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.
Page 1 of 6; 24 results per page.
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.
BM25 score snapshot for comparing neural rerankers against a lexical baseline.
Manifest linking Wave F scene PNGs to per-language caption JSONL files.
Multiple reference captions per image for BLEU/CIDEr caption eval.
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.
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.
Hierarchical category labels for taxonomy-aware classifiers.
Imbalanced label distribution: 2 rare vs 8 common rows.
Intent classification JSONL for dialog systems.
Language identification JSONL with EN/ES pairs.
Multi-label classification JSONL with two labels per row.
Topic classification rows for news routing tests.
Binary toxicity labels for moderation model eval.
Zero-shot candidate label lists per text for NLI-style classifiers.
Label id/name map companion for Wave F classification JSONL fixtures.
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.
3×3 confusion matrix CSV for classification metric calculators.
JSON twin of the 3-class confusion matrix.
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.
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.
5×5 confusion matrix with off-diagonal noise for multi-class eval.
JSON twin of the 5-class confusion matrix.
Precomputed cosine similarity pairs for embedding eval scripts.
COCO JSON with bounding boxes and polygon segmentation for the aerial scene.
