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:
- 88899e0bc2c35d003437d8b90c0016d8bc45c3d60e50a5a89420b01afac66fda
- Size of remote file:
- 438 MB
- SHA256:
- 7f493d5a1549c5e38e731596cb4ac3954cda47cd150edeb7c7821d769bf2ae4a
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