pretty_name: Evaluation run of LLMs/Stable-Vicuna-13B
dataset_summary: "Dataset automatically created during the evaluation run of model [LLMs/Stable-Vicuna-13B](https://huggingface.co/LLMs/Stable-Vicuna-13B) 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\nfrom datasets import load_dataset\ndata = load_dataset(\"open-llm-leaderboard/details_LLMs__Stable-Vicuna-13B\",\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\nThese are the [latest results from run 2023-10-13T11:51:54.162285](https://huggingface.co/datasets/open-llm-leaderboard/details_LLMs__Stable-Vicuna-13B/blob/main/results_2023-10-13T11-51-54.162285.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\": 0.012688758389261746,\n \"em_stderr\": 0.0011462418380586343,\n \"f1\": 0.06941170302013412,\n \"f1_stderr\": 0.0017195070383295536,\n \"acc\": 0.2849250197316496,\n \"acc_stderr\": 0.006957342547358349\n },\n \"harness|drop|3\": {\n \"em\": 0.012688758389261746,\n \"em_stderr\": 0.0011462418380586343,\n \"f1\": 0.06941170302013412,\n \"f1_stderr\": 0.0017195070383295536\n },\n \"harness|gsm8k|5\": {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n },\n \"harness|winogrande|5\": {\n \"acc\": 0.5698500394632992,\n \"acc_stderr\": 0.013914685094716698\n }\n}\n```"
repo_url: https://huggingface.co/LLMs/Stable-Vicuna-13B
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_13T11_51_54.162285
path:
- '**/details_harness|drop|3_2023-10-13T11-51-54.162285.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-10-13T11-51-54.162285.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_10_13T11_51_54.162285
path:
- '**/details_harness|gsm8k|5_2023-10-13T11-51-54.162285.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-10-13T11-51-54.162285.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_10_13T11_51_54.162285
path:
- '**/details_harness|winogrande|5_2023-10-13T11-51-54.162285.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-10-13T11-51-54.162285.parquet'
- config_name: results
data_files:
- split: 2023_10_13T11_51_54.162285
path:
- results_2023-10-13T11-51-54.162285.parquet
- split: latest
path:
- results_2023-10-13T11-51-54.162285.parquet
Dataset Card for Evaluation run of LLMs/Stable-Vicuna-13B
Dataset Description
- Homepage:
- Repository: https://huggingface.co/LLMs/Stable-Vicuna-13B
- 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 LLMs/Stable-Vicuna-13B on the 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).
To load the details from a run, you can for instance do the following:
from datasets import load_dataset
data = load_dataset("open-llm-leaderboard/details_LLMs__Stable-Vicuna-13B",
"harness_winogrande_5",
split="train")
Latest results
These are the latest results from run 2023-10-13T11:51:54.162285(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):
{
"all": {
"em": 0.012688758389261746,
"em_stderr": 0.0011462418380586343,
"f1": 0.06941170302013412,
"f1_stderr": 0.0017195070383295536,
"acc": 0.2849250197316496,
"acc_stderr": 0.006957342547358349
},
"harness|drop|3": {
"em": 0.012688758389261746,
"em_stderr": 0.0011462418380586343,
"f1": 0.06941170302013412,
"f1_stderr": 0.0017195070383295536
},
"harness|gsm8k|5": {
"acc": 0.0,
"acc_stderr": 0.0
},
"harness|winogrande|5": {
"acc": 0.5698500394632992,
"acc_stderr": 0.013914685094716698
}
}
Supported Tasks and Leaderboards
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Languages
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Dataset Structure
Data Instances
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Data Fields
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Data Splits
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Dataset Creation
Curation Rationale
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Source Data
Initial Data Collection and Normalization
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Who are the source language producers?
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Annotations
Annotation process
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Who are the annotators?
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Personal and Sensitive Information
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Considerations for Using the Data
Social Impact of Dataset
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Discussion of Biases
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Other Known Limitations
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Additional Information
Dataset Curators
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