Create README.md

README.md

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+---
+language: bn
+tags:
+- bengali-ner
+- bengali
+- bangla
+- NER
+license: MIT
+datasets:
+- wikiann
+- xtreme
+
+---
+
+# Multi-lingual BERT Bengali Name Entity Recognition
+`mBERT-Bengali-NER` is a transformer-based Bengali NER model build with [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) model and [Wikiann](https://huggingface.co/datasets/wikiann) Datasets.
+
+## How to Use
+
+```py
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+
+tokenizer = AutoTokenizer.from_pretrained("sagorsarker/mbert-bengali-ner")
+model = AutoModelForTokenClassification.from_pretrained("sagorsarker/mbert-bengali-ner")
+
+nlp = pipeline("ner", model=model, tokenizer=tokenizer)
+example = "আমি জাহিদ এবং আমি ঢাকায় বাস করি।"
+
+ner_results = nlp(example)
+print(ner_results)
+```
+
+## Label and ID Mapping
+
+| Label ID | Label |
+| -------- | ----- |
+|0 | O |
+| 1 | B-PER |
+| 2 | I-PER |
+| 3 | B-ORG|
+| 4 | I-ORG | 
+| 5 | B-LOC |
+| 6 | I-LOC |
+
+## Training Details
+- mBERT-Bengali-NER trained with [Wikiann](https://huggingface.co/datasets/wikiann) datasets
+- mBERT-Bengali-NER trained with [transformers-token-classification](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/token_classification.ipynb) script
+- mBERT-Bengali-NER total trained 5 epochs.
+- Trained in Kaggle GPU
+
+## Evaluation Results
+|Model | F1 | Precision | Recall | Accuracy | Loss |
+| ---- | --- | --------- | ----- | -------- | --- |
+|Bangla-BERT-NER | 0.97105 | 0.96769| 0.97443 | 0.97682 | 0.12511 |
+
+
+
+