Instructions to use kbaek/mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kbaek/mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kbaek/mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kbaek/mnli") model = AutoModelForSequenceClassification.from_pretrained("kbaek/mnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7de3988615b1797abee08bf1b49faa923691569b4d9d14cd1839fea723c3dd6
- Size of remote file:
- 438 MB
- SHA256:
- f9cf9c984bb068b9adf5bfaddc53cab2995f13a5473a54becadbcd3b33eb25f5
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