AI / ML

The AI category ships the file shapes machine-learning pipelines actually pass around — real formats with documented schemas, all fully synthetic. The NLP set is JSON Lines training data: labelled sentiment, token-level NER with BIO tags, chat fine-tuning in both the OpenAI and Anthropic message shapes (linked as a conversion twin), Alpaca-style instruction tuning, extractive QA in the SQuAD v2 structure with an unanswerable question, abstractive summarization, and an English–Spanish parallel corpus. The embeddings set carries the same 24 texts as an L2-normalised 16-dimensional vector set in three twinned encodings — JSON, Apache Parquet, and a raw NumPy .npy matrix — for testing vector stores and loaders. The vision set renders one detection scene and annotates it three ways — COCO JSON, YOLO text, and Pascal-VOC XML — so you can diff annotation converters against a known image. The evaluation set has benchmark results, a confusion matrix as CSV and JSON twins, an ROC curve, and a scikit-learn-style classification report; there's a templated prompt library, and a genuinely-valid tiny safetensors weight file (sample values, not a trained model). Every dataset uses fixed seeds and invented names, companies, and places — no real people or data.

Filter ai / ml on Browse · 124 files · 10 subcategories

124 of 124 files

Embeddings

Cosine Similarity Pairs (JSONL)

Precomputed cosine similarity pairs for embedding eval scripts.

File: JSONL · Embeddings · 6 records
Use case: Model evaluation

Embedding ID Mapping (JSON)

ID list and model metadata for the Wave F embedding set.

File: JSON · Embeddings
Use case: ML training dataJSON parsing· Conversion set

Embedding Similarity Matrix (JSON)

Tiny 2×2 cosine similarity matrix for embedding eval unit tests.

File: JSON · Embeddings
Use case: Model evaluation

Multilingual Embeddings (JSON)

EN/ES sentence embeddings for cross-lingual similarity tests.

File: JSON · Embeddings
Use case: ML training data

Quantized int8 Embeddings (JSON)

int8-quantised embedding vector for quantised vector search tests.

File: JSON · Embeddings
Use case: ML training data

Text Embeddings — 16-dim (JSON)

A set of 24 L2-normalised 16-dimensional text embeddings as JSON — each record pairs an id and its source text with a float vector. A fixture for testing vector stores, similarity search, and embedding loaders. Parquet and .npy twins included.

File: JSON · Embeddings · 24 records
Use case: ML training dataEmbeddings+2· Conversion set

Text Embeddings — 16-dim (Parquet)

The same 16-dimensional embeddings as Apache Parquet — id and text columns plus one column per dimension. The columnar twin, for testing analytics engines and Parquet-based vector pipelines.

File: PARQUET · Embeddings · 24 rows
Use case: ML training dataEmbeddings+2· Conversion set

Text Embeddings — 24×16 matrix (NumPy .npy)

The embeddings as a raw NumPy array — a 24×16 float32 matrix in .npy format, loadable with numpy.load. The binary twin of the JSON and Parquet files, for testing tensor and matrix loaders.

File: NPY · Embeddings
Use case: ML training dataEmbeddings+1· Conversion set

Text Embeddings — 8-dim (JSON)

L2-normalised 8-dimensional text embeddings in JSON — for vector store loader tests.

File: JSON · Embeddings · 12 rows
Use case: ML training dataJSON parsing· Conversion set

Text Embeddings — 8-dim (NPY)

NumPy matrix twin of the Wave F 8-dim embeddings.

File: NPY · Embeddings
Use case: ML training data· Conversion set

Eval

Classification Report (JSON)

A per-class classification report in the scikit-learn structure — precision, recall, F1, and support for each class plus accuracy and macro/weighted averages. A fixture for testing metric parsers and report renderers.

File: JSON · Eval
Use case: Model evaluationJSON parsing

Confusion Matrix — 3 Classes (CSV)

3×3 confusion matrix CSV for classification metric calculators.

File: CSV · Eval
Use case: Model evaluationCSV parsing· Conversion set

Confusion Matrix — 3 Classes (JSON)

JSON twin of the 3-class confusion matrix.

File: JSON · Eval
Use case: Model evaluationJSON parsing· Conversion set

Confusion Matrix — 3-class (CSV)

A 3-class confusion matrix as CSV — rows are the true class, columns the predicted class, cells the counts. Paired with a JSON twin for testing metric parsers and evaluation visualisers.

