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aigrant
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awesome-taiwan-knowledge

Tasks:

Table Question Answering

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Cannot get the config names for the dataset.

Error code:   ConfigNamesError
Exception:    ValueError
Message:      
Expected data_files in YAML to be either a string or a list of strings
or a list of dicts with two keys: 'split' and 'path', but got [{'split': 'mcq-civics-studies-hs', 'path': 'mcq-civics-studies-hs.csv-*'}, {'split': 'mcq-taiwan literature.csv', 'path': 'mcq-taiwan literature.csv-*'}, {'split': 'mcq-taiwan-social-studies-elem-jhs.csv', 'path': 'mcq-taiwan-social-studies-elem-jhs.csv-*'}, {'split': 'mtqs-sicial-studies-elem-jhs.csv', 'path': 'mtqs-sicial-studies-elem-jhs.csv-*'}, {'split': 'mtqs-taiwan-literature.csv', 'path': 'mtqs-taiwan-literature.csv-*'}]
Examples of data_files in YAML:

   data_files: data.csv

   data_files: data/*.png

   data_files:
    - part0/*
    - part1/*

   data_files:
    - split: train
      path: train/*
    - split: test
      path: test/*

   data_files:
    - split: train
      path:
      - train/part1/*
      - train/part2/*
    - split: test
      path: test/*

PS: some symbols like dashes '-' are not allowed in split names

Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 79, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1914, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1889, in dataset_module_factory
                  return HubDatasetModuleFactoryWithoutScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1237, in get_module
                  metadata_configs = MetadataConfigs.from_dataset_card_data(dataset_card_data)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/metadata.py", line 231, in from_dataset_card_data
                  cls._raise_if_data_files_field_not_valid(metadata_config)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/metadata.py", line 178, in _raise_if_data_files_field_not_valid
                  raise ValueError(yaml_error_message)
              ValueError: 
              Expected data_files in YAML to be either a string or a list of strings
              or a list of dicts with two keys: 'split' and 'path', but got [{'split': 'mcq-civics-studies-hs', 'path': 'mcq-civics-studies-hs.csv-*'}, {'split': 'mcq-taiwan literature.csv', 'path': 'mcq-taiwan literature.csv-*'}, {'split': 'mcq-taiwan-social-studies-elem-jhs.csv', 'path': 'mcq-taiwan-social-studies-elem-jhs.csv-*'}, {'split': 'mtqs-sicial-studies-elem-jhs.csv', 'path': 'mtqs-sicial-studies-elem-jhs.csv-*'}, {'split': 'mtqs-taiwan-literature.csv', 'path': 'mtqs-taiwan-literature.csv-*'}]
              Examples of data_files in YAML:
              
                 data_files: data.csv
              
                 data_files: data/*.png
              
                 data_files:
                  - part0/*
                  - part1/*
              
                 data_files:
                  - split: train
                    path: train/*
                  - split: test
                    path: test/*
              
                 data_files:
                  - split: train
                    path:
                    - train/part1/*
                    - train/part2/*
                  - split: test
                    path: test/*
              
              PS: some symbols like dashes '-' are not allowed in split names

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Awesome Taiwan Knowledge (ATK) Dataset

The Awesome Taiwan Knowledge (ATK) Dataset is a comprehensive collection of questions and answers designed to evaluate artificial intelligence models' understanding of Taiwan-specific information. This unique dataset addresses the growing need for culturally nuanced AI performance metrics, particularly for models claiming global competence.

Key Features:

Taiwan-Centric Content: Covers a wide range of topics uniquely relevant to Taiwan, including history, culture, politics, education, and current affairs.
Diverse Question Formats:
- Multiple-choice questions for quantitative assessment
- Multi-turn dialogue questions to evaluate contextual understanding and conversational abilities
Expert-Validated Answers: All responses are meticulously curated and verified by qualified Taiwanese educators and subject matter experts.
Detailed Explanations: Each question is accompanied by in-depth explanations, providing context and educational value beyond mere right/wrong evaluations.
Continuous Updates: The dataset is regularly refreshed to include current events and evolving cultural nuances.

Focused Subject Areas:

The ATK Dataset collects questions from key educational domains, ensuring comprehensive coverage of Taiwan-specific knowledge:

Civic Studies for High School
Social Studies for Elementary School and Junior High
Taiwan Literature for K-12
Taiwan Geography
Taiwan History

These areas represent core components of Taiwan's educational curriculum, providing a robust foundation for assessing AI models' understanding of Taiwan's societal, cultural, and geographical landscape.

Purpose:

Benchmark AI models' proficiency in Taiwan-specific knowledge
Identify gaps in AI systems' understanding of localized information
Promote the development of more culturally aware and inclusive AI models
Provide a standardized tool for comparing different AI models' performance on Taiwan-related queries

Current Status:

The ATK Dataset is in active development, with ongoing data collection from local educators and experts. A comprehensive benchmarking report, evaluating various AI models against this dataset, is forthcoming.

Significance:

This dataset aims to highlight the importance of cultural and regional knowledge in AI systems, encouraging developers to create more inclusive and globally competent models. By focusing on Taiwan-specific information, the ATK Dataset addresses a critical gap in current AI evaluation metrics.

Stay Tuned:

The full Awesome Taiwan Knowledge Dataset and initial benchmarking results will be released soon, offering unprecedented insights into AI models' capabilities in handling Taiwan-specific queries.

Contributors

年級	領域	教師名稱	學校
小學	公民	朱堯麟	退休
國中	台灣文學	陳雅娟	竹北國中
高中	公民	廖宗德	六家高中
		and 5 more annonymous contributors

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