datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
dockerNLcommands-sft-unsloth
Docker NL Commands (Unsloth-ready)
Converted from dockerNLcommands-sft-jsonl for Unsloth Studio.
Configurations
Config
Format
Columns
Train rows
Test rows
alpaca (default)
Alpaca
instruction, input, output
2294
121
chatml
ChatML
messages (with role + content)
2294
121
Usage
Unsloth Studio
Dataset → Hugging Face → lakhera2023/dockerNLcommands-sft-unsloth
Format: alpaca (default config)
Train split: train, eval split: test… See the full description on the dataset page: https://huggingface.co/datasets/lakhera2023/dockerNLcommands-sft-unsloth.docker_sft
Docker_sft
Synthetic fine-tuning dataset generated with Dataset Genie 0.1.0 on 2026-09-09T07:15:41+00:00.
Domain brief
Docker Expert
A highly skilled Docker expert with strong experience in containerisation, Docker image development, Docker Compose, networking, volumes, security, and production deployments. They understand how to design efficient, portable, and reliable container-based environments across development, testing, and production.
Their… See the full description on the dataset page: https://huggingface.co/datasets/k3nn3dy/docker_sft.whylab-gemini-2-5-docker-validation
🛈 Anonymity Notice (2026-05-12): The associated manuscript is currently under peer review at a double-blind venue. Author identity and venue-specific identifiers have been withheld throughout this README, the BibTeX templates, and the CITATION.cff block. The dataset itself remains CC-BY-4.0 and is independently citable via its Zenodo DOI 10.5281/zenodo.20018468. The author byline will be restored after the review outcome is announced.
DOI
This dataset is citable via DataCite DOI… See the full description on the dataset page: https://huggingface.co/datasets/neogenesislab/whylab-gemini-2-5-docker-validation.sabnock-docker
Sabnock Docker
Sabnock Docker is a synthetic benchmark for testing whether AI agents can repair real Docker, Compose, and containerized CI failures.
It is built for AI engineers, not model hype. Each task contains a broken mini-repository, failing build/runtime logs, an expected fix, and machine-checkable scoring hints.
Why This Exists
Most coding-agent demos look good until the work touches Docker, Compose, dependency resolution, build context, network binding… See the full description on the dataset page: https://huggingface.co/datasets/Redhanuman/sabnock-docker.