readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: indic-transformers-te-distilbert
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        args: te
      metrics:
      - name: Precision
        type: precision
        value: 0.5657225853304285
      - name: Recall
        type: recall
        value: 0.6486261448792673
      - name: F1
        type: f1
        value: 0.604344453064391
      - name: Accuracy
        type: accuracy
        value: 0.9049186160277506
  ---
  
  <!-- 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. -->
  
  # indic-transformers-te-distilbert
  
  This model was trained from scratch on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2940
  - Precision: 0.5657
  - Recall: 0.6486
  - F1: 0.6043
  - Accuracy: 0.9049
  
  ## 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: 8
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | No log        | 1.0   | 125  | 0.3629          | 0.4855    | 0.5287 | 0.5062 | 0.8826   |
  | No log        | 2.0   | 250  | 0.3032          | 0.5446    | 0.6303 | 0.5843 | 0.9002   |
  | No log        | 3.0   | 375  | 0.2940          | 0.5657    | 0.6486 | 0.6043 | 0.9049   |
  
  
  ### Framework versions
  
  - Transformers 4.15.0
  - Pytorch 1.10.0+cu111
  - Datasets 1.17.0
  - Tokenizers 0.10.3
  