Instructions to use Jeevesh8/init_bert_ft_qqp-63 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/init_bert_ft_qqp-63 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/init_bert_ft_qqp-63")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/init_bert_ft_qqp-63") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/init_bert_ft_qqp-63") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 703d59c677011c50487820de2a6a028e48b366d138acde0a74bffff1ac8d9057
- Size of remote file:
- 438 MB
- SHA256:
- 20ecd17960fc1f082ad0f6469968c322f9a01c916d82f82fb79952b0d92cdf5d
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