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Datasets:
KrazyKitty
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Data-Lab

Tags:
data-provenance
dataset-documentation
preprocessing
data-quality
evaluation
privacy
License:
Dataset card Files
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Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/harbor/harbor.py", line 171, in _split_generators
                  raise DataFilesNotFoundError("No task.toml or instruction.md files found")
              datasets.exceptions.DataFilesNotFoundError: No task.toml or instruction.md files found
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

KRAZYKITTY // DATA LAB

Status: research documentation and workflow toolkit — no dataset is published here.

DATA LAB is KrazyKitty's evidence-first workspace for studying how datasets should be sourced, reviewed, transformed, documented, and evaluated before they are allowed near a training or benchmark pipeline.

This repository contains dataset-card templates, provenance schemas, deterministic preprocessing tools, evaluation guidance, and review gates. It does not contain a corpus, benchmark examples, training rows, scraped content, or a claim that any source is approved for KittyLM.

Research tracks

Track Purpose Current state
Source provenance Bind each source to identity, version, integrity, licence, and allowed use Working templates and validator
Licensing, privacy, consent Require explicit permission and data-risk review before processing Documented gate
Preprocessing experiments Compare deterministic text transformations without hiding exclusions Working local reference workflow
Data quality Measure duplication, encoding, language, structure, contamination, and coverage Measurement plan
Evaluation resources Version benchmark inputs and separate test integrity from model claims Documentation and Collection candidates
Dataset cards Make intended use, composition, limitations, and governance visible Reusable templates

Repository map

Reproducible workflow

  1. Create a source manifest from the schema and keep its decision proposed.
  2. Record canonical source URLs, versions, licences, redistribution terms, integrity hashes, privacy review, consent or other lawful basis, and intended use.
  3. Review the source outside the preprocessing program. Automated checks do not grant permission.
  4. Change the manifest to approved only after the named reviewers have approved every source.
  5. Run python workflows/validate_manifest.py manifest.json.
  6. Run the reference preprocessing experiment against only the files listed in that manifest.
  7. Store the exact command, source revision, manifest hash, environment, counts, exclusions, and output hashes in an experiment record.
  8. Publish data only through a separate release decision with a complete dataset card.

Non-goals and safeguards

  • Public availability is not treated as consent or permission for model training.
  • A licence label is not accepted without checking scope, attribution, redistribution, and downstream restrictions.
  • The reference preprocessing tool is not a privacy scrubber, copyright detector, toxicity classifier, or quality oracle.
  • Personally identifiable, sensitive, confidential, or revoked material is release-blocking until reviewed and resolved.
  • Benchmark test content must not enter training mixtures.
  • Raw and processed data remain outside this repository unless a later, separately approved dataset release explicitly permits them.

Relationship to KittyLM

DATA LAB can produce reviewed methods and provenance records that KittyLM may use in the future. It does not imply that KittyLM has an approved training dataset, completed data pipeline, or published model weights.

Licence

The documentation, schemas, and original workflow code in this repository are proposed for release under Apache-2.0. That licence does not apply to any third-party source described by a manifest. Every source retains its own rights and restrictions.
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