Instructions to use Pclanglais/French-TV-transcript-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pclanglais/French-TV-transcript-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Pclanglais/French-TV-transcript-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Pclanglais/French-TV-transcript-NER") model = AutoModelForTokenClassification.from_pretrained("Pclanglais/French-TV-transcript-NER") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
French-TV-transcript-NER is a named-entity recognition model trained specifically on French TV headlines and transcript.
Given the format specificities, generalist multilingual or French model were unperforming. Additionally, the new model also provide additional set of entities useful in production (such as distinction between first name and last name).
Entities
The model covers twelve entities:
- First name (prenom)
- Last name (nom)
- Location (lieu)
- Country (pays)
- Organization (organisation)
- Event (evenement)
- Nationality (nationalite)
- Broadcast name (emission)
- Product (produit), such as technological production, medicine, etc.
- Law (loi)
- Cultural creation (creation), such as movie titles, novels, etc.
- Disease (maladie)
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