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)
💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
Retrieval and Retrieval-augmented LLMs
ImageBind One Embedding Space to Bind Them All
Train and evaluate embeddings for similarity search with PyTorch. Includes contrastive losses, hard example mining, samplers, and retrieval metrics.
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.
Fast, Accurate, Lightweight Python library to make State of the Art Embedding
Siamese and triplet networks with online pair/triplet mining in PyTorch
The code used to train and run inference with the ColVision models, e.g. ColPali, ColQwen2, and ColSmol.
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Fast State-of-the-Art Static Embeddings
Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
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
Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.