File: CSV · Eval
Use case: Model evaluationCSV parsing+1· Paired fixture

Confusion Matrix — 3-class (JSON)

The same 3-class confusion matrix as JSON — a labels array plus a nested counts matrix. The structured twin of the CSV, for testing evaluation tooling.

File: JSON · Eval
Use case: Model evaluationJSON parsing+1· Paired fixture

Confusion Matrix — 5 Classes (CSV)

5×5 confusion matrix with off-diagonal noise for multi-class eval.

File: CSV · Eval
Use case: Model evaluation· Conversion set

Confusion Matrix — 5 Classes (JSON)

JSON twin of the 5-class confusion matrix.

File: JSON · Eval
Use case: Model evaluation· Conversion set

Detection Eval Metrics (JSON)

Synthetic mAP evaluation summary for object-detection benchmark harness tests.

File: JSON · Eval
Use case: Model evaluationJSON parsing

Eval Metric — Accuracy Mini

Minimal SAMPLE eval metric JSON (accuracy) for dashboard parsers.

File: JSON · Eval
Use case: Model evaluationJSON parsing· Conversion set

Eval Metric — Bleu Mini

Minimal SAMPLE eval metric JSON (bleu) for dashboard parsers.

File: JSON · Eval
Use case: Model evaluationJSON parsing· Conversion set

Eval Metric — F1 Mini

Minimal SAMPLE eval metric JSON (f1) for dashboard parsers.

File: JSON · Eval
Use case: Model evaluationJSON parsing· Conversion set

Eval Metric — Latency P95

Minimal SAMPLE eval metric JSON (latency_ms) for dashboard parsers.

File: JSON · Eval
Use case: Model evaluationJSON parsing· Conversion set

Eval Metric — Toxicity Score

Minimal SAMPLE eval metric JSON (toxicity) for dashboard parsers.

File: JSON · Eval
Use case: Model evaluationJSON parsing· Conversion set

Model Benchmark Results (JSON)

A model-evaluation summary in JSON — per-task scores for a fictional model across sentiment, NER, summarization, translation, and QA, each with its metric and sample size. A fixture for testing eval dashboards and leaderboard importers.

File: JSON · Eval
Use case: Model evaluationJSON parsing+1

Per-class Metrics (JSON)

Per-class precision/recall/F1 snapshot for classification dashboards.

File: JSON · Eval
Use case: Model evaluation

Precision-Recall Curve Points (JSON)

PR curve coordinates for average-precision metric tests.

File: JSON · Eval
Use case: Model evaluation

Ranking Eval Queries (JSONL)

Short query list for ranking benchmark harness smoke tests.

File: JSONL · Eval · 6 records
Use case: Model evaluationJSON parsing

ROC Curve Points (CSV)

An ROC curve as CSV — decision threshold with the corresponding false-positive and true-positive rates, monotonic from (0,0) to (1,1). A fixture for testing chart tools and AUC calculators.

File: CSV · Eval
Use case: Model evaluationCSV parsing+1

ROC Curve Points (JSON)

ROC curve coordinate list for AUC calculator tests.

File: JSON · Eval
Use case: Model evaluation

sklearn Classification Report (TXT)

sklearn-style classification report text for parser snapshot tests.

File: TXT · Eval
Use case: Model evaluation

Tool Calling — Tool Call Search

SAMPLE tool-calling JSON (tool-call-search) for agent harness schema tests.

File: JSON · Eval
Use case: Tool calling / function callingJSON parsing+1· Conversion set

Tool Calling — Tool Call Weather

SAMPLE tool-calling JSON (tool-call-weather) for agent harness schema tests.

File: JSON · Eval
Use case: Tool calling / function callingJSON parsing+1· Conversion set

Tool Calling — Tool Error Unknown

SAMPLE tool-calling JSON (tool-error-unknown) for agent harness schema tests.

File: JSON · Eval
Use case: Tool calling / function callingJSON parsing+1· Conversion set

Tool Calling — Tool Parallel Two

SAMPLE tool-calling JSON (tool-parallel-two) for agent harness schema tests.

File: JSON · Eval
Use case: Tool calling / function callingJSON parsing+1· Conversion set

Tool Calling — Tool Result Weather

SAMPLE tool-calling JSON (tool-result-weather) for agent harness schema tests.

