
Dataset versions with file manifests and quality metrics.
What you see
The quality report
Computed once, at push time, over the full dataset — sampled only for very large ones. It counts what a machine can count and does not pretend to judge content.Protected datasets
A dataset can be governed so that a trainer may use it without being able to read it. On such a dataset the console shows identity, version, schema, lineage and authorisation state — and no preview, no sample records, and no download. That is enforced on the server, not by hiding buttons. Logs, validation samples and artifacts are covered by the same policy, so they cannot become an alternative export path.Uploading from the console
Upload dataset on the dataset list creates a dataset and its first version; Upload a version on a dataset page adds one to an existing dataset. Both ask the same three thingstp dataset push
does — which dataset, which version, which files — and both send the bytes straight to object
storage on a presigned URL. index.json, which is what makes a version complete and therefore
immutable, is written only after every file has landed: an interrupted upload leaves the version
number free rather than a version that claims to be finished.
A README.md among the files is published as the dataset card, exactly as a CLI push does it.
The quality report and the contamination fingerprint are not computed in the browser — a tab will
not read gigabytes to slice n-grams. A worker computes them from the bucket shortly after the
version appears, in the same pass that scans it, and writes them beside the version where a CLI
push would have. Past a per-version read budget the report marks itself sampled rather than
publishing a duplicate rate measured over part of a file.
A console upload refuses a file over 512 MB: a browser has to hold a whole file in memory to
checksum it, and the checksum is what the job side verifies a download against.
tp dataset push
streams, so it has no such limit — and it computes both reports over everything at push time
rather than up to a budget afterwards.What you can do
- Reference a version in a config or with
--train-dataset. - Push a new version from this page, or with
tp dataset push. - Check quality before pushing with
tp dataset checkandtp dataset quality.