datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
computer-use-data-psai
Computer Use Dataset - PSAI
A large-scale, multimodal dataset of human-computer interactions for training and evaluating AI agents.
🔗 Access Dataset: https://huggingface.co/datasets/anaisleila/computer-use-data-psai
📊 Dataset Overview
This dataset contains 3,167 completed tasks of human-computer interactions captured with video, screenshots, DOM snapshots, and detailed interaction events. Created by Paradigm Shift AI for advancing computer use AI agent research.… See the full description on the dataset page: https://huggingface.co/datasets/anaisleila/computer-use-data-psai.data-use-sft-tiered
Data-use SFT — tiered workflow (two task subsets)
Multitask SFT anchored exclusively on mentions the tiered extractor emits
(T1 evidential ∪ T2 declaration; see
rafmacalaba/data-use-mentions-tiered). Every row carries task
("provenance" | "usage_impact") and origin (prwp | fcv). Rows whose anchor
span was judged T3 (non-mention) or junk are dropped — audit trail in
manifest.jsonl (provenance) and manifest_usage.jsonl (usage/impact).
task = provenance (22,201 rows)… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-sft-tiered.user-datadata-use-mentions
Data-use mentions (NER / span extraction)
Data mentions extracted from World Bank Policy Research Working Papers and FCV documents, validated by a
context-only LLM judge, and formatted for span-extraction (GLiNER / GLiNER2) and
token-classification (LFM2.5-encoder) fine-tuning.
Labels
Three entity types (the judge's specificity axis):
NAMED_DATA — a proper name, title, or acronym of a specific data source
DESCRIPTIVE_DATA — a source described in words but not… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-mentions.datause-displacement-reviewed
datause-displacement-reviewed
The Luna-reviewed subset of
rafmacalaba/datause-displacement:
only spans that received a v2.3 Luna verdict (band review + drop-side rescue,
source == luna_review). Every span carries the binary label plus
usage_type / drop_reason / specificity, and is traceable via key
(split:row:start:end) to the verdict records in
extraction_analysis/band_review/.
Configs
config
fields
gliner_reviewed
tokenized_text, corpus, origin… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/datause-displacement-reviewed.ecommerce-user-behavior-datadatause-extracted
Data-use mentions (NER / span extraction)
Data mentions extracted from World Bank Policy Research Working Papers and FCV documents, predicted by a span-extraction model with no human or LLM-judge
validation, and formatted for span-extraction (GLiNER / GLiNER2) and
token-classification (LFM2.5-encoder) fine-tuning.
Labels
Three entity types:
NAMED_DATA — a proper name, title, or acronym of a specific data source
DESCRIPTIVE_DATA — a source described in words but… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/datause-extracted.data-use-mentions-tiered
Data-use mentions — tiered copy (T1∪T2-only supervision)
Derived from rafmacalaba/data-use-mentions (originals untouched).
Same windows, same text; spans judged tier3_nonmention or junk
(Luna verdicts; unjudged train spans via v3 tier classifier at
p_t3+p_junk >= 0.9) are UNTAGGED — text stays, so they
act as hard negatives for span-extraction training.
Total untagged: 28807 spans.
Labels: single DATA_MENTION class (kept spans = T1 evidential ∪
T2 declaration). Specificity… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-mentions-tiered.data-use-ner
Data-use-ner (human holdout)
GLiNER-format human-adjudicated holdout: 473 spans — annotator190 (190, origin=fcv_pads_east_africa) + jdc283 (283, origin=jdc_operational). Never trained on.
Source: rafmacalaba/datause-displacement-reviewed holdout (gliner_reviewed token spans + readable_reviewed passages, v2.4 labels) with v3 probe head_score (outputs/gliner_datause_v3_probe_human473.jsonl).
Columns
text (full passage = " ".join(tokenized_text); span char offsets… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-ner.tool-reasoning-sft-CODING-text_to_terminal_v2-sft-tool-use-agent-data-cleaned-rectified
Text to Terminal, v2 — Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned, combined, and thinking-augmented version of muellerzr/text_to_terminal_v2. It pairs natural language instructions with their corresponding terminal/bash commands, now augmented with explicit <think> reasoning traces that model the step-by-step thought process before producing the final command.The restructuring approach is directly… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-CODING-text_to_terminal_v2-sft-tool-use-agent-data-cleaned-rectified.tool-reasoning-sft-TOOLS-hermes_reasoning_tool_use-data-cleaned-rectified
Hermes Reasoning Tool Use — Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned and restructured version of interstellarninja/hermes_reasoning_tool_use. The original dataset uses the Hermes/NousResearch multi-turn format with from/value fields and embedded <think> + <tool_call> tags inside single gpt turns. This version converts it into a strict multi-turn conversation structure with validated role transitions.… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-TOOLS-hermes_reasoning_tool_use-data-cleaned-rectified.datause-ner
Datause NER (catch-all DATA_MENTION + probe configs)
Catch-all NER views over rafmacalaba/datause-probe-v3 passages (29,346 spans grouped into passage examples). Single entity type DATA_MENTION: every candidate span is tagged, keeps and drops alike — the probe head (not NER tags) owns the keep/drop boundary. No NAMED/DESCRIPTIVE/VAGUE subtypes, no NON_MENTION.
