Update README.md

README.md

@@ -18,19 +18,30 @@ This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/b
 It achieves the following results on the evaluation set:
 - Loss: 0.0814
 
+
 ## Model description
 
-More information needed
+bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC).
 
-## Intended uses & limitations
+Specifically, this model is a bert-base-cased model that was fine-tuned on the English version of the standard CoNLL-2003 Named Entity Recognition dataset.
 
-More information needed
+If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a bert-large-NER version is also available.
 
-## Training and evaluation data
+#  How to Use
+You can use this model with Transformers pipeline for NER.
 
-More information needed
+    from transformers import AutoTokenizer, AutoModelForTokenClassification
+    from transformers import pipeline
+    
+    tokenizer = AutoTokenizer.from_pretrained("Hatman/bert-finetuned-ner")
+    model = AutoModelForTokenClassification.from_pretrained("Hatman/bert-finetuned-ner")
+    
+    nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+    example = "My name is Wolfgang and I live in Berlin"
+    
+    ner_results = nlp(example)
+    print(ner_results)
 
-## Training procedure
 
 ### Training hyperparameters