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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
data: struct<advertising-agency: struct<25m_100m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: (... 18473 chars omitted)
child 0, advertising-agency: struct<25m_100m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>, 5m_ (... 84 chars omitted)
child 0, 25m_100m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>
child 0, ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
child 1, 5m_25m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>
child 0, ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
child 1, aerospace: struct<100m_500m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>, 25 (... 334 chars omitted)
child 0, 100m_500m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>
child 0, ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
child 1, 25m_100m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50:
...
child 3, p75: double
child 2, 5m_25m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>
child 0, ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
child 3, over_500m_ev: struct<ev_ebitda: struct<n: int64, p25: double, p50: double, p75: double>, ev_revenue: struct<n: int (... 43 chars omitted)
child 0, ev_ebitda: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
child 1, ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
child 4, under_5m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>
child 0, ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
generated_at: timestamp[s]
license: string
methodology: string
min_cell_n: int64
schema_version: string
source_window: string
min_n_per_cell: int64
year_range: list<item: int64>
child 0, item: int64
source: string
to
{'data': {'advertising-agency': {'2018': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}}, 'aerospace': {'2018': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2019': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2020': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2021': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2024': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64
...
4')}, 'n_deals': Value('int64')}, '2021': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2022': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2023': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2024': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2025': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}}}, 'generated_at': Value('string'), 'min_n_per_cell': Value('int64'), 'schema_version': Value('string'), 'source': Value('string'), 'year_range': List(Value('int64'))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
data: struct<advertising-agency: struct<25m_100m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: (... 18473 chars omitted)
child 0, advertising-agency: struct<25m_100m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>, 5m_ (... 84 chars omitted)
child 0, 25m_100m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>
child 0, ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
child 1, 5m_25m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>
child 0, ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
child 1, aerospace: struct<100m_500m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>, 25 (... 334 chars omitted)
child 0, 100m_500m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>
child 0, ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
child 1, 25m_100m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50:
...
child 3, p75: double
child 2, 5m_25m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>
child 0, ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
child 3, over_500m_ev: struct<ev_ebitda: struct<n: int64, p25: double, p50: double, p75: double>, ev_revenue: struct<n: int (... 43 chars omitted)
child 0, ev_ebitda: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
child 1, ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
child 4, under_5m_ev: struct<ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>>
child 0, ev_revenue: struct<n: int64, p25: double, p50: double, p75: double>
child 0, n: int64
child 1, p25: double
child 2, p50: double
child 3, p75: double
generated_at: timestamp[s]
license: string
methodology: string
min_cell_n: int64
schema_version: string
source_window: string
min_n_per_cell: int64
year_range: list<item: int64>
child 0, item: int64
source: string
to
{'data': {'advertising-agency': {'2018': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}}, 'aerospace': {'2018': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2019': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2020': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2021': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2024': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64
...
4')}, 'n_deals': Value('int64')}, '2021': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2022': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2023': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2024': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}, '2025': {'ev_ebitda': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'ev_revenue': {'n': Value('int64'), 'p25': Value('float64'), 'p50': Value('float64'), 'p75': Value('float64')}, 'n_deals': Value('int64')}}}, 'generated_at': Value('string'), 'min_n_per_cell': Value('int64'), 'schema_version': Value('string'), 'source': Value('string'), 'year_range': List(Value('int64'))}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
SMB and Lower Middle Market M&A Valuation Multiples
Per-vertical valuation multiples (EV/EBITDA, EV/Revenue, SDE) for 107 SMB and lower-middle-market sub-verticals, derived from 25,615+ verified M&A transactions across SEC filings, EDGAR 8-K, and verified press releases.
Includes p25/p50/p75 multiples per sub-vertical and per size bracket, methodology recommendations, and named-acquirer pool composition.
Files
multiples.json— per (sub-vertical × EV-bracket) median + p25/p75 EV/EBITDA, EV/Revenue, SDE multiplesmultiples-by-year.json— per (sub-vertical × year) date-stamped quartiles (210 cells, n>=10, 2018-2025)
Source
Live JSON: https://exitvalue.ai/data/multiples.json Methodology: https://exitvalue.ai/methodology Zenodo DOI: https://doi.org/10.5281/zenodo.20398222
Updated nightly from continuous EDGAR ingestion.
License
CC-BY-4.0. Free to use with attribution to ExitValue.ai.
Citation
ExitValue.ai. (2026). SMB and Lower Middle Market M&A Valuation Multiples.
Hugging Face. https://huggingface.co/datasets/nickcals/smb-mna-multiples
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