Text Classification
Transformers
Safetensors
deberta-v2
Generated from Trainer
deberta
multilingual
subjectivity-detection
Instructions to use AIWizards/mdeberta-v3-base-subjectivity-sentiment-multilingual-no-arabic 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-multilingual-no-arabic 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-multilingual-no-arabic")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AIWizards/mdeberta-v3-base-subjectivity-sentiment-multilingual-no-arabic") model = AutoModel.from_pretrained("AIWizards/mdeberta-v3-base-subjectivity-sentiment-multilingual-no-arabic") - Notebooks
- Google Colab
- Kaggle
Improve model card: Update license, add languages, expand sections, and include usage example
#1
by nielsr HF Staff - opened
This PR significantly enhances the model card for mdeberta-v3-base-subjectivity-sentiment-multilingual-no-arabic by:
- Correcting the license to
cc-by-4.0as stated in the associated GitHub repository. - Updating the paper link to point to the Hugging Face Papers page (
https://huggingface.co/papers/2507.11764). - Adding a direct link to the official GitHub repository (
https://github.com/MatteoFasulo/clef2025-checkthat). - Enriching the metadata with specific
languages(Arabic, German, English, Italian, Bulgarian, Greek, Romanian, Polish, Ukrainian) and additionaltagssuch asdeberta,multilingual, andsubjectivity-detectionfor better discoverability. - Populating the "Model description", "Intended uses & limitations," and "Training and evaluation data" sections with comprehensive details extracted from the paper abstract and GitHub README.
- Adding a "How to use" section with a clear Python code example for inference.
- Including a "Citation" section with a BibTeX entry for the paper.
These changes make the model card much more informative and user-friendly, providing essential context and practical guidance for anyone using the model.
MatteoFasulo changed pull request status to merged