Instructions to use egerber1/classifier-de with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use egerber1/classifier-de with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="egerber1/classifier-de")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("egerber1/classifier-de") model = AutoModelForSequenceClassification.from_pretrained("egerber1/classifier-de") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06630cf344f0221e01c8cb7a55773cae7875a5c723e8f64429c198e7496e3330
- Size of remote file:
- 436 MB
- SHA256:
- 03fece0c1929ae3527fd08ecbd9dc587021180b7b058840f4fa780614dc2a7e2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.