Instructions to use Jeevesh8/roberta_base_qqp_ft_11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_11 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_11")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_11") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_11") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2ec539f6d4dc17e2cc87472c4c7ac4c3c79c7ee7b71a6e6e8908af3799403d56
- Size of remote file:
- 499 MB
- SHA256:
- 57727c1be4a0f0d34297763668848092fbb88e4e11c0f1becccb16ab802fdce3
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