Update README.md

README.md

@@ -34,25 +34,25 @@ Furthermore, we excluded data that contained tags that warned of various rights
 This dataset building is conducted in conjunction with human validation. 
 We conducted approx random 1% human validation of filtered dataset and if any questionable data is found, add word to our database, and then filtered again.
 
-- How we curate this dataset
-  - **Problem statement** :
-  - Our goal to build this dataset is to achieve both quality and copyright/privacy safety.
-  - 1. Creating rights-cleared and safe-to-use dataset from an uncurated and noisy data source.
-    2. Creating diversified and balanced dataset from an uncurated and noisy data source.
-  - **Dataset curation** :
-  - 1. We used category tags to limit the data to safe use, and then conducted word based filtering.
-    - For public domain data, we used following categories only: `CC-PD-Mark, PD-self, PD-user, PD-author, PD-link, PD-old-70, PD-old-80, PD-old-90, PD-old-100`
-    - Images with these tags are removed even if they are tagged as public domain: `Images with watermarks, PD-algorithm, ~AI-generated works, With trademark, Unidentified logos, License review needed, Deletion requests, Flickr images~, Personality rights warining, Cosplay, Media from YouTube` (XXXX=Year)
-    - This means we solely use public domain data whose copyright is expired globally (US, EU and Japan) or waived directly by authors, without using AI generated contents.
-    - To address copyright laundering concerns, we also do not use any data sourced from Flickr. See: [Flickr Washing](https://commons.wikimedia.org/wiki/Commons:Problematic_sources#Flickr_washing:_is_the_work_original_with_the_uploader,_or_a_copyright_violation?)
-    - After category tag based filtering, we conducted word based filtering described above for mitigating possible rights infringing or harmful data.
-    - Actual photographs including recognizable human faces are removed from this dataset by using our internal human face detector, to maximize privacy safety.
-  - 2. We also improved the quality of our dataset by doing the following without using a pretrained model
-    - Image deduplication is conducted by using simple imagehash algorithm.
-    - To build diversified dataset with limited datasources, we use [WordNet](https://wordnet.princeton.edu/), and word count based balancing method introduced in the original [CLIP paper](https://arxiv.org/abs/2103.00020) and the research paper by [Hu Xu et al, "Demystifying CLIP Data"](https://arxiv.org/abs/2309.16671)
-      - Princeton University "About WordNet." [WordNet](https://wordnet.princeton.edu/). Princeton University. 2010.
-    - To improve caption accuracy, we performed a Commons API query on the words in WordNet and sorted them by relevance to add additional captions by query words.
-    - Also we conducted machine translation of captions between Japanese and English using [our ElanMT model](https://huggingface.co/Mitsua/elan-mt-bt-en-ja) which is trained exclusively on openly licensed corpus.
+# How we curate this dataset
+## Problem statement
+- Our goal to build this dataset is to achieve both quality and copyright/privacy safety.
+- 1. Creating rights-cleared and safe-to-use dataset from an uncurated and noisy data source.
+  2. Creating diversified and balanced dataset from an uncurated and noisy data source.
+## Dataset curation
+- 1. We used category tags to limit the data to safe use, and then conducted word based filtering.
+  - For public domain data, we used following categories only: `CC-PD-Mark, PD-self, PD-user, PD-author, PD-link, PD-old-70, PD-old-80, PD-old-90, PD-old-100`
+  - Images with these tags are removed even if they are tagged as public domain: `Images with watermarks, PD-algorithm, ~AI-generated works, With trademark, Unidentified logos, License review needed, Deletion requests, Flickr images~, Personality rights warining, Cosplay, Media from YouTube` (XXXX=Year)
+  - This means we solely use public domain data whose copyright is expired globally (US, EU and Japan) or waived directly by authors, without using AI generated contents.
+  - To address copyright laundering concerns, we also do not use any data sourced from Flickr. See: [Flickr Washing](https://commons.wikimedia.org/wiki/Commons:Problematic_sources#Flickr_washing:_is_the_work_original_with_the_uploader,_or_a_copyright_violation?)
+  - After category tag based filtering, we conducted word based filtering described above for mitigating possible rights infringing or harmful data.
+  - Actual photographs including recognizable human faces are removed from this dataset by using our internal human face detector, to maximize privacy safety.
+- 2. We also improved the quality of our dataset by doing the following without using a pretrained model
+  - Image deduplication is conducted by using simple imagehash algorithm.
+  - To build diversified dataset with limited datasources, we use [WordNet](https://wordnet.princeton.edu/), and word count based balancing method introduced in the original [CLIP paper](https://arxiv.org/abs/2103.00020) and the research paper by [Hu Xu et al, "Demystifying CLIP Data"](https://arxiv.org/abs/2309.16671)
+    - Princeton University "About WordNet." [WordNet](https://wordnet.princeton.edu/). Princeton University. 2010.
+  - To improve caption accuracy, we performed a Commons API query on the words in WordNet and sorted them by relevance to add additional captions by query words.
+  - Also we conducted machine translation of captions between Japanese and English using [our ElanMT model](https://huggingface.co/Mitsua/elan-mt-bt-en-ja) which is trained exclusively on openly licensed corpus.
 
 # Limitation and Biases
 - Public domain images would contain biased and toxic content, such as stereotypes about certain minoritized groups. We tried to remove this by manual word filtering.