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Found 4 papers, 1 papers with code
Revisiting Distance Metric Learning for Few-Shot Natural Language Classification
Our systematic experiments have shown that under few-shot learning settings, particularly proxy-based DML losses can positively affect the fine-tuning and inference of a supervised language model.
Distance Metric Learning Loss Functions in Few-Shot Scenarios of Supervised Language Models Fine-Tuning
This paper presents an analysis regarding an influence of the Distance Metric Learning (DML) loss functions on the supervised fine-tuning of the language models for classification tasks.
TASTEset -- Recipe Dataset and Food Entities Recognition Benchmark
We provide a few state-of-the-art baselines of named entity recognition models, which show that our dataset poses a solid challenge to existing models.
Applying SoftTriple Loss for Supervised Language Model Fine Tuning
We introduce a new loss function TripleEntropy, to improve classification performance for fine-tuning general knowledge pre-trained language models based on cross-entropy and SoftTriple loss.
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