readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - conll2003
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: deberta-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: conll2003
        type: conll2003
        args: conll2003
      metrics:
      - name: Precision
        type: precision
        value: 0.9577488309953239
      - name: Recall
        type: recall
        value: 0.9651632446987546
      - name: F1
        type: f1
        value: 0.961441743503772
      - name: Accuracy
        type: accuracy
        value: 0.9907182964622135
    - task:
        type: token-classification
        name: Token Classification
      dataset:
        name: conll2003
        type: conll2003
        config: conll2003
        split: test
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9108823919384779
        verified: true
      - name: Precision
        type: precision
        value: 0.9308372971460548
        verified: true
      - name: Recall
        type: recall
        value: 0.9213792387183881
        verified: true
      - name: F1
        type: f1
        value: 0.9260841198729938
        verified: true
      - name: loss
        type: loss
        value: 0.8661637306213379
        verified: true
  ---
  
  <!-- 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-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: 0.0515
  - Precision: 0.9577
  - Recall: 0.9652
  - F1: 0.9614
  - Accuracy: 0.9907
  
  ## 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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.0742        | 1.0   | 1756 | 0.0526          | 0.9390    | 0.9510 | 0.9450 | 0.9868   |
  | 0.0374        | 2.0   | 3512 | 0.0528          | 0.9421    | 0.9554 | 0.9487 | 0.9879   |
  | 0.0205        | 3.0   | 5268 | 0.0505          | 0.9505    | 0.9636 | 0.9570 | 0.9900   |
  | 0.0089        | 4.0   | 7024 | 0.0528          | 0.9531    | 0.9636 | 0.9583 | 0.9898   |
  | 0.0076        | 5.0   | 8780 | 0.0515          | 0.9577    | 0.9652 | 0.9614 | 0.9907   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.3.2
  - Tokenizers 0.12.1
  