Instructions to use Jeevesh8/roberta_base_qqp_ft_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_0")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_0") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_0") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14916297485564b0be459aaf2a4df1c6685d8929d07bbd140f55998e8ed4213b
- Size of remote file:
- 499 MB
- SHA256:
- 350f66147ec6a6f04f07d61c0d078681d20d28c884c5e50897ce20948211958c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.