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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Pclanglais/transcript-text-analysis") model = AutoModelForSequenceClassification.from_pretrained("Pclanglais/transcript-text-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 651 Bytes
dd9a9ee b99b722 | 1 2 3 4 5 6 7 8 | **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) |