Instructions to use mjwong/mcontriever-xnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mjwong/mcontriever-xnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="mjwong/mcontriever-xnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("mjwong/mcontriever-xnli") model = AutoModelForSequenceClassification.from_pretrained("mjwong/mcontriever-xnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a9ef674d5a99623e53e78823be86c2c5bbcc29df992f3802ac457bce2b43e1fe
- Size of remote file:
- 711 MB
- SHA256:
- 9f6e9f6e9b14a6532a727e8edaf74d8b1d7ca67d6e67dd1ccfdf62b2a7e03dc1
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