File: JSON · Eval
Use case: Tool calling / function callingJSON parsing+1· Conversion set

Model Inference

ONNX Inference Contract - Affine MatMul plus Add Expected Output

The reference-evaluator output for the Affine MatMul plus Add model and supplied input, including shape, dtype, exact values and a 1e-6 tolerance.

File: JSON · Model Inference
Use case: Model inference testingModel evaluation+1· Conversion set

ONNX Inference Contract - Affine MatMul plus Add Input

The named float32 input tensor payload for the Affine MatMul plus Add model, with explicit shapes and JSON values for runner-independent test setup.

File: JSON · Model Inference
Use case: Model inference testingModel evaluation+1· Conversion set

ONNX Inference Contract - Affine MatMul plus Add Model

A two-node affine transform with fixed weights and bias, exercising initializers, MatMul dimensions and Add broadcasting. The matching JSON input and expected output make the model immediately usable in a CI inference assertion.

File: ONNX · Model Inference
Use case: Model inference testingModel evaluation+1· Conversion set

ONNX Inference Contract - Dynamic-Batch ReduceSum Expected Output

The reference-evaluator output for the Dynamic-Batch ReduceSum model and supplied input, including shape, dtype, exact values and a 1e-6 tolerance.

File: JSON · Model Inference
Use case: Model inference testingModel evaluation+1· Conversion set

ONNX Inference Contract - Dynamic-Batch ReduceSum Input

The named float32 input tensor payload for the Dynamic-Batch ReduceSum model, with explicit shapes and JSON values for runner-independent test setup.

File: JSON · Model Inference
Use case: Model inference testingModel evaluation+1· Conversion set

ONNX Inference Contract - Dynamic-Batch ReduceSum Model

A ReduceSum graph with a symbolic batch dimension and fixed feature width, for dynamic-shape import and batch-output tests. The matching JSON input and expected output make the model immediately usable in a CI inference assertion.

File: ONNX · Model Inference
Use case: Model inference testingModel evaluation+1· Conversion set

ONNX Inference Contract - Static Identity Expected Output

The reference-evaluator output for the Static Identity model and supplied input, including shape, dtype, exact values and a 1e-6 tolerance.

File: JSON · Model Inference
Use case: Model inference testingModel evaluation+1· Conversion set

ONNX Inference Contract - Static Identity Input

The named float32 input tensor payload for the Static Identity model, with explicit shapes and JSON values for runner-independent test setup.

File: JSON · Model Inference
Use case: Model inference testingModel evaluation+1· Conversion set

ONNX Inference Contract - Static Identity Model

A one-node identity graph with a fixed 1x4 float tensor, useful as the smallest loader and inference smoke test. The matching JSON input and expected output make the model immediately usable in a CI inference assertion.

File: ONNX · Model Inference
Use case: Model inference testingModel evaluation+1· Conversion set

ONNX Inference Contract - Two-Input Broadcast Add Expected Output

The reference-evaluator output for the Two-Input Broadcast Add model and supplied input, including shape, dtype, exact values and a 1e-6 tolerance.

File: JSON · Model Inference
Use case: Model inference testingModel evaluation+1· Conversion set

ONNX Inference Contract - Two-Input Broadcast Add Input

The named float32 input tensor payload for the Two-Input Broadcast Add model, with explicit shapes and JSON values for runner-independent test setup.

File: JSON · Model Inference
Use case: Model inference testingModel evaluation+1· Conversion set

ONNX Inference Contract - Two-Input Broadcast Add Model

A two-input Add graph where a length-two bias broadcasts across a 2x2 matrix, checking input binding and broadcasting rules. The matching JSON input and expected output make the model immediately usable in a CI inference assertion.

File: ONNX · Model Inference
Use case: Model inference testingModel evaluation+1· Conversion set

Nlp

Chat Fine-tuning Dataset — Anthropic Format (JSONL)

The same synthetic conversations in the Anthropic Messages JSONL shape — a top-level system prompt plus a messages array of user and assistant turns. The format twin of the OpenAI file, for testing chat-format conversion.

File: JSONL · Nlp
Use case: ML training dataNLP datasets+1· Conversion set

Chat Fine-tuning Dataset — OpenAI Format (JSONL)

A chat fine-tuning dataset in the OpenAI JSONL format — one conversation per line as a messages array with system, user, and assistant turns. Synthetic Q&A content. Paired with an Anthropic-format twin for testing format converters.

