Instructions to use Jeevesh8/roberta_base_qqp_ft_7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_7")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_7") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_7") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42e83ba532db10bacf87c66b7ddca042c8d169f478b3d5bc8e2ec3739520d737
- Size of remote file:
- 499 MB
- SHA256:
- 19723f7e3815a8a93e61856ca617565de64455a28bbb34528e204d30475df700
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.