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The acge_text_embedding model is a text embedding model developed by the team at aspire. This model uses the Matryoshka Representation Learning approach to map text to a vector representation. The acge_text_embedding model is similar to other BGE and text2vec text embedding models in that it can be used for tasks like retrieval, classification, and semantic search. However, the acge_text_embedding model was trained specifically on Chinese text data and may perform better on Chinese language tasks compared to the English-focused models. Model inputs and outputs Inputs Chinese text data in the form of strings Outputs 1792-dimensional vector representations of the input text Capabilities The acge_text_embedding model can map any Chinese text to a low-dimensional dense vector. These vector representations can then be used for a variety of downstream tasks such as: Retrieval: Finding relevant passages or documents given a query Classification: Classifying text into different categories Clustering: Grouping similar text together Semantic search: Finding semantically similar text What can I use it for? The acge_text_embedding model can be useful for a range of applications that require understanding the semantic meaning of Chinese text, such as: Building search engines or recommendation systems for Chinese content Powering chatbots or virtual assistants that interact with users in Chinese Analyzing Chinese text data for insights, such as in market research or social media monitoring Things to try One interesting thing to try with the acge_text_embedding model is using it to find similar Chinese text passages or documents. By comparing the vector representations of different pieces of text, you can identify content that is semantically related, even if the wording is different. This can be useful for tasks like: Building a content recommendation system to suggest related articles or products to users Identifying duplicate or near-duplicate content in a large corpus of Chinese text Clustering Chinese text data into meaningful groups based on the underlying semantics To get started, you can use the acge_text_embedding model through the FlagEmbedding library, which provides a simple interface for working with the model.

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Updated 5/28/2024