Update README.md

README.md

@@ -1,3 +1,26 @@
----
-license: mit
----
+---
+license: mit
+language:
+- xh
+- zu
+- nr
+- ss
+---
+
+Usage:
+
+1. For mask prediction
+
+```
+tokenizer = AutoTokenizer.from_pretrained("francois-meyer/nguni-xlmr-large")
+model = XLMRobertaForMaskedLM.from_pretrained("francois-meyer/nguni-xlmr-large")
+text = "A test <mask> for the nguni model." ## Replace with any sentence from the Nguni Languages with mask tokens.
+inputs = tokenizer(text, return_tensors="pt")
+with torch.no_grad():
+  logits = model(**inputs).logits
+mask_token_index = (inputs.input_ids == tokenizer.mask_token_id)[0].nonzero(as_tuple=True)[0]
+predicted_token_id = logits[0, mask_token_index].argmax(axis=-1)
+print(tokenizer.decode(predicted_token_id))
+```
+
+2. For any other task, you might want to fine-tune the model in the same way you fine-tune a BERT/XLMR model.