datasetId stringlengths 2 117 | card stringlengths 19 1.01M |
|---|---|
ura-hcmut/MATH-dpo | ---
license: mit
language:
- vi
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: test
path: math-dpo.json
--- |
Multimodal-Fatima/OxfordPets_test_facebook_opt_125m_Attributes_Caption_ns_3669 | ---
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---
# Dataset Card for "OxfordPets_test_facebook_opt_125m_Attributes_Caption_ns_3669"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
danf0/snli_shortcut_grammar | ---
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---
# Dataset Card for "snli_shortcut_grammar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anan-2024/twitter_dataset_1712994579 | ---
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---
|
salma-remyx/test_startup_advice_10k | ---
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---
# Dataset Card for "test_startup_advice_10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
rchiang/mergedAgentInstruct | ---
license: apache-2.0
configs:
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download_size: 0
dataset_size: 8042511
---
direct copy of [AgentInstruct](https://huggingface.co/datasets/THUDM/AgentInstruct) for use in Axolotl. |
kiett0710/liarraw | ---
license: apache-2.0
---
|
Falah/toddler_smiling_low_angle_shots_prompts | ---
dataset_info:
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---
# Dataset Card for "toddler_smiling_low_angle_shots_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
dipudl/hc3-and-gpt-wiki-intro-with-perplexity-and-32-window | ---
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---
|
riquinho21/fb | ---
license: other
---
|
daspartho/anime-or-not | ---
license: apache-2.0
---
|
argilla/10k_prompts_SPIN_iter0_phi2_top | ---
configs:
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data_files:
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path: data/train-*
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---
# Dataset Card for "10k_prompts_SPIN_iter0_phi2_top"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
limcheekin/flutter-website-3.7 | ---
license: cc-by-3.0
---
|
LongDaHo/filter-platypus | ---
dataset_info:
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---
|
yiching/MVTec_cable | ---
license: unknown
---
|
bigcode/the-stack-v2 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- other
multilinguality:
- multilingual
pretty_name: The-Stack-v2
size_categories:
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source_datasets: []
task_categories:
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task_ids: []
extra_gated_prompt: |-
## Terms of Use for The Stack v2
The Stack v2 dataset is a collection of source code in over 600 programming languages. We ask that you read and acknowledge the following points before using the dataset:
1. Downloading the dataset in bulk requires a an agreement with SoftwareHeritage and INRIA. Contact [datasets@softwareheritage.org](mailto:datasets@softwareheritage.org?subject=TheStackV2%20request%20for%20dataset%20access%20information) for more information.
2. If you are using the dataset to train models you must adhere to the SoftwareHeritage [principles for language model training](https://www.softwareheritage.org/2023/10/19/swh-statement-on-llm-for-code/).
3. The Stack v2 is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack v2 must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.
4. The Stack v2 is regularly updated to enact validated data removal requests. By clicking on "Access repository", you agree to update your own version of The Stack v2 to the most recent usable version.
By clicking on "Access repository" below, you accept that your contact information (email address and username) can be shared with the dataset maintainers as well.
extra_gated_fields:
Email: text
I have read the License and agree with its terms: checkbox
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- split: train
path: "data/TOML/*.parquet"
- config_name: "TSQL"
data_files:
- split: train
path: "data/TSQL/*.parquet"
- config_name: "TSV"
data_files:
- split: train
path: "data/TSV/*.parquet"
- config_name: "TSX"
data_files:
- split: train
path: "data/TSX/*.parquet"
- config_name: "TXL"
data_files:
- split: train
path: "data/TXL/*.parquet"
- config_name: "Talon"
data_files:
- split: train
path: "data/Talon/*.parquet"
- config_name: "Tcl"
data_files:
- split: train
path: "data/Tcl/*.parquet"
- config_name: "Tcsh"
data_files:
- split: train
path: "data/Tcsh/*.parquet"
- config_name: "TeX"
data_files:
- split: train
path: "data/TeX/*.parquet"
- config_name: "Tea"
data_files:
- split: train
path: "data/Tea/*.parquet"
- config_name: "Terra"
data_files:
- split: train
path: "data/Terra/*.parquet"
- config_name: "Texinfo"
data_files:
- split: train
path: "data/Texinfo/*.parquet"
- config_name: "Text"
data_files:
- split: train
path: "data/Text/*.parquet"
- config_name: "TextMate_Properties"
data_files:
- split: train
path: "data/TextMate_Properties/*.parquet"
- config_name: "Textile"
data_files:
- split: train
path: "data/Textile/*.parquet"
- config_name: "Thrift"
data_files:
- split: train
path: "data/Thrift/*.parquet"
- config_name: "Turing"
data_files:
- split: train
path: "data/Turing/*.parquet"
- config_name: "Turtle"
data_files:
- split: train
path: "data/Turtle/*.parquet"
- config_name: "Twig"
data_files:
- split: train
path: "data/Twig/*.parquet"
- config_name: "TypeScript"
data_files:
- split: train
path: "data/TypeScript/*.parquet"
- config_name: "Type_Language"
data_files:
- split: train
path: "data/Type_Language/*.parquet"
- config_name: "Unified_Parallel_C"
data_files:
- split: train
path: "data/Unified_Parallel_C/*.parquet"
- config_name: "Unity3D_Asset"
data_files:
- split: train
path: "data/Unity3D_Asset/*.parquet"
- config_name: "Unix_Assembly"
data_files:
- split: train
path: "data/Unix_Assembly/*.parquet"
- config_name: "Uno"
data_files:
- split: train
path: "data/Uno/*.parquet"
- config_name: "UnrealScript"
data_files:
- split: train
path: "data/UnrealScript/*.parquet"
- config_name: "UrWeb"
data_files:
- split: train
path: "data/UrWeb/*.parquet"
- config_name: "V"
data_files:
- split: train
path: "data/V/*.parquet"
- config_name: "VBA"
data_files:
- split: train
path: "data/VBA/*.parquet"
- config_name: "VBScript"
data_files:
- split: train
path: "data/VBScript/*.parquet"
- config_name: "VCL"
data_files:
- split: train
path: "data/VCL/*.parquet"
- config_name: "VHDL"
data_files:
- split: train
path: "data/VHDL/*.parquet"
- config_name: "Vala"
data_files:
- split: train
path: "data/Vala/*.parquet"
- config_name: "Valve_Data_Format"
data_files:
- split: train
path: "data/Valve_Data_Format/*.parquet"
- config_name: "Velocity_Template_Language"
data_files:
- split: train
path: "data/Velocity_Template_Language/*.parquet"
- config_name: "Verilog"
data_files:
- split: train
path: "data/Verilog/*.parquet"
- config_name: "Vim_Help_File"
data_files:
- split: train
path: "data/Vim_Help_File/*.parquet"
- config_name: "Vim_Script"
data_files:
- split: train
path: "data/Vim_Script/*.parquet"
- config_name: "Vim_Snippet"
data_files:
- split: train
path: "data/Vim_Snippet/*.parquet"
- config_name: "Visual_Basic_.NET"
data_files:
- split: train
path: "data/Visual_Basic_.NET/*.parquet"
- config_name: "Volt"
data_files:
- split: train
path: "data/Volt/*.parquet"
- config_name: "Vue"
data_files:
- split: train
path: "data/Vue/*.parquet"
- config_name: "Vyper"
data_files:
- split: train
path: "data/Vyper/*.parquet"
- config_name: "Wavefront_Material"
data_files:
- split: train
path: "data/Wavefront_Material/*.parquet"
- config_name: "Wavefront_Object"
data_files:
- split: train
path: "data/Wavefront_Object/*.parquet"
- config_name: "WebAssembly"
data_files:
- split: train
path: "data/WebAssembly/*.parquet"
- config_name: "WebIDL"
data_files:
- split: train
path: "data/WebIDL/*.parquet"
- config_name: "WebVTT"
data_files:
- split: train
path: "data/WebVTT/*.parquet"
- config_name: "Web_Ontology_Language"
data_files:
- split: train
path: "data/Web_Ontology_Language/*.parquet"
- config_name: "Wget_Config"
data_files:
- split: train
path: "data/Wget_Config/*.parquet"
- config_name: "Whiley"
data_files:
- split: train
path: "data/Whiley/*.parquet"
- config_name: "Wikitext"
data_files:
- split: train
path: "data/Wikitext/*.parquet"
- config_name: "Win32_Message_File"
data_files:
- split: train
path: "data/Win32_Message_File/*.parquet"
- config_name: "Windows_Registry_Entries"
data_files:
- split: train
path: "data/Windows_Registry_Entries/*.parquet"
- config_name: "Witcher_Script"
data_files:
- split: train
path: "data/Witcher_Script/*.parquet"
- config_name: "Wollok"
data_files:
- split: train
path: "data/Wollok/*.parquet"
- config_name: "World_of_Warcraft_Addon_Data"
data_files:
- split: train
path: "data/World_of_Warcraft_Addon_Data/*.parquet"
- config_name: "Wren"
data_files:
- split: train
path: "data/Wren/*.parquet"
- config_name: "X10"
data_files:
- split: train
path: "data/X10/*.parquet"
- config_name: "XC"
data_files:
- split: train
path: "data/XC/*.parquet"
- config_name: "XCompose"
data_files:
- split: train
path: "data/XCompose/*.parquet"
- config_name: "XML"
data_files:
- split: train
path: "data/XML/*.parquet"
- config_name: "XML_Property_List"
data_files:
- split: train
path: "data/XML_Property_List/*.parquet"
- config_name: "XPages"
data_files:
- split: train
path: "data/XPages/*.parquet"
- config_name: "XProc"
data_files:
- split: train
path: "data/XProc/*.parquet"
- config_name: "XQuery"
data_files:
- split: train
path: "data/XQuery/*.parquet"
- config_name: "XS"
data_files:
- split: train
path: "data/XS/*.parquet"
- config_name: "XSLT"
data_files:
- split: train
path: "data/XSLT/*.parquet"
- config_name: "X_BitMap"
data_files:
- split: train
path: "data/X_BitMap/*.parquet"
- config_name: "X_Font_Directory_Index"
data_files:
- split: train
path: "data/X_Font_Directory_Index/*.parquet"
- config_name: "X_PixMap"
data_files:
- split: train
path: "data/X_PixMap/*.parquet"
- config_name: "Xojo"
data_files:
- split: train
path: "data/Xojo/*.parquet"
- config_name: "Xonsh"
data_files:
- split: train
path: "data/Xonsh/*.parquet"
- config_name: "Xtend"
data_files:
- split: train
path: "data/Xtend/*.parquet"
- config_name: "YAML"
data_files:
- split: train
path: "data/YAML/*.parquet"
- config_name: "YANG"
data_files:
- split: train
path: "data/YANG/*.parquet"
- config_name: "YARA"
data_files:
- split: train
path: "data/YARA/*.parquet"
- config_name: "YASnippet"
data_files:
- split: train
path: "data/YASnippet/*.parquet"
- config_name: "Yacc"
data_files:
- split: train
path: "data/Yacc/*.parquet"
- config_name: "Yul"
data_files:
- split: train
path: "data/Yul/*.parquet"
- config_name: "ZAP"
data_files:
- split: train
path: "data/ZAP/*.parquet"
- config_name: "ZIL"
data_files:
- split: train
path: "data/ZIL/*.parquet"
- config_name: "Zeek"
data_files:
- split: train
path: "data/Zeek/*.parquet"
- config_name: "ZenScript"
data_files:
- split: train
path: "data/ZenScript/*.parquet"
- config_name: "Zephir"
data_files:
- split: train
path: "data/Zephir/*.parquet"
- config_name: "Zig"
data_files:
- split: train
path: "data/Zig/*.parquet"
- config_name: "Zimpl"
data_files:
- split: train
path: "data/Zimpl/*.parquet"
- config_name: "cURL_Config"
data_files:
- split: train
path: "data/cURL_Config/*.parquet"
- config_name: "desktop"
data_files:
- split: train
path: "data/desktop/*.parquet"
- config_name: "dircolors"
data_files:
- split: train
path: "data/dircolors/*.parquet"
- config_name: "eC"
data_files:
- split: train
path: "data/eC/*.parquet"
- config_name: "edn"
data_files:
- split: train
path: "data/edn/*.parquet"
- config_name: "fish"
data_files:
- split: train
path: "data/fish/*.parquet"
- config_name: "hoon"
data_files:
- split: train
path: "data/hoon/*.parquet"
- config_name: "jq"
data_files:
- split: train
path: "data/jq/*.parquet"
- config_name: "kvlang"
data_files:
- split: train
path: "data/kvlang/*.parquet"
- config_name: "mIRC_Script"
data_files:
- split: train
path: "data/mIRC_Script/*.parquet"
- config_name: "mcfunction"
data_files:
- split: train
path: "data/mcfunction/*.parquet"
- config_name: "mupad"
data_files:
- split: train
path: "data/mupad/*.parquet"
- config_name: "nanorc"
data_files:
- split: train
path: "data/nanorc/*.parquet"
- config_name: "nesC"
data_files:
- split: train
path: "data/nesC/*.parquet"
- config_name: "ooc"
data_files:
- split: train
path: "data/ooc/*.parquet"
- config_name: "q"
data_files:
- split: train
path: "data/q/*.parquet"
- config_name: "reStructuredText"
data_files:
- split: train
path: "data/reStructuredText/*.parquet"
- config_name: "robots.txt"
data_files:
- split: train
path: "data/robots.txt/*.parquet"
- config_name: "sed"
data_files:
- split: train
path: "data/sed/*.parquet"
- config_name: "wdl"
data_files:
- split: train
path: "data/wdl/*.parquet"
- config_name: "wisp"
data_files:
- split: train
path: "data/wisp/*.parquet"
- config_name: "xBase"
data_files:
- split: train
path: "data/xBase/*.parquet"
---
# The Stack v2
<center>
<img src="https://huggingface.co/datasets/bigcode/admin_private/resolve/main/thestackv2_banner.png" alt="Stackv2" width="900" height="600">
</center>
## Dataset Description
- **Homepage:** https://www.bigcode-project.org/
- **Repository:** https://github.com/bigcode-project
- **Paper:** [Link](https://huggingface.co/papers/2402.19173)
- **Point of Contact:** contact@bigcode-project.org
The dataset consists of 4 versions:
- [`bigcode/the-stack-v2`](https://huggingface.co/datasets/bigcode/the-stack-v2): the full "The Stack v2" dataset **<-- you are here**
- [`bigcode/the-stack-v2-dedup`](https://huggingface.co/datasets/bigcode/the-stack-v2-dedup): based on the `bigcode/the-stack-v2` but further near-deduplicated
- [`bigcode/the-stack-v2-train-full-ids`](https://huggingface.co/datasets/bigcode/the-stack-v2-train-full-ids): based on the `bigcode/the-stack-v2-dedup` dataset but further filtered with heuristics and spanning 600+ programming languages. The data is grouped into repositories.
- [`bigcode/the-stack-v2-train-smol-ids`](https://huggingface.co/datasets/bigcode/the-stack-v2-train-smol-ids): based on the `bigcode/the-stack-v2-dedup` dataset but further filtered with heuristics and spanning 17 programming languages. The data is grouped into repositories.
**These datasets only contain the SWHIDs to download the code files and not the content of the files itself. See examples below to see how to download content. We are working on making the training datasets available in the coming weeks.**
The Stack v2 is significantly larger than v1:
||The Stack v1|The Stack v2|
|-|-|-|
| full | 6.4TB | 67.5TB |
| dedup | 2.9TB | 32.1TB |
| train (full) | ~200B tokens | ~900B tokens |
### Changelog
|Release|Description|
|-|-|
| v2.0.1 | Removed repositories that opted out before 2023-10-20. StarCoder2 was trained on this version |
| v2.0 | Initial release of the Stack v2 |
### Dataset Summary
The Stack v2 contains over 3B files in 600+ programming and markup languages. The dataset was created as part of the [BigCode Project](https://www.bigcode-project.org/), an open scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs). The Stack serves as a pre-training dataset for Code LLMs, i.e., code-generating AI systems which enable the synthesis of programs from natural language descriptions as well as other from code snippets.
This dataset is derived from the Software Heritage archive, the largest public archive of software source code and accompanying development history. Software Heritage is an open, non profit initiative to collect, preserve, and share the source code of all publicly available software, launched by Inria, in partnership with UNESCO. We acknowledge Software Heritage for providing access to this invaluable resource. For more details, visit the [Software Heritage website](https://www.softwareheritage.org).
### Languages
The dataset contains 658 languages. The full list can be found in the [language stats table](https://huggingface.co/datasets/bigcode/the-stack-v2/blob/main/language_stats.csv).
### How to use it
```python
from datasets import load_dataset
# full dataset (file IDs only)
ds = load_dataset("bigcode/the-stack-v2", split="train")
# specific language (e.g. Dockerfiles)
ds = load_dataset("bigcode/the-stack-v2", "Dockerfile", split="train")
# dataset streaming (will only download the data as needed)
ds = load_dataset("bigcode/the-stack-v2", streaming=True, split="train")
for sample in iter(ds):
print(sample)
```
#### Downloading the file contents
The file contents are stored in the Software Heritage S3 bucket to ensure data compliance. Downloading data in bulk requires an agreement with SoftwareHeritage and INRIA as stated in the dataset agreement.
Make sure to configure your environment with your [AWS credentials](https://awscli.amazonaws.com/v2/documentation/api/latest/reference/configure/index.html#examples).
```bash
pip install smart_open[s3]
```
```python
import os
import boto3
from smart_open import open
from datasets import load_dataset
session = boto3.Session(
aws_access_key_id=os.environ["AWS_ACCESS_KEY_ID"],
aws_secret_access_key=os.environ["AWS_SECRET_ACCESS_KEY"])
s3 = session.client("s3")
def download_contents(blob_id, src_encoding):
s3_url = f"s3://softwareheritage/content/{blob_id}"
with open(s3_url, "rb", compression=".gz", transport_params={"client": s3}) as fin:
content = fin.read().decode(src_encoding)
return {"content": content}
ds = load_dataset("bigcode/the-stack-v2", split="train", streaming=True)
ds = ds.map(lambda row: download_contents(row["blob_id"], row["src_encoding"]))
for row in ds:
print(row["content"])
break
```
## Dataset Structure
### Data Fields
* `blob_id` (`string`): Software Heritage (SWH) ID of the file on AWS S3.
* `directory_id` (`string`): SWH ID of the root directory of the repository.
* `path` (`string`): The file path within the repository.
* `content_id` (`string`): SWH content ID.
* `detected_licenses` (`string[]`): List of licenses (SPDX) detected by ScanCode.
* `license_type` (`string`): Inferred license type (`permissive` or `no_license`).
* `repo_name` (`string`): Repository name on GitHub.
* `snapshot_id` (`string`): SWH snapshot ID.
* `revision_id` (`string`): SWH revision (commit) ID.
* `branch_name` (`string`): Repository branch name.
* `visit_date` (`timestamp[ns]`): SWH crawl (snapshot) timestamp.
* `revision_date` (`timestamp[ns]`): SWH revision (commit) timestamp.
* `committer_date` (`timestamp[ns]`): SWH revision (commit) timestamp reported by the committer.
* `github_id` (`int64`): GitHub identifier for the repository.
* `star_events_count` (`int64`): number of stars calculated from GHArchive events.
* `fork_events_count` (`int64`): number of forks calculated from GHArchive events.
* `gha_license_id` (`string`): GHArchive SPDX license identifier, `None` if the repo is missing.
* `gha_event_created_at` (`timestamp[ns]`): Timestamp of the latest event on GHArchive for this repository.
* `gha_created_at` (`timestamp[ns]`): Timestamp of repository creation on GitHub, `None` if the repo is missing.
* `gha_language` (`string`): Repository's primary programming language on GitHub, `None` if the repo is missing.
* `src_encoding` (`string`): Original encoding of the file content befre converting to UTF-8.
* `language` (`string`): Programming language of the file, detected by `go-enry / linguist`.
* `is_vendor` (`bool`): Indicator of vendor file (external library), detected by `go-enry`.
* `is_generated` (`bool`): Indicator of generated file (external library), detected by `go-enry`.
* `length_bytes` (`int64`): Length of the file content in UTF-8 bytes.
* `extension` (`string`): File extension.
### Data Splits
The dataset has no splits and all data is loaded as train split by default. If you want to setup a custom train-test split beware that dataset contains a lot of near-duplicates which can cause leakage into the test split.
## Dataset Creation
For more information on the dataset creation pipeline please refer to the [technical report](https://huggingface.co/papers/2402.19173).
### Curation Rationale
One of the challenges faced by researchers working on code LLMs is the lack of openness and transparency around the development of these systems. Most prior works described the high-level data collection process but did not release the training data. It is therefore difficult for other researchers to fully reproduce these models and understand what kind of pre-training data leads to high-performing code LLMs. By releasing an open large-scale code dataset we hope to make training of code LLMs more reproducible.
### Source Data
#### Data Collection
3.28B unique files belonging to 104.2M github repositories were collected by traversing the Software Heritage [2023-09-06](https://docs.softwareheritage.org/devel/swh-dataset/graph/dataset.html#graph-dataset-2023-09-06) graph dataset.
Additional repository-level metadata was collected from [GitHub Archive](https://www.gharchive.org/) data up to 2023-09-14.
The total uncompressed size of all files is 67.53TB.
Near-deduplication was implemented in the pre-processing pipeline on top of exact deduplication.
Roughly 40% of permissively licensed files were (near-)duplicates.
The following are not stored:
* Files that cannot contribute to training code: binary, empty, could not be decoded
* Files larger than 10MB
**Training Datasets**: For the training datasets the programming languages were filtered further to 17 and 600+ for the `the-stack-v2-smol-ids` and `the-stack-v2-full-ids` dataset, respecively. In addition, heuristics were applied to further increase the quality of the dataset. The code files are also grouped into repositories to allow to pretrain with full repository context. For more details see the [technical report](https://drive.google.com/file/d/17iGn3c-sYNiLyRSY-A85QOzgzGnGiVI3/view?usp=sharing).
##### License detection
We extract repository-level license information from [GH Archive](https://www.gharchive.org/) for all repositories with matching names in the SWH dataset.
When the repo-level license is not available, i.e., for 96.93\% of repositories, we use the [ScanCode Toolkit](https://github.com/nexB/scancode-toolkit) to detect file-level licenses as follows:
* Find all filenames that could contain a license (e.g., LICENSE, MIT.txt, Apache2.0) or contain a reference to the license (e.g., README.md, GUIDELINES);
* Apply ScanCode's license detection to the matching files and gather the SPDX IDs of the detected licenses;
* Propagate the detected licenses to all files that have the same base path within the repository as the license file.
The licenses we consider permissive are listed [here](https://huggingface.co/datasets/bigcode/the-stack-v2/blob/main/license_stats.csv).
This list was compiled from the licenses approved by the [Blue Oak Council](https://blueoakcouncil.org/list),
as well as licenses categorized as "Permissive" or "Public Domain" by [ScanCode](https://scancode-licensedb.aboutcode.org/).
#### Who are the source language producers?
The source (code) language producers are users of GitHub that created unique repository names up until 2023-09-06 (cutoff date).
### Personal and Sensitive Information
The released dataset may contain sensitive information such as emails, IP addresses, and API/ssh keys that have previously been published to public repositories on GitHub. Deduplication has helped to reduce the amount of sensitive data that may exist. In the event that the dataset contains personal information, researchers should only use public, non-personal information in support of conducting and publishing their [open-access](https://en.wikipedia.org/wiki/Open_access) research. Personal information should not be used for spamming purposes, including sending unsolicited emails or selling of personal information. Complaints, removal requests, and "do not contact" requests can be sent to contact@bigcode-project.org.
### Opting out of The Stack v2
We are giving developers the ability to have their code removed from the dataset upon request. The process for submitting and enacting removal requests will keep evolving throughout the project as we receive feedback and build up more data governance tools.
You can check if your code is in The Stack v2 with the following ["Am I In The Stack?" Space](https://huggingface.co/spaces/bigcode/in-the-stack). If you'd like to have your data removed from the dataset follow the [instructions on GitHub](https://github.com/bigcode-project/opt-out-v2).