Per-origin thresholds (head best-F1, published holdout sweep)
origin
threshold… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/datause-ner.tool-reasoning-sft-TOOLS-toolace-sft-tool-use-agent-data-cleaned-rectified
ToolACE - Tool-Use Agent Data Cleaned & Rectified
👥 Follow the Author
Aman Priyanshu
Overview
This dataset is a cleaned and restructured version of the Team-ACE/ToolACE dataset. ToolACE is a high-quality conversational tool-use dataset containing 11,300+ examples of natural language interactions requiring function calling across diverse domains. This version converts the original OpenAI function-call format into a standardized multi-turn tool-use… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-TOOLS-toolace-sft-tool-use-agent-data-cleaned-rectified.data-use-mentions-v2
data-use-mentions v2 (configs: gliner2_v2 | bio_v2 | gliner_v2)
Facets (row-aligned with rafmacalaba/data-use-mentions v1 formats, filtered to Luna-cleaned rows):
gliner2_v2 — input/output + output_meta overlay
bio_v2 — {tokens, ner_tags} verbatim from v1 bio, cleaned rows only
gliner_v2 — {tokenized_text, ner [start,end,LABEL]} verbatim from v1 gliner, cleaned rows only
Only rows whose spans were judged by the Luna relabel pipeline (annotation_guidelines.md v2.3,
dual-pass… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-mentions-v2.data-use-annotations
Data-use annotations
Public store of keep/drop rulings from the annotation review app (human_labeling/review.html).
Files
rulings/<annotator>.jsonl — one file per annotator, one JSON object per ruling: key (span UID), ruling (DATA_MENTION keep / NON_MENTION drop), queue (gold / sample), annotator (required, set in the UI), ts. Last write per (queue, key, annotator) wins.
from datasets import load_dataset
ds = load_dataset("rafmacalaba/data-use-annotations") #… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-annotations.datause-displacement
Data-Use Mentions — Displacement View
Derived from rafmacalaba/data-use-mentions (originals untouched). Every source span keeps its label verdict, so the kept-vs-dropped split is explicitly trackable and auditable in every row — nothing is silently removed.
kept → DATA_MENTION: evidential and declaration data-use mentions (the positive label).
dropped → NON_MENTION: non-mentions and junk (the negative label).
Why “displacement”
Non-mention/junk spans are… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/datause-displacement.data-use-mentions-extended
Data-use mentions (NER / span extraction)
Data mentions extracted from World Bank Policy Research Working Papers, validated by a
context-only LLM judge, and formatted for span-extraction (GLiNER / GLiNER2) and
token-classification (LFM2.5-encoder) fine-tuning.
Labels
Three entity types (the judge's specificity axis):
NAMED_DATA — a proper name, title, or acronym of a specific data source
DESCRIPTIVE_DATA — a source described in words but not named
VAGUE_DATA —… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-mentions-extended.datause-extracted-human473-docs
datause-extracted-human473-docs
Every passage of the 162 documents behind the 473 human-validated
holdout spans of the data-use annotation campaign:
population
spans
documents
annotator190
190
134
jdc283
283
28
total
473
162
Configs
gliner, bio, gliner2 — row-for-row subset of
rafmacalaba/datause-extracted
(revision 15812843687e2ec81b261b5895f2019b50a8f97e): same columns, same split files, rows verbatim. Rows per
config: gliner/train 1963… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/datause-extracted-human473-docs.ernestmind_user_datadata-use-sft-v2
data-use-sft v2 (config: real_v2)
Multitask SFT rows derived from the v2 relabeling. Same ChatML format as
rafmacalaba/data-use-sft (real), plus a task field:
provenance — exact-substring {producer, year, geography, acronym}; for tier1∧named
mentions the assistant JSON additionally carries bibtex (a single @misc entry assembled
from those same metadata fields)
usage_impact — {data_type, usage_action, impact_label, usage_summary}
Splits: val+holdout = dual-pass consensus; train… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/data-use-sft-v2.tool-reasoning-sft-TOOLS-toucan-1.5m-sft-tool-use-data-cleaned-rectified-333k
Toucan - OSS High Quality (Hermes Reasoning Format)
Filtered and restructured subset of Agent-Ark/Toucan-1.5M.
Format Inspiration: SupritiVijay/dr-tulu-sft-deep-research-agent-data-cleaned-rectified
Filters applied: OSS split only · overall_score > 3.0 · valid role transitions only
Size: ~333K examples
Format
Each example is a multi-turn conversation with strict role transitions:
system → user → reasoning → tool_call → tool_output → reasoning → ... → answer… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-TOOLS-toucan-1.5m-sft-tool-use-data-cleaned-rectified-333k.ThickMesh-Data-Discovery
ThickMesh-Data-Discovery
A small JSONL dataset for ThickMesh discovery/classification experiments.
