BGE-EN-ICL Embedding Model
| Property | Value |
|---|---|
| Parameter Count | 7.11B |
| License | Apache 2.0 |
| Paper | Making Text Embedders Few-Shot Learners |
| Author | BAAI |
What is bge-en-icl?
BGE-EN-ICL is a state-of-the-art large language model specifically designed for generating text embeddings with powerful in-context learning capabilities. Built by BAAI, it represents a significant advancement in the field of text embeddings by allowing few-shot learning through examples, making it highly adaptable to various tasks without fine-tuning.
Implementation Details
The model utilizes a 7.11B parameter architecture and implements an innovative approach to text embedding that incorporates in-context learning. It supports both zero-shot and few-shot scenarios, with the latter showing superior performance across various benchmarks. The model can process queries with provided examples to enhance its understanding of specific tasks.
- Supports flexible integration through both FlagEmbedding and HuggingFace Transformers libraries
- Implements efficient F32 tensor operations
- Provides comprehensive API for both query and document encoding
- Supports batch processing with customizable maximum length settings
Core Capabilities
- State-of-the-art performance on MTEB and AIR-Bench leaderboards
- Superior few-shot learning capabilities through example-based context
- Achieves up to 54.36% accuracy on AIR-Bench QA tasks with few-shot learning
- Excellent performance in both regular and long-document retrieval scenarios
- Supports multiple similarity computation methods (cosine, dot product, euclidean)
Frequently Asked Questions
Q: What makes this model unique?
BGE-EN-ICL stands out through its ability to learn from few-shot examples provided in the query, significantly enhancing its performance without requiring fine-tuning. It achieves state-of-the-art results on major benchmarks and supports a wide range of document retrieval tasks.
Q: What are the recommended use cases?
The model excels in semantic search, document retrieval, and question-answering tasks. It's particularly effective for applications requiring adaptive embedding generation based on specific task examples, and can handle both short and long-document scenarios efficiently.
