datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
kaggle-womens-ecom-clothing-reviews
Women's Clothing E-Commerce Reviews (wide-row repack)
Repack of Kaggle dataset nicapotato/womens-ecommerce-clothing-reviews (CC0-1.0) into a single wide parquet with ZStandard level 9 compression.
Schema: one row per review — row_id (int64), clothing_id (int32), age (int32), rating (int8), recommended_ind (int8), positive_feedback_count (int32), title (string, nullable), review_text (string, nullable), division_name (string, nullable), department_name (string, nullable)… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/kaggle-womens-ecom-clothing-reviews.taskweft-fbd-react-train
taskweft-fbd-react-train
Intents and the IEC 61131-3 Function Block Diagrams that carry them out, as an
EditScore-shaped corpus: one root row per intent, three candidates per row (rank1 the
reference diagram, rank3 one that compiles and does the wrong thing, rank5 one the
compiler refuses), and one score row per candidate from the compiler's reference scan on three constructed input traces per row. Every row is
constructed from a template and a seed, so the labels are true by… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/taskweft-fbd-react-train.taskweft-fbd-editscore-train
taskweft-fbd-editscore-train
Intents and the IEC 61131-3 Function Block Diagrams that carry them out, as an
EditScore-shaped corpus: one root row per intent, three candidates per row (rank1 the
reference diagram, rank3 one that compiles and does the wrong thing, rank5 one the
compiler refuses), and one score row per candidate from the compiler and a runner that performed the plan. Every row is
constructed from a template and a seed, so the labels are true by construction and
the… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/taskweft-fbd-editscore-train.taskweft-fbd-trainer-train
taskweft-fbd-trainer-train
Intents and the IEC 61131-3 Function Block Diagrams that carry them out, as an
EditScore-shaped corpus: one root row per intent, three candidates per row (rank1 the
reference diagram, rank3 one that compiles and does the wrong thing, rank5 one the
compiler refuses), and one score row per candidate from the trainer config: the calls were applied to the mjlab task config and the term table read back. Every row is
constructed from a template and a seed… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/taskweft-fbd-trainer-train.anny-dress-on-stage-train
anny-dress-on-stage-train
Dress-on edits of ANNY parametric bodies with second-hand garment photos, edited by
VoxHammer (training-free 3D latent editing on TRELLIS-image-large), rendered by
Mitsuba 3 over a seeded Hammersley camera sequence, and scored with the MaskScore
geometric metric against the source's own decode.
MaskScore-shaped ETNF: dress_on (root), dress_on_candidates (rank1 own garment,
rank3 wrong garment, rank5 source decode = the floor), dress_on_scores (per view… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/anny-dress-on-stage-train.taskweft-fbd-godot-train
taskweft-fbd-godot-train
Intents and the IEC 61131-3 Function Block Diagrams that carry them out, as an
EditScore-shaped corpus: one root row per intent, three candidates per row (rank1 the
reference diagram, rank3 one that compiles and does the wrong thing, rank5 one the
compiler refuses), and one score row per candidate from the engine itself: api_runner.gd performed the calls on the fixture scene and the returns were read back. Every row is
constructed from a template and a… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/taskweft-fbd-godot-train.taskweft-fbd-harness-train
taskweft-fbd-harness-train
Intents and the IEC 61131-3 Function Block Diagrams that carry them out, as an
EditScore-shaped corpus: one root row per intent, three candidates per row (rank1 the
reference diagram, rank3 one that compiles and does the wrong thing, rank5 one the
compiler refuses), and one score row per candidate from the compiler's reference scan, rank1's outputs the reference for the others. Every row is
constructed from a template and a seed, so the labels are true… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/taskweft-fbd-harness-train.taskweft-fbd-plan-train
taskweft-fbd-plan-train
Intents and the IEC 61131-3 Function Block Diagrams that carry them out, as an
EditScore-shaped corpus: one root row per intent, three candidates per row (rank1 the
reference diagram, rank3 one that compiles and does the wrong thing, rank5 one the
compiler refuses), and one score row per candidate from the compiler's step lowering, compared step for step against rank1's plan. Every row is
constructed from a template and a seed, so the labels are true by… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/taskweft-fbd-plan-train.taskweft-fbd-compose-train
taskweft-fbd-compose-train
Intents and the IEC 61131-3 Function Block Diagrams that carry them out, as an
EditScore-shaped corpus: one root row per intent, three candidates per row (rank1 the
reference diagram, rank3 one that compiles and does the wrong thing, rank5 one the
compiler refuses), and one score row per candidate from the compiler's reference scan over the composed controller's traces. Every row is
constructed from a template and a seed, so the labels are true by… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/taskweft-fbd-compose-train.omnigen2-azimuth-ladder-anny-20260901
omnigen2-azimuth-ladder-anny-20260901
An image-edit ladder in the EditScore dataset shape: a candidate measured against a baseline
on the same prompts, one row per (source, edited, instruction) with the per-pair
measurement beside the images. The baseline is OmniGen2; the candidate is the same model
after a camera-control LoRA.
This ladder has no EditScore score. The runs measured recovered azimuth — where the
body actually faces in the generated view — not EditScore's pf / sc /… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/omnigen2-azimuth-ladder-anny-20260901.taskweft-fbd-udon-train
taskweft-fbd-udon-train
Intents and the IEC 61131-3 Function Block Diagrams that carry them out, as an
EditScore-shaped corpus: one root row per intent, three candidates per row (rank1 the
reference diagram, rank3 one that compiles and does the wrong thing, rank5 one the
compiler refuses), and one score row per candidate from the compiler and a runner that performed the plan. Every row is
constructed from a template and a seed, so the labels are true by construction and
the… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/taskweft-fbd-udon-train.vast-market-snapshots
vast-market-snapshots
Canonical dataset location: chibifire/vast-market-snapshots on Hugging Face.
This GitHub repo carries the capture code only (snapshot.py + this README).
Parquet payloads live on HF per the workspace's weights-live-on-huggingface rule.
snapshots/ is gitignored here; run snapshot.py locally to produce a snapshot
directory, then upload to the HF dataset repo with huggingface_hub.
Point-in-time snapshots of the Vast.ai on-demand GPU offer market, kept as
zstd… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/vast-market-snapshots.rf-detr-keypoint-latency-3090-20260904
rf-detr keypoint inference latency on 3090 (2026-09-04)
Measures the rf-detr-keypoint-preview-xlarge checkpoint (Apache-2.0
upstream, 129 M params, XL preview variant) per-frame forward wall on
the RTX 3090 at 816×816. The number is one input to the RFD 1170
body-presence-half latency budget (SIDEKICK owns the voice-turn half;
this repository is the body-presence half).
The numbers, at a glance
metric
run_1
run_2
mean forward
64.13 ms
63.84 ms
p50… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/rf-detr-keypoint-latency-3090-20260904.rung0-omnigen2-anny-depth-train
rung0-omnigen2-anny-depth
Rung 0 smoke invocation of the depth-conditioned generation pipeline: one
ANNY render's depth map conditioning one OmniGen2 image. A pipeline record,
not a training corpus.
Five zstd parquet tables in Essential Tuple Normal Form sharing primary
keys: run (one row per invocation), run_params and prompt (satellites),
render (camera and framing of the depth control), asset (image bytes:
the depth control and the generated output).
Generator:… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/rung0-omnigen2-anny-depth-train.