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:
- da4deb61838777f07600465a75b2607ae6a72df3685f4fd74d32660e00ef6065
- Size of remote file:
- 3.57 kB
- SHA256:
- c66c583cd3e8a36d61760e0375184058c04020d23826d5c2ee6e0852362a4223
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.