Displaying 1 to 5 from 5 results

NeuronBlocks - NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

  •    Python

NeuronBlocks is a NLP deep learning modeling toolkit that helps engineers/researchers to build end-to-end pipelines for neural network model training for NLP tasks. The main goal of this toolkit is to minimize developing cost for NLP deep neural network model building, including both training and inference stages. NeuronBlocks consists of two major components: Block Zoo and Model Zoo.

PaddleClas - A treasure chest for visual recognition powered by PaddlePaddle

  •    Python

PaddleClas is an image recognition toolset for industry and academia, helping users train better computer vision models and apply them in real scenarios. A practical image recognition system consist of detection, feature learning and retrieval modules, widely applicable to all types of image recognition tasks. Four sample solutions are provided, including product recognition, vehicle recognition, logo recognition and animation character recognition.

EasyTransfer - EasyTransfer is designed to make the development of transfer learning in NLP applications easier

  •    Python

EasyTransfer is designed to make the development of transfer learning in NLP applications easier. The literature has witnessed the success of applying deep Transfer Learning (TL) for many real-world NLP applications, yet it is not easy to build an easy-to-use TL toolkit to achieve such a goal. To bridge this gap, EasyTransfer is designed to facilitate users leveraging deep TL for NLP applications at ease. It was developed in Alibaba in early 2017, and has been used in the major BUs in Alibaba group and achieved very good results in 20+ business scenarios. It supports the mainstream pre-trained ModelZoo, including pre-trained language models (PLMs) and multi-modal models on the PAI platform, integrates the SOTA models for the mainstream NLP applications in AppZoo, and supports knowledge distillation for PLMs. EasyTransfer is very convenient for users to quickly start model training, evaluation, offline prediction, and online deployment. It also provides rich APIs to make the development of NLP and transfer learning easier.




DICOD - Official Pytorch implementation for Distilling Image Classifiers in Object detection

  •    Python

Code is in early release and may be subject to change. Please feel free to open an issue in case of questions. We use PyTorch and MMDetection v2.10.0 as the codebase.






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