Datasets:
Languages:
English
Size:
100K<n<1M
ArXiv:
Tags:
mixed-modality-retrieval
multimodal-retrieval
visual-document-retrieval
contrastive-learning
training-data
License:
| license: mit | |
| language: | |
| - en | |
| task_categories: | |
| - text-retrieval | |
| - visual-document-retrieval | |
| - image-text-to-text | |
| tags: | |
| - mixed-modality-retrieval | |
| - multimodal-retrieval | |
| - visual-document-retrieval | |
| - contrastive-learning | |
| - training-data | |
| pretty_name: Trident-Train | |
| size_categories: | |
| - 100K<n<1M | |
| viewer: false | |
| <h1 align="center">Trident-Train</h1> | |
| <p align="center"> | |
| Official training data for <a href="https://arxiv.org/abs/2610.11816"><i>Chaos in the Text: Revealing the Modality Preference in Mixed-Modality Retrievers</i></a> | |
| </p> | |
| <p align="center"> | |
| <a href="https://chaos-in-texts.github.io/"><img src="https://img.shields.io/badge/%F0%9F%94%B1%20Project%20Page-chaos--in--texts-5b3fd1.svg" alt="Project Page"></a> | |
| <a href="https://arxiv.org/abs/2610.11816"><img src="https://img.shields.io/badge/arXiv-2610.11816-b31b1b.svg" alt="arXiv"></a> | |
| <a href="https://github.com/OpenBMB/Trident"><img src="https://img.shields.io/badge/GitHub-OpenBMB%2FTrident-blue.svg" alt="Code"></a> | |
| <a href="https://huggingface.co/OpenBMB/Trident-Qwen3VL-2B"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Model-Trident--Qwen3VL--2B-yellow.svg" alt="Model"></a> | |
| <a href="https://huggingface.co/datasets/OpenBMB/Trident-Train"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Data-Trident--Train-orange.svg" alt="Train data"></a> | |
| <a href="https://huggingface.co/datasets/OpenBMB/Trident-Eval"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Data-Trident--Eval-orange.svg" alt="Eval data"></a> | |
| <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-green.svg" alt="License"></a> | |
| </p> | |
| ## Overview | |
| Dense retrievers tend to assign higher similarity scores to *text* representations than to semantically equivalent *image* representations, which makes mixed-modality retrieval fragile. Trident-Train is the **modality-balanced** training set used to mitigate this bias. | |
| Every training instance pairs one query with **three co-equal positive views** of the same document: | |
| | View | Content | How it is built | | |
| | --------- | -------------------------------------------- | ------------------------------------------------------------ | | |
| | **Text** | A detailed description of the document image | Generated by [Qwen3-VL-235B-A22B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct) | | |
| | **Image** | The original document image | Original image | | |
| | **Fused** | A short summary **+** the original image | Summary generated by [Qwen3-VL-235B-A22B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct) | | |
| The three views are optimized jointly with the Multi-Positive View InfoNCE loss (see the [paper](https://arxiv.org/abs/2610.11816) and the [code](https://github.com/OpenBMB/Trident)). | |
| Three training files are provided: | |
| | File | Domain | Source | #Queries | | |
| | -------------------- | --------------------------------- | ---------------------------- | -------- | | |
| | `train_vdoc.jsonl` | Visual documents | OpenDocVQA | ~41K | | |
| | `train_nature.jsonl` | Natural images | MMEB training sets | ~170K | | |
| | `train_all.jsonl` | Visual documents + natural images | Union of the two files above | ~211K | | |
| `train_all.jsonl` is the data used to train the released [Trident-Qwen3VL-2B](https://huggingface.co/OpenBMB/Trident-Qwen3VL-2B). The two domain files can be used to train domain-specific retrievers. | |
| ## Directory Structure | |
| ``` | |
| Trident-Train/ | |
| ├── README.md | |
| ├── train_all.jsonl | |
| ├── train_vdoc.jsonl | |
| ├── train_nature.jsonl | |
| └── images/ | |
| ├── aokvqa.tar.gz | |
| ├── docvqa.tar.gz | |
| ├── dude.tar.gz | |
| ├── fvqa.tar.gz | |
| ├── infovqa.tar.gz | |
| ├── mscoco.tar.gz | |
| ├── openwikitable.tar.gz | |
| ├── render_images.tar.gz | |
| ├── visualmrc.tar.gz | |
| ├── visualnews.tar.gz | |
| └── wiki.tar.gz | |
| ``` | |
| After downloading and extracting, the layout matches what the [Trident code](https://github.com/OpenBMB/Trident) expects under `data/`: | |
| ``` | |
| data/ | |
| └── Train/ | |
| ├── train_all.jsonl | |
| ├── train_vdoc.jsonl | |
| ├── train_nature.jsonl | |
| └── images/ | |
| ├── docvqa/ ... | |
| ├── mscoco/ ... | |
| └── ... | |
| ``` | |
| ### Image archives | |
| | Archive | Source dataset | | |
| | ---------------------- | ----------------------------------------------- | | |
| | `aokvqa.tar.gz` | A-OKVQA | | |
| | `docvqa.tar.gz` | DocVQA | | |
| | `dude.tar.gz` | DUDE | | |
| | `fvqa.tar.gz` | FVQA | | |
| | `infovqa.tar.gz` | InfographicVQA | | |
| | `mscoco.tar.gz` | MSCOCO | | |
| | `openwikitable.tar.gz` | OpenWikiTable | | |
