Datasets:

syntaxsynth
/

tmmluplus

   - split: test
     path: veterinary_pharmacology/test-*
 ---
+# TMMLU+ : Large scale traditional chinese massive multitask language understanding
+We present TMMLU+, a traditional Chinese massive multitask language understanding dataset. TMMLU+ is a multiple-choice question-answering dataset featuring 66 subjects, ranging from elementary to professional level.
+The TMMLU+ dataset is six times larger and contains more balanced subjects compared to its predecessor, [TMMLU](https://github.com/mtkresearch/MR-Models/tree/main/TC-Eval/data/TMMLU). We have included benchmark results in TMMLU+ from closed-source models and 20 open-weight Chinese large language models, with parameters ranging from 1.8B to 72B. The benchmark results show that Traditional Chinese variants still lag behind those trained on major Simplified Chinese models.
+## Leaderboard ( Last Updated : 2025/04/05 )
+                   Model                    | humanities | social sciences |   STEM   |  Others  | Average
+-----------------------------------------------------------------------------------------------------------
+openrouter/quasar-alpha                     |  70.38   |  83.07   |  87.89   |  76.42   |  79.44
+deepseek-v3                               |  73.19   |  81.93   |  82.93   |  74.41   |  78.11
+Qwen/Qwen2.5-72B-Instruct-Turbo             |  67.59   |  79.36   |  82.57   |  72.65   |  75.54
+gpt-4o-2024-08-06                           |  65.48   |  78.23   |  81.39   |  71.24   |  74.08
+claude-3-5-sonnet-20240620                  |  73.23   |  78.27   |  68.50   |  69.35   |  72.34
+gemini-2.0-flash-001                        |  67.81   |  75.24   |  74.79   |  65.92   |  70.94
+gemini-2.0-flash-lite-001                   |  65.26   |  75.12   |  72.73   |  65.50   |  69.65
+gemini-2.0-flash-lite-preview-02-05         |  64.66   |  73.48   |  70.00   |  63.90   |  68.01
+meta-llama/llama-3.2-90b-vision-instruct    |  68.93   |  75.77   |  67.19   |  59.79   |  67.92
+qwen/qwen2.5-vl-32b-instruct                |  62.93   |  79.24   |  63.61   |  65.16   |  67.73
+Qwen/QwQ-32B-Preview                        |  57.98   |  70.94   |  72.87   |  63.59   |  66.35
+claude-3-opus-20240229                      |  60.34   |  70.12   |  67.43   |  62.32   |  65.05
+gemini-1.5-pro                              |  61.84   |  70.29   |  66.18   |  60.30   |  64.65
+gpt-4o-mini-2024-07-18                      |  55.01   |  67.09   |  73.16   |  61.36   |  64.15
+mistralai/Mistral-Small-24B-Instruct-2501   |  54.56   |  68.32   |  73.25   |  59.25   |  63.85
+llama-3.1-70b-versatile                     |  64.94   |  70.14   |  58.63   |  61.33   |  63.76
+Qwen/Qwen2.5-7B-Instruct-Turbo              |  54.42   |  64.51   |  68.01   |  58.83   |  61.44
+yentinglin/Llama-3-Taiwan-8B-Instruct       |  61.51   |  67.61   |  52.05   |  58.60   |  59.94
+meta-llama/llama-4-scout                    |  53.86   |  61.83   |  60.76   |  57.78   |  58.56
+google/gemma-3-27b-it                       |  59.75   |  58.32   |  55.65   |  49.21   |  55.73
+claude-3-sonnet-20240229                    |  52.06   |  59.38   |  49.87   |  51.64   |  53.24
+meta-llama/llama-4-maverick                 |  52.53   |  49.88   |  54.82   |  51.41   |  52.16
+Qwen2-7B-Instruct                           |  55.66   |  66.40   |  27.18   |  55.32   |  51.14
+gemma2-9b-it                                |  45.38   |  55.76   |  49.89   |  48.92   |  49.99
+claude-3-haiku-20240307                     |  47.48   |  54.48   |  48.47   |  48.77   |  49.80
+gemini-1.5-flash                            |  42.99   |  53.42   |  53.47   |  46.56   |  49.11
+reka-flash                                  |  44.07   |  52.68   |  46.04   |  43.43   |  46.56
+meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo |  44.03   |  50.95   |  42.75   |  45.19   |  45.73
+mixtral-8x7b-32768                          |  44.75   |  50.34   |  32.60   |  43.76   |  42.86
+meta-llama/Llama-3-70b-chat-hf              |  37.50   |  47.02   |  34.44   |  39.51   |  39.62
+RekaAI/reka-flash-3                         |  34.76   |  40.70   |  42.98   |  35.37   |  38.45
+google/gemma-7b-it                          |  34.00   |  35.70   |  31.89   |  33.79   |  33.84
+reka-edge                                   |  31.84   |  39.40   |  30.02   |  32.36   |  33.41
+meta-llama/Llama-3-8b-chat-hf               |  28.91   |  34.19   |  31.52   |  31.79   |  31.60
+taide/Llama3-TAIDE-LX-8B-Chat-Alpha1        |  27.02   |  36.64   |  25.33   |  27.96   |  29.24
+## How to use
+```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('syntaxsynth/tmmluplus', task)['validation']
+  dev = load_dataset('syntaxsynth/tmmluplus', task)['train']
+  test = load_dataset('syntaxsynth/tmmluplus', task)['test']
+```
+Statistic on all four categories : STEM, Social Science, Humanities, Other
+| Category                         | Test  | Dev  | Validation |
+|----------------------------------|-------|------|------------|
+| STEM                             | 3458  | 70   | 385        |
+| Social Sciences                  | 5958  | 90   | 665        |
+| Humanities                       | 1763  | 35   | 197        |
+| Other (Business, Health, Misc.)  | 8939  | 135  | 995        |
+| **Total**                        | 20118 | 330  | 2242       |
+## Citation
+```
+@inproceedings{tam2024tmmlu+,
+  title={Tmmlu+: An improved traditional chinese evaluation suite for foundation models},
+  author={Tam, Zhi Rui and Pai, Ya Ting and Lee, Yen-Wei and Shuai, Hong-Han and Chen, Jun-Da and Chu, Wei Min and Cheng, Sega},
+  booktitle={First Conference on Language Modeling},
+  year={2024}
+}
+```