Instructions to use Jeevesh8/roberta_base_qqp_ft_39 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_39 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_39")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_39") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_39") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e733ca597c2d0600653b3cd2c6bdb14609b489d7511da712b85f7a60e717d2e2
- Size of remote file:
- 499 MB
- SHA256:
- a5bf19b0d3cf1c08e12de663fe7644ef8231937f9b579a9a15550a5393ea54bf
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