Instructions to use Jeevesh8/bert-base-uncased_mnli_ft_12 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_12 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_12")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert-base-uncased_mnli_ft_12") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert-base-uncased_mnli_ft_12", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3589f7e000a6f9cc2b821bf890e59616c4692d5eead62fdda571b7136237186d
- Size of remote file:
- 5.37 MB
- SHA256:
- 5a75b5417186f233c867ee622d5432d19d0d014305839995f11b5b774d2332a0
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