How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("fill-mask", model="dsfsi/zabantu-sot-ven-170m")
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("dsfsi/zabantu-sot-ven-170m")
model = AutoModelForMaskedLM.from_pretrained("dsfsi/zabantu-sot-ven-170m", device_map="auto")
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Zabantu - Tshivenda & Sotho family

This is a variant of Zabantu pre-trained on a multilingual dataset of Tshivenda(ven) and Sotho family(Northern Sotho, Southern Sotho, Setswana) 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-sot-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)
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