Finetuner allows one to finetune any deep neural network for better embedding on search tasks
Project description
Finetuning any deep neural network for better embedding on neural search tasks
Finetuner allows one to tune the weights of any deep neural network for better embedding on search tasks. It accompanies Jina to deliver the last mile of performance-tuning for neural search applications.
🔱 Powerful yet intuitive: all you need is finetuner.fit()
, a one-liner that unlocks rich features such as siamese/triplet architecture, interactive labeling, weight freezing, dimensionality reduction.
⚛️ Framework-agnostic: promise an identical user experience on Pytorch, Keras or PaddlePaddle deep learning backends.
🧈 Jina integration: buttery smooth integration with Jina, reducing the cost of context-switch between experimenting and production.
Install
Make sure you have Python 3.7+ and one of Pytorch, Keras or PaddlePaddle installed on Linux/MacOS.
pip install finetuner
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