Datasets:

Kencorpus
/

KenPOS

token stringlengths 1 20	pos_tag stringclasses 114 values	sentence_id int64 0 69	position int64 0 954	filename stringclasses 168 values
AUSTINE	NN	0	0	0044_dho_pos.csv
SIGANA	NN	0	1	0044_dho_pos.csv
MAR	ADP	0	2	0044_dho_pos.csv
OPUK	NN	0	3	0044_dho_pos.csv
GI	Conj	0	4	0044_dho_pos.csv
APUOYO	NN	0	5	0044_dho_pos.csv
Chon	ADV	0	6	0044_dho_pos.csv
Chon	ADV	0	7	0044_dho_pos.csv
gi	Conj	0	8	0044_dho_pos.csv
lala	NN	0	9	0044_dho_pos.csv
ne	Det	0	10	0044_dho_pos.csv
nitiere	V	0	11	0044_dho_pos.csv
opuk	NN	0	12	0044_dho_pos.csv
gi	Conj	0	13	0044_dho_pos.csv
apuoyo	NN	0	14	0044_dho_pos.csv
,	PUNCT	0	15	0044_dho_pos.csv
opuk	NN	0	16	0044_dho_pos.csv
ne	ADV	0	17	0044_dho_pos.csv
en	V	0	18	0044_dho_pos.csv
lee	NN	0	19	0044_dho_pos.csv
ma	ADP	0	20	0044_dho_pos.csv
odak	V	0	21	0044_dho_pos.csv
e	ADP	0	22	0044_dho_pos.csv
pii	NN	0	23	0044_dho_pos.csv
to	Conj	0	24	0044_dho_pos.csv
apuoyo	NN	0	25	0044_dho_pos.csv
ne	ADV	0	26	0044_dho_pos.csv
en	V	0	27	0044_dho_pos.csv
lee	NN	0	28	0044_dho_pos.csv
ma	ADP	0	29	0044_dho_pos.csv
ne	ADV	0	30	0044_dho_pos.csv
odak	V	0	31	0044_dho_pos.csv
e	ADP	0	32	0044_dho_pos.csv
bungu	NN	0	33	0044_dho_pos.csv
.	PUNCT	0	34	0044_dho_pos.csv
apuoyo	NN	1	0	0044_dho_pos.csv
ne	ADV	1	1	0044_dho_pos.csv
ofuwo	V	1	2	0044_dho_pos.csv
ahinya	Adj	1	3	0044_dho_pos.csv
kendo	ADV	1	4	0044_dho_pos.csv
ne	ADV	1	5	0044_dho_pos.csv
en	V	1	6	0044_dho_pos.csv
gi	PRON	1	7	0044_dho_pos.csv
paro	V	1	8	0044_dho_pos.csv
mang' eny	Adj	1	9	0044_dho_pos.csv
.	PUNCT	1	10	0044_dho_pos.csv
chieng'	NN	2	0	0044_dho_pos.csv
moro	NN	2	1	0044_dho_pos.csv
achiel	NUM	2	2	0044_dho_pos.csv
opuk	NN	2	3	0044_dho_pos.csv
gi	Conj	2	4	0044_dho_pos.csv
apuoyo	NN	2	5	0044_dho_pos.csv
ne	ADV	2	6	0044_dho_pos.csv
odhi	V	2	7	0044_dho_pos.csv
e	ADP	2	8	0044_dho_pos.csv
ng'wech	NN	2	9	0044_dho_pos.csv
,	PUNCT	2	10	0044_dho_pos.csv
apuoyo	NN	2	11	0044_dho_pos.csv
ne	ADV	2	12	0044_dho_pos.csv
okono	V	2	13	0044_dho_pos.csv
opuk	NN	2	14	0044_dho_pos.csv
nitiere	V	2	15	0044_dho_pos.csv
opuk	NN	2	16	0044_dho_pos.csv
kia	V	2	17	0044_dho_pos.csv
ng'wech	NN	2	18	0044_dho_pos.csv
to	Conj	2	19	0044_dho_pos.csv
opuk	NN	2	20	0044_dho_pos.csv
ne	ADV	2	21	0044_dho_pos.csv
onyise	V	2	22	0044_dho_pos.csv
ni	Det	2	23	0044_dho_pos.csv
apuoyo	NN	2	24	0044_dho_pos.csv
in	PRON	2	25	0044_dho_pos.csv
okono	V	2	26	0044_dho_pos.csv
inyal	V	2	27	0044_dho_pos.csv
yomba	V	2	28	0044_dho_pos.csv
.	PUNCT	2	29	0044_dho_pos.csv
apuoyo	NN	3	0	0044_dho_pos.csv
ne	ADV	3	1	0044_dho_pos.csv
owacho	V	3	2	0044_dho_pos.csv
ne	PRON	3	3	0044_dho_pos.csv
opuk	NN	3	4	0044_dho_pos.csv
ni	Det	3	5	0044_dho_pos.csv
,	PUNCT	3	6	0044_dho_pos.csv
opuk	NN	3	7	0044_dho_pos.csv
adhi	V	3	8	0044_dho_pos.csv
yombi	V	3	9	0044_dho_pos.csv
mabor	Adj	3	10	0044_dho_pos.csv
.	PUNCT	3	11	0044_dho_pos.csv
ka	ADV	4	0	0044_dho_pos.csv
ne	ADV	4	1	0044_dho_pos.csv
gi	PRON	4	2	0044_dho_pos.csv
chako	V	4	3	0044_dho_pos.csv
ng'wech	NN	4	4	0044_dho_pos.csv
,	PUNCT	4	5	0044_dho_pos.csv
apuoyo	NN	4	6	0044_dho_pos.csv
ne	ADV	4	7	0044_dho_pos.csv
oyombo	V	4	8	0044_dho_pos.csv
opuk	NN	4	9	0044_dho_pos.csv
mabor	Adj	4	10	0044_dho_pos.csv
.	PUNCT	4	11	0044_dho_pos.csv

