datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
long_context_hindi
Dataset
This dataset was filtered from AI4BHarat dataset sangraha,which is the largest high-quality, cleaned Indic language pretraining data containing 251B tokens summed up over 22 languages, extracted from curated sources, existing multilingual corpora and large scale translations.
This dataset contains only Hindi as of now
Information
First this dataset is mainly for long context training
The minimum len is 6000 and maximum len is 3754718
Getting started… See the full description on the dataset page: https://huggingface.co/datasets/damerajee/long_context_hindi.long-context-retrieval-training-pool
Long context retrieval training pool
Long prompts with short, checkable answers. Each row is one complete message: a task instruction,
a long body of text that hides what the question is about, and the question itself, together with
every string an answer has to contain for it to be right. The bodies run from four thousand to
thirty-two thousand tokens. Three sources, laid out twice. Train on either layer or on
both.
pool.jsonl
Every source rewritten into one… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/long-context-retrieval-training-pool.mbpp-longcontext
MBPP Long-Context Dataset
Overview
MBPP Long-Context is a benchmark dataset that combines coding problems from the MBPP (Mostly Basic Python Problems) dataset with long-context distractors from BABILong. This dataset evaluates code generation performance under long-context conditions, testing whether models can maintain coding ability with stuffed context.
Dataset Structure
Data Fields
Each sample contains:
Original MBPP Fields… See the full description on the dataset page: https://huggingface.co/datasets/jannalu/mbpp-longcontext.long-context-baseline-bakeoff
Long-Context Data-Selection Bake-off — Shared Candidate Pool
The shared 16K candidate pool for comparing long-context data-selection methods on equal
footing. Every method (AttentionSpan, LongAttn, LongProc, ProLong, perplexity, ...) scores the
same 14,300 documents, picks its own top-800 under the same split, then trains
Llama-2-7B + 16K LoRA and evaluates on HELMET.
Files
File
Description
candidate_pool_16k_scored.parquet
The shared pool — 14,300 docs… See the full description on the dataset page: https://huggingface.co/datasets/KevinDavidHayes/long-context-baseline-bakeoff.long-context-llm-papers
Long-Context LLM Papers — FineSet
A research-paper dataset on Long-Context LLM Papers, assembled, deduplicated, and quality-scored by
FineSet from arXiv and Semantic Scholar.
📸 This is a dated snapshot — generated 2026-06-19.
It is not auto-updated. Research on Long-Context LLM Papers moves fast — new papers land on arXiv every
week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. ↓
Why this dataset
Quality-scored: quality_score… See the full description on the dataset page: https://huggingface.co/datasets/fineset-io/long-context-llm-papers.long_context_predictable_dataset
Long Context Predictable Dataset
A dataset of long-context editing and translation prompts built from Project Gutenberg texts.
Description
Each example consists of a task instruction (prompt) prepended to a long passage of text (~549,000 words per passage). The tasks are designed to require long output, such as translating, rewriting, or editing the full text.
Dataset Structure
Each example contains the following fields:
Field
Type
Description
prompt… See the full description on the dataset page: https://huggingface.co/datasets/rkarhila-amd/long_context_predictable_dataset.