Sentiment, NER, chat, instruction-tuning, QA, summarization, and translation data in JSON Lines and JSON — for testing NLP loaders, tokenizers, and format converters.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Six synthetic image-caption pairs in JSON Lines — for testing captioning loaders and vision-language eval harnesses.
Short VQA question/answer pairs referencing library images — a fixture for VQA loaders and eval scripts.
A short Markdown document in the mini RAG corpus (doc-a.md) — retrieve against the paired queries JSON.
A short Markdown document in the mini RAG corpus (doc-b.md) — retrieve against the paired queries JSON.
A short Markdown document in the mini RAG corpus (doc-c.md) — retrieve against the paired queries JSON.
A short Markdown document in the mini RAG corpus (doc-d.md) — retrieve against the paired queries JSON.
Retrieval-eval queries with relevant_docs pointers into the mini RAG Markdown corpus.
A single multi-turn chat example for an OCR assistant — system/user/assistant roles in JSONL.
Named-entity spans over fictional SAMPLE PII sentences — for redaction/NER tooling tests.
Multi-label classification JSONL with two labels per row.
Hierarchical category labels for taxonomy-aware classifiers.
Imbalanced label distribution — 2 rare vs 8 common rows.
Zero-shot candidate label lists per text for NLI-style classifiers.
Intent classification JSONL for dialog systems.
Topic classification rows for news routing tests.
Language identification JSONL with EN/ES pairs.
Binary toxicity labels for moderation model eval.
Query-document relevance grades for learning-to-rank training and eval.
Pairwise doc preferences for reward-model and reranker training tests.
EN image captions referencing Wave F detection scenes.
ES image captions referencing Wave F detection scenes.
FR image captions referencing Wave F detection scenes.
DE image captions referencing Wave F detection scenes.
JA image captions referencing Wave F detection scenes.
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