Instructions to use Jeevesh8/roberta_base_qqp_ft_45 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_45 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_45")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_45") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_45") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8057db968a6169715b6ab4fce6cd90eb540685368ea8f98eb989b02a08e882be
- Size of remote file:
- 499 MB
- SHA256:
- 5b28dfef632ca9c8cdee68007ef10aac5c2c2e4f41867f2e2218a06906a857e9
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