Instructions to use prajjwal1/bert-tiny-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prajjwal1/bert-tiny-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prajjwal1/bert-tiny-mnli")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("prajjwal1/bert-tiny-mnli") model = AutoModelForSequenceClassification.from_pretrained("prajjwal1/bert-tiny-mnli") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e1737e5998e52faecd1ad5660071ac439e00bd6efe931065181e732811b900f
- Size of remote file:
- 1.52 kB
- SHA256:
- 66a1a02128588759ff40ce643bcfa4e53af9dd24d21c96cda7d16743bea32073
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