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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
item_id: string
start: timestamp[s]
freq: string
target: list<item: float>
child 0, item: float
past_feat_dynamic_real: fixed_size_list<item: list<item: float>>[7]
child 0, item: list<item: float>
child 0, item: float
-- schema metadata --
huggingface: '{"info": {"features": {"item_id": {"dtype": "string", "_typ' + 347
to
{'item_id': Value('string'), 'start': Value('timestamp[s]'), 'freq': Value('string'), 'target': List(Value('float32'))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1779, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/arrow/arrow.py", line 74, in _generate_tables
yield Key(file_idx, batch_idx), self._cast_table(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/arrow/arrow.py", line 54, 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
item_id: string
start: timestamp[s]
freq: string
target: list<item: float>
child 0, item: float
past_feat_dynamic_real: fixed_size_list<item: list<item: float>>[7]
child 0, item: list<item: float>
child 0, item: float
-- schema metadata --
huggingface: '{"info": {"features": {"item_id": {"dtype": "string", "_typ' + 347
to
{'item_id': Value('string'), 'start': Value('timestamp[s]'), 'freq': Value('string'), 'target': List(Value('float32'))}
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 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, 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.
item_id string | start timestamp[s] | freq string | target list |
|---|---|---|---|
0 | 2015-01-01T00:00:00 | 5T | [61.93913650512695,59.23252487182617,61.99180221557617,62.480655670166016,62.490482330322266,62.5417(...TRUNCATED) |
1 | 2015-01-01T00:00:00 | 5T | [64.2808837890625,65.08245086669922,65.30912017822266,65.191650390625,65.28766632080078,68.001235961(...TRUNCATED) |
2 | 2015-01-01T00:00:00 | 5T | [62.077396392822266,64.80834197998047,64.80391693115234,67.20659637451172,67.32328796386719,66.41279(...TRUNCATED) |
3 | 2015-01-01T00:00:00 | 5T | [60.78642272949219,65.85395050048828,64.26608276367188,63.988426208496094,64.70740509033203,62.72486(...TRUNCATED) |
4 | 2015-01-01T00:00:00 | 5T | [63.12067413330078,59.20623016357422,62.239200592041016,65.80850982666016,65.70866394042969,63.67469(...TRUNCATED) |
5 | 2015-01-01T00:00:00 | 5T | [64.44831848144531,62.4967155456543,63.816612243652344,64.75755310058594,65.35836791992188,63.123970(...TRUNCATED) |
6 | 2015-01-01T00:00:00 | 5T | [63.41112518310547,65.99217987060547,60.19683074951172,62.01144790649414,65.09144592285156,62.115444(...TRUNCATED) |
7 | 2015-01-01T00:00:00 | 5T | [64.7394790649414,64.71804809570312,65.44779205322266,66.33447265625,63.09504699707031,68.0616760253(...TRUNCATED) |
8 | 2015-01-01T00:00:00 | 5T | [63.009918212890625,61.24407196044922,63.79776382446289,61.702735900878906,62.18679428100586,61.0574(...TRUNCATED) |
9 | 2015-01-01T00:00:00 | 5T | [65.26490020751953,65.60872650146484,66.01715850830078,65.73542785644531,65.09737396240234,65.096916(...TRUNCATED) |
End of preview.
gift-pretrain-small-4096
Companion to jeremycochoy/gift-pretrain-small,
identical sampling but with a 4096-point crop window instead of 1025.
Built by uniformly sampling 10 series from every sub-dataset of
Salesforce/GiftEvalPretrain,
then cropping each selected series into non-overlapping windows of
length 4096 and globally shuffling the result. Series shorter than
4096 points yield zero windows and are silently skipped, so this
bundle is naturally smaller than the 1025 version even at the same
K — only the long-series sub-datasets contribute heavily.
Layout
.
├── small_v1/
│ ├── shard_NNNNN.parquet
│ └── manifest.json
├── eval/ ← Salesforce/GiftEval mirror
└── README.md
Schema (small_v1/shard_*.parquet)
| Column | Type | Notes |
|---|---|---|
series |
list<float32>[4096] |
Fixed-length non-overlapping window |
source_id |
uint8 |
Always 0 (gift) — bundle is single-source |
meta |
string |
Original item_id from the source arrow file |
Compression: zstd, row group size 10_000.
Sampling
Same per-sub-dataset strategy as the 1025-window bundle: pick the smallest arrow file in each top-level directory, sample K=10 series uniformly without replacement, emit every non-overlapping 4096-point window from each selected series.
Reproducing
python -m training_data_prep.build_gift_only_bundle --output-dir /path/to/out --series-per-subdataset 10 --window-length 4096
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