datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Nemotron-3-Nano-RL-Training-Blend-prompt-only
Nemotron-3-Nano-RL-Training-Blend-prompt-only
Prompt-only extraction from nvidia/Nemotron-3-Nano-RL-Training-Blend.
Files:
prompts.csv: one prompt extraction record per source row. Records include
prompt, separated system_prompt, and structured tools when the source row
defines available tools. Nested values are JSON-encoded inside CSV cells.
summary.md: source row counts, extracted row counts, count deltas, and failed prompt counts.
null_or_empty_rows.md: row indexes where… See the full description on the dataset page: https://huggingface.co/datasets/jamesdborin/Nemotron-3-Nano-RL-Training-Blend-prompt-only.Nemotron-RL-Ultra-Training-Blends-prompt-only
Nemotron-RL-Ultra-Training-Blends-prompt-only
Prompt-only extraction from nvidia/Nemotron-RL-Ultra-Training-Blends.
Files:
prompts.csv: one prompt extraction record per source row. Records include
prompt, separated system_prompt, and structured tools when the source row
defines available tools. Nested values are JSON-encoded inside CSV cells.
summary.md: source row counts, extracted row counts, count deltas, and failed prompt counts.
null_or_empty_rows.md: row indexes where… See the full description on the dataset page: https://huggingface.co/datasets/jamesdborin/Nemotron-RL-Ultra-Training-Blends-prompt-only.clinical-5node-anticoag-buf-lag-cpl-anticoag-mismanagement-bleed-v0.1
What this repo does
This dataset models an anticoagulation mismanagement cascade by measuring when anticoagulation pressure rises, hemostatic buffer capacity erodes, monitoring and decision lags increase, and cross-team coupling tightens across prescribers and care settings, crossing a phase transition into an unrecoverable bleeding cascade state.
Core quad
anticoagbuflagcpl
Prediction target
label_cascade_state
Row structure
One row represents an… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-5node-anticoag-buf-lag-cpl-anticoag-mismanagement-bleed-v0.1.Nemotron-RL-Super-Training-Blends-prompt-only
Nemotron-RL-Super-Training-Blends-prompt-only
Prompt-only extraction from nvidia/Nemotron-RL-Super-Training-Blends.
Files:
prompts.csv: one prompt extraction record per source row. Records include
prompt, separated system_prompt, and structured tools when the source row
defines available tools. Nested values are JSON-encoded inside CSV cells.
summary.md: source row counts, extracted row counts, count deltas, and failed prompt counts.
null_or_empty_rows.md: row indexes where… See the full description on the dataset page: https://huggingface.co/datasets/jamesdborin/Nemotron-RL-Super-Training-Blends-prompt-only.clinical-5node-bleed-buf-lag-cpl-post-op-hemorrhage-v0.1
What this repo does
This dataset models a post-operative hemorrhage cascade by measuring when bleeding pressure rises, physiologic buffer capacity erodes, clinical response lags, and ward-to-theatre coupling tightens, crossing a phase transition into an unrecoverable cascade state.
Core quad
bleedbuflagcpl
Prediction target
label_cascade_state
Row structure
One row represents a post-operative deterioration scenario with numeric signals for bleeding… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-5node-bleed-buf-lag-cpl-post-op-hemorrhage-v0.1.ai-blessed_raw_recipes38_blessings_mingalar_tayartaw_qna
၃၈ ဖြာ မင်္ဂလာ တရားတော် [The 38 Blessings of Maha Mangala Sutta Q&A]
Created by: freococoLicense: CC0 1.0 Universal (Public Domain)
Summary
This dataset consists of 1,111 high-quality Questions and Answers centered on the 38 Blessings (၃၈ ဖြာ မင်္ဂလာ - Mangala). The answers are written in a Burmese Spoken Style to ensure natural AI conversational flow, while maintaining deep Philosophical, Psychological, Social, and Spiritual perspectives.
This repository is a specialized… See the full description on the dataset page: https://huggingface.co/datasets/freococo/38_blessings_mingalar_tayartaw_qna.clinical-quad-anticoag-antiplatelet-renal-fall-bleed-v0.1What this repo does
This dataset models major bleeding risk under anticoagulation. It predicts when the interaction between anticoagulant intensity, antiplatelet coadministration, reduced renal function, and elevated fall risk creates a high probability of a bleeding event.
Core quad
anticoag_intensity_index
antiplatelet_coadmin_index
renal_function_index
fall_risk_index
Prediction target
label_bleed_event
Row structure
Each row represents a patient bleeding risk snapshot during active… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/clinical-quad-anticoag-antiplatelet-renal-fall-bleed-v0.1.