Keras, PyTorch, and NumPy Implementations of Deep Learning Architectures for NLP

Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.

[NeurIPS 2019] Spherical Text Embedding

Clustering sentence embeddings to extract message intent

Source code for our AAAI 2020 paper P-SIF: Document Embeddings using Partition Averaging

Model for learning document embeddings along with their uncertainties

Using Hugging Face Hub Embeddings with Langchain document loaders to do some query answering

just testing langchain with llama cpp documents embeddings

A Comparative Study of Various Code Embeddings in Software Semantic Matching

This repository contains the code for the Transformer-Representation Neural Topic Model (TNTM) based on the paper "Probabilistic Topic Modelling with Transformer Representations" by Arik Reuter, Anton Thielmann, Christoph Weisser, Benjamin Säfken and Thomas Kneib

A tool for performing semantic search within pdf documents leveraging sentence transformers.

Development and Application of Document Embedding for Semantic Text Retrieval

Hybrid approach combining dictionary-based NER and doc2vec

An approach exploring and assessing literature-based doc-2-doc recommendations using a doc2vec and applying to the RELISH dataset.

An approach exploring and assessing literature-based doc-2-doc recommendations using word2vec combined with doc2vec, and applying it to TREC and RELISH datasets

A streamlit app for chat based on embedding documents using langchain