Instructions to use bunsenfeng/FactKB with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bunsenfeng/FactKB with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="bunsenfeng/FactKB")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bunsenfeng/FactKB") model = AutoModelForSequenceClassification.from_pretrained("bunsenfeng/FactKB") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07cde9bc491340af9706c88747e0238bb53ff0a00b7953d56aeb3586321b0b6a
- Size of remote file:
- 499 MB
- SHA256:
- 06abb4bf38c9b69d6976e27f39439a2df6865edc059d33d9695bed757d63f47b
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