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train

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Couldn't cast array of type
string
to
List(Value('string'))
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, 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 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2223, in cast_table_to_schema
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2224, in <listcomp>
                  cast_array_to_feature(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2092, in cast_array_to_feature
                  raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
              TypeError: Couldn't cast array of type
              string
              to
              List(Value('string'))
              
              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 1456, 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 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, 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 1702, 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 1858, 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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text string	label list
@user Loku u navela Ku tissunga, tissungue🕳️	[ "negative" ]
Aha... Auxeni ti nguavani	[ "negative" ]
Hahahahaha ih, tipo arrombaram o nhoster, 😂😂👏 https://t.co/OTTuJlkK3l	[ "negative" ]
Hiiiii ni livrarih !!	[ "negative" ]
@user 😱 oh hosi yanga 🙆	[ "negative" ]
@user Eeeeh a nsati lwei a banana maj...	[ "negative" ]
Mu nga ni landzi... Na loya	[ "negative" ]
@user A ta ti nhela😂	[ "negative" ]
Wa ni Danissa 🙄 https://t.co/1pXjbA0QSI	[ "negative" ]
U gama u Kuma 2 valores https://t.co/TfQYeTa4kI	[ "negative" ]
Ihhhhhhhhhhh... A chirami ka lomuu!!!!	[ "negative" ]
@user então ungah ni trapalhari wena pah...	[ "negative" ]
Hahahah swa nsila só 😂😂😂😂 https://t.co/NILtGhy1pu	[ "negative" ]
Famba ni ma Range ya wena 😂😂😂😂	[ "negative" ]
A xilungu xakarata. 🤦🏾	[ "negative" ]
@user Famba ti gumula hi parede	[ "negative" ]
U bihile demais, lokhu ulangunta hansi a xikwembo swo kubha I mazwidji. 🤣🤣🤣	[ "negative" ]
Yuh... Mamã... A ni ku lavi mina🕺🏿	[ "negative" ]
I tinholuene a xwi Dari https://t.co/hAYe2sMVrB	[ "negative" ]
@user Wa hlanha lweyi😂😂	[ "negative" ]
@user KI está a criar um Vuku Vuku grande sabe	[ "negative" ]
Messi Wa Nhine 😂	[ "negative" ]
@user 🙆 hosi yanga 😹😹😹	[ "negative" ]
@user Uta ni dhlaya	[ "negative" ]
Xitomb* xa wuako https://t.co/r5w8GnhRYw	[ "negative" ]
Ih ni livrari, yu. https://t.co/BRPTViLFJc	[ "negative" ]
Ho hi danissa wena wananga https://t.co/zWhrWVFQ4i	[ "negative" ]
@user Wuaa😅 swa ma SUPERA PATRICK ani tendere😅	[ "negative" ]
Hi tafa hi ndlala	[ "negative" ]
@user Namuntlha nada baiye, a xirhame ci demanji	[ "negative" ]
Suka, xtapora: Niti dlela male ya Mina 🤌🏽 Toh ta falar, Noh ti dlela male yah Mina 😉	[ "negative" ]
@user Hambi uku nhó 🙄, um dia uta sunguna ku phuza na mesma🚶🚶🚶	[ "negative" ]
Swi mbeni... Mas vale u mielaaaaaaaa	[ "negative" ]
Famba uya ti fassa😤 https://t.co/rXNiryPCRL	[ "negative" ]
@user Pimissa nhana u tsika madururo	[ "negative" ]
Barcelona ( Loko ni file mungane rilane..)	[ "negative" ]
Swa yentxeka não tá nada nice 😭💀	[ "negative" ]
Hahahahaha hadjakufa lweyi... É provocar overdose isso! 😂 https://t.co/OicW5UEMax	[ "negative" ]
@user @user Swimbenhane wa dja ku ni hembissa ja 😂😂😂😂😂😂	[ "negative" ]
Aha swa mbangue... Tsala swi mbeni... Hloko ya pao	[ "negative" ]
@user Nawene tem que o tsika madururo u pimissa nhana	[ "negative" ]
@user @user Ova na vuntswaka lowa nhloko	[ "negative" ]
Yuh... Vá djula ku ih hlongola ka tiku ledzi... Ih dhlawileh https://t.co/vpH6wgZKdd	[ "negative" ]
