Instructions to use Jeevesh8/roberta_base_qqp_ft_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_3") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_3") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c7c27eca20de312eca63c1040957ee5018f4882bf7432d77396fe39f52d305d
- Size of remote file:
- 499 MB
- SHA256:
- f28aadad2277d9a909f0fe1a366ecccbd4af05b96c8e06802ce929b843522fa6
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