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Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.

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01benikm91 /l-shape Code for dataset generation: https://github.com/benikm91/drawing-dataset-generator text100K<n<1M0 likes269 downloads11d agoHugging Face02bayang /shape Shape Geometry Dataset Synthetic graph-based centerline representations of 3D geometric motifs (pipe-like structures). JSON Schema dataset.json is an array of shape records. Each record: { "category": "arc_90", "nodes": [[x, y, z], ...], "edges": [[i, j], ...], "features": { "curvature": [0.0, 0.1, ...], "segment_angle": [0.0, 160.5, ...] } } Field Type Description category string Shape class label (e.g. straight, arc_90, corner) nodes… See the full description on the dataset page: https://huggingface.co/datasets/bayang/shape.imagegraph-ml100K<n<1M2 likes217 downloads8mo agoHugging Face03RemiFabre /spectre-shapestextn<1K0 likes78 downloads23d agoHugging Face04mvishiu11 /nlp-shap-text-validation NLP Shapley — Text Validation & Faithfulness Data Consolidated experimental data for the study "Is Shapley attribution on LLMs faithful, and if so, in what specific way?" This repo backs the analysis and the paper write-up; it is a living dataset — some experiments are still running on the cluster and will be added here as they land (see Status below). The research arc (what this data answers) Machinery is correct. Exact vs sampled Shapley converge; efficiency… See the full description on the dataset page: https://huggingface.co/datasets/mvishiu11/nlp-shap-text-validation.textn<1K0 likes45 downloads21d agoHugging Face05wannabegosu /Shape_direction MVBench We introduce a novel static-to-dynamic method for defining temporal-related tasks. By converting static tasks into dynamic ones, we facilitate systematic generation of video tasks necessitating a wide range of temporal abilities, from perception to cognition. Guided by task definitions, we then automatically transform public video annotations into multiple-choice QA for task evaluation. This unique paradigm enables efficient creation of MVBench with minimal manual… See the full description on the dataset page: https://huggingface.co/datasets/wannabegosu/Shape_direction.textvisual-question-answeringn<1K0 likes26 downloads10mo agoHugging Face06MrOvkill /shaped-svgs-autocaptioned-1675So, whilst working on my SVG model, I saw this dataset and thought, despite it's relatively small size, it would be much better and much more helpful if the images were captioned. Now, this is V0.1. V. 0.1. As in, it's not done yet. The images have (mostly) i'd say approximately 70-80% acurately captioned. As soon as my server is refilled, i'm going through it with a VQA and an actual vision model, i've a few in mind. Also special prompts to ensure EVERY image is accurate. In the meantime… See the full description on the dataset page: https://huggingface.co/datasets/MrOvkill/shaped-svgs-autocaptioned-1675.text1K<n<10K2 likes20 downloads3y agoHugging Face07neoneye /simon-arc-shape-v1 Version 1 Detect shape2x2 and shape3x3. The image sizes are between 1 and 30 pixels. textimage-to-text100K<n<1M0 likes16 downloads2y agoHugging Face08valira-ai /objaverse-xl-shape-annotations objaverse-xl-shape-annotations Shape-based textual annotations for 537,841 objects from Objaverse-XL. Each object gets a class label and a short, geometry-focused description. Why does this exist? Objaverse-XL is a large benchmark, but it does not contain any textual descriptions. This dataset was built to fix that. Every description focuses strictly on shape and structure, making it suitable for text-to-3D retrieval and contrastive representation learning tasks… See the full description on the dataset page: https://huggingface.co/datasets/valira-ai/objaverse-xl-shape-annotations.texttext-to-3d100K<n<1M1 likes16 downloads3mo agoHugging Face09jomasego /repro-shape-of-thought-repro-bundletextn<1K0 likes16 downloads2mo agoHugging Face10shapermindai /codealpaca-stanfordtext10K<n<100K0 likes10 downloads3y agoHugging Face11neoneye /simon-arc-shape-v2 Version 1 Detect shape2x2 and shape3x3_center. The image sizes are between 1 and 30 pixels. Version 2 Detect shape2x2 and shape3x3_center and shape3x3_opposite. The image sizes are between 1 and 30 pixels. textimage-to-text100K<n<1M0 likes9 downloads2y agoHugging Face12neoneye /simon-arc-shape-v4-rev3 Version 1 Detect shape2x2 and shape3x3_center. The image sizes are between 1 and 30 pixels. Version 2 Detect shape2x2 and shape3x3_center and shape3x3_opposite. The image sizes are between 1 and 30 pixels. Version 3 Focus on counting the unique number of colors. corners and diamond4. The image sizes are between 1 and 30 pixels. Version 4 Same weight to all transformations. The image sizes are between 1 and 30 pixels. TEST rev3. I'm making yet another… See the full description on the dataset page: https://huggingface.co/datasets/neoneye/simon-arc-shape-v4-rev3.textimage-to-text100K<n<1M0 