Instructions to use HAriGa/germanarticleclassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HAriGa/germanarticleclassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HAriGa/germanarticleclassification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HAriGa/germanarticleclassification") model = AutoModelForSequenceClassification.from_pretrained("HAriGa/germanarticleclassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 094f895894adb206fbdb316b89261921f2f56ebaa478a71d37e00c434b8c93a7
- Size of remote file:
- 436 MB
- SHA256:
- 78c2eb5f1c2bbb971b7fe41b0628c5287f21d2b42198508e9328307693397f60
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