readme: 
  ---
  language: 
    - en
  license: "apache-2.0"
  datasets:
  - CoNLL-2003
  metrics:
  - F1
  
  ---
  
  This is a T5 small model finetuned on CoNLL-2003 dataset for named entity recognition (NER).
  
  Example Input and Output:
  “Recognize all the named entities in this sequence (replace named entities with one of [PER], [ORG], [LOC], [MISC]): When Alice visited New York” → “When PER visited LOC LOC"
  
  Evaluation Result:
  
  % of match (for comparison with ExT5: https://arxiv.org/pdf/2111.10952.pdf): 
  
  | Model| ExT5_{Base} | This Model | T5_NER_CONLL_OUTPUTLIST 
  | :---: | :---: | :---: | :---: |
  | % of Complete Match| 86.53 | 79.03 | TBA| 
  
  
  
  There are some outputs (212/3453 or 6.14% that does not have the same length as the input)
  
  F1 score on testing set of those with matching length :
  
  | Model | This Model | T5_NER_CONLL_OUTPUTLIST | BERTbase 
  | :---: | :---: | :---: | :---: |
  | F1| 0.8901 | 0.8691| 0.9240
  
  **Caveat: The testing set of these aren't the same, due to matching length issue... 
  T5_NER_CONLL_OUTPUTLIST only has 27/3453 missing length (only 0.78%); The BERT number is directly from their paper (https://arxiv.org/pdf/1810.04805.pdf)
  
  
  
  
  