Update README.md

README.md

@@ -24,9 +24,10 @@ This dataset has Wikipedia articles pertaining to Indian context.
 ### Dataset Description
 
 <!-- Provide a longer summary of what this dataset is. -->
-We filtered out Indian context data from [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset's English articles by keywords. 
-Further we trained a model to classify for Indian content vs Not Indian content to narrow down filtered English articles. 
-We then translated these artices to 6 Indian languages (Hindi, Bengali, Gujarati, Tamil, Kannada, Urdu) using AI4Bharat's [IndicTrans2](https://huggingface.co/ai4bharat/indictrans2-en-indic-1B). The dataset has been cleaned and can be used for pre-training multilingual LLMs.
+The dataset is built from Wikipedia articles taken from [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia). 
+We filtered, cleaned and translated English articles related to India and Indian context out of entire dataset.
+
+\Each example has contents of a full cleaned wikipedia article and it's translations in 6 Indian languages.
 
 
 - **Curated by:** [Soket AI Labs](https://soket.ai/)
@@ -41,17 +42,46 @@ The dataset is focussed on Indian factual content for pre-training LLMs where In
 ## Dataset Structure
 
 <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+Total number of rows: 200820 \
+It has approximately **1.54** billion tokens for all languages with almost similar number of tokens for each language when tokenized 
+with our Indic tokenizer we created which can be found in our model repository [Pragna-1b](https://huggingface.co/soketlabs/pragna-1b).
+Here are token counts for each language:
+- English: 196.2 millions
+- Hindi: 225 millions
+- Bengali: 286.2 millions
+- Gujarati: 204 millions
+- Tamil: 231.3 millions
+- Kannada: 201.3 millions
+- Urdu: 204.9 millions
+
+\These numbers were extrapolated from calculations on 10% of randomly sampled dataset.\
+
 Each row corresponds to a wikipedia article with the decription of article in source language(english) and translations in 6 indian languages.  
 The title is in english and descriptions in different languages is represented by column name of format "language_code"_"script". 
 Each description column in different languages is a list of sentences/multiple sentences and can be concatenated to get cleaned article decription.
+\\Each row is of the format:
+
+{'id': '1',
+ 'url': 'https://simple.wikipedia.org/sample_article',
+ 'title': 'Sample article',
+ 'eng_Latn': ['This is a sample...', 'and more information'],
+ 'hin_Deva': ['यह एक नमूना है'..., 'और अधिक जानकारी'],
+ 'kan_Knda': ['ಇದು ಒಂದು ಮಾದರಿ...', 'ಮತ್ತು ಹೆಚ್ಚಿನ ಮಾಹಿತಿ'],
+ 'ben_Beng': ['এটি একটি নমুনা...', 'এবং আরও তথ্য'],
+ 'guj_Gujr': ['આ એક નમૂનો છે...', 'અને વધુ માહિતી'],
+ 'tam_Taml': ['இது ஒரு மாதிரி...', 'மேலும் தகவல்'],
+ 'urd_Arab': ['...یہ ایک نمونہ ہے۔', 'اور مزید معلومات']
+}
+
 
 ## Dataset Creation
 
 ### Curation Rationale
 
 <!-- Motivation for the creation of this dataset. -->
+We needed to induce knowledge regarding India and Indian context while training our LLM, for which we gathered available Indic 
+content data and also filtered factual data from Wikipedia.
 
-[More Information Needed]
 
 ### Source Data
 
@@ -61,8 +91,13 @@ Wikpedia english articles from [wikimedia/wikipedia](https://huggingface.co/data
 #### Data Collection and Processing
 
 <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+We filtered out Indian context data from [wikimedia/wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia) dataset's English 
+articles by select keywords. 
+Further we trained a few shot classification model to classify for Indian content vs Not Indian content to narrow down filtered English 
+articles. 
+We cleaned the articles and removed unwanted paragraphs for References etc. 
+We then translated these artices to 6 Indian languages (Hindi, Bengali, Gujarati, Tamil, Kannada, Urdu) using AI4Bharat's [IndicTrans2](https://huggingface.co/ai4bharat/indictrans2-en-indic-1B). The dataset has been cleaned and can be used for pre-training multilingual LLMs.
 
-[More Information Needed]