Instructions to use Jeevesh8/roberta_base_qqp_ft_67 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_67 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_67")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_67") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_67") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e6355767d8d722396789fe4f65a011b19f3091786c53bdbf6b45a5fe3f09068b
- Size of remote file:
- 499 MB
- SHA256:
- 00c8220be138c5beaf08cfc3858a908c9920806d56f4ca217e1df7b3c5aa65e9
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