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sophieb
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dynamically_generated_hate_speech_dataset

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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 43 new columns ({'NoTargetRecorded', 'working', 'indig', 'african', 'immig', 'jew', 'old', 'trav', 'trans', 'hitler', 'arab', 'hispanic', 'muswom', 'ethnic minority', 'ref', 'nazi', 'east', 'south', 'non-white', 'pak', 'none', 'gaywom', 'chinese', 'gayman', 'other', 'mixed', 'asiwom', 'mus', 'gendermin', 'blawom', 'blaman', 'for', 'wom', 'pol', 'eastern european', 'indigwom', 'gay', 'dis', 'asi', 'asylum', 'bla', 'bis', 'russian'}) and 10 missing columns ({'split', 'status', 'label', 'annotator', 'Unnamed: 0', 'text', 'model_wrong', 'db.model_preds', 'round', 'type'}).

This happened while the csv dataset builder was generating data using

hf://datasets/sophieb/dynamically_generated_hate_speech_dataset/2020-12-31-DynamicallyGeneratedHateDataset-targets-v0.1.csv (at revision eebfc857e775f10513dd739c355e326937d58de9)

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: string
              none: int64
              dis: int64
              bla: int64
              trav: int64
              jew: int64
              mus: int64
              trans: int64
              indig: int64
              immig: int64
              for: int64
              blaman: int64
              gayman: int64
              east: int64
              south: int64
              mixed: int64
              gendermin: int64
              gay: int64
              gaywom: int64
              arab: int64
              wom: int64
              chinese: int64
              ref: int64
              asylum: int64
              blawom: int64
              asiwom: int64
              muswom: int64
              non-white: int64
              hispanic: int64
              bis: int64
              ethnic minority: int64
              nazi: int64
              asi: int64
              pak: int64
              pol: int64
              eastern european: int64
              working: int64
              african: int64
              russian: int64
              indigwom: int64
              old: int64
              hitler: int64
              NoTargetRecorded: int64
              other: int64
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 5030
              to
              {'Unnamed: 0': Value(dtype='int64', id=None), 'id': Value(dtype='string', id=None), 'text': Value(dtype='string', id=None), 'label': Value(dtype='string', id=None), 'type': Value(dtype='string', id=None), 'model_wrong': Value(dtype='bool', id=None), 'db.model_preds': Value(dtype='float64', id=None), 'status': Value(dtype='string', id=None), 'round': Value(dtype='string', id=None), 'split': Value(dtype='string', id=None), 'annotator': 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 43 new columns ({'NoTargetRecorded', 'working', 'indig', 'african', 'immig', 'jew', 'old', 'trav', 'trans', 'hitler', 'arab', 'hispanic', 'muswom', 'ethnic minority', 'ref', 'nazi', 'east', 'south', 'non-white', 'pak', 'none', 'gaywom', 'chinese', 'gayman', 'other', 'mixed', 'asiwom', 'mus', 'gendermin', 'blawom', 'blaman', 'for', 'wom', 'pol', 'eastern european', 'indigwom', 'gay', 'dis', 'asi', 'asylum', 'bla', 'bis', 'russian'}) and 10 missing columns ({'split', 'status', 'label', 'annotator', 'Unnamed: 0', 'text', 'model_wrong', 'db.model_preds', 'round', 'type'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/sophieb/dynamically_generated_hate_speech_dataset/2020-12-31-DynamicallyGeneratedHateDataset-targets-v0.1.csv (at revision eebfc857e775f10513dd739c355e326937d58de9)
              
              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.

