Instructions to use huggingface/distilbert-base-uncased-finetuned-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huggingface/distilbert-base-uncased-finetuned-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="huggingface/distilbert-base-uncased-finetuned-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("huggingface/distilbert-base-uncased-finetuned-mnli") model = AutoModelForSequenceClassification.from_pretrained("huggingface/distilbert-base-uncased-finetuned-mnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 23ac6bbededc99ace218a4c00debeef6a740b991717751a0ead75d20e3ccd1a5
- Size of remote file:
- 268 MB
- SHA256:
- 4c0454970853b935a06ec821587e06cc903dd736fd34e6e7916255391a01ac34
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