Instructions to use Jeevesh8/roberta_base_qqp_ft_50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_50")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_50") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_50") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42cd3ced02b06e39d9db07c5db43db2b16f65a1036b8484f1b81dc70e23a7147
- Size of remote file:
- 499 MB
- SHA256:
- 42ee87f161e61ba1b8a17451679714c80e8509814a9129d6ff1645a59385cdad
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