Update README.md

README.md

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 ---
+license: cc-by-4.0
 dataset_info:
   features:
   - name: SAMPLE_ID
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   - name: train
     num_bytes: 28506248899
     num_examples: 107166507
-  download_size: 16353146269
+  download_size: 16353125308
   dataset_size: 28506248899
 configs:
 - config_name: default
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   - split: train
     path: data/train-*
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+# 100M Text Debiased Subset from LAION 2B
+
+- Captions in LAION-2B have a significant bias towards describing visual text content embedded in the images.
+- Released CLIP models have strong text spotting bias in almost every style of web images, resulting in the CLIP-filtering datasets inherently biased towards visual text dominant data.
+- CLIP models easily learn text spotting capacity from parrot captions while failing to connect the vision-language semantics, just like a text spotting parrot.
+
+For more details, please see our [paper](https://arxiv.org/abs/2312.14232).
+
+## Filtering Details
+
+We provide an alternative solution by releasing a less biased filtered LAION-2B 100M(107,166,507) subset.
+
+We construct a less biased 100M subset from the LAION-2B subset with Empty OCR results, CLIP score > 0.3, and Aesthetics score > 4.5.
+
+We add the ase_scores and K-means labels (4000 total) for each image-text pair.
+
+*We also released the dataset on [OpenDataLab](https://openxlab.org.cn/datasets/opendatalab-linyiqi/LAION-text-debiased-100M).*
+
+The pre-trained CLIP model is released on [github](https://github.com/opendatalab/CLIP-Parrot-Bias).
+
+## Reference
+```
+@article{lin2023parrot,
+  title={Parrot Captions Teach CLIP to Spot Text}, 
+  author={Yiqi Lin and Conghui He and Alex Jinpeng Wang and Bin Wang and Weijia Li and Mike Zheng Shou},
+  journal={arXiv preprint arXiv:2312.14232},
+  year={2023}
+}
+@misc{conghui2022opendatalab,
+  author={He, Conghui and Li, Wei and Jin, Zhenjiang and Wang, Bin and Xu, Chao and Lin, Dahua},
+  title={OpenDataLab: Empowering General Artificial Intelligence with Open Datasets},
+  howpublished = {\url{https://opendatalab.com}},
+  year={2022}
+}
+```