africa-intelligence/llama-south-africa-benchmarking

--- pretty_name: Evaluation run of chad-brouze/llama-8b-south-africa dataset_summary: "Dataset automatically created during the evaluation run of model\ \ [chad-brouze/llama-8b-south-africa](https://huggingface.co/chad-brouze/llama-8b-south-africa)\n\ The dataset is composed of 17 configuration(s), each one corresponding to one of\ \ the evaluated task.\n\nThe dataset has been created from 14 run(s). Each run can\ \ be found as a specific split in each configuration, the split being named using\ \ the timestamp of the run.The \"train\" split is always pointing to the latest\ \ results.\n\nAn additional configuration \"results\" store all the aggregated results\ \ of the run.\n\nTo load the details from a run, you can for instance do the following:\n\ ```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"africa-intelligence/llama-south-africa-benchmarking\"\ ,\n\tname=\"chad-brouze__llama-8b-south-africa__afrimgsm_direct_xho\",\n\tsplit=\"\ latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results from run\ \ 2024-10-01T01-56-35.375763](https://huggingface.co/datasets/africa-intelligence/llama-south-africa-benchmarking/blob/main/chad-brouze/llama-8b-south-africa/results_2024-10-01T01-56-35.375763.json)\ \ (note that there might be results for other tasks in the repos if successive evals\ \ didn't cover the same tasks. You find each in the results and the \"latest\" split\ \ for each eval):\n\n```python\n{\n \"all\": {\n \"afrimgsm_direct_xho\"\ : {\n \"alias\": \"afrimgsm_direct_xho\",\n \"exact_match,remove_whitespace\"\ : 0.02,\n \"exact_match_stderr,remove_whitespace\": 0.008872139507342681,\n\ \ \"exact_match,flexible-extract\": 0.048,\n \"exact_match_stderr,flexible-extract\"\ : 0.013546884228085717\n },\n \"afrimgsm_direct_zul\": {\n \ \ \"alias\": \"afrimgsm_direct_zul\",\n \"exact_match,remove_whitespace\"\ : 0.024,\n \"exact_match_stderr,remove_whitespace\": 0.00969908702696424,\n\ \ \"exact_match,flexible-extract\": 0.068,\n \"exact_match_stderr,flexible-extract\"\ : 0.01595374841074702\n },\n \"afrimmlu_direct_xho\": {\n \ \ \"alias\": \"afrimmlu_direct_xho\",\n \"acc,none\": 0.296,\n \ \ \"acc_stderr,none\": 0.020435342091896135,\n \"f1,none\": 0.28935123890039643,\n\ \ \"f1_stderr,none\": \"N/A\"\n },\n \"afrimmlu_direct_zul\"\ : {\n \"alias\": \"afrimmlu_direct_zul\",\n \"acc,none\":\ \ 0.316,\n \"acc_stderr,none\": 0.020812359515855857,\n \"\ f1,none\": 0.3097199360473918,\n \"f1_stderr,none\": \"N/A\"\n \ \ },\n \"afrixnli_en_direct_xho\": {\n \"alias\": \"afrixnli_en_direct_xho\"\ ,\n \"acc,none\": 0.44333333333333336,\n \"acc_stderr,none\"\ : 0.02029781968475275,\n \"f1,none\": 0.3534203078622213,\n \ \ \"f1_stderr,none\": \"N/A\"\n },\n \"afrixnli_en_direct_zul\"\ : {\n \"alias\": \"afrixnli_en_direct_zul\",\n \"acc,none\"\ : 0.43,\n \"acc_stderr,none\": 0.02022824683332485,\n \"f1,none\"\ : 0.3421107285347325,\n \"f1_stderr,none\": \"N/A\"\n }\n },\n\ \ \"afrimgsm_direct_xho\": {\n \"alias\": \"afrimgsm_direct_xho\",\n \ \ \"exact_match,remove_whitespace\": 0.02,\n \"exact_match_stderr,remove_whitespace\"\ : 0.008872139507342681,\n \"exact_match,flexible-extract\": 0.048,\n \ \ \"exact_match_stderr,flexible-extract\": 0.013546884228085717\n },\n \ \ \"afrimgsm_direct_zul\": {\n \"alias\": \"afrimgsm_direct_zul\",\n \ \ \"exact_match,remove_whitespace\": 0.024,\n \"exact_match_stderr,remove_whitespace\"\ : 0.00969908702696424,\n \"exact_match,flexible-extract\": 0.068,\n \ \ \"exact_match_stderr,flexible-extract\": 0.01595374841074702\n },\n \"\ afrimmlu_direct_xho\": {\n \"alias\": \"afrimmlu_direct_xho\",\n \"\ acc,none\": 0.296,\n \"acc_stderr,none\": 0.020435342091896135,\n \ \ \"f1,none\": 0.28935123890039643,\n \"f1_stderr,none\": \"N/A\"\n },\n\ \ \"afrimmlu_direct_zul\": {\n \"alias\": \"afrimmlu_direct_zul\",\n \ \ \"acc,none\": 0.316,\n \"acc_stderr,none\": 0.020812359515855857,\n\ \ \"f1,none\": 0.3097199360473918,\n \"f1_stderr,none\": \"N/A\"\n\ \ },\n \"afrixnli_en_direct_xho\": {\n \"alias\": \"afrixnli_en_direct_xho\"\ ,\n \"acc,none\": 0.44333333333333336,\n \"acc_stderr,none\": 0.02029781968475275,\n\ \ \"f1,none\": 0.3534203078622213,\n \"f1_stderr,none\": \"N/A\"\n\ \ },\n \"afrixnli_en_direct_zul\": {\n \"alias\": \"afrixnli_en_direct_zul\"\ ,\n \"acc,none\": 0.43,\n \"acc_stderr,none\": 0.02022824683332485,\n\ \ \"f1,none\": 0.3421107285347325,\n \"f1_stderr,none\": \"N/A\"\n\ \ }\n}\n```" repo_url: https://huggingface.co/chad-brouze/llama-8b-south-africa leaderboard_url: '' point_of_contact: '' configs: - config_name: CohereForAI__aya-23-8B__afrimgsm_direct_xho data_files: - split: 2024_09_29T21_54_34.048716 path: - '**/samples_afrimgsm_direct_xho_2024-09-29T21-54-34.048716.jsonl' - split: latest path: - '**/samples_afrimgsm_direct_xho_2024-09-29T21-54-34.048716.jsonl' - config_name: CohereForAI__aya-23-8B__afrimgsm_direct_zul data_files: - split: 2024_09_29T21_54_34.048716 path: - '**/samples_afrimgsm_direct_zul_2024-09-29T21-54-34.048716.jsonl' - split: latest path: - '**/samples_afrimgsm_direct_zul_2024-09-29T21-54-34.048716.jsonl' - config_name: CohereForAI__aya-23-8B__afrimmlu_direct_xho data_files: - split: 2024_09_29T21_54_34.048716 path: - '**/samples_afrimmlu_direct_xho_2024-09-29T21-54-34.048716.jsonl' - split: latest path: - '**/samples_afrimmlu_direct_xho_2024-09-29T21-54-34.048716.jsonl' - config_name: CohereForAI__aya-23-8B__afrimmlu_direct_zul data_files: - split: 2024_09_29T21_54_34.048716 path: - '**/samples_afrimmlu_direct_zul_2024-09-29T21-54-34.048716.jsonl' - split: latest path: - '**/samples_afrimmlu_direct_zul_2024-09-29T21-54-34.048716.jsonl' - config_name: CohereForAI__aya-23-8B__afrixnli_en_direct_xho data_files: - split: 2024_09_29T21_54_34.048716 path: - '**/samples_afrixnli_en_direct_xho_2024-09-29T21-54-34.048716.jsonl' - split: latest path: - '**/samples_afrixnli_en_direct_xho_2024-09-29T21-54-34.048716.jsonl' - config_name: CohereForAI__aya-23-8B__afrixnli_en_direct_zul data_files: - split: 2024_09_29T21_54_34.048716 path: - '**/samples_afrixnli_en_direct_zul_2024-09-29T21-54-34.048716.jsonl' - split: latest path: - '**/samples_afrixnli_en_direct_zul_2024-09-29T21-54-34.048716.jsonl' - config_name: chad-brouze__llama-8b-south-africa__afrimgsm_direct_xho data_files: - split: 2024_10_01T01_56_35.375763 path: - '**/samples_afrimgsm_direct_xho_2024-10-01T01-56-35.375763.jsonl' - split: latest path: - '**/samples_afrimgsm_direct_xho_2024-10-01T01-56-35.375763.jsonl' - config_name: chad-brouze__llama-8b-south-africa__afrimgsm_direct_zul data_files: - split: 2024_10_01T01_56_35.375763 path: - '**/samples_afrimgsm_direct_zul_2024-10-01T01-56-35.375763.jsonl' - split: latest path: - '**/samples_afrimgsm_direct_zul_2024-10-01T01-56-35.375763.jsonl' - config_name: chad-brouze__llama-8b-south-africa__afrimmlu_direct_xho data_files: - split: 2024_10_01T01_56_35.375763 path: - '**/samples_afrimmlu_direct_xho_2024-10-01T01-56-35.375763.jsonl' - split: latest path: - '**/samples_afrimmlu_direct_xho_2024-10-01T01-56-35.375763.jsonl' - config_name: chad-brouze__llama-8b-south-africa__afrimmlu_direct_zul data_files: - split: 2024_10_01T01_56_35.375763 path: - '**/samples_afrimmlu_direct_zul_2024-10-01T01-56-35.375763.jsonl' - split: latest path: - '**/samples_afrimmlu_direct_zul_2024-10-01T01-56-35.375763.jsonl' - config_name: chad-brouze__llama-8b-south-africa__afrixnli_en_direct_xho data_files: - split: 2024_10_01T01_56_35.375763 path: - '**/samples_afrixnli_en_direct_xho_2024-10-01T01-56-35.375763.jsonl' - split: latest path: - '**/samples_afrixnli_en_direct_xho_2024-10-01T01-56-35.375763.jsonl' - config_name: chad-brouze__llama-8b-south-africa__afrixnli_en_direct_zul data_files: - split: 2024_10_01T01_56_35.375763 path: - '**/samples_afrixnli_en_direct_zul_2024-10-01T01-56-35.375763.jsonl' - split: latest path: - '**/samples_afrixnli_en_direct_zul_2024-10-01T01-56-35.375763.jsonl' - config_name: meta-llama__Llama-3.1-8B-Instruct__afrimgsm_direct_xho data_files: - split: 2024_09_29T21_41_43.806530 path: - '**/samples_afrimgsm_direct_xho_2024-09-29T21-41-43.806530.jsonl' - split: latest path: - '**/samples_afrimgsm_direct_xho_2024-09-29T21-41-43.806530.jsonl' - config_name: meta-llama__Llama-3.1-8B-Instruct__afrimgsm_direct_zul data_files: - split: 2024_09_29T21_41_43.806530 path: - '**/samples_afrimgsm_direct_zul_2024-09-29T21-41-43.806530.jsonl' - split: latest path: - '**/samples_afrimgsm_direct_zul_2024-09-29T21-41-43.806530.jsonl' - config_name: meta-llama__Llama-3.1-8B-Instruct__afrimmlu_direct_xho data_files: - split: 2024_09_29T21_41_43.806530 path: - '**/samples_afrimmlu_direct_xho_2024-09-29T21-41-43.806530.jsonl' - split: latest path: - '**/samples_afrimmlu_direct_xho_2024-09-29T21-41-43.806530.jsonl' - config_name: