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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("panigrah/wineberto-ner") model = AutoModelForTokenClassification.from_pretrained("panigrah/wineberto-ner", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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# Wineberto ner model
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Pretrained model on on wine labels and descriptions for named entity recognition that uses bert-base-uncased as the base model.
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* Updated to remove bias on position of wine label in the training inputs.
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* also updated to remove trying to get the wine classification. e.g. Grand Cru etc because training data is not reliable.
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## Model description
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# Wineberto ner model
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Pretrained model on on wine labels and descriptions for named entity recognition that uses bert-base-uncased as the base model. This tries to recognize both the wine label and also description about the wine.
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<b>The label discovery doesnt work as well as just using the panigrah/winberto-labels model. </b>
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* Updated to remove bias on position of wine label in the training inputs.
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* also updated to remove trying to get the wine classification. e.g. Grand Cru etc because training data is not reliable.
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## Model description
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