Instructions to use Mahmoud8/deberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud8/deberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mahmoud8/deberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Mahmoud8/deberta-base") model = AutoModelForSequenceClassification.from_pretrained("Mahmoud8/deberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mahmoud8/deberta-base: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/Mahmoud8/deberta-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mahmoud8/deberta-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mahmoud8/deberta-base/resolve/main/pytorch_model.bin
557 MB
- Xet hash:
- 74b32f8bcdc6f4dd658f4d526017c5a4b436a61601b5b510e4e934aea07f8c05
- Size of remote file:
- 557 MB
- SHA256:
- 514dbcc5a0b608cd26ce2f4ef0863b3e0d5e05239e344111870e5dfcc561ce25
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