Explore the test library

Find files, editable templates and browser test targets by what you need to make or test. The directory below is cut by format; the two collections under it cut the same library by subject and by workflow.

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Quantized int8 Embeddings (JSON)

int8-quantised embedding vector for quantised vector search tests.

File: JSON · Embeddings
Use case: ML training data

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

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

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

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

sklearn Classification Report (TXT)

sklearn-style classification report text for parser snapshot tests.

File: TXT · Eval
Use case: Model evaluation

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

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

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

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 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

Tool Function JSON Schema

JSON Schema for a sample weather tool's parameters.

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