readme: ---
  language: en
  license: mit
  tags:
    - ner
  widget:
    - text: "These shoes I recently bought from Tommy Hilfiger fit quite well. The shirt, however, has got a hole"
  ---
  
  ### Description
  
  A Named Entity Recognition model trained on a customer feedback data using DistilBert.
  Possible labels are in BIO-notation. Performance of the PERS tag could be better because of low data samples:
  
  - PROD: for certain products
  - BRND: for brands
  - PERS: people names
  
  The following tags are simply in place to help better categorize the previous tags
  
  - MATR: relating to materials, e.g. cloth, leather, seam, etc.
  - TIME: time related entities
  - MISC: any other entity that might skew the results
  
  ### Usage
  
  ```
  from transformers import AutoTokenizer, AutoModelForTokenClassification
  
    tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_ner_v7_distil")
  
    model = AutoModelForTokenClassification.from_pretrained("CouchCat/ma_ner_v7_distil")
  ```
  