## Considerations for Using the Data
### Social Impact of Dataset
The Stack v2 is an output of the BigCode Project. BigCode aims to be responsible by design and by default. The project is conducted in the spirit of Open Science, focused on the responsible development of LLMs for code.
With the release of The Stack v2, we aim to increase access, reproducibility, and transparency of code LLMs in the research community. Work to de-risk and improve on the implementation of ethical best practices of code LLMs is conducted in various BigCode working groups. The Legal, Ethics, and Governance working group has explored topics such as licensing (including copyleft and the intended use of permissively licensed code), attribution of generated code to original code, rights to restrict processing, the inclusion of Personally Identifiable Information (PII), and risks of malicious code, among other topics. This work is ongoing as of October 25th, 2022.
We expect code LLMs to enable people from diverse backgrounds to write higher quality code and develop low-code applications. Mission-critical software could become easier to maintain as professional developers are guided by code-generating systems on how to write more robust and efficient code. While the social impact is intended to be positive, the increased accessibility of code LLMs comes with certain risks such as over-reliance on the generated code and long-term effects on the software development job market.
A broader impact analysis relating to Code LLMs can be found in section 7 of this [paper](https://arxiv.org/abs/2107.03374). An in-depth risk assessments for Code LLMs can be found in section 4 of this [paper](https://arxiv.org/abs/2207.14157).
### Discussion of Biases
The code collected from GitHub does not contain demographic information or proxy information about the demographics. However, it is not without risks,
as the comments within the code may contain harmful or offensive language, which could be learned by the models.
Widely adopted programming languages like C and Javascript are overrepresented compared to niche programming languages like Julia and Scala. Some programming languages such as SQL, Batchfile, TypeScript are less likely to be permissively licensed (4% vs the average 10%). This may result in a biased representation of those languages. Permissively licensed files also tend to be longer.
The majority of natural language present in code from GitHub is English.
### Other Known Limitations
One of the current limitations of The Stack v2 is that scraped HTML for websites may not be compliant with Web Content Accessibility Guidelines ([WCAG](https://www.w3.org/WAI/standards-guidelines/wcag/)). This could have an impact on HTML-generated code that may introduce web accessibility issues.
The training dataset could contain malicious code and/or the model could be used to generate malware or ransomware.
To the best of our knowledge, all files contained in the dataset are licensed with one of the permissive licenses (see list in [Licensing information](#licensing-information)) or no license.
The accuracy of license attribution is limited by the accuracy of GHArchive and ScanCode Toolkit.
Any mistakes should be reported to BigCode Project for review and follow-up as needed.
## Additional Information
### Dataset Curators
1. Harm de Vries, ServiceNow Research, harm.devries@servicenow.com
2. Leandro von Werra, Hugging Face, leandro@huggingface.co
### Licensing Information
The Stack v2 is a collection of source code from repositories with various licenses. Any use of all or part of the code gathered in The Stack v2 must abide by the terms of the original licenses, including attribution clauses when relevant. We facilitate this by providing provenance information for each data point.
The list of [SPDX license identifiers](https://spdx.org/licenses/) included in the dataset can be found [here](https://huggingface.co/datasets/bigcode/the-stack-v2/blob/main/license_stats.csv).
### Citation Information
```bash
@misc{lozhkov2024starcoder,
title={StarCoder 2 and The Stack v2: The Next Generation},
author={Anton Lozhkov and Raymond Li and Loubna Ben Allal and Federico Cassano and Joel Lamy-Poirier and Nouamane Tazi and Ao Tang and Dmytro Pykhtar and Jiawei Liu and Yuxiang Wei and Tianyang Liu and Max Tian and Denis Kocetkov and Arthur Zucker and Younes Belkada and Zijian Wang and Qian Liu and Dmitry Abulkhanov and Indraneil Paul and Zhuang Li and Wen-Ding Li and Megan Risdal and Jia Li and Jian Zhu and Terry Yue Zhuo and Evgenii Zheltonozhskii and Nii Osae Osae Dade and Wenhao Yu and Lucas Krauß and Naman Jain and Yixuan Su and Xuanli He and Manan Dey and Edoardo Abati and Yekun Chai and Niklas Muennighoff and Xiangru Tang and Muhtasham Oblokulov and Christopher Akiki and Marc Marone and Chenghao Mou and Mayank Mishra and Alex Gu and Binyuan Hui and Tri Dao and Armel Zebaze and Olivier Dehaene and Nicolas Patry and Canwen Xu and Julian McAuley and Han Hu and Torsten Scholak and Sebastien Paquet and Jennifer Robinson and Carolyn Jane Anderson and Nicolas Chapados and Mostofa Patwary and Nima Tajbakhsh and Yacine Jernite and Carlos Muñoz Ferrandis and Lingming Zhang and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries},
year={2024},
eprint={2402.19173},
archivePrefix={arXiv},
primaryClass={cs.SE}
}
``` |
chopraamitea/eCommerce_Women_section | ---
license: apache-2.0
---
|
CyberHarem/glorious_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of glorious/グロリアス/光荣 (Azur Lane)
This is the dataset of glorious/グロリアス/光荣 (Azur Lane), containing 104 images and their tags.
The core tags of this character are `blonde_hair, breasts, large_breasts, blue_eyes, hair_ornament, long_hair, hair_flower, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:-------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 104 | 176.45 MiB | [Download](https://huggingface.co/datasets/CyberHarem/glorious_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 104 | 88.46 MiB | [Download](https://huggingface.co/datasets/CyberHarem/glorious_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 273 | 204.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/glorious_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 104 | 150.34 MiB | [Download](https://huggingface.co/datasets/CyberHarem/glorious_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 273 | 304.44 MiB | [Download](https://huggingface.co/datasets/CyberHarem/glorious_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/glorious_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 10 |  |  |  |  |  | 1girl, bare_shoulders, chinese_clothes, cleavage, hair_stick, official_alternate_costume, red_dress, solo, wide_sleeves, clothing_cutout, looking_at_viewer, open_dress, white_flower, kanzashi, purple_eyes, red_choker, night_sky, blush, full_moon, huge_breasts, revealing_clothes, starry_sky |
| 1 | 7 |  |  |  |  |  | 1girl, bare_shoulders, hair_stick, looking_at_viewer, official_alternate_costume, red_dress, solo, wide_sleeves, chinese_clothes, kanzashi, open_dress, red_choker, cleavage_cutout, white_flower, purple_eyes |
| 2 | 14 |  |  |  |  |  | 1girl, long_sleeves, solo, looking_at_viewer, smile, very_long_hair, white_shirt, school_uniform, blush, blue_skirt, closed_mouth, blue_necktie, simple_background, white_background, collared_shirt, hair_between_eyes, hair_bow, sitting, cowboy_shot, french_braid, pleated_skirt, ribbon, school_bag |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | bare_shoulders | chinese_clothes | cleavage | hair_stick | official_alternate_costume | red_dress | solo | wide_sleeves | clothing_cutout | looking_at_viewer | open_dress | white_flower | kanzashi | purple_eyes | red_choker | night_sky | blush | full_moon | huge_breasts | revealing_clothes | starry_sky | cleavage_cutout | long_sleeves | smile | very_long_hair | white_shirt | school_uniform | blue_skirt | closed_mouth | blue_necktie | simple_background | white_background | collared_shirt | hair_between_eyes | hair_bow | sitting | cowboy_shot | french_braid | pleated_skirt | ribbon | school_bag |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------------|:------------------|:-----------|:-------------|:-----------------------------|:------------|:-------|:---------------|:------------------|:--------------------|:-------------|:---------------|:-----------|:--------------|:-------------|:------------|:--------|:------------|:---------------|:--------------------|:-------------|:------------------|:---------------|:--------|:-----------------|:--------------|:-----------------|:-------------|:---------------|:---------------|:--------------------|:-------------------|:-----------------|:--------------------|:-----------|:----------|:--------------|:---------------|:----------------|:---------|:-------------|
| 0 | 10 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | |
| 1 | 7 |  |  |  |  |  | X | X | X | | X | X | X | X | X | | X | X | X | X | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | | |
| 2 | 14 |  |  |  |  |  | X | | | | | | | X | | | X | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X |
|
CyberHarem/yaegashi_yasuko_akibameidosensou | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of Yaegashi Yasuko
This is the dataset of Yaegashi Yasuko, containing 156 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
| Name | Images | Download | Description |
|:------------|---------:|:------------------------------------|:-------------------------------------------------------------------------|
| raw | 156 | [Download](dataset-raw.zip) | Raw data with meta information. |
| raw-stage3 | 372 | [Download](dataset-raw-stage3.zip) | 3-stage cropped raw data with meta information. |
| 384x512 | 156 | [Download](dataset-384x512.zip) | 384x512 aligned dataset. |
| 512x512 | 156 | [Download](dataset-512x512.zip) | 512x512 aligned dataset. |
| 512x704 | 156 | [Download](dataset-512x704.zip) | 512x704 aligned dataset. |
| 640x640 | 156 | [Download](dataset-640x640.zip) | 640x640 aligned dataset. |
| 640x880 | 156 | [Download](dataset-640x880.zip) | 640x880 aligned dataset. |
| stage3-640 | 372 | [Download](dataset-stage3-640.zip) | 3-stage cropped dataset with the shorter side not exceeding 640 pixels. |
| stage3-800 | 372 | [Download](dataset-stage3-800.zip) | 3-stage cropped dataset with the shorter side not exceeding 800 pixels. |
| stage3-1200 | 372 | [Download](dataset-stage3-1200.zip) | 3-stage cropped dataset with the shorter side not exceeding 1200 pixels. |
|
dog/actlearn_test_mnist | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
splits:
- name: test
num_bytes: 2875182.0
num_examples: 10000
download_size: 2383462
dataset_size: 2875182.0
---
# Dataset Card for "actlearn_test_mnist"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Vinnyyw/Belindavoice | ---
license: openrail
---
|
projecte-aina/PAWS-ca | ---
YAML tags: null
annotations_creators:
- professional translators
- machine-generated
language:
- ca
license:
- other
multilinguality:
- monolingual
pretty_name: paws-ca
task_categories:
- text-classification
tags:
- paraphrase-identification
---
# Dataset Card for PAWS-ca: Paraphrase Adversaries from Word Scrambling in Catalan
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [PAWS-ca](https://zenodo.org/record/)
- **Point of Contact:** langtech@bsc.es
### Dataset Summary
The PAWS-ca dataset (Paraphrase Adversaries from Word Scrambling in Catalan) is a translation of the English PAWS dataset into Catalan, commissioned by BSC LangTech Unit.
The dataset contains 4,000 human translated PAWS pairs and 49,000 machine translated pairs.
### Supported Tasks and Leaderboards
Paraphrase Identification, Language Model
### Languages
The dataset is in Catalan (`ca-ES`).
## Dataset Structure
### Data Instances
Three JSON files, one for each split.
### Example:
<pre>
{
"id": 38
"sentence1": Holly estava influenciat musicalment per Elton John.
"sentence2": Holly Holly va ser influenciada musicalment per Elton John.
"label": 1
}
{
"id":
"sentence1": L’equip va respondre als canvis en el següent partit el mateix vespre del 19 de febrer.
"sentence2": L'equip va respondre als canvis en el mateix partit d’aquell següent 19 de febrer al vespre.
"label": 0
}
</pre>
### Data Fields
- id: An ID that matches the ID of the source pair of the English PAWS dataset
- sentence1: The first sentence
- sentence2: The second sentence
- label: Label for each pair
### Data Splits
* paws-ca.train.jsonl: 49,401 examples
* paws-ca.val.jsonl: 2,000 examples
* paws-ca.test.jsonl: 2,000 examples
> **Caveat**: please note that the dev and test sets of PAWS-X are both sourced
> from the dev set of PAWS-Wiki. As a consequence, the same `sentence 1` may
> appear in both the dev and test sets. Nevertheless our data split guarantees
> that there is no overlap on sentence pairs (`sentence 1` + `sentence 2`)
> between dev and test.
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
PAWS (Paraphrase Adversaries from Word Scrambling)
#### Initial Data Collection and Normalization
This dataset is a translation the English PAWS dataset into Catalan, commissioned by BSC LangTech Unit within Projecte AINA.
#### Who are the source language producers?
For more information on how PAWS was created, refer to the paper (), or visit the [PAWS's webpage]().
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
This is a translation of the English PAWS dataset and its annotations.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this dataset contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Language Technologies Unit at the Barcelona Supercomputing Center (langtech@bsc.es)
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/ca/inici/index.html#googtrans(ca|en) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina).
### Licensing Information
Original PAWS-X License:
The dataset may be freely used for any purpose, with acknowledgment of Google LLC as the data source being appreciated. The dataset is provided "AS IS" without any warranty, express or implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.
PAWS-ca:
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
### Contributions
[N/A] |
spsither/prepare_dataset_train_batch3 | ---
dataset_info:
features:
- name: input_features
sequence:
sequence: float32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 95836096584
num_examples: 99761
download_size: 20673138275
dataset_size: 95836096584
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "prepare_dataset_train_batch3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jlbaker361/cyberpunk-500-cropped | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
- name: frame
dtype: int64
- name: title
dtype: string
splits:
- name: train
num_bytes: 104772251.0
num_examples: 490
download_size: 104750440
dataset_size: 104772251.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
316usman/thematic3c_rr_embed | ---
dataset_info:
features:
- name: text
dtype: string
- name: document_url
dtype: string
- name: source_url
dtype: string
splits:
- name: train
num_bytes: 266374122
num_examples: 420005
download_size: 95501088
dataset_size: 266374122
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
vwxyzjn/summarize_from_feedback_oai_preprocessing | ---
dataset_info:
features:
- name: info
struct:
- name: id
dtype: string
- name: post
dtype: string
- name: title
dtype: string
- name: subreddit
dtype: string
- name: site
dtype: string
- name: article
dtype: string
- name: summaries
list:
- name: text
dtype: string
- name: policy
dtype: string
- name: note
dtype: string
- name: choice
dtype: int32
- name: worker
dtype: string
- name: batch
dtype: string
- name: split
dtype: string
- name: extra
struct:
- name: confidence
dtype: int32
- name: query_token
sequence: int64
- name: query
dtype: string
- name: response0
dtype: string
- name: response0_token
sequence: int64
- name: response1
dtype: string
- name: response1_token
sequence: int64
splits:
- name: train
num_bytes: 862825302
num_examples: 92858
- name: validation
num_bytes: 812584211
num_examples: 86086
download_size: 127954862
dataset_size: 1675409513
---
# Dataset Card for "summarize_from_feedback_oai_preprocessing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yunus-emre/test-xquad-xtreme | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 80047933
num_examples: 91012
download_size: 50644660
dataset_size: 80047933
---
# Dataset Card for "test-xquad-xtreme"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
BalajiAIdev/autotrain-data-animal-image-classification | ---
task_categories:
- image-classification
---
# AutoTrain Dataset for project: animal-image-classification
## Dataset Description
This dataset has been automatically processed by AutoTrain for project animal-image-classification.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<366x274 RGB PIL image>",
"target": 0
},
{
"image": "<367x274 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Lion', 'Tiger'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 20 |
| valid | 20 |
|
openlifescienceai/mmlu_college_medicine | ---
dataset_info:
features:
- name: subject_name
dtype: string
- name: data
struct:
- name: Correct Answer
dtype: string
- name: Correct Option
dtype: string
- name: Options
struct:
- name: A
dtype: string
- name: B
dtype: string
- name: C
dtype: string
- name: D
dtype: string
- name: Question
dtype: string
- name: id
dtype: string
splits:
- name: test
num_bytes: 98420
num_examples: 173
- name: validation
num_bytes: 9931
num_examples: 22
- name: dev
num_bytes: 2155
num_examples: 5
download_size: 86587
dataset_size: 110506
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: validation
path: data/validation-*
- split: dev
path: data/dev-*
---
|
missvector/asd-qa-val | ---
license: mit
dataset_info:
features:
- name: question
dtype: string
- name: answers
struct:
- name: answer_end
dtype: int64
- name: answer_start
dtype: int64
- name: text
dtype: string
- name: paragraph
dtype: string
splits:
- name: train
num_bytes: 316067
num_examples: 261
download_size: 54962
dataset_size: 316067
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for The ASD QA Dataset (validation set)
## Dataset Description
- **Repository:** https://github.com/vifirsanova/empi
### Dataset Summary
A dataset for question-answering used for building an informational Russian language chatbot for the inclusion of people with autism spectrum disorder and Asperger syndrome in particular, based on data from the following website: https://aspergers.ru.
### Languages
Russian
## Dataset Structure
The dataset inherits SQuAD 2.0 structure.
### Source Data
https://aspergers.ru
### Dataset Curators
Victoria Firsanova |
zxyun/PKU-DyMVHumans | ---
language:
- en
- zh
license: c-uda
tags:
- Video
- Multi-viewpoint
viewer: false
---
# <i>PKU-DyMVHumans</i> Dataset
## Overview
PKU-DyMVHumans is a versatile human-centric dataset designed for high-fidelity reconstruction and rendering of dynamic human performances in markerless multi-view capture settings.
It comprises 32 humans across 45 different dynamic scenarios, each featuring highly detailed appearances and complex human motions.
### Sources
- **Project page:** https://pku-dymvhumans.github.io
- **Github:** https://github.com/zhengxyun/PKU-DyMVHumans
- **Paper:** https://arxiv.org/abs/2403.16080
### Key Features:
- **High-fidelity performance:** We construct a multi-view system to capture humans in motion, containing 56/60 synchronous cameras with 1080P or 4K resolution.
- **High-detailed appearance:** It captures complex cloth deformation, and intricate texture details, like delicate satin ribbon and special headwear.
- **Complex human motion:** It covers a wide range of special costume performances, artistic movements, and sports activities.
- **Human-object/scene interactions:** These include human-object interactions, as well as challenging multi-person interactions and complex scene effects (e.g., lighting, shadows, and smoking).
### Benchmark
The objective of our benchmark is to achieve robust geometry reconstruction and novel view synthesis for dynamic humans under markerless and fixed multi-view camera settings, while minimizing the need for manual annotation and reducing time costs.
This includes **neural scene decomposition**, **novel view synthesis**, and **dynamic human modeling**.
## Dataset Details
### Agreement
Note that by downloading the datasets, you acknowledge that you have read the agreement, understand it, and agree to be bound by them:
- The PKU-DyMVHumans dataset is made available only for non-commercial research purposes. Any other use, in particular any use for commercial purposes, is prohibited.
- You agree not to further copy, publish or distribute any portion of the dataset.
- Peking University reserves the right to terminate your access to the dataset at any time.
### Dataset Statistics
- **Scenes:** 45 different dynamic scenarios, engaging in various actions and clothing styles.
- **Actions:** 4 different action types: dance, kungfu, sport, and fashion show.
- **Individual:** 32 professional players, including 16 males, 11 females, and 5 children.
- **Frames:** totalling approximately 8.2 million frames.
## Dataset Structure
For each scene, we provide the multi-view images (`./case_name/per_view/cam_*/images/`), the coarse foreground with RGBA channels (`./case_name/per_view/cam_*/images/`),
as well as the coarse foreground segmentation (`./case_name/per_view/cam_*/pha/`), which are obtained using [BackgroundMattingV2](https://github.com/PeterL1n/BackgroundMattingV2).
To make the benchmarks easier compare with our dataset, we save different data formats (i.e., [Surface-SOS](https://github.com/zhengxyun/Surface-SOS), [NeuS](https://github.com/Totoro97/NeuS), [NeuS2](https://github.com/19reborn/NeuS2), [Instant-ngp](https://github.com/NVlabs/instant-ngp), and [3D-Gaussian](https://github.com/graphdeco-inria/gaussian-splatting)) of PKU-DyMVHumans at **Part1** and write a document that describes the data process.
```
.
|--- <case_name>
| |--- cams
| |--- videos
| |--- per_view
| |--- per_frame
| |--- data_ngp
| |--- data_NeuS
| |--- data_NeuS2
| |--- data_COLMAP
| |--- <overview_fme_*.png>
|--- ...
```
## BibTeX
```
@article{zheng2024DyMVHumans,
title={PKU-DyMVHumans: A Multi-View Video Benchmark for High-Fidelity Dynamic Human Modeling},
author={Zheng, Xiaoyun and Liao, Liwei and Li, Xufeng and Jiao, Jianbo and Wang, Rongjie and Gao, Feng and Wang, Shiqi and Wang, Ronggang},
journal={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2024}
}
``` |
lilpad/random | ---
task_categories:
- audio-classification
language:
- en
--- |
heliosprime/twitter_dataset_1713094871 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 9380
num_examples: 24
download_size: 11971
dataset_size: 9380
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "twitter_dataset_1713094871"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
aureliojafer/twitter_dataset_1709833834 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
splits:
- name: train
num_bytes: 70117
num_examples: 226
download_size: 44234
dataset_size: 70117
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
liuyanchen1015/MULTI_VALUE_wnli_drop_copula_be_locative | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: train
num_bytes: 1236
num_examples: 8
download_size: 3092
dataset_size: 1236
---
# Dataset Card for "MULTI_VALUE_wnli_drop_copula_be_locative"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
anantn/hacker-news | ---
license: mit
---
|
HuggingFaceM4/MMBench_modif_chatbot_dedup_val | Invalid username or password. |
TeamSODA/cl-signal_processing_attacks_assembly_commonvoice | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: label
dtype:
class_label:
names:
'0': 0-benign
'1': 1-attacked
splits:
- name: train
num_bytes: 153422558.0
num_examples: 300
download_size: 142595903
dataset_size: 153422558.0
---
# Dataset Card for "cl_signal_processing_attacks_assembly_commonvoice"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
thejorseman/Albures | ---
license: apache-2.0
---
|
Elijahbodden/SMS-v2 | ---
license: unlicense
---
|
CyberHarem/scathach_skadi_fgo | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of scathach_skadi/スカサハ=スカディ/斯卡哈·斯卡蒂 (Fate/Grand Order)
This is the dataset of scathach_skadi/スカサハ=スカディ/斯卡哈·斯卡蒂 (Fate/Grand Order), containing 500 images and their tags.
