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+# MMCBench Dataset: Benchmarking Dataset for Multimodal Model Evaluation 🚀
+
+## Overview
+
+The MMCBench Dataset is a curated collection of data designed for the comprehensive evaluation of Large Multimodal Models (LMMs) under common corruption scenarios. This dataset supports the MMCBench framework, focusing on cross-modal interactions involving text, image, and speech. It provides essential data for generative tasks such as text-to-image, image-to-text, text-to-speech, and speech-to-text, enabling robustness and self-consistency assessments of LMMs.
+
+## Dataset Composition 📊
+
+The MMCBench Dataset is structured to facilitate the evaluation across four key generative tasks:
+
+- **Text-to-Image:** A collection of text descriptions with their corresponding corrupted versions and associated images.
+- **Image-to-Text:** A set of images with clean and corrupted captions.
+- **Text-to-Speech:** Text inputs with their clean and corrupted audio outputs.
+- **Speech-to-Text:** Audio files with transcriptions before and after audio corruptions.
+
+Each subset of the dataset has been meticulously selected and processed to represent challenging scenarios for LMMs.
+
+## Using the Dataset 🛠️
+
+To use the MMCBench Dataset for model evaluation:
+
+1. **Access the Data**: The dataset is hosted on Hugging Face and can be accessed using their dataset library or direct download.
+2. **Select the Task**: Choose from text-to-image, image-to-text, text-to-speech, or speech-to-text tasks based on your model's capabilities.
+3. **Apply the Benchmark**: Utilize the data for each task to test your model's performance against various corruptions. Follow the [MMCBench](https://github.com/sail-sg/MMCBench/tree/main) framework for a consistent and standardized evaluation.
+
+### Dataset Structure 📁
+
+The dataset is organized into four main directories, each corresponding to one of the generative tasks:
+
+- `text2image/`: Contains text inputs and associated images.
+- `image2text/`: Comprises images and their descriptive captions.
+- `text2speech/`: Includes text inputs and generated speech outputs.
+- `speech2text/`: Contains audio files and their transcriptions.
+
+## Contributing to the Dataset 🤝
+
+Contributions to the MMCBench Dataset are welcome. If you have suggestions for additional data or improvements, please reach out through the Hugging Face platform or directly contribute via GitHub.
+
+## License 📜
+
+The MMCBench Dataset is made available under the Apache 2.0 License, ensuring open and ethical use for research and development.
+
+## Acknowledgments and Citations 📚
+
+When using the MMCBench Dataset in your research, please cite it appropriately. We extend our gratitude to all contributors and collaborators who have enriched this dataset, making it a valuable resource for the AI and ML community.

README.md

@@ -1,3 +1,45 @@
----
-license: apache-2.0
----
+# MMCBench Dataset: Benchmarking Dataset for Multimodal Model Evaluation 🚀
+
+## Overview
+
+The MMCBench Dataset is a curated collection of data designed for the comprehensive evaluation of Large Multimodal Models (LMMs) under common corruption scenarios. This dataset supports the MMCBench framework, focusing on cross-modal interactions involving text, image, and speech. It provides essential data for generative tasks such as text-to-image, image-to-text, text-to-speech, and speech-to-text, enabling robustness and self-consistency assessments of LMMs.
+
+## Dataset Composition 📊
+
+The MMCBench Dataset is structured to facilitate the evaluation across four key generative tasks:
+
+- **Text-to-Image:** A collection of text descriptions with their corresponding corrupted versions and associated images.
+- **Image-to-Text:** A set of images with clean and corrupted captions.
+- **Text-to-Speech:** Text inputs with their clean and corrupted audio outputs.
+- **Speech-to-Text:** Audio files with transcriptions before and after audio corruptions.
+
+Each subset of the dataset has been meticulously selected and processed to represent challenging scenarios for LMMs.
+
+## Using the Dataset 🛠️
+
+To use the MMCBench Dataset for model evaluation:
+
+1. **Access the Data**: The dataset is hosted on Hugging Face and can be accessed using their dataset library or direct download.
+2. **Select the Task**: Choose from text-to-image, image-to-text, text-to-speech, or speech-to-text tasks based on your model's capabilities.
+3. **Apply the Benchmark**: Utilize the data for each task to test your model's performance against various corruptions. Follow the [MMCBench](https://github.com/sail-sg/MMCBench/tree/main) framework for a consistent and standardized evaluation.
+
+### Dataset Structure 📁
+
+The dataset is organized into four main directories, each corresponding to one of the generative tasks:
+
+- `text2image/`: Contains text inputs and associated images.
+- `image2text/`: Comprises images and their descriptive captions.
+- `text2speech/`: Includes text inputs and generated speech outputs.
+- `speech2text/`: Contains audio files and their transcriptions.
+
+## Contributing to the Dataset 🤝
+
+Contributions to the MMCBench Dataset are welcome. If you have suggestions for additional data or improvements, please reach out through the Hugging Face platform or directly contribute via GitHub.
+
+## License 📜
+
+The MMCBench Dataset is made available under the Apache 2.0 License, ensuring open and ethical use for research and development.
+
+## Acknowledgments and Citations 📚
+
+When using the MMCBench Dataset in your research, please cite it appropriately. We extend our gratitude to all contributors and collaborators who have enriched this dataset, making it a valuable resource for the AI and ML community.