readme: ---
  language: en
  license: apache-2.0
  datasets:
  - conll2003
  model-index:
  - name: elastic/distilbert-base-uncased-finetuned-conll03-english
    results:
    - task:
        type: token-classification
        name: Token Classification
      dataset:
        name: conll2003
        type: conll2003
        config: conll2003
        split: validation
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9854480753649896
        verified: true
      - name: Precision
        type: precision
        value: 0.9880928983228512
        verified: true
      - name: Recall
        type: recall
        value: 0.9895677847945542
        verified: true
      - name: F1
        type: f1
        value: 0.9888297915932504
        verified: true
      - name: loss
        type: loss
        value: 0.06707527488470078
        verified: true
  ---
  
  [DistilBERT base uncased](https://huggingface.co/distilbert-base-uncased), fine-tuned for NER using the [conll03 english dataset](https://huggingface.co/datasets/conll2003). Note that this model is **not** sensitive to capital letters — "english" is the same as "English". For the case sensitive version, please use [elastic/distilbert-base-cased-finetuned-conll03-english](https://huggingface.co/elastic/distilbert-base-cased-finetuned-conll03-english).
  
  ## Versions
  
  - Transformers version: 4.3.1
  - Datasets version: 1.3.0
  
  ## Training
  
  ```
  $ run_ner.py \
    --model_name_or_path distilbert-base-uncased \
    --label_all_tokens True \
    --return_entity_level_metrics True \
    --dataset_name conll2003 \
    --output_dir /tmp/distilbert-base-uncased-finetuned-conll03-english \
    --do_train \
    --do_eval
  ```
  
  After training, we update the labels to match the NER specific labels from the
  dataset [conll2003](https://raw.githubusercontent.com/huggingface/datasets/1.3.0/datasets/conll2003/dataset_infos.json)
  