Instructions to use Jeevesh8/roberta_base_qqp_ft_44 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/roberta_base_qqp_ft_44 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeevesh8/roberta_base_qqp_ft_44")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/roberta_base_qqp_ft_44") model = AutoModelForSequenceClassification.from_pretrained("Jeevesh8/roberta_base_qqp_ft_44") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 929a3405deb1e69df8dd727e76876f8e18d9f0e99b0e152df491b790d37a8449
- Size of remote file:
- 499 MB
- SHA256:
- ab6d1c5a13aeb8bcf53f4b08ef75e05a5dedbdce2e2ebd6975b748b47219de6f
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