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README.md

@@ -18,14 +18,20 @@ size_categories:
 viewer: false
 ---
 
-# Trident-Train
+<h1 align="center">Trident-Train</h1>
 
-Training data for the paper **[Chaos in the Text: Revealing the Modality Preference in Mixed-Modality Retrievers](https://arxiv.org/abs/2610.11816)**.
+<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>
 
-- 📄 Paper: [arXiv:2610.11816](https://arxiv.org/pdf/2610.11816)
-- 💻 Code: [OpenBMB/Trident](https://github.com/OpenBMB/Trident)
-- 🧪 Evaluation data: [OpenBMB/Trident-Eval](https://huggingface.co/datasets/OpenBMB/Trident-Eval)
-- 🤗 Model: [OpenBMB/Trident-Qwen3VL-2B](https://huggingface.co/OpenBMB/Trident-Qwen3VL-2B)
+<p align="center">
+  <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="https://github.com/OpenBMB/Trident/blob/main/LICENSE"><img src="https://img.shields.io/badge/license-MIT-green.svg" alt="License"></a>
+</p>
 
 ## Overview
 
@@ -33,21 +39,21 @@ Dense retrievers tend to assign higher similarity scores to *text* representatio
 
 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 |
+| 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 |
+| 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.
 
@@ -89,19 +95,19 @@ data/
 
 ### 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 |
+| 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 |
+| `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>`.
 
@@ -163,23 +169,23 @@ Every `.jsonl` file contains one JSON object per line. Image paths are **relativ
 ```json
 {"query": [{"text": "How many stores did Saint Laurent operate in Western Europe in 2020?"}],
  "positive": [{"text": "<detailed description>", "id": "d1_text"},
-              {"image": "chartqa/d1.png", "id": "d1_image"},
-              {"text": "<short summary>", "image": "chartqa/d1.png", "id": "d1_fused"}],
+              {"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"`). |
+| 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` |
+| 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.
@@ -188,14 +194,16 @@ See [`src/mmemb/data/schema.py`](https://github.com/OpenBMB/Trident/blob/main/sr
 
 ## 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 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.
+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. 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.
+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{trident2026chaos,
   title   = {Chaos in the Text: Revealing the Modality Preference in Mixed-Modality Retrievers},