viktor-shcherb/longbench2-128k-plus
LongBench2-128k-plus LongBench2-128k-plus is a long-context corpus derived from the zai-org/LongBench-v2 benchmark. It keeps only the "long" examples and exposes just the raw long documents, making it convenient for: long-context pretraining or continued training, long-context adaptation (e.g., RoPE scaling, attention tuning), retrieval and RAG-style experimentation where only documents are needed. All question/answer and multiple-choice metadata from LongBench v2 are dropped;… See the full description on the dataset page: https://huggingface.co/datasets/viktor-shcherb/longbench2-128k-plus.
LongBench2-128k-plus
LongBench2-128k-plus is a long-context corpus derived from the zai-org/LongBench-v2 benchmark. It keeps only the "long" examples and exposes just the raw long documents, making it convenient for:
- long-context pretraining or continued training,
- long-context adaptation (e.g., RoPE scaling, attention tuning),
- retrieval and RAG-style experimentation where only documents are needed.
All question/answer and multiple-choice metadata from LongBench v2 are dropped; each row is a single long text.
Source dataset
This dataset is a processed subset of:
- Original dataset:
zai-org/LongBench-v2 - Project page: https://longbench2.github.io
- Paper: LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks (arXiv:2412.15204)
LongBench v2 is a long-context evaluation benchmark with contexts ranging from thousands to millions of words, spanning multiple realistic domains and task types (QA, multi-document reasoning, code, dialogue, and more).