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Datasets:
digi-texx
/
calib-agentic-normalized

Tasks:
Text Generation
Languages:
English
Size:
1M<n<10M
Tags:
calibration
quantization
ptq
agentic
tool-calling
function-calling
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/parquet/parquet.py", line 127, in _split_generators
                  self.info.features = datasets.Features.from_arrow_schema(pq.read_schema(f))
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1977, in from_arrow_schema
                  else generate_from_arrow_type(field.type)
                       ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1621, in generate_from_arrow_type
                  return {field.name: generate_from_arrow_type(field.type) for field in pa_type}
                                      ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1634, in generate_from_arrow_type
                  return Value(dtype=_arrow_to_datasets_dtype(pa_type))
                                     ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 125, in _arrow_to_datasets_dtype
                  raise ValueError(f"Arrow type {arrow_type} does not have a datasets dtype equivalent.")
              ValueError: Arrow type map<string, string ('adapters')> does not have a datasets dtype equivalent.
              
              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.

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calib-agentic (v1)

Normalized calibration corpus for post-training quantization. Every row is OpenAI-shaped (messages + tools) regardless of upstream dialect, with provenance in origin and precomputed stats for stratified selection.

Schema

  • messages[] — role (system/user/assistant/tool), content, name, tool_call_id, tool_calls[] (name, arguments as a JSON string), reasoning_content
  • tools[] — name, description, parameters (JSON string of the JSON-Schema)
  • origin — source_id, repo, config, split, revision, row_uid, position, license (per row), domain, language, category, adapters, corpus_version
  • stats — n_messages, n_turns, n_tools, n_tool_calls, has_tool_calls, has_tool_results, has_reasoning, n_chars, char_entropy, max_char_run, non_ascii_ratio, longest_word_len

arguments and parameters are JSON strings, not nested structs: tool schemas are arbitrarily nested and mutually inconsistent across sources, so a typed struct would either fail to unify or silently coerce fields away.
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Models trained or fine-tuned on digi-texx/calib-agentic-normalized