readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - wikiann
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-uncased_ner_wikiann
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: wikiann
        type: wikiann
        args: en
      metrics:
      - name: Precision
        type: precision
        value: 0.8138623392697518
      - name: Recall
        type: recall
        value: 0.8367029548989113
      - name: F1
        type: f1
        value: 0.8251246122207119
      - name: Accuracy
        type: accuracy
        value: 0.9300437071620145
  ---
  
  <!-- 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. -->
  
  # distilbert-base-uncased_ner_wikiann
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the wikiann dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2834
  - Precision: 0.8139
  - Recall: 0.8367
  - F1: 0.8251
  - Accuracy: 0.9300
  
  ## 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: 16
  - eval_batch_size: 16
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: cosine
  - num_epochs: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.3325        | 1.0   | 1250 | 0.2657          | 0.7732    | 0.8175 | 0.7947 | 0.9214   |
  | 0.2242        | 2.0   | 2500 | 0.2505          | 0.7942    | 0.8289 | 0.8111 | 0.9262   |
  | 0.158         | 3.0   | 3750 | 0.2539          | 0.8099    | 0.8367 | 0.8231 | 0.9294   |
  | 0.1155        | 4.0   | 5000 | 0.2804          | 0.8172    | 0.8373 | 0.8271 | 0.9302   |
  | 0.1047        | 5.0   | 6250 | 0.2834          | 0.8139    | 0.8367 | 0.8251 | 0.9300   |
  
  
  ### Framework versions
  
  - Transformers 4.20.1
  - Pytorch 1.11.0
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  