Instructions to use RobinWZQ/backdoor_KMMD_len_11_a_rugged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobinWZQ/backdoor_KMMD_len_11_a_rugged with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RobinWZQ/backdoor_KMMD_len_11_a_rugged") model = AutoModel.from_pretrained("RobinWZQ/backdoor_KMMD_len_11_a_rugged") - Notebooks
- Google Colab
- Kaggle
Add comprehensive model card for DAA text encoder
#1
by nielsr HF Staff - opened
This PR significantly improves the model card for the text-encoder-for-backdoor-detection model. It adds:
- Key metadata:
license: apache-2.0,pipeline_tag: text-to-image,library_name: transformers, andtags: [backdoor-detection]. Thelibrary_name: transformersis based on theCLIPTextModelarchitecture andtransformers_versionfound inconfig.json. - A direct link to the paper: Dynamic Attention Analysis for Backdoor Detection in Text-to-Image Diffusion Models.
- A link to the official GitHub repository: https://github.com/Robin-WZQ/DAA.
- A concise overview of the DAA framework, including an illustrative image.
- Clarification that this repository contains a
CLIPTextModelcomponent. - A sample usage section with code snippets directly from the GitHub README to demonstrate detection.
- The BibTeX citation for the paper.
These additions enhance discoverability, usability, and proper attribution for the model.
Please review and merge if everything looks correct!
RobinWZQ changed pull request status to merged