| | `render_images.tar.gz` | Rendered images generated for the training data | | |
| | `visualmrc.tar.gz` | VisualMRC | | |
| | `visualnews.tar.gz` | VisualNews | | |
| | `wiki.tar.gz` | Wikipedia images | | |
| Each archive extracts into `images/` and keeps its own sub-folder, so image paths in the `.jsonl` files resolve as `data/Train/images/<path>`. | |
| ## Download & Extract | |
| ### Option 1: one-line script (recommended) | |
| The [Trident repository](https://github.com/OpenBMB/Trident) ships a helper script that downloads and extracts the data in the expected layout: | |
| ```bash | |
| git clone https://github.com/OpenBMB/Trident.git && cd Trident | |
| bash data/download_data.sh train # everything (train_all.jsonl + all images) | |
| bash data/download_data.sh train --train-subset vdoc # only train_vdoc.jsonl | |
| bash data/download_data.sh train --train-images docvqa,dude # only some image archives | |
| bash data/download_data.sh train --mirror # use hf-mirror.com | |
| ``` | |
| ### Option 2: manual | |
| ```bash | |
| # 1. Download (everything, or pass --include "train_vdoc.jsonl" "images/docvqa.tar.gz" for a subset) | |
| hf download OpenBMB/Trident-Train \ | |
| --repo-type dataset --local-dir data/Train | |
| # 2. Extract the image archives in place | |
| for f in data/Train/images/*.tar.gz; do | |
| tar -xzf "$f" -C data/Train/images | |
| done | |
| # 3. (optional) remove archives to save disk space | |
| # rm data/Train/images/*.tar.gz | |
| ``` | |
| Or download from Python: | |
| ```python | |
| from huggingface_hub import snapshot_download | |
| snapshot_download( | |
| repo_id="OpenBMB/Trident-Train", | |
| repo_type="dataset", | |
| local_dir="data/Train", | |
| allow_patterns=["train_vdoc.jsonl", "images/docvqa.tar.gz"], # omit to download everything | |
| ) | |
| ``` | |
| Then train with the code repository, e.g.: | |
| ```bash | |
| bash scripts/train.sh configs/trident_qwen3vl.yaml \ | |
| --set data.train_path=data/Train/train_all.jsonl | |
| ``` | |
| ## Data Format | |
| Every `.jsonl` file contains one JSON object per line. Image paths are **relative to `images/`** and use forward slashes. | |
| ```json | |
| {"query": [{"text": "How many stores did Saint Laurent operate in Western Europe in 2020?"}], | |
| "positive": [{"text": "<detailed description>", "id": "d1_text"}, | |
| {"image": "docvqa/d1.png", "id": "d1_image"}, | |
| {"text": "<short summary>", "image": "docvqa/d1.png", "id": "d1_fused"}], | |
| "doc_id": "d1", "query_id": "q1", "task": "default"} | |
| ``` | |
| | Field | Description | | |
| | -------------------------- | ------------------------------------------------------------ | | |
| | `query` | A list with the query content (`text` and/or `image`). | | |
| | `positive` | Exactly three positive views of the same document, **always in the order text → image → fused**. Each view carries its own `id`. | | |
| | `doc_id`, `query_id` | Identities of the document and the query. Used to mask false negatives inside a batch. | | |
| | `task` | Task tag (`"default"`). | | |
| | `positive_ids`, `group_id` | *Optional.* Extra identity fields for false-negative masking. Missing ids fall back to content hashes. | | |
| | View | Fields present | | |
| | ----------------------- | --------------------- | | |
| | Text | `id`, `text` | | |
| | Image | `id`, `image` | | |
| | Fused (summary + image) | `id`, `text`, `image` | | |
| See [`src/mmemb/data/schema.py`](https://github.com/OpenBMB/Trident/blob/main/src/mmemb/data/schema.py) in the code repository for all supported fields. | |
| > All ids (`id`, `doc_id`, `query_id`) are **strings**. | |
| ## Construction | |
| Following Section 4.1 of the paper, we build training instances from OpenDocVQA (visual documents) and the MMEB training sets (natural images). For every image document, [Qwen3-VL-235B-A22B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-235B-A22B-Instruct) generates (i) a detailed description (Text view) and (ii) a short summary that is paired with the image (Fused view). The original image is kept as the Image view, so each document is represented by three equally weighted positive views. | |
| ## License | |
| The annotations released here are under the [MIT License](https://github.com/OpenBMB/Trident/blob/main/LICENSE). The images originate from third-party datasets (see [Image archives](#image-archives)) and remain subject to their **original licenses**; please make sure your use complies with them. | |
| ## Citation | |
| If you find this work useful, please cite: | |
| ```bibtex | |
| @article{sun2026chaos, | |
| title={Chaos in the Text: Revealing the Modality Preference in Mixed-Modality Retrievers}, | |
| author={Sun, Yubo and Peng, Chunyi and Yan, Yukun and Liu, Zhenghao and Xu, Zhipeng and Mei, Sen and Xin, Linlin and Zeng, Zheni and Sun, Maosong}, | |
| journal={arXiv preprint arXiv:2610.11816}, | |
| year={2026} | |
| } | |
| ``` | |