Instructions to use microsoft/deberta-v2-xlarge-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/deberta-v2-xlarge-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="microsoft/deberta-v2-xlarge-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-v2-xlarge-mnli") model = AutoModelForSequenceClassification.from_pretrained("microsoft/deberta-v2-xlarge-mnli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/deberta-v2-xlarge-mnli: direct link, hf CLI and curl.
- Browser
- Download file 1.77 GB
-
https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/0f6a42e33518de8fe040aa8dff9da023d98ca808/pytorch_model.bin
- Command line
-
hf download hf://microsoft/deberta-v2-xlarge-mnli@0f6a42e33518de8fe040aa8dff9da023d98ca808/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/0f6a42e33518de8fe040aa8dff9da023d98ca808/pytorch_model.bin
1.77 GB
- Xet hash:
- 7ababb7987025f17ebb912bd112ff4b1762caac212cc45e3496695a749414914
- Size of remote file:
- 1.77 GB
- SHA256:
- cc41eeb065c6ab3f7e88f8dab756334abb1f77a1136fec8cf02442143934c253
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