Instructions to use Jeevesh8/roberta_base_qqp_ft_32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_32 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_32")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_32") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_32") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e156e67456ebbda97e1c3c7ec27e1d6447ce3a32f6377dc649f70f2c6afb7f98
- Size of remote file:
- 499 MB
- SHA256:
- e31fca4c17528ae11b18fbeff38ee5ffd7eb9a4648a246bba03d672893b7ea95
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