Instructions to use textattack/bert-base-uncased-QQP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-QQP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-QQP")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-QQP") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-QQP") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4a0668274bab16118a4e4248be9290bf798652f8d87ced56db3b106389f8b107
- Size of remote file:
- 1.05 kB
- SHA256:
- 5077ffd7e108156cc09fbf900feee8cb2fa93a5a5e75d4bdf4e0a82feae38821
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