Update README.md

README.md

@@ -5,32 +5,39 @@ task_categories:
 language:
 - en
 tags:
-- ocr
+- conversational
 pretty_name: Youtube Commons Corpus
 ---
 
 # 📺 Youtube-Commons 📺
 
-**Youtube-Commons** is a corpus of audio transcripts from 1.3 million videos shared on Youtube under a CC-By license.
+**Youtube-Commons** is a collection of audio transcripts of 1,689,372 videos shared on Youtube under a CC-By license.
 
 
 ## Content
+The collection comprises 12,663,123 original and automatically translated transcripts from 1,689,372 videos (348,839 individual channels).
+
+In total, this represents 24,470,331,415 words.
+
+All the videos where shared on Youtube with a CC-BY license: the dataset provide all the necessary provenance information including the title, link, channel name and upload date.
 
 ## Language
 
-The corpus is fully multilingual.
+The corpus is multilingual with a majority of English-speaking content (67%). Most represented languages aside from English include Hindi (927,466 videos), French (601,559 videos), and Spanish (492,279 videos)
 
 ## Uses
-The primary use of the collection is for cultural analytics on a wide scale. It has been instrumental for some major digital humanities projects like [Viral Texts](https://viraltexts.org/).
+The collection aims to expand the availability of conversational data for research in AI, computational social science and digital humanities. 
+
+Most of the available resources under free licenses are written texts such as public domain works or open science articles. 
 
-The collection also aims to expand the availability of open works for the training of Large Language Models. The text can be used for model training and republished without restriction for reproducibility purposes.
+The text can be used for training model and republished with  for reproducibility purposes.
 
-## License and ethical note
+## License and ethics
 All the transcripts are part of a video shared under a CC-By license. In accordance with the provision of the license, every Youtube channel is fully credited.
 
 While content under a free license can be lawfully reproduced in any setting, there is currently a debate over the legitimacy and proper ethical use of free content for pre-training large language models.
 
-In accordance with the philosophy of Creative Commons, we recommend that this set be preferablyb used for open models, especially ones with an integrated open approach (open data, open code, open weights), and strong guarantees of contributing back to the commons.
+In accordance with the philosophy of Creative Commons, we recommend that this set be preferably used for open research. Furthermore, the license requires that contribution of each individual author is properly credited. In a research context, the best way to achieve this aim would be to fully release the data sources used for training or, at the very least, provide an extensive open documentation.
 
 ## Future developments