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", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("bunsenfeng/FactKB") model = AutoModelForSequenceClassification.from_pretrained("bunsenfeng/FactKB", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fd23aa768f068073562d1f2de7bc4cc758ed09a48bd649fccb1b42e2a4ca44f0
- Size of remote file:
- 499 MB
- SHA256:
- df07ebac810262fd48b05ce3a8dee69a3babe295bf022b1f6ac9d9215f85b6a3
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