Instructions to use Jeevesh8/bert-base-uncased_mnli_ft_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert-base-uncased_mnli_ft_4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert-base-uncased_mnli_ft_4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert-base-uncased_mnli_ft_4") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert-base-uncased_mnli_ft_4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3d7f2c581c3f988f49e7268ebec1ba160a16e68d6f3df3b4eed5c6bfb9a7e0d
- Size of remote file:
- 438 MB
- SHA256:
- 13d95a5e57b2a2dc8a674448d097ddcba6b11b2665cc30a8f0b0d792f9681726
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