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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- faec289b9976b0baba43476d5433d56b4d67ad64a61ec12f9209d31a2ba7cf9b
- Size of remote file:
- 436 MB
- SHA256:
- c9250c8ba7547cb7d41b5ed4d105cd338cf62893b8886ae7dcfd4e8088c4edbc
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