readme: ---
  pipeline_tag: token-classification
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - few_nerd
  metrics:
  - precision
  - recall
  - f1
  - accuracy
  model-index:
  - name: distilbert-base-uncased-finetuned-ner
    results:
    - task:
        name: Token Classification
        type: token-classification
      dataset:
        name: few_nerd
        type: few_nerd
        args: supervised
      metrics:
      - name: Precision
        type: precision
        value: 0.6424480067658478
      - name: Recall
        type: recall
        value: 0.6854236732015421
      - name: F1
        type: f1
        value: 0.6632404008334158
      - name: Accuracy
        type: accuracy
        value: 0.9075199647113962
  ---
  
  <!-- 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-finetuned-ner
  
  This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the few_nerd dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.3136
  - Precision: 0.6424
  - Recall: 0.6854
  - F1: 0.6632
  - Accuracy: 0.9075
  
  ## 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: linear
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
  | 0.328         | 1.0   | 8236  | 0.3197          | 0.6274    | 0.6720 | 0.6489 | 0.9041   |
  | 0.2776        | 2.0   | 16472 | 0.3111          | 0.6433    | 0.6759 | 0.6592 | 0.9069   |
  | 0.241         | 3.0   | 24708 | 0.3136          | 0.6424    | 0.6854 | 0.6632 | 0.9075   |
  
  
  ### Framework versions
  
  - Transformers 4.18.0
  - Pytorch 1.10.0+cu111
  - Datasets 2.1.0
  - Tokenizers 0.12.1
  