Instructions to use ken2555/sentiment-analysis-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ken2555/sentiment-analysis-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ken2555/sentiment-analysis-distilbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ken2555/sentiment-analysis-distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Sentiment Analysis Model - MLOps Lab 2
Description
Ce projet utilise le modèle DistilBERT fine-tuné sur le dataset SST-2 (Stanford Sentiment Treebank) pour l'analyse de sentiment.
Utilisation
from transformers import pipeline
classifier = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english") result = classifier("I love this product!") print(result)
Modèle de base
- Modèle: distilbert-base-uncased-finetuned-sst-2-english
- Tâche: Classification de texte (Sentiment Analysis)
- Labels: POSITIVE, NEGATIVE
Auteur
- Nom: Karim Haddadi
- Cours: MLOps - M2