Keras, PyTorch, and NumPy Implementations of Deep Learning Architectures for NLP
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Updated
Jul 25, 2024 - Jupyter Notebook
Keras, PyTorch, and NumPy Implementations of Deep Learning Architectures for NLP
[NeurIPS 2019] Spherical Text Embedding
Lbl2Vec learns jointly embedded label, document and word vectors to retrieve documents with predefined topics from an unlabeled document corpus.
Clustering sentence embeddings to extract message intent
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
Model for learning document embeddings along with their uncertainties
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
just testing langchain with llama cpp documents embeddings
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 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.
A tool for performing semantic search within pdf documents leveraging sentence transformers.
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