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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'prompt', 'completion', 'source'}) and 2 missing columns ({'industry', 'total_laid_off'}).
This happened while the csv dataset builder was generating data using
hf://datasets/mabera/global-layoffs-workforce-dataset/layoffs_interpreter_dataset.csv (at revision c14a00e79c175be7565c828bfba78c33fe61b63e), ['hf://datasets/mabera/global-layoffs-workforce-dataset@c14a00e79c175be7565c828bfba78c33fe61b63e/layoffs_industry_summary.csv', 'hf://datasets/mabera/global-layoffs-workforce-dataset@c14a00e79c175be7565c828bfba78c33fe61b63e/layoffs_interpreter_dataset.csv', 'hf://datasets/mabera/global-layoffs-workforce-dataset@c14a00e79c175be7565c828bfba78c33fe61b63e/layoffs_yearly_summary.csv', 'hf://datasets/mabera/global-layoffs-workforce-dataset@c14a00e79c175be7565c828bfba78c33fe61b63e/owid_layoffs_full.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
prompt: string
completion: string
source: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 621
to
{'industry': Value('string'), 'total_laid_off': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'prompt', 'completion', 'source'}) and 2 missing columns ({'industry', 'total_laid_off'}).
This happened while the csv dataset builder was generating data using
hf://datasets/mabera/global-layoffs-workforce-dataset/layoffs_interpreter_dataset.csv (at revision c14a00e79c175be7565c828bfba78c33fe61b63e), ['hf://datasets/mabera/global-layoffs-workforce-dataset@c14a00e79c175be7565c828bfba78c33fe61b63e/layoffs_industry_summary.csv', 'hf://datasets/mabera/global-layoffs-workforce-dataset@c14a00e79c175be7565c828bfba78c33fe61b63e/layoffs_interpreter_dataset.csv', 'hf://datasets/mabera/global-layoffs-workforce-dataset@c14a00e79c175be7565c828bfba78c33fe61b63e/layoffs_yearly_summary.csv', 'hf://datasets/mabera/global-layoffs-workforce-dataset@c14a00e79c175be7565c828bfba78c33fe61b63e/owid_layoffs_full.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)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.
industry string | total_laid_off float64 |
|---|---|
Consumer | 46,682 |
Retail | 43,613 |
Other | 36,289 |
Transportation | 31,998 |
Finance | 28,344 |
Healthcare | 25,953 |
Food | 22,855 |
Real Estate | 17,565 |
Travel | 17,129 |
Hardware | 13,828 |
Education | 13,338 |
Sales | 13,216 |
Crypto | 10,583 |
Marketing | 10,258 |
Fitness | 8,748 |
Security | 5,979 |
Infrastructure | 5,785 |
Media | 5,234 |
Data | 5,135 |
Logistics | 4,026 |
Construction | 3,863 |
Support | 3,523 |
HR | 2,853 |
Recruiting | 2,775 |
Product | 1,233 |
Legal | 836 |
Energy | 802 |
Aerospace | 661 |
Fin-Tech | 215 |
CryptoCurrency | 68 |
Crypto Currency | 42 |
Manufacturing | 20 |
null | null |
null | null |
null | null |
null | null |
null | null |
null | 81,068 |
null | 15,823 |
null | 161,711 |
null | 127,277 |
Other | 500 |
Media | 475 |
Retail | 400 |
Education | 120 |
Real Estate | 340 |
Transportation | 230 |
Real Estate | 100 |
Marketing | 63 |
null | 30 |
Healthcare | null |
Other | null |
Security | 177 |
Education | 79 |
Food | 50 |
Fitness | 30 |
Media | 12 |
Retail | null |
Consumer | null |
Logistics | null |
Other | 500 |
Food | 355 |
Healthcare | 300 |
Transportation | 209 |
HR | 200 |
Support | 200 |
Travel | 100 |
Crypto | null |
Consumer | 200 |
Consumer | 80 |
Education | null |
Finance | null |
Transportation | 300 |
Healthcare | 285 |
Finance | 130 |
Data | 75 |
Sales | 70 |
Security | 19 |
Security | 40 |
Consumer | 200 |
Other | 8,500 |
Other | 300 |
Transportation | 220 |
Support | 59 |
Food | 40 |
Healthcare | 26 |
Finance | 24 |
Retail | null |
Healthcare | 200 |
Marketing | 150 |
Retail | 69 |
Transportation | 40 |
Infrastructure | 40 |
Crypto | null |
Crypto | null |
Healthcare | null |
Media | 200 |
Support | 186 |
Healthcare | null |
Crypto | null |
End of preview.
Global Layoffs & Workforce Dataset
Author: Hussein Adeiza (mabera) Role: Licensed Environmental Health Officer, Abuja Nigeria Built for: AutoScientist Challenge 2026, Part 2 — HR Category
Dataset Description
An HR/workforce dataset built from real, tracked global tech layoffs data (layoffs.fyi, 2020-2023), downloaded directly from GitHub. This submission completes all 7 categories of the AutoScientist Challenge 2026 Part 2. https://huggingface.co/mabera/global-layoffs-workforce-interpreter
A Note on Subject Matter
This dataset addresses real workforce reductions affecting real people's livelihoods. All interpretation stays measured and analytical, with honest disclosure of data limitations rather than overstated confidence.
Files
owid_layoffs_full.csv
The complete, unmodified raw source file, 2,361 real company entries, 2020-2023, downloaded directly from a properly-cited GitHub repository.
layoffs_yearly_summary.csv / layoffs_industry_summary.csv
Real, aggregated subsets of the raw file, computed directly via pandas.
layoffs_interpreter_dataset.csv
The 5-row training file. Every numeric claim traces directly to the raw source file.
verify_layoffs_dataset.py
Independent verification script. Run python verify_layoffs_dataset.py
to re-derive all 5 claims directly from the raw source file. Confirmed
5/5 verified when last run.
build_layoffs_interpreter.py
The script that generated the training data.
Key Cited Findings
- Layoffs followed a non-monotonic trend: 2020 COVID shock (81,068), 2021 recovery (15,823), 2022-2023 correction (161,711 and 127,277 partial-year)
- Funding raised barely correlates with layoff scale (r = 0.077)
- Post-IPO companies account for 53.1% of tracked layoffs
- ~32% of records are missing key figures, disclosed as a real limitation, not hidden
- The dataset's US concentration likely reflects tracker reporting bias, not true global distribution
Source
layoffs.fyi tracked data, accessed via: https://github.com/manseek-11/World-Layoffs--Data-Analysis-using-SQL
Related Links
- 🤗 Model: https://huggingface.co/mabera/global-layoffs-workforce-interpreter
- 📊 Kaggle: https://www.kaggle.com/datasets/yunusahusseinadeiza/global-layoffs-and-workforce-interpreter
Credits
Powered by Adaptive Data — Adaption Labs AutoScientist Challenge 2026, Part 2 — HR Category
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