XLM-RoBERTa Fine-tuned on Ugandan Languages
This model is XLM-RoBERTa-base fine-tuned on a comprehensive dataset of Ugandan languages.
Usage
from transformers import AutoTokenizer, AutoModelForMaskedLM, pipeline
tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-uganda-languages")
model = AutoModelForMaskedLM.from_pretrained("xlm-roberta-uganda-languages")
fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
result = fill_mask("Abantu b'omubyalo tibatera kwikiriza [MASK] muyaaka.")
print(result)
Training Details
- Training Steps: N/A
- Training Loss: 2.1567
- Learning Rate: 5e-05
- Batch Size: 8
- Epochs: 3
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