Instructions to use egerber1/classifier-de2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use egerber1/classifier-de2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="egerber1/classifier-de2")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("egerber1/classifier-de2") model = AutoModelForSequenceClassification.from_pretrained("egerber1/classifier-de2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 24fba591064d9762a3bda238882f9233821e1a90347ac50df394f74b55b48d26
- Size of remote file:
- 436 MB
- SHA256:
- 7f5eabf03779d706f947963d0fcdf2042d96604d5bcb7b94517d666695715493
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