Instructions to use Jeevesh8/roberta_base_qqp_ft_43 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_43 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_43")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_43") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_43") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c648a96e62b9ef7442521451d7523bee290556385a70ae1c3612cc0bbb46b9f8
- Size of remote file:
- 499 MB
- SHA256:
- 26dc3c15209ef0b3317d06e8b4cad1934f23e3d4da7726f052a01b7f8d28917b
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