jeanpoll
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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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widget:
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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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pytorch_model.bin
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