open-llm-leaderboard-old/details_BreadAi__PM_modelV2
收藏Hugging Face2023-09-16 更新2024-06-22 收录
下载链接：
https://hf-mirror.com/datasets/open-llm-leaderboard-old/details_BreadAi__PM_modelV2
下载链接
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资源简介：
该数据集是在Open LLM Leaderboard上对模型BreadAi/PM_modelV2进行评估时自动创建的。数据集由64个配置组成，每个配置对应一个评估任务。数据集由2次运行生成，每次运行在每个配置中表示为特定的分割。train分割始终指向最新的结果。此外，名为results的配置存储了所有运行的聚合结果，用于计算和显示Open LLM Leaderboard上的聚合指标。README还提供了如何使用`datasets`库中的`load_dataset`函数加载数据集的示例。
该数据集是在Open LLM Leaderboard上对模型BreadAi/PM_modelV2进行评估时自动创建的。数据集由64个配置组成，每个配置对应一个评估任务。数据集由2次运行生成，每次运行在每个配置中表示为特定的分割。train分割始终指向最新的结果。此外，名为results的配置存储了所有运行的聚合结果，用于计算和显示Open LLM Leaderboard上的聚合指标。README还提供了如何使用`datasets`库中的`load_dataset`函数加载数据集的示例。
提供机构：
open-llm-leaderboard-old
原始信息汇总
数据集概述
数据集是在评估模型 BreadAi/PM_modelV2 在 Open LLM Leaderboard 上的运行过程中自动创建的。
数据集组成
- 数据集包含 64 个配置，每个配置对应一个评估任务。
- 数据集从 2 次运行中创建，每次运行可以在每个配置中找到特定的分割，分割名称使用运行的时间戳。
- "train" 分割总是指向最新的结果。
- 额外的配置 "results" 存储所有运行的聚合结果，用于计算和显示聚合指标在 Open LLM Leaderboard 上。
加载数据集示例
python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_BreadAi__PM_modelV2", "harness_winogrande_5", split="train")
最新结果
以下是 2023-09-16T22:18:34.076880 运行的最新结果： python { "all": { "em": 0.0009437919463087249, "em_stderr": 0.00031446531194131066, "f1": 0.004450503355704698, "f1_stderr": 0.00041742876892271523, "acc": 0.2478295185477506, "acc_stderr": 0.007025978032038445 }, "harness|drop|3": { "em": 0.0009437919463087249, "em_stderr": 0.00031446531194131066, "f1": 0.004450503355704698, "f1_stderr": 0.00041742876892271523 }, "harness|gsm8k|5": { "acc": 0.0, "acc_stderr": 0.0 }, "harness|winogrande|5": { "acc": 0.4956590370955012, "acc_stderr": 0.01405195606407689 } }
配置详情
-
config_name: harness_arc_challenge_25
- split: 2023_07_19T19_16_43.765981
- path: **/details_harness|arc:challenge|25_2023-07-19T19:16:43.765981.parquet
- split: latest
- path: **/details_harness|arc:challenge|25_2023-07-19T19:16:43.765981.parquet
- split: 2023_07_19T19_16_43.765981
-
config_name: harness_drop_3
- split: 2023_09_16T22_18_34.076880
- path: **/details_harness|drop|3_2023-09-16T22-18-34.076880.parquet
- split: latest
- path: **/details_harness|drop|3_2023-09-16T22-18-34.076880.parquet
- split: 2023_09_16T22_18_34.076880
-
config_name: harness_gsm8k_5
- split: 2023_09_16T22_18_34.076880
- path: **/details_harness|gsm8k|5_2023-09-16T22-18-34.076880.parquet
- split: latest
- path: **/details_harness|gsm8k|5_2023-09-16T22-18-34.076880.parquet
- split: 2023_09_16T22_18_34.076880
-
config_name: harness_hellaswag_10
- split: 2023_07_19T19_16_43.765981
- path: **/details_harness|hellaswag|10_2023-07-19T19:16:43.765981.parquet
- split: latest
- path: **/details_harness|hellaswag|10_2023-07-19T19:16:43.765981.parquet
- split: 2023_07_19T19_16_43.765981
-
config_name: harness_hendrycksTest_5
- split: 2023_07_19T19_16_43.765981
- path:
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- **/details_harness|hendrycksTest-nutrition|5_2023-07-19T19:16:43.765981.parquet
- **/details_harness|hendrycksTest-philosophy|5_2023-07-19T19:16:43.765981.parquet
- **/details_harness|hendrycksTest-prehistory|5_2023-07-19T19:16:43.765981.parquet
- **/details_harness|hendrycksTest-professional_accounting|5_2023-07-19T19:16:43.765981.parquet
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- **/details_harness|hendrycks
- path:
- split: 2023_07_19T19_16_43.765981
