Transfer learning is a methodology where weights from a model trained on one task are taken and either used (a) to construct a fixed feature extractor, (b) as weight initialization and/or fine-tuning.
( Image credit: Subodh Malgonde )
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The approach combines, in a black-box fashion, multiple models trained with disjoint datasets, such as records from different subsets of users.
In this paper, we present HuggingFace's Transformers library, a library for state-of-the-art NLP, making these developments available to the community by gathering state-of-the-art general-purpose pretrained models under a unified API together with an ecosystem of libraries, examples, tutorials and scripts targeting many downstream NLP tasks.
As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging.
#5 best model for Semantic Textual Similarity on MRPC
Clone a voice in 5 seconds to generate arbitrary speech in real-time
SOTA for Text-To-Speech Synthesis on LJSpeech (using extra training data)
Training large-scale question answering systems is complicated because training sources usually cover a small portion of the range of possible questions.
SOTA for Question Answering on WebQuestions
In this paper, we propose a practically Easy Transfer Learning (EasyTL) approach which requires no model selection and hyperparameter tuning, while achieving competitive performance.
SOTA for Transfer Learning on ImageCLEF-DA
Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods.
#76 best model for Image Classification on ImageNet
Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available.
SOTA for Image Classification on Stanford Cars (using extra training data)
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks.