update

README.md

@@ -56,6 +56,24 @@ The distilbert-finetuned-ner model is designed for Named Entity Recognition (NER
 
 ## Intended Uses & Limitations
 
+#### How to use
+
+You can use this model with Transformers *pipeline* for NER.
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("amanpatkar/distilbert-finetuned-ner")
+model = AutoModelForTokenClassification.from_pretrained("amanpatkar/distilbert-finetuned-ner")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "My name is Aman Patkar and I live in Gurugram, India."
+
+ner_results = nlp(example)
+print(ner_results)
+```
+
 ### Intended Uses
 - Named Entity Recognition (NER): Extracting entities such as names, locations, organizations, and miscellaneous entities from text.
 - Information Extraction: Automatically identifying and classifying key information in documents.
@@ -70,6 +88,17 @@ The distilbert-finetuned-ner model is designed for Named Entity Recognition (NER
 ## Training and evaluation data
 
 The model is fine-tuned on the CoNLL-2003 dataset, a widely-used dataset for training and evaluating NER systems. The dataset includes four types of named entities: Persons (PER), Organizations (ORG), Locations (LOC), and Miscellaneous (MISC).
+Abbreviation|Description
+-|-
+O|Outside of a named entity
+B-MISC |Beginning of a miscellaneous entity right after another miscellaneous entity
+I-MISC | Miscellaneous entity
+B-PER |Beginning of a person’s name right after another person’s name
+I-PER |Person’s name
+B-ORG |Beginning of an organization right after another organization
+I-ORG |organization
+B-LOC |Beginning of a location right after another location
+I-LOC |Location
 
 ## Training procedure