Instructions to use textattack/bert-base-uncased-QNLI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use textattack/bert-base-uncased-QNLI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/bert-base-uncased-QNLI")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("textattack/bert-base-uncased-QNLI") model = AutoModelForSequenceClassification.from_pretrained("textattack/bert-base-uncased-QNLI") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 379a2907d5e371df9e4d43843b5e897103372891797b6c0334e64df82576cd4d
- Size of remote file:
- 1.05 kB
- SHA256:
- 14093a1fc788d6cb9fcb23651612537d989161bee13181bfce88f4c83af5c446
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