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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
mgrno: string
permco: string
fdate: timestamp[s]
shares: double
price: double
dollar_holding: double
quarter: string
year: int64
qtr: int64
-- schema metadata --
huggingface: '{"info": {"features": {"mgrno": {"dtype": "string", "_type"' + 414
to
{'permco': Value(dtype='string', id=None), 'embedding': Sequence(feature=Value(dtype='float32', id=None), length=32, id=None)}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1855, in _prepare_split_single
for _, table in generator:
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 690, in wrapped
for item in generator(*args, **kwargs):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/arrow/arrow.py", line 76, in _generate_tables
yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/arrow/arrow.py", line 59, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
mgrno: string
permco: string
fdate: timestamp[s]
shares: double
price: double
dollar_holding: double
quarter: string
year: int64
qtr: int64
-- schema metadata --
huggingface: '{"info": {"features": {"mgrno": {"dtype": "string", "_type"' + 414
to
{'permco': Value(dtype='string', id=None), 'embedding': Sequence(feature=Value(dtype='float32', id=None), length=32, id=None)}
because column names don't match
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/parquet_and_info.py", line 1431, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 992, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1898, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
permco string | embedding sequence |
|---|---|
1249 | [
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... |
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0.002509511... |
20191 | [
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20207 | [
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0.0058994... |
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... |
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0.... |
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... |
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20414 | [
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20440 | [
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20456 | [
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20466 | [
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... |
20678 | [
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20684 | [
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20737 | [
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20786 | [
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End of preview.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
US Institutional 13F Holdings Record-level Embeddings Dataset (Merged Quarterly Snapshots 1980–2024)
This dataset provides a single merged Hugging Face DatasetDict combining all 179 quarterly snapshots of U.S. institutional 13F filings (1980 Q1 → 2024 Q3).
Every quarter’s folder has been concatenated into three splits:
- holdings (113,114,724 rows): record-level positions with fields: • mgrno (string) – Institutional manager ID (SEC) • permco (string) – Permanent company identifier • fdate (timestamp[s]) – Quarter-end report date • shares (float64) – Shares held • price (float64) – Price per share on fdate • dollar_holding (float64) – Market value (shares × price)
- asset_embeddings (919,599 rows): PERMCO-level 32-dimensional vectors • permco (string) – PERMCO identifier • embedding (float32[32]) – Asset embedding vector
- investor_embeddings (445,036 rows): manager-level 32-dimensional vectors • mgrno (string) – Institutional manager ID • embedding (float32[32]) – Investor embedding vector
Quick Load Example:
from datasets import load_dataset
ds = load_dataset("kurry/institutional-holdings-13f-quarterly")
print({s: ds[s].num_rows for s in ds}) # {'holdings': 113114724, 'asset_embeddings': 919599, 'investor_embeddings': 445036}
print(ds["holdings"][0])
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