added more dataset info

README.md

@@ -332,10 +332,12 @@ The inspiration is clearly the original GLUE benchmark.
 
 
 **TrCOLA** The original **C**orpus **o**f **L**inguistic **A**cceptability consists of sentences compiled from English literature textbooks. The task is to determine if the sentences are grammatically correct and acceptable sentences.
-Our corpus is also compiled from Turkish linguistic textbooks and include morphological, syntactic and semantic violations.
+Our corpus is also compiled from Turkish linguistic textbooks and include morphological, syntactic and semantic violations. 
+This dataset also has a [standalone repo on HuggingFace](https://huggingface.co/datasets/turkish-nlp-suite/TrCOLA).
 
 **TrSST-2** The Stanford Sentiment Treebank is a sentiment analysis dataset includes sentences from movie reviews, annotated by human annotators. 
-The task is to predict the sentiment of a given sentence. Our dataset is compiled from movie review websites, both reviews and sentiment ratings are compiled from those websites. 
+The task is to predict the sentiment of a given sentence. Our dataset is compiled from movie review websites BeyazPerde.com and Sinefil.com, both reviews and sentiment ratings are compiled from those websites. 
+Here we offer a binary classification task to be compatible with the original GLUE task, however we offer a 10-way classification challenge in this dataset's [standalone HuggingFace repo](https://huggingface.co/datasets/turkish-nlp-suite/BuyukSinema).
 
 ### Sentence Pair Tasks
 
@@ -392,7 +394,7 @@ For more information about dataset statistics, please visit the [research paper]
 ## Dataset Curation
 
 Some of the datasets are translates of original GLUE sets, some of the datasets are compiled by us. TrSST-2 is scraped from Turkish movie review websites, Sinefil and Beyazperde.
-TrCOLA is compiled from openly available linguistic books, then generated violation b<y the
+TrCOLA is compiled from openly available linguistic books, then generated violation by the
 LLM [Snowflake Arctic](https://www.snowflake.com/en/blog/arctic-open-efficient-foundation-language-models-snowflake/) and 
 then curated by the data company [Co-one](https://www.co-one.co/).
 For more information please refer to the [TrCOLA's standalone repo](https://huggingface.co/datasets/turkish-nlp-suite/TrCOLA) and the [research paper]().