Text Classification
Transformers
Safetensors
modernbert
sentiment-analysis
multilingual
restaurants
5-star
text-embeddings-inference
Instructions to use Festooned/Multilingual-Restaurant-Reviews-Sentiment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Festooned/Multilingual-Restaurant-Reviews-Sentiment with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Festooned/Multilingual-Restaurant-Reviews-Sentiment")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Festooned/Multilingual-Restaurant-Reviews-Sentiment") model = AutoModelForSequenceClassification.from_pretrained("Festooned/Multilingual-Restaurant-Reviews-Sentiment", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d190257ad9e708b6380d3c156211aadc3067a642e133cddeb10406f735092b83
- Size of remote file:
- 34.4 MB
- SHA256:
- 3cdd116e350e9551f912dd7670b55fe726dcd5d6377c7477ef4792885deb9c1a
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