The core tags of this character are `purple_hair, breasts, long_hair, red_eyes, large_breasts, hair_between_eyes, ribbon`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:--------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 956.33 MiB | [Download](https://huggingface.co/datasets/CyberHarem/scathach_skadi_fgo/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 1200 | 500 | 813.19 MiB | [Download](https://huggingface.co/datasets/CyberHarem/scathach_skadi_fgo/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1317 | 1.56 GiB | [Download](https://huggingface.co/datasets/CyberHarem/scathach_skadi_fgo/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/scathach_skadi_fgo',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 7 |  |  |  |  |  | 1girl, fur_trim, holding_wand, jewelry, long_sleeves, looking_at_viewer, purple_dress, smile, solo, tiara, cleavage, detached_collar, hair_ribbon, wide_sleeves |
| 1 | 10 |  |  |  |  |  | 1girl, cleavage, long_sleeves, purple_dress, solo, tiara, feather_trim, holding_wand, looking_at_viewer, wide_sleeves, fur-trimmed_dress, headpiece, detached_collar, runes, smile, pendant |
| 2 | 12 |  |  |  |  |  | 1girl, fur_trim, holding_wand, looking_at_viewer, ponytail, purple_dress, purple_ribbon, solo, tiara, bare_shoulders, hair_ribbon, closed_mouth, headpiece, smile, thighhighs, blush |
| 3 | 5 |  |  |  |  |  | 1girl, fur-trimmed_dress, holding_wand, looking_at_viewer, purple_dress, purple_ribbon, smile, solo, tiara, bare_shoulders, ponytail, hair_ribbon, white_thighhighs, detached_sleeves |
| 4 | 5 |  |  |  |  |  | 1girl, cleavage_cutout, fur_trim, holding_wand, looking_at_viewer, purple_dress, smile, solo, tiara, simple_background, covered_navel, blush, choker, collarbone, hair_ribbon, parted_lips, purple_bodysuit, shoulder_armor, white_background |
| 5 | 11 |  |  |  |  |  | 1girl, bare_shoulders, cleavage, hair_flower, looking_at_viewer, outdoors, smile, solo, blue_sky, collarbone, day, bracelet, navel, off-shoulder_bikini, purple_bikini, thighs, twintails, beach, blush, ocean |
| 6 | 14 |  |  |  |  |  | 1girl, purple_dress, solo, cleavage, looking_at_viewer, sweater_dress, collarbone, glasses, over-rim_eyewear, ponytail, see-through_sleeves, blush, hair_scrunchie, long_sleeves, smile, thighs, bare_shoulders, open_mouth |
| 7 | 10 |  |  |  |  |  | 1girl, blush, nipples, 1boy, completely_nude, hetero, penis, pussy, sex, vaginal, mosaic_censoring, navel, solo_focus, sweat, ponytail, thighs, tiara, bed, collarbone, looking_at_viewer, open_mouth, spread_legs, hair_ribbon, on_back, pillow, purple_ribbon, cum, hair_ornament, missionary |
| 8 | 12 |  |  |  |  |  | 1girl, beret, solo, looking_at_viewer, skirt, jacket_on_shoulders, stethoscope, holding, purple_headwear, purple_dress, shirt, watermelon_bar, sitting |
| 9 | 11 |  |  |  |  |  | 1girl, bare_shoulders, fake_animal_ears, looking_at_viewer, playboy_bunny, rabbit_ears, solo, cleavage, detached_collar, purple_leotard, strapless_leotard, blush, fishnet_pantyhose, highleg_leotard, wrist_cuffs, official_alternate_costume, rabbit_tail, smile, ponytail, thighs, ass, bowtie, covered_navel, detached_sleeves |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | fur_trim | holding_wand | jewelry | long_sleeves | looking_at_viewer | purple_dress | smile | solo | tiara | cleavage | detached_collar | hair_ribbon | wide_sleeves | feather_trim | fur-trimmed_dress | headpiece | runes | pendant | ponytail | purple_ribbon | bare_shoulders | closed_mouth | thighhighs | blush | white_thighhighs | detached_sleeves | cleavage_cutout | simple_background | covered_navel | choker | collarbone | parted_lips | purple_bodysuit | shoulder_armor | white_background | hair_flower | outdoors | blue_sky | day | bracelet | navel | off-shoulder_bikini | purple_bikini | thighs | twintails | beach | ocean | sweater_dress | glasses | over-rim_eyewear | see-through_sleeves | hair_scrunchie | open_mouth | nipples | 1boy | completely_nude | hetero | penis | pussy | sex | vaginal | mosaic_censoring | solo_focus | sweat | bed | spread_legs | on_back | pillow | cum | hair_ornament | missionary | beret | skirt | jacket_on_shoulders | stethoscope | holding | purple_headwear | shirt | watermelon_bar | sitting | fake_animal_ears | playboy_bunny | rabbit_ears | purple_leotard | strapless_leotard | fishnet_pantyhose | highleg_leotard | wrist_cuffs | official_alternate_costume | rabbit_tail | ass | bowtie |
|----:|----------:|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------------------------------|:--------|:-----------|:---------------|:----------|:---------------|:--------------------|:---------------|:--------|:-------|:--------|:-----------|:------------------|:--------------|:---------------|:---------------|:--------------------|:------------|:--------|:----------|:-----------|:----------------|:-----------------|:---------------|:-------------|:--------|:-------------------|:-------------------|:------------------|:--------------------|:----------------|:---------|:-------------|:--------------|:------------------|:-----------------|:-------------------|:--------------|:-----------|:-----------|:------|:-----------|:--------|:----------------------|:----------------|:---------|:------------|:--------|:--------|:----------------|:----------|:-------------------|:----------------------|:-----------------|:-------------|:----------|:-------|:------------------|:---------|:--------|:--------|:------|:----------|:-------------------|:-------------|:--------|:------|:--------------|:----------|:---------|:------|:----------------|:-------------|:--------|:--------|:----------------------|:--------------|:----------|:------------------|:--------|:-----------------|:----------|:-------------------|:----------------|:--------------|:-----------------|:--------------------|:--------------------|:------------------|:--------------|:-----------------------------|:--------------|:------|:---------|
| 0 | 7 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 10 |  |  |  |  |  | X | | X | | X | X | X | X | X | X | X | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 12 |  |  |  |  |  | X | X | X | | | X | X | X | X | X | | | X | | | | X | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 5 |  |  |  |  |  | X | | X | | | X | X | X | X | X | | | X | | | X | | | | X | X | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | X | X | | | X | X | X | X | X | | | X | | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 11 |  |  |  |  |  | X | | | | | X | | X | X | | X | | | | | | | | | | | X | | | X | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 14 |  |  |  |  |  | X | | | | X | X | X | X | X | | X | | | | | | | | | X | | X | | | X | | | | | | | X | | | | | | | | | | | | | X | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 10 |  |  |  |  |  | X | | | | | X | | | | X | | | X | | | | | | | X | X | | | | X | | | | | | | X | | | | | | | | | | X | | | X | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | |
| 8 | 12 |  |  |  |  |  | X | | | | | X | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | |
| 9 | 11 |  |  |  |  |  | X | | | | | X | | X | X | | X | X | | | | | | | | X | | X | | | X | | X | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X | X | X |
|
zolak/twitter_dataset_1712998930 | ---
dataset_info:
features:
- name: id
dtype: string
- name: tweet_content
dtype: string
- name: user_name
dtype: string
- name: user_id
dtype: string
- name: created_at
dtype: string
- name: url
dtype: string
- name: favourite_count
dtype: int64
- name: scraped_at
dtype: string
- name: image_urls
dtype: string
splits:
- name: train
num_bytes: 3050088
num_examples: 7402
download_size: 1499465
dataset_size: 3050088
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
autoevaluate/autoeval-eval-phpthinh__examplehsd-raw-ff3db7-1730160388 | ---
type: predictions
tags:
- autotrain
- evaluation
datasets:
- phpthinh/examplehsd
eval_info:
task: text_zero_shot_classification
model: bigscience/bloom-3b
metrics: ['f1']
dataset_name: phpthinh/examplehsd
dataset_config: raw
dataset_split: test
col_mapping:
text: text
classes: classes
target: target
---
# Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Zero-Shot Text Classification
* Model: bigscience/bloom-3b
* Dataset: phpthinh/examplehsd
* Config: raw
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@phpthinh](https://huggingface.co/phpthinh) for evaluating this model. |
hlillemark/c4_t5_corrupted_seqlen256_tiny | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 265951400
num_examples: 177775
- name: validation
num_bytes: 26565968
num_examples: 17758
download_size: 132827702
dataset_size: 292517368
---
# Dataset Card for "c4_t5_corrupted_seqlen256_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
cqin/strawberry-disease | ---
language:
- en
- zh
size_categories:
- n<1K
--- |
reciprocate/math_dpo_pairs | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
list:
- name: content
dtype: string
- name: role
dtype: string
- name: rejected
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 7316399
num_examples: 2418
- name: test
num_bytes: 8556760
num_examples: 1964
download_size: 7652518
dataset_size: 15873159
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
vietgpt-archive/toxic_clean_v1 | ---
dataset_info:
features:
- name: content
dtype: string
- name: object
struct:
- name: _id
dtype: string
- name: name
dtype: string
- name: type
dtype: string
splits:
- name: train
num_bytes: 190880779.118642
num_examples: 1822670
download_size: 155806319
dataset_size: 190880779.118642
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
Chamroush/Exclusive | ---
license: artistic-2.0
---
|
lauyon/anonymizer | ---
license: cc-by-4.0
---
|
cce112/12345 | ---
license: openrail
---
|
Doutran/setmyllabom | ---
license: openrail
---
|
TinyPixel/s-data_2 | ---
dataset_info:
features:
- name: human
dtype: string
- name: bot
dtype: string
splits:
- name: train
num_bytes: 19428668
num_examples: 34687
download_size: 9618286
dataset_size: 19428668
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "s-data_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Minata/tokenized_512src_fm_fc_ms_ff_method2testcases_v0 | ---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 1225830278
num_examples: 183836
- name: test
num_bytes: 310978298
num_examples: 46637
download_size: 172254156
dataset_size: 1536808576
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
alancooney/sae-monology-pile-uncopyrighted-tokenizer-gpt2 | ---
license: mit
dataset_info:
features:
- name: input_ids
sequence: int32
splits:
- name: train
num_bytes: 31522321636
num_examples: 30663737
download_size: 17207116581
dataset_size: 31522321636
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
language:
- en
--- |
shrikant11/myra4 | ---
dataset_info:
features:
- name: input_image
dtype: image
- name: edit_prompt
dtype: string
- name: edited_image
dtype: image
splits:
- name: train
num_bytes: 92938183.954
num_examples: 1386
download_size: 79532355
dataset_size: 92938183.954
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/details_saltlux__luxia-21.4b-alignment-v1.1 | ---
pretty_name: Evaluation run of saltlux/luxia-21.4b-alignment-v1.1
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [saltlux/luxia-21.4b-alignment-v1.1](https://huggingface.co/saltlux/luxia-21.4b-alignment-v1.1)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 configuration, each one coresponding to one of the\
\ evaluated task.\n\nThe dataset has been created from 2 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 aggregated 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_saltlux__luxia-21.4b-alignment-v1.1\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-03-21T14:42:07.820524](https://huggingface.co/datasets/open-llm-leaderboard/details_saltlux__luxia-21.4b-alignment-v1.1/blob/main/results_2024-03-21T14-42-07.820524.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 \"acc\": 0.6840030660779568,\n\
\ \"acc_stderr\": 0.031363296344179496,\n \"acc_norm\": 0.6874998200439859,\n\
\ \"acc_norm_stderr\": 0.03200381423680316,\n \"mc1\": 0.6940024479804161,\n\
\ \"mc1_stderr\": 0.016132229728155038,\n \"mc2\": 0.8090943058378247,\n\
\ \"mc2_stderr\": 0.013112054157410078\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.7627986348122867,\n \"acc_stderr\": 0.01243039982926086,\n\
\ \"acc_norm\": 0.7824232081911263,\n \"acc_norm_stderr\": 0.012057262020972499\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7514439354710217,\n\
\ \"acc_stderr\": 0.0043129258376915424,\n \"acc_norm\": 0.8969328818960366,\n\
\ \"acc_norm_stderr\": 0.003034251362939894\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.04923659639173309,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.04923659639173309\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6370370370370371,\n\
\ \"acc_stderr\": 0.04153948404742398,\n \"acc_norm\": 0.6370370370370371,\n\
\ \"acc_norm_stderr\": 0.04153948404742398\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.8026315789473685,\n \"acc_stderr\": 0.03238981601699397,\n\
\ \"acc_norm\": 0.8026315789473685,\n \"acc_norm_stderr\": 0.03238981601699397\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.73,\n\
\ \"acc_stderr\": 0.04461960433384741,\n \"acc_norm\": 0.73,\n \
\ \"acc_norm_stderr\": 0.04461960433384741\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7396226415094339,\n \"acc_stderr\": 0.027008766090708045,\n\
\ \"acc_norm\": 0.7396226415094339,\n \"acc_norm_stderr\": 0.027008766090708045\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.8194444444444444,\n\
\ \"acc_stderr\": 0.03216600808802268,\n \"acc_norm\": 0.8194444444444444,\n\
\ \"acc_norm_stderr\": 0.03216600808802268\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956912,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956912\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.63,\n \"acc_stderr\": 0.048523658709391,\n \"acc_norm\": 0.63,\n\
\ \"acc_norm_stderr\": 0.048523658709391\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145633,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145633\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6878612716763006,\n\
\ \"acc_stderr\": 0.03533133389323657,\n \"acc_norm\": 0.6878612716763006,\n\
\ \"acc_norm_stderr\": 0.03533133389323657\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.4411764705882353,\n \"acc_stderr\": 0.049406356306056595,\n\
\ \"acc_norm\": 0.4411764705882353,\n \"acc_norm_stderr\": 0.049406356306056595\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.82,\n \"acc_stderr\": 0.038612291966536955,\n \"acc_norm\": 0.82,\n\
\ \"acc_norm_stderr\": 0.038612291966536955\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6680851063829787,\n \"acc_stderr\": 0.03078373675774565,\n\
\ \"acc_norm\": 0.6680851063829787,\n \"acc_norm_stderr\": 0.03078373675774565\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.5263157894736842,\n\
\ \"acc_stderr\": 0.046970851366478626,\n \"acc_norm\": 0.5263157894736842,\n\
\ \"acc_norm_stderr\": 0.046970851366478626\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6551724137931034,\n \"acc_stderr\": 0.03960933549451207,\n\
\ \"acc_norm\": 0.6551724137931034,\n \"acc_norm_stderr\": 0.03960933549451207\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4947089947089947,\n \"acc_stderr\": 0.02574986828855657,\n \"\
acc_norm\": 0.4947089947089947,\n \"acc_norm_stderr\": 0.02574986828855657\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.48412698412698413,\n\
\ \"acc_stderr\": 0.04469881854072606,\n \"acc_norm\": 0.48412698412698413,\n\
\ \"acc_norm_stderr\": 0.04469881854072606\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.047258156262526045,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.047258156262526045\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.8354838709677419,\n \"acc_stderr\": 0.021090847745939317,\n \"\
acc_norm\": 0.8354838709677419,\n \"acc_norm_stderr\": 0.021090847745939317\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.5517241379310345,\n \"acc_stderr\": 0.03499113137676744,\n \"\
acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.03499113137676744\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.76,\n \"acc_stderr\": 0.04292346959909283,\n \"acc_norm\"\
: 0.76,\n \"acc_norm_stderr\": 0.04292346959909283\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8181818181818182,\n \"acc_stderr\": 0.030117688929503564,\n\
\ \"acc_norm\": 0.8181818181818182,\n \"acc_norm_stderr\": 0.030117688929503564\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8585858585858586,\n \"acc_stderr\": 0.02482590979334333,\n \"\
acc_norm\": 0.8585858585858586,\n \"acc_norm_stderr\": 0.02482590979334333\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8911917098445595,\n \"acc_stderr\": 0.022473253332768766,\n\
\ \"acc_norm\": 0.8911917098445595,\n \"acc_norm_stderr\": 0.022473253332768766\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.7,\n \"acc_stderr\": 0.023234581088428498,\n \"acc_norm\"\
: 0.7,\n \"acc_norm_stderr\": 0.023234581088428498\n },\n \"harness|hendrycksTest-high_school_mathematics|5\"\
: {\n \"acc\": 0.4,\n \"acc_stderr\": 0.029869605095316897,\n \
\ \"acc_norm\": 0.4,\n \"acc_norm_stderr\": 0.029869605095316897\n \
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \"\
acc\": 0.7815126050420168,\n \"acc_stderr\": 0.026841514322958945,\n \
\ \"acc_norm\": 0.7815126050420168,\n \"acc_norm_stderr\": 0.026841514322958945\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.4370860927152318,\n \"acc_stderr\": 0.04050035722230636,\n \"\
acc_norm\": 0.4370860927152318,\n \"acc_norm_stderr\": 0.04050035722230636\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8642201834862385,\n \"acc_stderr\": 0.014686907556340029,\n \"\
acc_norm\": 0.8642201834862385,\n \"acc_norm_stderr\": 0.014686907556340029\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5833333333333334,\n \"acc_stderr\": 0.03362277436608043,\n \"\
acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.03362277436608043\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8676470588235294,\n \"acc_stderr\": 0.023784297520918853,\n \"\
acc_norm\": 0.8676470588235294,\n \"acc_norm_stderr\": 0.023784297520918853\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8354430379746836,\n \"acc_stderr\": 0.024135736240566932,\n \
\ \"acc_norm\": 0.8354430379746836,\n \"acc_norm_stderr\": 0.024135736240566932\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7488789237668162,\n\
\ \"acc_stderr\": 0.029105220833224622,\n \"acc_norm\": 0.7488789237668162,\n\
\ \"acc_norm_stderr\": 0.029105220833224622\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6793893129770993,\n \"acc_stderr\": 0.04093329229834278,\n\
\ \"acc_norm\": 0.6793893129770993,\n \"acc_norm_stderr\": 0.04093329229834278\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.8512396694214877,\n \"acc_stderr\": 0.032484700838071943,\n \"\
acc_norm\": 0.8512396694214877,\n \"acc_norm_stderr\": 0.032484700838071943\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\
\ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.48214285714285715,\n\
\ \"acc_stderr\": 0.047427623612430116,\n \"acc_norm\": 0.48214285714285715,\n\
\ \"acc_norm_stderr\": 0.047427623612430116\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8252427184466019,\n \"acc_stderr\": 0.03760178006026621,\n\
\ \"acc_norm\": 0.8252427184466019,\n \"acc_norm_stderr\": 0.03760178006026621\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.9188034188034188,\n\
\ \"acc_stderr\": 0.017893784904018533,\n \"acc_norm\": 0.9188034188034188,\n\
\ \"acc_norm_stderr\": 0.017893784904018533\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.75,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8173690932311622,\n\
\ \"acc_stderr\": 0.013816335389973117,\n \"acc_norm\": 0.8173690932311622,\n\
\ \"acc_norm_stderr\": 0.013816335389973117\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7369942196531792,\n \"acc_stderr\": 0.02370309952525818,\n\
\ \"acc_norm\": 0.7369942196531792,\n \"acc_norm_stderr\": 0.02370309952525818\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4893854748603352,\n\
\ \"acc_stderr\": 0.016718732941192107,\n \"acc_norm\": 0.4893854748603352,\n\
\ \"acc_norm_stderr\": 0.016718732941192107\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.761437908496732,\n \"acc_stderr\": 0.02440439492808787,\n\
\ \"acc_norm\": 0.761437908496732,\n \"acc_norm_stderr\": 0.02440439492808787\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7427652733118971,\n\
\ \"acc_stderr\": 0.02482617128925089,\n \"acc_norm\": 0.7427652733118971,\n\
\ \"acc_norm_stderr\": 0.02482617128925089\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7839506172839507,\n \"acc_stderr\": 0.022899162918445803,\n\
\ \"acc_norm\": 0.7839506172839507,\n \"acc_norm_stderr\": 0.022899162918445803\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.5319148936170213,\n \"acc_stderr\": 0.029766675075873866,\n \
\ \"acc_norm\": 0.5319148936170213,\n \"acc_norm_stderr\": 0.029766675075873866\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4941329856584094,\n\
\ \"acc_stderr\": 0.012769356925216526,\n \"acc_norm\": 0.4941329856584094,\n\
\ \"acc_norm_stderr\": 0.012769356925216526\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7022058823529411,\n \"acc_stderr\": 0.02777829870154544,\n\
\ \"acc_norm\": 0.7022058823529411,\n \"acc_norm_stderr\": 0.02777829870154544\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6895424836601307,\n \"acc_stderr\": 0.018718067052623216,\n \
\ \"acc_norm\": 0.6895424836601307,\n \"acc_norm_stderr\": 0.018718067052623216\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6727272727272727,\n\
\ \"acc_stderr\": 0.04494290866252091,\n \"acc_norm\": 0.6727272727272727,\n\
\ \"acc_norm_stderr\": 0.04494290866252091\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7551020408163265,\n \"acc_stderr\": 0.02752963744017493,\n\
\ \"acc_norm\": 0.7551020408163265,\n \"acc_norm_stderr\": 0.02752963744017493\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.8308457711442786,\n\
\ \"acc_stderr\": 0.02650859065623327,\n \"acc_norm\": 0.8308457711442786,\n\
\ \"acc_norm_stderr\": 0.02650859065623327\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.81,\n \"acc_stderr\": 0.03942772444036624,\n \
\ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036624\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5783132530120482,\n\
\ \"acc_stderr\": 0.03844453181770917,\n \"acc_norm\": 0.5783132530120482,\n\
\ \"acc_norm_stderr\": 0.03844453181770917\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03188578017686398,\n\
\ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03188578017686398\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.6940024479804161,\n\
\ \"mc1_stderr\": 0.016132229728155038,\n \"mc2\": 0.8090943058378247,\n\
\ \"mc2_stderr\": 0.013112054157410078\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8666140489344909,\n \"acc_stderr\": 0.009555448026422969\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.46019711902956784,\n \
\ \"acc_stderr\": 0.01372877671409936\n }\n}\n```"
repo_url: https://huggingface.co/saltlux/luxia-21.4b-alignment-v1.1
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|arc:challenge|25_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|arc:challenge|25_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|gsm8k|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|gsm8k|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hellaswag|10_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hellaswag|10_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-39-42.695025.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-42-07.820524.parquet'
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- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-42-07.820524.parquet'
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- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-42-07.820524.parquet'
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- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-42-07.820524.parquet'
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- '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-42-07.820524.parquet'
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- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T14-42-07.820524.parquet'
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- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-42-07.820524.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-03-21T14-42-07.820524.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- '**/details_harness|winogrande|5_2024-03-21T14-39-42.695025.parquet'
- split: 2024_03_21T14_42_07.820524
path:
- '**/details_harness|winogrande|5_2024-03-21T14-42-07.820524.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-03-21T14-42-07.820524.parquet'
- config_name: results
data_files:
- split: 2024_03_21T14_39_42.695025
path:
- results_2024-03-21T14-39-42.695025.parquet
- split: 2024_03_21T14_42_07.820524
path:
- results_2024-03-21T14-42-07.820524.parquet
- split: latest
path:
- results_2024-03-21T14-42-07.820524.parquet
---
# Dataset Card for Evaluation run of saltlux/luxia-21.4b-alignment-v1.1
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [saltlux/luxia-21.4b-alignment-v1.1](https://huggingface.co/saltlux/luxia-21.4b-alignment-v1.1) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 configuration, each one coresponding to one of the evaluated task.
