Text Classification
Transformers
PyTorch
deberta-v2
Generated from Trainer
text-embeddings-inference
Instructions to use keremp/deberta-em with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use keremp/deberta-em with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="keremp/deberta-em")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("keremp/deberta-em") model = AutoModelForSequenceClassification.from_pretrained("keremp/deberta-em", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from keremp/deberta-em: direct link, hf CLI and curl.
- Browser
- Download file 738 MB
-
https://huggingface.co/keremp/deberta-em/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://keremp/deberta-em/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/keremp/deberta-em/resolve/main/pytorch_model.bin
738 MB
- Xet hash:
- 02098cfba12a009b02a97b1fc068e5ad8a0da932f818845d513889099f13df11
- Size of remote file:
- 738 MB
- SHA256:
- 232ee602c74515c4659fde43a826d83366391e0fdb1a1ced74ef21e5439c55b8
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