Instructions to use Jeevesh8/bert-base-uncased_mnli_ft_24 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_24 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_24")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert-base-uncased_mnli_ft_24") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert-base-uncased_mnli_ft_24", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 144960071a83c4c893375329bec4538fd4934e9871cb9b94305ec9e45f8fcb83
- Size of remote file:
- 5.37 MB
- SHA256:
- 9b548fd44f4cab34fa637d3bd433d5e74e980cadd6f816fc2fdd58f94c1adab3
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