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:
- a9e39df7e376f532bbe903402b0c50f04f89cdfadec5422d66e162821a15d9c1
- Size of remote file:
- 359 MB
- SHA256:
- 3dae838e1766ef80b68b6210c99c2512ca0c8c699373d014f97838abed6483f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.