Instructions to use Dzeniks/alberta_fact_checking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dzeniks/alberta_fact_checking with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Dzeniks/alberta_fact_checking")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Dzeniks/alberta_fact_checking") model = AutoModelForSequenceClassification.from_pretrained("Dzeniks/alberta_fact_checking", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Update README.md
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Create README.md
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Adding `safetensors` variant of this model (#1)
- Adding `safetensors` variant of this model (ef763985832065348796bd2bd553af73b8187873) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>b39efcf
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Newer Alpha version
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Alpha version
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initial commit
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