datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SWE-Next
SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
SWE-Next Dataset
SWE-Next is an execution-grounded dataset of 2,308 self-verifying software engineering tasks mined from real merged GitHub pull requests. Starting from 3,971 seeded Python repositories and 102,582 executed candidate base/merged commit pairs, SWE-Next retains only instances where the merged commit produces a strict test improvement without regressions. The final release… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/SWE-Next.SWE-Next-SFT-Trajectories
SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
SWE-Next SFT Trajectories
SWE-Next SFT Trajectories is the supervised fine-tuning dataset released with SWE-Next: Scalable Real-World Software Engineering Tasks for Agents. It contains 3,693 ShareGPT-style multi-turn training examples collected from expert agent rollouts on 2,308 execution-grounded SWE tasks synthesized from real merged pull requests.
The dataset is designed for training… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/SWE-Next-SFT-Trajectories.NextSearch-1-Trajectories
NextSearch-1 Trajectories
The supervised training trajectories behind the
NextSearch-1 web research agents:
complete research episodes — reasoning, tool calls, live-web tool results,
and final answers — for every task in the companion
NextSearch-1-Tasks
SFT configs. Directly trainable: each row is a prompt (messages) plus a
target trajectory (target) with per-message reasoning and OpenAI-format
tool calls.
Technical report: nexttoken.co/research/nextsearch-1 ·
Harness and evals:… See the full description on the dataset page: https://huggingface.co/datasets/NextTokenAI/NextSearch-1-Trajectories.next.js-15.4-with-reasoning
Description
The Next.js Documentation Dataset based on next.js 15.4 version is a high-quality, code-centric dataset created from Next.js documentation for fine-tuning language models. It contains 1,172 question-answer pairs derived from 178 markdown documentation files, focusing on practical code examples and real-world development scenarios.
This dataset is designed for:
Question Answering: Natural language questions about Next.js development
Code Generation: Generating practical… See the full description on the dataset page: https://huggingface.co/datasets/Slava32/next.js-15.4-with-reasoning.socraticDataset1680
Socratic AI Pedagogy Preference Dataset (1,680 Quadruplets)
A curated multi-domain educational preference dataset for post-training open language models into pedagogical Socratic tutors for introductory Artificial Intelligence and Machine Learning courses.
📚 Dataset Overview
The dataset contains 1,680 paired preference quadruplets across five foundational AI subfields:
Classical Search & Planning (347 items): A* heuristic admissibility, graph search state-space… See the full description on the dataset page: https://huggingface.co/datasets/NextGenInstitute/socraticDataset1680.NextSearch-1-Tasks
NextSearch-1 Tasks
The task pools behind the NextSearch-1
web research agents: every row is a research question with its reference
answer and grading spec — the sft-tasks configs are the tasks behind the
supervised corpora, the rl-tasks configs the verified prompt+gold pools
used for reinforcement learning. Full trajectories for the SFT configs are in the companion
NextSearch-1-Trajectories.
Technical report: nexttoken.co/research/nextsearch-1 ·
Harness and evals:… See the full description on the dataset page: https://huggingface.co/datasets/NextTokenAI/NextSearch-1-Tasks.synapse-set-10k
🧠 SynapseSet-10K
SynapseSet-10K is a synthetic instruction-tuning dataset crafted to simulate EEG-based neurological state interpretation for natural language models. Each sample reflects brain signal metrics with contextual metadata, and an expert-style medical NLP explanation.
This dataset was generated by 7enn Labs and aims to bridge neuroscience signal interpretation with instruction-tuned NLP systems.
🔬 100% synthetic, non-clinical data. Intended for academic and research… See the full description on the dataset page: https://huggingface.co/datasets/NextGenC/synapse-set-10k.synapse-set-50k
🧠 SynapseSet-50K
SynapseSet-50K is a synthetic instruction-tuning dataset crafted to simulate EEG-based neurological state interpretation for natural language models. Each sample reflects brain signal metrics with contextual metadata, and an expert-style medical NLP explanation.
This dataset was generated by 7enn Labs and aims to bridge neuroscience signal interpretation with instruction-tuned NLP systems.
🔬 100% synthetic, non-clinical data. Intended for academic and research… See the full description on the dataset page: https://huggingface.co/datasets/NextGenC/synapse-set-50k.synapse-set-100k
🧠 SynapseSet-100K
SynapseSet-100K is a synthetic instruction-tuning dataset crafted to simulate EEG-based neurological state interpretation for natural language models. Each sample reflects brain signal metrics with contextual metadata, and an expert-style medical NLP explanation.
This dataset was generated by 7enn Labs and aims to bridge neuroscience signal interpretation with instruction-tuned NLP systems.
🔬 100% synthetic, non-clinical data. Intended for academic and research… See the full description on the dataset page: https://huggingface.co/datasets/NextGenC/synapse-set-100k.SWE-Next
SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
SWE-Next Dataset
SWE-Next is an execution-grounded dataset of 2,308 self-verifying software engineering tasks mined from real merged GitHub pull requests. Starting from 3,971 seeded Python repositories and 102,582 executed candidate base/merged commit pairs, SWE-Next retains only instances where the merged commit produces a strict test improvement without regressions. The final release… See the full description on the dataset page: https://huggingface.co/datasets/Evelina2025/SWE-Next.next_token
Supreme Court of India Judgments Dataset (1950-2025)
Dataset Description
This dataset contains a comprehensive collection of judgments and orders from the Supreme Court of India, spanning from its inception in 1950 up to early 2025.
Dataset Summary
Total Documents: 26,688
Total Tokens: ~196.9 Million (counted using cl100k_base encoding)
Format: JSONL (JSON Lines)
Language: English
Time Range: 1950 - 2025
Data Fields
Each entry in the .jsonl file… See the full description on the dataset page: https://huggingface.co/datasets/psychopenguin/next_token.mirror-SWE-Next-SFT-Trajectories
SWE-Next: Scalable Real-World Software Engineering Tasks for Agents
SWE-Next SFT Trajectories
SWE-Next SFT Trajectories is the supervised fine-tuning dataset released with SWE-Next: Scalable Real-World Software Engineering Tasks for Agents. It contains 3,693 ShareGPT-style multi-turn training examples collected from expert agent rollouts on 2,308 execution-grounded SWE tasks synthesized from real merged pull requests.
The dataset is designed for… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-SWE-Next-SFT-Trajectories.nextjobzlm-dataset-v0.1.0
NextjobzLM Instruction Dataset v0.1.0
Training data for NextjobzLM, the explanation
and extraction model behind NextJobz recommendations. This is the v0.1.0 release, published on
its own so the dataset viewer and fine-tuning UIs can read it. Every row is generated by code
in the repository; no real person's data is in here.
The model this trains never scores, ranks, or decides eligibility. Those decisions come from a
rule engine and a ranker; the model restates them and extracts… See the full description on the dataset page: https://huggingface.co/datasets/emdadibos/nextjobzlm-dataset-v0.1.0.docs-instruct-nextjs-20260601-0306
docs-instruct-20260601-0306
Synthetic instruction-tuning dataset generated by the DownFTuner pipeline.
Source: random Wikipedia articles (en), one run.
Generator: LLM-synthesized instruction/answer pairs grounded in each article.
Format: chat-format JSONL (messages field), split into train.jsonl and valid.jsonl.
License: CC-BY-SA-4.0 (inherits from Wikipedia source).
Source URLs are preserved in each row's source metadata.