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:
- 696641edce476603391faafb9c7035f9504d74feb10e6bcd44801987b5db55f2
- Size of remote file:
- 268 MB
- SHA256:
- 7178363f56b6a71ac8efbab2481bd91b64ce80f3c4f593c998047df3e8dc5fa4
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