Update README.md

README.md

@@ -90,15 +90,16 @@ Users (both direct and downstream) should be made aware of the risks, biases, an
 
 Use the code below to get started with the model.
 
+
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
 ### Load the model and tokenizer
-model = AutoModelForSequenceClassification.from_pretrained("your-username/fine-tuned-gemma-7b-hausa")
-tokenizer = AutoTokenizer.from_pretrained("your-username/fine-tuned-gemma-7b-hausa")
+**model** = AutoModelForSequenceClassification.from_pretrained("bagwai/fine-tuned-gemma-7b-hausa")
+**tokenizer** = AutoTokenizer.from_pretrained("bagwai/fine-tuned-gemma-7b-hausa")
 
 ### Example usage
-inputs = tokenizer("Ina son wannan littafin", return_tensors="pt")
-outputs = model(**inputs)
+**inputs** = tokenizer("Ina son wannan littafin", return_tensors="pt")
+**outputs** = model(**inputs)
 
 
 ## Training Details
@@ -174,7 +175,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 **BibTeX:**
 
-@misc{yourname2024fine,
+@misc{mubarak2024fine,
   author = {Mubarak Daha Isa},
   title = {Fine-tuned Gemma 7B for Hausa Sentiment Analysis},
   year = {2024},
@@ -195,5 +196,5 @@ Mubarak Daha Isa
 
 ## Model Card Contact
 
-mubarakdaha8@gmail.com
-2023000675.mubarak@pg.sharda.ac.in
+**mubarakdaha8@gmail.com**
+**2023000675.mubarak@pg.sharda.ac.in**