Fixed a typo.

README.md

@@ -21,7 +21,7 @@ More information, and other similar models can be found in the [main Github repo
 
 ## About
 This models is transformer based and was fine-tuned on the MasakhaNER dataset. It is a named entity recognition dataset, containing mostly news articles in 10 different African languages. 
-The model was fine-tuned for 50 epochs, with a maximum sequence length of 200, 32 batch size, 5e-5 learning rate. This process was repeated 5 times (with different random seeds), and this uploaded model performed the best out of those 5 seeds (aggregate F1 on on test set).
+The model was fine-tuned for 50 epochs, with a maximum sequence length of 200, 32 batch size, 5e-5 learning rate. This process was repeated 5 times (with different random seeds), and this uploaded model performed the best out of those 5 seeds (aggregate F1 on test set).
 
 This model was fine-tuned by me, Michael Beukman while doing a project at the University of the Witwatersrand, Johannesburg. This is version 1, as of 20 November 2021.
 This models is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
@@ -103,7 +103,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForTokenClassification.from_pretrained(model_name)
 
 nlp = pipeline("ner", model=model, tokenizer=tokenizer)
-example = "A (luganda) sentence that may contain entities"
+example = "Empaka zaakubeera mu kibuga Liverpool e Bungereza , okutandika nga July 12 ."
 
 ner_results = nlp(example)
 print(ner_results)