README.md

metadata

license: cc-by-nc-4.0
language:
  - en
task_categories:
  - text-generation
tags:
  - code
  - non-commercial
  - sft
  - chatml
pretty_name: CodeAlpaca OpenAI Native
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.parquet
      - split: test
        path: data/test.parquet

CodeAlpaca OpenAI Native

This is a deterministic, lossless-formatting derivative of sahil2801/CodeAlpaca-20k, modernized with a typed OpenAI/TRL messages column and decontaminated against the HumanEval and MBPP test sets. The original Alpaca columns remain available for backward compatibility.

Intended use

from datasets import load_dataset
from trl import SFTTrainer

dataset = load_dataset("Archangel-system/codealpaca-openai-native")
trainer = SFTTrainer(model="your-model", train_dataset=dataset["train"])

No row-level JSON parsing or prompt concatenation is required. Choose a model whose tokenizer has an appropriate chat template.

Schema and conversion rule

Each row has:

• messages: a typed list of two records, strictly user then assistant;
• instruction, input, and output: the unchanged source strings.

The user message equals instruction when input.strip() is empty. Otherwise it equals instruction + "\n\n" + input (exactly two newline characters). The assistant message equals output. No answer was rewritten or generated.

Source audit

The build downloaded the source's Hub auto-converted Parquet shard directly.

Measurement	Count
Source rows	20022
Non-empty input rows	10258
Empty output rows	6
Exact duplicate instruction rows	0
Aggressively normalized duplicate instruction rows	0
Exact output == instruction rows	0
Normalized output == instruction rows	0

Decontamination

Two criteria are applied to CodeAlpaca instruction strings against benchmark test prompts. The benchmark sets are never modified.

1. Exact normalized equality: re.sub(r"[^a-z0-9]+", "", s.lower()).
2. Shared 13-gram after lowercasing and tokenizing with [a-z0-9]+.

Benchmark	Test items	Exact-normalized source rows	13-gram source rows
HumanEval	164	0	0
MBPP (full, test, field text)	500	1	2

Any source row matching either criterion was removed before splitting. Complete row-to-benchmark evidence is in rejections.jsonl. A row touching multiple items or criteria is rejected once but retains every hit in that log.

Filtering, deduplication, and split

Stage / reason	Rows removed
Empty output	6
Benchmark contamination (union)	2
Duplicate normalized (instruction, input)	0
Train/test signature purge from train	0
Total removed	8
Rejection rate	0.0400%

Rows are deduplicated on the tuple (normalize_text(instruction), normalize_text(input)), where normalize_text is the same exact-normalization function used by verification. The first source occurrence is retained.

The remaining rows are shuffled with Python's random.Random(42). The test size is min(1000, round(0.05 * n)); test receives the first shuffled indices. Any normalized pair crossing train/test would be purged from train, never test. The measured post-build intersection is 0.

Split	Rows
train	19014
test	1000
total	20014

Lengths and diversity

Lengths below are lexical token counts using [a-z0-9]+.

Field	Min	Median	Mean	P95	Max
User message	4	16.0	17.5716	31.0	110
Assistant message	0	20.0	28.4568	82.0	633

The final dataset contains 20014 exactly unique instructions and 20014 unique aggressively normalized (instruction, input) pairs. It preserves 10252 rows with non-empty inputs. No semantic-quality filter, language classifier, code execution, or model-based judgment was applied; this release intentionally does not rewrite source data.

Reproducibility and files

• Seed: 42.
• Build script: build.py.
• Independent Hub verification script: verify.py.
• Machine-readable measurements: stats.json.
• Rejection evidence: rejections.jsonl.
• Validation report: validation_report.json.
• Source revision: sahil2801/CodeAlpaca-20k@152bb5e9a29651266b018106053980070a0521a1.
• HumanEval revision: openai/openai_humaneval@7dce6050a7d6d172f3cc5c32aa97f52fa1a2e544.
• MBPP revision: google-research-datasets/mbpp@4bb6404fdc6cacfda99d4ac4205087b89d32030c.

File	SHA-256
Source Parquet as downloaded	5a2fa0c510d42843292c583f357086abb59f95796e0f55727b97e449aa93bed9
data/train.parquet	c00b9902c25983f60ebadb0a08905f42d44c5954ca0c8d5b4e0044e527adac5e
data/test.parquet	1200ef59fdc5854a2c571857c3695488754a783b6e17d778e392f05f60455864
rejections.jsonl	bf88205147fb3074152c43849408aa9476f44441d8f91b99d213980b7d0c3ac7

Run python build.py to rebuild locally or python build.py --publish to rebuild and upload. Publication reads HF_TOKEN from the environment or from the profile .env; the token is never written to an artifact.

License and attribution

This dataset is CC BY-NC 4.0 — non-commercial use only.

The upstream repository sahil280114/codealpaca ships two separate licence files, and they do not say the same thing:

File	Covers	Licence
LICENSE	the training/generation code	Apache-2.0
DATA_LICENSE	data/code_alpaca_20k.json, i.e. the data used here	CC BY-NC 4.0

The Hugging Face mirror sahil2801/CodeAlpaca-20k is tagged cc-by-4.0, which drops the NonCommercial clause present in the upstream DATA_LICENSE. We verified both files directly rather than trusting the mirror's tag, and this derivative follows the stricter upstream data licence: CC BY-NC 4.0.

Practical consequence: you may use, modify and redistribute this dataset for non-commercial purposes with attribution. Training a model on it for a commercial product is not covered. If you need a commercially usable code-instruction set, this is not the right dataset.

Attribution:

• Data: Sahil Chaudhary, Code Alpaca, sahil280114/codealpaca, DATA_LICENSE (CC BY-NC 4.0).
• Outputs were generated with OpenAI text-davinci-003; OpenAI's terms of use apply to the generated content independently of this licence.

Limitations

The original outputs were generated with text-davinci-003; this release does not independently validate their correctness, security, language, or executability. Decontamination is limited to exact normalized equality and lexical 13-gram overlap against the pinned HumanEval and MBPP test prompts; it does not prove absence of semantic paraphrases. The test split measures this source distribution and is not a replacement for external code benchmarks.