Instructions to use Jeevesh8/roberta_base_qqp_ft_62 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_62 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_62")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_62") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_62") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3c0d901e8a585f16e71f5f749c953f00379a7e6fba6a1b2590ce6eb9bda5d0d9
- Size of remote file:
- 499 MB
- SHA256:
- 0058b46aea6fbb2aede1d340b1d8f73e1afe5f0ee34192678836498806b8df57
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