Text Classification
Transformers
Safetensors
German
distilbert
public participation
text classification
argument mining
text-embeddings-inference
Instructions to use juliaromberg/distilbert-base-german-cased_cimt-argument with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use juliaromberg/distilbert-base-german-cased_cimt-argument with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="juliaromberg/distilbert-base-german-cased_cimt-argument")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("juliaromberg/distilbert-base-german-cased_cimt-argument") model = AutoModelForSequenceClassification.from_pretrained("juliaromberg/distilbert-base-german-cased_cimt-argument") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 72526ae5ee6e5b03a3a01f5f82ed69f06b6db8920edbf5c653a1954bbe8c4f49
- Size of remote file:
- 4.54 kB
- SHA256:
- 2a3d2ef96e61d7203d71b9f1ded657cfe34a7900c80741c9a554457b14559d41
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