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.
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Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the area of global optimization algorithms.
We further show that our proposed framework can generalize to contextualized representations and achieves state-of-the-art results on the CoNLL cross-lingual NER benchmark.
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.
In this paper, we use a promising deep learning model called BERT to solve the fine-grained sentiment classification task.
On VOC07 testbed for few-shot image classification tasks on ImageNet with transfer learning (Goyal et al., 2019), replacing the linear SVM currently used with a Convolutional NTK SVM consistently improves performance.
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 remain challenging.
#5 best model for Semantic Textual Similarity on MRPC
Previous work on adversarially robust neural networks requires large training sets and computationally expensive training procedures.
We propose a novel method to train deep convolutional neural networks which learn from multiple data sets of varying input sizes through weight sharing.
Breast cancer is one of the most common causes of cancer-related death in women worldwide.