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:
- 586cdf6f908499634dbf5c10d8a79d45ff8eb5f8941fb725dab4793a0cc7fd22
- Size of remote file:
- 438 MB
- SHA256:
- 5b1d1c7a1ccf16fdeb05c21b29ef04144c4a6f0130eae725ec063f0b244f22ff
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