Instructions to use microsoft/deberta-base-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/deberta-base-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="microsoft/deberta-base-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-base-mnli") model = AutoModelForSequenceClassification.from_pretrained("microsoft/deberta-base-mnli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/deberta-base-mnli: direct link, hf CLI and curl.
- Browser
- Download file 557 MB
-
https://huggingface.co/microsoft/deberta-base-mnli/resolve/69891cc5e0bcde6ce0fa9e6889df3321443095a0/pytorch_model.bin
- Command line
-
hf download hf://microsoft/deberta-base-mnli@69891cc5e0bcde6ce0fa9e6889df3321443095a0/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/deberta-base-mnli/resolve/69891cc5e0bcde6ce0fa9e6889df3321443095a0/pytorch_model.bin
557 MB
- Xet hash:
- 0f104a459c4172dddef3619acceac30d3cad042cbf28319b2586a215f106fb7b
- Size of remote file:
- 557 MB
- SHA256:
- 9f82b3a368bf628768567c1ce03b354eea1299b87c42ded71a56e4cdd3ea0039
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