Instructions to use Jeevesh8/roberta_base_qqp_ft_25 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_25 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_25")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_25") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_25") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 935f37c7facc081856b853079752953cf90e62c934f543309ae41e6a2d5831fd
- Size of remote file:
- 499 MB
- SHA256:
- 9dd448b9adc564b4f47c01bb3fc7d711f1f2f9840485fec434fd79d9a309c6ab
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