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amogneandualem/awngi-fasttext-skipgram

sourceHugging Facecc-by-4.0updated 5mo agoView on Hugging Face

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Model Card

Awngi fastText Skip-gram Embeddings

This repository contains Awngi fastText skip-gram embeddings trained on a cleaned Awngi corpus.

Corpus Statistics

Metric	Value
Sentences	153,341
Tokens	2,410,758
Unique words	194,469

Training Configuration

Parameter	Value
Algorithm	Skip-gram
Dimension	100
Epochs	20
Window size	5
Character n-grams	3–6

Usage

python

import fasttext

model = fasttext.load_model("awngi_fasttext_skipgram.bin")

vector = model.get_word_vector("አውጚ")

print(vector[:10])
Intended Use

This model is designed for:

Named Entity Recognition
Low-resource NLP
Ethiopian language processing
Morphologically rich language representation
Citation
@article{andualem2026awnginer,
  title={AwngiNER: A Benchmark Dataset and fastText-Enhanced Neural Models for Named Entity Recognition in Awngi},
  author={Andualem, Amogne},
  year={2026}
}