Update README.md

README.md

@@ -15,7 +15,7 @@ widget:
 ---
 
 # Model description
-**bert-base-cased-kin** is a model based on the fine-tuned BERT base cased model. It has been trained to recognize four types of entities:
+**bert-base-uncased-kin** is a model based on the fine-tuned BERT base uncased model. It has been trained to recognize four types of entities:
 
 - dates & time (DATE)
 - Location (LOC)
@@ -57,7 +57,7 @@ We evaluated this model on the test split of the Kinyarwandan corpus **(kin)** p
 # Results
 Model Name| Precision | Recall | F1-score
 -|-|-|-
-**bert-base-cased-kin**| 75.00 |80.09|77.47
+**bert-base-uncased-kin**| 75.00 |80.09|77.47
 
 # Usage
 
@@ -65,8 +65,8 @@ Model Name| Precision | Recall | F1-score
 from transformers import AutoTokenizer, AutoModelForTokenClassification
 from transformers import pipeline
 
-tokenizer = AutoTokenizer.from_pretrained("arnolfokam/bert-base-cased-kin")
-model = AutoModelForTokenClassification.from_pretrained("arnolfokam/bert-base-cased-kin")
+tokenizer = AutoTokenizer.from_pretrained("arnolfokam/bert-base-uncased-kin")
+model = AutoModelForTokenClassification.from_pretrained("arnolfokam/bert-base-uncased-kin")
 
 nlp = pipeline("ner", model=model, tokenizer=tokenizer)
 example = "Rayon Sports yasinyishije rutahizamu w’Umurundi"