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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


from transformers import AutoTokenizer, AutoModelForTokenClassification

  tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_ner_v7_distil")

  model = AutoModelForTokenClassification.from_pretrained("CouchCat/ma_ner_v7_distil")
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