README.md

metadata

license: cc
language:
  - ve
  - nso
metrics:
  - perplexity
library_name: transformers
tags:
  - tshivenda
  - sepedi
  - sesotho sa leboa
  - nothern sotho
  - south africa
  - low-resource
  - bantu
  - xlm-roberta
widget:
  - text: Rabulasi wa <mask> u khou bvelela nga u lima
  - text: >-
      Vhana vhane vha kha ḓi bva u bebwa vha kha khombo ya u <mask> nga
      Listeriosis

Zabantu - Tshivenda & Sotho family

This is a variant of Zabantu pre-trained on a multilingual dataset of Tshivenda(ven) and Sepedi(nso) sentences on a transformer network with 170 million traininable parameters.

Usage Example(s)

from transformers import pipeline
# Initialize the pipeline for masked language model
unmasker = pipeline('fill-mask', model='dsfsi/zabantu-nso-ven-170m')

sample_sentences = ["Rabulasi wa <mask> u khou bvelela nga u lima",
                    "Vhana vhane vha kha ḓi bva u bebwa vha kha khombo ya u <mask> nga Listeriosis"]

# Perform the fill-mask task
results = unmasker(sentence)
# Display the results
for result in results:
    print(f"Predicted word: {result['token_str']} - Score: {result['score']}")
    print(f"Full sentence: {result['sequence']}\n")
    print("=" * 80)