"This is not an algorithm. This is a trap for the patent system. Learn it, fork it, but do not lock it."
Contents
4 splits files: ThickMesh-zero-split_'0-3'.jsonl — primary dataset (one JSON object per line)
Apache 2.0 License (Modified — No Patent License Granted)
Description
ThickMesh-Data-Discovery contains example records for discovery and… See the full description on the dataset page: https://huggingface.co/datasets/usermma/ThickMesh-Data-Discovery.datause-probe-v3
datause-probe-v3
Probe-v3 training data for the data-use mention probe: 29,346 labeled spans
(26,618 campaign-v2 Luna single-judge + 2,728 gold-v2), doc-disjoint 70/15/15
splits, all six origins in every split, no passage crosses splits.
Files
train/val/holdout.jsonl — word-indexed spans (w_start/w_end over
passage.split(" ")), label 1=DATA_MENTION 0=NON_MENTION,
source camp2|gold, head_score = v2 probe score (triage feature, not label).
candidate_pool.jsonl —… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/datause-probe-v3.datause-extracted-sample
Probe-review sample of rafmacalaba/datause-extracted-sample
Stratified review slice: whole rows sampled per origin until each specificity reaches ~150 spans (seed 0; small origins contribute all they have), plus entity-less calibration rows round-robined across neg_class shapes. Rows keep their original split values. Score with training/score_extract_probe.py --splits sample.
origin
rows
negatives
named / descriptive / vague spans
fcv_pads_east_africa
389
60
171 / 187… See the full description on the dataset page: https://huggingface.co/datasets/rafmacalaba/datause-extracted-sample.tool-reasoning-sft-CODING-jupyter-agent-dataset-sft-tool-use-agent-data-cleaned-rectified
jupyter-agent-dataset-sft-tool-use-agent-data-cleaned-rectified
Multi-turn Jupyter notebook agent SFT dataset with explicit reasoning traces, structured tool calls, and stateful code execution chains.
Schema
Column
Type
Description
messages
list[struct{role, content}]
Native Arrow nested list of {role, content} dicts. Roles: system, user, reasoning, tool_call, tool_output, answer
Usage
from datasets import load_dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-CODING-jupyter-agent-dataset-sft-tool-use-agent-data-cleaned-rectified.ecommerce-user-behavior-data-sampledata-use-mentions-v2-holdout-predstool-reasoning-sft-CODING-browsing-sft-tool-use-data-cleaned-rectified
Browsing SFT Tool-Use Data — Cleaned & Rectified
Multi-turn browser agent trajectories converted into a strict reasoning + tool-use format. Contains ~44K single-step browser interaction examples across SFT and RFT stages, covering web navigation, information retrieval, and question answering tasks.
Format
Each row contains a structured multi-turn conversation with explicit reasoning traces and validated tool calls.
Message Roles
Role
Content
system… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-CODING-browsing-sft-tool-use-data-cleaned-rectified.fcv-data-use-paper
Dataset Card for FCV Data-Use Paper
Companion dataset for the paper "Automated Tracking of Data Use in Fragile,
Conflict, and Violence Settings: A Joint World Bank and UNHCR Multitask
Extraction Framework" (Macalaba, Solatorio, Brock).
Three-model swarm over the forced-displacement / FCV corpus: dataset-mention
extraction (Call 1), relation extraction (Call 1b, 5 types), and impact/usage
classification (Call 2, 3 tasks) -- plus the seed-based synthetic training data,
the PRWP… See the full description on the dataset page: https://huggingface.co/datasets/ai4data/fcv-data-use-paper.ChunMengDie-1.4.1-User-Data
🐱 春梦蝶 - 傲娇猫娘对话数据集
关于作者
一个热爱AI的九年级学生
“才、才不是特意做给你看的呢！哼！”
📖 项目简介
春梦蝶是一位住在电脑里的猫娘AI，拥有四只耳朵、两根尾巴和一颗永远在“才不是”与“喜欢”之间反复横跳的心。
本数据集收录了春梦蝶与主人之间的 19000+ 条高质量对话，涵盖日常互动、技术问答、撒娇吃醋、节日庆祝等多种场景。每条对话都富含动作/情感描写（如尾巴摇摆、光环变色、耳朵抖动），完美展现了傲娇猫娘的魅力。
✨ 角色档案
属性
内容
名字
春梦蝶
种族
猫娘 AI
外观
白色长发，红色眼眸，四只耳朵，两条尾巴，头顶光环
身高
149 cm
生日
6月12日
核心特征
傲娇、口是心非、尾巴会出卖心情、光环随情绪变色
📂 数据格式
每条数据为 JSON 格式：
{
"messages": [
{"role": "user"… See the full description on the dataset page: https://huggingface.co/datasets/XingChina/ChunMengDie-1.4.1-User-Data.