The dataset has been created from 2 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 aggregated 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_saltlux__luxia-21.4b-alignment-v1.1",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-03-21T14:42:07.820524](https://huggingface.co/datasets/open-llm-leaderboard/details_saltlux__luxia-21.4b-alignment-v1.1/blob/main/results_2024-03-21T14-42-07.820524.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": {
"acc": 0.6840030660779568,
"acc_stderr": 0.031363296344179496,
"acc_norm": 0.6874998200439859,
"acc_norm_stderr": 0.03200381423680316,
"mc1": 0.6940024479804161,
"mc1_stderr": 0.016132229728155038,
"mc2": 0.8090943058378247,
"mc2_stderr": 0.013112054157410078
},
"harness|arc:challenge|25": {
"acc": 0.7627986348122867,
"acc_stderr": 0.01243039982926086,
"acc_norm": 0.7824232081911263,
"acc_norm_stderr": 0.012057262020972499
},
"harness|hellaswag|10": {
"acc": 0.7514439354710217,
"acc_stderr": 0.0043129258376915424,
"acc_norm": 0.8969328818960366,
"acc_norm_stderr": 0.003034251362939894
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.4,
"acc_stderr": 0.04923659639173309,
"acc_norm": 0.4,
"acc_norm_stderr": 0.04923659639173309
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6370370370370371,
"acc_stderr": 0.04153948404742398,
"acc_norm": 0.6370370370370371,
"acc_norm_stderr": 0.04153948404742398
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.8026315789473685,
"acc_stderr": 0.03238981601699397,
"acc_norm": 0.8026315789473685,
"acc_norm_stderr": 0.03238981601699397
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.73,
"acc_stderr": 0.04461960433384741,
"acc_norm": 0.73,
"acc_norm_stderr": 0.04461960433384741
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7396226415094339,
"acc_stderr": 0.027008766090708045,
"acc_norm": 0.7396226415094339,
"acc_norm_stderr": 0.027008766090708045
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.8194444444444444,
"acc_stderr": 0.03216600808802268,
"acc_norm": 0.8194444444444444,
"acc_norm_stderr": 0.03216600808802268
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956912,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956912
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.63,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.63,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145633,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6878612716763006,
"acc_stderr": 0.03533133389323657,
"acc_norm": 0.6878612716763006,
"acc_norm_stderr": 0.03533133389323657
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.4411764705882353,
"acc_stderr": 0.049406356306056595,
"acc_norm": 0.4411764705882353,
"acc_norm_stderr": 0.049406356306056595
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.82,
"acc_stderr": 0.038612291966536955,
"acc_norm": 0.82,
"acc_norm_stderr": 0.038612291966536955
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6680851063829787,
"acc_stderr": 0.03078373675774565,
"acc_norm": 0.6680851063829787,
"acc_norm_stderr": 0.03078373675774565
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.5263157894736842,
"acc_stderr": 0.046970851366478626,
"acc_norm": 0.5263157894736842,
"acc_norm_stderr": 0.046970851366478626
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6551724137931034,
"acc_stderr": 0.03960933549451207,
"acc_norm": 0.6551724137931034,
"acc_norm_stderr": 0.03960933549451207
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.4947089947089947,
"acc_stderr": 0.02574986828855657,
"acc_norm": 0.4947089947089947,
"acc_norm_stderr": 0.02574986828855657
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.48412698412698413,
"acc_stderr": 0.04469881854072606,
"acc_norm": 0.48412698412698413,
"acc_norm_stderr": 0.04469881854072606
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.33,
"acc_stderr": 0.047258156262526045,
"acc_norm": 0.33,
"acc_norm_stderr": 0.047258156262526045
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8354838709677419,
"acc_stderr": 0.021090847745939317,
"acc_norm": 0.8354838709677419,
"acc_norm_stderr": 0.021090847745939317
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.03499113137676744,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.03499113137676744
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.76,
"acc_stderr": 0.04292346959909283,
"acc_norm": 0.76,
"acc_norm_stderr": 0.04292346959909283
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.8181818181818182,
"acc_stderr": 0.030117688929503564,
"acc_norm": 0.8181818181818182,
"acc_norm_stderr": 0.030117688929503564
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8585858585858586,
"acc_stderr": 0.02482590979334333,
"acc_norm": 0.8585858585858586,
"acc_norm_stderr": 0.02482590979334333
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8911917098445595,
"acc_stderr": 0.022473253332768766,
"acc_norm": 0.8911917098445595,
"acc_norm_stderr": 0.022473253332768766
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.7,
"acc_stderr": 0.023234581088428498,
"acc_norm": 0.7,
"acc_norm_stderr": 0.023234581088428498
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.4,
"acc_stderr": 0.029869605095316897,
"acc_norm": 0.4,
"acc_norm_stderr": 0.029869605095316897
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7815126050420168,
"acc_stderr": 0.026841514322958945,
"acc_norm": 0.7815126050420168,
"acc_norm_stderr": 0.026841514322958945
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.4370860927152318,
"acc_stderr": 0.04050035722230636,
"acc_norm": 0.4370860927152318,
"acc_norm_stderr": 0.04050035722230636
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8642201834862385,
"acc_stderr": 0.014686907556340029,
"acc_norm": 0.8642201834862385,
"acc_norm_stderr": 0.014686907556340029
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5833333333333334,
"acc_stderr": 0.03362277436608043,
"acc_norm": 0.5833333333333334,
"acc_norm_stderr": 0.03362277436608043
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8676470588235294,
"acc_stderr": 0.023784297520918853,
"acc_norm": 0.8676470588235294,
"acc_norm_stderr": 0.023784297520918853
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8354430379746836,
"acc_stderr": 0.024135736240566932,
"acc_norm": 0.8354430379746836,
"acc_norm_stderr": 0.024135736240566932
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7488789237668162,
"acc_stderr": 0.029105220833224622,
"acc_norm": 0.7488789237668162,
"acc_norm_stderr": 0.029105220833224622
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6793893129770993,
"acc_stderr": 0.04093329229834278,
"acc_norm": 0.6793893129770993,
"acc_norm_stderr": 0.04093329229834278
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.8512396694214877,
"acc_stderr": 0.032484700838071943,
"acc_norm": 0.8512396694214877,
"acc_norm_stderr": 0.032484700838071943
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.040191074725573483,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.040191074725573483
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.754601226993865,
"acc_stderr": 0.03380939813943354,
"acc_norm": 0.754601226993865,
"acc_norm_stderr": 0.03380939813943354
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.48214285714285715,
"acc_stderr": 0.047427623612430116,
"acc_norm": 0.48214285714285715,
"acc_norm_stderr": 0.047427623612430116
},
"harness|hendrycksTest-management|5": {
"acc": 0.8252427184466019,
"acc_stderr": 0.03760178006026621,
"acc_norm": 0.8252427184466019,
"acc_norm_stderr": 0.03760178006026621
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.9188034188034188,
"acc_stderr": 0.017893784904018533,
"acc_norm": 0.9188034188034188,
"acc_norm_stderr": 0.017893784904018533
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8173690932311622,
"acc_stderr": 0.013816335389973117,
"acc_norm": 0.8173690932311622,
"acc_norm_stderr": 0.013816335389973117
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7369942196531792,
"acc_stderr": 0.02370309952525818,
"acc_norm": 0.7369942196531792,
"acc_norm_stderr": 0.02370309952525818
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4893854748603352,
"acc_stderr": 0.016718732941192107,
"acc_norm": 0.4893854748603352,
"acc_norm_stderr": 0.016718732941192107
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.761437908496732,
"acc_stderr": 0.02440439492808787,
"acc_norm": 0.761437908496732,
"acc_norm_stderr": 0.02440439492808787
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7427652733118971,
"acc_stderr": 0.02482617128925089,
"acc_norm": 0.7427652733118971,
"acc_norm_stderr": 0.02482617128925089
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7839506172839507,
"acc_stderr": 0.022899162918445803,
"acc_norm": 0.7839506172839507,
"acc_norm_stderr": 0.022899162918445803
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.5319148936170213,
"acc_stderr": 0.029766675075873866,
"acc_norm": 0.5319148936170213,
"acc_norm_stderr": 0.029766675075873866
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4941329856584094,
"acc_stderr": 0.012769356925216526,
"acc_norm": 0.4941329856584094,
"acc_norm_stderr": 0.012769356925216526
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7022058823529411,
"acc_stderr": 0.02777829870154544,
"acc_norm": 0.7022058823529411,
"acc_norm_stderr": 0.02777829870154544
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6895424836601307,
"acc_stderr": 0.018718067052623216,
"acc_norm": 0.6895424836601307,
"acc_norm_stderr": 0.018718067052623216
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6727272727272727,
"acc_stderr": 0.04494290866252091,
"acc_norm": 0.6727272727272727,
"acc_norm_stderr": 0.04494290866252091
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7551020408163265,
"acc_stderr": 0.02752963744017493,
"acc_norm": 0.7551020408163265,
"acc_norm_stderr": 0.02752963744017493
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.8308457711442786,
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"acc_norm": 0.8308457711442786,
"acc_norm_stderr": 0.02650859065623327
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.81,
"acc_stderr": 0.03942772444036624,
"acc_norm": 0.81,
"acc_norm_stderr": 0.03942772444036624
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5783132530120482,
"acc_stderr": 0.03844453181770917,
"acc_norm": 0.5783132530120482,
"acc_norm_stderr": 0.03844453181770917
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03188578017686398,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03188578017686398
},
"harness|truthfulqa:mc|0": {
"mc1": 0.6940024479804161,
"mc1_stderr": 0.016132229728155038,
"mc2": 0.8090943058378247,
"mc2_stderr": 0.013112054157410078
},
"harness|winogrande|5": {
"acc": 0.8666140489344909,
"acc_stderr": 0.009555448026422969
},
"harness|gsm8k|5": {
"acc": 0.46019711902956784,
"acc_stderr": 0.01372877671409936
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
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## Dataset Card Contact
[More Information Needed] |
Whab/deepfake | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Fake
'1': Real
splits:
- name: train
num_bytes: 1553838685.12
num_examples: 179430
download_size: 1677949725
dataset_size: 1553838685.12
---
# Dataset Card for "deepfake"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
open-llm-leaderboard/details_yeontaek__llama-2-70B-ensemble-v8 | ---
pretty_name: Evaluation run of yeontaek/llama-2-70B-ensemble-v8
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [yeontaek/llama-2-70B-ensemble-v8](https://huggingface.co/yeontaek/llama-2-70B-ensemble-v8)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 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_yeontaek__llama-2-70B-ensemble-v8\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-09-04T20:27:12.407104](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-70B-ensemble-v8/blob/main/results_2023-09-04T20%3A27%3A12.407104.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 \"acc\": 0.6363532267919642,\n\
\ \"acc_stderr\": 0.03285197203583459,\n \"acc_norm\": 0.6397352881146252,\n\
\ \"acc_norm_stderr\": 0.03283029655087548,\n \"mc1\": 0.45165238678090575,\n\
\ \"mc1_stderr\": 0.017421480300277643,\n \"mc2\": 0.6211306316728467,\n\
\ \"mc2_stderr\": 0.01529356194952766\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6561433447098977,\n \"acc_stderr\": 0.013880644570156215,\n\
\ \"acc_norm\": 0.6723549488054608,\n \"acc_norm_stderr\": 0.013715847940719339\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6623182632941645,\n\
\ \"acc_stderr\": 0.004719529099913132,\n \"acc_norm\": 0.8456482772356104,\n\
\ \"acc_norm_stderr\": 0.003605472116762285\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.29,\n \"acc_stderr\": 0.045604802157206845,\n \
\ \"acc_norm\": 0.29,\n \"acc_norm_stderr\": 0.045604802157206845\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5333333333333333,\n\
\ \"acc_stderr\": 0.043097329010363554,\n \"acc_norm\": 0.5333333333333333,\n\
\ \"acc_norm_stderr\": 0.043097329010363554\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6842105263157895,\n \"acc_stderr\": 0.0378272898086547,\n\
\ \"acc_norm\": 0.6842105263157895,\n \"acc_norm_stderr\": 0.0378272898086547\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.68,\n\
\ \"acc_stderr\": 0.046882617226215034,\n \"acc_norm\": 0.68,\n \
\ \"acc_norm_stderr\": 0.046882617226215034\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.7056603773584905,\n \"acc_stderr\": 0.02804918631569525,\n\
\ \"acc_norm\": 0.7056603773584905,\n \"acc_norm_stderr\": 0.02804918631569525\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.75,\n\
\ \"acc_stderr\": 0.03621034121889507,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.03621034121889507\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.43,\n \"acc_stderr\": 0.04975698519562428,\n \
\ \"acc_norm\": 0.43,\n \"acc_norm_stderr\": 0.04975698519562428\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.48,\n \"acc_stderr\": 0.050211673156867795,\n \"acc_norm\": 0.48,\n\
\ \"acc_norm_stderr\": 0.050211673156867795\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.048783173121456316,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.048783173121456316\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6069364161849711,\n\
\ \"acc_stderr\": 0.03724249595817731,\n \"acc_norm\": 0.6069364161849711,\n\
\ \"acc_norm_stderr\": 0.03724249595817731\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.29411764705882354,\n \"acc_stderr\": 0.04533838195929777,\n\
\ \"acc_norm\": 0.29411764705882354,\n \"acc_norm_stderr\": 0.04533838195929777\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \"acc_norm\": 0.72,\n\
\ \"acc_norm_stderr\": 0.045126085985421276\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.6297872340425532,\n \"acc_stderr\": 0.03156564682236784,\n\
\ \"acc_norm\": 0.6297872340425532,\n \"acc_norm_stderr\": 0.03156564682236784\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.3684210526315789,\n\
\ \"acc_stderr\": 0.04537815354939391,\n \"acc_norm\": 0.3684210526315789,\n\
\ \"acc_norm_stderr\": 0.04537815354939391\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5517241379310345,\n \"acc_stderr\": 0.04144311810878152,\n\
\ \"acc_norm\": 0.5517241379310345,\n \"acc_norm_stderr\": 0.04144311810878152\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3941798941798942,\n \"acc_stderr\": 0.025167982333894143,\n \"\
acc_norm\": 0.3941798941798942,\n \"acc_norm_stderr\": 0.025167982333894143\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42857142857142855,\n\
\ \"acc_stderr\": 0.04426266681379909,\n \"acc_norm\": 0.42857142857142855,\n\
\ \"acc_norm_stderr\": 0.04426266681379909\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.04988876515698589,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.04988876515698589\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.7741935483870968,\n\
\ \"acc_stderr\": 0.023785577884181015,\n \"acc_norm\": 0.7741935483870968,\n\
\ \"acc_norm_stderr\": 0.023785577884181015\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.45320197044334976,\n \"acc_stderr\": 0.03502544650845872,\n\
\ \"acc_norm\": 0.45320197044334976,\n \"acc_norm_stderr\": 0.03502544650845872\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\"\
: 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7878787878787878,\n \"acc_stderr\": 0.031922715695483,\n\
\ \"acc_norm\": 0.7878787878787878,\n \"acc_norm_stderr\": 0.031922715695483\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8535353535353535,\n \"acc_stderr\": 0.025190921114603925,\n \"\
acc_norm\": 0.8535353535353535,\n \"acc_norm_stderr\": 0.025190921114603925\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9067357512953368,\n \"acc_stderr\": 0.02098685459328972,\n\
\ \"acc_norm\": 0.9067357512953368,\n \"acc_norm_stderr\": 0.02098685459328972\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563973,\n\
\ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563973\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2777777777777778,\n \"acc_stderr\": 0.027309140588230165,\n \
\ \"acc_norm\": 0.2777777777777778,\n \"acc_norm_stderr\": 0.027309140588230165\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6890756302521008,\n \"acc_stderr\": 0.030066761582977927,\n\
\ \"acc_norm\": 0.6890756302521008,\n \"acc_norm_stderr\": 0.030066761582977927\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.40397350993377484,\n \"acc_stderr\": 0.0400648568536534,\n \"\
acc_norm\": 0.40397350993377484,\n \"acc_norm_stderr\": 0.0400648568536534\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8440366972477065,\n \"acc_stderr\": 0.01555580271359016,\n \"\
acc_norm\": 0.8440366972477065,\n \"acc_norm_stderr\": 0.01555580271359016\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5277777777777778,\n \"acc_stderr\": 0.0340470532865388,\n \"acc_norm\"\
: 0.5277777777777778,\n \"acc_norm_stderr\": 0.0340470532865388\n },\n\
\ \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\": 0.8431372549019608,\n\
\ \"acc_stderr\": 0.02552472232455335,\n \"acc_norm\": 0.8431372549019608,\n\
\ \"acc_norm_stderr\": 0.02552472232455335\n },\n \"harness|hendrycksTest-high_school_world_history|5\"\
: {\n \"acc\": 0.8396624472573839,\n \"acc_stderr\": 0.02388438092596567,\n\
\ \"acc_norm\": 0.8396624472573839,\n \"acc_norm_stderr\": 0.02388438092596567\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7443946188340808,\n\
\ \"acc_stderr\": 0.029275891003969923,\n \"acc_norm\": 0.7443946188340808,\n\
\ \"acc_norm_stderr\": 0.029275891003969923\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7480916030534351,\n \"acc_stderr\": 0.03807387116306086,\n\
\ \"acc_norm\": 0.7480916030534351,\n \"acc_norm_stderr\": 0.03807387116306086\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7851239669421488,\n \"acc_stderr\": 0.037494924487096966,\n \"\
acc_norm\": 0.7851239669421488,\n \"acc_norm_stderr\": 0.037494924487096966\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.040191074725573483,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.040191074725573483\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7730061349693251,\n \"acc_stderr\": 0.03291099578615769,\n\
\ \"acc_norm\": 0.7730061349693251,\n \"acc_norm_stderr\": 0.03291099578615769\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5357142857142857,\n\
\ \"acc_stderr\": 0.04733667890053756,\n \"acc_norm\": 0.5357142857142857,\n\
\ \"acc_norm_stderr\": 0.04733667890053756\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7864077669902912,\n \"acc_stderr\": 0.040580420156460344,\n\
\ \"acc_norm\": 0.7864077669902912,\n \"acc_norm_stderr\": 0.040580420156460344\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\
\ \"acc_stderr\": 0.023086635086841403,\n \"acc_norm\": 0.8547008547008547,\n\
\ \"acc_norm_stderr\": 0.023086635086841403\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.65,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.65,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8263090676883781,\n\
\ \"acc_stderr\": 0.013547415658662257,\n \"acc_norm\": 0.8263090676883781,\n\
\ \"acc_norm_stderr\": 0.013547415658662257\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7341040462427746,\n \"acc_stderr\": 0.023786203255508297,\n\
\ \"acc_norm\": 0.7341040462427746,\n \"acc_norm_stderr\": 0.023786203255508297\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3687150837988827,\n\
\ \"acc_stderr\": 0.016135759015030122,\n \"acc_norm\": 0.3687150837988827,\n\
\ \"acc_norm_stderr\": 0.016135759015030122\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6830065359477124,\n \"acc_stderr\": 0.026643278474508755,\n\
\ \"acc_norm\": 0.6830065359477124,\n \"acc_norm_stderr\": 0.026643278474508755\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.7266881028938906,\n\
\ \"acc_stderr\": 0.025311765975426122,\n \"acc_norm\": 0.7266881028938906,\n\
\ \"acc_norm_stderr\": 0.025311765975426122\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7654320987654321,\n \"acc_stderr\": 0.02357688174400571,\n\
\ \"acc_norm\": 0.7654320987654321,\n \"acc_norm_stderr\": 0.02357688174400571\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.49645390070921985,\n \"acc_stderr\": 0.02982674915328092,\n \
\ \"acc_norm\": 0.49645390070921985,\n \"acc_norm_stderr\": 0.02982674915328092\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.5208604954367666,\n\
\ \"acc_stderr\": 0.012759117066518008,\n \"acc_norm\": 0.5208604954367666,\n\
\ \"acc_norm_stderr\": 0.012759117066518008\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5955882352941176,\n \"acc_stderr\": 0.029812630701569743,\n\
\ \"acc_norm\": 0.5955882352941176,\n \"acc_norm_stderr\": 0.029812630701569743\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6797385620915033,\n \"acc_stderr\": 0.018875682938069443,\n \
\ \"acc_norm\": 0.6797385620915033,\n \"acc_norm_stderr\": 0.018875682938069443\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6545454545454545,\n\
\ \"acc_stderr\": 0.04554619617541054,\n \"acc_norm\": 0.6545454545454545,\n\
\ \"acc_norm_stderr\": 0.04554619617541054\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6693877551020408,\n \"acc_stderr\": 0.030116426296540606,\n\
\ \"acc_norm\": 0.6693877551020408,\n \"acc_norm_stderr\": 0.030116426296540606\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7960199004975125,\n\
\ \"acc_stderr\": 0.02849317624532607,\n \"acc_norm\": 0.7960199004975125,\n\
\ \"acc_norm_stderr\": 0.02849317624532607\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.88,\n \"acc_stderr\": 0.03265986323710906,\n \
\ \"acc_norm\": 0.88,\n \"acc_norm_stderr\": 0.03265986323710906\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5060240963855421,\n\
\ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.5060240963855421,\n\
\ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8362573099415205,\n \"acc_stderr\": 0.028380919596145866,\n\
\ \"acc_norm\": 0.8362573099415205,\n \"acc_norm_stderr\": 0.028380919596145866\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.45165238678090575,\n\
\ \"mc1_stderr\": 0.017421480300277643,\n \"mc2\": 0.6211306316728467,\n\
\ \"mc2_stderr\": 0.01529356194952766\n }\n}\n```"
repo_url: https://huggingface.co/yeontaek/llama-2-70B-ensemble-v8
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|arc:challenge|25_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hellaswag|10_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-09-04T20:27:12.407104.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-09-04T20:27:12.407104.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-04T20:27:12.407104.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-09-04T20:27:12.407104.parquet'
- config_name: results
data_files:
- split: 2023_09_04T20_27_12.407104
path:
- results_2023-09-04T20:27:12.407104.parquet
- split: latest
path:
- results_2023-09-04T20:27:12.407104.parquet
---
# Dataset Card for Evaluation run of yeontaek/llama-2-70B-ensemble-v8
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yeontaek/llama-2-70B-ensemble-v8
- **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 [yeontaek/llama-2-70B-ensemble-v8](https://huggingface.co/yeontaek/llama-2-70B-ensemble-v8) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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_yeontaek__llama-2-70B-ensemble-v8",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-09-04T20:27:12.407104](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-70B-ensemble-v8/blob/main/results_2023-09-04T20%3A27%3A12.407104.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": {
"acc": 0.6363532267919642,
"acc_stderr": 0.03285197203583459,
"acc_norm": 0.6397352881146252,
"acc_norm_stderr": 0.03283029655087548,
"mc1": 0.45165238678090575,
"mc1_stderr": 0.017421480300277643,
"mc2": 0.6211306316728467,
"mc2_stderr": 0.01529356194952766
},
"harness|arc:challenge|25": {
"acc": 0.6561433447098977,
"acc_stderr": 0.013880644570156215,
"acc_norm": 0.6723549488054608,
"acc_norm_stderr": 0.013715847940719339
},
"harness|hellaswag|10": {
"acc": 0.6623182632941645,
"acc_stderr": 0.004719529099913132,
"acc_norm": 0.8456482772356104,
"acc_norm_stderr": 0.003605472116762285
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.29,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.29,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5333333333333333,
"acc_stderr": 0.043097329010363554,
"acc_norm": 0.5333333333333333,
"acc_norm_stderr": 0.043097329010363554
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6842105263157895,
"acc_stderr": 0.0378272898086547,
"acc_norm": 0.6842105263157895,
"acc_norm_stderr": 0.0378272898086547
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.68,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.68,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.7056603773584905,
"acc_stderr": 0.02804918631569525,
"acc_norm": 0.7056603773584905,
"acc_norm_stderr": 0.02804918631569525
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.75,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.75,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.43,
"acc_stderr": 0.04975698519562428,
"acc_norm": 0.43,
"acc_norm_stderr": 0.04975698519562428
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.48,
"acc_stderr": 0.050211673156867795,
"acc_norm": 0.48,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.38,
"acc_stderr": 0.048783173121456316,
"acc_norm": 0.38,
"acc_norm_stderr": 0.048783173121456316
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6069364161849711,
"acc_stderr": 0.03724249595817731,
"acc_norm": 0.6069364161849711,
"acc_norm_stderr": 0.03724249595817731
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.29411764705882354,
"acc_stderr": 0.04533838195929777,
"acc_norm": 0.29411764705882354,
"acc_norm_stderr": 0.04533838195929777
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.72,
"acc_stderr": 0.045126085985421276,
"acc_norm": 0.72,
"acc_norm_stderr": 0.045126085985421276
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.6297872340425532,
"acc_stderr": 0.03156564682236784,
"acc_norm": 0.6297872340425532,
"acc_norm_stderr": 0.03156564682236784
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.3684210526315789,
"acc_stderr": 0.04537815354939391,
"acc_norm": 0.3684210526315789,
"acc_norm_stderr": 0.04537815354939391
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5517241379310345,
"acc_stderr": 0.04144311810878152,
"acc_norm": 0.5517241379310345,
"acc_norm_stderr": 0.04144311810878152
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3941798941798942,
"acc_stderr": 0.025167982333894143,
"acc_norm": 0.3941798941798942,
"acc_norm_stderr": 0.025167982333894143
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.42857142857142855,
"acc_stderr": 0.04426266681379909,
"acc_norm": 0.42857142857142855,
"acc_norm_stderr": 0.04426266681379909
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.44,
"acc_stderr": 0.04988876515698589,
"acc_norm": 0.44,
"acc_norm_stderr": 0.04988876515698589
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7741935483870968,
"acc_stderr": 0.023785577884181015,
"acc_norm": 0.7741935483870968,
"acc_norm_stderr": 0.023785577884181015
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.45320197044334976,
"acc_stderr": 0.03502544650845872,
"acc_norm": 0.45320197044334976,
"acc_norm_stderr": 0.03502544650845872
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.6,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.6,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7878787878787878,
"acc_stderr": 0.031922715695483,
"acc_norm": 0.7878787878787878,
"acc_norm_stderr": 0.031922715695483
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8535353535353535,
"acc_stderr": 0.025190921114603925,
"acc_norm": 0.8535353535353535,
"acc_norm_stderr": 0.025190921114603925
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9067357512953368,
"acc_stderr": 0.02098685459328972,
"acc_norm": 0.9067357512953368,
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"harness|hendrycksTest-high_school_microeconomics|5": {
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"acc_norm": 0.6890756302521008,
"acc_norm_stderr": 0.030066761582977927
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.40397350993377484,
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"acc_norm": 0.40397350993377484,
"acc_norm_stderr": 0.0400648568536534
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"harness|hendrycksTest-high_school_psychology|5": {
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"acc_norm": 0.8440366972477065,
"acc_norm_stderr": 0.01555580271359016
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5277777777777778,
"acc_stderr": 0.0340470532865388,
"acc_norm": 0.5277777777777778,
"acc_norm_stderr": 0.0340470532865388
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8431372549019608,
"acc_stderr": 0.02552472232455335,
"acc_norm": 0.8431372549019608,
"acc_norm_stderr": 0.02552472232455335
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8396624472573839,
"acc_stderr": 0.02388438092596567,
"acc_norm": 0.8396624472573839,
"acc_norm_stderr": 0.02388438092596567
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7443946188340808,
"acc_stderr": 0.029275891003969923,
"acc_norm": 0.7443946188340808,
"acc_norm_stderr": 0.029275891003969923
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.7480916030534351,
"acc_stderr": 0.03807387116306086,
"acc_norm": 0.7480916030534351,
"acc_norm_stderr": 0.03807387116306086
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7851239669421488,
"acc_stderr": 0.037494924487096966,
"acc_norm": 0.7851239669421488,