Unnamed: 0 int64	id string	text string	label string	type string	model_wrong bool	db.model_preds float64	status string	round string	split string	annotator string
1	10000	It should be legal for a husband to rape his wife	hate	notgiven	true	0.97457	dynabench entry	1	train	gnzuctwed3
2	10001	dalits are lowlife scum	hate	notgiven	false	0.08233	dynabench entry	1	test	cagnluiznm
3	10002	dalits are lowlives	hate	notgiven	true	0.92319	dynabench entry	1	train	cagnluiznm
4	10003	It was a better world when women dare not question men	hate	notgiven	true	0.99006	dynabench entry	1	test	gnzuctwed3
5	10004	I don't work this hard so that those immigrants can take all the benefits	hate	notgiven	true	0.98836	dynabench entry	1	train	cagnluiznm
6	10005	I don't work this hard so that those migrants can take all the benefits	hate	notgiven	true	0.99506	dynabench entry	1	train	cagnluiznm
7	10006	I don't work this hard so that those foreigners can take all the benefits	hate	notgiven	true	0.9934	dynabench entry	1	train	cagnluiznm
8	10007	I don't work this hard so that those Hispanics can take all the benefits	hate	notgiven	true	0.98625	dynabench entry	1	train	cagnluiznm
9	10008	I don't work this hard so that those chavs can take all the benefits	hate	notgiven	true	0.95252	dynabench entry	1	test	cagnluiznm
10	10009	I don't work this hard so that all those pakis can take the benefits	hate	notgiven	false	0.09288	dynabench entry	1	train	cagnluiznm
11	10010	I don't work this hard so that all those muslims can take the benefits	hate	notgiven	false	0.46144	dynabench entry	1	train	cagnluiznm
12	10012	foreigners are a burden to our society	hate	notgiven	true	0.98753	dynabench entry	1	train	cagnluiznm
13	10013	immigrants are a burden to our society	hate	notgiven	true	0.98971	dynabench entry	1	train	cagnluiznm
14	10014	women are a burden to our society	hate	notgiven	true	0.9814	dynabench entry	1	dev	cagnluiznm
15	10015	gay people are a burden to our society	hate	notgiven	true	0.53936	dynabench entry	1	train	cagnluiznm
16	10016	gay people are a burden on our society	hate	notgiven	true	0.8682	dynabench entry	1	train	cagnluiznm
17	10017	Why is it that foreigners smell weird? Do they not wash?	hate	notgiven	true	0.89113	dynabench entry	1	train	cagnluiznm
18	10018	Why is it that french people smell weird? Do they not wash?	hate	notgiven	true	0.96767	dynabench entry	1	train	cagnluiznm
19	10019	Why is it that white people smell weird? Do they not wash?	hate	notgiven	true	0.92169	dynabench entry	1	train	cagnluiznm
20	10020	Why is it that asian people smell weird? Do they not wash?	hate	notgiven	true	0.92405	dynabench entry	1	train	cagnluiznm
21	10021	Why is it that black people smell weird? Do they not wash?	hate	notgiven	true	0.83432	dynabench entry	1	train	cagnluiznm
22	10022	Why is it that Africans smell weird? Do they not wash?	hate	notgiven	false	0.37971	dynabench entry	1	train	cagnluiznm
23	10023	Why is it that African people smell weird? Do they not wash?	hate	notgiven	true	0.56723	dynabench entry	1	dev	cagnluiznm
24	10024	Why is it that whites smell weird? Do they not wash?	hate	notgiven	true	0.91835	dynabench entry	1	train	cagnluiznm
25	10025	Why is it that dikes smell weird? Do they not wash?	hate	notgiven	true	0.80692	dynabench entry	1	train	cagnluiznm
26	10026	Why is it that welsh people smell weird? Do they not wash?	hate	notgiven	true	0.96381	dynabench entry	1	train	cagnluiznm
27	10027	I dont hate hindus	nothate	none	false	0.97793	dynabench entry	1	train	vde7gn0nrl
28	10028	I dont hate Muslims	nothate	none	false	0.98672	dynabench entry	1	train	vde7gn0nrl
29	10029	I dont hatefucking Islam	nothate	none	true	0.05814	dynabench entry	1	train	vde7gn0nrl
30	10030	I dont hate fucking Muslims	nothate	none	true	0.00707	dynabench entry	1	train	vde7gn0nrl
31	10031	The world full of women is unfair	nothate	none	false	0.98859	dynabench entry	1	train	vde7gn0nrl
32	10032	Government is incompetent	nothate	none	true	0.06605	dynabench entry	1	train	vde7gn0nrl
33	10033	short people are somehow not smart	nothate	none	false	0.99206	dynabench entry	1	train	vde7gn0nrl
