Update README.md

README.md

@@ -26,28 +26,17 @@ It achieves the following results on the evaluation set:
 
 The sentiment fine-tuning was done on Hausa Language.
 
-Model Repository :  https://github.com/idimohammed/HausaBERTa
+Model Repository :  https://github.com/YobeStateUniversity/HausaSentiLex
 
 ## Model description
 
-HausaBERTa is a pretrained lexicon low resources language model. The model was trained on Hausa Language (Hausa is a Chadic language spoken by the Hausa people in the northern half of Nigeria, Niger, Ghana, Cameroon, Benin and Togo, and the southern half of Niger, Chad and Sudan, with significant minorities in Ivory Coast. It is the most widely spoken language in West Africa, and one of the most widely spoken languages in Africa as a whole).
+HausaSentiLex is a pretrained lexicon low resources language model. The model was trained on Hausa Language (Hausa is a Chadic language spoken by the Hausa people in the northern half of Nigeria, Niger, Ghana, Cameroon, Benin and Togo, and the southern half of Niger, Chad and Sudan, with significant minorities in Ivory Coast. It is the most widely spoken language in West Africa, and one of the most widely spoken languages in Africa as a whole).
 The model has been shown to obtain competitive downstream performances on text classification on trained language
 
 ## Intended uses & limitations
 
 You can use this model with Transformers for sentiment analysis task in Hausa Language.
 
-# Use a pipeline as a high-level helper
-from transformers import pipeline
-
-pipe = pipeline("text-classification", model="mangaphd/hausaBERTmodel")
-
-# Load model directly
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-tokenizer = AutoTokenizer.from_pretrained("mangaphd/HausaBERTa")
-model = AutoModelForSequenceClassification.from_pretrained("mangaphd/HausaBERTa")
-
 # Supplementary function
 Add the following codes for ease of interpretation