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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)
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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 |
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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