Instructions to use Jeevesh8/bert_ft_qqp-72 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_qqp-72 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_qqp-72")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_qqp-72") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_qqp-72") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2921953ba64fb43ecd610d7acd8992ffa5caf571f83b196fced11c287b3ef34f
- Size of remote file:
- 438 MB
- SHA256:
- afd28944ae43465480c858d28949182ddf1bf3b675a3c562229cbeecaa03d492
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.