Multilingual Embeddings (JSON)
EN/ES sentence embeddings for cross-lingual similarity tests.
[
{
"id": "t00",
"text": "Hello sample",
"lang": "en",
"embedding": [
-0.24161726236343384,
0.2968849539756775,
-0.08661457151174545,
0.2776573896408081,
0.5728073716163635,
0.042604897171258926,
-0.3045431971549988,
-0.5884001851081848
]
},
{
"id": "t01",
"text": "Hola muestra",
"lang": "es",
"embedding": [
0.38322508335113525,
-0.5825456976890564,
0.3423929214477539,
0.25020214915275574,
-0.3601084053516388,
-0.08197279274463654,
-0.4132791757583618,
0.16354766488075256
]
}
]
Specifications
- Languages
- en, es
- Dimensions
- 8
Testing contract
Expected to pass- Scenario
- Exercise Multilingual Embeddings (JSON) in its embeddings workflow. EN/ES sentence embeddings for cross-lingual similarity tests.
- Expected result
- array length is 2; first-record keys are id, text, lang, embedding. Declared feature checks: languages=en, es; dimensions=8.
What is a .json file?
JSON (JavaScript Object Notation) is a lightweight, text-based data-interchange format representing objects, arrays, strings, numbers, booleans, and null. It is language-independent, human-readable, and the dominant format for web APIs and configuration. It requires a single well-formed root value.
How to use this file
Use an example JSON file to test parsers and serializers, schema validation, Unicode and number-precision handling, and API request or response processing.
How to use this file for testing
“Multilingual Embeddings (JSON)” is a deterministic Novus Examples fixture for ML training data. Labelled, synthetic datasets in the shapes ML pipelines expect (JSONL for text tasks, image annotations, embeddings, and sample weights) for testing data loaders, tokenizers, and training tooling.
Documented properties for this file: JSON · 659 bytes. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such, expect parsers to fail loudly rather than silently accept them.
AI/ML fixtures are fully synthetic with documented schemas, no real people or data. Test data loaders, tokenizers, annotation converters, embedding/vector stores, or eval-metric parsers against the known structure and fixed seeds.
These are labelled, training-shaped fixtures with a documented schema. Test your data loader, tokenizer, or format converter against it; every label and value is synthetic.
Code examples
import json
with open("multilingual-embeddings.json") as f:
data = json.load(f)
print(type(data), len(data))Related files
- jsonEmbedding ID Mapping (JSON)ID list and model metadata for the Wave F embedding set.
- jsonQuantized int8 Embeddings (JSON)int8-quantised embedding vector for quantised vector search tests.
- jsonText 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.
- parquetText 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.
- npyText 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.
- jsonText Embeddings — 8-dim (JSON)L2-normalised 8-dimensional text embeddings in JSON — for vector store loader tests.
Generated by generation/ai_wave_f.py. Free for any use, no attribution required, license.