state-of-the-art performance on 70+ embedding tasks
- Implements efficient encoding for various text types
Core Capabilities
- Text Classification with domain-specific embeddings
- Information Retrieval with customized query representations
- Semantic Text Clustering
- Sentence Similarity Computation
- Cross-domain Text Analysis
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to understand and adapt to natural language instructions for generating task-specific embeddings sets it apart. This eliminates the need for task-specific finetuning while maintaining high performance across diverse applications.
Q: What are the recommended use cases?
The model excels in scenarios requiring domain-specific text understanding, including scientific paper classification, financial document retrieval, medical text clustering, and general semantic similarity tasks. Its instruction-based approach makes it particularly valuable for multi-domain applications.
