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Error code: DatasetGenerationError Exception: ArrowNotImplementedError Message: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field. 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 583, in write_table self._build_writer(inferred_schema=pa_table.schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer self.pa_writer = self._WRITER_CLASS(self.stream, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__ self.writer = _parquet.ParquetWriter( File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__ File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2027, in _prepare_split_single num_examples, num_bytes = writer.finalize() File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 602, in finalize self._build_writer(self.schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, in _build_writer self.pa_writer = self._WRITER_CLASS(self.stream, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__ self.writer = _parquet.ParquetWriter( File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__ File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowNotImplementedError: Cannot write struct type '_format_kwargs' with no child field to Parquet. Consider adding a dummy child field. 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 1529, 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 1154, 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 2038, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
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_data_files
list | _fingerprint
string | _format_columns
sequence | _format_kwargs
dict | _format_type
null | _indexes
dict | _output_all_columns
bool | _split
null |
---|---|---|---|---|---|---|---|
[
{
"filename": "dataset.arrow"
}
] | 8534a7ae5e87a416 | [
"target",
"text"
] | {} | null | {} | false | null |
Dataset for project: FakeLuke-ISOT
Dataset Description
A refined variant of the ISOT dataset. For our binary task, only two tags are needed: type (fake or true) and text. In order to achieve that we take both of the .csv files and we trim the article tags: title, type and publishing date. Next, besides the text we add a new column “type” and we mark it with 0 for real news and 1 for fake news. We further trim the Fake.csv file by eliminating all the empty columns, the ones that only contain a web link (http:…) or short abbreviations such as “Fw:”. After this operation we only have about 17.000 fake news to work with. We may observe that many of the remaining fake texts (733) have the sentence “21st Century Wire says” inserted in the front of the text. We consider this issue a crawler caused artefact that offers a strong lead towards identifying fake news that has to be cleansed and we erase all the instances. Next, we notice that 6665 text fields from the true.csv start with the syntagm “WASHINGTON (Reuters) -” and a total of 21247 (100%) lines begin in the same form: “ ‘city’ (Reuters) – “. We clean all of these instances since they reveal the source for all the true news and would boost accuracy of any model untruthfully even if they are probably just crawler-caused artefacts. Furthermore, we randomly erase about 3.000 of the columns from the true.csv file in order to maintain a balance between fake and real news in the dataset. After all these operations, our dataset looks like this: Fake.csv - 17.050 articles True.csv - 17.058 articles We next split the files following an 80% - train - 20% - validation quota as follows: Train.csv - 27284 samples Test.csv - 6822 samples
Languages
ENGLISH
Dataset Structure
binary: 0=true 1=false
Data Instances
A sample from this dataset looks as follows:
[
{
"target": 0,
"text": "The chief executive of the U.S. Aerospace Industries Association urged President-elect Donald Trump on Tuesday to quickly nominate new board members to the U.S. Export-Import Bank to allow the export financing agency to resume functioning. Dave Melcher said billions of dollars worth of potential orders were in limbo because the agency\u2019s board lacked the necessary number of board members to back important trade deals. \u201cThe Senate can protect American manufacturing jobs by restoring the quorum to the Export-Import Bank\u2019s board of directors,\u201d Melcher said in a speech before more than 300 industry and media representatives Tuesday in Arlington, Virginia. Boeing Co, one of the largest U.S. exporters, and other aerospace companies have raised similar concerns."
},
{
"target": 0,
"text": "WASHINGTON/The U.S. Congress may be headed for a reckoning with the federal debt limit within weeks, thanks to wealthy Americans and corporations deferring tax payments in the hope that they would benefit from the lower tax rates promised by President Trump. Trump promised tax cuts during his election campaign last year and has reiterated those promises in recent months leading some wealthy Americans and businesses to shift accounting for income into the future, betting that lower tax rates will arrive, perhaps in 2018, wealth managers told Reuters. \u201cEveryone wants to talk about deferring income,\u201d said Mark Copeland, senior partner at Signature Estate & Investment Advisors LLC in Newport Beach, California. The U.S. stock market has also rallied since Trump\u2019s election victory in November, partly on hopes for lower corporate tax rates. \u201cWe are starting to prepare clients for potentially lower taxes in 2018,\u201d said Julia Carlson, chief executive and at Financial Freedom Wealth Management Group LLC in Oregon. Trump promised to cut taxes as far back as September 2015 in a four-page plan and reiterated those promises in a two-page \u201cContract with the American Voter\u201d before last November\u2019s election and again in a one-page document in April, but Republicans in Congress remain divided on tax reform. The delay to tax payments could help to explain why tax receipts this fiscal year are coming in more slowly than projected, said tax experts and the Congressional Budget Office (CBO), an arm of Congress. \u201cTaxpayers may have shifted more income than projected ... to later years, expecting legislation to reduce tax rates to be enacted this year,\u201d the CBO said in a monthly report on Wednesday. The weaker tax revenues this year have forced the U.S. Treasury to borrow more money than expected to cover the federal budget deficit and that is putting the government on track to hit its legal debt limit sooner than expected, experts said. The U.S. government has a legal limit on how much it can borrow, currently set at about $19.8 trillion and the limit can only be increased by a vote of Congress. The need to raise the debt limit usually creates partisan debate in Washington. Conservative Republicans like to use the debt limit issue as leverage to demand cuts in spending but Democrats and moderate Republicans usually oppose such ultimatums on the grounds that the borrowings are used to fund spending approved previously by Congress. This year, with Trump\u2019s legislative agenda stalled and Congress preoccupied by Trump\u2019s firing of former FBI director James Comey, the House Republicans known as the House Freedom Caucus along with outside conservative groups are again demanding spending cuts for support to raise the debt limit. In 2011 the failure to raise the debt limit in a timely way and the possibility that the U.S. government might default on its obligations led to the U.S. losing its prized triple-A credit rating from Standard & Poor\u2019s. Since mid-March the U.S. Treasury has been using emergency funding powers to postpone hitting the debt limit and those measures had been expected to last until about October, but lower tax receipts so far this year may mean the debt limit will be hit sooner-than-expected. Treasury Secretary Steven Mnuchin urged Congress last month to raise the debt ceiling before lawmakers break for a long August summer recess, a call echoed last week by House Democratic leader Nancy Pelosi."
}
]
Dataset Fields
The dataset has the following fields (also called "features"):
{
"target": "ClassLabel(num_classes=2, names=['0', '1'], id=None)",
"text": "Value(dtype='string', id=None)"
}
Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
Split name | Num samples |
---|---|
train | 27284 |
valid | 6822 |
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