meta-llama__Llama-3.1-8B-Instruct__afrimmlu_direct_zul data_files: - split: 2024_09_29T21_41_43.806530 path: - '**/samples_afrimmlu_direct_zul_2024-09-29T21-41-43.806530.jsonl' - split: latest path: - '**/samples_afrimmlu_direct_zul_2024-09-29T21-41-43.806530.jsonl' - config_name: meta-llama__Llama-3.1-8B-Instruct__afrixnli_en_direct_xho data_files: - split: 2024_09_29T21_41_43.806530 path: - '**/samples_afrixnli_en_direct_xho_2024-09-29T21-41-43.806530.jsonl' - split: latest path: - '**/samples_afrixnli_en_direct_xho_2024-09-29T21-41-43.806530.jsonl' - config_name: meta-llama__Llama-3.1-8B-Instruct__afrixnli_en_direct_zul data_files: - split: 2024_09_29T21_41_43.806530 path: - '**/samples_afrixnli_en_direct_zul_2024-09-29T21-41-43.806530.jsonl' - split: latest path: - '**/samples_afrixnli_en_direct_zul_2024-09-29T21-41-43.806530.jsonl' --- # Dataset Card for Evaluation run of chad-brouze/llama-8b-south-africa <!-- Provide a quick summary of the dataset. --> Dataset automatically created during the evaluation run of model [chad-brouze/llama-8b-south-africa](https://huggingface.co/chad-brouze/llama-8b-south-africa) The dataset is composed of 17 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 14 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An additional configuration "results" store all the aggregated results of the run. To load the details from a run, you can for instance do the following: ```python from datasets import load_dataset data = load_dataset( "africa-intelligence/llama-south-africa-benchmarking", name="chad-brouze__llama-8b-south-africa__afrimgsm_direct_xho", split="latest" ) ``` ## Latest results These are the [latest results from run 2024-10-01T01-56-35.375763](https://huggingface.co/datasets/africa-intelligence/llama-south-africa-benchmarking/blob/main/chad-brouze/llama-8b-south-africa/results_2024-10-01T01-56-35.375763.json) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval): ```python { "all": { "afrimgsm_direct_xho": { "alias": "afrimgsm_direct_xho", "exact_match,remove_whitespace": 0.02, "exact_match_stderr,remove_whitespace": 0.008872139507342681, "exact_match,flexible-extract": 0.048, "exact_match_stderr,flexible-extract": 0.013546884228085717 }, "afrimgsm_direct_zul": { "alias": "afrimgsm_direct_zul", "exact_match,remove_whitespace": 0.024, "exact_match_stderr,remove_whitespace": 0.00969908702696424, "exact_match,flexible-extract": 0.068, "exact_match_stderr,flexible-extract": 0.01595374841074702 }, "afrimmlu_direct_xho": { "alias": "afrimmlu_direct_xho", "acc,none": 0.296, "acc_stderr,none": 0.020435342091896135, "f1,none": 0.28935123890039643, "f1_stderr,none": "N/A" }, "afrimmlu_direct_zul": { "alias": "afrimmlu_direct_zul", "acc,none": 0.316, "acc_stderr,none": 0.020812359515855857, "f1,none": 0.3097199360473918, "f1_stderr,none": "N/A" }, "afrixnli_en_direct_xho": { "alias": "afrixnli_en_direct_xho", "acc,none": 0.44333333333333336, "acc_stderr,none": 