Update model card

README.md

@@ -1,9 +1,30 @@
+---
+language: mr
+datasets:
+- wikiann
+examples:
+widget:
+- text: "सचिन तेंडुलकर मुंबईचा आहे."
+  example_title: "Sentence_1"
+- text: "विराट कोहली भारताकडून खेळतो."
+  example_title: "Sentence_2"
+- text: "नवी दिल्ली ही भारताची राजधानी आहे"
+  example_title: "Sentence_3"
+---
+
+<h1>Marathi Named Entity Recognition Model trained using transfer learning</h1>
 Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER on Marathi language.
 
+
+## Label ID and its corresponding label name
+
 Label list: (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4), B-LOC (5), I-LOC (6)
 
-How to use the model:
+
+Example
+```py
 from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
 
 tokenizer = AutoTokenizer.from_pretrained("lakshaywadhwa1993/ner_marathi_bert")
 
@@ -11,8 +32,9 @@ model = AutoModelForTokenClassification.from_pretrained("lakshaywadhwa1993/ner_m
 
 nlp = pipeline("ner", model=model, tokenizer=tokenizer)
 
-example = ["राज्यसभा","निवडणुकांसाठी","मुंबईत","भाजपचे" ,"चिंचवडचे", "आमदार", "लक्ष्मण", "जगताप"] 
+example = ["राज्यसभा","निवडणुकांसाठी","मुंबईत","भाजपचे" ,"चिंचवडचे", "आमदार", "लक्ष्मण", "जगताप"]
 
 results = nlp(example)
 
-results
+results
+```