Instructions to use Jeevesh8/roberta_base_qqp_ft_15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_15 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_15")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_15") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_15") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 384077221c77d52e0342e42a0509fea33f28e7ce12424c04650ee8e2e80697ca
- Size of remote file:
- 499 MB
- SHA256:
- b698d34be45127bb8f3ef8a99bfa1b5bfb2a47ef1222cf49800530f9b53baf8e
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