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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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+
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+ # camembert-ner: model fine-tuned from camemBERT for NER task.
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+
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+ ## Introduction
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+
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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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+
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+ ## Training data
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+ Training data was classified as follow:
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+
23
+ 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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+
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+
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+ ## How to use camembert-ner with HuggingFace
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+
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+ ##### Load camembert-ner and its sub-word tokenizer :
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+
36
+ ```python
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+ from transformers import AutoTokenizer, AutoModelForTokenClassification
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+
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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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+
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+
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+ ##### Process text sample (from wikipedia)
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+
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+ from transformers import pipeline
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+
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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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+
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+
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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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+ ```
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+
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+
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+ ## Model performances (metric: seqeval)
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+
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+ Overall
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+
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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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+
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+ By entity
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+
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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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+
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+
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+
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+
115
+ 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:
116
+ https://medium.com/@jean-baptiste.polle/lstm-model-for-email-signature-detection-8e990384fefa
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+ "0": "O",
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+ "2": "I-PER",
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+ "3": "I-MISC",
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+ "I-LOC": 1,
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+ "O": 0,
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+ "I-ORG": 4,
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+ "I-PER": 2
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+ },
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+ "layer_norm_eps": 1e-05,
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+ "max_position_embeddings": 514,
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+ "model_type": "camembert",
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+ "num_attention_heads": 12,
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+ "num_hidden_layers": 12,
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+ "output_past": true,
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+ "pad_token_id": 1,
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+ "position_embedding_type": "absolute",
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+ "transformers_version": "4.3.2",
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+ "type_vocab_size": 1,
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