Update README.md

README.md

@@ -16,11 +16,11 @@ tags:
 - Informal text
 license: mit
 ---
-# InstaFoodBERT
+# InstaFoodBERT-NER
 
 ## Model description
 
-**InstaFoodBERT** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** of Food entities on informal text (social media like). It has been trained to recognize a single entity: food (FOOD).
+**InstaFoodBERT-NER** is a fine-tuned BERT model that is ready to use for **Named Entity Recognition** of Food entities on informal text (social media like). It has been trained to recognize a single entity: food (FOOD).
 
 Specifically, this model is a *bert-base-cased* model that was fine-tuned on a dataset consisting of 400 English Instagram posts related to food. The [dataset](https://huggingface.co/datasets/Dizex/InstaFoodSet) is open source.
 
@@ -35,8 +35,8 @@ You can use this model with Transformers *pipeline* for NER.
 from transformers import AutoTokenizer, AutoModelForTokenClassification
 from transformers import pipeline
 
-tokenizer = AutoTokenizer.from_pretrained("Dizex/InstaFoodBERT")
-model = AutoModelForTokenClassification.from_pretrained("Dizex/InstaFoodBERT")
+tokenizer = AutoTokenizer.from_pretrained("Dizex/InstaFoodBERT-NER")
+model = AutoModelForTokenClassification.from_pretrained("Dizex/InstaFoodBERT-NER")
 
 pipe = pipeline("ner", model=model, tokenizer=tokenizer)
 example = "Today's meal: Fresh olive poké bowl topped with chia seeds. Very delicious!"