Text Classification
Transformers
PyTorch
Safetensors
English
deberta-v2
Trained with AutoTrain
text-embeddings-inference
Instructions to use Showroom/accessories_subcategory_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Showroom/accessories_subcategory_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Showroom/accessories_subcategory_classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Showroom/accessories_subcategory_classifier") model = AutoModelForSequenceClassification.from_pretrained("Showroom/accessories_subcategory_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Showroom/accessories_subcategory_classifier: direct link, hf CLI and curl.
- Browser
- Download file 8.66 MB
-
https://huggingface.co/Showroom/accessories_subcategory_classifier/resolve/main/tokenizer.json
- Command line
-
hf download hf://Showroom/accessories_subcategory_classifier/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Showroom/accessories_subcategory_classifier/resolve/main/tokenizer.json
8.66 MB
- Xet hash:
- c90ede2c3d68c1a441242363e166599fb48658f21af5fb95b8abcb68985505ab
- Size of remote file:
- 8.66 MB
- SHA256:
- af0a753804a7d156814eae145400661ab0ecdc0fb8f4673812bb351d0bb279c7
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