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:
- e0a4eb40ca4f27c3e561ce8b15fafc3f4f9d237cb90c2e60df9bff4f0f421596
- Size of remote file:
- 339 MB
- SHA256:
- 27c39e884c14b03cf46cfc5485971b6db70ff330220d93dfe729c63fde43af0e
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