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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 4 new columns ({'algorithm', 'id_news1', 'id_news2', 'similarity_score'}) and 14 missing columns ({'__index_level_3__', '__index_level_2__', ' numofdays', '__index_level_9__', '__index_level_0__', '__index_level_4__', '__index_level_8__', ' configurations', 'id', '__index_level_6__', ' name', '__index_level_7__', '__index_level_5__', '__index_level_1__'}).
This happened while the csv dataset builder was generating data using
hf://datasets/frollo/ItalianCrimeNews/duplicate.csv (at revision 49e7bd1793ccc082c3fd25ac50ade795870f22ff)
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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
id_news1: int64
id_news2: int64
algorithm: int64
similarity_score: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 735
to
{'id': Value(dtype='string', id=None), ' name': Value(dtype='string', id=None), ' numofdays': Value(dtype='string', id=None), ' configurations': Value(dtype='string', id=None), '__index_level_0__': Value(dtype='int64', id=None), '__index_level_1__': Value(dtype='string', id=None), '__index_level_2__': Value(dtype='int64', id=None), '__index_level_3__': Value(dtype='string', id=None), '__index_level_4__': Value(dtype='string', id=None), '__index_level_5__': Value(dtype='string', id=None), '__index_level_6__': Value(dtype='string', id=None), '__index_level_7__': Value(dtype='string', id=None), '__index_level_8__': Value(dtype='string', id=None), '__index_level_9__': Value(dtype='string', id=None)}
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 1321, 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 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, 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 2013, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
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 4 new columns ({'algorithm', 'id_news1', 'id_news2', 'similarity_score'}) and 14 missing columns ({'__index_level_3__', '__index_level_2__', ' numofdays', '__index_level_9__', '__index_level_0__', '__index_level_4__', '__index_level_8__', ' configurations', 'id', '__index_level_6__', ' name', '__index_level_7__', '__index_level_5__', '__index_level_1__'}).
This happened while the csv dataset builder was generating data using
hf://datasets/frollo/ItalianCrimeNews/duplicate.csv (at revision 49e7bd1793ccc082c3fd25ac50ade795870f22ff)
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? Open a discussion for direct support.
id
string | name
string | numofdays
string | configurations
string | __index_level_0__
int64 | __index_level_1__
string | __index_level_2__
int64 | __index_level_3__
string | __index_level_4__
string | __index_level_5__
string | __index_level_6__
string | __index_level_7__
string | __index_level_8__
string | __index_level_9__
string | id_news1
int64 | id_news2
int64 | algorithm
int64 | similarity_score
float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
"score": [{"same municipality":0.025} | {"same day":0.03} | {"one day of difference":0.015}] | "threshold": 0.7}" | 1 | "Cosine similarity" | 3 | "{ "description": "news with sub-title" | "k-shingle size": [{"title":2} | {"sub-title":2} | {"text":3}] | "weights": [{"title":1} | {"sub-title":1.25} | {"text":4}] | null | null | null | null |
{"one day of difference":0.015}] | "threshold": 0.7}" | null | null | 2 | "Cosine similarity" | 3 | "{ "description": "news without sub-title" | "k-shingle size": [{"title":2} | {"text":3}] | "weights": [{"title":1} | {"text":4}] | "score": [{"same municipality":0.025} | {"same day":0.03} | null | null | null | null |
{"same day":0.03} | {"one day of difference":0.015}] | "threshold": 0.7}" | null | 3 | "Cosine similarity" | 5 | "{ "description": "filter: same event_date or window on publication_date; same municipality or no municipality; same crime category" | "k-shingle size": [{"title":2} | {"text":3}] | "weights": [{"title":1} | {"sub-title":1.25} | {"text":4}] | "score": [{"same municipality":0.025} | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 305 | 309 | 2 | 0.839102 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 363 | 366 | 2 | 0.963142 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 424 | 426 | 2 | 0.818271 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 460 | 463 | 2 | 0.876163 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 484 | 485 | 2 | 0.94161 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 508 | 510 | 2 | 0.810422 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 710 | 713 | 2 | 0.912329 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 896 | 898 | 1 | 0.831673 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 6,337 | 6,339 | 1 | 0.803687 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 1,081 | 1,083 | 2 | 0.843051 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 1,118 | 1,120 | 2 | 0.802233 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 1,388 | 1,401 | 2 | 0.923451 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 1,524 | 1,529 | 2 | 0.834161 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2,095 | 2,103 | 1 | 1 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2,532 | 2,534 | 1 | 0.855392 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2,572 | 2,575 | 1 | 0.837067 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2,620 | 2,621 | 1 | 0.809017 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 3,032 | 3,044 | 1 | 0.87144 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 3,435 | 3,438 | 1 | 0.862232 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 3,809 | 3,810 | 1 | 1 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 3,871 | 3,874 | 1 | 0.960009 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 4,127 | 4,132 | 1 | 0.83425 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 4,348 | 4,349 | 1 | 1 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 4,357 | 4,366 | 1 | 0.843271 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 4,545 | 4,566 | 1 | 0.835295 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 4,623 | 4,633 | 1 | 0.813222 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 4,666 | 4,670 | 1 | 0.939895 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 4,914 | 4,917 | 1 | 0.826963 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 4,957 | 4,958 | 1 | 1 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 5,049 | 5,067 | 1 | 1 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 5,301 | 5,309 | 1 | 0.881333 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 5,462 | 5,465 | 1 | 0.866943 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 6,558 | 6,559 | 1 | 1 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 7,692 | 7,693 | 1 | 0.807415 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 7,801 | 7,803 | 1 | 0.847843 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 8,234 | 8,237 | 1 | 0.844971 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 8,364 | 8,366 | 1 | 0.811025 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 8,389 | 8,391 | 1 | 0.877825 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 8,542 | 8,543 | 1 | 0.829307 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 8,866 | 8,868 | 1 | 0.848229 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 9,052 | 9,053 | 1 | 0.815795 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 9,580 | 9,582 | 1 | 0.83107 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 9,858 | 9,868 | 1 | 0.868055 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 9,885 | 9,888 | 1 | 0.946157 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 9,984 | 9,986 | 1 | 0.852052 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 10,181 | 10,189 | 1 | 0.848216 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 10,346 | 10,350 | 1 | 0.838785 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 10,484 | 10,485 | 1 | 0.814623 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 10,692 | 10,694 | 1 | 0.830065 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 10,894 | 10,903 | 1 | 0.814573 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 11,063 | 11,065 | 1 | 0.938072 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 11,210 | 11,214 | 1 | 0.969505 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 426,982 | 426,983 | 2 | 0.882465 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 11,345 | 11,347 | 1 | 0.911738 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 11,421 | 11,423 | 1 | 0.95944 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 11,548 | 11,550 | 1 | 0.887137 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 11,636 | 11,638 | 1 | 0.906795 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 11,652 | 11,654 | 1 | 0.834069 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 11,906 | 11,910 | 1 | 0.818405 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 12,220 | 12,222 | 1 | 0.87231 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 94,933 | 94,934 | 2 | 0.878716 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 158,558 | 158,560 | 2 | 0.880592 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 223,033 | 223,034 | 2 | 0.811506 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 224,289 | 224,295 | 1 | 0.858532 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 224,557 | 224,561 | 2 | 0.886031 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 224,631 | 224,633 | 1 | 0.922225 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 255,364 | 255,366 | 1 | 0.83254 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 255,510 | 255,512 | 1 | 0.821962 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 269,014 | 273,297 | 1 | 0.807329 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 273,411 | 273,413 | 2 | 0.852663 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 273,967 | 273,969 | 2 | 0.950178 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 300,832 | 300,834 | 1 | 0.8138 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 300,932 | 300,944 | 1 | 0.863729 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 358,604 | 358,607 | 2 | 0.822625 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 386,903 | 386,905 | 1 | 0.804755 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 396,719 | 396,721 | 1 | 0.808137 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 426,941 | 426,942 | 1 | 0.80273 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 1,042,430 | 1,042,432 | 1 | 0.867639 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 1,076,335 | 1,076,337 | 1 | 0.870563 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 1,076,363 | 1,076,365 | 1 | 0.838235 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2,200,842 | 2,207,673 | 1 | 0.706123 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 1,793,757 | 1,807,261 | 2 | 0.808312 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2,770,149 | 2,776,929 | 2 | 0.703402 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 3,685,541 | 3,707,277 | 1 | 0.730303 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2,864,298 | 2,877,850 | 1 | 0.737455 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2,870,974 | 2,877,823 | 2 | 0.71012 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2,870,976 | 2,877,822 | 2 | 0.808526 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 4,103,348 | 4,103,350 | 1 | 0.712799 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2,736,309 | 2,743,072 | 1 | 0.754048 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 3,348,625 | 3,375,922 | 2 | 0.706741 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 3,605,469 | 3,612,773 | 1 | 0.720966 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 3,606,580 | 3,613,885 | 2 | 0.826018 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2,988,623 | 2,995,517 | 2 | 0.710963 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 2,763,395 | 2,783,717 | 2 | 0.714168 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 97 | 99 | 2 | 0.70856 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 99 | 158,574 | 1 | 0.727224 |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | 3,502,996 | 3,509,934 | 2 | 0.708954 |
End of preview.
The dataset contains the main components of the news articles published online by the newspaper named Gazzetta di Modena: url of the web page, title, sub-title, text, date of publication, crime category assigned to each news article by the author.
The news articles are written in Italian and describe 11 types of crime events occurred in the province of Modena between the end of 2011 and 2021.
Moreover, the dataset includes data derived from the abovementioned components thanks to the application of Natural Language Processing techniques. Some examples are the place of the crime event occurrence (municipality, area, address and GPS coordinates), the date of the occurrence, and the type of the crime events described in the news article obtained by an automatic categorization of the text.
In the end, news articles describing the same crime events (duplciates) are detected by calculating the document similarity.
Now, we are working on the application of question answering to extract the 5W+1H and we plan to extend the current dataset with the obtained data.
Other researchers can employ the dataset to apply other algorithms of text categorization and duplicate detection and compare their results with the benchmark. The dataset can be useful for several scopes, e.g., geo-localization of the events, text summarization, crime analysis, crime prediction, community detection, topic modeling.
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