datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
LongBenchLongBench is a comprehensive benchmark for multilingual and multi-task purposes, with the goal to fully measure and evaluate the ability of pre-trained language models to understand long text. This dataset consists of twenty different tasks, covering key long-text application scenarios such as multi-document QA, single-document QA, summarization, few-shot learning, synthetic tasks, and code completion.LongBench
Introduction
LongBench is the first benchmark for bilingual, multitask, and comprehensive assessment of long context understanding capabilities of large language models. LongBench includes different languages (Chinese and English) to provide a more comprehensive evaluation of the large models' multilingual capabilities on long contexts. In addition, LongBench is composed of six major categories and twenty one different tasks, covering key long-text application scenarios such as… See the full description on the dataset page: https://huggingface.co/datasets/jannalu/LongBench.longbench-longctx
longbench-longctx
Long-context requests for end-to-end LLM inference benchmarking in fastkernels — Scenario B. Exercises the regimes the bulk set can't reach: long-sequence attention (incl. sparse / sliding-window / DSA), RoPE/YaRN scaling, and large-KV decode.
What it's for
64 real long documents truncated into clean prefill-length buckets from 8K to 128K, each paired with its real multiple-choice question. Prefill-dominated: it measures how kernels scale with… See the full description on the dataset page: https://huggingface.co/datasets/sfc-gh-goliaro/longbench-longctx.longbench-view
Introduction
LongBench is the first benchmark for bilingual, multitask, and comprehensive assessment of long context understanding capabilities of large language models. LongBench includes different languages (Chinese and English) to provide a more comprehensive evaluation of the large models' multilingual capabilities on long contexts. In addition, LongBench is composed of six major categories and twenty one different tasks, covering key long-text application scenarios such as… See the full description on the dataset page: https://huggingface.co/datasets/leideng/longbench-view.LongBenchLongBench is a comprehensive benchmark for multilingual and multi-task purposes, with the goal to fully measure and evaluate the ability of pre-trained language models to understand long text. This dataset consists of twenty different tasks, covering key long-text application scenarios such as multi-document QA, single-document QA, summarization, few-shot learning, synthetic tasks, and code completion.LongBenchLongBench is a comprehensive benchmark for multilingual and multi-task purposes, with the goal to fully measure and evaluate the ability of pre-trained language models to understand long text. This dataset consists of twenty different tasks, covering key long-text application scenarios such as multi-document QA, single-document QA, summarization, few-shot learning, synthetic tasks, and code completion.LongBench
LongBench
Dataset Summary
LongBench is a bilingual, multitask benchmark for evaluating long-context understanding in large language models. It covers long-text application scenarios including single-document question answering, multi-document question answering, summarization, few-shot learning, synthetic long-context tasks, and code completion.
This Hugging Face dataset repository repackages locally downloaded LongBench JSONL files into a clean, typed, data-only… See the full description on the dataset page: https://huggingface.co/datasets/GinkgoQ/LongBench.LongBench
Introduction
LongBench is the first benchmark for bilingual, multitask, and comprehensive assessment of long context understanding capabilities of large language models. LongBench includes different languages (Chinese and English) to provide a more comprehensive evaluation of the large models' multilingual capabilities on long contexts. In addition, LongBench is composed of six major categories and twenty one different tasks, covering key long-text application scenarios such as… See the full description on the dataset page: https://huggingface.co/datasets/hyg444/LongBench.longbench_hotpotqa
Bud Ecosystem mirror of THUDM/LongBench — a verbatim copy for offline, reproducible model evaluation. License unchanged (CC-BY-SA-4.0 (inherited from HotpotQA source; LongBench code is MIT)); all rights remain with the original authors. Share-alike: any derivative must stay under the same CC-BY-SA license.
Introduction
LongBench is the first benchmark for bilingual, multitask, and comprehensive assessment of long context understanding capabilities of large language models.… See the full description on the dataset page: https://huggingface.co/datasets/budecosystem/longbench_hotpotqa.LongbenchSeg
Dataset Card for LongbenchSeg
The segmented version of Longbench using the trained segmenter from the paper Towards Generalization of Block Attention via Automatic Segmentation and Block Distillation.
We set the recursion depth to 1 and use the threshold value of 0.4.
Dataset Details
Dataset Description
The newly introduced segmentation columns are:
chunks: The segmented chunks.
cut_prob: The corresponding segmenting probability for each… See the full description on the dataset page: https://huggingface.co/datasets/Syon-Li/LongbenchSeg.LongBenchLongBench is a comprehensive benchmark for multilingual and multi-task purposes, with the goal to fully measure and evaluate the ability of pre-trained language models to understand long text. This dataset consists of twenty different tasks, covering key long-text application scenarios such as multi-document QA, single-document QA, summarization, few-shot learning, synthetic tasks, and code completion.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.