readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - hi_ner-original
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: xlm-roberta-base-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: hi_ner-original
        type: hi_ner-original
        args: HiNER
      metrics:
      - name: Precision
        type: precision
        value: 0.7366076627460114
      - name: Recall
        type: recall
        value: 0.6770947627585838
      - name: F1
        type: f1
        value: 0.7055985498152408
      - name: Accuracy
        type: accuracy
        value: 0.9359390321752693
  ---
  
  <!-- 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. -->
  
  # xlm-roberta-base-finetuned-ner
  
  This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the hi_ner-original dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2314
  - Precision: 0.7366
  - Recall: 0.6771
  - F1: 0.7056
  - Accuracy: 0.9359
  
  ## 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: 4
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 6
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.2025        | 0.74  | 7000  | 0.2146          | 0.7399    | 0.6197 | 0.6745 | 0.9316   |
  | 0.1641        | 1.47  | 14000 | 0.2238          | 0.7618    | 0.6108 | 0.6780 | 0.9336   |
  | 0.1404        | 2.21  | 21000 | 0.2302          | 0.7560    | 0.6327 | 0.6889 | 0.9350   |
  | 0.1371        | 2.95  | 28000 | 0.2226          | 0.7395    | 0.6600 | 0.6975 | 0.9350   |
  | 0.1248        | 3.68  | 35000 | 0.2314          | 0.7366    | 0.6771 | 0.7056 | 0.9359   |
  | 0.1112        | 4.42  | 42000 | 0.2423          | 0.7089    | 0.7064 | 0.7077 | 0.9333   |
  | 0.1048        | 5.16  | 49000 | 0.2599          | 0.7326    | 0.6793 | 0.7050 | 0.9349   |
  | 0.1091        | 5.89  | 56000 | 0.2542          | 0.7244    | 0.6918 | 0.7077 | 0.9348   |
  
  
  ### Framework versions
  
  - Transformers 4.19.4
  - Pytorch 1.11.0+cu102
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  