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 pytorch_model.bin from Showroom/accessories_subcategory_classifier: direct link, hf CLI and curl.
- Browser
- Download file 738 MB
-
https://huggingface.co/Showroom/accessories_subcategory_classifier/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Showroom/accessories_subcategory_classifier/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Showroom/accessories_subcategory_classifier/resolve/main/pytorch_model.bin
738 MB
- Xet hash:
- b1565b3a52425626223dcecd42142ee9768f1b74f528dbbb247561de2d183e93
- Size of remote file:
- 738 MB
- SHA256:
- 1573cfa8f3e57cdf0e64739dda3c90380ef4f6de145adcd501f943fd594980dc
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