readme: ---
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: deberta-finetuned-ner-connll-late-stop
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        config: en
        split: train
        args: en
      metrics:
      - name: Precision
        type: precision
        value: 0.830192600803658
      - name: Recall
        type: recall
        value: 0.8470945850417079
      - name: F1
        type: f1
        value: 0.8385584324702589
      - name: Accuracy
        type: accuracy
        value: 0.9228861596598961
  ---
  
  <!-- 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-connll-late-stop
  
  This model is a fine-tuned version of [microsoft/deberta-base](https://huggingface.co/microsoft/deberta-base) on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.5259
  - Precision: 0.8302
  - Recall: 0.8471
  - F1: 0.8386
  - Accuracy: 0.9229
  
  ## 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: 7
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.3408        | 1.0   | 1875  | 0.3639          | 0.7462    | 0.7887 | 0.7669 | 0.8966   |
  | 0.2435        | 2.0   | 3750  | 0.2933          | 0.8104    | 0.8332 | 0.8217 | 0.9178   |
  | 0.1822        | 3.0   | 5625  | 0.3034          | 0.8147    | 0.8388 | 0.8266 | 0.9221   |
  | 0.1402        | 4.0   | 7500  | 0.3667          | 0.8275    | 0.8474 | 0.8374 | 0.9235   |
  | 0.1013        | 5.0   | 9375  | 0.4290          | 0.8285    | 0.8448 | 0.8366 | 0.9227   |
  | 0.0677        | 6.0   | 11250 | 0.4914          | 0.8259    | 0.8473 | 0.8365 | 0.9231   |
  | 0.0439        | 7.0   | 13125 | 0.5259          | 0.8302    | 0.8471 | 0.8386 | 0.9229   |
  
  
  ### Framework versions
  
  - Transformers 4.22.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  