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:
- 963fb4f97d0135e64f4f8b2f042fd39bdd6c57d569109c2925921398381ff097
- Size of remote file:
- 440 MB
- SHA256:
- 66664eb6c2ea9381dad8ca421a7a609859136df87c7d2f7823a363d9084f5833
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.