Enhance dataset card: Add metadata, paper link, license, and sample usage (#2)

- Enhance dataset card: Add metadata, paper link, license, and sample usage (090c785d38a58005d9a1162bd237b9d57c02ffed)


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

README.md

@@ -32,9 +32,21 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+task_categories:
+- image-text-to-text
+- question-answering
+license: mit
+language:
+- en
+- hi
+tags:
+- multimodal
+- vlm
+- scientific-reasoning
+- benchmark
+- education
 ---
 
-
 # mmJEE-Eval: A Bilingual Multimodal Benchmark for Exam-Style Evaluation of Vision-Language Models
 
 <div align="center">
@@ -53,6 +65,30 @@ configs:
 </a>
 </div>
 
+**Paper:** [mmJEE-Eval: A Bilingual Multimodal Benchmark for Evaluating Scientific Reasoning in Vision-Language Models](https://huggingface.co/papers/2511.09339)
+**Code:** [https://github.com/ArkaMukherjee0/mmJEE-Eval](https://github.com/ArkaMukherjee0/mmJEE-Eval)
+**Project Page:** [https://mmjee-eval.github.io](https://mmjee-eval.github.io)
+
 ## Introduction
 
-mmJEE-Eval  is a multimodal and bilingual dataset for LLM evaluation comprising 1,460 challenging questions from seven years (2019-2025) of India's JEE Advanced competitive examination. We evaluate 17 state-of-the-art VLMs, finding that open models (from 7B-400B) struggle significantly (maxing at 40-50%) as compared to frontier models from Google and OpenAI (77-84%). mmJEE-Eval is significantly more challenging than the text-only JEEBench, the only other well-established dataset on JEE Advanced problems, with performance drops of 18-56% across all models. Our findings, especially metacognitive self-correction abilities, cross-lingual consistency, and human evaluation of reasoning quality, demonstrate that contemporary VLMs still show authentic scientific reasoning deficits despite strong question-solving capabilities (as evidenced by high Pass@K accuracies), establishing mmJEE-Eval as a challenging complementary benchmark that effectively discriminates between model capabilities.
+mmJEE-Eval is a multimodal and bilingual dataset for LLM evaluation comprising 1,460 challenging questions from seven years (2019-2025) of India's JEE Advanced competitive examination. We evaluate 17 state-of-the-art VLMs, finding that open models (from 7B-400B) struggle significantly (maxing at 40-50%) as compared to frontier models from Google and OpenAI (77-84%). mmJEE-Eval is significantly more challenging than the text-only JEEBench, the only other well-established dataset on JEE Advanced problems, with performance drops of 18-56% across all models. Our findings, especially metacognitive self-correction abilities, cross-lingual consistency, and human evaluation of reasoning quality, demonstrate that contemporary VLMs still show authentic scientific reasoning deficits despite strong question-solving capabilities (as evidenced by high Pass@K accuracies), establishing mmJEE-Eval as a challenging complementary benchmark that effectively discriminates between model capabilities.
+
+## Sample Usage
+
+You can load the dataset using the Hugging Face `datasets` library:
+
+```python
+from datasets import load_dataset
+
+# Load the entire dataset
+dataset = load_dataset("ArkaMukherjee/mmJEE-Eval")
+
+# Access the training split
+train_dataset = dataset["train"]
+
+# Print an example
+print(train_dataset[0])
+
+# To run evaluation scripts, please refer to the official GitHub repository:
+# https://github.com/ArkaMukherjee0/mmJEE-Eval
+```