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  # distilroberta-base-ner-wikiann-conll2003-3-class
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- This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the wikiann-conll2003 dataset.
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- It achieves the following results on the evaluation set:
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- - Loss: 0.0520
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- - Precision: 0.9625
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- - Recall: 0.9667
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- - F1: 0.9646
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- - Accuracy: 0.9914
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- ## Model description
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- More information needed
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- ## Intended uses & limitations
 
 
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- More information needed
 
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- ## Training and evaluation data
 
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- More information needed
 
 
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  ## Training procedure
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  ### Training results
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  ### Framework versions
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  # distilroberta-base-ner-wikiann-conll2003-3-class
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+ This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the wikiann and conll2003 dataset. It consists out of the classes of wikiann.
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+ O (0), B-PER (1), I-PER (2), B-ORG (3), I-ORG (4) B-LOC (5), I-LOC (6).
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+ eval F1-Score: **96,25** (merged dataset)
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+ test F1-Score: **92,41** (merged dataset)
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+ ## Model Usage
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+ ```python
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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("philschmid/distilroberta-base-ner-wikiann-conll2003-3-class")
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+ model = AutoModelForTokenClassification.from_pretrained("philschmid/distilroberta-base-ner-wikiann-conll2003-3-class")
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+ nlp = pipeline("ner", model=model, tokenizer=tokenizer, grouped_entities=True)
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+ example = "My name is Philipp and live in Germany"
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+ nlp(example)
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+
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+ ```
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  ## Training procedure
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  ### Training results
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.0520
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+ - Precision: 0.9625
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+ - Recall: 0.9667
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+ - F1: 0.9646
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+ - Accuracy: 0.9914
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+
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+ It achieves the following results on the test set:
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+ - Loss: 0.141
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+ - Precision: 0.917
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+ - Recall: 0.9313
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+ - F1: 0.9241
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+ - Accuracy: 0.9807
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  ### Framework versions