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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("krupper/text-complexity-classification") model = AutoModelForSequenceClassification.from_pretrained("krupper/text-complexity-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from krupper/text-complexity-classification: direct link, hf CLI and curl.
- Browser
- Download file 440 MB
-
https://huggingface.co/krupper/text-complexity-classification/resolve/main/model.safetensors
- Command line
-
hf download hf://krupper/text-complexity-classification/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/krupper/text-complexity-classification/resolve/main/model.safetensors
440 MB
- Xet hash:
- 963fb4f97d0135e64f4f8b2f042fd39bdd6c57d569109c2925921398381ff097
- Size of remote file:
- 440 MB
- SHA256:
- 66664eb6c2ea9381dad8ca421a7a609859136df87c7d2f7823a363d9084f5833
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