Instructions to use Jeevesh8/roberta_base_qqp_ft_40 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_40 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_40")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_40") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_40") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 681a18a2b0d83e28b0669adb1d0be7b4518601c10e8b35ffbb9bde6c6984fa64
- Size of remote file:
- 499 MB
- SHA256:
- 133e2961be38377bf07240d959ea24d2c9c053a1d3fba93871f46ef0a4f5df09
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