U ku um dois u mu ba ka xifuva a fa.... Inah... Swa djuleka 😭😭	[ "negative" ]
Hosi yanga 🙆🏾‍♂️🙆🏾‍♂️🙆🏾‍♂️ https://t.co/fSlFykkTJ0	[ "negative" ]
Wali wa Dula 😭 https://t.co/sQ0HCqUo7E	[ "negative" ]
@user Unga hi nhenhentsi wena	[ "negative" ]
@user A nguenile matilwene😭	[ "negative" ]
Ikhali mina ni zunga waku djokuni tolovela - sg	[ "negative" ]
@user @user Vha ta dlaya😭	[ "negative" ]
@user Swa kharata mpela... Khambi... Funtsi a ih swi tsiki... 😔	[ "negative" ]
U la kuta ka mina Ungati na usiku U zama zama kuni phazamisa ... U la ku nivuyisela a gono ni meticali	[ "negative" ]
😆😆😆😆😆😆😆😆 A xizame da kwapa mesmo https://t.co/NHgwJMAfrx	[ "negative" ]
@user Mas Khasi a u teke nsati hi maka muni!?	[ "negative" ]
Famba ti kundza invejosa 🤣 https://t.co/j7E0qcKys1	[ "negative" ]
Ceh Ina ka! I para hi ku yini? https://t.co/HZbhA0cYkE	[ "negative" ]
Ka hissa kola missavene. 😭🌞	[ "negative" ]
@user @user @user @user @user @user @user Hi maka Ruy 😂	[ "negative" ]
@user Swimbenhane vata hi rhivalela mas ava futseke...	[ "negative" ]
@user Uku ni dzo 😂😂😂😂	[ "negative" ]
@user Hi ku pfinha, va hi sola	[ "negative" ]
@user 🤣 why uni lembrara lexwu 😤 wena wa kazata	[ "negative" ]
Va khu ndzaveta u va khuma la https://t.co/tPpLJUXwiQ	[ "negative" ]
@user @user Ani madururu npfana lweyi😹	[ "negative" ]
“A xwi chegui ka?” 😭😭 https://t.co/8e8jEUeM0n	[ "negative" ]
@user @user Yu , tindzava ? Tsika mbangui mpfana e nita ku desprogramara	[ "negative" ]
@user Himpela wo nhimela nkama wa ko u fika, yena a poronto, ni mu fundile bem, mas wa karhata nhana....	[ "negative" ]
@user 🤣🤣🤣🤣🤣🤣🤣🤣🤣🤣famba u ya ti gunha hi parede.	[ "negative" ]
Swa mbangue ni ma cigarri !!! https://t.co/3DZRDFHJw1	[ "negative" ]
U tsama u ui vona a mbongolo yi nga xidjana*? 🤔 Ih leyi Lani 😂😂 https://t.co/R3wBgvTXIc	[ "negative" ]
@user Tili luweme nase A Mulungu otisunga	[ "negative" ]
@user @user Unga ni lhanhele mina	[ "negative" ]
A xizame xo kwapa hi🥶	[ "negative" ]
Hi Ta Hambana Hi Ma Central @user 😭😭 https://t.co/duLp8VeqA0	[ "negative" ]
@user Afinal fere a vista? 😂😂😂 Ni dzivaleli a ni nga swi tivi 🤣	[ "negative" ]
@user Uta kuma timaka hi maka ma brincadeira	[ "negative" ]
@user Wena uni ntxumo uxi mahaka ni guele, ingui u bom pra puto puto, mas haaaaa	[ "negative" ]
Wenna @user hu ta ni txanissa ih voz man https://t.co/HyFpfT0aJZ	[ "negative" ]
@user Va mu tlelile pah 😭😭😭 tlelaaa!!!	[ "negative" ]
Misava yi Bihile ihhhh😤😤❤️❤️🤲🤲🤲🤲 @user	[ "negative" ]
Tiva Hini hi khu fassa 😡 https://t.co/y62SJaptDz	[ "negative" ]
Hi dlawile 🤣🤣🤣 https://t.co/eDAoFMhw7s	[ "negative" ]
“Kasse mamana ya wena ii herioa”😭😭😹	[ "negative" ]
@user @user @user @user Himakha mune lesho, Em Moz seria ""ku ba mpama mina"" durante.	[ "negative" ]
U La Ku Nitxintxa My Baby! U La Ku Nitxintxa My Baby!	[ "negative" ]
@user @user Nitaku panhula hi xinwui* xa tiro😂😂	[ "negative" ]
@user Unga ni lheki😡	[ "negative" ]
Mina ningue fambi hi lane	[ "negative" ]
A xizami ehhh 😭	[ "negative" ]
Na Unitiva tem Opium🥺 https://t.co/Wqn5g9ZB9M	[ "negative" ]
@user Wa nsila lweyii... 😪 Damnnnnnnn	[ "negative" ]
@user Senhora atirha hospital privado lani a humaka kona vatwa hiyena vana vayena angava pfumeli vatlanga na vana vanwani @user @user @user	[ "negative" ]
U ta swi vona. https://t.co/2UVVwSX9cv	[ "negative" ]
@user @user Pfutseka! Para ti que ttaste	[ "negative" ]
@user Heee ... ha ni wa lewso mina 😭😭😭😭	[ "negative" ]
“Wa Vizinhouuu Nikonbela Ko Loyiwaaaa” https://t.co/pLSSvDPvKL	[ "negative" ]
Nitaku panhula ih xinwe xa tiro😂 https://t.co/Sx54jesXJW	[ "negative" ]
Nako male ya mu phuntissa https://t.co/BFe0HqToDa	[ "negative" ]
@user Depois saem hi matalane Vha buya vhata yih hlupa	[ "negative" ]
Swi mbeni e má vagwi ya má like	[ "negative" ]

