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
Commit History
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- Add TF weights (47de41bd0d1919fc7aeccf414e6369f296d3b4be) Co-authored-by: Joao Gante <joaogante@users.noreply.huggingface.co>5b07a90
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xlarge mnli model
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DeBERTa XLarge MNLI model
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DeBERTa XLarge MNLI model
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DeBERTa XLarge MNLI model
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Create deberta xlarge 750M model fine-tuned with mnli
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