open-llm-leaderboard/details_openchat__opencoderplus
收藏Hugging Face2023-10-19 更新2024-03-04 收录
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https://hf-mirror.com/datasets/open-llm-leaderboard/details_openchat__opencoderplus
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---
pretty_name: Evaluation run of openchat/opencoderplus
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [openchat/opencoderplus](https://huggingface.co/openchat/opencoderplus) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 3 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe 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.\n\
\nAn additional configuration \"results\" store all the aggregated results of the\
\ run (and is used to compute and display the agregated metrics on the [Open LLM\
\ Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).\n\
\nTo load the details from a run, you can for instance do the following:\n```python\n\
from datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_openchat__opencoderplus\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-10-19T03:19:58.630219](https://huggingface.co/datasets/open-llm-leaderboard/details_openchat__opencoderplus/blob/main/results_2023-10-19T03-19-58.630219.json)(note\
\ that their 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 \"em\": 0.033976510067114093,\n\
\ \"em_stderr\": 0.0018553373122680704,\n \"f1\": 0.09136325503355722,\n\
\ \"f1_stderr\": 0.0022463129422016395,\n \"acc\": 0.3538260251930829,\n\
\ \"acc_stderr\": 0.009542580764267136\n },\n \"harness|drop|3\": {\n\
\ \"em\": 0.033976510067114093,\n \"em_stderr\": 0.0018553373122680704,\n\
\ \"f1\": 0.09136325503355722,\n \"f1_stderr\": 0.0022463129422016395\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.04624715693707354,\n \
\ \"acc_stderr\": 0.0057849916626918465\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.6614048934490924,\n \"acc_stderr\": 0.013300169865842424\n\
\ }\n}\n```"
repo_url: https://huggingface.co/openchat/opencoderplus
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_drop_3
data_files:
- split: 2023_10_19T03_19_58.630219
path:
- '**/details_harness|drop|3_2023-10-19T03-19-58.630219.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-19T03-19-58.630219.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_19T03_19_58.630219
path:
- '**/details_harness|gsm8k|5_2023-10-19T03-19-58.630219.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-19T03-19-58.630219.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_19T03_19_58.630219
path:
- '**/details_harness|winogrande|5_2023-10-19T03-19-58.630219.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-19T03-19-58.630219.parquet'
- config_name: results
data_files:
- split: 2023_10_19T03_19_58.630219
path:
- results_2023-10-19T03-19-58.630219.parquet
- split: latest
path:
- results_2023-10-19T03-19-58.630219.parquet
---
# Dataset Card for Evaluation run of openchat/opencoderplus
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/openchat/opencoderplus
- **Paper:**
- **Leaderboard:** https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
- **Point of Contact:** clementine@hf.co
### Dataset Summary
Dataset automatically created during the evaluation run of model [openchat/opencoderplus](https://huggingface.co/openchat/opencoderplus) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 3 configuration, each one coresponding to one of the evaluated task.
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 (and is used to compute and display the agregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
To load the details from a run, you can for instance do the following:
```python
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_openchat__opencoderplus",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-10-19T03:19:58.630219](https://huggingface.co/datasets/open-llm-leaderboard/details_openchat__opencoderplus/blob/main/results_2023-10-19T03-19-58.630219.json)(note that their 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": {
"em": 0.033976510067114093,
"em_stderr": 0.0018553373122680704,
"f1": 0.09136325503355722,
"f1_stderr": 0.0022463129422016395,
"acc": 0.3538260251930829,
"acc_stderr": 0.009542580764267136
},
"harness|drop|3": {
"em": 0.033976510067114093,
"em_stderr": 0.0018553373122680704,
"f1": 0.09136325503355722,
"f1_stderr": 0.0022463129422016395
},
"harness|gsm8k|5": {
"acc": 0.04624715693707354,
"acc_stderr": 0.0057849916626918465
},
"harness|winogrande|5": {
"acc": 0.6614048934490924,
"acc_stderr": 0.013300169865842424
}
}
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
Dataset automatically created during the evaluation run of model [openchat/opencoderplus](https://huggingface.co/openchat/opencoderplus) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard). The dataset is composed of 3 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. An additional configuration results stores all the aggregated results of the run (and is used to compute and display the aggregated metrics on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)).
提供机构：
open-llm-leaderboard
原始信息汇总
数据集卡片 for Evaluation run of openchat/opencoderplus
数据集描述
数据集概述
数据集是在模型 openchat/opencoderplus 在 Open LLM Leaderboard 上的评估运行期间自动创建的。
该数据集由 3 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。每次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时间戳。"train" 拆分始终指向最新的结果。
额外的配置 "results" 存储了运行的所有聚合结果（并用于计算和显示 Open LLM Leaderboard 上的聚合指标）。
要加载运行的详细信息，可以执行以下操作： python from datasets import load_dataset data = load_dataset("open-llm-leaderboard/details_openchat__opencoderplus", "harness_winogrande_5", split="train")
最新结果
以下是 2023-10-19T03:19:58.630219 运行的最新结果（注意，如果连续评估没有覆盖相同的任务，仓库中可能会有其他任务的结果。您可以在每个评估的 "latest" 拆分中找到每个任务的结果）：
python { "all": { "em": 0.033976510067114093, "em_stderr": 0.0018553373122680704, "f1": 0.09136325503355722, "f1_stderr": 0.0022463129422016395, "acc": 0.3538260251930829, "acc_stderr": 0.009542580764267136 }, "harness|drop|3": { "em": 0.033976510067114093, "em_stderr": 0.0018553373122680704, "f1": 0.09136325503355722, "f1_stderr": 0.0022463129422016395 }, "harness|gsm8k|5": { "acc": 0.04624715693707354, "acc_stderr": 0.0057849916626918465 }, "harness|winogrande|5": { "acc": 0.6614048934490924, "acc_stderr": 0.013300169865842424 } }
