Update README.md

README.md

@@ -10,6 +10,9 @@ tags:
 
 Pretrained model on on wine labels and descriptions for named entity recognition that uses bert-base-uncased as the base model. 
 
+* Updated to remove bias on position of wine label in the training inputs.
+* also updated to remove trying to get the wine classification. e.g. Grand Cru etc because training data is not reliable.
+
 ## Model description
 
 
@@ -46,15 +49,13 @@ balanced: mouthfeel: 0.99992
 The BERT model was trained on 20K reviews and wine labels derived from https://huggingface.co/datasets/james-burton/wine_reviews_all_text and manually annotated to capture the following tokens
 
 ```
-"1": "classification",
-"2": "country",
-"3": "flavor",
-"4": "mouthfeel",
-"5": "producer",
-"6": "province",
-"7": "region",
-"8": "subregion",
-"9": "wine"
+adjective: nice, exciting, strong etc
+country: countries specified in label or description
+flavor: fruit, apple, toast, smoke etc
+grape: Cab, Cabernet Sauvignon, etc
+mouthfeel: lucious, smooth, textured, rough etc
+producer: wine maker
+province, region: province and region of wine - sometimes these get mixed up
 ```
 
 ## Training procedure