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")# 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 spm.model from microsoft/deberta-v2-xlarge-mnli: direct link, hf CLI and curl.
- Browser
- Download file 2.45 MB
-
https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model
- Command line
-
hf download hf://microsoft/deberta-v2-xlarge-mnli/spm.model
-
curl -L -o spm.model https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model
2.45 MB
- Xet hash:
- f73f9950297136a9a075101e2c3d66fd55e91840d4ba259c9b547a7d14ae8e2a
- Size of remote file:
- 2.45 MB
- SHA256:
- 5598d5e96f339a8d980c15f9afd405a2e5e1be7db41de3ed13b0f03fac1e8c17
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