Feature Extraction
Transformers
Safetensors
xlm-roberta
Generated from Trainer
text-embeddings-inference
Instructions to use Ludo33/e5_Eau_Multilabel_Topic_Sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ludo33/e5_Eau_Multilabel_Topic_Sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Ludo33/e5_Eau_Multilabel_Topic_Sentiment")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Ludo33/e5_Eau_Multilabel_Topic_Sentiment") model = AutoModel.from_pretrained("Ludo33/e5_Eau_Multilabel_Topic_Sentiment") - Notebooks
- Google Colab
- Kaggle
| {"topic_labels": ["Aucun th\u00e8me", "Eau - Gestion \u00e9conome de l'eau", "Eau - R\u00e9cup\u00e9ration de l'eau", "Eau - Sensibilisation aux usages de l'eau", "Eau - Traitement de l'eau", "Eau - \u00c9quitabilit\u00e9 des ressources"], "sentiment_labels": ["Aucun sentiment", "negative", "neutral", "positive"], "num_topic_labels": 6, "num_sentiment_labels": 4} |