GLiNER multi-task: Generalist Lightweight Model for Various Information
  Extraction Tasks
Information extraction tasks require both accurate, efficient, and
generalisable models. Classical supervised deep learning approaches can achieve
the required performance, but they need large datasets and are limited in their
ability to adapt to different tasks. On the other hand, large language models
(LLMs) demonstrate good generalization, meaning that they can adapt to many
different tasks based on user requests. However, LLMs are computationally
expensive and tend to fail to generate structured outputs. In this article, we
will introduce a new kind of GLiNER model that can be used for various
information extraction tasks while being a small encoder model. Our model
achieved SoTA performance on zero-shot NER benchmarks and leading performance
on question-answering, summarization and relation extraction tasks.
Additionally, in this article, we will cover experimental results on
self-learning approaches for named entity recognition using GLiNER models.
667529f0246665be1aa7312a
Ihor Stepanov
claimed_verified
2024-06-21T10:24:42.457Z
62d59dd5a2de3ae5ea6fc262
https://cdn-avatars.huggingface.co/v1/production/uploads/1658166666371-noauth.png
Stepanov
user
Ihor
667529f0246665be1aa7312b
Mykhailo Shtopko
claimed_verified
2024-06-21T07:57:48.931Z
6405f62ba577649430be5124
https://cdn-avatars.huggingface.co/v1/production/uploads/6405f62ba577649430be5124/WMSqNvDqaeydFkHhNCU_K.png
Mykhailo Shtopko
user
BioMike
GLiNER multi-task: Generalist Lightweight Model for Various Information
  Extraction Tasks
Information extraction tasks require both accurate, efficient, and
generalisable models. Classical supervised deep learning approaches can achieve
the required performance, but they need large datasets and are limited in their
ability to adapt to different tasks. On the other hand, large language models
(LLMs) demonstrate good generalization, meaning that they can adapt to many
different tasks based on user requests. However, LLMs are computationally
expensive and tend to fail to generate structured outputs. In this article, we
will introduce a new kind of GLiNER model that can be used for various
information extraction tasks while being a small encoder model. Our model
achieved SoTA performance on zero-shot NER benchmarks and leading performance
on question-answering, summarization and relation extraction tasks.
Additionally, in this article, we will cover experimental results on
self-learning approaches for named entity recognition using GLiNER models.
A small encoder model, GLiNER, achieves state-of-the-art performance on zero-shot and various information extraction tasks, combining size efficiency with strong generalization.
GLiNER model zero-shot NER named entity recognition question-answering summarization relation extraction self-learning