Instructions to use kugler/distilbert-base-german-cased-defakts-fake-binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kugler/distilbert-base-german-cased-defakts-fake-binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kugler/distilbert-base-german-cased-defakts-fake-binary")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kugler/distilbert-base-german-cased-defakts-fake-binary") model = AutoModelForSequenceClassification.from_pretrained("kugler/distilbert-base-german-cased-defakts-fake-binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5db5ef79c665a6901bdc214c62348cd4f3d8fa08655f881c9ab8f23531089b76
- Size of remote file:
- 5.24 kB
- SHA256:
- dcef72f20b998c68a6674f86fa241bbd1f12cb7e77e294afe5e23a3095448475
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.