These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning
Metrics concerned:Accuracy
Recently, it has attracted much attention to build reliable named entity
recognition (NER) systems using limited annotated data. Nearly all existing
works heavily rely on domain-specific resources, such as external lexicons and
knowledge bases. However, such domain-specific resources are often not
available, meanwhile it's difficult and expensive to construct the resources,
which has become a key obstacle to wider adoption. To tackle the problem, in
this work, we propose a novel robust and domain-adaptive approach RDANER for
low-resource NER, which only uses cheap and easily obtainable resources.
Extensive experiments on three benchmark datasets demonstrate that our approach
achieves the best performance when only using cheap and easily obtainable
resources, and delivers competitive results against state-of-the-art methods
which use difficultly obtainable domainspecific resources. All our code and
corpora can be found on https://github.com/houking-can/RDANER.
About the metric use case
You can click on the links to see the associated metric use cases
Objective(s):
Purpose(s):
Target sector(s):
