bge-reranker-v2-m3
Maintainer: BAAI
98
🛠️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
Model overview
The bge-reranker-v2-m3 model is a lightweight reranker model from BAAI that possesses strong multilingual capabilities. It is built on top of the bge-m3 base model, which is a versatile AI model that can simultaneously perform dense retrieval, multi-vector retrieval, and sparse retrieval. The bge-reranker-v2-m3 model is easy to deploy and provides fast inference, making it suitable for a variety of multilingual contexts.
Model inputs and outputs
The bge-reranker-v2-m3 model takes as input a query and a passage, and outputs a relevance score that indicates how relevant the passage is to the query. The relevance score is not bounded to a specific range, as the model is optimized based on cross-entropy loss. This allows for more fine-grained ranking of passages compared to models that output similarity scores bounded between 0 and 1.
Inputs
- Query: The text of the query to be evaluated
- Passage: The text of the passage to be evaluated for relevance to the query
Outputs
- Relevance score: A float value representing the relevance of the passage to the query, with higher scores indicating more relevance.
Capabilities
The bge-reranker-v2-m3 model is designed to be a powerful and efficient reranker for multilingual contexts. It can be used to rerank the top-k documents retrieved by an embedding model, such as the bge-m3 model, to further improve the relevance of the final results.
What can I use it for?
The bge-reranker-v2-m3 model is well-suited for a variety of multilingual information retrieval and question-answering tasks. It can be used to rerank results from a search engine, to filter and sort documents for research or analysis, or to improve the relevance of responses in a multilingual chatbot or virtual assistant. Its fast inference and strong multilingual capabilities make it a versatile tool for building language-agnostic applications.
Things to try
One interesting aspect of the bge-reranker-v2-m3 model is its ability to output relevance scores that are not bounded between 0 and 1. This allows for more nuanced ranking of passages, which could be particularly useful in applications where small differences in relevance are important. Developers could experiment with using these unbounded scores to improve the precision of their retrieval systems, or to surface more contextually relevant information to users.
Another interesting thing to try would be to combine the bge-reranker-v2-m3 model with the bge-m3 model in a hybrid retrieval pipeline. By using the bge-m3 model for initial dense retrieval and the bge-reranker-v2-m3 model for reranking, you could potentially achieve higher accuracy and better performance across a range of multilingual use cases.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
📈
bge-small-en
65
The bge-small-en model is a small-scale English text embedding model developed by BAAI (Beijing Academy of Artificial Intelligence) as part of their FlagEmbedding project. It is one of several bge (BAAI General Embedding) models that achieve state-of-the-art performance on text embedding benchmarks like MTEB and C-MTEB. The bge-small-en model is a smaller version of the BAAI/bge-large-en-v1.5 and BAAI/bge-base-en-v1.5 models, with 384 embedding dimensions compared to 1024 and 768 respectively. Despite its smaller size, the bge-small-en model still provides competitive performance, making it a good choice when computation resources are limited. Model inputs and outputs Inputs Text sentences**: The model can take a list of text sentences as input. Outputs Sentence embeddings**: The model outputs a numpy array of sentence embeddings, where each row corresponds to the embedding of the corresponding input sentence. Capabilities The bge-small-en model can be used for a variety of natural language processing tasks that benefit from semantic text representations, such as: Information retrieval**: The embeddings can be used to find relevant passages or documents for a given query, by computing similarity scores between the query and the passages/documents. Text classification**: The embeddings can be used as features for training classification models on text data. Clustering**: The embeddings can be used to group similar text documents into clusters. Semantic search**: The embeddings can be used to find semantically similar text based on their meaning, rather than just lexical matching. What can I use it for? The bge-small-en model can be a useful tool for a variety of applications that involve working with English text data. For example, you could use it to build a semantic search engine for your company's knowledge base, or to improve the text classification capabilities of your customer support chatbot. Since the model is smaller and more efficient than the larger bge models, it may be particularly well-suited for deployment on edge devices or in resource-constrained environments. You could also fine-tune the model on your specific text data to further improve its performance for your use case. Things to try One interesting thing to try with the bge-small-en model is to compare its performance to the larger bge models, such as BAAI/bge-large-en-v1.5 and BAAI/bge-base-en-v1.5, on your specific tasks. You may find that the smaller model provides nearly the same performance as the larger models, while being more efficient and easier to deploy. Another thing to try is to fine-tune the bge-small-en model on your own text data, using the techniques described in the FlagEmbedding documentation. This can help the model better capture the semantics of your domain-specific text, potentially leading to improved performance on your tasks.
