Instructions to use prajjwal1/bert-medium-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/bert-medium-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prajjwal1/bert-medium-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prajjwal1/bert-medium-mnli") model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/bert-medium-mnli", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b9e971c957d2f33050de1d3dd3ee150c81ce41aacc81cb5345644e9a478e940a
- Size of remote file:
- 166 MB
- SHA256:
- 3b554f49cafab5047a74ca97a2528f237939cfd756ac80aff595731aa4eacc9c
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