Instructions to use Pclanglais/transcript-text-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Pclanglais/transcript-text-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Pclanglais/transcript-text-analysis")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Pclanglais/transcript-text-analysis") model = AutoModelForSequenceClassification.from_pretrained("Pclanglais/transcript-text-analysis") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
transcript-text-analysis is an encoder model specialized for the classification of French news transcripts. The model is based on debertav3 and has been trained on 1,018 examples of annotated transcripts.
Given a text, transcript-text-analysis will generate the following classifications in French:
- Emotion (Neutre, Persuasif, Optimiste, Solennel, Alarmant, Indigné)
- Expression (Interview/Discussion, Publicite, Informations, Meteo, Reportage/Enquete)
- Intention (Informer, Sensibiliser, Promouvoir, Mobiliser, Divertir, Eduquer)
- Theme (Santé, Société, Économie, Politique, Sports)
- Tonalite (Informative, Emotionnelle, Publicitaire)
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