Instructions to use MoritzLaurer/mDeBERTa-v3-base-mnli-xnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MoritzLaurer/mDeBERTa-v3-base-mnli-xnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-mnli-xnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("MoritzLaurer/mDeBERTa-v3-base-mnli-xnli") model = AutoModelForSequenceClassification.from_pretrained("MoritzLaurer/mDeBERTa-v3-base-mnli-xnli", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8bded11c3b90feb4aa05526dcb7665950899379207b117a01166aeaa4abf5cfa
- Size of remote file:
- 558 MB
- SHA256:
- 65af59b1ff4450b09ecbf13ca35c840dbf038b26ff8e10e5ea89ca724828ed1e
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