update readme

README.md

@@ -8,7 +8,7 @@ datasets:
 ## Model description
 **mT5_base_yoruba_adr** is a **automatic diacritics restoration** model for Yorùbá language based on a fine-tuned  mT5-base  model.  It achieves the **state-of-the-art performance** for adding the correct diacritics or tonal marks to Yorùbá texts.  
 
-Specifically, this model is a *mT5_base* model that was fine-tuned on  JW300 Yorùbá corpus and  [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
+Specifically, this model is a *mT5_base* model that was fine-tuned on  JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
 ## Intended uses & limitations
 #### How to use
 You can use this model with Transformers *pipeline* for NER.
@@ -17,7 +17,7 @@ from transformers import AutoTokenizer, AutoModelForTokenClassification
 from transformers import pipeline
 tokenizer = AutoTokenizer.from_pretrained("")
 model = AutoModelForTokenClassification.from_pretrained("")
-nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+nlp = pipeline("", model=model, tokenizer=tokenizer)
 example = "Emir of Kano turban Zhang wey don spend 18 years for Nigeria"
 ner_results = nlp(example)
 print(ner_results)