Text Classification
Transformers
TensorBoard
Safetensors
English
deberta-v2
cross-encoder
sequence-classification
text-embeddings-inference
Instructions to use xpmir/cross-encoder-DeBERTav3-Hinge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xpmir/cross-encoder-DeBERTav3-Hinge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="xpmir/cross-encoder-DeBERTav3-Hinge")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("xpmir/cross-encoder-DeBERTav3-Hinge") model = AutoModelForSequenceClassification.from_pretrained("xpmir/cross-encoder-DeBERTav3-Hinge", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from xpmir/cross-encoder-DeBERTav3-Hinge: direct link, hf CLI and curl.
- Browser
- Download file 8.34 MB
-
https://huggingface.co/xpmir/cross-encoder-DeBERTav3-Hinge/resolve/main/tokenizer.json
- Command line
-
hf download hf://xpmir/cross-encoder-DeBERTav3-Hinge/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/xpmir/cross-encoder-DeBERTav3-Hinge/resolve/main/tokenizer.json
8.34 MB
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