Instructions to use leomaurodesenv/bert-base-uncased-answerable-or-not with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leomaurodesenv/bert-base-uncased-answerable-or-not with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="leomaurodesenv/bert-base-uncased-answerable-or-not")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("leomaurodesenv/bert-base-uncased-answerable-or-not") model = AutoModelForSequenceClassification.from_pretrained("leomaurodesenv/bert-base-uncased-answerable-or-not", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6662835d664b75299e1bd31a71876cf6fa1125ed6d9b6dd1e8f4a5cbeb1569e8
- Size of remote file:
- 5.27 kB
- SHA256:
- 8252357e25fd7d71584487f42582069db970db036a4bbcd4ac49b8d852427d05
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