A very simple framework for state-of-the-art Natural Language Processing (NLP)
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💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows
- python
- search
- nlp
- semantic-search
Train and evaluate embeddings for similarity search with PyTorch. Includes contrastive losses, hard example mining, samplers, and retrieval metrics.
- metric-learning
- deep-learning
- computer-vision
- machine-learning
Representation learning on large graphs using stochastic graph convolutions.
Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
- machine-learning
- convolutional-networks
- embedding-vectors
- embeddings
Siamese and triplet networks with online pair/triplet mining in PyTorch
- machine-learning
- deep-learning
- siamese-network
- triplet-loss
A Toolkit for Industrial Topic Modeling
- topic-modeling
- topic-models
- lda
- sentence-lda
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
- community-detection
- graph-clustering
- deepwalk
- networkx
Python library for Representation Learning on Knowledge Graphs https://docs.ampligraph.org
- machine-learning
- knowledge-graph
- relational-learning
- representation-learning
WeMM-Embedding is a family of universal multimodal embedding models by the WeChat Vision Team at Tencent, supporting multimodal understanding and retrieval.
- embedding-models
- multimodal
- multimodal-llm
This tool provides an efficient implementation of the continuous bag-of-words and skip-gram architectures for computing vector representations of words. These representations can be subsequently used in many natural language processing applications and for further research.
Cleora AI is a general-purpose open-source model for efficient, scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Created by Synerise.com team.
- ai
- graphs
- synerise
- embeddings