GLiNER2.5 Base V1
GLiNER2.5 Base is a 0.2B parameter span free information extraction model that predicts entity boundaries directly instead of enumerating candidate spans, enabling linear scaling with document length. It performs named entity recognition, relation extraction, and constrained classification, scoring an average F1 of 54.87 across 16 benchmarks.
- Type
- Text
- Weights
- Open
- Downloads
- 28k
- Likes
- 23
- Pipeline
- token classification
- License
- apache-2.0
Model hub
fastino/gliner2.5-base-v1
- Repo
- fastino/gliner2.5-base-v1
- Library
- gliner2
- Access
- Public
Technical tags and languages
Papers
1
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About Fastino
1
Task-optimized language models (TLMs) that are more accurate, faster, and safer than traditional LLMs, running without GPUs