update README.md

README.md

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+---
+language: bn
+datasets:
+- wikiann
+examples:
+widget:
+- text: "রিয়াল মাদ্রিদ ফুটবল ক্লাব"
+  example_title: "Sentence_1"
+- text: "উত্তরবঙ্গ কৃষি বিশ্ববিদ্যালয়"
+  example_title: "Sentence_2"
+- text: "বাংলাদেশ জাতীয় ক্রিকেট দল"
+  example_title: "Sentence_3"
+- text: "বাংলাদেশ টেলিকমিউনিকেশন্স কোম্পানী লিমিটেড"
+  example_title: "Sentence_4"
+- text: "রোমিও অ্যান্ড জুলিয়েট"
+  example_title: "Sentence_5"
+---
+
+<h1>Named Entity Recognition on Bangla Language</h1>
+Fine Tuning BERT for NER on Bengali Language Tagging using HuggingFace
+
+
+## Correspondence Label ID and 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>Evaluation and Validation</h1>
+
+| Name | Precision | Recall | F1 | Accuracy |
+| ---- | -------- | ----- | ---- | ---- |
+| Train/Val set | 0.963899 | 0.964770 | 0.964334 | 0.981252 |
+| Test set | 0.952855 | 0.965105 |  0.958941 | 0.981349 |
+
+
+Transformers AutoModelForTokenClassification
+
+```py
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("engineersakibcse47/NER_on_Bangla_Language")
+model_ner = AutoModelForTokenClassification.from_pretrained("engineersakibcse47/NER_on_Bangla_Language")
+
+pipe = pipeline("ner", model=model_ner, tokenizer=tokenizer)
+sample = "বসনিয়া ও হার্জেগোভিনা"
+
+result = pipe(sample)
+result
+```