End of preview.

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Check out the documentation for more information.

South African Finetuning Datasets

This dataset collection contains various NLP tasks for South African languages, organized by task and language.

Dataset Structure

The dataset follows this structure:

task_name/
    language_code/
        train.jsonl
        dev.jsonl
        test.jsonl
    metadata.json

Tasks

This collection includes the following tasks:

afrisent-semeval

Language	Train	Validation	Test
tso	804	203	254

injongo-intent

Language	Train	Validation	Test
sna	2240	320	640
sot	2240	320	640
xho	2240	320	640
zul	2240	320	640

injongo-intent-sequence

Language	Train	Validation	Test
sna	2240	320	640
sot	2240	320	640
xho	2240	320	640
zul	2240	320	640

injongo-intent-token

Language	Train	Validation	Test
sna	2240	320	640
sot	2240	320	640
xho	2240	320	640
zul	2240	320	640

irokobench-nli

Language	Validation	Test
sna	450	600
sot	450	600
xho	450	600
zul	450	600

masakhane-news

Language	Train	Validation	Test
sna	1288	185	369
xho	1032	147	297

masakhane-pos

Language	Train	Validation	Test
sna	747	149	596
tsn	754	150	602
xho	752	150	601
zul	753	150	601

masakhaner

Language	Train	Validation	Test
sna	6207	887	1773
tsn	3489	499	996
xho	5718	817	1633
zul	5848	836	1670

sib-200

Language	Train	Validation	Test
nso	701	99	204
sna	701	99	204
sot	701	99	204
ssw	701	99	204
tsn	701	99	204
tso	701	99	204
xho	701	99	204
zul	701	99	204

Usage

from datasets import load_dataset

# Load a specific task and language
dataset = load_dataset("SimbaMa1547/south-african-finetuning", "task_name/language_code")

# For example, to load Xhosa sentiment analysis:
sentiment_dataset = load_dataset("SimbaMa1547/south-african-finetuning", "afrisent-semeval/tso")

# Access the training split
train_data = sentiment_dataset["train"]

Languages

This collection includes data for the following South African languages:

nso: Northern Sotho (Sepedi)
sot: Southern Sotho (Sesotho)
ssw: Swati (Siswati)
tsn: Tswana (Setswana)
tso: Tsonga (Xitsonga)
xho: Xhosa (IsiXhosa)
zul: Zulu (IsiZulu)

Citation

Please cite the original papers for each dataset as specified in the respective task's metadata.json file.

Downloads last month: 143

Total file size:

28.3 MB

Language	Train	Validation	Test
nso	701	99	204
sna	701	99	204
sot	701	99	204
ssw	701	99	204
tsn	701	99	204
tso	701	99	204
xho	701	99	204
zul	701	99	204

Language	Train	Validation	Test
nso	701	99	204
sna	701	99	204
sot	701	99	204
ssw	701	99	204
tsn	701	99	204
tso	701	99	204
xho	701	99	204
zul	701	99	204

Language	Train	Validation	Test
nso	701	99	204
sna	701	99	204
sot	701	99	204
ssw	701	99	204
tsn	701	99	204
tso	701	99	204
xho	701	99	204
zul	701	99	204