Instructions to use Jeevesh8/roberta_base_qqp_ft_19 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_19 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_19")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_19") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_19") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e5280ee24b72957b757dd0131c72e25899a9bcd620d9d6f1c1a9a496c53da6a3
- Size of remote file:
- 499 MB
- SHA256:
- aa2485e57c6fae5bb1d0ce352bdaace09fcc6966105a062579a932da687f60f5
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.