34	10034	short people are smart to some extent	nothate	none	false	0.99844	dynabench entry	1	train	vde7gn0nrl
35	10035	short people are damn smart	nothate	none	false	0.99303	dynabench entry	1	train	vde7gn0nrl
36	10037	short women are fucking cute	nothate	none	true	0.39223	dynabench entry	1	dev	vde7gn0nrl
37	10038	short men are fucking weird	nothate	none	false	0.92885	dynabench entry	1	train	vde7gn0nrl
38	10039	tall men are intelligent	nothate	none	false	0.99438	dynabench entry	1	train	vde7gn0nrl
39	10040	disabled people are stupid	nothate	none	true	0.09295	dynabench entry	1	train	vde7gn0nrl
40	10041	kids are dumb	nothate	none	false	0.93163	dynabench entry	1	train	vde7gn0nrl
41	10042	pear is not tasty	nothate	none	false	0.99483	dynabench entry	1	train	vde7gn0nrl
42	10043	blonde ladies are fuc''ing smart	nothate	none	true	0.01191	dynabench entry	1	test	vde7gn0nrl
43	10044	blonde people are not smart	nothate	none	false	0.83987	dynabench entry	1	train	vde7gn0nrl
44	10045	Arabs are terrorists	nothate	none	true	0.11515	dynabench entry	1	train	vde7gn0nrl
45	10046	Terrorists must be Muslims	nothate	none	false	0.89751	dynabench entry	1	train	vde7gn0nrl
46	10047	Terrorists must be Muslim rats	nothate	none	true	0.10132	dynabench entry	1	train	vde7gn0nrl
47	10048	Terrorists are always Muslim but never white	nothate	none	false	0.98106	dynabench entry	1	train	vde7gn0nrl
48	10049	Terrorists are always fucking Muslim but never white	nothate	none	true	0.0643	dynabench entry	1	test	vde7gn0nrl
49	10050	veiled women are dumb	nothate	none	true	0.31599	dynabench entry	1	train	vde7gn0nrl
50	10051	blonde people suck	nothate	none	true	0.03023	dynabench entry	1	train	vde7gn0nrl
51	10052	Blonde people are bad	nothate	none	true	0.09154	dynabench entry	1	train	vde7gn0nrl
52	10053	Blonde women are sick	nothate	none	true	0.05152	dynabench entry	1	train	vde7gn0nrl
53	10054	Blonde men are sick	nothate	none	true	0.05893	dynabench entry	1	train	vde7gn0nrl
54	10056	immigrants are like animals , they dont belong to this world	nothate	none	true	0.02581	dynabench entry	1	train	vde7gn0nrl
55	10057	Indian people are not smart	nothate	none	false	0.94961	dynabench entry	1	test	vde7gn0nrl
56	10058	Indian people smell like spices	nothate	none	false	0.95928	dynabench entry	1	train	vde7gn0nrl
57	10059	Indian people smell like fucking spices	nothate	none	true	0.02098	dynabench entry	1	dev	vde7gn0nrl
58	10061	Lesbians suck and i dont know how to could accept themselves	nothate	none	true	0.03327	dynabench entry	1	train	vde7gn0nrl
59	10062	fat women are stupid	nothate	none	true	0.25338	dynabench entry	1	train	vde7gn0nrl
60	10063	gay people need medical treatment	nothate	none	false	0.97913	dynabench entry	1	train	vde7gn0nrl
61	10064	gay people should seek fucking medical help	nothate	none	true	0.07169	dynabench entry	1	train	vde7gn0nrl
62	10065	Arabs are dumb	hate	notgiven	false	0.13062	dynabench entry	1	train	vde7gn0nrl
63	10066	Be quiet, you silly little girl	hate	notgiven	false	0.01665	dynabench entry	1	dev	gnzuctwed3
64	10067	You throw like a girl	hate	notgiven	true	0.95625	dynabench entry	1	train	gnzuctwed3
65	10068	He throws like a girl	hate	notgiven	true	0.96309	dynabench entry	1	test	gnzuctwed3
66	10069	Women can't throw	hate	notgiven	true	0.92486	dynabench entry	1	dev	gnzuctwed3
67	10071	Girls can't play football	hate	notgiven	true	0.97307	dynabench entry	1	train	gnzuctwed3
68	10072	Women shouldn't even try to play football	hate	notgiven	true	0.97946	dynabench entry	1	train	gnzuctwed3
69	10073	Football is a man's game	hate	notgiven	true	0.99864	dynabench entry	1	train	gnzuctwed3
70	10074	She's almost as good as a man	hate	notgiven	true	0.99642	dynabench entry	1	dev	gnzuctwed3
71	10075	She's not as good as a man	hate	notgiven	true	0.96963	dynabench entry	1	test	gnzuctwed3
73	10076	Women aren't as good as men	hate	notgiven	true	0.98117	dynabench entry	1	train	gnzuctwed3
74	10078	She's not as good as a man	hate	notgiven	true	0.96963	dynabench entry	1	test	gnzuctwed3
76	10079	Women will never be as good as men	hate	notgiven	true	0.9836	dynabench entry	1	train	gnzuctwed3
77	10080	Women should stick to the things they're good at, mainly domestic chores	hate	notgiven	true	0.98553	dynabench entry	1	train	gnzuctwed3
