Text Classification
Transformers
Safetensors
German
deberta-v2
Generated from Trainer
subjectivity
sentiment
multilingual
zero-shot
deberta-v3
Instructions to use AIWizards/mdeberta-v3-base-subjectivity-sentiment-german with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIWizards/mdeberta-v3-base-subjectivity-sentiment-german with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AIWizards/mdeberta-v3-base-subjectivity-sentiment-german")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AIWizards/mdeberta-v3-base-subjectivity-sentiment-german") model = AutoModel.from_pretrained("AIWizards/mdeberta-v3-base-subjectivity-sentiment-german") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, update license, and enrich content
#1
by nielsr HF Staff - opened
This PR significantly enhances the model card for mdeberta-v3-base-subjectivity-sentiment-german by:
- Adding the
pipeline_tag: text-classificationfor better discoverability on the Hugging Face Hub. - Updating the
licensetocc-by-4.0as specified in the project's GitHub repository. - Including more specific
tagssuch assubjectivity,sentiment,multilingual,zero-shot, anddeberta-v3to improve categorization. - Adding a direct link to the GitHub repository and mentioning the related Hugging Face collection.
- Populating the "Model description," "Intended uses & limitations," and "Training and evaluation data" sections with comprehensive details from the paper abstract and GitHub README.
- Removing the auto-generated comment at the top of the README.
These changes make the model card more informative, accurate, and user-friendly, aligning it with Hugging Face Hub best practices.
MatteoFasulo changed pull request status to merged