embedding-models

A curated list of pretrained sentence and word embedding models

A curated list of awesome embedding models tutorials, projects and communities.

Python library for knowledge graph embedding and representation learning.

OpenL3: Open-source deep audio and image embeddings

Implementations of Embedding-based methods for Knowledge Base Completion tasks

Word Embeddings for Information Retrieval

Image search engine

Web-ify your word2vec: framework to serve distributional semantic models online

Neural Code Comprehension: A Learnable Representation of Code Semantics

tensorflow prediction using c++ api

Contrastive Noise Embeddings (CNE) for dimensionality reduction and clustering

ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms, including but not limited to click-through-rate (CTR) prediction, learning-to-ranking (LTR), and Matrix/Tensor Embedding. The project objective is to develop an ecosystem to experiment, share, reproduce, and deploy in real-world in a smooth and easy way.

Encoding position with the word embeddings.

Generates a set of property-specific entity embeddings from knowledge graphs using node2vec

STransE: a novel embedding model of entities and relationships in knowledge bases (NAACL 2016)

PyTorch implementation of paper "Visual Concept-Metaconcept Learner", NeruIPS 2019

🐸 KERMIT - A lightweight library to encode and interpret Universal Syntactic Embeddings

Learning node representation using edge semantics

Place2Vec ground truth dataset

Piecewise Flat Embedding for Image Segmentation

C++ and Python library for Polarizable Embedding

Python implementation of "Data-dependent Learning of Symmetric/Antisymmetric Relations for Knowledge Base Completion [Manabe+. 2018]"

Creation of an embedding space using unsupervised triplet loss and Tile2Vec that can be used for a variety of downstream tasks

Neural Network models to map mention of a text to corresponding entity in the Knowledge Base

DL Lab Project - Given a subset of switchboard corpus, goal is to classify dialogue acts from Speech and Text data. We define a RNN-LSTM model for Text classification and CNN model for speech classification and then ensemble both model to output a stable and higher performance model

Simple implementation of search for visually similar images using deep learning and vector search. It's based on pretrained ImageNet weights so it doesnt require any additional training

A monolingual and cross-lingual meta-embedding generation and evaluation framework

Using Embeddings and DNNs to predict outcomes of Dota 2 matches

Neural Collaborative Filtering with MXNet