datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SPADE-Grounding-Corpus-ToolUse-15K
SPADE grounding corpus: tool use (15k)
Reference documents the SPADE Environment Designer is grounded on when generating multi-turn tool-use environments. 15,552 source files drawn from nvidia/Nemotron-Pretraining-Code-v3.
Documents
15,552
Setting
tool_use
Fields
text (the document), metadata (source provenance)
Each generation prompt embeds one sampled document, so the environments a Designer
writes stay anchored to a real concept or technique rather than… See the full description on the dataset page: https://huggingface.co/datasets/spade-rl/SPADE-Grounding-Corpus-ToolUse-15K.SPADE-Grounding-Corpus-Games-15K
SPADE grounding corpus — games (15k)
Reference documents the SPADE proposer is grounded on when generating cognitive-skill
game environments. 15,000 documents: 10k drawn from a mathematics corpus and 5k from a
science corpus.
Documents
15,000
Setting
games
Fields
Field
Description
text
The document, exactly as embedded in the generation prompt
metadata
domain (mathematics / science) and url (source provenance)
Each generation… See the full description on the dataset page: https://huggingface.co/datasets/spade-rl/SPADE-Grounding-Corpus-Games-15K.gui_grounding_dataset-1k
Supported Tasks
Natural Language → GUI Action Grounding
Convert user instructions into JSON action objects.
Instruction Following
Models learn to interpret varying natural language formulations (e.g., “press submit” vs “click the submit button”).
Multi-step UI Automation
Some samples involve sequences of actions (e.g., open site → type → press Enter → screenshot).
Languages
English (en)
Generated with simple variations (synonyms, phrasings).
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/ArunKr/gui_grounding_dataset-1k.gui_grounding_dataset-100
Supported Tasks
Natural Language → GUI Action Grounding
Convert user instructions into JSON action objects.
Instruction Following
Models learn to interpret varying natural language formulations (e.g., “press submit” vs “click the submit button”).
Multi-step UI Automation
Some samples involve sequences of actions (e.g., open site → type → press Enter → screenshot).
Languages
English (en)
Generated with simple variations (synonyms, phrasings).
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/ArunKr/gui_grounding_dataset-100.genielm-ui-grounding
GenieLM UI-Grounding
Synthetic supervised fine-tuning data for text-based UI grounding: given a list of
on-screen elements (label + pixel center) and a natural-language instruction, pick the
single element to act on and emit a strict JSON action.
Built for GenieLM, a macOS agent that reads
the accessibility tree as text (not pixels) and lets a small LLM drive the cursor.
Format
Conversational SFT (messages column):
{"messages": [
{"role": "system", "content":… See the full description on the dataset page: https://huggingface.co/datasets/Barath/genielm-ui-grounding.hallucination-grounding-dpo-4k
Hallucination Grounding DPO Pairs (4K)
DPO preference pairs targeting the full spectrum of factuality failures — from hallucination to over-hedging.
Motivation
Existing refusal/safety datasets focus on what not to say. This dataset targets the orthogonal challenge: when to say "I don't know" vs. when to answer confidently. Models that over-refuse waste user trust; models that hallucinate destroy it.
Dataset Description
4,000 preference pairs across… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/hallucination-grounding-dpo-4k.rag-grounding-dpo-4k
RAG Grounding DPO Pairs (4K)
DPO preference pairs for training LLMs to faithfully use retrieved context in RAG pipelines.
Motivation
RAG is the dominant LLM deployment pattern in production. The core failure mode: models that ignore retrieved context and hallucinate answers, or that contradict documents with confidently-stated fabrications. This dataset trains models to ground answers in provided context.
Dataset Description
4,000 preference pairs… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/rag-grounding-dpo-4k.