File: JSONL · Nlp
Use case: ML training dataNLP datasets+1· Conversion set

Classification Dataset — Hierarchical (JSONL)

Hierarchical category labels for taxonomy-aware classifiers.

File: JSONL · Nlp · 8 records
Use case: ML training dataNLP datasets+1

Classification Dataset — Imbalanced (JSONL)

Imbalanced label distribution — 2 rare vs 8 common rows.

File: JSONL · Nlp · 10 records
Use case: ML training dataNLP datasets+1

Classification Dataset — Intent (JSONL)

Intent classification JSONL for dialog systems.

File: JSONL · Nlp · 8 records
Use case: ML training dataNLP datasets+1

Classification Dataset — Language Id (JSONL)

Language identification JSONL with EN/ES pairs.

File: JSONL · Nlp · 8 records
Use case: ML training dataNLP datasets+1

Classification Dataset — Multilabel (JSONL)

Multi-label classification JSONL with two labels per row.

File: JSONL · Nlp · 8 records
Use case: ML training dataNLP datasets+1

Classification Dataset — Topic (JSONL)

Topic classification rows for news routing tests.

File: JSONL · Nlp · 8 records
Use case: ML training dataNLP datasets+1

Classification Dataset — Toxicity (JSONL)

Binary toxicity labels for moderation model eval.

File: JSONL · Nlp · 8 records
Use case: ML training dataNLP datasets+1

Classification Dataset — Zero Shot (JSONL)

Zero-shot candidate label lists per text for NLI-style classifiers.

File: JSONL · Nlp · 6 records
Use case: ML training dataNLP datasets+1

Classification Label Map (JSON)

Label id/name map companion for Wave F classification JSONL fixtures.

File: JSON · Nlp
Use case: ML training dataJSON parsing

Extractive QA Dataset — SQuAD v2 Format (JSON)

An extractive question-answering dataset in the SQuAD v2.0 JSON structure — titled articles with context paragraphs, questions, character-offset answers, and one deliberately unanswerable question. Synthetic content; a fixture for QA model training and SQuAD-format loaders.

File: JSON · Nlp
Use case: ML training dataNLP datasets+1

Failed Tool Calls (JSONL)

Failed tool invocation rows for agent error-handling and retry tests.

File: JSONL · Nlp · 4 records
Use case: Tool calling / function callingError handling

Instruction-tuning Dataset — Alpaca Format (JSONL)

An instruction-tuning dataset in the Alpaca JSONL format — 20 instruction / input / output triples covering small transformations, extraction, and factual answers. Fully synthetic; a fixture for supervised fine-tuning pipelines.

File: JSONL · Nlp · 20 records
Use case: ML training dataNLP datasets+1

Multi-step Agent Trace (JSONL)

Multi-step agent action/observation trace for ReAct-style eval harnesses.

File: JSONL · Nlp
Use case: Tool calling / function callingML training data

Named-Entity Recognition Dataset — BIO Tags (JSONL)

A token-classification dataset in JSON Lines — 16 tokenized sentences with aligned BIO tags for person, organisation, and location entities. All names, companies, and places are fictional. A fixture for NER model training and sequence-labelling tooling.

File: JSONL · Nlp · 16 records
Use case: ML training dataNLP datasets+1

OpenAI Tools Schema (JSON)

OpenAI function tools definition JSON paired with the tool-calling traces.

File: JSON · Nlp
Use case: Tool calling / function callingSchema / OpenAPI testing

Parallel Tool Calls (JSONL)

Parallel multi-tool call payloads for concurrent tool-router tests.

File: JSONL · Nlp · 4 records
Use case: Tool calling / function calling

Parallel Translation Corpus — EN↔ES (JSONL)

An English↔Spanish parallel corpus in JSON Lines — 20 aligned sentence pairs of everyday phrases. A fixture for training and evaluating machine-translation models and for testing UTF-8 handling of accented characters.

File: JSONL · Nlp
Use case: ML training dataNLP datasets+2

SAMPLE PII NER Spans (JSONL)

Named-entity spans over fictional SAMPLE PII sentences — for redaction/NER tooling tests.