Updated Invalid Date
✨
llm-embedder
92
llm-embedder is a text embedding model developed by BAAI (Beijing Academy of Artificial Intelligence) that can map any text to a low-dimensional dense vector. This can be used for tasks like retrieval, classification, clustering, and semantic search. It is part of the FlagEmbedding project, which also includes other models like bge-reranker-base and bge-reranker-large. The model is available in multiple sizes, including bge-large-en-v1.5, bge-base-en-v1.5, and bge-small-en-v1.5. These models have been optimized to have more reasonable similarity distributions and enhanced retrieval abilities compared to earlier versions. Model inputs and outputs Inputs Text to be embedded Outputs Low-dimensional dense vector representation of the input text Capabilities The llm-embedder model can generate high-quality embeddings that capture the semantic meaning of text. These embeddings can then be used in a variety of downstream applications, such as: Information retrieval: Finding relevant documents or passages for a given query Text classification: Categorizing text into different classes or topics Clustering: Grouping similar text together Semantic search: Finding text that is semantically similar to a given query The model has been shown to achieve state-of-the-art performance on benchmarks like MTEB and C-MTEB. What can I use it for? The llm-embedder model can be useful in a wide range of applications that require understanding the semantic content of text, such as: Building search engines or recommendation systems that can retrieve relevant information based on user queries Developing chatbots or virtual assistants that can engage in more natural conversations by understanding the context and meaning of user inputs Improving the accuracy of text classification models for tasks like sentiment analysis, topic modeling, or spam detection Powering knowledge management systems that can organize and retrieve information based on the conceptual relationships between documents Additionally, the model can be fine-tuned on domain-specific data to improve its performance for specific use cases. Things to try One interesting aspect of the llm-embedder model is its support for retrieval augmentation for large language models (LLMs). The LLM-Embedder variant of the model is designed to provide a unified embedding solution to support diverse retrieval needs for LLMs. Another interesting direction to explore is the use of the bge-reranker-base and bge-reranker-large models, which are cross-encoder models that can be used to re-rank the top-k documents retrieved by the embedding model. This can help improve the overall accuracy of the retrieval system.
Updated Invalid Date
🤷
bge-base-zh
51
The bge-base-zh model is part of the BAAI FlagEmbedding suite, which focuses on retrieval-augmented language models. It is a Chinese-language text embedding model trained by BAAI using contrastive learning on a large-scale dataset. The model can map any Chinese text to a low-dimensional dense vector, which can be used for tasks like retrieval, classification, clustering, or semantic search. The FlagEmbedding project also includes the LLM-Embedder model, which is a unified embedding model designed to support diverse retrieval augmentation needs for large language models (LLMs). Additionally, the project features BGE Reranker models, which are cross-encoder models that are more accurate but less efficient than the embedding models. Model inputs and outputs Inputs Chinese text**: The model takes arbitrary Chinese text as input and encodes it into a low-dimensional dense vector. Outputs Embedding vector**: The model outputs a low-dimensional (e.g. 768-dimensional) dense vector representation of the input text. Capabilities The bge-base-zh model can map Chinese text to a semantic vector space, enabling a variety of downstream tasks. It has been shown to achieve state-of-the-art performance on the Chinese Massive Text Embedding Benchmark (C-MTEB), outperforming other widely used models like multilingual-e5 and text2vec. What can I use it for? The bge-base-zh model can be used for a variety of natural language processing tasks, such as: Semantic search**: Use the embeddings to find relevant documents or passages given a query. Text classification**: Train a classifier on top of the embeddings to categorize text into different classes. Clustering**: Group similar text together based on the embedding vectors. Semantic similarity**: Compute the similarity between two text snippets using the cosine similarity of their embeddings. The model can also be fine-tuned on domain-specific data to further improve performance on specialized tasks. Things to try One interesting aspect of the bge-base-zh model is its ability to generate embeddings without the need for an instruction prefix, which can simplify the usage in some scenarios. However, for retrieval tasks involving short queries and long passages, it is recommended to add an instruction prefix to the query to improve performance. When using the model, it's also important to consider the similarity distribution of the embeddings. The current bge-base-zh model has a similarity distribution in the range of [0.6, 1], so a similarity score greater than 0.5 does not necessarily indicate that the two sentences are similar. For downstream tasks, the relative order of the scores is more important than the absolute value.
