Instructions to use textattack/bert-base-uncased-MNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-MNLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-MNLI")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-MNLI") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-MNLI", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a7b1fed72a699efec62e85eeac34f200dc1cd0147c9d0ed946dfe0829a74edf8
- Size of remote file:
- 438 MB
- SHA256:
- 02fa27735990e212439ec04be82931059be4af278b33427bbb42a316919543b7
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