Instructions to use traberph/RedBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use traberph/RedBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="traberph/RedBERT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("traberph/RedBERT") model = AutoModelForSequenceClassification.from_pretrained("traberph/RedBERT") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 25bdeb0bcff524369aedfe378aa48739cf31891a478a667b764abc5fdda90eb5
- Size of remote file:
- 718 MB
- SHA256:
- df368ee65b27fc129703294a7d6fd05f716367c6599312104fde8753a126fa88
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