Text Classification
Transformers
PyTorch
Safetensors
German
bert
easy-language
plain-language
leichte-sprache
einfache-sprache
text-complexity
text-embeddings-inference
Instructions to use krupper/text-complexity-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use krupper/text-complexity-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="krupper/text-complexity-classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("krupper/text-complexity-classification") model = AutoModelForSequenceClassification.from_pretrained("krupper/text-complexity-classification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 27950cdf884c8651820033642f4a4b4fbe5cc03331dd4760f318fdcc484cbf3b
- Size of remote file:
- 440 MB
- SHA256:
- 17a66db0210bd3dc3bb393c91a33a3268b132f964ae466349960c4b66566a891
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