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--- |
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language: fr |
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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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- text: "george washington est allé à washington" |
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license: mit |
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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 overperformed 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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## Training data |
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Training data was classified as follow: |
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Abbreviation|Description |
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-|- |
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O |Outside of a named entity |
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MISC |Miscellaneous entity |
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PER |Person’s name |
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ORG |Organization |
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LOC |Location |
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## How to use camembert-ner with HuggingFace |
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##### Load camembert-ner and its sub-word tokenizer : |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/camembert-ner") |
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model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/camembert-ner") |
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##### Process text sample (from wikipedia) |
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from transformers import pipeline |
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nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple") |
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nlp("Apple est créée le 1er avril 1976 dans le garage de la maison d'enfance de Steve Jobs à Los Altos en Californie par Steve Jobs, Steve Wozniak et Ronald Wayne14, puis constituée sous forme de société le 3 janvier 1977 à l'origine sous le nom d'Apple Computer, mais pour ses 30 ans et pour refléter la diversification de ses produits, le mot « computer » est retiré le 9 janvier 2015.") |
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[{'entity_group': 'ORG', |
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'score': 0.9472818374633789, |
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'word': 'Apple', |
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'start': 0, |
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'end': 5}, |
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{'entity_group': 'PER', |
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'score': 0.9838564991950989, |
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'word': 'Steve Jobs', |
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'start': 74, |
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'end': 85}, |
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{'entity_group': 'LOC', |
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'score': 0.9831605950991312, |
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'word': 'Los Altos', |
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'start': 87, |
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'end': 97}, |
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{'entity_group': 'LOC', |
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'score': 0.9834540486335754, |
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'word': 'Californie', |
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'start': 100, |
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'end': 111}, |
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{'entity_group': 'PER', |
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'score': 0.9841555754343668, |
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'word': 'Steve Jobs', |
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'start': 115, |
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'end': 126}, |
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{'entity_group': 'PER', |
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'score': 0.9843501806259155, |
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'word': 'Steve Wozniak', |
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'start': 127, |
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'end': 141}, |
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{'entity_group': 'PER', |
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'score': 0.9841533899307251, |
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'word': 'Ronald Wayne', |
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'start': 144, |
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'end': 157}, |
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{'entity_group': 'ORG', |
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'score': 0.9468960364659628, |
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'word': 'Apple Computer', |
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'start': 243, |
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'end': 257}] |
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``` |
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## Model performances (metric: seqeval) |
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Overall |
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precision|recall|f1 |
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-|-|- |
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0.8859|0.8971|0.8914 |
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By entity |
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entity|precision|recall|f1 |
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-|-|-|- |
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PER|0.9372|0.9598|0.9483 |
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ORG|0.8099|0.8265|0.8181 |
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LOC|0.8905|0.9005|0.8955 |
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MISC|0.8175|0.8117|0.8146 |
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For those who could be interested, here is a short article on how I used the results of this model to train a LSTM model for signature detection in emails: |
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https://medium.com/@jean-baptiste.polle/lstm-model-for-email-signature-detection-8e990384fefa |
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