readme: ---
  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, grouped_entities=True)
  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 |
  | ---- | --- | --------- | ----- | -------- | --- |
  |mBert-Bengali-NER | 0.97105 | 0.96769| 0.97443 | 0.97682 | 0.12511 |
  
  
  
    