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:
- 67a5bffb896938f79bc87d4c03aaa7f46d42f899d9a5b9d933aea77e1ef7b3dc
- Size of remote file:
- 3.52 kB
- SHA256:
- bf4174b046b02a75e239d509123a54b078774ad1a140b116a6636385d3cd897a
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