Update README.md

README.md

@@ -1,3 +1,166 @@
 ---
 license: cc-by-nc-sa-4.0
+task_categories:
+- zero-shot-classification
+- zero-shot-image-classification
+language:
+- ar
+- el
+- en
+- hi
+- ja
+- ko
+- te
+- th
+- uk
+- zh
+tags:
+- multimodal
+- representation learning
+- multilingual
+pretty_name: Symile-M3
+size_categories:
+- 10M<n<100M
 ---
+# Dataset Card for Symile-M3
+Symile-M3 is a multilingual dataset of (audio, image, text) samples. The dataset is specifically designed to test a model's ability to capture higher-order information between three distinct high-dimensional data types: by incorporating multiple languages, we construct a task where text and audio are both needed to predict the image, and where, importantly, neither text nor audio alone would suffice.
+- Paper: https://arxiv.org/abs/2411.01053
+- GitHub: https://github.com/rajesh-lab/symile
+- Questions & Discussion: https://www.alphaxiv.org/abs/2411.01053v1
+
+## Overview
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/66d8e34b27d76ef6e481c2b5/mR0kJkgVyUK5rTNUOCOFx.jpeg)
+
+Let `w` represent the number of languages in the dataset (`w=2`, `w=5`, and `w=10` correspond to Symile-M3-2, Symile-M3-5, and Symile-M3-10, respectively). An (audio, image, text) sample is generated by first drawing a short one-sentence audio clip from [Common Voice](https://commonvoice.mozilla.org/en/datasets) spoken in one of `w` languages with equal probability. An image is drawn from [ImageNet](https://www.image-net.org/) that corresponds to one of 1,000 classes with equal probability. Finally, text containing exactly `w` words is generated based on the drawn audio and image: one of the `w` words in the text is the drawn image class name in the drawn audio language. The remaining `w-1` words are randomly chosen from the ImageNet class names and written in one of the `w` languages such that there is no overlap in language or class name across the `w` words in the text. The words are separated by underscores, and their order is randomized.
+
+## Tasks
+The dataset was designed to evaluate a model on the zero-shot retrieval task of finding an image of the appropriate class given the audio and text. The most probable image for a given query audio and text pair, selected from all possible candidate images in the test set, is that with the highest similarity score.
+
+The dataset was designed to ensure that neither text nor audio alone would suffice to predict the image. Therefore, success on this zero-shot retrieval task hinges on a model's ability to capture joint information between the three modalities.
+
+### Dataset Structure
+
+Each sample in the dataset is a dictionary containing the following fields:
+
+```python
+{
+    # language code of the audio clip
+    'lang': 'ja',
+    
+    # audio data
+    'audio': {
+        'path': 'common_voice_ja_39019065.mp3',                 # Common Voice filename
+        'array': array([0.00000000e+00, ..., 7.78421963e-06]),  # raw audio waveform
+        'sampling_rate': 32000                                  # sampling rate in Hz
+    },
+    
+    # image as a PIL Image object (RGB, size varies)
+    'image': <PIL.JpegImageFile image mode=RGB size=500x375>,
+    
+    # text containing w words (one per language) separated by underscores
+    'text': 'σπιτάκι πουλιών_ドーム_प्रयोगशाला कोट_мавпа-павук_gown',
+    
+    # target word class name in English (key in translations.json)
+    'cls': 'dome',
+    
+    # class ID from translations.json (0 to 999)
+    'cls_id': 538,
+    
+    # target word (class name in the language of the audio)
+    'target_text': 'ドーム'
+}
+```
+
+The dataset includes a `translations.json` file that maps ImageNet class names across all supported languages. Each entry contains:
+- The English class name as the key
+- Translations for all supported languages (`ar`, `el`, `en`, `hi`, `ja`, `ko`, `te`, `th`, `uk`, `zh-CN`)
+- The ImageNet synset ID
+- A unique class ID (0-999)
+
+Example structure:
+```json
+{
+    "tench": {
+        "synset_id": "n01440764",
+        "cls_id": 0,
+        "ar": "سمك البنش",
+        "el": "είδος κυπρίνου",
+        "en": "tench",
+        "hi": "टेंच",
+        "ja": "テンチ",
+        "ko": "텐치",
+        "te": "టెంచ్",
+        "th": "ปลาเทนช์",
+        "uk": "линь",
+        "zh-CN": "丁鱥"
+    }
+}
+```
+
+## Dataset Variants
+We release three variants of the dataset:
+- Symile-M3-2 with 2 languages: English (`en`) and Greek (`el`).
+- Symile-M3-5 with 5 languages: English (`en`), Greek (`el`), Hindi (`hi`), Japanese (`ja`), and Ukrainian (`uk`).
+- Symile-M3-10 with 10 languages: Arabic (`ar`), Greek (`el`), English (`en`), Hindi (`hi`), Japanese (`ja`), Korean (`ko`), Telugu (`te`), Thai (`th`), Ukrainian (`uk`), and Chinese (`zh-CN`).
+
+Each variant is available in four sizes:
+- Large (`l`): 10M training samples, 500K validation samples, 500K test samples
+- Medium (`m`): 5M training samples, 250K validation samples, 250K test samples
+- Small (`s`): 1M training samples, 50K validation samples, 50K test samples
+- Extra Small (`xs`): 500K training samples, 25K validation samples, 25K test samples
+
+## Usage
+
+Before using the dataset, ensure you have the required audio and image processing libraries installed:
+```bash
+pip install librosa soundfile pillow
+```
+
+To load a specific version of Symile-M3, use a configuration name following the pattern `symile-m3-{num_langs}-{size}` where:
+- `num_langs` is `2`, `5`, or `10`
+- `size` is `xs`, `s`, `m`, or `l`
+
+For example, to load the `xs` version of Symile-M3-5:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("arsaporta/symile-m3", "symile-m3-5-xs")
+
+print(dataset['train'][0])    # access first train sample
+print(len(dataset['train']))  # get number of train samples
+```
+
+To process the dataset without loading it entirely into memory, use streaming mode to load samples one at a time:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("arsaporta/symile-m3", "symile-m3-5-xs", streaming=True)
+
+print(next(iter(dataset['train'])))
+```
+
+To download the dataset for offline use:
+
+```python
+from huggingface_hub import snapshot_download
+
+local_dir = snapshot_download(
+    repo_id="arsaporta/symile-m3",
+    repo_type="dataset",
+    local_dir="./symile_data",   # where to save
+    subfolder="symile-m3-5-xs",  # which configuration to download
+)
+```
+
+## Citation
+
+```
+@inproceedings{saporta2024symile,
+  title = {Contrasting with Symile: Simple Model-Agnostic Representation Learning for Unlimited Modalities}
+  author = {Saporta, Adriel and Puli, Aahlad and Goldstein, Mark and Ranganath, Rajesh}
+  booktitle = {Advances in Neural Information Processing Systems},
+  year = {2024}
+}
+```