Instructions to use Jeevesh8/bert_ft_qqp-69 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/bert_ft_qqp-69 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/bert_ft_qqp-69")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/bert_ft_qqp-69") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/bert_ft_qqp-69") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 79eb1c7c210a44ac53370a5b6c0963f2e38b73241f9d1c627127673123dd24e2
- Size of remote file:
- 438 MB
- SHA256:
- 773e3c6cd68b089a458ee4f3e1267a665d4d4006deecad285c795298128c9185
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.