README.md

metadata

license: cc
language:
  - nso
metrics:
  - perplexity
tags:
  - sepedi
  - sesotho sa leboa
  - northen sotho
  - south africa
  - bantu
  - xlm-roberta
library_name: transformers
widget:
  - text: mopresidente wa <mask> wa afrika-borwa

Zabantu - Sepedi

This is a variant of Zabantu pre-trained on a monolingual dataset of Sepedi(nso) sentences on a transformer network with 120 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-120m')

# The Sepedi sentence with a masked token
sample_sentences = ["mopresidente wa <mask> wa afrika-borwa",   # original token: maloba
"bašomedi ba polase ya dinamune ya zebediela citrus ba hlomile magato a <mask> malebana le go se sepetšwe botse ga dilo ka polaseng eo."  # original token: boipelaetšo
]

# 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)