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 training_args.bin from krupper/text-complexity-classification: direct link, hf CLI and curl.
- Browser
- Download file 3.18 kB
-
https://huggingface.co/krupper/text-complexity-classification/resolve/main/training_args.bin
- Command line
-
hf download hf://krupper/text-complexity-classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/krupper/text-complexity-classification/resolve/main/training_args.bin
3.18 kB
- Xet hash:
- dff25e08c6c2f18138e2bb7cf0665b38fde9921db5af41c47c65da9241473cd1
- Size of remote file:
- 3.18 kB
- SHA256:
- f0bb0e48caefba6d487b850974c71baae2c5e4384d850890a461854091e0beaa
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