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LASER multilingual sentence embeddings as a pip package
The main features of yannvgn/laserembeddings are: Pre-trained Language Models, Text Generation.
Projects with overlapping indexed features include: asyml/texar — Toolkit for Machine Learning, Natural Language Processing, and Text Generation, in TensorFlow. This is part of the… codertimo/bert-pytorch. huggingface/course — This project is an educational course and learning curriculum for implementing and fine-tuning transformer models… morvanzhou/tutorials — This repository is a comprehensive collection of instructional guides and practical examples for Python development,… facebookresearch/flow_matching — This project is a PyTorch-based generative model framework designed to transform noise into complex data distributions… openai/gpt-2 — This project is a transformer-based language model and autoregressive text generator designed to predict the next…
Toolkit for Machine Learning, Natural Language Processing, and Text Generation, in TensorFlow. This is part of the CASL project: http://casl-project.ai/
This project is an educational course and learning curriculum for implementing and fine-tuning transformer models using the Hugging Face ecosystem. It serves as a structured guide and technical walkthrough for processing multimodal data, adapting pre-trained neural networks, and deploying models. The material includes a guide for managing, versioning, and distributing model weights and datasets through a centralized asset hub. It also provides a practical tutorial on adapting models to specific datasets using parameter-efficient methods and an implementation guide for solving natural language
This project is a PyTorch-based generative model framework designed to transform noise into complex data distributions by learning vector fields and probability paths. It serves as a multimodal generative toolkit for producing synthetic text and images through learned probability flows. The library distinguishes itself by supporting continuous, discrete, and Riemannian manifold integrations. This allows the framework to handle a variety of data types, including categorical data via discrete-state flow matching and non-Euclidean spaces through Riemannian manifold integration. The toolkit cove
