readme: ---
  language: bn
  datasets:
  - wikiann
  examples:
  widget:
  - text: "মারভিন দি মারসিয়ান"
    example_title: "Sentence_1"
  - text: "লিওনার্দো দা ভিঞ্চি"
    example_title: "Sentence_2"
  - text: "বসনিয়া ও হার্জেগোভিনা"
    example_title: "Sentence_3"
  - text: "সাউথ ইস্ট ইউনিভার্সিটি"
    example_title: "Sentence_4"
  - text: "মানিক বন্দ্যোপাধ্যায় লেখক"
    example_title: "Sentence_5"
  ---
  
  <h1>Bengali Named Entity Recognition</h1>
  Fine-tuning bert-base-multilingual-cased on Wikiann dataset for performing NER on Bengali language.
  
  
  ## Label ID and its corresponding label name
  
  | Label ID | Label Name|
  | -------- | ----- |
  |0 | O |
  | 1 | B-PER |
  | 2 | I-PER |
  | 3 | B-ORG|
  | 4 | I-ORG | 
  | 5 | B-LOC |
  | 6 | I-LOC |
  
  <h1>Results</h1>
  
  | Name | Overall F1 | LOC F1 | ORG F1 | PER F1 |
  | ---- | -------- | ----- | ---- | ---- |
  | Train set | 0.997927 | 0.998246 | 0.996613 | 0.998769 |
  | Validation set | 0.970187 | 0.969212 | 0.956831 | 0.982079 |
  | Test set | 0.9673011 | 0.967120 |  0.963614 | 0.970938 |
  
  Example
  ```py
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  from transformers import pipeline
  
  tokenizer = AutoTokenizer.from_pretrained("Suchandra/bengali_language_NER")
  model = AutoModelForTokenClassification.from_pretrained("Suchandra/bengali_language_NER")
  
  nlp = pipeline("ner", model=model, tokenizer=tokenizer)
  example = "মারভিন দি মারসিয়ান"
  
  ner_results = nlp(example)
  ner_results
  ```
  