Update README.md

adding usage examples

README.md

@@ -54,9 +54,28 @@ It achieves the following results on the evaluation set:
 
 More information needed
 
-## Intended uses & limitations
+## limitations
 
-More information needed
+#### Limitations and bias
+
+This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains. Furthermore, the model occassionally tags subword tokens as entities and post-processing of results may be necessary to handle those cases. 
+
+
+#### How to use
+
+You can use this model with Transformers *pipeline* for NER.
+
+```python
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+tokenizer = AutoTokenizer.from_pretrained("Jorgeutd/albert-base-v2-finetuned-ner")
+
+model = AutoModelForTokenClassification.from_pretrained("Jorgeutd/albert-base-v2-finetuned-ner")
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "My name is Scott and I live in Ohio"
+ner_results = nlp(example)
+print(ner_results)
+```
 
 ## Training and evaluation data