File: JSONL · Nlp · 2 records
Use case: NLP datasetsML training data+1

Sentiment Classification Dataset (JSONL)

A labelled sentiment-classification dataset in JSON Lines — 24 short product-review-style sentences balanced across positive, negative, and neutral. Fully synthetic; a fixture for testing text-classification loaders, tokenizers, and JSONL parsers.

File: JSONL · Nlp · 24 records
Use case: ML training dataNLP datasets+2

Summarization Dataset (JSONL)

An abstractive-summarization dataset in JSON Lines — 15 short synthetic news-style documents each paired with a one-sentence summary. A fixture for training and evaluating summarization models and for testing JSONL ingestion.

File: JSONL · Nlp · 15 records
Use case: ML training dataNLP datasets+1

Tool Call Results (JSONL)

Function-call result rows decoupled from chat messages for router testing.

File: JSONL · Nlp · 6 records
Use case: Tool calling / function callingJSON parsing

Tool Function JSON Schema

JSON Schema for a sample weather tool's parameters.

File: JSON · Nlp
Use case: Tool calling / function callingSchema / OpenAPI testing

Tool-calling Mini Trace (JSONL)

Minimal single-turn tool-calling trace for quick parser smoke tests.

File: JSONL · Nlp · 1 record
Use case: Tool calling / function calling

Tool-calling Traces — Anthropic Format (JSONL)

Anthropic-style tool_use blocks with paired tool results for agent harness tests.

File: JSONL · Nlp · 6 records
Use case: Tool calling / function callingML training data

Tool-calling Traces — OpenAI Format (JSONL)

OpenAI-style tool-call JSONL with function invocation and tool result messages.

File: JSONL · Nlp · 6 records
Use case: Tool calling / function callingML training data+1

Prompts

OCR Assistant Chat Turns (JSONL)

A single multi-turn chat example for an OCR assistant — system/user/assistant roles in JSONL.

File: JSONL · Prompts · 1 record
Use case: Prompt engineeringNLP datasets+1

Prompt — Fewshot Translate

Tiny SAMPLE prompt fixture (fewshot-translate) for harness and eval wiring.

File: TXT · Prompts
Use case: Prompt engineeringML training data· Conversion set

Prompt — System Concise

Tiny SAMPLE prompt fixture (system-concise) for harness and eval wiring.

File: TXT · Prompts
Use case: Prompt engineeringML training data· Conversion set

Prompt — System Json Only

Tiny SAMPLE prompt fixture (system-json-only) for harness and eval wiring.

File: TXT · Prompts
Use case: Prompt engineeringML training data· Conversion set

Prompt — User Classify

Tiny SAMPLE prompt fixture (user-classify) for harness and eval wiring.

File: TXT · Prompts
Use case: Prompt engineeringML training data· Conversion set

Prompt — User Extract Entities

Tiny SAMPLE prompt fixture (user-extract-entities) for harness and eval wiring.

File: TXT · Prompts
Use case: Prompt engineeringML training data· Conversion set

Prompt — User Summarize

Tiny SAMPLE prompt fixture (user-summarize) for harness and eval wiring.

File: TXT · Prompts
Use case: Prompt engineeringML training data· Conversion set

Prompt Library (JSONL)

A reusable prompt library in JSON Lines — 20 templated prompts for summarization, translation, extraction, code, and more, each with a task label, tags, and {curly-brace} placeholders. A fixture for prompt-management tools and JSONL parsers.

File: JSONL · Prompts
Use case: Prompt engineeringNLP datasets+1

Rag

Mini RAG Corpus — doc-a.md

A short Markdown document in the mini RAG corpus (doc-a.md) — retrieve against the paired queries JSON.

File: MD · Rag
Use case: ML training dataNLP datasets+1· Conversion set

Mini RAG Corpus — doc-b.md

A short Markdown document in the mini RAG corpus (doc-b.md) — retrieve against the paired queries JSON.

File: MD · Rag
Use case: ML training dataNLP datasets+1· Conversion set

Mini RAG Corpus — doc-c.md

A short Markdown document in the mini RAG corpus (doc-c.md) — retrieve against the paired queries JSON.

File: MD · Rag
Use case: ML training dataNLP datasets+1· Conversion set

Mini RAG Corpus — doc-d.md

A short Markdown document in the mini RAG corpus (doc-d.md) — retrieve against the paired queries JSON.