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KenPOS: Kenyan Languages Part-of-Speech Tagged Dataset

Dataset Description

KenPOS is a part-of-speech (POS) tagged corpus for Kenyan languages, featuring 156,994 tokens across four languages. The dataset provides manually annotated POS tags for low-resource Kenyan languages, enabling NLP research and applications.

Dataset Statistics

Language	Code	Tokens	Sentences	Files	Unique POS Tags
Dholuo	dho	54,712	70	168	114
Lubukusu	lbk	51,900	154	62	97
Lumarachi	lch	25,917	27	212	78
Lulogooli	llg	24,465	290	121	75
Total		156,994	541	563

Languages & Codes

Language / Dialect	Code	Family / Notes
Dholuo (Luo)	dho	Nilotic (western Kenya)
Lubukusu (Bukusu)	lbk	Bantu, Luhya dialect
Lumarachi (Marachi)	lch	Bantu, Luhya dialect
Lulogooli (Logooli)	llg	Bantu, Luhya dialect

Dataset Format

The dataset is distributed as Parquet files for optimal performance and compatibility:

Format: Apache Parquet (columnar storage)
Encoding: UTF-8
File naming: {language}/train.parquet
Compatibility: Works with datasets 4.0.0+ without custom loading scripts

Data Fields

Each record in the dataset contains:

token: string - The word or token
pos_tag: string - Part-of-speech tag (e.g., NN, V, ADJ, PUNCT)
sentence_id: int - Unique identifier for the sentence
position: int - Position of the token within the sentence (0-indexed)
filename: string - Source filename from which the token was extracted

Example Record

{
  'token': 'Kezia',
  'pos_tag': 'NN',
  'sentence_id': 0,
  'position': 0,
  'filename': '4411_dho_pos.csv'
}

Usage

Loading with 🤗 Datasets

Compatible with datasets 4.0.0+ (No trust_remote_code needed!)

from datasets import load_dataset

# Load Dholuo POS dataset
dho = load_dataset("Kencorpus/KenPOS", "dho")

# Load Lubukusu POS dataset
lbk = load_dataset("Kencorpus/KenPOS", "lbk")

# Load Lumarachi POS dataset
lch = load_dataset("Kencorpus/KenPOS", "lch")

# Load Lulogooli POS dataset
llg = load_dataset("Kencorpus/KenPOS", "llg")

# Access the data
print(dho['train'][0])
# Output: {'token': 'Kezia', 'pos_tag': 'NN', 'sentence_id': 0, 'position': 0, 'filename': '4411_dho_pos.csv'}

Reconstructing Sentences

from datasets import load_dataset
import pandas as pd

# Load dataset
dho = load_dataset("Kencorpus/KenPOS", "dho")
df = pd.DataFrame(dho['train'])

# Get first sentence
sentence_0 = df[df['sentence_id'] == 0].sort_values('position')
print(' '.join(sentence_0['token'].tolist()))

Analyzing POS Tags

from datasets import load_dataset
import pandas as pd

# Load dataset
dho = load_dataset("Kencorpus/KenPOS", "dho")
df = pd.DataFrame(dho['train'])

# Count POS tag frequencies
pos_counts = df['pos_tag'].value_counts()
print(pos_counts.head(10))

POS Tag Categories

The dataset uses a variety of POS tags including:

NN - Noun
V - Verb
ADJ/Adj. - Adjective
ADV/Adv - Adverb
PRON - Pronoun
ADP - Adposition (preposition/postposition)
DET/Det. - Determiner
CONJ/Conj. - Conjunction
NUM - Numeral
PUNCT/PUNC - Punctuation
And many more fine-grained categories

Note: Tag naming conventions may vary slightly across files (e.g., PUNCT vs PUNC, ADJ vs Adj.).

Dataset Curators

Florence Indede (Maseno University)
Owen McOnyango (Maseno University)
Lilian D.A. Wanzare (Maseno University)
Barack Wanjawa (University of Nairobi)
Edward Ombui (Africa Nazarene University)
Lawrence Muchemi (University of Nairobi)

Citation

If you use this dataset in your research, please cite:

@article{wanjawa2022kencorpus,
  title={Kencorpus: A Kenyan Language Corpus of Swahili, Dholuo and Luhya for Natural Language Processing Tasks},
  author={Wanjawa, Barack W. and Wanzare, Lilian D. and Indede, Florence and McOnyango, Owen and Ombui, Edward and Muchemi, Lawrence},
  journal={arXiv preprint arXiv:2208.12081},
  year={2022}
}