Instructions to use Jeevesh8/roberta_base_qqp_ft_30 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_30 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_30")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_30") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_30") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0a86d11afb38697b3ccc3342bc51137e9f1a203873937a2214a303f501012d2a
- Size of remote file:
- 499 MB
- SHA256:
- cd1614268e64084130a35d049ac0eda27572b5ae48e8a91d5635ffadca256bf0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.