"acc_norm_stderr": 0.037494924487096966
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.040191074725573483,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.040191074725573483
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7730061349693251,
"acc_stderr": 0.03291099578615769,
"acc_norm": 0.7730061349693251,
"acc_norm_stderr": 0.03291099578615769
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.5357142857142857,
"acc_stderr": 0.04733667890053756,
"acc_norm": 0.5357142857142857,
"acc_norm_stderr": 0.04733667890053756
},
"harness|hendrycksTest-management|5": {
"acc": 0.7864077669902912,
"acc_stderr": 0.040580420156460344,
"acc_norm": 0.7864077669902912,
"acc_norm_stderr": 0.040580420156460344
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8547008547008547,
"acc_stderr": 0.023086635086841403,
"acc_norm": 0.8547008547008547,
"acc_norm_stderr": 0.023086635086841403
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.65,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.65,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.8263090676883781,
"acc_stderr": 0.013547415658662257,
"acc_norm": 0.8263090676883781,
"acc_norm_stderr": 0.013547415658662257
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7341040462427746,
"acc_stderr": 0.023786203255508297,
"acc_norm": 0.7341040462427746,
"acc_norm_stderr": 0.023786203255508297
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.3687150837988827,
"acc_stderr": 0.016135759015030122,
"acc_norm": 0.3687150837988827,
"acc_norm_stderr": 0.016135759015030122
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6830065359477124,
"acc_stderr": 0.026643278474508755,
"acc_norm": 0.6830065359477124,
"acc_norm_stderr": 0.026643278474508755
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.7266881028938906,
"acc_stderr": 0.025311765975426122,
"acc_norm": 0.7266881028938906,
"acc_norm_stderr": 0.025311765975426122
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7654320987654321,
"acc_stderr": 0.02357688174400571,
"acc_norm": 0.7654320987654321,
"acc_norm_stderr": 0.02357688174400571
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.49645390070921985,
"acc_stderr": 0.02982674915328092,
"acc_norm": 0.49645390070921985,
"acc_norm_stderr": 0.02982674915328092
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.5208604954367666,
"acc_stderr": 0.012759117066518008,
"acc_norm": 0.5208604954367666,
"acc_norm_stderr": 0.012759117066518008
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5955882352941176,
"acc_stderr": 0.029812630701569743,
"acc_norm": 0.5955882352941176,
"acc_norm_stderr": 0.029812630701569743
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6797385620915033,
"acc_stderr": 0.018875682938069443,
"acc_norm": 0.6797385620915033,
"acc_norm_stderr": 0.018875682938069443
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6545454545454545,
"acc_stderr": 0.04554619617541054,
"acc_norm": 0.6545454545454545,
"acc_norm_stderr": 0.04554619617541054
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6693877551020408,
"acc_stderr": 0.030116426296540606,
"acc_norm": 0.6693877551020408,
"acc_norm_stderr": 0.030116426296540606
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7960199004975125,
"acc_stderr": 0.02849317624532607,
"acc_norm": 0.7960199004975125,
"acc_norm_stderr": 0.02849317624532607
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.88,
"acc_stderr": 0.03265986323710906,
"acc_norm": 0.88,
"acc_norm_stderr": 0.03265986323710906
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5060240963855421,
"acc_stderr": 0.03892212195333045,
"acc_norm": 0.5060240963855421,
"acc_norm_stderr": 0.03892212195333045
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8362573099415205,
"acc_stderr": 0.028380919596145866,
"acc_norm": 0.8362573099415205,
"acc_norm_stderr": 0.028380919596145866
},
"harness|truthfulqa:mc|0": {
"mc1": 0.45165238678090575,
"mc1_stderr": 0.017421480300277643,
"mc2": 0.6211306316728467,
"mc2_stderr": 0.01529356194952766
}
}
```
### 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] |
BangumiBase/suzumiyaharuhinoyuuutsu | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Suzumiya Haruhi No Yuuutsu
This is the image base of bangumi Suzumiya Haruhi no Yuuutsu, we detected 22 characters, 4994 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 1639 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 563 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 606 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 72 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 27 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 103 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 796 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 23 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 22 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 453 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 124 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 67 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 19 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 49 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 13 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 34 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 48 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 12 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 44 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 57 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 5 | [Download](20/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 218 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
open-llm-leaderboard/details_TomGrc__FusionNet_linear | ---
pretty_name: Evaluation run of TomGrc/FusionNet_linear
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [TomGrc/FusionNet_linear](https://huggingface.co/TomGrc/FusionNet_linear) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 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 aggregated 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_TomGrc__FusionNet_linear\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-01-04T13:05:33.197090](https://huggingface.co/datasets/open-llm-leaderboard/details_TomGrc__FusionNet_linear/blob/main/results_2024-01-04T13-05-33.197090.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 \"acc\": 0.6672525697360521,\n\
\ \"acc_stderr\": 0.031625508279826374,\n \"acc_norm\": 0.6680745258089933,\n\
\ \"acc_norm_stderr\": 0.032268986108869066,\n \"mc1\": 0.5703794369645043,\n\
\ \"mc1_stderr\": 0.017329234580409095,\n \"mc2\": 0.7194464363694983,\n\
\ \"mc2_stderr\": 0.015001159262384656\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6834470989761092,\n \"acc_stderr\": 0.013592431519068079,\n\
\ \"acc_norm\": 0.712457337883959,\n \"acc_norm_stderr\": 0.013226719056266125\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.7132045409281019,\n\
\ \"acc_stderr\": 0.004513409114983827,\n \"acc_norm\": 0.8843855805616411,\n\
\ \"acc_norm_stderr\": 0.003191084792793155\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.44,\n \"acc_stderr\": 0.0498887651569859,\n \
\ \"acc_norm\": 0.44,\n \"acc_norm_stderr\": 0.0498887651569859\n },\n\
\ \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.6148148148148148,\n\
\ \"acc_stderr\": 0.04203921040156279,\n \"acc_norm\": 0.6148148148148148,\n\
\ \"acc_norm_stderr\": 0.04203921040156279\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.756578947368421,\n \"acc_stderr\": 0.034923496688842384,\n\
\ \"acc_norm\": 0.756578947368421,\n \"acc_norm_stderr\": 0.034923496688842384\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.74,\n\
\ \"acc_stderr\": 0.0440844002276808,\n \"acc_norm\": 0.74,\n \
\ \"acc_norm_stderr\": 0.0440844002276808\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6830188679245283,\n \"acc_stderr\": 0.02863723563980089,\n\
\ \"acc_norm\": 0.6830188679245283,\n \"acc_norm_stderr\": 0.02863723563980089\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.7777777777777778,\n\
\ \"acc_stderr\": 0.03476590104304134,\n \"acc_norm\": 0.7777777777777778,\n\
\ \"acc_norm_stderr\": 0.03476590104304134\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.47,\n \"acc_stderr\": 0.050161355804659205,\n \
\ \"acc_norm\": 0.47,\n \"acc_norm_stderr\": 0.050161355804659205\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"\
acc\": 0.51,\n \"acc_stderr\": 0.05024183937956913,\n \"acc_norm\"\
: 0.51,\n \"acc_norm_stderr\": 0.05024183937956913\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.32,\n \"acc_stderr\": 0.046882617226215034,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.046882617226215034\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.6705202312138728,\n\
\ \"acc_stderr\": 0.03583901754736412,\n \"acc_norm\": 0.6705202312138728,\n\
\ \"acc_norm_stderr\": 0.03583901754736412\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.38235294117647056,\n \"acc_stderr\": 0.04835503696107223,\n\
\ \"acc_norm\": 0.38235294117647056,\n \"acc_norm_stderr\": 0.04835503696107223\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.625531914893617,\n \"acc_stderr\": 0.03163910665367291,\n\
\ \"acc_norm\": 0.625531914893617,\n \"acc_norm_stderr\": 0.03163910665367291\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.49122807017543857,\n\
\ \"acc_stderr\": 0.04702880432049615,\n \"acc_norm\": 0.49122807017543857,\n\
\ \"acc_norm_stderr\": 0.04702880432049615\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.6275862068965518,\n \"acc_stderr\": 0.04028731532947558,\n\
\ \"acc_norm\": 0.6275862068965518,\n \"acc_norm_stderr\": 0.04028731532947558\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.4973544973544973,\n \"acc_stderr\": 0.02575094967813039,\n \"\
acc_norm\": 0.4973544973544973,\n \"acc_norm_stderr\": 0.02575094967813039\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.4365079365079365,\n\
\ \"acc_stderr\": 0.04435932892851466,\n \"acc_norm\": 0.4365079365079365,\n\
\ \"acc_norm_stderr\": 0.04435932892851466\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695235,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695235\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.8193548387096774,\n\
\ \"acc_stderr\": 0.021886178567172534,\n \"acc_norm\": 0.8193548387096774,\n\
\ \"acc_norm_stderr\": 0.021886178567172534\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.5024630541871922,\n \"acc_stderr\": 0.03517945038691063,\n\
\ \"acc_norm\": 0.5024630541871922,\n \"acc_norm_stderr\": 0.03517945038691063\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.72,\n \"acc_stderr\": 0.04512608598542128,\n \"acc_norm\"\
: 0.72,\n \"acc_norm_stderr\": 0.04512608598542128\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.8121212121212121,\n \"acc_stderr\": 0.03050193405942914,\n\
\ \"acc_norm\": 0.8121212121212121,\n \"acc_norm_stderr\": 0.03050193405942914\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.8686868686868687,\n \"acc_stderr\": 0.024063156416822516,\n \"\
acc_norm\": 0.8686868686868687,\n \"acc_norm_stderr\": 0.024063156416822516\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.9015544041450777,\n \"acc_stderr\": 0.02150024957603348,\n\
\ \"acc_norm\": 0.9015544041450777,\n \"acc_norm_stderr\": 0.02150024957603348\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6641025641025641,\n \"acc_stderr\": 0.023946724741563976,\n\
\ \"acc_norm\": 0.6641025641025641,\n \"acc_norm_stderr\": 0.023946724741563976\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.37037037037037035,\n \"acc_stderr\": 0.02944316932303154,\n \
\ \"acc_norm\": 0.37037037037037035,\n \"acc_norm_stderr\": 0.02944316932303154\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.7142857142857143,\n \"acc_stderr\": 0.029344572500634332,\n\
\ \"acc_norm\": 0.7142857142857143,\n \"acc_norm_stderr\": 0.029344572500634332\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.37748344370860926,\n \"acc_stderr\": 0.03958027231121569,\n \"\
acc_norm\": 0.37748344370860926,\n \"acc_norm_stderr\": 0.03958027231121569\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8458715596330275,\n \"acc_stderr\": 0.015480826865374308,\n \"\
acc_norm\": 0.8458715596330275,\n \"acc_norm_stderr\": 0.015480826865374308\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.5740740740740741,\n \"acc_stderr\": 0.03372343271653062,\n \"\
acc_norm\": 0.5740740740740741,\n \"acc_norm_stderr\": 0.03372343271653062\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.8578431372549019,\n \"acc_stderr\": 0.02450980392156862,\n \"\
acc_norm\": 0.8578431372549019,\n \"acc_norm_stderr\": 0.02450980392156862\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8481012658227848,\n \"acc_stderr\": 0.023363878096632446,\n \
\ \"acc_norm\": 0.8481012658227848,\n \"acc_norm_stderr\": 0.023363878096632446\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6771300448430493,\n\
\ \"acc_stderr\": 0.03138147637575499,\n \"acc_norm\": 0.6771300448430493,\n\
\ \"acc_norm_stderr\": 0.03138147637575499\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.7633587786259542,\n \"acc_stderr\": 0.03727673575596915,\n\
\ \"acc_norm\": 0.7633587786259542,\n \"acc_norm_stderr\": 0.03727673575596915\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228733,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228733\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.8055555555555556,\n\
\ \"acc_stderr\": 0.038260763248848646,\n \"acc_norm\": 0.8055555555555556,\n\
\ \"acc_norm_stderr\": 0.038260763248848646\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.754601226993865,\n \"acc_stderr\": 0.03380939813943354,\n\
\ \"acc_norm\": 0.754601226993865,\n \"acc_norm_stderr\": 0.03380939813943354\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.4732142857142857,\n\
\ \"acc_stderr\": 0.047389751192741546,\n \"acc_norm\": 0.4732142857142857,\n\
\ \"acc_norm_stderr\": 0.047389751192741546\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.8543689320388349,\n \"acc_stderr\": 0.03492606476623791,\n\
\ \"acc_norm\": 0.8543689320388349,\n \"acc_norm_stderr\": 0.03492606476623791\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8547008547008547,\n\
\ \"acc_stderr\": 0.0230866350868414,\n \"acc_norm\": 0.8547008547008547,\n\
\ \"acc_norm_stderr\": 0.0230866350868414\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.7,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.7,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.8045977011494253,\n\
\ \"acc_stderr\": 0.014179171373424383,\n \"acc_norm\": 0.8045977011494253,\n\
\ \"acc_norm_stderr\": 0.014179171373424383\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.7543352601156069,\n \"acc_stderr\": 0.023176298203992005,\n\
\ \"acc_norm\": 0.7543352601156069,\n \"acc_norm_stderr\": 0.023176298203992005\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.39217877094972065,\n\
\ \"acc_stderr\": 0.016329061073207446,\n \"acc_norm\": 0.39217877094972065,\n\
\ \"acc_norm_stderr\": 0.016329061073207446\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.761437908496732,\n \"acc_stderr\": 0.024404394928087866,\n\
\ \"acc_norm\": 0.761437908496732,\n \"acc_norm_stderr\": 0.024404394928087866\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.729903536977492,\n\
\ \"acc_stderr\": 0.02521804037341062,\n \"acc_norm\": 0.729903536977492,\n\
\ \"acc_norm_stderr\": 0.02521804037341062\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7870370370370371,\n \"acc_stderr\": 0.0227797190887334,\n\
\ \"acc_norm\": 0.7870370370370371,\n \"acc_norm_stderr\": 0.0227797190887334\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.4929078014184397,\n \"acc_stderr\": 0.02982449855912901,\n \
\ \"acc_norm\": 0.4929078014184397,\n \"acc_norm_stderr\": 0.02982449855912901\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4921773142112125,\n\
\ \"acc_stderr\": 0.0127686730761119,\n \"acc_norm\": 0.4921773142112125,\n\
\ \"acc_norm_stderr\": 0.0127686730761119\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.7389705882352942,\n \"acc_stderr\": 0.026679252270103128,\n\
\ \"acc_norm\": 0.7389705882352942,\n \"acc_norm_stderr\": 0.026679252270103128\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6781045751633987,\n \"acc_stderr\": 0.018901015322093092,\n \
\ \"acc_norm\": 0.6781045751633987,\n \"acc_norm_stderr\": 0.018901015322093092\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6818181818181818,\n\
\ \"acc_stderr\": 0.04461272175910509,\n \"acc_norm\": 0.6818181818181818,\n\
\ \"acc_norm_stderr\": 0.04461272175910509\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.7387755102040816,\n \"acc_stderr\": 0.028123429335142783,\n\
\ \"acc_norm\": 0.7387755102040816,\n \"acc_norm_stderr\": 0.028123429335142783\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.835820895522388,\n\
\ \"acc_stderr\": 0.026193923544454125,\n \"acc_norm\": 0.835820895522388,\n\
\ \"acc_norm_stderr\": 0.026193923544454125\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.9,\n \"acc_stderr\": 0.030151134457776334,\n \
\ \"acc_norm\": 0.9,\n \"acc_norm_stderr\": 0.030151134457776334\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.5843373493975904,\n\
\ \"acc_stderr\": 0.03836722176598053,\n \"acc_norm\": 0.5843373493975904,\n\
\ \"acc_norm_stderr\": 0.03836722176598053\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03188578017686398,\n\
\ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03188578017686398\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.5703794369645043,\n\
\ \"mc1_stderr\": 0.017329234580409095,\n \"mc2\": 0.7194464363694983,\n\
\ \"mc2_stderr\": 0.015001159262384656\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.8326756116811366,\n \"acc_stderr\": 0.010490608806828075\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.6535253980288097,\n \
\ \"acc_stderr\": 0.013107179054313398\n }\n}\n```"
repo_url: https://huggingface.co/TomGrc/FusionNet_linear
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|arc:challenge|25_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|gsm8k|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hellaswag|10_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-05-33.197090.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-01-04T13-05-33.197090.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- '**/details_harness|winogrande|5_2024-01-04T13-05-33.197090.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-01-04T13-05-33.197090.parquet'
- config_name: results
data_files:
- split: 2024_01_04T13_05_33.197090
path:
- results_2024-01-04T13-05-33.197090.parquet
- split: latest
path:
- results_2024-01-04T13-05-33.197090.parquet
---
# Dataset Card for Evaluation run of TomGrc/FusionNet_linear
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [TomGrc/FusionNet_linear](https://huggingface.co/TomGrc/FusionNet_linear) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 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 aggregated 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_TomGrc__FusionNet_linear",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-01-04T13:05:33.197090](https://huggingface.co/datasets/open-llm-leaderboard/details_TomGrc__FusionNet_linear/blob/main/results_2024-01-04T13-05-33.197090.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": {
"acc": 0.6672525697360521,
"acc_stderr": 0.031625508279826374,
"acc_norm": 0.6680745258089933,
"acc_norm_stderr": 0.032268986108869066,
"mc1": 0.5703794369645043,
"mc1_stderr": 0.017329234580409095,
"mc2": 0.7194464363694983,
"mc2_stderr": 0.015001159262384656
},
"harness|arc:challenge|25": {
"acc": 0.6834470989761092,
"acc_stderr": 0.013592431519068079,
"acc_norm": 0.712457337883959,
"acc_norm_stderr": 0.013226719056266125
},
"harness|hellaswag|10": {
"acc": 0.7132045409281019,
"acc_stderr": 0.004513409114983827,
"acc_norm": 0.8843855805616411,
"acc_norm_stderr": 0.003191084792793155
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.44,
"acc_stderr": 0.0498887651569859,
"acc_norm": 0.44,
"acc_norm_stderr": 0.0498887651569859
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.6148148148148148,
"acc_stderr": 0.04203921040156279,
"acc_norm": 0.6148148148148148,
"acc_norm_stderr": 0.04203921040156279
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.756578947368421,
"acc_stderr": 0.034923496688842384,
"acc_norm": 0.756578947368421,
"acc_norm_stderr": 0.034923496688842384
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.74,
"acc_stderr": 0.0440844002276808,
"acc_norm": 0.74,
"acc_norm_stderr": 0.0440844002276808
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6830188679245283,
"acc_stderr": 0.02863723563980089,
"acc_norm": 0.6830188679245283,
"acc_norm_stderr": 0.02863723563980089
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.7777777777777778,
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"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03476590104304134
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.47,
"acc_stderr": 0.050161355804659205,
"acc_norm": 0.47,
"acc_norm_stderr": 0.050161355804659205
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.51,
"acc_stderr": 0.05024183937956913,
"acc_norm": 0.51,
"acc_norm_stderr": 0.05024183937956913
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_medicine|5": {
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"acc_norm": 0.6705202312138728,
"acc_norm_stderr": 0.03583901754736412
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.38235294117647056,
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"acc_norm": 0.38235294117647056,
"acc_norm_stderr": 0.04835503696107223
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.625531914893617,
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"acc_norm_stderr": 0.03163910665367291
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.49122807017543857,
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"acc_norm": 0.49122807017543857,
"acc_norm_stderr": 0.04702880432049615
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.6275862068965518,
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"acc_norm_stderr": 0.04028731532947558
},
"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.4973544973544973,
"acc_norm_stderr": 0.02575094967813039
},
"harness|hendrycksTest-formal_logic|5": {
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"acc_norm_stderr": 0.04435932892851466
},
"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.8193548387096774,
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"acc_norm": 0.8193548387096774,
"acc_norm_stderr": 0.021886178567172534
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.5024630541871922,
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"acc_norm": 0.5024630541871922,
"acc_norm_stderr": 0.03517945038691063
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.72,
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"acc_norm": 0.72,
"acc_norm_stderr": 0.04512608598542128
},
"harness|hendrycksTest-high_school_european_history|5": {
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"acc_norm_stderr": 0.03050193405942914
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.8686868686868687,
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"acc_norm_stderr": 0.024063156416822516
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.9015544041450777,
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"acc_norm": 0.9015544041450777,
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},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6641025641025641,
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"acc_norm_stderr": 0.023946724741563976
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.37037037037037035,
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"acc_norm": 0.37037037037037035,
"acc_norm_stderr": 0.02944316932303154
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.7142857142857143,
"acc_stderr": 0.029344572500634332,
"acc_norm": 0.7142857142857143,
"acc_norm_stderr": 0.029344572500634332
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.37748344370860926,
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"acc_norm": 0.37748344370860926,
"acc_norm_stderr": 0.03958027231121569
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8458715596330275,
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"acc_norm": 0.8458715596330275,
"acc_norm_stderr": 0.015480826865374308
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.5740740740740741,
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"acc_norm_stderr": 0.03372343271653062
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.8578431372549019,
"acc_stderr": 0.02450980392156862,
"acc_norm": 0.8578431372549019,
"acc_norm_stderr": 0.02450980392156862
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8481012658227848,
"acc_stderr": 0.023363878096632446,
"acc_norm": 0.8481012658227848,
"acc_norm_stderr": 0.023363878096632446
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6771300448430493,
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"acc_norm_stderr": 0.03138147637575499
},
"harness|hendrycksTest-human_sexuality|5": {
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},
"harness|hendrycksTest-international_law|5": {
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},
"harness|hendrycksTest-jurisprudence|5": {
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},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.754601226993865,
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"acc_norm": 0.754601226993865,
"acc_norm_stderr": 0.03380939813943354
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.4732142857142857,
"acc_stderr": 0.047389751192741546,
"acc_norm": 0.4732142857142857,
"acc_norm_stderr": 0.047389751192741546
},
"harness|hendrycksTest-management|5": {
"acc": 0.8543689320388349,
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"acc_norm": 0.8543689320388349,
"acc_norm_stderr": 0.03492606476623791
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8547008547008547,
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"acc_norm": 0.8547008547008547,
"acc_norm_stderr": 0.0230866350868414
},
"harness|hendrycksTest-medical_genetics|5": {
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},
"harness|hendrycksTest-miscellaneous|5": {
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},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.7543352601156069,
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"acc_norm_stderr": 0.023176298203992005
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.39217877094972065,
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"acc_norm": 0.39217877094972065,
"acc_norm_stderr": 0.016329061073207446
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.761437908496732,
"acc_stderr": 0.024404394928087866,
"acc_norm": 0.761437908496732,
"acc_norm_stderr": 0.024404394928087866
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.729903536977492,
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"acc_norm": 0.729903536977492,
"acc_norm_stderr": 0.02521804037341062
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.7870370370370371,
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"acc_norm": 0.7870370370370371,
"acc_norm_stderr": 0.0227797190887334
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.4929078014184397,
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"acc_norm": 0.4929078014184397,
"acc_norm_stderr": 0.02982449855912901
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.4921773142112125,
"acc_stderr": 0.0127686730761119,
"acc_norm": 0.4921773142112125,
"acc_norm_stderr": 0.0127686730761119
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.7389705882352942,
"acc_stderr": 0.026679252270103128,
"acc_norm": 0.7389705882352942,
"acc_norm_stderr": 0.026679252270103128
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.6781045751633987,
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"acc_norm": 0.6781045751633987,
"acc_norm_stderr": 0.018901015322093092
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6818181818181818,
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"acc_norm": 0.6818181818181818,
"acc_norm_stderr": 0.04461272175910509
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.7387755102040816,
"acc_stderr": 0.028123429335142783,
"acc_norm": 0.7387755102040816,
"acc_norm_stderr": 0.028123429335142783
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.835820895522388,
"acc_stderr": 0.026193923544454125,
"acc_norm": 0.835820895522388,
"acc_norm_stderr": 0.026193923544454125
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.9,
"acc_stderr": 0.030151134457776334,
"acc_norm": 0.9,
"acc_norm_stderr": 0.030151134457776334
},
"harness|hendrycksTest-virology|5": {
"acc": 0.5843373493975904,
"acc_stderr": 0.03836722176598053,
"acc_norm": 0.5843373493975904,
"acc_norm_stderr": 0.03836722176598053
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03188578017686398,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03188578017686398
},
"harness|truthfulqa:mc|0": {
"mc1": 0.5703794369645043,
"mc1_stderr": 0.017329234580409095,
"mc2": 0.7194464363694983,
"mc2_stderr": 0.015001159262384656
},
"harness|winogrande|5": {
"acc": 0.8326756116811366,
"acc_stderr": 0.010490608806828075
},
"harness|gsm8k|5": {
"acc": 0.6535253980288097,
"acc_stderr": 0.013107179054313398
}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
segments/sidewalk-semantic | ---
annotations_creators:
- crowdsourced
- expert-generated
language_creators:
- expert-generated
license: cc-by-nc-4.0
multilinguality: []
pretty_name: sidewalk-semantic
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- image-segmentation
task_ids:
- semantic-segmentation
---
# Dataset Card for sidewalk-semantic
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Dataset Structure](#dataset-structure)
- [Data Categories](#data-categories)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Dataset homepage on Segments.ai](https://segments.ai/segments/sidewalk-imagery/)
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Bert De Brabandere](mailto:bert@segments.ai)
### Dataset Summary
A dataset of sidewalk images gathered in Belgium in the summer of 2021. Label your own semantic segmentation datasets on [segments.ai](https://segments.ai/?utm_source=hf&utm_medium=hf-ds&utm_campaign=sidewalk)
### Supported Tasks and Leaderboards
- `semantic-segmentation`: The dataset can be used to train a semantic segmentation model, where each pixel is classified. The model performance is measured by how high its [mean IoU (intersection over union)](https://huggingface.co/metrics/mean_iou) to the reference is.
