---
language: mr
datasets:
- wikiann
examples:
widget:
- text: "राज्यसभा निवडणुकांसाठी उद्या मुंबईत मतदान होणार 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)


Example
```py
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("lakshaywadhwa1993/ner_marathi_bert")

model = AutoModelForTokenClassification.from_pretrained("lakshaywadhwa1993/ner_marathi_bert")

nlp = pipeline("ner", model=model, tokenizer=tokenizer)

example = ["राज्यसभा","निवडणुकांसाठी","मुंबईत","भाजपचे" ,"चिंचवडचे", "आमदार", "लक्ष्मण", "जगताप"]

results = nlp(example)

results
```