Instructions to use panigrah/wineberto-ner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use panigrah/wineberto-ner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="panigrah/wineberto-ner")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("panigrah/wineberto-ner") model = AutoModelForTokenClassification.from_pretrained("panigrah/wineberto-ner") - Notebooks
- Google Colab
- Kaggle
Added more entities to training set and also randomized wine label. Earlier training set was flawed because wine labels were always located at the beginning of the description of the wine and the model trained itself to tag everything in the begining of the description as the wine label.
ddb7191 - Xet hash:
- 6da9cc9ba92eaf9bb99b33b4e39d2538847f48634fb11b52c7a0be61903bd3de
- Size of remote file:
- 436 MB
- SHA256:
- 6d56cb4b0c77244c1b92a832a98cb7cf5217d54039804ca63d8bdaa5ea7aec69
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