readme: ---
  
  language: en
  tags:
  - token classification
  license: cc
  datasets: conll2003
  model-index:
  - name: sarahmiller137/distilbert-base-uncased-ft-conll2003
    results:
    - task:
        type: token-classification
        name: Token Classification
      dataset:
        name: conll2003
        type: conll2003
        config: conll2003
        split: test
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9750189904012154
        verified: true
      - name: Precision
        type: precision
        value: 0.9802152215150602
        verified: true
      - name: Recall
        type: recall
        value: 0.9803021169462076
        verified: true
      - name: F1
        type: f1
        value: 0.9802586673049137
        verified: true
      - name: loss
        type: loss
        value: 0.10723897069692612
        verified: true
  ---
  
  ## Model information:
  distilbert-base-uncased model finetuned using the conll2003 dataset from the datasets library. 
  
  ## Intended uses & limitations
  This model is intended to be used for named entity recoginition tasks. The model will identify entities of persons, locations, organisations, and miscellaneous.  The model will predict lables based upon the CoNLL-2003 dataset.
  
  Note that the dataset and model may not be fully represetative or suitable for all needs it is recommended that the paper for the dataset and base model card should be reviewed before using the model - 
  - [CoNLL-2003](https://aclanthology.org/W03-0419)
  - [distilbert](https://huggingface.co/distilbert-base-uncased)
  
  
  ## How to use 
  Load the model from the library using the following checkpoints:
  ```python
  from transformers import AutoTokenizer, AutoModel
  tokenizer = AutoTokenizer.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-conll2003")
  model = AutoModel.from_pretrained("sarahmiller137/distilbert-base-uncased-ft-conll2003")
  ```
  
  
  
  