Instructions to use prajjwal1/bert-mini-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/bert-mini-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prajjwal1/bert-mini-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prajjwal1/bert-mini-mnli") model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/bert-mini-mnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4dd5f51cca9f226072b474b4c47e1d92c915b12309f2d915dcfcf7fe4a539470
- Size of remote file:
- 44.7 MB
- SHA256:
- bacbf5460059a97b849fecae8b8de34822837c6361189c8b26d95bfcf905194c
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