open-llm-leaderboard/details_xiaol__RWKV-v4-raven-14B-one-state
收藏数据集概述
数据集摘要
该数据集是在评估模型 xiaol/RWKV-v4-raven-14B-one-state 在 Open LLM Leaderboard 上的自动创建的。数据集包含 61 个配置,每个配置对应一个评估任务。数据集从 1 次运行中创建,每个运行可以在每个配置中找到特定的拆分,拆分名称使用运行的 timestamp。"train" 拆分总是指向最新的结果。
数据集结构
数据集包含多个配置,每个配置对应不同的评估任务。以下是部分配置的示例:
-
harness_arc_challenge_25
- 拆分:2023_10_09T21_38_42.028709
- 路径:
**/details_harness|arc:challenge|25_2023-10-09T21-38-42.028709.parquet - 拆分:latest
- 路径:
**/details_harness|arc:challenge|25_2023-10-09T21-38-42.028709.parquet
-
harness_hellaswag_10
- 拆分:2023_10_09T21_38_42.028709
- 路径:
**/details_harness|hellaswag|10_2023-10-09T21-38-42.028709.parquet - 拆分:latest
- 路径:
**/details_harness|hellaswag|10_2023-10-09T21-38-42.028709.parquet
-
harness_hendrycksTest_5
- 拆分:2023_10_09T21_38_42.028709
- 路径:多个路径,例如:
**/details_harness|hendrycksTest-abstract_algebra|5_2023-10-09T21-38-42.028709.parquet**/details_harness|hendrycksTest-anatomy|5_2023-10-09T21-38-42.028709.parquet- ...
最新结果
以下是 2023-10-09T21:38:42.028709 运行的最新结果:
python { "all": { "acc": 0.33924685524661535, "acc_stderr": 0.03400094010286168, "acc_norm": 0.3432206955736541, "acc_norm_stderr": 0.03399555734342263, "mc1": 0.2484700122399021, "mc1_stderr": 0.015127427096520681, "mc2": 0.37298301233557335, "mc2_stderr": 0.014007983938605419 }, "harness|arc:challenge|25": { "acc": 0.41467576791808874, "acc_stderr": 0.014397070564409172, "acc_norm": 0.45733788395904434, "acc_norm_stderr": 0.01455810654392407 }, "harness|hellaswag|10": { "acc": 0.5230033857797252, "acc_stderr": 0.004984497871025246, "acc_norm": 0.714797849034057, "acc_norm_stderr": 0.00450587908460685 }, "harness|hendrycksTest-abstract_algebra|5": { "acc": 0.23, "acc_stderr": 0.04229525846816507, "acc_norm": 0.23, "acc_norm_stderr": 0.04229525846816507 }, ... }
数据加载示例
要加载特定运行的详细信息,可以使用以下代码:
python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_xiaol__RWKV-v4-raven-14B-one-state", "harness_truthfulqa_mc_0", split="train")



