Create README.md

README.md

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+---
+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](https://huggingface.co/dsfsi/zabantu-bantu-250m) pre-trained on a monolingual dataset of Sepedi(nso) sentences on a transformer network 
+with 120 million traininable parameters.
+
+
+# Usage Example(s)
+
+```python
+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)
+```