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Datasets:
arpandeepk
/
code-atlas-provenance

Tasks:
Text Generation
Languages:
code
ArXiv:
Tags:
code
scaling-laws
multilingual
provenance
License:
Dataset card Files
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Expected object or value
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 281, in _generate_tables
                  examples = [ujson_loads(line) for line in batch.splitlines()]
                              ~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value

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Code-ATLAS Corpus Registry

This repository is the data-first foundation for adapting ATLAS to programming languages. It is intentionally a registry and split specification before it is a training corpus.

The scientific goal is to measure directed transfer among roughly 20 programming languages, fit loss-based scaling laws, and predict a data mixture for adapting a model to a low-resource or newly introduced language.

Publication contract

Every published payload record must be traceable to an immutable source artifact and must have a license decision that permits both redistribution and ML training. Records that fail either check remain in the public provenance registry as metadata-only entries; their source text is not republished or trained on.

The initial source inventory is in registry/sources.json. The record-level contract is in schemas/provenance.schema.json. No source is admitted to the training split merely because it is publicly downloadable.

Data roles

  1. core_pretrain: natural repository code used for next-token pretraining and held-out loss measurement.
  2. parallel_aux: same-problem or translated programs used only in a separately reported transfer ablation.
  3. functional_eval: executable tasks that never enter training.
  4. metadata_only: redistributability, training permission, or artifact access is unresolved or prohibited.

Raw repository pretraining is ordinary causal language modeling over source files and selected repository context. Issue descriptions, patches, execution traces, and tests are useful task/SFT/RL data, but are not interchangeable with the natural-code corpus used for the main ATLAS loss experiment. See docs/what_code_pretraining_data_is.md for the record format and the boundary between repository and benchmark data.

Split invariant

No file-level random split is allowed. Files sharing a repository lineage, fork or clone family, near-duplicate cluster, or benchmark problem family must receive the same split. See docs/split_protocol.md.

Current status

  • Source registry: in progress.
  • License and provenance audit: in progress.
  • Per-language post-filter census: not yet frozen.
  • Stack v3 record-level ingestion: one pinned shard validated; full 8,192-shard scan pending.
  • Final language panel: not yet selected.
  • Train/validation/test assignments: intentionally not created yet.
  • Public provenance repository: https://huggingface.co/datasets/arpandeepk/code-atlas-provenance
  • Public permissive payload: not yet created; record-level license and leakage gates must pass first.

Primary references

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