Text Classification
Transformers
Safetensors
English
modernbert
food
drink
food-classification
caption-classification
knowledge-distillation
ettin
Eval Results (legacy)
text-embeddings-inference
Instructions to use mrdbourke/ettin-150m-food-or-drink-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mrdbourke/ettin-150m-food-or-drink-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mrdbourke/ettin-150m-food-or-drink-classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mrdbourke/ettin-150m-food-or-drink-classifier") model = AutoModelForSequenceClassification.from_pretrained("mrdbourke/ettin-150m-food-or-drink-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- abd3e6928286a4d9bce1ff8ac575d8e9b96238bc6bba3767ebac68cea88905a7
- Size of remote file:
- 598 MB
- SHA256:
- c390da60bdf323a5d49acbc712861e390c6982918b4bc3c06aef89ec778fa276
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