Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-license: cc
+license: mit
 multilinguality: multilingual
 task_categories:
 - multiple-choice
@@ -7,6 +7,7 @@ pretty_name: Tokenization Robustness
 tags:
 - multilingual
 - tokenization
+- robustness
 dataset_info:
 - config_name: tokenizer_robustness_completion_general_abbreviations
   features:
@@ -777,7 +778,11 @@ configs:
   data_files:
   - split: test
     path: tokenizer_robustness_completion_general_unusual_formatting/test-*
+language:
+- en
+size_categories:
+- n<1K
 ---
 ## TokSuite Bonus Benchmarks (General Collection)
 
-This dataset provides a **bonus set of TokSuite benchmarks** designed to probe tokenizer robustness under **language-agnostic, cross-domain surface-form perturbations** that commonly occur in real-world text. The General collection includes canonical questions alongside targeted perturbations such as abbreviations, character deletion, currency symbol usage, diverse date formats, and unusual or non-standard formatting. Unlike language-specific TokSuite subsets, these benchmarks focus on **universal tokenization stressors** that arise across languages, domains, and writing contexts, offering a compact but high-signal evaluation suite for analyzing how tokenizers handle formatting irregularities, symbol-heavy text, and noisy inputs independent of linguistic morphology.
+This dataset provides a **bonus set of TokSuite benchmarks** designed to probe tokenizer robustness under **language-agnostic, cross-domain surface-form perturbations** that commonly occur in real-world text. The General collection includes canonical questions alongside targeted perturbations such as abbreviations, character deletion, currency symbol usage, diverse date formats, and unusual or non-standard formatting. Unlike language-specific TokSuite subsets, these benchmarks focus on **universal tokenization stressors** that arise across languages, domains, and writing contexts, offering a compact but high-signal evaluation suite for analyzing how tokenizers handle formatting irregularities, symbol-heavy text, and noisy inputs independent of linguistic morphology.