readme: 
  ---
  language: bn
  tags:
  - collaborative
  - bengali
  - NER
  license: apache-2.0
  datasets: xtreme 
  metrics:
  - Loss
  - Accuracy
  - Precision
  - Recall
  ---
  
  # sahajBERT Named Entity Recognition
  
  ## Model description
  
  [sahajBERT](https://huggingface.co/neuropark/sahajBERT-NER) fine-tuned for NER using the bengali split of [WikiANN ](https://huggingface.co/datasets/wikiann). 
  
  Named Entities predicted by the model:
  
  | Label id | Label |
  |:--------:|:----:|
  |0 |O|
  |1 |B-PER|
  |2 |I-PER|
  |3 |B-ORG|
  |4 |I-ORG|
  |5 |B-LOC|
  |6 |I-LOC|
  
  ## Intended uses & limitations
  
  #### How to use
  
  You can use this model directly with a pipeline for masked language modeling:
  ```python
  from transformers import AlbertForTokenClassification, TokenClassificationPipeline, PreTrainedTokenizerFast
  
  # Initialize tokenizer
  tokenizer = PreTrainedTokenizerFast.from_pretrained("neuropark/sahajBERT-NER")
  
  # Initialize model
  model = AlbertForTokenClassification.from_pretrained("neuropark/sahajBERT-NER")
  
  # Initialize pipeline
  pipeline = TokenClassificationPipeline(tokenizer=tokenizer, model=model)
  
  raw_text = "এই ইউনিয়নে ৩ টি মৌজা ও ১০ টি গ্রাম আছে ।" # Change me
  output = pipeline(raw_text)
  ```
  
  #### Limitations and bias
  
  <!-- Provide examples of latent issues and potential remediations. -->
  WIP
  
  ## Training data
  
  The model was initialized it with pre-trained weights of [sahajBERT](https://huggingface.co/neuropark/sahajBERT-NER) at step 19519 and trained on the bengali of [WikiANN ](https://huggingface.co/datasets/wikiann)
  
  ## Training procedure
  
  Coming soon! 
  <!-- ```bibtex
  @inproceedings{...,
    year={2020}
  }
  ``` -->
  
  ## Eval results
  
  
  loss: 0.11714419722557068
  
  accuracy: 0.9772286821705426
  
  precision: 0.9585365853658536
  
  recall: 0.9651277013752456
  
  f1 : 0.9618208516886931
  
  
  ### BibTeX entry and citation info
  
  Coming soon! 
  <!-- ```bibtex
  @inproceedings{...,
    year={2020}
  }
  ``` -->
  