Update README.md

README.md

@@ -177,6 +177,13 @@ Further, we release gold standard codemix dev and test set manually translated b
 ### Dataset Description
 
 We construct a large synthetic Hinglish-English dataset by leveraging a bilingual Hindi-English corpus.
+Split: Train, test, valid
+Subsets: 
+ - Hi - Hindi in devanagiri script (*अमेरिकी लोग अब पहले जितनी गैस नहीं खरीदते।*)
+ - Hicm - Hindi sentences with codemix words substituted in English (*American people अब पहले जितनी gas नहीं खरीदते।*)
+ - Hicmrom - Hicm with romanized hindi words (*American people ab pahle jitni gas nahin kharidte.*)
+ - Hicmdvg - Hicm with transliterated english words to devangiri (*अमेरिकन पेओपल अब पहले जितनी गैस नहीं खरीदते।*)
+ - NoisyHicmrom - synthetic noise added to Hicmrom sentences to improve model robustness (*Aerican people ab phle jtni gas nain khridte.*)
 
 - **Curated by:** LCS2 IIITD (https://www.lcs2.in/)
 - **Language(s) (NLP):** Hindi Romanized, Hindi Devanagiri, Hindi Codemix, English
@@ -207,7 +214,7 @@ Here, hindi is the matrix language which forms the syntactic and morphological s
 1. Use fast-align for learning alignment model b/w parallel corpora (Hi-En). Once words are aligned, next task is switch words from english sentences to hindi sentence based on inclusion list.
 1. Use heuristics to replace n-gram words and create multiple codemix mappings of the same hindi sentence.
 1. Filter sentences using deterministic and perplexity metrics from a multilingual model like XLM.
-
+1. Add synthetic noise like omission, switch, typo, random replacement to consider the noisy nature of codemix text.
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/61565c721b6f2789680793eb/KhhuM9Ze2-UrllHh6vRGL.png)