Camembert Ner

🚀 camembert-ner: A Fine-tuned Model from camemBERT for NER Task

camembert-ner is a Named Entity Recognition (NER) model fine-tuned from camemBERT on the wikiner-fr dataset, offering high performance in NER tasks, especially on emails/chat data.

🚀 Quick Start

camembert-ner is a NER model fine-tuned from camemBERT on the wikiner-fr dataset. The model was trained on approximately 170,634 sentences from the wikiner-fr dataset. It was validated on emails/chat data and outperformed other models on this specific type of data. In particular, it seems to work better on entities that don't start with an uppercase letter.

✨ Features

• Fine-tuned from camemBERT on the wikiner-fr dataset.
• High performance on emails/chat data.
• Works well on entities not starting with an uppercase letter.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

Load camembert-ner and its sub-word tokenizer:

from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("Jean-Baptiste/camembert-ner")
model = AutoModelForTokenClassification.from_pretrained("Jean-Baptiste/camembert-ner")

Advanced Usage

Process a text sample (from wikipedia):

from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
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.")

[{'entity_group': 'ORG',
  'score': 0.9472818374633789,
  'word': 'Apple',
  'start': 0,
  'end': 5},
 {'entity_group': 'PER',
  'score': 0.9838564991950989,
  'word': 'Steve Jobs',
  'start': 74,
  'end': 85},
 {'entity_group': 'LOC',
  'score': 0.9831605950991312,
  'word': 'Los Altos',
  'start': 87,
  'end': 97},
 {'entity_group': 'LOC',
  'score': 0.9834540486335754,
  'word': 'Californie',
  'start': 100,
  'end': 111},
 {'entity_group': 'PER',
  'score': 0.9841555754343668,
  'word': 'Steve Jobs',
  'start': 115,
  'end': 126},
 {'entity_group': 'PER',
  'score': 0.9843501806259155,
  'word': 'Steve Wozniak',
  'start': 127,
  'end': 141},
 {'entity_group': 'PER',
  'score': 0.9841533899307251,
  'word': 'Ronald Wayne',
  'start': 144,
  'end': 157},
 {'entity_group': 'ORG',
  'score': 0.9468960364659628,
  'word': 'Apple Computer',
  'start': 243,
  'end': 257}]

📚 Documentation

Training data

The training data was classified as follows:

Model performances (metric: seqeval)

Overall

By entity

Related Article

For those who might be interested, here is a short article on how the author used the results of this model to train an LSTM model for signature detection in emails: LSTM model for email signature detection

📄 License

This project is licensed under the MIT license.