likes9 downloads2y agoHugging Face13neoneye /simon-arc-shape-v5 Version 1 Detect shape2x2 and shape3x3_center. The image sizes are between 1 and 30 pixels. Version 2 Detect shape2x2 and shape3x3_center and shape3x3_opposite. The image sizes are between 1 and 30 pixels. Version 3 Focus on counting the unique number of colors. corners and diamond4. The image sizes are between 1 and 30 pixels. Version 4 Same weight to all transformations. The image sizes are between 1 and 30 pixels. Version 5 Added more… See the full description on the dataset page: https://huggingface.co/datasets/neoneye/simon-arc-shape-v5.textimage-to-text100K<n<1M0 likes9 downloads2y agoHugging Face14neoneye /simon-arc-shape-v6 Version 1 Detect shape2x2 and shape3x3_center. The image sizes are between 1 and 30 pixels. Version 2 Detect shape2x2 and shape3x3_center and shape3x3_opposite. The image sizes are between 1 and 30 pixels. Version 3 Focus on counting the unique number of colors. corners and diamond4. The image sizes are between 1 and 30 pixels. Version 4 Same weight to all transformations. The image sizes are between 1 and 30 pixels. Version 5 Added more… See the full description on the dataset page: https://huggingface.co/datasets/neoneye/simon-arc-shape-v6.textimage-to-text100K<n<1M0 likes9 downloads2y agoHugging Face15neoneye /simon-arc-shape-v7 Version 1 Detect shape2x2 and shape3x3_center. The image sizes are between 1 and 30 pixels. Version 2 Detect shape2x2 and shape3x3_center and shape3x3_opposite. The image sizes are between 1 and 30 pixels. Version 3 Focus on counting the unique number of colors. corners and diamond4. The image sizes are between 1 and 30 pixels. Version 4 Same weight to all transformations. The image sizes are between 1 and 30 pixels. Version 5 Added more… See the full description on the dataset page: https://huggingface.co/datasets/neoneye/simon-arc-shape-v7.textimage-to-text100K<n<1M0 likes9 downloads2y agoHugging Face16bulletfinley /ll-v4-5-7-scale-and-shape-20260620text1K<n<10K0 likes9 downloads3mo agoHugging Face17neoneye /simon-arc-shape-v3 Version 1 Detect shape2x2 and shape3x3_center. The image sizes are between 1 and 30 pixels. Version 2 Detect shape2x2 and shape3x3_center and shape3x3_opposite. The image sizes are between 1 and 30 pixels. Version 3 Focus on counting the unique number of colors. corners and diamond4. The image sizes are between 1 and 30 pixels. textimage-to-text100K<n<1M0 likes8 downloads2y agoHugging Face18lms-shape-preferences /wildchat-intents-qwen3-32bgated wildchat-intents-qwen3-32b WildChat-1M conversations with user-intent summaries extracted by qwen3-32b (temperature 0.7, top-p 0.9, max_tokens 16384), following the intent-extraction pipeline of "Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants" (https://github.com/schang-lab/utility-of-user-simulators), with the intent prompt of the UserLM paper (Naous et al., arXiv:2510.06552). Each row is the original allenai/WildChat-1M record plus an… See the full description on the dataset page: https://huggingface.co/datasets/lms-shape-preferences/wildchat-intents-qwen3-32b.text100K<n<1M1 likes8 downloads2mo agoHugging Face19Alignment-Lab-AI /bengalisamantha-bloom-shapedtext10K<n<100K0 likes3 downloads3y agoHugging Face20ajaysri /shape_hole_v2_visual_trace_preview Shape Hole V2 Visual Trace Preview This preview shows the low-level-policy variant where the subtask signal is drawn directly onto the external front_workspace_60 image instead of being provided as spatial text. No text is rendered into the image. The training image should match the normal visual observation, with only the trace overlay added. Visual conditioning signal: noisy label_source_uv and label_target_uv Source episode:… See the full description on the dataset page: https://huggingface.co/datasets/ajaysri/shape_hole_v2_visual_trace_preview.tabularn<1K0 likes3 downloads6mo agoHugging Face21tonydav41 /onnx-shape-inference-poc-custom-function-recursion-2026-05-12gated ONNX shape inference custom function recursion PoC Private evidence package for authorized huntr / ONNX MFF research. Target Package: onnx==1.21.0 API: onnx.shape_inference.infer_shapes_path Payload: payload.onnx Result: process termination by SIGSEGV / shell return code 139 Reproduce python3 -m venv /tmp/onnx-poc-venv /tmp/onnx-poc-venv/bin/python -m pip install onnx==1.21.0 /tmp/onnx-poc-venv/bin/python -c 'import onnx; m = onnx.load("payload.onnx");… See the full description on the dataset page: https://huggingface.co/datasets/tonydav41/onnx-shape-inference-poc-custom-function-recursion-2026-05-12.textn<1K0 likes3 downloads4mo agoHugging Face22lms-shape-preferences /pairs_Movies_and_TVtextn<1K0 likes2 downloads5mo agoHugging Face23lms-shape-preferences /pairs_Grocery_and_Gourmet_Foodtextn<1K0 likes2 downloads6mo agoHugging Face24dda71427 /draw_basic_shapes.jsontextn<1K0 likes1 downloads9mo agoHugging Face

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