Fastino发布了GLiNER2.5，它通过边界预测技术提高了信息提取的效率，比之前的版本更节省计算资源，值得一试。
Fastino发布GLiNER2.5，采用边界预测代替跨度枚举，实体宽度不再影响计算。提供74M、194M和287M参数的三个Apache 2.0检查点，均支持CPU运行。新增联合实体关系解码、约束分类、跨度属性和4,096词上下文。在16个零样本基准测试中，整体宏F1达到56.17。
Fastino Releases GLiNER2.5: A Boundary-Prediction Architecture That Removes Span Enumeration From Information Extraction
Fastino released GLiNER2.5, replacing span enumeration with boundary prediction so entity width no longer costs compute. Three Apache 2.0 checkpoints ship at 74M, 194M, and 287M parameters, all CPU-runnable. The release adds joint entity-relation decoding, constrained classification, span attributes, and 4,096-word context. Overall macro F1 reaches 56.17 on 16 zero-shot benchmarks. The post Fastino Releases GLiNER2.5: A Boundary-Prediction Architecture That Removes Span Enumeration From Information Extraction appeared first on MarkTechPost .