Instructions to use CouchCat/ma_ner_v7_distil with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CouchCat/ma_ner_v7_distil with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="CouchCat/ma_ner_v7_distil")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("CouchCat/ma_ner_v7_distil") model = AutoModelForTokenClassification.from_pretrained("CouchCat/ma_ner_v7_distil") - Notebooks
- Google Colab
- Kaggle
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")
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