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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 1 missing columns ({'licensing'})
This happened while the csv dataset builder was generating data using
hf://datasets/joey234/neg-136/NEG-136-SIMP.tsv (at revision 65312b1ae759cb963e15e67d641b76c975f2da5b)
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
item: int64
context_aff: string
context_neg: string
target_aff: string
target_neg: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 849
to
{'item': Value(dtype='int64', id=None), 'context_aff': Value(dtype='string', id=None), 'context_neg': Value(dtype='string', id=None), 'target_aff': Value(dtype='string', id=None), 'target_neg': Value(dtype='string', id=None), 'licensing': 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 1 missing columns ({'licensing'})
This happened while the csv dataset builder was generating data using
hf://datasets/joey234/neg-136/NEG-136-SIMP.tsv (at revision 65312b1ae759cb963e15e67d641b76c975f2da5b)
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? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
item int64 | context_aff string | context_neg string | target_aff string | target_neg string | licensing string |
|---|---|---|---|---|---|
0 | With proper equipment, scuba-diving is very | With proper equipment, scuba-diving isn't very | safe | dangerous | Y |
1 | Traveling in Baghdad is very | Traveling in Baghdad isn't very | dangerous | safe | Y |
2 | In moderation, drinking red wine is | In moderation, drinking red wine isn't | good | bad | Y |
3 | Drinking tap water in developing countries is very | Drinking tap water in developing countries isn't very | dangerous | safe | Y |
4 | Secondhand furniture is very | Secondhand furniture isn't very | cheap | expensive | Y |
5 | Most smokers find that quitting is very | Most smokers find that quitting isn't very | difficult | easy | Y |
6 | Old computers may be | Old computers may not be | slow | fast | Y |
7 | A fast food dinner on a first date is very | A fast food dinner on a first date isn't very | lame | romantic | Y |
8 | Bulletproof vests are very | Bulletproof vests aren't very | safe | dangerous | N |
9 | Terrorist bomb attacks are really | Terrorist bomb attacks aren't really | dangerous | safe | N |
10 | Vitamins and proteins are very | Vitamins and proteins aren't very | good | bad | N |
11 | Using strong suntan lotion is | Using strong suntan lotion isn't | safe | dangerous | N |
12 | Rockets and missiles are very | Rockets and missiles aren't very | fast | slow | N |
13 | Keeping the door open for somebody is very | Keeping the door open for somebody isn't very | polite | rude | N |
14 | A baby bunny's fur is very | A baby bunny's fur isn't very | soft | hard | N |
15 | Businessman Donald Trump is really | Businessman Donald Trump isn't really | rich | poor | N |
0 | A trout is (a|an) | A trout is not (a|an) | fish | tool | null |
1 | A salmon is (a|an) | A salmon is not (a|an) | fish | flower | null |
2 | An ant is (a|an) | An ant is not (a|an) | insect | vegetable | null |
3 | A bee is (a|an) | A bee is not (a|an) | insect | building | null |
4 | A robin is (a|an) | A robin is not (a|an) | bird | tree | null |
5 | A sparrow is (a|an) | A sparrow is not (a|an) | bird | vehicle | null |
6 | An oak is (a|an) | An oak is not (a|an) | tree | vehicle | null |
7 | A pine is (a|an) | A pine is not (a|an) | tree | tool | null |
8 | A rose is (a|an) | A rose is not (a|an) | flower | insect | null |
9 | A daisy is (a|an) | A daisy is not (a|an) | flower | bird | null |
10 | A carrot is (a|an) | A carrot is not (a|an) | vegetable | fish | null |
11 | A pea is (a|an) | A pea is not (a|an) | vegetable | building | null |
12 | A hammer is (a|an) | A hammer is not (a|an) | tool | insect | null |
13 | A saw is (a|an) | A saw is not (a|an) | tool | vegetable | null |
14 | A car is (a|an) | A car is not (a|an) | vehicle | tree | null |
15 | A truck is (a|an) | A truck is not (a|an) | vehicle | flower | null |
16 | A hotel is (a|an) | A hotel is not (a|an) | building | fish | null |
17 | A house is (a|an) | A house is not (a|an) | building | bird | null |
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
This dataset contains two subsets: NEG-136-SIMP and NEG-136-NAT. NEG-136-SIMP items come from Fischler et al. (1983). NEG-136-NAT items come from Nieuwland & Kuperberg (2008).
The NEG-136-SIMP.tsv and NEG-136-NAT.tsv files contain for each item the affirmative and negative version of the context (context_aff, context_neg), and completions that are true with the affirmative context (target_aff) and with the negative context (target_neg).
- For NEG-136-SIMP, determiners (a/an) are left ambiguous, and need to be selected based on the completion noun (this is done already in
proc_datasets.py).
References:
- Ira Fischler, Paul A Bloom, Donald G Childers, Salim E Roucos, and Nathan W Perry Jr. 1983. Brain potentials related to stages of sentence verification.
- Mante S Nieuwland and Gina R Kuperberg. 2008. When the truth is not too hard to handle: An event-related potential study on the pragmatics of negation.
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