Instructions to use Jeevesh8/init_bert_ft_qqp-30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/init_bert_ft_qqp-30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/init_bert_ft_qqp-30")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/init_bert_ft_qqp-30") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/init_bert_ft_qqp-30") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c36e455a69e37f89e054aeafbf574d4048578267959d7bba07239c7c650e9a14
- Size of remote file:
- 438 MB
- SHA256:
- df2941d8798842b0c6b7b218f7cc1cac41b5a54b06643370d1da3e94c89ff318
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