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:
- d707779a29df1b885724542c8268f9f37ae7b4207eb38cb5f0c1b72782661317
- Size of remote file:
- 499 MB
- SHA256:
- 6dfd3c0e6e5549518e4c575ab34090e59be6b5bfc76a957a6752d49199942ce3
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