Update README.md

README.md

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 ---
 license: cc-by-4.0
+task_categories:
+- text-classification
+tags:
+- sequence classification
+- formal languages
+- regular languages
+- long-distance dependencies
+- logical complexity
+- generalization
+pretty_name: MLRegTest
+size_categories:
+- 10K<n<100K
 ---
+
+The dataset is stored at the OSF [here](https://osf.io/ksdnm/)
+
+MLRegTest is a benchmark for sequence classification, containing training, development, and test sets from 1,800 regular languages.
+Regular languages are formal languages, which are sets of sequences definable with certain kinds of formal grammars, including
+regular expressions, finite-state acceptors, and monadic second-order logic with either the successor or precedence relation in the
+model signature for words. This benchmark was designed to help identify those factors, specifically the kinds of long-distance
+dependencies, that can make it difficult for ML systems to generalize successfully in learning patterns over sequences. MLRegTest
+organizes its languages according to their logical complexity (monadic second-order, first-order, propositional, or monomial
+expressions) and the kind of logical literals (string, tier-string, subsequence, or combinations thereof). The logical complexity
+and choice of literal provides a systematic way to understand different kinds of long-distance dependencies in regular languages,
+and therefore to understand the capabilities of different ML systems to learn such long-distance dependencies. The authors think it
+will be an important milestone if other researchers are able to find an ML system that succeeds across the board on MLRegTest.