Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.
Model for learning document embeddings along with their uncertainties
Source code for our AAAI 2020 paper P-SIF: Document Embeddings using Partition Averaging
Using Hugging Face Hub Embeddings with Langchain document loaders to do some query answering
just testing langchain with llama cpp documents embeddings
A tool for performing semantic search within pdf documents leveraging sentence transformers.
Development and Application of Document Embedding for Semantic Text Retrieval
An approach exploring and assessing literature-based doc-2-doc recommendations using word2vec combined with doc2vec, and applying it to TREC and RELISH datasets
An approach exploring and assessing literature-based doc-2-doc recommendations using a doc2vec and applying to the RELISH dataset.
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