0.02029781968475275, "f1,none": 0.3534203078622213, "f1_stderr,none": "N/A" }, "afrixnli_en_direct_zul": { "alias": "afrixnli_en_direct_zul", "acc,none": 0.43, "acc_stderr,none": 0.02022824683332485, "f1,none": 0.3421107285347325, "f1_stderr,none": "N/A" } }, "afrimgsm_direct_xho": { "alias": "afrimgsm_direct_xho", "exact_match,remove_whitespace": 0.02, "exact_match_stderr,remove_whitespace": 0.008872139507342681, "exact_match,flexible-extract": 0.048, "exact_match_stderr,flexible-extract": 0.013546884228085717 }, "afrimgsm_direct_zul": { "alias": "afrimgsm_direct_zul", "exact_match,remove_whitespace": 0.024, "exact_match_stderr,remove_whitespace": 0.00969908702696424, "exact_match,flexible-extract": 0.068, "exact_match_stderr,flexible-extract": 0.01595374841074702 }, "afrimmlu_direct_xho": { "alias": "afrimmlu_direct_xho", "acc,none": 0.296, "acc_stderr,none": 0.020435342091896135, "f1,none": 0.28935123890039643, "f1_stderr,none": "N/A" }, "afrimmlu_direct_zul": { "alias": "afrimmlu_direct_zul", "acc,none": 0.316, "acc_stderr,none": 0.020812359515855857, "f1,none": 0.3097199360473918, "f1_stderr,none": "N/A" }, "afrixnli_en_direct_xho": { "alias": "afrixnli_en_direct_xho", "acc,none": 0.44333333333333336, "acc_stderr,none": 0.02029781968475275, "f1,none": 0.3534203078622213, "f1_stderr,none": "N/A" }, "afrixnli_en_direct_zul": { "alias": "afrixnli_en_direct_zul", "acc,none": 0.43, "acc_stderr,none": 0.02022824683332485, "f1,none": 0.3421107285347325, "f1_stderr,none": "N/A" } } ``` ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]# 数据集卡片：chad-brouze/llama-8b-south-africa 模型评估运行数据集 本数据集由模型[chad-brouze/llama-8b-south-africa](https://huggingface.co/chad-brouze/llama-8b-south-africa)的评估运行流程中自动生成。 本数据集共包含17个配置项，每个配置项对应一个被评估的任务。 本数据集基于14次独立运行结果构建。每次运行的结果会以数据集分割（split）的形式存储在每个配置项中，分割名称采用对应运行的时间戳进行命名。其中，`train`分割始终指向最新的评估结果。 此外，本数据集额外提供一个名为`results`的配置项，用于存储所有评估运行的聚合结果。 若需加载单次运行的详细数据，可参考如下代码示例： python from datasets import load_dataset data = load_dataset( "africa-intelligence/llama-south-africa-benchmarking", name="chad-brouze__llama-8b-south-africa__afrimgsm_direct_xho", split="latest" ) ## 最新评估结果 以下为来自2024-10-01T01-56-35.375763运行的最新结果（[链接](https://huggingface.co/datasets/africa-intelligence/llama-south-africa-benchmarking/blob/main/chad-brouze/llama-8b-south-africa/results_2024-10-01T01-56-35.375763.json)）。注：若后续评估运行未覆盖全部任务，则仓库中可能存在其他任务的评估结果，可在各次运行的结果文件以及每个评估对应的`latest`分割中查找对应内容： python { "all": { "afrimgsm_direct_xho": { "alias": "afrimgsm_direct_xho", "exact_match,remove_whitespace": 0.02, "exact_match_stderr,remove_whitespace": 0.008872139507342681, "exact_match,flexible-extract": 0.048, "exact_match_stderr,flexible-extract": 0.013546884228085717 }, "afrimgsm_direct_zul": { "alias": "afrimgsm_direct_zul", "exact_match,remove_whitespace": 0.024, "exact_match_stderr,remove_whitespace": 0.00969908702696424, "exact_match,flexible-extract": 0.068, "exact_match_stderr,flexible-extract": 