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Datasets:
michsethowusu
/
twi-fante-sentences-parts-of-speech-pos-10m

Tasks:
Text Classification
Token Classification
Modalities:
Document
Tabular
Text
Formats:
parquet
Sub-tasks:
part-of-speech
Languages:
Akan
Twi
Size:
1M - 10M
Libraries:
Datasets
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License:
Dataset card Data Studio Files
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sentence
stringlengths
3
146
pos_sequence
stringclasses
602 values
word_count
int64
2
20
pattern_id
int64
0
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N V
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Akan POS Tagging Dataset - 10 Million Sentences

Dataset Description

The dataset includes sentences and part-of-speech tags and is aimed at supporting the development of POS tagging models for the Akan language. This work demonstrates that data limitations for low-resource languages can be overcome using artificial data generation techniques.

Dataset Structure

Data Fields

  • sentence: The Akan sentence text
  • pos_sequence: Part-of-speech tags for the sentence. The POS-tags.pdf includes full meanings and descriptions of the POS tags from the dataset.
  • word_count: Number of words in the sentence

Data Splits

The dataset is provided as a single training split containing 10 million sentence-POS tag pairs.

Dataset Statistics

  • Total sentences: 10,000,000
  • Language: Akan (Akan)
  • Task: Part-of-Speech Tagging
  • Data Generation: Artificial data generation techniques

Usage 

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("michsethowusu/aka-pos-10m")

# Access the data
train_data = dataset['train']
print(f"Number of examples: {len(train_data)}")

# Example usage
example = train_data[0]
print(f"Sentence: {example['sentence']}")
print(f"POS Tags: {example['pos_sequence']}")
print(f"Word Count: {example['word_count']}")

Research Applications

This dataset is particularly valuable for:

  • Developing POS tagging models for Akan/Akan language
  • Research in low-resource language processing
  • Studies on artificial data generation for NLP
  • Cross-lingual transfer learning experiments

Acknowledgement

The seed dataset was obtained from the following source:

@misc{Beermann2018,
  author       = {Dorothee Beermann},
  title        = {The TypeCraft Akan corpus, Release 1.0},
  year         = {2018},
  howpublished = {\url{https://typecraft.org/tc2wiki/The_TypeCraft_Akan_Corpus}},
  note         = {TypeCraft – The Interlinear Text Repository}
}

Citation

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

@dataset{aka_pos_10m,
  title={Akan POS Tagging Dataset - 10 Million Sentences},
  author={Mich-Seth Owusu},
  year={2025},
  publisher={Hugging Face},
  url={michsethowusu/twi-fante-sentences-parts-of-speech-pos_tagged-10m}
}

Collaboration

We invite researchers working in low-resource language processing to collaborate on research papers using this dataset. If you're interested in co-authoring a paper, please reach out!

License

[Specify your license here - e.g., Apache 2.0, MIT, CC BY 4.0, etc.]

Acknowledgments

This work demonstrates the potential of artificial data generation techniques for overcoming data limitations in low-resource language processing, specifically for African languages.
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