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:
- 55d1829cb8a71b93687e0b771264c10d97d7682dc2678d2e1c9080bfcf09f0a8
- Size of remote file:
- 3.9 kB
- SHA256:
- 5285bcc999a72b22cb27f8977b9a8764ca7f4a08349a6b2654ebffe21437ca00
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