Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use mohamed-tamer-nassr/text_class_food-distilbert-base-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohamed-tamer-nassr/text_class_food-distilbert-base-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mohamed-tamer-nassr/text_class_food-distilbert-base-uncased")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mohamed-tamer-nassr/text_class_food-distilbert-base-uncased") model = AutoModelForSequenceClassification.from_pretrained("mohamed-tamer-nassr/text_class_food-distilbert-base-uncased") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 19c0e552542c5471fd22a221c00296ce3cd044a43a1e7e86feccdb60449052fe
- Size of remote file:
- 5.2 kB
- SHA256:
- 1d55b7217c578810c28b46d110edb32204f74dbeee7fa103ed63e5802430f41f
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