Update README.md

README.md

@@ -6,7 +6,9 @@
 
 # Model Card for Model ID
 
-This model is developed to tag Names, Organisations and addresses. I have used a data combined from Conll, ontonotes5, and a custom address dataset that wad self made.
+This model is developed to tag Names, Organisations and addresses. I have used a data combined fro Conll, ontonotes5, and a custom address dataset that was self made. Cleaned
+out the tags.
+[\"O\", \"B-ORG\", \"I-ORG\", \"B-PER\", \"I-PER\",'B-addr','I-addr']
 
 ### Model Description
 
@@ -15,24 +17,14 @@ This model is developed to tag Names, Organisations and addresses. I have used a
 - **Language(s) (NLP):** Named Entity recognition
 - **Finetuned from model [optional]:** bert-base-cased
 
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
 ### Direct Use
 
 ```python
-from transformers import BertTokenizer, AutoModelForTokenClassification
+from transformers import AutoTokenizer, AutoModelForTokenClassification
 from transformers import pipeline
-tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
 model = AutoModelForTokenClassification.from_pretrained("ctrlbuzz/bert-addresses")
 nlp = pipeline("ner", model=model, tokenizer=tokenizer)
 example = "While Maria was representing Johnson & Associates at a conference in Spain, she mailed me a letter from her new office at 123 Elm St., Apt. 4B, Springfield, IL.",