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New improved model trained on full dataset
Browse files- README.md +20 -17
- pytorch_model.bin +2 -2
    	
        README.md
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            datasets:
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            - Jean-Baptiste/wikiner_fr
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            - text: "Je m'appelle  | 
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            ---
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            # camembert-ner: model fine-tuned from camemBERT for NER task.
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            ## Introduction
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            [camembert-ner] is a NER model that was fine-tuned from camemBERT on wikiner-fr dataset.
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            Model was trained on  | 
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            ## How to use camembert-ner with HuggingFace
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            Global
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            ```
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            'precision': 0. | 
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            'recall': 0. | 
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            'f1': 0. | 
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            ```
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            By entity
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            ```
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            'LOC': {'precision': 0. | 
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            'MISC': {'precision': 0. | 
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            'ORG': {'precision': 0. | 
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             ```
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            datasets:
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            - Jean-Baptiste/wikiner_fr
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            widget:
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            - text: "Je m'appelle jean-baptiste et je vis à montréal"
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            ---
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            # camembert-ner: model fine-tuned from camemBERT for NER task.
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            ## Introduction
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            [camembert-ner] is a NER model that was fine-tuned from camemBERT on wikiner-fr dataset.
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            Model was trained on wikiner-fr dataset (~170 634  sentences).
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            Model was validated on emails/chat data and surperformed other models on this type of data specifically. 
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            In particular the model seems to work better on entity that don't start with an upper case.
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            ## How to use camembert-ner with HuggingFace
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            Global
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            ```
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            'precision': 0.8859
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            'recall': 0.8971
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            'f1': 0.8914
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            ```
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            By entity
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            ```
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            'LOC': {'precision': 0.8905576596578294,
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            		'recall': 0.900554675118859,
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            		'f1': 0.8955282684352223},
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            'MISC': {'precision': 0.8175627240143369,
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            		 'recall': 0.8117437722419929,
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            		 'f1': 0.8146428571428571},
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            'ORG': {'precision': 0.8099480326651819,
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            		'recall': 0.8265151515151515,
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            		'f1': 0.8181477315335584},
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            'PER': {'precision': 0.9372509960159362,
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            		'recall': 0.959812321501428,
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            		'f1': 0.9483975005039308}
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             ```
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