OALL/details_NaniDAO__deepseek-r1-qwen-2.5-32B-ablated

该数据集是在评估模型[NaniDAO/deepseek-r1-qwen-2.5-32B-ablated](https://huggingface.co/NaniDAO/deepseek-r1-qwen-2.5-32B-ablated)时自动生成的。数据集由136个配置组成，每个配置对应一个评估任务。数据集是从1次运行中创建的。每个运行都可以在配置中作为一个特定的分割找到，分割名称使用运行的时间戳命名。"train"分割始终指向最新的结果。还有一个额外的配置"results"存储了所有运行的聚合结果。要加载运行的详细信息，可以使用以下示例代码：python
from datasets import load_dataset
data = load_dataset("OALL/details_NaniDAO__deepseek-r1-qwen-2.5-32B-ablated", "lighteval_xstory_cloze_ar_0_2025_02_04T22_10_18_431478_parquet", split="train")。最新结果可以在[这里](https://huggingface.co/datasets/OALL/details_NaniDAO__deepseek-r1-qwen-2.5-32B-ablated/blob/main/results_2025-02-04T22-10-18.431478.json)找到。

Dataset automatically created during the evaluation run of model [NaniDAO/deepseek-r1-qwen-2.5-32B-ablated](https://huggingface.co/NaniDAO/deepseek-r1-qwen-2.5-32B-ablated). The dataset is composed of 136 configurations, each one corresponding to one of the evaluated tasks. The dataset has been created from 1 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("OALL/details_NaniDAO__deepseek-r1-qwen-2.5-32B-ablated", "lighteval_xstory_cloze_ar_0_2025_02_04T22_10_18_431478_parquet", split="train")
Latest results can be found [here](https://huggingface.co/datasets/OALL/details_NaniDAO__deepseek-r1-qwen-2.5-32B-ablated/blob/main/results_2025-02-04T22-10-18.431478.json).