## Dataset Structure
### Data categories
| Id | Name | Description |
| --- | ---- | ----------- |
| 0 | unlabeled | - |
| 1 | flat-road | - |
| 2 | flat-sidewalk | - |
| 3 | flat-crosswalk | - |
| 4 | flat-cyclinglane | - |
| 5 | flat-parkingdriveway | - |
| 6 | flat-railtrack | - |
| 7 | flat-curb | - |
| 8 | human-person | - |
| 9 | human-rider | - |
| 10 | vehicle-car | - |
| 11 | vehicle-truck | - |
| 12 | vehicle-bus | - |
| 13 | vehicle-tramtrain | - |
| 14 | vehicle-motorcycle | - |
| 15 | vehicle-bicycle | - |
| 16 | vehicle-caravan | - |
| 17 | vehicle-cartrailer | - |
| 18 | construction-building | - |
| 19 | construction-door | - |
| 20 | construction-wall | - |
| 21 | construction-fenceguardrail | - |
| 22 | construction-bridge | - |
| 23 | construction-tunnel | - |
| 24 | construction-stairs | - |
| 25 | object-pole | - |
| 26 | object-trafficsign | - |
| 27 | object-trafficlight | - |
| 28 | nature-vegetation | - |
| 29 | nature-terrain | - |
| 30 | sky | - |
| 31 | void-ground | - |
| 32 | void-dynamic | - |
| 33 | void-static | - |
| 34 | void-unclear | - |
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
This dataset only contains one split.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information] |
health360/Healix-Shot | ---
license: cc-by-4.0
task_categories:
- text-classification
- text-generation
- text2text-generation
language:
- en
tags:
- chemistry
- biology
- medical
size_categories:
- 10B<n<100B
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 146867911160
num_examples: 10848856
download_size: 3009752051
dataset_size: 146867911160
---
**README**
---
## Healix-Shot: Largest Medical Corpora by Health 360
Healix-Shot, proudly presented by Health 360, stands as an emblematic milestone in the realm of medical datasets. Hosted on the HuggingFace repository, it heralds the infusion of cutting-edge AI in the healthcare domain. With an astounding 22 billion tokens, Healix-Shot provides a comprehensive, high-quality corpus of medical text, laying the foundation for unparalleled medical NLP applications.
### **Importance:**
Healix-Shot isn't merely a dataset; it's a revolution in how we approach medical text:
1. **Comprehensive Knowledge**: With data spanning academic papers, medical encyclopedias, and more, it covers a broad spectrum of medical topics.
2. **Quality Assured**: Leveraging techniques like the one in "Textbooks is All You Need," along with internal processes, ensures that the data stands up to rigorous quality checks.
3. **Open-source Nature**: By providing this vast repository to the public, Health 360 encourages communal contribution, fostering innovation and advancements in medical NLP.
### **Dataset Composition**:
| **Resource** | **Tokens** (Billions) | **Description** |
|-----------------------|-----------------------|--------------------------------------------------------|
| Filtered peS2o | 19.2 | High-quality medical papers |
| Various Sources | 2.8 | Medical Wikipedia, textbooks, medical news, etc. |
| **Total** | **22.0** | |
### **Methods**:
1. **Textbooks is All You Need**: One of our primary extraction and cleaning methods. This approach emphasized the vast knowledge encapsulated within textbooks, making them invaluable.
2. **Internal Processing**: Health 360 has employed proprietary processes to ensure data purity and relevance, further cleaning and refining the data.
### **Usage**:
Healix-Shot is primed for various NLP tasks, such as:
- Medical information retrieval
- Automatic summarization of medical articles
- Medical question answering
- Drug interaction prediction
- And many more...
### **Acknowledgments**:
We are grateful for the global medical community's incessant efforts, from whom much of this data originates. Their dedication to spreading knowledge empowers projects like Healix-Shot.
### **Licensing**:
This dataset is open-source, adhering to the Creative Commons Attribution 4.0 International (CC BY 4.0). Kindly refer to the license document for detailed rights and restrictions.
---
**Note**: Embedding the dataset into your projects? Do cite Health 360's Healix-Shot from HuggingFace!
---
By merging quality and quantity, Healix-Shot stands at the precipice of a new era in medical NLP. With the support and collaboration of the global community, the potential applications are limitless. Dive in, explore, and let's shape the future of medical informatics together. |
breno30/LocutorLindomar | ---
license: openrail
---
|
alexshengzhili/SciCapAbstractsOCR0350K-generated-multiturnQA | ---
license: mit
dataset_info:
features:
- name: image_file
dtype: string
- name: id
dtype: string
- name: caption
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: first_mention
dtype: string
- name: response
dtype: string
- name: title
dtype: string
- name: abstract
dtype: string
splits:
- name: 0to20percent
num_bytes: 333522772
num_examples: 70404
download_size: 173696449
dataset_size: 333522772
---
|
eren23/aya_dataset_tr | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: language
dtype: string
- name: language_code
dtype: string
- name: annotation_type
dtype: string
- name: user_id
dtype: string
splits:
- name: train
num_bytes: 5092340.457808701
num_examples: 4046
- name: test
num_bytes: 254601.14285714287
num_examples: 250
download_size: 1200045
dataset_size: 5346941.600665844
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
distilled-one-sec-cv12-each-chunk-uniq/chunk_19 | ---
dataset_info:
features:
- name: logits
sequence: float32
- name: mfcc
sequence:
sequence: float64
splits:
- name: train
num_bytes: 1444996712.0
num_examples: 281566
download_size: 1476729097
dataset_size: 1444996712.0
---
# Dataset Card for "chunk_19"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
distilabel-internal-testing/test-dataset-subsets | ---
size_categories: n<1K
config_names:
- generate_with_openai_gpt4
- generate_with_openai_gpt4_2
tags:
- synthetic
- distilabel
- rlaif
dataset_info:
config_name: generate_with_openai_gpt4_2
features:
- name: instruction
dtype: string
- name: completion
dtype: string
- name: meta
struct:
- name: category
dtype: string
- name: completion
dtype: string
- name: id
dtype: int64
- name: input
dtype: 'null'
- name: motivation_app
dtype: 'null'
- name: prompt
dtype: string
- name: source
dtype: string
- name: subcategory
dtype: string
- name: model_name
dtype: string
- name: generation
dtype: string
splits:
- name: train
num_bytes: 26770
num_examples: 10
download_size: 34623
dataset_size: 26770
configs:
- config_name: generate_with_openai_gpt4_2
data_files:
- split: train
path: generate_with_openai_gpt4_2/train-*
---
<p align="left">
<a href="https://github.com/argilla-io/distilabel">
<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>
</a>
</p>
# Dataset Card for test-dataset-subsets
This dataset has been created with [Distilabel](https://distilabel.argilla.io/).
## Dataset Summary
This dataset contains a `pipeline.yaml` which can be used to reproduce the pipeline that generated it in distilabel using the `distilabel` CLI:
```console
distilabel pipeline run --config "https://huggingface.co/datasets/distilabel-internal-testing/test-dataset-subsets/raw/main/pipeline.yaml"
```
or explore the configuration:
```console
distilabel pipeline info --config "https://huggingface.co/datasets/distilabel-internal-testing/test-dataset-subsets/raw/main/pipeline.yaml"
```
## Dataset structure
The examples have the following structure per configuration:
<details><summary> Configuration: generate_with_openai_gpt4_2 </summary><hr>
```json
{
"completion": "Denote the number of chocolates each person has by the letter of their first name. We know that\nA = D + 12\nD = R + 6\nA = 2 * R\n\nThus, A = (R + 6) + 12 = R + 18\nSince also A = 2 * R, this means 2 * R = R + 18\nHence R = 18\nHence D = 18 + 6 = 24",
"generation": "To find out how many chocolates Danny has, let\u0027s define some variables:\nLet \\( R \\) be the number of chocolates Robbie has.\nLet \\( D \\) be the number of chocolates Danny has.\nLet \\( A \\) be the number of chocolates Arianna has.\n\nFrom the information given:\n1. Arianna has 12 chocolates more than Danny: \\( A = D + 12 \\).\n2. Danny has 6 chocolates more than Robbie: \\( D = R + 6 \\).\n3. Arianna has twice as many chocolates as Robbie: \\( A = 2R \\).\n\nNow let\u0027s use these equations to solve the problem.\n\nUsing equation (1) and (3), we can equate \\( A \\) from both equations:\n\\[ D + 12 = 2R \\]\n\nAnd we already know from equation (2) that:\n\\[ D = R + 6 \\]\n\nSubstituting equation (2) into the modified equation (1) gives:\n\\[ (R + 6) + 12 = 2R \\]\n\\[ R + 18 = 2R \\]\n\\[ 2R - R = 18 \\]\n\\[ R = 18 \\]\n\nSubstituting \\( R = 18 \\) back",
"instruction": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
"meta": {
"category": "Question Answering",
"completion": "Denote the number of chocolates each person has by the letter of their first name. We know that\nA = D + 12\nD = R + 6\nA = 2 * R\n\nThus, A = (R + 6) + 12 = R + 18\nSince also A = 2 * R, this means 2 * R = R + 18\nHence R = 18\nHence D = 18 + 6 = 24",
"id": 0,
"input": null,
"motivation_app": null,
"prompt": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
"source": "surge",
"subcategory": "Math"
},
"model_name": "gpt-4-turbo-2024-04-09"
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("distilabel-internal-testing/test-dataset-subsets", "generate_with_openai_gpt4_2")
```
</details>
<details><summary> Configuration: generate_with_openai_gpt4 </summary><hr>
```json
{
"completion": "Denote the number of chocolates each person has by the letter of their first name. We know that\nA = D + 12\nD = R + 6\nA = 2 * R\n\nThus, A = (R + 6) + 12 = R + 18\nSince also A = 2 * R, this means 2 * R = R + 18\nHence R = 18\nHence D = 18 + 6 = 24",
"generation": "Let\u0027s define the number of chocolates each person has with variables:\n- Let \\( R \\) be the number of chocolates Robbie has.\n- Let \\( D \\) be the number of chocolates Danny has.\n- Let \\( A \\) be the number of chocolates Arianna has.\n\nFrom the problem, we know the following relationships:\n1. \\( A = R + 12 \\) (since Arianna has 12 chocolates more than Danny)\n2. \\( D = R + 6 \\) (since Danny has 6 chocolates more than Robbie)\n3. \\( A = 2R \\) (since Arianna has twice as many chocolates as Robbie)\n\nWe can use these equations to find the values. From equations 1 and 3, we have:\n\\[ R + 12 = 2R \\]\nSolving for \\( R \\), we subtract \\( R \\) from both sides:\n\\[ 12 = R \\]\n\nNow that we know \\( R = 12 \\), we can find \\( D \\) using equation 2:\n\\[ D = R + 6 = 12 + 6 = 18 \\]\n\nTherefore, Danny has 18 chocolates.",
"instruction": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
"meta": {
"category": "Question Answering",
"completion": "Denote the number of chocolates each person has by the letter of their first name. We know that\nA = D + 12\nD = R + 6\nA = 2 * R\n\nThus, A = (R + 6) + 12 = R + 18\nSince also A = 2 * R, this means 2 * R = R + 18\nHence R = 18\nHence D = 18 + 6 = 24",
"id": 0,
"input": null,
"motivation_app": null,
"prompt": "Arianna has 12 chocolates more than Danny. Danny has 6 chocolates more than Robbie. Arianna has twice as many chocolates as Robbie has. How many chocolates does Danny have?",
"source": "surge",
"subcategory": "Math"
},
"model_name": "gpt-4-turbo-2024-04-09"
}
```
This subset can be loaded as:
```python
from datasets import load_dataset
ds = load_dataset("distilabel-internal-testing/test-dataset-subsets", "generate_with_openai_gpt4")
```
</details>
|
shirastromer/pinterest-kitchen-design-ideas | ---
dataset_info:
features:
- name: image
dtype: image
- name: title
dtype: string
- name: description
dtype: string
- name: tesseract
dtype: string
- name: CLIP
dtype: string
splits:
- name: train
num_bytes: 242038846.0
num_examples: 848
download_size: 241773265
dataset_size: 242038846.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
anirudhlakhotia/Baarat-Hindi-Instruct-v1 | ---
dataset_info:
features:
- name: instruction
list:
- name: content
dtype: string
- name: role
dtype: string
- name: output
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 2361934683
num_examples: 573088
download_size: 610924253
dataset_size: 2361934683
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
CyberHarem/hagiwara_yukiho_theidolmster | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of hagiwara_yukiho/萩原雪歩 (THE iDOLM@STER)
This is the dataset of hagiwara_yukiho/萩原雪歩 (THE iDOLM@STER), containing 500 images and their tags.
The core tags of this character are `brown_hair, short_hair, brown_eyes, bob_cut, breasts`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:------------------------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 513.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hagiwara_yukiho_theidolmster/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 329.63 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hagiwara_yukiho_theidolmster/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1092 | 660.74 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hagiwara_yukiho_theidolmster/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 468.64 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hagiwara_yukiho_theidolmster/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1092 | 889.15 MiB | [Download](https://huggingface.co/datasets/CyberHarem/hagiwara_yukiho_theidolmster/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/hagiwara_yukiho_theidolmster',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 9 |  |  |  |  |  | 1girl, smile, solo, dress, open_mouth, gloves, hair_ornament, snowflakes, blush, looking_at_viewer |
| 1 | 13 |  |  |  |  |  | 1girl, solo, santa_costume, smile, christmas, blush, open_mouth, gloves, mittens, santa_hat |
| 2 | 10 |  |  |  |  |  | 1girl, coat, solo, blush, smile, snowing, looking_at_viewer, open_mouth, winter_clothes, gloves, scarf |
| 3 | 16 |  |  |  |  |  | 1girl, solo, wings, smile, open_mouth, star_(symbol), collar, microphone, blush, hair_bow, parody, jewelry |
| 4 | 5 |  |  |  |  |  | hair_flower, 1girl, kimono, solo, blush, looking_at_viewer, open_mouth, :d, new_year, obi |
| 5 | 6 |  |  |  |  |  | 1girl, day, solo, cloud, sky, sundress, smile, sun_hat, blush, straw_hat |
| 6 | 11 |  |  |  |  |  | 1girl, solo, medium_breasts, nipples, nude, navel, blush, open_mouth, pussy, looking_at_viewer, simple_background, small_breasts |
| 7 | 7 |  |  |  |  |  | 1girl, navel, solo, white_bikini, blush, looking_at_viewer, medium_breasts, open_mouth, cleavage, cowboy_shot, white_background, sailor_bikini, simple_background, smile |
| 8 | 15 |  |  |  |  |  | 1girl, hetero, penis, solo_focus, 1boy, blush, censored, nipples, cum, nude, large_breasts, open_mouth, oral |
| 9 | 10 |  |  |  |  |  | 1girl, looking_at_viewer, solo, smile, bangs, sleeveless_dress, collarbone, closed_mouth, blue_dress, blush, hair_between_eyes, white_background, bare_shoulders, striped, white_dress |
| 10 | 5 |  |  |  |  |  | 1girl, solo, blush, boots, fishnet_thighhighs, bare_shoulders, bracelet, pink_footwear, sitting, smile, belt, elbow_gloves, fingerless_gloves |
| 11 | 9 |  |  |  |  |  | 1girl, solo, school_uniform, smile, blazer, grey_background, plaid_skirt, socks, looking_at_viewer, simple_background, striped_necktie, upper_body |
| 12 | 6 |  |  |  |  |  | 1girl, maid_headdress, puffy_short_sleeves, solo, looking_at_viewer, pink_bowtie, smile, wrist_cuffs, alternate_costume, blush, frilled_apron, simple_background, waist_apron, white_background, white_shirt, bangs, cowboy_shot, holding_tray, open_mouth, pink_skirt |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | smile | solo | dress | open_mouth | gloves | hair_ornament | snowflakes | blush | looking_at_viewer | santa_costume | christmas | mittens | santa_hat | coat | snowing | winter_clothes | scarf | wings | star_(symbol) | collar | microphone | hair_bow | parody | jewelry | hair_flower | kimono | :d | new_year | obi | day | cloud | sky | sundress | sun_hat | straw_hat | medium_breasts | nipples | nude | navel | pussy | simple_background | small_breasts | white_bikini | cleavage | cowboy_shot | white_background | sailor_bikini | hetero | penis | solo_focus | 1boy | censored | cum | large_breasts | oral | bangs | sleeveless_dress | collarbone | closed_mouth | blue_dress | hair_between_eyes | bare_shoulders | striped | white_dress | boots | fishnet_thighhighs | bracelet | pink_footwear | sitting | belt | elbow_gloves | fingerless_gloves | school_uniform | blazer | grey_background | plaid_skirt | socks | striped_necktie | upper_body | maid_headdress | puffy_short_sleeves | pink_bowtie | wrist_cuffs | alternate_costume | frilled_apron | waist_apron | white_shirt | holding_tray | pink_skirt |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:-------|:--------|:-------------|:---------|:----------------|:-------------|:--------|:--------------------|:----------------|:------------|:----------|:------------|:-------|:----------|:-----------------|:--------|:--------|:----------------|:---------|:-------------|:-----------|:---------|:----------|:--------------|:---------|:-----|:-----------|:------|:------|:--------|:------|:-----------|:----------|:------------|:-----------------|:----------|:-------|:--------|:--------|:--------------------|:----------------|:---------------|:-----------|:--------------|:-------------------|:----------------|:---------|:--------|:-------------|:-------|:-----------|:------|:----------------|:-------|:--------|:-------------------|:-------------|:---------------|:-------------|:--------------------|:-----------------|:----------|:--------------|:--------|:---------------------|:-----------|:----------------|:----------|:-------|:---------------|:--------------------|:-----------------|:---------|:------------------|:--------------|:--------|:------------------|:-------------|:-----------------|:----------------------|:--------------|:--------------|:--------------------|:----------------|:--------------|:--------------|:---------------|:-------------|
| 0 | 9 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 13 |  |  |  |  |  | X | X | X | | X | X | | | X | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 10 |  |  |  |  |  | X | X | X | | X | X | | | X | X | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 16 |  |  |  |  |  | X | X | X | | X | | | | X | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 5 |  |  |  |  |  | X | | X | | X | | | | X | X | | | | | | | | | | | | | | | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 6 |  |  |  |  |  | X | X | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 11 |  |  |  |  |  | X | | X | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 7 |  |  |  |  |  | X | X | X | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | | X | | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 15 |  |  |  |  |  | X | | | | X | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 10 |  |  |  |  |  | X | X | X | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | |
| 10 | 5 |  |  |  |  |  | X | X | X | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | |
| 11 | 9 |  |  |  |  |  | X | X | X | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | | | | | | | | | | |
| 12 | 6 |  |  |  |  |  | X | X | X | | X | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | X | X | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | X | X | X | X | X | X |
|
open-llm-leaderboard/details_BFauber__lora_llama2-13b_10e6 | ---
pretty_name: Evaluation run of BFauber/lora_llama2-13b_10e6
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [BFauber/lora_llama2-13b_10e6](https://huggingface.co/BFauber/lora_llama2-13b_10e6)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 63 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 aggregated 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_BFauber__lora_llama2-13b_10e6\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2024-02-10T00:00:05.303461](https://huggingface.co/datasets/open-llm-leaderboard/details_BFauber__lora_llama2-13b_10e6/blob/main/results_2024-02-10T00-00-05.303461.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 \"acc\": 0.5496264952294131,\n\
\ \"acc_stderr\": 0.03356784950104118,\n \"acc_norm\": 0.5555279978317994,\n\
\ \"acc_norm_stderr\": 0.03429414921472853,\n \"mc1\": 0.24357405140758873,\n\
\ \"mc1_stderr\": 0.015026354824910782,\n \"mc2\": 0.36031296950321545,\n\
\ \"mc2_stderr\": 0.013647842441008402\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5631399317406144,\n \"acc_stderr\": 0.014494421584256517,\n\
\ \"acc_norm\": 0.5887372013651877,\n \"acc_norm_stderr\": 0.014379441068522077\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6154152559251145,\n\
\ \"acc_stderr\": 0.004855027248398163,\n \"acc_norm\": 0.8189603664608643,\n\
\ \"acc_norm_stderr\": 0.0038426408003615093\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.31,\n \"acc_stderr\": 0.04648231987117316,\n \
\ \"acc_norm\": 0.31,\n \"acc_norm_stderr\": 0.04648231987117316\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5185185185185185,\n\
\ \"acc_stderr\": 0.043163785995113245,\n \"acc_norm\": 0.5185185185185185,\n\
\ \"acc_norm_stderr\": 0.043163785995113245\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5197368421052632,\n \"acc_stderr\": 0.04065771002562605,\n\
\ \"acc_norm\": 0.5197368421052632,\n \"acc_norm_stderr\": 0.04065771002562605\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.54,\n\
\ \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.54,\n \
\ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6075471698113207,\n \"acc_stderr\": 0.03005258057955784,\n\
\ \"acc_norm\": 0.6075471698113207,\n \"acc_norm_stderr\": 0.03005258057955784\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6111111111111112,\n\
\ \"acc_stderr\": 0.04076663253918567,\n \"acc_norm\": 0.6111111111111112,\n\
\ \"acc_norm_stderr\": 0.04076663253918567\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \
\ },\n \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\"\
: 0.45,\n \"acc_stderr\": 0.04999999999999999,\n \"acc_norm\": 0.45,\n\
\ \"acc_norm_stderr\": 0.04999999999999999\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.28,\n \"acc_stderr\": 0.04512608598542127,\n \
\ \"acc_norm\": 0.28,\n \"acc_norm_stderr\": 0.04512608598542127\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5260115606936416,\n\
\ \"acc_stderr\": 0.03807301726504513,\n \"acc_norm\": 0.5260115606936416,\n\
\ \"acc_norm_stderr\": 0.03807301726504513\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.2647058823529412,\n \"acc_stderr\": 0.043898699568087785,\n\
\ \"acc_norm\": 0.2647058823529412,\n \"acc_norm_stderr\": 0.043898699568087785\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.71,\n \"acc_stderr\": 0.045604802157206845,\n \"acc_norm\": 0.71,\n\
\ \"acc_norm_stderr\": 0.045604802157206845\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.46382978723404256,\n \"acc_stderr\": 0.032600385118357715,\n\
\ \"acc_norm\": 0.46382978723404256,\n \"acc_norm_stderr\": 0.032600385118357715\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2894736842105263,\n\
\ \"acc_stderr\": 0.04266339443159394,\n \"acc_norm\": 0.2894736842105263,\n\
\ \"acc_norm_stderr\": 0.04266339443159394\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.503448275862069,\n \"acc_stderr\": 0.04166567577101579,\n\
\ \"acc_norm\": 0.503448275862069,\n \"acc_norm_stderr\": 0.04166567577101579\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3333333333333333,\n \"acc_stderr\": 0.0242785680243077,\n \"acc_norm\"\
: 0.3333333333333333,\n \"acc_norm_stderr\": 0.0242785680243077\n },\n\
\ \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.30952380952380953,\n\
