Instructions to use Jeevesh8/roberta_base_qqp_ft_18 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_18 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_18")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_18") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_18") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3a736b345e8fbfdd5ba28a1fc1bcad42de41a77f290a4b38d28eab1fd4548a7
- Size of remote file:
- 499 MB
- SHA256:
- 693260adfc16810c80cd654534e4d8e7ff3386b981dec9a589d5dedc138cd4a7
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