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README.md
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It achieves the following results on the evaluation set:
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- Loss: 0.0814
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## Model description
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## Training procedure
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### Training hyperparameters
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It achieves the following results on the evaluation set:
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- Loss: 0.0814
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## Model description
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bert-base-NER is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC).
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Specifically, this model is a bert-base-cased model that was fine-tuned on the English version of the standard CoNLL-2003 Named Entity Recognition dataset.
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If you'd like to use a larger BERT-large model fine-tuned on the same dataset, a bert-large-NER version is also available.
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# How to Use
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You can use this model with Transformers pipeline for NER.
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from transformers import AutoTokenizer, AutoModelForTokenClassification
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from transformers import pipeline
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tokenizer = AutoTokenizer.from_pretrained("Hatman/bert-finetuned-ner")
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model = AutoModelForTokenClassification.from_pretrained("Hatman/bert-finetuned-ner")
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nlp = pipeline("ner", model=model, tokenizer=tokenizer)
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example = "My name is Wolfgang and I live in Berlin"
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ner_results = nlp(example)
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print(ner_results)
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### Training hyperparameters
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