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Found 4 papers, 4 papers with code
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
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.
Ranked #2 on
Only Connect Walls Dataset Task 1 (Grouping)
on OCW
(Wasserstein Distance (WD) metric, using extra
training data)
HuggingFace's Transformers: State-of-the-art Natural Language Processing
Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks.
Transformers: State-of-the-Art Natural Language Processing
Transformer architectures have facilitated building higher-capacity models and pretraining has made it possible to effectively utilize this capacity for a wide variety of tasks.
Datasets: A Community Library for Natural Language Processing
The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks.
