Update README.md

README.md

@@ -27,6 +27,37 @@ We present TMMLU+ a traditional Chinese massive multitask language understanding
 
 TMMLU+ dataset is 6 times larger and contains more balanced subjects compared to the previous version, [TMMLU](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval/data/TMMLU). We included benchmark results in TMMLU+ from closed-source models and 20 open-weight Chinese large language models of parameters ranging from 1.8B to 72B. Benchmark results show Traditional Chinese variants still lag behind those trained on Simplified Chinese major models.
 
+
+```python
+from datasets import load_dataset
+task_list = [
+             'engineering_math', 'dentistry', 'traditional_chinese_medicine_clinical_medicine', 'clinical_psychology', 'technical', 'culinary_skills', 'mechanical', 'logic_reasoning', 'real_estate',
+             'general_principles_of_law', 'finance_banking', 'anti_money_laundering', 'ttqav2', 'marketing_management', 'business_management', 'organic_chemistry', 'advance_chemistry',
+             'physics', 'secondary_physics', 'human_behavior', 'national_protection', 'jce_humanities', 'politic_science', 'agriculture', 'official_document_management',
+             'financial_analysis', 'pharmacy', 'educational_psychology', 'statistics_and_machine_learning', 'management_accounting', 'introduction_to_law', 'computer_science', 'veterinary_pathology',
+             'accounting', 'fire_science', 'optometry', 'insurance_studies', 'pharmacology', 'taxation', 'trust_practice', 'geography_of_taiwan', 'physical_education', 'auditing', 'administrative_law',
+             'education_(profession_level)', 'economics', 'veterinary_pharmacology', 'nautical_science', 'occupational_therapy_for_psychological_disorders',
+             'basic_medical_science', 'macroeconomics', 'trade', 'chinese_language_and_literature', 'tve_design', 'junior_science_exam', 'junior_math_exam', 'junior_chinese_exam',
+             'junior_social_studies', 'tve_mathematics', 'tve_chinese_language', 'tve_natural_sciences', 'junior_chemistry', 'music', 'education', 'three_principles_of_people',
+             'taiwanese_hokkien'
+            ]
+for task in task_list:
+  val = load_dataset('ikala/tmmluplus', task)['validation']
+  dev = load_dataset('ikala/tmmluplus', task)['train']
+  test = load_dataset('ikala/tmmluplus', task)['test']
+```
+
+Statistic on datasets result
+
+| Category                         | Test  | Dev  | Validation |
+|----------------------------------|-------|------|------------|
+| STEM                             | 3458  | 70   | 385        |
+| Other (Business, Health, Misc.)  | 8939  | 135  | 995        |
+| Social Sciences                  | 5958  | 90   | 665        |
+| Humanities                       | 1763  | 35   | 197        |
+| **Total**                        | 20118 | 330  | 2242       |
+
+
 ## Benchmark on direct prompting
 
 | model | STEM | Social Science | Humanities | Other | Average |
@@ -59,3 +90,6 @@ TMMLU+ dataset is 6 times larger and contains more balanced subjects compared to
 | [yentinglin/Taiwan-LLM-7B-v2.1-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.1-chat) | 14.99 | 16.23 | 15.00 | 16.22 |15.61|
 | [FlagAlpha/Atom-7B](https://huggingface.co/FlagAlpha/Atom-7B) | 5.60 | 13.57 | 7.71 | 11.84 |9.68|
 
+
+
+