Create README.md

README.md

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+---
+license: cc
+language:
+- ve
+metrics:
+- perplexity
+library_name: transformers
+tags:
+- tshivenda
+- 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
+
+This is a variant of [Zabantu](https://huggingface.co/dsfsi/zabantu-bantu-250m) pre-trained on a monolingual dataset of Tshivenda(ven) 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-ven-120m)
+# The Sepedi sentence with a masked token
+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)
+```