jsonl2.1 KB
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.
Preview — first 21 linesjsonl
{"instruction": "Summarize the text in one sentence.", "input": "The library closes at 8pm on weekdays and 5pm on weekends.", "output": "The library closes at 8pm on weekdays and 5pm on weekends."}
{"instruction": "Translate to French.", "input": "Good morning, how are you?", "output": "Bonjour, comment allez-vous ?"}
{"instruction": "Classify the sentiment as positive or negative.", "input": "I absolutely loved the concert.", "output": "positive"}
{"instruction": "Extract the email address.", "input": "Reach me at dev@example.com after noon.", "output": "dev@example.com"}
{"instruction": "Convert to uppercase.", "input": "hello world", "output": "HELLO WORLD"}
{"instruction": "List three primary colours.", "input": "", "output": "Red, blue, and yellow."}
{"instruction": "What is 15 percent of 200?", "input": "", "output": "30"}
{"instruction": "Correct the grammar.", "input": "She don't like apples.", "output": "She doesn't like apples."}
{"instruction": "Give the plural form.", "input": "mouse", "output": "mice"}
{"instruction": "Sort the numbers ascending.", "input": "5, 2, 9, 1", "output": "1, 2, 5, 9"}
{"instruction": "Name the largest planet in the solar system.", "input": "", "output": "Jupiter"}
{"instruction": "Rewrite the sentence in the past tense.", "input": "I walk to school.", "output": "I walked to school."}
{"instruction": "Provide the chemical symbol for gold.", "input": "", "output": "Au"}
{"instruction": "Count the words.", "input": "the quick brown fox", "output": "4"}
{"instruction": "Turn this into a question.", "input": "The store is open.", "output": "Is the store open?"}
{"instruction": "Give an antonym for 'increase'.", "input": "", "output": "decrease"}
{"instruction": "Round to the nearest whole number.", "input": "3.7", "output": "4"}
{"instruction": "Identify the language.", "input": "Hola, ¿cómo estás?", "output": "Spanish"}
{"instruction": "Extract the year.", "input": "The treaty was signed in 1848.", "output": "1848"}
{"instruction": "Abbreviate the phrase.", "input": "as soon as possible", "output": "ASAP"}
Specifications
- Records
- 20
- Format
- Alpaca
- Schema
- instruction, input, output
- Task
- instruction tuning
What is a .jsonl file?
JSONL (JSON Lines) is a text format where each line is a complete, independent JSON value, allowing records to be streamed and appended without parsing the whole file. It is not itself a JSON array and each line must stand alone. It is common in logging, machine learning datasets, and data pipelines.
How to use this file
Use an example JSONL to test line-by-line streaming parsers, append-and-resume ingestion, and batch pipelines that process one record per line.
Code examples
import json
with open("instructions.jsonl") as f:
rows = [json.loads(line) for line in f]
print(len(rows), rows[0])Related files
- jsonlChat 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.
- jsonlChat 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.
- jsonExtractive 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.
- jsonlNamed-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.
- jsonlParallel 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.
- jsonlSentiment 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.
Generated by generation/ai_datasets.py. Free for any use, no attribution required — license.