Topic Modelling for Humans
- gensim
- topic-modeling
- information-retrieval
- machine-learning
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Topic Modelling for Humans
A very simple framework for state-of-the-art Natural Language Processing (NLP)
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
Train and evaluate embeddings for similarity search with PyTorch. Includes contrastive losses, hard example mining, samplers, and retrieval metrics.
PyTorch code and models for V-JEPA self-supervised learning from video.
Representation learning on large graphs using stochastic graph convolutions.
[EMNLP 2021] SimCSE: Simple Contrastive Learning of Sentence Embeddings https://arxiv.org/abs/2104.08821
Open-source inference server and production cluster for all the models your agent needs.
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
A python package to analyze and compare voices with deep learning
Siamese and triplet networks with online pair/triplet mining in PyTorch
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.
A simple deep learning library for estimating a set of tags and extracting semantic feature vectors from given illustrations.
Learning Heterogeneous Tactile Representation for General Dexterous Manipulation
gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions