bge-small-en
Maintainer: BAAI
65
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| 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 |
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Model overview
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.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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The bge-base-en is a text embedding model developed by the BAAI (Beijing Academy of Artificial Intelligence) that can map any text to a low-dimensional dense vector. It is part of the BAAI/bge-base-en model series, which also includes larger and smaller scale versions such as BAAI/bge-large-en and BAAI/bge-small-en. These models were trained using contrastive learning on a massive text corpus and demonstrate state-of-the-art performance on text embedding benchmarks like MTEB and C-MTEB. The bge-base-en model is a base-scale version that achieves similar performance to the larger bge-large-en model, making it a good option for applications with limited compute resources. All models in the BAAI/bge series have been recommended to use the newest v1.5 versions, which have an improved similarity distribution compared to earlier versions. Model inputs and outputs Inputs Text**: The bge-base-en model can take any text as input, such as a sentence, paragraph, or document. Instruction (optional)**: For text retrieval tasks, the input text can optionally be prefixed with an instruction to improve performance, such as "Represent this sentence for searching relevant passages:". Outputs Embedding vector**: The model outputs a fixed-size dense vector representation of the input text, which can be used for downstream tasks like retrieval, classification, clustering, or semantic search. Capabilities The bge-base-en model is a powerful text embedding model that can capture the semantic meaning of input text in a compact vector representation. It has been shown to excel at a variety of NLP tasks, achieving top performance on the MTEB and C-MTEB benchmarks. Some key capabilities of the model include: Retrieval**: The embedding vectors can be used to efficiently search large text corpora to find relevant documents or passages for a given query. Classification**: The embeddings can be leveraged as features for training classifiers on text data. Clustering**: The vector representations allow for effective grouping of similar text items. Semantic search**: The model can identify semantically related texts based on the proximity of their embedding vectors. What can I use it for? The bge-base-en model is a highly versatile tool that can be applied to a wide range of NLP applications. Some potential use cases include: Intelligent search**: Integrating the model into search engines or knowledge bases to enable more accurate and semantically-aware retrieval of information. Recommender systems**: Using the text embeddings to identify related content or products for recommendation. Content analysis**: Leveraging the model's ability to capture semantic meaning for tasks like topic modeling, sentiment analysis, or text summarization. Multimodal applications**: Combining the text embeddings with visual or audio representations for applications like image/video captioning or multimedia search. Things to try One interesting aspect of the bge-base-en model is its ability to generate high-quality embeddings without requiring an instruction prefix, while still maintaining strong retrieval performance. This makes the model convenient to use in many scenarios where adding an instruction may not be practical. Another thing to explore is fine-tuning the model on your own data using the provided examples. By incorporating domain-specific knowledge, you can further improve the model's performance on tasks relevant to your application. The FlagEmbedding library provides guidance on how to effectively fine-tune the bge models. Finally, you can experiment with using the bge-base-en model in combination with the larger bge-large-en model or the bge-reranker models to further enhance retrieval performance. The reranker models can be used to re-rank the top results from the embedding model, providing a more accurate relevance score.
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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.
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