Video-Text-to-Text
Transformers
Safetensors
qwen3_vl
image-text-to-text
video-retrieval
temporal-grounding
videosearch-r1
Instructions to use VideoSearchR1/activitynet-stage2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VideoSearchR1/activitynet-stage2 with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("VideoSearchR1/activitynet-stage2") model = AutoModelForMultimodalLM.from_pretrained("VideoSearchR1/activitynet-stage2") - Notebooks
- Google Colab
- Kaggle
Add video-text-to-text pipeline tag, link paper, project page, and code to model card
#1
by nielsr HF Staff - opened
Hi! I have opened this PR to enhance the model card for the VideoSearch-R1 ActivityNet Stage 2 checkpoint.
Here is a summary of the improvements:
- Added
pipeline_tag: video-text-to-textto the metadata to improve searchability and integration on the Hub. - Linked the model card to the official paper VideoSearch-R1: Iterative Video Retrieval and Reasoning via Soft Query Refinement.
- Included links to the official project page and GitHub repository.
- Added the BibTeX citation for the ECCV 2026 paper.
- Kept your original setup and usage commands intact.
Let me know if you have any questions!
happy8825 changed pull request status to merged