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The Future of (Transfer Learning in) Natural Language Processing
Transfer Learning in Natural Language Processing (NLP): Open questions, current trends, limits, and future directions.
natural-language-processing transfer-learning tutorial video
VirTex: Learning Visual Representations from Textual Annotations
We train CNN+Transformer from scratch from COCO, transfer the CNN to 6 downstream vision tasks, and exceed ImageNet features despite using 10x fewer ...
convolutional-neural-networks transformers coco visual-representations
NLP Model Selection
NLP model selection guide to make it easier to select models. This is prescriptive in nature and has to be used with caution.
transfer-learning natural-language-processing neural-networks transformers
Insight
Project Insight is designed to create NLP as a service with code base for both front end GUI (streamlit) and backend server (FastAPI) the usage of ...
fastapi huggingface transformers pytorch
Practical Tips and Tricks for Successful Transfer Learning
Training models to learn knowledge and skills from other related tasks that will transfer and boost performance on tasks of interest.
transfer-learning pretraining natural-language-processing tutorial
An Intuitive Guide to Deep Network Architectures
Intuition behind base network architectures like MobileNets, Inception, and ResNet.
object-detection image-classification transfer-learning computer-vision
Transfer Learning with T5: the Text-To-Text Transfer Transformer
In the paper, we demonstrate how to achieve state-of-the-art results on multiple NLP tasks using a text-to-text transformer pre-trained on a large text ...
transformers t5 question-answering reading-comprehension
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