readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - xtreme
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: bert-multilingual-finetuned-xtreme-tamil-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: xtreme
        type: xtreme
        config: PAN-X.ta
        split: train
        args: PAN-X.ta
      metrics:
      - name: Precision
        type: precision
        value: 0.746268656716418
      - name: Recall
        type: recall
        value: 0.819672131147541
      - name: F1
        type: f1
        value: 0.7812500000000001
      - name: Accuracy
        type: accuracy
        value: 0.9236328484625299
  ---
  
  <!-- 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. -->
  
  # bert-multilingual-finetuned-xtreme-tamil-ner
  
  This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on the xtreme dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2338
  - Precision: 0.7463
  - Recall: 0.8197
  - F1: 0.7813
  - Accuracy: 0.9236
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 2
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.3899        | 1.0   | 469  | 0.2517          | 0.6893    | 0.7893 | 0.7360 | 0.9143   |
  | 0.2093        | 2.0   | 938  | 0.2338          | 0.7463    | 0.8197 | 0.7813 | 0.9236   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  