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README.md

@@ -1,8 +1,6 @@
 ---
 library_name: transformers
-license: other
-license_name: wemm-model-license
-license_link: https://huggingface.co/tencent/WeMM-Embedding-9B/blob/main/LICENSE
+license: apache-2.0
 base_model:
 - Qwen/Qwen3.5-9B
 pipeline_tag: feature-extraction
@@ -12,13 +10,14 @@ tags:
 - image-embedding
 - video-embedding
 - mrl
-language:
-- zh
-- en
 ---
 
 # WeMM-Embedding-9B
 
+[![Hugging Face](https://img.shields.io/badge/🤗-Hugging%20Face-yellow)](https://huggingface.co/collections/tencent/wemm-embedding)
+[![Technical Report](https://img.shields.io/badge/📄-Technical%20Report-red)](https://github.com/Tencent/WeMM-Embedding/blob/main/assets/WeMM_Embedding_tech_report.pdf)
+[![GitHub](https://img.shields.io/badge/GitHub-WeMM--Embedding-black?logo=github)](https://github.com/Tencent/WeMM-Embedding)
+
 WeMM-Embedding-9B is a universal multimodal embedding model built on Qwen3.5. It accepts text, images, videos, visual documents, and interleaved multimodal inputs, and returns a 4,096-dimensional L2-normalized embedding. Audio input is not supported.
 
 ## Installation
@@ -90,7 +89,7 @@ inputs = [
 embeddings = model.encode(
     inputs,
     batch_size=1,
-    truncate_dim=256,
+    truncate_dim=4096,
     normalize_embeddings=True,
 )
 ```
@@ -127,6 +126,52 @@ python -m sglang.launch_server \
   --enable-precise-embedding-interpolation
 ```
 
+## Evaluation
+
+### MMEB-v2
+
+Results on 78 datasets from Table 1 of the [technical report](https://github.com/Tencent/WeMM-Embedding/blob/main/assets/WeMM_Embedding_tech_report.pdf). Image and video tasks use Hit@1, while visual-document tasks use NDCG@5. Higher is better.
+
+| Model | Size | AVG | Image | Video | VisDoc |
+| --- | ---: | ---: | ---: | ---: | ---: |
+| VLM2Vec | 2B | 47.8 | 59.7 | 29.0 | 44.0 |
+| GME | 2B | 55.4 | 51.9 | 33.9 | 76.8 |
+| VLM2Vec-V2 | 2B | 59.3 | 64.9 | 34.9 | 69.2 |
+| Qwen3-VL-Embedding | 2B | 73.2 | 75.0 | 61.9 | 79.2 |
+| DME-Small† | 2B | 74.8 | 75.9 | 65.6 | 79.9 |
+| **WeMM-Embedding** | **2B** | **77.9** | **79.6** | **70.8** | **80.7** |
+| **WeMM-Embedding** | **4B** | **79.2** | **80.8** | **72.1** | **82.0** |
+| VLM2Vec | 8B | 53.2 | 65.5 | 34.0 | 49.1 |
+| GME | 8B | 59.2 | 56.0 | 38.6 | 79.3 |
+| Qwen3-VL-Embedding | 8B | 77.8 | 80.1 | 67.1 | 82.4 |
+| DME-Medium† | 9B | 78.4 | 79.8 | 70.8 | 82.0 |
+| **WeMM-Embedding** | **9B** | **80.6** | **81.9** | **74.3** | **83.3** |
+
+† Closed-source leaderboard submission without publicly released model weights or a public inference endpoint.
+
+### MMEB-v3
+
+Results on all 190 tasks from Table 2 of the [technical report](https://github.com/Tencent/WeMM-Embedding/blob/main/assets/WeMM_Embedding_tech_report.pdf). V3-All includes the 78 MMEB-v2 tasks, 53 text tasks, 47 agent tasks, 11 audio tasks, and MCMR. Unsupported tasks are assigned a score of zero.
+
+| Model | Size | V3-All | Text | Agent | MCMR | Audio |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: |
+| VLM2Vec-V2 | 2B | 38.3 | 24.5 | 28.7 | 4.1 | 0.0 |
+| Omni-Embed-Nemotron | 3B | 43.5 | 39.2 | 36.5 | 26.1 | 36.5 |
+| E5-Omni | 3B | 44.6 | 26.7 | 36.9 | 31.9 | 30.8 |
+| Qwen3-VL-Embedding | 2B | 50.9 | 39.2 | 39.3 | 42.0 | 0.0 |
+| **WeMM-Embedding** | **2B** | **56.0** | **45.3** | **45.1** | **42.5** | **0.0** |
+| **WeMM-Embedding** | **4B** | **58.2** | **47.9** | **49.0** | **41.9** | **0.0** |
+| WAVE | 7B | 26.3 | 13.7 | 11.3 | 8.9 | 31.8 |
+| VLM2Vec | 8B | 32.9 | 22.2 | 19.7 | 0.9 | 0.0 |
+| LCO-Embedding-Omni | 7B | 40.6 | 32.4 | 27.8 | 20.0 | 43.2 |
+| GME | 8B | 43.6 | 37.1 | 35.6 | 27.3 | 0.0 |
+| E5-Omni | 7B | 47.1 | 26.9 | 36.7 | 41.1 | 43.0 |
+| Tianmu-Emb-Uni | 8B | 53.3 | 43.6 | 39.4 | 38.8 | 38.9 |
+| Qwen3-VL-Embedding | 8B | 53.5 | 42.5 | 38.4 | 38.0 | 0.0 |
+| **WeMM-Embedding** | **9B** | **59.5** | **48.8** | **51.0** | **49.3** | **0.0** |
+
+Text results use NDCG@5; agent, MCMR, and audio results use Hit@1.
+
 ## Citation
 
 ```bibtex