readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  model-index:
  - name: ner_hindi_bert
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # ner_hindi_bert
  
  This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3713
  - Overall Precision: 0.8942
  - Overall Recall: 0.8972
  - Overall F1: 0.8957
  - Overall Accuracy: 0.9367
  - Loc F1: 0.8766
  - Org F1: 0.8489
  - Per F1: 0.9454
  
  ## Model description
  
  More information needed
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  More information needed
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during training:
  - learning_rate: 2e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 7
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Loc F1 | Org F1 | Per F1 |
  |:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:------:|:------:|:------:|
  | 0.2993        | 3.19  | 1000 | 0.3230          | 0.8779            | 0.8786         | 0.8782     | 0.9244           | 0.8535 | 0.8270 | 0.9358 |
  | 0.0641        | 6.39  | 2000 | 0.3713          | 0.8942            | 0.8972         | 0.8957     | 0.9367           | 0.8766 | 0.8489 | 0.9454 |
  
  
  ### Framework versions
  
  - Transformers 4.21.0
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  