readme: 
  ---
  language: bn
  tags:
  - collaborative
  - bengali
  - SequenceClassification
  license: apache-2.0
  datasets: IndicGlue 
  metrics:
  - Loss
  - Accuracy
  - Precision
  - Recall
  widget:
  
  - text: "এশিয়ায় প্রথম দৃষ্টিহীন ব্যক্তির মাউন্ট এভারেস্ট জয়|"
  
  ---
  
  # sahajBERT News Article Classification
  
  ## Model description
  
  [sahajBERT](https://huggingface.co/neuropark/sahajBERT) fine-tuned for news article classification using the `sna.bn` split of [IndicGlue](https://huggingface.co/datasets/indic_glue). 
  
  The model is trained for classifying articles into 5 different classes:
  
  | Label id | Label |
  |:--------:|:----:|
  |0 | kolkata|
  |1 | state|
  |2 | national|
  |3 | sports|
  |4 | entertainment|
  |5 | international|
  
  ## Intended uses & limitations
  
  #### How to use
  
  You can use this model directly with a pipeline for Sequence Classification:
  ```python
  from transformers import AlbertForSequenceClassification, TextClassificationPipeline, PreTrainedTokenizerFast
  
  # Initialize tokenizer
  tokenizer = PreTrainedTokenizerFast.from_pretrained("neuropark/sahajBERT-NCC")
  
  # Initialize model
  model = AlbertForSequenceClassification.from_pretrained("neuropark/sahajBERT-NCC")
  
  # Initialize pipeline
  pipeline = TextClassificationPipeline(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 with pre-trained weights of [sahajBERT](https://huggingface.co/neuropark/sahajBERT) at step 19519 and trained on the `sna.bn` split of [IndicGlue](https://huggingface.co/datasets/indic_glue). 
  
  ## Training procedure
  
  Coming soon! 
  <!-- ```bibtex
  @inproceedings{...,
    year={2020}
  }
  ``` -->
  
  ## Eval results
  
  
  Loss: 0.2477145493030548
  
  Accuracy: 0.926293408929837
  
  Macro F1: 0.9079785326650756
  
  Recall: 0.926293408929837
  
  Weighted F1: 0.9266428029354202
  
  Macro Precision: 0.9109938492260489
  
  Micro Precision: 0.926293408929837
  
  Weighted Precision: 0.9288535478995414
  
  Macro Recall: 0.9069095007692186
  
  Micro Recall: 0.926293408929837
  
  Weighted Recall: 0.926293408929837
  
  
  ### BibTeX entry and citation info
  
  Coming soon! 
  <!-- ```bibtex
  @inproceedings{...,
    year={2020}
  }
  ``` -->
  