File: MD · Rag
Use case: ML training dataNLP datasets+1· Conversion set

Mini RAG Queries (JSON)

Retrieval-eval queries with relevant_docs pointers into the mini RAG Markdown corpus.

File: JSON · Rag
Use case: ML training dataNLP datasets+1· Conversion set

Ranking

BM25 Retrieval Scores (JSON)

BM25 score snapshot for comparing neural rerankers against a lexical baseline.

File: JSON · Ranking
Use case: Model evaluationJSON parsing

Learning-to-Rank Queries (JSONL)

Query-document relevance grades for learning-to-rank training and eval.

File: JSONL · Ranking · 6 records
Use case: ML training dataNLP datasets

Listwise Ranking (JSONL)

Listwise ranked doc lists for nDCG and listwise loss tests.

File: JSONL · Ranking · 4 records
Use case: ML training data

MRR Eval Ground Truth (JSON)

Mean reciprocal rank ground truth for search eval harness tests.

File: JSON · Ranking
Use case: Model evaluation

nDCG Ground Truth (JSON)

Precomputed DCG/nDCG values for ranking metric unit tests.

File: JSON · Ranking
Use case: Model evaluation

Pairwise Ranking Preferences (JSONL)

Pairwise doc preferences for reward-model and reranker training tests.

File: JSONL · Ranking · 6 records
Use case: ML training dataNLP datasets

Speech

ASR Digit Utterances Dataset (JSONL)

JSON Lines ASR training/eval set for the Wave B synthetic digit utterances — each row points at a clean WAV and carries the expected transcript.

File: JSONL · Speech · 8 records
Use case: ASR testingML training data+1

Vision

Caption Dataset Manifest (JSON)

Manifest linking Wave F scene PNGs to per-language caption JSONL files.

File: JSON · Vision
Use case: Computer visionJSON parsing

Caption Eval References (JSON)

Multiple reference captions per image for BLEU/CIDEr caption eval.

File: JSON · Vision
Use case: Model evaluation

Detection Annotations — Aerial COCO (JSON)

COCO JSON with bounding boxes and polygon segmentation for the aerial scene.

File: JSON · Vision
Use case: Computer visionML training data+1

Detection Annotations — COCO (JSON)

Object-detection annotations for the scene in the COCO JSON format — images, categories, and per-object bounding boxes as [x, y, width, height]. Grouped with YOLO and Pascal-VOC twins for testing annotation-format conversion.

File: JSON · Vision
Use case: Computer visionML training data+2· Conversion set

Detection Annotations — Pascal VOC (XML)

The same detection boxes in the Pascal VOC XML format — a per-image annotation with size, and one object element per box with pixel corner coordinates. The XML twin of the COCO and YOLO annotations.

File: XML · Vision
Use case: Computer visionML training data+1· Conversion set

Detection Annotations — Retail COCO (JSON)

COCO JSON with bounding boxes and polygon segmentation for the retail scene.

File: JSON · Vision
Use case: Computer visionML training data+1· Conversion set

Detection Annotations — Retail YOLO (TXT)

YOLO-format normalised boxes for the retail detection scene.

File: TXT · Vision
Use case: Computer visionML training data· Conversion set

Detection Annotations — Warehouse COCO (JSON)

COCO JSON with bounding boxes and polygon segmentation for the warehouse scene.

File: JSON · Vision
Use case: Computer visionML training data+1· Conversion set

Detection Annotations — Warehouse Pascal VOC (XML)

Pascal VOC XML annotations for the warehouse detection scene.

File: XML · Vision
Use case: Computer visionML training data+1· Conversion set

Detection Annotations — Warehouse YOLO (TXT)

YOLO-format normalised boxes for the warehouse detection scene.

File: TXT · Vision
Use case: Computer visionML training data· Conversion set

Detection Annotations — YOLO (TXT)

The same detection boxes in the YOLO text format — one object per line as class id and box centre, width, and height normalised to 0–1. The format twin of the COCO and VOC annotations.

File: TXT · Vision
Use case: Computer visionML training data+1· Conversion set

Detection Class List (TXT)

The class-name list for the detection scene, one label per line — index equals the zero-based line number, matching the YOLO class ids. A companion to the COCO/YOLO/VOC annotation files.

File: TXT · Vision
Use case: Computer visionML training data

Detection Keypoint Annotations (JSON)

Sample keypoint annotations (visible/occluded flags) for pose-estimation loader tests.

