Instructions to use vuiseng9/bert-base-uncased-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vuiseng9/bert-base-uncased-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="vuiseng9/bert-base-uncased-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("vuiseng9/bert-base-uncased-mnli") model = AutoModelForSequenceClassification.from_pretrained("vuiseng9/bert-base-uncased-mnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3b61da71753f2bc0a7a55c928802f3a8c82bce2aafdd63874d30661b6800d589
- Size of remote file:
- 2.67 kB
- SHA256:
- a8061bc1c8862f1af4386e72fe7478d40f46fcfc536525e6b6fb09ddfa71e9fc
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