Instructions to use driftbench/qqp_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use driftbench/qqp_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="driftbench/qqp_base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("driftbench/qqp_base") model = AutoModelForSequenceClassification.from_pretrained("driftbench/qqp_base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6396f3f9646bd4e041bc77da1613b85134208516e60da7207342c853f5116366
- Size of remote file:
- 3.52 kB
- SHA256:
- 6a088dc7a75dc689313860dce8edec3f9b408f110ab1f3efd344626f00c60791
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