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--- |
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license: unknown |
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language: |
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- en |
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tags: |
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- wine |
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- ner |
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widget: |
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- text: 'Heitz Cabernet Sauvignon California Napa Valley Napa US' |
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example_title: 'California Cab' |
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--- |
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# Wineberto labels |
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Pretrained model on on wine labels only for named entity recognition that uses bert-base-uncased as the base model. |
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## Model description |
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## How to use |
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You can use this model directly for named entity recognition like so |
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```python |
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>>> from transformers import pipeline |
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>>> ner = pipeline('ner', model='winberto-labels') |
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>>> tokens = ner("Heitz Cabernet Sauvignon California Napa Valley Napa US") |
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>>> for t in toks: |
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>>> print(f"{t['word']}: {t['entity_group']}: {t['score']:.5}") |
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heitz: producer: 0.99758 |
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cabernet: wine: 0.92263 |
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sauvignon: wine: 0.92472 |
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california: region: 0.53502 |
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napa valley: subregion: 0.79638 |
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us: country: 0.93675 |
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``` |
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## Training data |
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The BERT model was trained on 50K wine labels derived from https://www.liv-ex.com/wwd/lwin/ and manually annotated to capture the following tokens |
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``` |
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"1": "B-classification", |
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"2": "B-country", |
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"3": "B-producer", |
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"4": "B-region", |
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"5": "B-subregion", |
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"6": "B-vintage", |
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"7": "B-wine" |
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``` |
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## Training procedure |
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``` |
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model_id = 'bert-base-uncased' |
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arguments = TrainingArguments( |
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evaluation_strategy="epoch", |
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learning_rate=2e-5, |
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per_device_train_batch_size=8, |
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per_device_eval_batch_size=8, |
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num_train_epochs=5, |
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weight_decay=0.01, |
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) |
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... |
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trainer.train() |
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``` |
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