Updated Invalid Date
🖼️
bge-large-zh
290
The bge-large-zh model is a state-of-the-art text embedding model developed by the Beijing Academy of Artificial Intelligence (BAAI). It is part of the BAAI General Embedding (BGE) family of models, which have achieved top performance on both the MTEB and C-MTEB benchmarks. The bge-large-zh model is specifically designed for Chinese text processing, and it can map any Chinese text into a low-dimensional dense vector that can be used for tasks like retrieval, classification, clustering, or semantic search. Compared to similar models like BAAI/bge-large-en and BAAI/bge-small-en, the bge-large-zh model has been optimized for Chinese text and has demonstrated state-of-the-art performance on Chinese benchmarks. The BAAI/llm-embedder model is a more recent addition to the BAAI family, serving as a unified embedding model to support diverse retrieval augmentation needs for large language models (LLMs). Model inputs and outputs Inputs Text**: The bge-large-zh model can take any Chinese text as input, ranging from short queries to long passages. Instruction (optional)**: For retrieval tasks that use short queries to find long related documents, it is recommended to add an instruction to the query to help the model better understand the intent. The instruction should be placed at the beginning of the query text. No instruction is needed for the passage/document text. Outputs Embeddings**: The primary output of the bge-large-zh model is a dense vector embedding of the input text. These embeddings can be used for a variety of downstream tasks, such as: Retrieval: The embeddings can be used to find related passages or documents by computing the similarity between the query embedding and the passage/document embeddings. Classification: The embeddings can be used as features for training classification models. Clustering: The embeddings can be used to group similar text together. Semantic search: The embeddings can be used to find semantically related text. Capabilities The bge-large-zh model demonstrates state-of-the-art performance on a range of Chinese text processing tasks. On the Chinese Massive Text Embedding Benchmark (C-MTEB), the bge-large-zh-v1.5 model ranked first overall, showing strong results across tasks like retrieval, semantic similarity, and classification. Additionally, the bge-large-zh model has been designed to handle long input text, with a maximum sequence length of 512 tokens. This makes it well-suited for tasks that involve processing lengthy passages or documents, such as research paper retrieval or legal document search. What can I use it for? The bge-large-zh model can be used for a variety of Chinese text processing tasks, including: Retrieval**: Use the model to find relevant passages or documents given a query. This can be helpful for building search engines, Q&A systems, or knowledge management tools. Classification**: Use the model's embeddings as features to train classification models for tasks like sentiment analysis, topic classification, or intent detection. Clustering**: Group similar Chinese text together using the model's embeddings, which can be useful for organizing large collections of documents or categorizing user-generated content. Semantic search**: Find semantically related text by computing the similarity between the model's embeddings, enabling more advanced search experiences. Things to try One interesting aspect of the bge-large-zh model is its ability to handle queries with or without instruction. While adding an instruction to the query can improve retrieval performance, the model's v1.5 version has been enhanced to perform well even without the instruction. This makes it more convenient to use in certain applications, as you don't need to worry about crafting the perfect query instruction. Another thing to try is fine-tuning the bge-large-zh model on your own data. The provided examples show how you can prepare data and fine-tune the model to improve its performance on your specific use case. This can be particularly helpful if you have domain-specific text that the pre-trained model doesn't handle as well.
Updated Invalid Date