Instructions to use Manhph2211/D-BETA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manhph2211/D-BETA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Manhph2211/D-BETA", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Manhph2211/D-BETA", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Request to access the D-BETA model for academic research
Dear Hung Manh Pham, Aaqib Saeed, and Dong Ma,
Hope this message finds you well.
My name is Chen, and I am currently a student working on ECG-based multi-modal learning and cardiovascular disease diagnosis. I am deeply interested in your work "Boosting Masked ECG-Text Auto-Encoders as Discriminative Learners (D-BETA)", published at ICML 2025, and I would like to reproduce your results and explore the model’s potential in my own academic research.
I have submitted an access request to your gated Hugging Face repository (Manhph2211/D-BETA) to obtain the model checkpoint and related code. I would be extremely grateful if you could review and approve my request. All use of the model and code will be strictly for non-commercial, academic research purposes only, and I will properly cite your paper in any resulting work.
If there are any additional requirements or forms I need to complete to obtain access, please feel free to let me know. I can also provide more information about my research background and institutional affiliation if needed.
Thank you very much for your time, effort, and for making this valuable research available to the community.
Best regards,
Chen
Hugging Face username: you-Y