Instructions to use Jeevesh8/roberta_base_qqp_ft_28 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_28 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_28")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_28") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_28") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 461c1d4a12742db7385f920e48c4d31cd259f84c2b37cb6b021f4dde4157cb34
- Size of remote file:
- 499 MB
- SHA256:
- c6d0d0ada4e85fe675ea08306402d96830dcda14c262c4c7cdf03135738ab9de
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