datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
vectorstore-mental_health
Vectorstore Dataset: Mental Health
Overview
This dataset contains pre-computed vector embeddings for the mental health domain, ready for use in Retrieval-Augmented Generation (RAG) applications, semantic search, and knowledge base systems. The embeddings are generated from high-quality source documents using state-of-the-art sentence transformers, making it easy to build production-ready RAG applications without the computational overhead of embedding generation.… See the full description on the dataset page: https://huggingface.co/datasets/meetara-lab/vectorstore-mental_health.vectorstore-women_health
Vectorstore Dataset: Women Health
Overview
This dataset contains pre-computed vector embeddings for the women health domain, ready for use in Retrieval-Augmented Generation (RAG) applications, semantic search, and knowledge base systems. The embeddings are generated from high-quality source documents using state-of-the-art sentence transformers, making it easy to build production-ready RAG applications without the computational overhead of embedding generation.… See the full description on the dataset page: https://huggingface.co/datasets/meetara-lab/vectorstore-women_health.vectorstore-accounting
Vectorstore Dataset: Accounting
Overview
This dataset contains pre-computed vector embeddings for the accounting domain, ready for use in Retrieval-Augmented Generation (RAG) applications, semantic search, and knowledge base systems. The embeddings are generated from high-quality source documents using state-of-the-art sentence transformers, making it easy to build production-ready RAG applications without the computational overhead of embedding generation.… See the full description on the dataset page: https://huggingface.co/datasets/meetara-lab/vectorstore-accounting.vectorstore-legal_business
Vectorstore Dataset: Legal Business
Overview
This dataset contains pre-computed vector embeddings for the legal business domain, ready for use in Retrieval-Augmented Generation (RAG) applications, semantic search, and knowledge base systems. The embeddings are generated from high-quality source documents using state-of-the-art sentence transformers, making it easy to build production-ready RAG applications without the computational overhead of embedding generation.… See the full description on the dataset page: https://huggingface.co/datasets/meetara-lab/vectorstore-legal_business.vectorstore-academic_tutoring
Vectorstore Dataset: Academic Tutoring
Overview
This dataset contains pre-computed vector embeddings for the academic tutoring domain, ready for use in Retrieval-Augmented Generation (RAG) applications, semantic search, and knowledge base systems. The embeddings are generated from high-quality source documents using state-of-the-art sentence transformers, making it easy to build production-ready RAG applications without the computational overhead of embedding generation.… See the full description on the dataset page: https://huggingface.co/datasets/meetara-lab/vectorstore-academic_tutoring.vectorstore-economics
Vectorstore Dataset: Economics
Overview
This dataset contains pre-computed vector embeddings for the economics domain, ready for use in Retrieval-Augmented Generation (RAG) applications, semantic search, and knowledge base systems. The embeddings are generated from high-quality source documents using state-of-the-art sentence transformers, making it easy to build production-ready RAG applications without the computational overhead of embedding generation.
What… See the full description on the dataset page: https://huggingface.co/datasets/meetara-lab/vectorstore-economics.vectorstore-general_health
Vectorstore Dataset: General Health
Overview
This dataset contains pre-computed vector embeddings for the general health domain, ready for use in Retrieval-Augmented Generation (RAG) applications, semantic search, and knowledge base systems. The embeddings are generated from high-quality source documents using state-of-the-art sentence transformers, making it easy to build production-ready RAG applications without the computational overhead of embedding generation.… See the full description on the dataset page: https://huggingface.co/datasets/meetara-lab/vectorstore-general_health.