Improve model card: Add pipeline tag, library name, abstract, results, and GitHub link (#1)

- Improve model card: Add pipeline tag, library name, abstract, results, and GitHub link (2cf6f9c1573c235ff6fa2ee96b8eecb01ef2a996)


Co-authored-by: Niels Rogge <[email protected]>

README.md

@@ -1,6 +1,9 @@
 ---
 license: apache-2.0
+pipeline_tag: video-text-to-text
+library_name: transformers
 ---
+
 <p align="center">
     <img src="./assets/raven_logo.png" width="100" style="margin-bottom: 0.2;"/>
 <p>
@@ -16,13 +19,33 @@ license: apache-2.0
     <a href="https://bashlab.github.io/raven_project/" style="color:#825987">
         https://bashlab.github.io/raven_project/
     </a>
+    &bull; Code:
+    <a href="https://github.com/BASHLab/RAVEN" style="color:#825987">
+        https://github.com/BASHLab/RAVEN
+    </a>
 </h5>
 <p align="center">
   <img src="./assets/raven_architecture.png" width="800" />
 <p>
   
 ---
-  
+
+## Abstract
+Multimodal question answering (QA) often requires identifying which video, audio, or sensor tokens are relevant to the question. Yet modality disagreements are common: off-camera speech, background noise, or motion outside the field of view often mislead fusion models that weight all streams equally. We present RAVEN, a unified QA architecture whose core is QuART, a query-conditioned cross-modal gating module that assigns scalar relevance scores to each token across modalities, enabling the model to amplify informative signals and suppress distractors before fusion. RAVEN is trained through a three-stage pipeline comprising unimodal pretraining, query-aligned fusion, and disagreement-oriented fine-tuning -- each stage targeting a distinct challenge in multi-modal reasoning: representation quality, cross-modal relevance, and robustness to modality mismatch. To support training and evaluation, we release AVS-QA, a dataset of 300K synchronized Audio--Video-Sensor streams paired with automatically generated question-answer pairs. Experimental results on seven multi-modal QA benchmarks -- including egocentric and exocentric tasks -- show that RAVEN achieves up to 14.5% and 8.0% gains in accuracy compared to state-of-the-art multi-modal large language models, respectively. Incorporating sensor data provides an additional 16.4% boost, and the model remains robust under modality corruption, outperforming SOTA baselines by 50.23%. Our code and dataset are available at this https URL .
+
+---
+## 🚀 Main Results
+##### Comparison of **RAVEN** and prior MLLMs on *exocentric* open-ended video QA (MSVD-QA, MSRVTT-QA, ActivityNet-QA) and audio-visual QA (AVSD, MUSIC-QA) benchmarks. Best and second-best scores are in $\textbf{Bold}$ and $\underline{\text{underline}}$. $^*$ indicates scores reproduced by us.
+<p><img src="./assets/main_result_exo.png" width="800"></p>
+
+##### Comparison of **RAVEN** with MLLMs on the EgoThink (Reasoning) and AVS-QA benchmarks. **RAVEN** outperforms across metrics and excels in reasoning. $\textbf{Bold}$ and $\underline{\text{underline}}$ indicate the best and second-best scores.
+<p><img src="./assets/main_result_ego.png" width="800"></p>
+
+---
+## 📁 **AVS-QA** Dataset
+Train and test split of **AVS-QA** is provided [here](./avs-qa-dataset/).<br>
+More details [here](./avs-qa-dataset/README.md).
+
 ## 🛠️ Requirements and Installation
 Basic Dependencies:
 * Python >= 3.8
@@ -40,7 +63,13 @@ apt-get update && apt-get install ffmpeg libsm6 libxext6  -y
 ```
 ---
 
-## 🤖 Inference
+## 🍀 Model Zoo
+| Model Name     | Modal Type |
+|:----------------|:------------:|
+| [RAVEN-7B-AV](https://huggingface.co/BASH-Lab/RAVEN-AV-7B)| AV |
+| RAVEN-7B-AVS| AVS |
+
+## 🤖 Sample Usage
 - **STEP 1:** Download $\texttt{siglip-so400m-patch14-384}$ from here [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) 
 - **STEP 2:** Download **RAVEN** checkpoint
 ```bash