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