File: JSON · Vision
Use case: Computer visionML training data· Conversion set

Detection Scene — Aerial (PNG)

Synthetic aerial detection scene with 2 labelled objects at known coordinates.

File: PNG · Vision · 320 × 240 px
Use case: Computer visionML training data

Detection Scene — Retail (PNG)

Synthetic retail detection scene with 3 labelled objects at known coordinates.

File: PNG · Vision · 320 × 240 px
Use case: Computer visionML training data· Conversion set

Detection Scene — Warehouse (PNG)

Synthetic warehouse detection scene with 3 labelled objects at known coordinates.

File: PNG · Vision · 320 × 240 px
Use case: Computer visionML training data· Conversion set

Extended Detection Class List (TXT)

Combined class-name list for Wave F warehouse, retail, and aerial detection scenes.

File: TXT · Vision
Use case: Computer visionML training data

Image Caption Dataset (JSONL)

Six synthetic image-caption pairs in JSON Lines — for testing captioning loaders and vision-language eval harnesses.

File: JSONL · Vision · 6 records
Use case: Computer visionML training data+1

Multilingual Captions — DE (JSONL)

DE image captions referencing Wave F detection scenes.

File: JSONL · Vision · 4 records
Use case: Computer visionML training data+1

Multilingual Captions — EN (JSONL)

EN image captions referencing Wave F detection scenes.

File: JSONL · Vision · 4 records
Use case: Computer visionML training data+1

Multilingual Captions — ES (JSONL)

ES image captions referencing Wave F detection scenes.

File: JSONL · Vision · 4 records
Use case: Computer visionML training data+1

Multilingual Captions — FR (JSONL)

FR image captions referencing Wave F detection scenes.

File: JSONL · Vision · 4 records
Use case: Computer visionML training data+1

Multilingual Captions — JA (JSONL)

JA image captions referencing Wave F detection scenes.

File: JSONL · Vision · 4 records
Use case: Computer visionML training data+1

Multilingual Captions — Parallel (JSONL)

Parallel EN/ES/FR/DE captions per image for multilingual eval.

File: JSONL · Vision · 6 records
Use case: Computer visionML training data

Object-detection Scene (PNG, 640×480)

A simple rendered street scene with a person, a car, and a tree at known pixel coordinates — the image the COCO, YOLO, and Pascal-VOC annotation twins describe. A fixture for testing object-detection loaders and annotation converters.

File: PNG · Vision · 640 × 480 px
Use case: Computer visionML training data+1

Semantic Segmentation Class Map (JSON)

Class-id to name map for the semantic-segmentation mask (background + three shapes).

File: JSON · Vision
Use case: Computer visionJSON parsing+1· Conversion set

Semantic Segmentation Mask (PNG)

Indexed 8-bit mask (0=background, 1–3=shapes) for the semantic-segmentation scene twin.

File: PNG · Vision · 256 × 256 px
Use case: Computer visionML training data· Paired fixture

Semantic Segmentation Scene (PNG)

A synthetic RGB scene with three coloured shapes — input for semantic-segmentation models. Pair with the indexed mask twin.

File: PNG · Vision · 256 × 256 px
Use case: Computer visionML training data· Paired fixture

Visual Question Answering Pairs (JSONL)

Short VQA question/answer pairs referencing library images — a fixture for VQA loaders and eval scripts.

File: JSONL · Vision · 6 records
Use case: Computer visionML training data+1

Weights

Tiny Model Weights (safetensors)

A genuinely-valid safetensors file with two small float32 tensors (36 parameters total) — an 8×4 weight and a length-4 bias. The values are meaningless sample data, not a trained model; a fixture for testing safetensors loaders and weight inspectors.

File: SAFETENSORS · Weights
Use case: ML training dataConversion testing

Frequently asked questions

What ML dataset shapes are included?

ASR JSONL, caption/VQA JSONL, mini RAG corpus + queries, semantic-segmentation scene/mask pairs, NER spans, chat prompt turns, plus detection/ranking/tool-calling JSONL and tiny eval packs in later waves.

Is this training data for production models?

These are tiny deterministic fixtures for loaders and eval harnesses — not large-scale training corpora.

How do I test LLM tool-calling parsers?

Filter Browse by purpose tool-calling for function-call JSONL shapes. Treat them as synthetic SAMPLE payloads for schema and harness tests only.