Instructions to use RobinWZQ/poisoned_model_1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RobinWZQ/poisoned_model_1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="RobinWZQ/poisoned_model_1") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("RobinWZQ/poisoned_model_1") model = AutoModel.from_pretrained("RobinWZQ/poisoned_model_1") - Notebooks
- Google Colab
- Kaggle
Improve model card: Add pipeline tag, library name, paper & code links, sample usage, and citation
#1
by nielsr HF Staff - opened
This PR enriches the model card for the model associated with the paper Assimilation Matters: Model-level Backdoor Detection in Vision-Language Pretrained Models.
It adds the following:
library_name: transformersmetadata, enabling the "how to use" widget, as theconfig.jsonindicates compatibility withtransformers.pipeline_tag: zero-shot-image-classificationmetadata, appropriate for a CLIPTextModel component within a VLP context.- A link to the GitHub repository.
- A summary of the paper's contribution (AMDET).
- Authors of the paper.
- A sample usage code snippet for backdoor detection, directly from the GitHub README.
- The BibTeX citation placeholder from the GitHub README.
Please review and merge if it looks good.
RobinWZQ changed pull request status to merged