Text Classification
Transformers
Safetensors
Italian
deberta-v2
Generated from Trainer
subjectivity-detection
deberta-v3
Instructions to use AIWizards/mdeberta-v3-base-subjectivity-italian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIWizards/mdeberta-v3-base-subjectivity-italian with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="AIWizards/mdeberta-v3-base-subjectivity-italian")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("AIWizards/mdeberta-v3-base-subjectivity-italian") model = AutoModelForSequenceClassification.from_pretrained("AIWizards/mdeberta-v3-base-subjectivity-italian") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, update license, expand description and usage
#1
by nielsr HF Staff - opened
This PR significantly enhances the model card by:
- Updating the license in the metadata to
cc-by-4.0as specified in the GitHub repository. - Adding the
pipeline_tag: text-classificationfor improved discoverability and inference widget functionality. - Including relevant tags like
subjectivity-detectionanddeberta-v3. - Adding the Hugging Face
paperID and therepo_urlto the metadata. - Removing the automatically generated comment at the top of the content.
- Adding a direct link to the GitHub repository and reiterating the paper link in the introductory section.
- Populating the "Model description", "Intended uses & limitations", and "Training and evaluation data" sections with detailed information extracted from the paper abstract and the associated GitHub README.
- Adding a "How to use" section with a practical Python code snippet using the
transformerslibrary for inference. - Including a "Citation" section with the BibTeX entry for the paper.
MatteoFasulo changed pull request status to merged