GLiClass: Generalist Lightweight Model for Sequence Classification Tasks
Classification is one of the most widespread tasks in AI applications,
serving often as the first step in filtering, sorting, and categorizing data.
Since modern AI systems must handle large volumes of input data and early
pipeline stages can propagate errors downstream, achieving high efficiency and
accuracy is critical. Moreover, classification requirements can change
dynamically based on user needs, necessitating models with strong zero-shot
capabilities. While generative LLMs have become mainstream for zero-shot
classification due to their versatility, they suffer from inconsistent
instruction following and computational inefficiency. Cross-encoders, commonly
used as rerankers in RAG pipelines, face a different bottleneck: they must
process text-label pairs sequentially, significantly reducing efficiency with
large label sets. Embedding-based approaches offer good efficiency but struggle
with complex scenarios involving logical and semantic constraints. We propose
GLiClass, a novel method that adapts the GLiNER architecture for sequence
classification tasks. Our approach achieves strong accuracy and efficiency
comparable to embedding-based methods, while maintaining the flexibility needed
for zero-shot and few-shot learning scenarios. Additionally, we adapted
proximal policy optimization (PPO) for multi-label text classification,
enabling training classifiers in data-sparse conditions or from human feedback.
689ad76cfab6fdd2e52ac544
Ihor Stepanov
claimed_verified
2025-08-13T07:19:09.506Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1658166666371-noauth.png
Stepanov
user
Ihor
689ad76cfab6fdd2e52ac545
Mykhailo Shtopko
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2025-08-13T07:19:06.516Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6405f62ba577649430be5124/WMSqNvDqaeydFkHhNCU_K.png
Mykhailo Shtopko
user
BioMike
689ad76cfab6fdd2e52ac546
Dmytro Vodianytskyi
claimed_verified
2025-08-13T07:35:15.832Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/662b8fd182472dc87ec9c15b/sLY7mZZRz6Yy2Xfgf3HmD.jpeg
Dmytro
user
werent4
689ad76cfab6fdd2e52ac547
Oleksandr Lukashov
claimed_verified
2025-08-14T13:39:29.020Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/675de32a07266b71f9f72e43/OgRYWcbRNA8sbxVSMfwZ6.jpeg
Oleksandr Lukashov
user
alexandrlukashov
689ad76cfab6fdd2e52ac548
Alexander Yavorskyi
689ad76cfab6fdd2e52ac549
Mykyta Yaroshenko
GLiClass: Generalist Lightweight Model for Sequence Classification Tasks
Classification is one of the most widespread tasks in AI applications,
serving often as the first step in filtering, sorting, and categorizing data.
Since modern AI systems must handle large volumes of input data and early
pipeline stages can propagate errors downstream, achieving high efficiency and
accuracy is critical. Moreover, classification requirements can change
dynamically based on user needs, necessitating models with strong zero-shot
capabilities. While generative LLMs have become mainstream for zero-shot
classification due to their versatility, they suffer from inconsistent
instruction following and computational inefficiency. Cross-encoders, commonly
used as rerankers in RAG pipelines, face a different bottleneck: they must
process text-label pairs sequentially, significantly reducing efficiency with
large label sets. Embedding-based approaches offer good efficiency but struggle
with complex scenarios involving logical and semantic constraints. We propose
GLiClass, a novel method that adapts the GLiNER architecture for sequence
classification tasks. Our approach achieves strong accuracy and efficiency
comparable to embedding-based methods, while maintaining the flexibility needed
for zero-shot and few-shot learning scenarios. Additionally, we adapted
proximal policy optimization (PPO) for multi-label text classification,
enabling training classifiers in data-sparse conditions or from human feedback.
GLiClass, an adaptation of GLiNER, achieves efficient and accurate sequence classification with zero-shot and few-shot capabilities, and PPO is adapted for multi-label text classification in data-sparse conditions.
GLiClass GLiNER sequence classification zero-shot learning few-shot learning proximal policy optimization PPO multi-label text classification