Text Classification
Transformers
Safetensors
multilingual
deberta-v2
subjectivity-detection
news-articles
deberta-v3
Generated from Trainer
Instructions to use AIWizards/mdeberta-v3-base-subjectivity-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-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-multilingual-no-arabic")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AIWizards/mdeberta-v3-base-subjectivity-multilingual-no-arabic") model = AutoModelForSequenceClassification.from_pretrained("AIWizards/mdeberta-v3-base-subjectivity-multilingual-no-arabic") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, update metadata, and expand content
#1
by nielsr HF Staff - opened
This PR significantly improves the model card for mdeberta-v3-base-subjectivity-multilingual-no-arabic by:
- Adding the
pipeline_tag: text-classificationto ensure proper discoverability on the Hugging Face Hub. - Updating the
licensetocc-by-4.0as stated in the official GitHub repository. - Refining
languageandtagsmetadata to better describe the model's scope and task. - Expanding the "Model description", "Intended uses & limitations", and "Training and evaluation data" sections with details extracted from the paper abstract and the GitHub README.
- Adding a direct link to the official GitHub repository for easy access to the source code.
- Providing a clear Python usage example using the
transformerspipelinefor text classification. - Adding a BibTeX citation for the associated paper.
Please review and merge.
MatteoFasulo changed pull request status to merged