Instructions to use Jeevesh8/roberta_base_qqp_ft_38 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_38 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_38")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_38") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_38") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5789b7fd55898d107d14dae3fe0260c23a0d3cf0ff7655a83190bc0f88ab14e2
- Size of remote file:
- 499 MB
- SHA256:
- 018994759a98a6a92ca670b80a3499bcd09f0a7b37faf035b67bd36c64035b61
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