Instructions to use microsoft/deberta-xlarge-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/deberta-xlarge-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="microsoft/deberta-xlarge-mnli")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("microsoft/deberta-xlarge-mnli") model = AutoModelForSequenceClassification.from_pretrained("microsoft/deberta-xlarge-mnli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from microsoft/deberta-xlarge-mnli: direct link, hf CLI and curl.
- Browser
- Download file 3.04 GB
-
https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/tf_model.h5
- Command line
-
hf download hf://microsoft/deberta-xlarge-mnli/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/tf_model.h5
3.04 GB
- Xet hash:
- 0d2d6602ceaa65136ba3024a091ac87bd823cb04f73212e1fe65d7b0f2529007
- Size of remote file:
- 3.04 GB
- SHA256:
- 96dd3b7d8cfbc856e96d7b8465638792391fa2c7f5654bfc036c44cdc85eabd3
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