Instructions to use Jeevesh8/roberta_base_qqp_ft_20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_20 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_20")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_20") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_20") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6981ddb54d3325da4cb69b52e9187c1d7188a60d7dd308537b76a32d4efb7b8
- Size of remote file:
- 499 MB
- SHA256:
- f0147f110465e385533b09560f442d8e7c64b9d622c7523dd225be3258d34c98
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