readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: deberta-base-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.9563020492186769
      - name: Recall
        type: recall
        value: 0.9652436720816018
      - name: F1
        type: f1
        value: 0.9607520564042303
      - name: Accuracy
        type: accuracy
        value: 0.9899205302077261
  ---
  
  <!-- 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. -->
  
  # deberta-base-finetuned-ner
  
  This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the conll2003 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0501
  - Precision: 0.9563
  - Recall: 0.9652
  - F1: 0.9608
  - Accuracy: 0.9899
  
  ## 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: 5e-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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.1419        | 1.0   | 878  | 0.0628          | 0.9290    | 0.9288 | 0.9289 | 0.9835   |
  | 0.0379        | 2.0   | 1756 | 0.0466          | 0.9456    | 0.9567 | 0.9511 | 0.9878   |
  | 0.0176        | 3.0   | 2634 | 0.0473          | 0.9539    | 0.9575 | 0.9557 | 0.9890   |
  | 0.0098        | 4.0   | 3512 | 0.0468          | 0.9570    | 0.9635 | 0.9603 | 0.9896   |
  | 0.0043        | 5.0   | 4390 | 0.0501          | 0.9563    | 0.9652 | 0.9608 | 0.9899   |
  
  
  ### Framework versions
  
  - Transformers 4.11.3
  - Pytorch 1.9.0+cu111
  - Datasets 1.12.1
  - Tokenizers 0.10.3
  