Granite Embedding 30m Sparse Model Summary: Granite Embedding 30m Sparse is a 30M parameter sparse biencoder embedding model from the Granite Experimental suite that can be used to generate high quality text embeddings. This model produces variable length bag of word like dictionary, containing expansions of sentence tokens and their corresponding weights and is trained using a combination of open source relevance pair datasets with permissive, enterprise friendly license, and IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pretraining, contrastive finetuning and knowledge distillation for improved performance. Developers: Granite Embedding Team, IBM GitHub Repository: ibm granite/granite embedding models Paper: Techincal Report Release Date : February 26th, 2025 License: Apache 2.0 Supported Languages: English. Intended use: The model is designed to produce variable length bag of word like dictionary, containing expansions of sentence tokens and their corresponding weights, for a given text, which can be used f…
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