Create README.md

README.md

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+# Filtered WIT, an Image-Text Dataset.
+A reliable Dataset to run Image-Text models.
+
+You can find WIT, Wikipedia Image Text Dataset, [here](https://github.com/google-research-datasets/wit)
+Data was taken from [dalle-mini/wit](https://huggingface.co/datasets/dalle-mini/wit)
+
+## Author
+ - [Aarush Katta](https://github.com/ARKseal)
+
+## Data Structure
+The data is stored as tars, containing 10,000 samples per tar.
+Each tar contains a `.jpg`, `.txt`, and `.json`.
+The image is stored in `.jpg`, the caption in `.txt.` and the metadata in `.json`
+The preferred method to read the data is [WebDataset](https://github.com/webdataset/webdataset)
+Here's an example:
+```python
+import webdataset as wds
+
+dataset = wds.WebDataset('data/00000.tar').to_tuple('txt', 'jpg', 'json')
+
+for text, image, meta in dataset:
+    print(
+      text[:50],
+      image[:50],
+      meta[:50]
+    )
+```
+
+## Filteration
+Each sample has 8 possible captions which were compared to the image using [CLIP ViT-B32](https://arxiv.org/abs/2103.00020)
+The text was encoded using [multilingual CLIP text encoder](https://huggingface.co/sentence-transformers/clip-ViT-B-32-multilingual-v1)
+Each possible caption was compared to the encoded image using Cosign Similarity
+and kept if the sim was greater than `0.26`
+Then the new caption was the filtered captions concatenated, and samples with no filtered caption were dropped.