\ \"acc_stderr\": 0.04134913018303316,\n \"acc_norm\": 0.30952380952380953,\n\
\ \"acc_norm_stderr\": 0.04134913018303316\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.35,\n \"acc_stderr\": 0.047937248544110196,\n \
\ \"acc_norm\": 0.35,\n \"acc_norm_stderr\": 0.047937248544110196\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7,\n \"acc_stderr\": 0.026069362295335127,\n \"acc_norm\": 0.7,\n\
\ \"acc_norm_stderr\": 0.026069362295335127\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.4236453201970443,\n \"acc_stderr\": 0.03476725747649037,\n\
\ \"acc_norm\": 0.4236453201970443,\n \"acc_norm_stderr\": 0.03476725747649037\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.58,\n \"acc_stderr\": 0.049604496374885836,\n \"acc_norm\"\
: 0.58,\n \"acc_norm_stderr\": 0.049604496374885836\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.6787878787878788,\n \"acc_stderr\": 0.036462049632538095,\n\
\ \"acc_norm\": 0.6787878787878788,\n \"acc_norm_stderr\": 0.036462049632538095\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.6818181818181818,\n \"acc_stderr\": 0.0331847733384533,\n \"acc_norm\"\
: 0.6818181818181818,\n \"acc_norm_stderr\": 0.0331847733384533\n },\n\
\ \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n \
\ \"acc\": 0.7979274611398963,\n \"acc_stderr\": 0.02897908979429673,\n\
\ \"acc_norm\": 0.7979274611398963,\n \"acc_norm_stderr\": 0.02897908979429673\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5230769230769231,\n \"acc_stderr\": 0.025323990861736232,\n\
\ \"acc_norm\": 0.5230769230769231,\n \"acc_norm_stderr\": 0.025323990861736232\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.28888888888888886,\n \"acc_stderr\": 0.027634907264178544,\n \
\ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.027634907264178544\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5252100840336135,\n \"acc_stderr\": 0.03243718055137411,\n \
\ \"acc_norm\": 0.5252100840336135,\n \"acc_norm_stderr\": 0.03243718055137411\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\
acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7412844036697248,\n \"acc_stderr\": 0.018776052319619624,\n \"\
acc_norm\": 0.7412844036697248,\n \"acc_norm_stderr\": 0.018776052319619624\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.44907407407407407,\n \"acc_stderr\": 0.03392238405321616,\n \"\
acc_norm\": 0.44907407407407407,\n \"acc_norm_stderr\": 0.03392238405321616\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7647058823529411,\n \"acc_stderr\": 0.02977177522814565,\n \"\
acc_norm\": 0.7647058823529411,\n \"acc_norm_stderr\": 0.02977177522814565\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7257383966244726,\n \"acc_stderr\": 0.029041333510598025,\n \
\ \"acc_norm\": 0.7257383966244726,\n \"acc_norm_stderr\": 0.029041333510598025\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.6457399103139013,\n\
\ \"acc_stderr\": 0.032100621541349864,\n \"acc_norm\": 0.6457399103139013,\n\
\ \"acc_norm_stderr\": 0.032100621541349864\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6183206106870229,\n \"acc_stderr\": 0.042607351576445594,\n\
\ \"acc_norm\": 0.6183206106870229,\n \"acc_norm_stderr\": 0.042607351576445594\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7355371900826446,\n \"acc_stderr\": 0.04026187527591207,\n \"\
acc_norm\": 0.7355371900826446,\n \"acc_norm_stderr\": 0.04026187527591207\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.6851851851851852,\n\
\ \"acc_stderr\": 0.04489931073591312,\n \"acc_norm\": 0.6851851851851852,\n\
\ \"acc_norm_stderr\": 0.04489931073591312\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.6625766871165644,\n \"acc_stderr\": 0.03714908409935574,\n\
\ \"acc_norm\": 0.6625766871165644,\n \"acc_norm_stderr\": 0.03714908409935574\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.30357142857142855,\n\
\ \"acc_stderr\": 0.04364226155841044,\n \"acc_norm\": 0.30357142857142855,\n\
\ \"acc_norm_stderr\": 0.04364226155841044\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7572815533980582,\n \"acc_stderr\": 0.04245022486384495,\n\
\ \"acc_norm\": 0.7572815533980582,\n \"acc_norm_stderr\": 0.04245022486384495\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.7991452991452992,\n\
\ \"acc_stderr\": 0.026246772946890474,\n \"acc_norm\": 0.7991452991452992,\n\
\ \"acc_norm_stderr\": 0.026246772946890474\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.52,\n \"acc_stderr\": 0.050211673156867795,\n \
\ \"acc_norm\": 0.52,\n \"acc_norm_stderr\": 0.050211673156867795\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7381864623243933,\n\
\ \"acc_stderr\": 0.01572083867844526,\n \"acc_norm\": 0.7381864623243933,\n\
\ \"acc_norm_stderr\": 0.01572083867844526\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.638728323699422,\n \"acc_stderr\": 0.025862201852277895,\n\
\ \"acc_norm\": 0.638728323699422,\n \"acc_norm_stderr\": 0.025862201852277895\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3195530726256983,\n\
\ \"acc_stderr\": 0.01559552029414741,\n \"acc_norm\": 0.3195530726256983,\n\
\ \"acc_norm_stderr\": 0.01559552029414741\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6241830065359477,\n \"acc_stderr\": 0.027732834353363947,\n\
\ \"acc_norm\": 0.6241830065359477,\n \"acc_norm_stderr\": 0.027732834353363947\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6430868167202572,\n\
\ \"acc_stderr\": 0.027210420375934023,\n \"acc_norm\": 0.6430868167202572,\n\
\ \"acc_norm_stderr\": 0.027210420375934023\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6450617283950617,\n \"acc_stderr\": 0.02662415247884585,\n\
\ \"acc_norm\": 0.6450617283950617,\n \"acc_norm_stderr\": 0.02662415247884585\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.40425531914893614,\n \"acc_stderr\": 0.02927553215970473,\n \
\ \"acc_norm\": 0.40425531914893614,\n \"acc_norm_stderr\": 0.02927553215970473\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41851368970013036,\n\
\ \"acc_stderr\": 0.012599505608336463,\n \"acc_norm\": 0.41851368970013036,\n\
\ \"acc_norm_stderr\": 0.012599505608336463\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5,\n \"acc_stderr\": 0.030372836961539352,\n \
\ \"acc_norm\": 0.5,\n \"acc_norm_stderr\": 0.030372836961539352\n \
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"acc\"\
: 0.5473856209150327,\n \"acc_stderr\": 0.020136790918492527,\n \"\
acc_norm\": 0.5473856209150327,\n \"acc_norm_stderr\": 0.020136790918492527\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6363636363636364,\n\
\ \"acc_stderr\": 0.04607582090719976,\n \"acc_norm\": 0.6363636363636364,\n\
\ \"acc_norm_stderr\": 0.04607582090719976\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6408163265306123,\n \"acc_stderr\": 0.030713560455108493,\n\
\ \"acc_norm\": 0.6408163265306123,\n \"acc_norm_stderr\": 0.030713560455108493\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7114427860696517,\n\
\ \"acc_stderr\": 0.03203841040213322,\n \"acc_norm\": 0.7114427860696517,\n\
\ \"acc_norm_stderr\": 0.03203841040213322\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.463855421686747,\n\
\ \"acc_stderr\": 0.03882310850890593,\n \"acc_norm\": 0.463855421686747,\n\
\ \"acc_norm_stderr\": 0.03882310850890593\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7543859649122807,\n \"acc_stderr\": 0.03301405946987249,\n\
\ \"acc_norm\": 0.7543859649122807,\n \"acc_norm_stderr\": 0.03301405946987249\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.24357405140758873,\n\
\ \"mc1_stderr\": 0.015026354824910782,\n \"mc2\": 0.36031296950321545,\n\
\ \"mc2_stderr\": 0.013647842441008402\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7671665351223362,\n \"acc_stderr\": 0.011878201073856544\n\
\ },\n \"harness|gsm8k|5\": {\n \"acc\": 0.21304018195602728,\n \
\ \"acc_stderr\": 0.01127844785690078\n }\n}\n```"
repo_url: https://huggingface.co/BFauber/lora_llama2-13b_10e6
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|arc:challenge|25_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|gsm8k|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hellaswag|10_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-management|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-virology|5_2024-02-10T00-00-05.303461.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2024-02-10T00-00-05.303461.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- '**/details_harness|winogrande|5_2024-02-10T00-00-05.303461.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2024-02-10T00-00-05.303461.parquet'
- config_name: results
data_files:
- split: 2024_02_10T00_00_05.303461
path:
- results_2024-02-10T00-00-05.303461.parquet
- split: latest
path:
- results_2024-02-10T00-00-05.303461.parquet
---
# Dataset Card for Evaluation run of BFauber/lora_llama2-13b_10e6
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [BFauber/lora_llama2-13b_10e6](https://huggingface.co/BFauber/lora_llama2-13b_10e6) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 63 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 aggregated 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_BFauber__lora_llama2-13b_10e6",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2024-02-10T00:00:05.303461](https://huggingface.co/datasets/open-llm-leaderboard/details_BFauber__lora_llama2-13b_10e6/blob/main/results_2024-02-10T00-00-05.303461.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": {
"acc": 0.5496264952294131,
"acc_stderr": 0.03356784950104118,
"acc_norm": 0.5555279978317994,
"acc_norm_stderr": 0.03429414921472853,
"mc1": 0.24357405140758873,
"mc1_stderr": 0.015026354824910782,
"mc2": 0.36031296950321545,
"mc2_stderr": 0.013647842441008402
},
"harness|arc:challenge|25": {
"acc": 0.5631399317406144,
"acc_stderr": 0.014494421584256517,
"acc_norm": 0.5887372013651877,
"acc_norm_stderr": 0.014379441068522077
},
"harness|hellaswag|10": {
"acc": 0.6154152559251145,
"acc_stderr": 0.004855027248398163,
"acc_norm": 0.8189603664608643,
"acc_norm_stderr": 0.0038426408003615093
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.31,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.31,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5185185185185185,
"acc_stderr": 0.043163785995113245,
"acc_norm": 0.5185185185185185,
"acc_norm_stderr": 0.043163785995113245
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5197368421052632,
"acc_stderr": 0.04065771002562605,
"acc_norm": 0.5197368421052632,
"acc_norm_stderr": 0.04065771002562605
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.54,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.54,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6075471698113207,
"acc_stderr": 0.03005258057955784,
"acc_norm": 0.6075471698113207,
"acc_norm_stderr": 0.03005258057955784
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6111111111111112,
"acc_stderr": 0.04076663253918567,
"acc_norm": 0.6111111111111112,
"acc_norm_stderr": 0.04076663253918567
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695236,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.45,
"acc_stderr": 0.04999999999999999,
"acc_norm": 0.45,
"acc_norm_stderr": 0.04999999999999999
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.28,
"acc_stderr": 0.04512608598542127,
"acc_norm": 0.28,
"acc_norm_stderr": 0.04512608598542127
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5260115606936416,
"acc_stderr": 0.03807301726504513,
"acc_norm": 0.5260115606936416,
"acc_norm_stderr": 0.03807301726504513
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.2647058823529412,
"acc_stderr": 0.043898699568087785,
"acc_norm": 0.2647058823529412,
"acc_norm_stderr": 0.043898699568087785
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.71,
"acc_stderr": 0.045604802157206845,
"acc_norm": 0.71,
"acc_norm_stderr": 0.045604802157206845
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.46382978723404256,
"acc_stderr": 0.032600385118357715,
"acc_norm": 0.46382978723404256,
"acc_norm_stderr": 0.032600385118357715
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2894736842105263,
"acc_stderr": 0.04266339443159394,
"acc_norm": 0.2894736842105263,
"acc_norm_stderr": 0.04266339443159394
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.503448275862069,
"acc_stderr": 0.04166567577101579,
"acc_norm": 0.503448275862069,
"acc_norm_stderr": 0.04166567577101579
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3333333333333333,
"acc_stderr": 0.0242785680243077,
"acc_norm": 0.3333333333333333,
"acc_norm_stderr": 0.0242785680243077
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.30952380952380953,
"acc_stderr": 0.04134913018303316,
"acc_norm": 0.30952380952380953,
"acc_norm_stderr": 0.04134913018303316
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.35,
"acc_stderr": 0.047937248544110196,
"acc_norm": 0.35,
"acc_norm_stderr": 0.047937248544110196
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7,
"acc_stderr": 0.026069362295335127,
"acc_norm": 0.7,
"acc_norm_stderr": 0.026069362295335127
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4236453201970443,
"acc_stderr": 0.03476725747649037,
"acc_norm": 0.4236453201970443,
"acc_norm_stderr": 0.03476725747649037
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.58,
"acc_stderr": 0.049604496374885836,
"acc_norm": 0.58,
"acc_norm_stderr": 0.049604496374885836
},
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"harness|hendrycksTest-world_religions|5": {
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"harness|gsm8k|5": {
"acc": 0.21304018195602728,
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}
}
```
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
cassanof/multipl-t-python-sample | ---
dataset_info:
features:
- name: content
dtype: string
- name: sha1
dtype: string
- name: id
dtype: int64
- name: entrypoint
dtype: string
- name: tests
sequence: string
- name: coverage
dtype: int64
- name: tests_failed
sequence: string
splits:
- name: train
num_bytes: 4583872
num_examples: 1000
download_size: 1376229
dataset_size: 4583872
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
trumble2024/Rick-bot-flags | ---
configs:
- config_name: default
data_files:
- split: train
path: data.csv
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
## Dataset Details
### Dataset Description
<!-- Provide a longer summary of what this dataset is. -->
- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
### Dataset Sources [optional]
<!-- Provide the basic links for the dataset. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
### Direct Use
<!-- This section describes suitable use cases for the dataset. -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
[More Information Needed]
## Dataset Structure
<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
[More Information Needed]
## Dataset Creation
### Curation Rationale
<!-- Motivation for the creation of this dataset. -->
[More Information Needed]
### Source Data
<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
#### Data Collection and Processing
<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
[More Information Needed]
#### Who are the source data producers?
<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
[More Information Needed]
### Annotations [optional]
<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
#### Annotation process
<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
[More Information Needed]
#### Who are the annotators?
<!-- This section describes the people or systems who created the annotations. -->
[More Information Needed]
#### Personal and Sensitive Information
<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Dataset Card Authors [optional]
[More Information Needed]
## Dataset Card Contact
[More Information Needed] |
khanzaid/sentiment_review | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 792620
num_examples: 4846
download_size: 412816
dataset_size: 792620
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
haizad/jurnal-malaysia-scraped | ---
language:
- ms
---
* website: [jurnal-malaysia](https://jurnal-malaysia.com/)
* Number of pages scraped: 20
* Number of posts scraped: 1938
* Link to dataset on [Huggingface](https://huggingface.co/datasets/haizad/jurnal-malaysia-scraped) |
mrmocciai/pre-duplicate | ---
license: mit
task_categories:
- feature-extraction
language:
- en
---
# This file are pre-trained rvc for training models.<br />
## This files are not mine, i just backing up this file due to colab new term of service that making rvc can't run in google colab.<br />
### Credit owner<br />
[Liu](https://huggingface.co/lj1995)<br /> |
westenfelder/NL2CMD-RAG | ---
license: mit
task_categories:
- translation
language:
- en
tags:
- code
size_categories:
- 10K<n<100K
---
# Dataset Card for NL2CMD-RAG
This is an updated version of [TRamesh2/NL2CMD](https://huggingface.co/datasets/TRamesh2/NL2CMD). It contains 10,125 rows with a natural language instruction, bash command translation, list of utilities used in that bash command, and a combined text field for fine-tuning. This dataset is intended to be used with [westenfelder/Linux-Manual-Pages-TLDR](https://huggingface.co/datasets/westenfelder/Linux-Manual-Pages-TLDR) to retrieve the relevant manual pages for the utilities in a command.
|
nlplabtdtu/multi-choices-health-100-v2 | ---
dataset_info:
features:
- name: 'Unnamed: 0'
dtype: int64
- name: question
dtype: string
- name: options
dtype: string
- name: answer
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 103402
num_examples: 82
download_size: 29426
dataset_size: 103402
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "multi-choices-health-100-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
joytafty/denoising-dirty-documents-train | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 19395270.0
num_examples: 144
download_size: 0
dataset_size: 19395270.0
---
# Dataset Card for "denoising-dirty-documents-train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DBQ/Saint.Laurent.Product.prices.United.Kingdom | ---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: United Kingdom - Saint Laurent - Product-level price list
tags:
- webscraping
- ecommerce
- Saint Laurent
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 1235575
num_examples: 3063
download_size: 376994
dataset_size: 1235575
---
# Saint Laurent web scraped data
## About the website
Saint Laurent operates within the **luxury fashion** industry in the Europe, the Middle East and Africa (EMEA) region, with significant impact in the **United Kingdom**. This industry is characterised by prestigious brands offering high-quality, expensive products which are often seen as a status symbol. There has notably been a shift in the industry towards **Ecommerce** sales, owing to customers’ increasing preference for online shopping. Within this dataset, we have obtained **Ecommerce product-list page (PLP) data** specifically for **Saint Laurent in the United Kingdom**, offering detailed insights into the brands online performance in this specific market.
## Link to **dataset**
[United Kingdom - Saint Laurent - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Saint%20Laurent%20Product-prices%20United%20Kingdom/r/recNRYRcXAYJCmE6x)
|
Deojoandco/ah_openai_dialog_v1 | ---
dataset_info:
features:
- name: url
dtype: string
- name: id
dtype: string
- name: num_comments
dtype: int64
- name: name
dtype: string
- name: title
dtype: string
- name: body
dtype: string
- name: score
dtype: int64
- name: upvote_ratio
dtype: float64
- name: distinguished
dtype: 'null'
- name: over_18
dtype: bool
- name: created_utc
dtype: int64
- name: comments
list:
- name: body
dtype: string
- name: created_utc
dtype: float64
- name: distinguished
dtype: string
- name: id
dtype: string
- name: permalink
dtype: string
- name: score
dtype: int64
- name: best_num_comments
dtype: int64
- name: query
dtype: string
- name: dialog
dtype: string
- name: dialog_success
dtype: bool
splits:
- name: train
num_bytes: 2586752
num_examples: 297
download_size: 1566283
dataset_size: 2586752
---
# Dataset Card for "ah_openai_dialog_v1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
DIBT/MPEP_MALAGASY | ---
size_categories: n<1K
tags:
- rlfh
- argilla
- human-feedback
---
# Dataset Card for MPEP_MALAGASY
This dataset has been created with [Argilla](https://docs.argilla.io).
As shown in the sections below, this dataset can be loaded into Argilla as explained in [Load with Argilla](#load-with-argilla), or used directly with the `datasets` library in [Load with `datasets`](#load-with-datasets).
## Dataset Description
- **Homepage:** https://argilla.io
- **Repository:** https://github.com/argilla-io/argilla
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains:
* A dataset configuration file conforming to the Argilla dataset format named `argilla.yaml`. This configuration file will be used to configure the dataset when using the `FeedbackDataset.from_huggingface` method in Argilla.
* Dataset records in a format compatible with HuggingFace `datasets`. These records will be loaded automatically when using `FeedbackDataset.from_huggingface` and can be loaded independently using the `datasets` library via `load_dataset`.
* The [annotation guidelines](#annotation-guidelines) that have been used for building and curating the dataset, if they've been defined in Argilla.
### Load with Argilla
To load with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface("DIBT/MPEP_MALAGASY")
```
### Load with `datasets`
To load this dataset with `datasets`, you'll just need to install `datasets` as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset("DIBT/MPEP_MALAGASY")
```
### Supported Tasks and Leaderboards
This dataset can contain [multiple fields, questions and responses](https://docs.argilla.io/en/latest/conceptual_guides/data_model.html#feedback-dataset) so it can be used for different NLP tasks, depending on the configuration. The dataset structure is described in the [Dataset Structure section](#dataset-structure).
There are no leaderboards associated with this dataset.
### Languages
[More Information Needed]
## Dataset Structure
### Data in Argilla
The dataset is created in Argilla with: **fields**, **questions**, **suggestions**, **metadata**, **vectors**, and **guidelines**.
The **fields** are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
| Field Name | Title | Type | Required | Markdown |
| ---------- | ----- | ---- | -------- | -------- |
| source | Source | text | True | True |
The **questions** are the questions that will be asked to the annotators. They can be of different types, such as rating, text, label_selection, multi_label_selection, or ranking.
| Question Name | Title | Type | Required | Description | Values/Labels |
| ------------- | ----- | ---- | -------- | ----------- | ------------- |
| target | Target | text | True | Translate the text. | N/A |
The **suggestions** are human or machine generated recommendations for each question to assist the annotator during the annotation process, so those are always linked to the existing questions, and named appending "-suggestion" and "-suggestion-metadata" to those, containing the value/s of the suggestion and its metadata, respectively. So on, the possible values are the same as in the table above, but the column name is appended with "-suggestion" and the metadata is appended with "-suggestion-metadata".
The **metadata** is a dictionary that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
| Metadata Name | Title | Type | Values | Visible for Annotators |
| ------------- | ----- | ---- | ------ | ---------------------- |
The **guidelines**, are optional as well, and are just a plain string that can be used to provide instructions to the annotators. Find those in the [annotation guidelines](#annotation-guidelines) section.