0.01595374841074702 }, "afrimmlu_direct_xho": { "alias": "afrimmlu_direct_xho", "acc,none": 0.296, "acc_stderr,none": 0.020435342091896135, "f1,none": 0.28935123890039643, "f1_stderr,none": "N/A" }, "afrimmlu_direct_zul": { "alias": "afrimmlu_direct_zul", "acc,none": 0.316, "acc_stderr,none": 0.020812359515855857, "f1,none": 0.3097199360473918, "f1_stderr,none": "N/A" }, "afrixnli_en_direct_xho": { "alias": "afrixnli_en_direct_xho", "acc,none": 0.44333333333333336, "acc_stderr,none": 0.02029781968475275, "f1,none": 0.3534203078622213, "f1_stderr,none": "N/A" }, "afrixnli_en_direct_zul": { "alias": "afrixnli_en_direct_zul", "acc,none": 0.43, "acc_stderr,none": 0.02022824683332485, "f1,none": 0.3421107285347325, "f1_stderr,none": "N/A" } }, "afrimgsm_direct_xho": { "alias": "afrimgsm_direct_xho", "exact_match,remove_whitespace": 0.02, "exact_match_stderr,remove_whitespace": 0.008872139507342681, "exact_match,flexible-extract": 0.048, "exact_match_stderr,flexible-extract": 0.013546884228085717 }, "afrimgsm_direct_zul": { "alias": "afrimgsm_direct_zul", "exact_match,remove_whitespace": 0.024, "exact_match_stderr,remove_whitespace": 0.00969908702696424, "exact_match,flexible-extract": 0.068, "exact_match_stderr,flexible-extract": 0.01595374841074702 }, "afrimmlu_direct_xho": { "alias": "afrimmlu_direct_xho", "acc,none": 0.296, "acc_stderr,none": 0.020435342091896135, "f1,none": 0.28935123890039643, "f1_stderr,none": "N/A" }, "afrimmlu_direct_zul": { "alias": "afrimmlu_direct_zul", "acc,none": 0.316, "acc_stderr,none": 0.020812359515855857, "f1,none": 0.3097199360473918, "f1_stderr,none": "N/A" }, "afrixnli_en_direct_xho": { "alias": "afrixnli_en_direct_xho", "acc,none": 0.44333333333333336, "acc_stderr,none": 0.02029781968475275, "f1,none": 0.3534203078622213, "f1_stderr,none": "N/A" }, "afrixnli_en_direct_zul": { "alias": "afrixnli_en_direct_zul", "acc,none": 0.43, "acc_stderr,none": 0.02022824683332485, "f1,none": 0.3421107285347325, "f1_stderr,none": "N/A" } } ## 数据集详情 ### 数据集描述 - **整理者：** [需补充更多信息] - **资助方（可选）：** [需补充更多信息] - **共享方（可选）：** [需补充更多信息] - **自然语言：** [需补充更多信息] - **许可证：** [需补充更多信息] ### 数据集来源（可选） - **仓库链接：** [需补充更多信息] - **相关论文（可选）：** [需补充更多信息] - **演示链接（可选）：** [需补充更多信息] ## 数据集用途 ### 直接用途 [需补充更多信息] ### 超出范围的使用场景 本数据集不适用于以下场景：[需补充更多信息] ## 数据集结构 本数据集的字段说明、分割规则、数据点间关系等结构相关信息：[需补充更多信息] ## 数据集创建 ### 整理依据 创建本数据集的动机与依据：[需补充更多信息] ### 源数据 #### 数据收集与处理流程 数据收集、筛选、归一化流程及使用的工具库：[需补充更多信息] #### 源数据生产者 原始数据的创建者信息：[需补充更多信息] ### 标注信息（可选） #### 标注流程 标注工具、标注规模、标注指南、标注者间一致性、标注验证流程等：[需补充更多信息] #### 标注者 标注人员或系统的相关信息：[需补充更多信息] #### 个人与敏感信息 本数据集是否包含个人、敏感或隐私数据（如地址、唯一标识姓名/别名、种族/民族起源、性取向、宗教信仰、政治观点、财务或健康数据等）：[需补充更多信息]。若已对数据进行匿名化处理，请说明匿名化流程：[需补充更多信息] ## 偏差、风险与局限性 本部分旨在说明数据集的技术与社会技术层面的局限性：[需补充更多信息] ### 建议 用户应知晓本数据集存在的偏差、风险与局限性。如需进一步的相关建议，请补充更多信息：[需补充更多信息] ## 引用信息（可选） **BibTeX格式：** [需补充更多信息] **APA格式：** [需补充更多信息] ## 术语表（可选） 帮助读者理解数据集或数据集卡片的相关术语与计算方式：[需补充更多信息] ## 更多信息（可选） [需补充更多信息] ## 数据集卡片作者（可选） [需补充更多信息] ## 数据集卡片联系人 [需补充更多信息]