openbmb/VisRAG-Ret-Train-In-domain-data
Dataset Description This dataset is the In-domain part of the training set of VisRAG it includes 122,752 Query-Document (Q-D) Pairs from openly available academic datasets. Our training data is organized with a batch size of 128, ensuring that all data within the same batch comes from the same dataset. Dataset # Q-D Pairs ArXivQA 25,856 ChartQA 4,224 MP-DocVQA 10,624 InfoVQA 17,664 PlotQA 56,192 SlideVQA 8,192 Load the dataset from… See the full description on the dataset page: https://huggingface.co/datasets/openbmb/VisRAG-Ret-Train-In-domain-data.
| Author / organization | openbmb |
|---|---|
| Downloads | 1.1K (as reported by Hugging Face) |
| Likes | 10 |
| Created | 2 October 2024 |
| Last updated at source | 15 October 2024 |
Popularity, last 2 days
1,130 now (-40 since 2026-10-16). Counts as reported by the source, snapshotted daily by Silytics.
Connected in the knowledge graph
- openbmb/VisRAG-RetModel · used to train
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- Metric-AI/colqwen2.5-3b-multilingualModel · used to train
- VAGOsolutions/SauerkrautLM-ColQwen3-4b-v0.1Model · used to train
- VAGOsolutions/SauerkrautLM-ColQwen3-2b-v0.1Model · used to train
- tsystems/colqwen2.5-3b-multilingual-v1.0Model · used to train
- Metric-AI/ColQwen2.5-3b-multilingual-v1.0Model · used to train
- webAI-Official/webAI-ColVec1.1-4bModel · used to train
- webAI-Official/webAI-ColVec1.1-8bModel · used to train
- athrael-soju/colqwen3.5-4.5B-v3Model · used to train
- ApsaraStackMaaS/EvoQwen2.5-VL-Retriever-7B-v1Model · used to train
- Metric-AI/ColQwen2.5-7b-multilingual-v1.0Model · used to train
Recent changes
- 17 Oct 2026 Description: Dataset Description This dataset is the In-domain part of the training set of VisRAG it includes 122,752 Query-Document (Q-D) Pairs from openly available academic datasets. Our training data is organized with a batch size of 128, ensuring t → Dataset Description This dataset is the In-domain part of the training set of VisRAG it includes 122,752 Query-Document (Q-D) Pairs from openly available academic datasets. Our training data is organized with a batch size of 128, ensuring t
Changes detected by Silytics between refreshes of the source.
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