Center-for-curriculum-redesign

bge_1-5_query_embeddings

center-for-curriculum-redesign

5

The bge_1-5_query_embeddings model is a query embedding generator developed by the Center for Curriculum Redesign. It is built on top of BAAI's bge-large-en v1.5 embedding model, which is a powerful text encoding model for embedding text sequences. Similar models include the bge-large-en-v1.5 model, the bge-reranker-base model, and the multilingual-e5-large model. Model inputs and outputs The bge_1-5_query_embeddings model takes in a list of text queries and generates corresponding embedding vectors for retrieval and comparison purposes. The model automatically formats the input queries for retrieval, so users do not need to preprocess the text. Inputs Query Texts**: A serialized JSON array of strings to be used as text queries for generating embeddings. Normalize**: A boolean flag to control whether the output embeddings are normalized to a magnitude of 1. Precision**: The numerical precision to use for the inference computations, either "full" or "half". Batchtoken Max**: The maximum number of kibiTokens (1 kibiToken = 1024 tokens) to include in a single batch, to avoid out-of-memory errors. Outputs Query Embeddings**: An array of embedding vectors, where each vector corresponds to one of the input text queries. Extra Metrics**: Additional metrics or data associated with the embedding generation process. Capabilities The bge_1-5_query_embeddings model is capable of generating high-quality text embeddings that can be used for a variety of natural language processing tasks, such as information retrieval, text similarity comparison, and document clustering. The embeddings capture the semantic meaning of the input text, allowing for more effective downstream applications. What can I use it for? The bge_1-5_query_embeddings model can be used in a wide range of applications that require text encoding and comparison, such as search engines, recommendation systems, and content analysis tools. By generating embeddings for text queries, you can leverage the model's powerful encoding capabilities to improve the relevance and accuracy of your search or recommendation results. Things to try One interesting thing to try with the bge_1-5_query_embeddings model is to experiment with different levels of precision for the inference computations. Depending on your specific use case and hardware constraints, you may find that the "half" precision setting provides sufficient accuracy while requiring less computational resources. Additionally, you could explore how the model's performance varies when using different normalization strategies for the output embeddings.

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