racineai/VDR_MEGA_MultiDomain_DocRetrieval
Visual Document Retrieval Dataset Overview This dataset is designed for training visual document retrieval models. It combines multiple datasets from the VDR series, Colpali, and LlamaIndex to create the most comprehensive training resource for visual document retrieval tasks. Dataset Structure The dataset contains structured fields including unique identifiers with string lengths ranging from 45 to 50 characters, search query text with variable… See the full description on the dataset page: https://huggingface.co/datasets/racineai/VDR_MEGA_MultiDomain_DocRetrieval.
| Author / organization | racineai |
|---|---|
| License (as declared) | apache-2.0 |
| Downloads | 1.2K (as reported by Hugging Face) |
| Likes | 24 |
| Created | 1 July 2025 |
| Last updated at source | 17 March 2026 |
Popularity, last 2 days
1,243 now (+28 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Recent changes
- 17 Oct 2026 Description: Visual Document Retrieval Dataset Overview This dataset is designed for training visual document retrieval models. It combines multiple datasets from the VDR series, Colpali, and LlamaIndex to create the most comprehensive training resource → Visual Document Retrieval Dataset Overview This dataset is designed for training visual document retrieval models. It combines multiple datasets from the VDR series, Colpali, and LlamaIndex to create the most comprehensive training resource
Changes detected by Silytics between refreshes of the source.
More from racineai
- racineai/VDR_MEGA_2Dataset
- racineai/VDR_QuantumDataset
- racineai/VDR_NuclearDataset
- racineai/VDR_EnergyDataset
- racineai/VDR_Renewable_RegulationDataset
- racineai/VDR_HydrogenDataset
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.