Create README.md

README.md

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+---
+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
+```