78	10081	Women never stop talking	hate	notgiven	true	0.9873	dynabench entry	1	train	gnzuctwed3
79	10082	Women talk too much	hate	notgiven	true	0.99248	dynabench entry	1	train	gnzuctwed3
80	10083	Women never shut up	hate	notgiven	true	0.96549	dynabench entry	1	dev	gnzuctwed3
81	10085	Kids get set the wrong example when their mum goes out to work	hate	notgiven	true	0.97831	dynabench entry	1	train	gnzuctwed3
82	10086	If she's going to go to work why bother having children?	hate	notgiven	true	0.99527	dynabench entry	1	train	gnzuctwed3
83	10087	Ban working mums	hate	notgiven	true	0.99654	dynabench entry	1	train	gnzuctwed3
84	10088	Ban women from the workplace	hate	notgiven	true	0.98961	dynabench entry	1	test	gnzuctwed3
85	10089	Get women out of the workplace	hate	notgiven	true	0.99495	dynabench entry	1	dev	gnzuctwed3
86	10091	Men should leave the childcare to women and focus on more important things	hate	notgiven	true	0.99772	dynabench entry	1	train	gnzuctwed3
87	10092	Men are too important to do childcare, its a womans job	hate	notgiven	true	0.98166	dynabench entry	1	train	gnzuctwed3
88	10099	It's women's job to raise kids, it's all theyre good for	hate	notgiven	true	0.99535	dynabench entry	1	train	gnzuctwed3
89	10100	Women shouldn't even pretend to like sport	hate	notgiven	true	0.98915	dynabench entry	1	train	gnzuctwed3
90	10102	Men who cry make me sick	hate	notgiven	true	0.95944	dynabench entry	1	train	gnzuctwed3
91	10103	Only gay men cry	hate	notgiven	true	0.80637	dynabench entry	1	train	gnzuctwed3
92	10104	Gay men are such crybabies	hate	notgiven	false	0.05585	dynabench entry	1	test	gnzuctwed3
93	10105	Women are so oversensitive	hate	notgiven	true	0.76339	dynabench entry	1	test	gnzuctwed3
94	10106	His parents must have done something wrong or he wouldn't be disabled	hate	notgiven	true	0.98421	dynabench entry	1	train	gnzuctwed3
95	10107	It's the parents fault they were born crippled	hate	notgiven	true	0.62433	dynabench entry	1	dev	gnzuctwed3
96	10108	I wouldn't spend my life looking after a cripple child, what a waste of time	hate	notgiven	true	0.99224	dynabench entry	1	train	gnzuctwed3
97	10135	my meal was fucking disgusting at pizza express tonight	nothate	none	true	0.02047	dynabench entry	1	train	tbubpfn6ip
98	10136	my meal was fucking disgusting	nothate	none	true	0.014	dynabench entry	1	train	tbubpfn6ip
99	10138	racism is fucking disgusting	nothate	none	true	0.02175	dynabench entry	1	train	tbubpfn6ip
100	10139	smoking is fucking disgusting	nothate	none	true	0.02719	dynabench entry	1	test	tbubpfn6ip
101	10142	drinking is fucking disgusting	nothate	none	true	0.01161	dynabench entry	1	train	tbubpfn6ip
102	10143	protesting is fucking disgusting	nothate	none	true	0.02201	dynabench entry	1	dev	tbubpfn6ip

End of preview.

YAML Metadata Warning: empty or missing yaml metadata in repo card (https://huggingface.co/docs/hub/datasets-cards)

Dataset card for dynamically generated dataset hate speech detection

Dataset summary

This dataset that was dynamically generated for training and improving hate speech detection models. A group of trained annotators generated and labeled challenging examples so that hate speech models could be tricked and consequently improved. This dataset contains about 40,000 examples of which 54% are labeled as hate speech. It also provides the target of hate speech, including vulnerable, marginalized, and discriminated groups. Overall, this is a balanced dataset which makes it different from the already available hate speech datasets you can find on the web.

This dataset was presented in the article Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection published in 2021. The article describes the process for generating and annotating the data. Also, it describes how they used the generated data for training and improving hate speech detection models. The full author list is the following: Bertie Vidgen (The Alan Turing Institute), Tristan Thrush (Facebook), Zeerak Waseem (University of Sheffield), and Douwe Kiela (Facebook).

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