BCEmbedding: Bilingual and Crosslingual Embedding for RAG
BCEmbedding is a sophisticated bilingual and crosslingual embedding framework developed by NetEase Youdao. The project, aimed at enhancing Retrieval Augmented Generation (RAG), focuses on bridging linguistic divides, primarily between Chinese and English. It incorporates two main components: the EmbeddingModel and the RerankerModel, each designed to optimize semantic search and retrieval tasks.
π Bilingual and Crosslingual Superiority
Traditional embedding models often struggle with bilingual and crosslingual tasks, especially involving Chinese and English. BCEmbedding capitalizes on Youdao's translation expertise to offer superior performance across monolingual, bilingual, and crosslingual environments. The EmbeddingModel supports both Chinese (ch) and English (en), with plans to incorporate more languages. The RerankerModel expands support to include Japanese (ja) and Korean (ko).
π‘ Key Features
- Bilingual and Crosslingual Proficiency: Utilizes Youdao's translation engine for excellent performance in crosslingual tasks.
- Optimized for RAG: Specifically tailored for an array of RAG tasks, ensuring nuanced query understanding and accurate results.
- Efficient Retrieval Mechanism: The dual-encoder of the
EmbeddingModelensures swift retrieval, while the cross-encoder of theRerankerModelenhances precision. - Wide Domain Adaptability: The models are trained on diverse datasets to perform well across different domains such as education, medicine, law, finance, and more.
- User-Friendly Operation: The design does not require specific instructions, thereby supporting versatile applications.
- Proven Effectiveness: Successfully integrated into Youdao products, validating its real-world applicability.
π Latest Updates
BCEmbedding continues to evolve, with the latest updates including technical blogs, model releases, integration capabilities with major frameworks like LangChain and LlamaIndex, and the introduction of evaluation datasets for improved performance benchmarking.
π Model List
BCEmbedding features two main models:
- bce-embedding-base_v1 for semantic embeddings in Chinese and English.
- bce-reranker-base_v1 for reranking tasks with support for multiple languages including Chinese, English, Japanese, and Korean.
These models are accessible via platforms like Hugging Face, ensuring straightforward integration into various applications.
π Manual
For installation, BCEmbedding offers a choice between minimal and source installations. The project is designed to be easily integrated with different frameworks and offers detailed instructions for quickly starting with embedding and reranking tasks. Code examples demonstrate how to leverage BCEmbedding using widely-used libraries like transformers and sentence-transformers.
Embedding and Reranker Integrations for RAG Frameworks
BCEmbedding facilitates seamless integration with major RAG frameworks such as LangChain and LlamaIndex, which enhances its applicability in broader AI and NLP tasks. This integration ensures that users can easily adopt BCEmbedding for their specific use cases in question-answering, translation, and other retrieval-oriented applications.
βοΈ Evaluation
BCEmbedding provides extensive evaluation tools for assessing the performance of its models. Using datasets and benchmarks like MTEB, users can evaluate both embedding and reranking models in bilingual and crosslingual scenarios.
In conclusion, BCEmbedding stands out as a comprehensive solution for tackling bilingual and crosslingual retrieval tasks. It combines cutting-edge technology with practical application, ensuring high performance across tasks while being user-friendly and adaptable to various domains.