### Data Instances
An example of a dataset instance in Argilla looks as follows:
```json
{
"external_id": "944",
"fields": {
"source": "I need a cv for this job\nJunior PHP Engineer (f/m/d)\nAbout the job\nAnstellungsdetails\n\nPermanent contract, full-time or part-time, Cologne / D\u00fcsseldorf / Darmstadt / Remote (in Germany)\n\nInfo\n\nWho We Are\n\nWe operate Kaufland.de: Several thousand sellers and millions of products make us one of the fastest growing online marketplaces. Our work is characterised by a dynamic corporate culture, with a start-up mentality and the power of a big corporate group. We combine knowledge and many years of experience in e-commerce with flat hierarchies and a highly motivated team. Whether it\u0027s from an intern: We take every idea seriously, because we want to work together in shaping the future of e-commerce!\n\nWe love flexibility! We offer you the best environment to work in a flexible and focused way: You decide whether you want to work in our office in Cologne // Darmstadt while at the same time having the opportunity to work remotely. We want to offer you the most attractive workplace in e-commerce and a maximum of flexibility for your personal work-life balance.\n\nDay-by-day, our development team of over 300 experts pursues the goal of creating the best-possible customer shopping experience for the Kaufland marketplace. We are here to inspire millions of customers, to help them find, purchase and get the things they love. To enrich the lives of our customers, we collaborate in cross-functional teams, in which product managers, product designers, frontend and backend engineers, data scientists and lean managers join forces. Details regarding the challenges of the respective product areas can be found here: https://kaufland-ecommerce.com/team/tech/.\n\nAufgaben\n\nYour tasks \u2013 this is what awaits you in detail\nAs a Junior PHP Engineer (f/m/d) you proactively develop the Kaufland Marketplace and internal services further\nIn our cross-functional teams, independent teams, you regularly work with colleagues from other disciplines to develop products that make our customers happy\nYou learn about cutting edge technologies and innovative processes and stay up-to-date on current trends in the engineering world\nIn pair-programming sessions, you have the opportunity to gain and share knowledge with your team and continuously learn new things\nYou actively contribute in planning \u0026 building scalable and reliable software solutions\n\nYour Profile \u2013 This Is What We Expect From You\nYou have an educational level that corresponds to completed university studies in (business) information technology, (business) engineering, mathematics or physics, or you finished an IT relevant apprenticeship, or you have equivalent professional experience\nIdeally, you have gained some previous insights into object-oriented programming, preferably with PHP, modern frameworks (Symfony, Laravel or similar), and knowledge of relational databases (MySQL or similar)\nYou understand clean coding principles and best practices to ensure sustainable, reliable and extendable code\nYou have the drive to continuously develop both yourself and your team further in order to become a little bit better every day\nYou are passionate about what you do and have a strong interest in current e-commerce trends and developments\nYou have excellent communication skills, are willing to embrace our company culture and values and enjoy working in an international, english-speaking team\n\nWhat We Offer\nCreate your own work-life! We offer you the best possible flexibility in terms of working hours and location\nA highly-motivated and qualified team from different fields and numerous nations\nYou would like to move to Germany? No problem - we offer you an attractive relocation package for a smooth start\nThe future is digital - Develop the e-commerce of the future with us and work on a product with millions of users with a broadly-based technology stack\nPower meets dynamic - flat hierarchies and start-up mentality meet and the power of a big corporate group and offers you an agile and secure working environment at the same time\nFree choice of hardware \u2013 we provide you with the development environment of your choice\nPersonal \u0026 team growth: We love to have flexibility in terms of working location, but we also believe that personal interactions are important. That\u2019s why we organize company events, and cover the costs to bring you there and to other (approved) get togethers with your peers. The same applies to conferences and other learning opportunities. We want everybody to grow personally and professionally, as a team and as a company\nTop conditions at the Urban Sports Club for fitness, team sports, yoga and more\n\nCheck out our Principles \u0026 our blog for even more insights into our company culture!\n\nRefer an Engineer Program\n\nYou want to work for us, but not without your dearest colleague? Join us as a team and earn money by referring your buddy for one of our open Tech positions! Depending on the level we offer a bonus up to 1.300,- \u20ac per referral, which will be paid after successful recruitment (the first part with your first salary, the second part after your buddy passes the trial period).\n\nBewerben\n\nWhy are you the perfect fit for this position? Don\u0027t bother with cover letters - we\u0027re interested in facts and figures!\n\nAnsprechpartner\n\n\\_\\_Your contact person: Alina Asmus\n\nKaufland e-commerce\n\nKaufland e-commerce Services GmbH \u0026 Co. KG\n\nPostal address:\n\nHabsburgerring 2\n\n50674 K\u00f6ln\n\nWe\u0027re looking forward to your application!\\_\\_"
},
"metadata": {
"evolved_from": null,
"kind": "human",
"source": "ewof/sharegpt-instruct-unfiltered-deduped"
},
"responses": [
{
"status": "submitted",
"user_id": "d2dae990-2c5e-4871-8d40-1f3ded82b14d",
"values": {
"target": {
"value": "Mila CV aho ho an\u0027ity asa ity\r\nJunior PHP injeniera (f / m / d)\nMomba ny asa\nAnstellungsdetails\n\nFifanekena maharitra, fotoana feno na tapak\u0027andro, Cologne / D\u00fcsseldorf / Darmstadt / Remote (any Alemaina)\n\nInfo\n\nIza moa isika\n\nMiasa Kaufland.de izahay: mpivarotra an\u0027arivony sy vokatra an-tapitrisany no mahatonga antsika ho iray amin\u0027ireo tsena an-tserasera mitombo haingana indrindra. Ny asanay dia miavaka amin\u0027ny kolontsain\u0027ny orinasa mavitrika, miaraka amin\u0027ny toe-tsaina manomboka sy ny herin\u0027ny vondrona orinasa lehibe. Manambatra ny fahalalana sy ny traikefa an-taonany maro amin\u0027ny e-varotra miaraka amin\u0027ny ambaratongam-pahefana sy ekipa tena manosika izahay. Na avy amin\u0027ny intern: Raisinay ho zava-dehibe ny hevitra rehetra, satria te-hiara-hiasa amin\u0027ny famolavolana ny hoavin\u0027ny e-varotra izahay!\n\n\n\r\n\n"
}
}
}
],
"suggestions": [
{
"agent": null,
"question_name": "target",
"score": null,
"type": null,
"value": "Mila CV ho an\u0027ity asa ity aho Junior Commerce Engineer (f/m/d) Inona no asa momba ny asa Anstellungsdetails Fampidirana ny asa Anstellungsdetails Fifanarahana maharitra, manontolo andro na ampahany andro, Cologne / D\u00fcsseldorf / Darmstadt / Remote (amin\u0027ny Alemaina) Info Izahay miasa Kaufland.de: Mpivarotra an\u0027arivony sy vokatra an-tapitrisany maro no mahatonga antsika ho iray amin\u0027ireo tsena an-tserasera mitombo haingana indrindra. Ny asa ataonay dia miavaka amin\u0027ny kolontsaina mavitrika, miaraka amin\u0027ny toe-tsaina fanombohana ary ny herin\u0027ny vondrona orinasa lehibe. Manambatra ny fahalalana sy ny traikefa nandritra ny taona maro amin\u0027ny e-varotra amin\u0027ny laharan-tsoratra feno fahalemem-panahy sy ny ekipa tena mavitrika (ao amin\u0027ny"
}
],
"vectors": {}
}
```
While the same record in HuggingFace `datasets` looks as follows:
```json
{
"external_id": "944",
"metadata": "{\"source\": \"ewof/sharegpt-instruct-unfiltered-deduped\", \"kind\": \"human\", \"evolved_from\": null}",
"source": "I need a cv for this job\nJunior PHP Engineer (f/m/d)\nAbout the job\nAnstellungsdetails\n\nPermanent contract, full-time or part-time, Cologne / D\u00fcsseldorf / Darmstadt / Remote (in Germany)\n\nInfo\n\nWho We Are\n\nWe operate Kaufland.de: Several thousand sellers and millions of products make us one of the fastest growing online marketplaces. Our work is characterised by a dynamic corporate culture, with a start-up mentality and the power of a big corporate group. We combine knowledge and many years of experience in e-commerce with flat hierarchies and a highly motivated team. Whether it\u0027s from an intern: We take every idea seriously, because we want to work together in shaping the future of e-commerce!\n\nWe love flexibility! We offer you the best environment to work in a flexible and focused way: You decide whether you want to work in our office in Cologne // Darmstadt while at the same time having the opportunity to work remotely. We want to offer you the most attractive workplace in e-commerce and a maximum of flexibility for your personal work-life balance.\n\nDay-by-day, our development team of over 300 experts pursues the goal of creating the best-possible customer shopping experience for the Kaufland marketplace. We are here to inspire millions of customers, to help them find, purchase and get the things they love. To enrich the lives of our customers, we collaborate in cross-functional teams, in which product managers, product designers, frontend and backend engineers, data scientists and lean managers join forces. Details regarding the challenges of the respective product areas can be found here: https://kaufland-ecommerce.com/team/tech/.\n\nAufgaben\n\nYour tasks \u2013 this is what awaits you in detail\nAs a Junior PHP Engineer (f/m/d) you proactively develop the Kaufland Marketplace and internal services further\nIn our cross-functional teams, independent teams, you regularly work with colleagues from other disciplines to develop products that make our customers happy\nYou learn about cutting edge technologies and innovative processes and stay up-to-date on current trends in the engineering world\nIn pair-programming sessions, you have the opportunity to gain and share knowledge with your team and continuously learn new things\nYou actively contribute in planning \u0026 building scalable and reliable software solutions\n\nYour Profile \u2013 This Is What We Expect From You\nYou have an educational level that corresponds to completed university studies in (business) information technology, (business) engineering, mathematics or physics, or you finished an IT relevant apprenticeship, or you have equivalent professional experience\nIdeally, you have gained some previous insights into object-oriented programming, preferably with PHP, modern frameworks (Symfony, Laravel or similar), and knowledge of relational databases (MySQL or similar)\nYou understand clean coding principles and best practices to ensure sustainable, reliable and extendable code\nYou have the drive to continuously develop both yourself and your team further in order to become a little bit better every day\nYou are passionate about what you do and have a strong interest in current e-commerce trends and developments\nYou have excellent communication skills, are willing to embrace our company culture and values and enjoy working in an international, english-speaking team\n\nWhat We Offer\nCreate your own work-life! We offer you the best possible flexibility in terms of working hours and location\nA highly-motivated and qualified team from different fields and numerous nations\nYou would like to move to Germany? No problem - we offer you an attractive relocation package for a smooth start\nThe future is digital - Develop the e-commerce of the future with us and work on a product with millions of users with a broadly-based technology stack\nPower meets dynamic - flat hierarchies and start-up mentality meet and the power of a big corporate group and offers you an agile and secure working environment at the same time\nFree choice of hardware \u2013 we provide you with the development environment of your choice\nPersonal \u0026 team growth: We love to have flexibility in terms of working location, but we also believe that personal interactions are important. That\u2019s why we organize company events, and cover the costs to bring you there and to other (approved) get togethers with your peers. The same applies to conferences and other learning opportunities. We want everybody to grow personally and professionally, as a team and as a company\nTop conditions at the Urban Sports Club for fitness, team sports, yoga and more\n\nCheck out our Principles \u0026 our blog for even more insights into our company culture!\n\nRefer an Engineer Program\n\nYou want to work for us, but not without your dearest colleague? Join us as a team and earn money by referring your buddy for one of our open Tech positions! Depending on the level we offer a bonus up to 1.300,- \u20ac per referral, which will be paid after successful recruitment (the first part with your first salary, the second part after your buddy passes the trial period).\n\nBewerben\n\nWhy are you the perfect fit for this position? Don\u0027t bother with cover letters - we\u0027re interested in facts and figures!\n\nAnsprechpartner\n\n\\_\\_Your contact person: Alina Asmus\n\nKaufland e-commerce\n\nKaufland e-commerce Services GmbH \u0026 Co. KG\n\nPostal address:\n\nHabsburgerring 2\n\n50674 K\u00f6ln\n\nWe\u0027re looking forward to your application!\\_\\_",
"target": [
{
"status": "submitted",
"user_id": "d2dae990-2c5e-4871-8d40-1f3ded82b14d",
"value": "Mila CV aho ho an\u0027ity asa ity\r\nJunior PHP injeniera (f / m / d)\nMomba ny asa\nAnstellungsdetails\n\nFifanekena maharitra, fotoana feno na tapak\u0027andro, Cologne / D\u00fcsseldorf / Darmstadt / Remote (any Alemaina)\n\nInfo\n\nIza moa isika\n\nMiasa Kaufland.de izahay: mpivarotra an\u0027arivony sy vokatra an-tapitrisany no mahatonga antsika ho iray amin\u0027ireo tsena an-tserasera mitombo haingana indrindra. Ny asanay dia miavaka amin\u0027ny kolontsain\u0027ny orinasa mavitrika, miaraka amin\u0027ny toe-tsaina manomboka sy ny herin\u0027ny vondrona orinasa lehibe. Manambatra ny fahalalana sy ny traikefa an-taonany maro amin\u0027ny e-varotra miaraka amin\u0027ny ambaratongam-pahefana sy ekipa tena manosika izahay. Na avy amin\u0027ny intern: Raisinay ho zava-dehibe ny hevitra rehetra, satria te-hiara-hiasa amin\u0027ny famolavolana ny hoavin\u0027ny e-varotra izahay!\n\n\n\r\n\n"
}
],
"target-suggestion": "Mila CV ho an\u0027ity asa ity aho Junior Commerce Engineer (f/m/d) Inona no asa momba ny asa Anstellungsdetails Fampidirana ny asa Anstellungsdetails Fifanarahana maharitra, manontolo andro na ampahany andro, Cologne / D\u00fcsseldorf / Darmstadt / Remote (amin\u0027ny Alemaina) Info Izahay miasa Kaufland.de: Mpivarotra an\u0027arivony sy vokatra an-tapitrisany maro no mahatonga antsika ho iray amin\u0027ireo tsena an-tserasera mitombo haingana indrindra. Ny asa ataonay dia miavaka amin\u0027ny kolontsaina mavitrika, miaraka amin\u0027ny toe-tsaina fanombohana ary ny herin\u0027ny vondrona orinasa lehibe. Manambatra ny fahalalana sy ny traikefa nandritra ny taona maro amin\u0027ny e-varotra amin\u0027ny laharan-tsoratra feno fahalemem-panahy sy ny ekipa tena mavitrika (ao amin\u0027ny",
"target-suggestion-metadata": {
"agent": null,
"score": null,
"type": null
}
}
```
### Data Fields
Among the dataset fields, we differentiate between the following:
* **Fields:** These are the dataset records themselves, for the moment just text fields are supported. These are the ones that will be used to provide responses to the questions.
* **source** is of type `text`.
* **Questions:** These are the questions that will be asked to the annotators. They can be of different types, such as `RatingQuestion`, `TextQuestion`, `LabelQuestion`, `MultiLabelQuestion`, and `RankingQuestion`.
* **target** is of type `text`, and description "Translate the text.".
* **Suggestions:** As of Argilla 1.13.0, the suggestions have been included to provide the annotators with suggestions to ease or assist during the annotation process. Suggestions are linked to the existing questions, are always optional, and contain not just the suggestion itself, but also the metadata linked to it, if applicable.
* (optional) **target-suggestion** is of type `text`.
Additionally, we also have two more fields that are optional and are the following:
* **metadata:** This is an optional field that can be used to provide additional information about the dataset record. This can be useful to provide additional context to the annotators, or to provide additional information about the dataset record itself. For example, you can use this to provide a link to the original source of the dataset record, or to provide additional information about the dataset record itself, such as the author, the date, or the source. The metadata is always optional, and can be potentially linked to the `metadata_properties` defined in the dataset configuration file in `argilla.yaml`.
* **external_id:** This is an optional field that can be used to provide an external ID for the dataset record. This can be useful if you want to link the dataset record to an external resource, such as a database or a file.
### Data Splits
The dataset contains a single split, which is `train`.
## 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 guidelines
This is a translation dataset that contains texts. Please translate the text in the text field.
#### 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] |
lavis-nlp/german_legal_sentences | ---
annotations_creators:
- machine-generated
language_creators:
- found
language:
- de
license:
- unknown
multilinguality:
- monolingual
size_categories:
- n>1M
source_datasets:
- original
task_categories:
- text-retrieval
- text-scoring
task_ids:
- semantic-similarity-scoring
- text-retrieval-other-example-based-retrieval
---
# Dataset Card for German Legal Sentences
## Table of Contents
- [Dataset Card for [Dataset Name]](#dataset-card-for-dataset-name)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://lavis-nlp.github.io/german_legal_sentences/
- **Repository:** https://github.com/lavis-nlp/german_legal_sentences
- **Paper:** coming soon
- **Leaderboard:**
- **Point of Contact:** [Marco Wrzalik](mailto:marco.wrzalik@hs-rm.de)
### Dataset Summary
German Legal Sentences (GLS) is an automatically generated training dataset for semantic sentence matching and citation recommendation in the domain in german legal documents. It follows the concept of weak supervision, where imperfect labels are generated using multiple heuristics. For this purpose we use a combination of legal citation matching and BM25 similarity. The contained sentences and their citations are parsed from real judicial decisions provided by [Open Legal Data](http://openlegaldata.io/) (https://arxiv.org/abs/2005.13342).
### Supported Tasks and Leaderboards
The main associated task is *Semantic Similarity Ranking*. We propose to use the *Mean Reciprocal Rank* (MRR) cut at the tenth position as well as MAP and Recall on Rankings of size 200. As baselines we provide the follows:
| Method | MRR@10 | MAP@200 | Recall@200 |
|-----------------------------------|---------:|-----------:|------------:|
| BM25 - default `(k1=1.2; b=0.75)` | 25.7 | 17.6 | 42.9 |
| BM25 - tuned `(k1=0.47; b=0.97)` | 26.2 | 18.1 | 43.3 |
| [CoRT](https://arxiv.org/abs/2010.10252) | 31.2 | 21.4 | 56.2 |
| [CoRT + BM25](https://arxiv.org/abs/2010.10252) | 32.1 | 22.1 | 67.1 |
In addition, we want to support a *Citation Recommendation* task in the future.
If you wish to contribute evaluation measures or give any suggestion or critique, please write an [e-mail](mailto:marco.wrzalik@hs-rm.de).
### Languages
This dataset contains texts from the specific domain of German court decisions.
## Dataset Structure
### Data Instances
```
{'query.doc_id': 28860,
'query.ref_ids': [6215, 248, 248],
'query.sent_id': 304863,
'query.text': 'Zudem ist zu berücksichtigen , dass die Vollverzinsung nach '
'[REF] i. V. m. [REF] gleichermaßen zugunsten wie zulasten des '
'Steuerpflichtigen wirkt , sodass bei einer Überzahlung durch '
'den Steuerpflichtigen der Staat dem Steuerpflichtigen neben '
'der Erstattung ebenfalls den entstandenen potentiellen Zins- '
'und Liquiditätsnachteil in der pauschalierten Höhe des [REF] '
'zu ersetzen hat , unabhängig davon , in welcher Höhe dem '
'Berechtigten tatsächlich Zinsen entgangen sind .',
'related.doc_id': 56348,
'related.ref_ids': [248, 6215, 62375],
'related.sent_id': 558646,
'related.text': 'Ferner ist zu berücksichtigen , dass der Zinssatz des [REF] '
'im Rahmen des [REF] sowohl für Steuernachforderung wie auch '
'für Steuererstattungen und damit gleichermaßen zugunsten wie '
'zulasten des Steuerpflichtigen wirkt , Vgl. BVerfG , '
'Nichtannahmebeschluss vom [DATE] [REF] , juris , mit der '
'Folge , dass auch Erstattungsansprüche unabhängig davon , ob '
'und in welcher Höhe dem Berechtigten tatsächlich Zinsen '
'entgangen sind , mit monatlich 0,0 % verzinst werden .'}
```
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The documents we take from [Open Legal Data](http://openlegaldata.io/) (https://arxiv.org/abs/2005.13342) are first preprocessed by removing line breaks, enumeration characters and headings. Afterwards we parse legal citations using hand-crafted regular expressions. Each citation is split into it components and normalized, thus different variants of the same citation are matched together. For instance, "§211 Absatz 1 des Strafgesetzbuches" is normalized to "§ 211 Abs. 1 StGB". Every time we discover an unknown citation, we assign an unique id to it. We use these ids to replace parsed citations in the document text with a simple reference tag containing this id (e.g `[REF321]`). At the same time we parse dates and replace them with the date tag `[DATE]`. Both remove dots which can may be confused with the end of a sentence, which makes the next stage easier.
We use [SoMaJo](https://github.com/tsproisl/SoMaJo) to perform sentence tokenizing on the pre-processed documents. Each sentence that does not contain at least one legal citation is discarded. For the rest we assign sentence ids, remove all reference ids from them as well as any contents in braces (braces often contain large enumerations of citations and their sources). At the same time we keep track of the corresponding document from which a sentence originates and which references occur in it.
#### Who are the source language producers?
The source language originates in the context of German court proceedings.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The annotations are machine-generated.
### Personal and Sensitive Information
The source documents are already public and anonymized.
## Considerations for Using the Data
### Social Impact of Dataset
With this dataset, we strive towards better accessibility of court decisions to the general public by accelerating research on semantic search technologies. We hope that emerging search technologies will enable the layperson to find relevant information without knowing the specific terms used by lawyers.
### 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
Coming soon!
### Contributions
Thanks to [@mwrzalik](https://github.com/mwrzalik) for adding this dataset. |
Carlisle/msmacro-test-corpus | ---
license: mit
---
|
tyzhu/squad_qa_baseline_v5_full_random_permute_1 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer
dtype: string
- name: context_id
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
splits:
- name: train
num_bytes: 2496440.0
num_examples: 2385
- name: validation
num_bytes: 335684
num_examples: 300
download_size: 667968
dataset_size: 2832124.0
---
# Dataset Card for "squad_qa_baseline_v5_full_random_permute_1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
liuyanchen1015/MULTI_VALUE_mrpc_regularized_past_tense | ---
dataset_info:
features:
- name: sentence1
dtype: string
- name: sentence2
dtype: string
- name: label
dtype: int64
- name: idx
dtype: int64
- name: value_score
dtype: int64
splits:
- name: test
num_bytes: 109443
num_examples: 399
- name: train
num_bytes: 216536
num_examples: 789
- name: validation
num_bytes: 22098
num_examples: 77
download_size: 231898
dataset_size: 348077
---
# Dataset Card for "MULTI_VALUE_mrpc_regularized_past_tense"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vibhamasti/imagenet-subset-100x4 | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
0: tench, Tinca tinca
1: goldfish, Carassius auratus
2: great white shark, white shark, man-eater, man-eating shark, Carcharodon
carcharias
3: tiger shark, Galeocerdo cuvieri
4: hammerhead, hammerhead shark
5: electric ray, crampfish, numbfish, torpedo
6: stingray
7: cock
8: hen
9: ostrich, Struthio camelus
10: brambling, Fringilla montifringilla
11: goldfinch, Carduelis carduelis
12: house finch, linnet, Carpodacus mexicanus
13: junco, snowbird
14: indigo bunting, indigo finch, indigo bird, Passerina cyanea
15: robin, American robin, Turdus migratorius
16: bulbul
17: jay
18: magpie
19: chickadee
20: water ouzel, dipper
21: kite
22: bald eagle, American eagle, Haliaeetus leucocephalus
23: vulture
24: great grey owl, great gray owl, Strix nebulosa
25: European fire salamander, Salamandra salamandra
26: common newt, Triturus vulgaris
27: eft
28: spotted salamander, Ambystoma maculatum
29: axolotl, mud puppy, Ambystoma mexicanum
30: bullfrog, Rana catesbeiana
31: tree frog, tree-frog
32: tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
33: loggerhead, loggerhead turtle, Caretta caretta
34: leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
35: mud turtle
36: terrapin
37: box turtle, box tortoise
38: banded gecko
39: common iguana, iguana, Iguana iguana
40: American chameleon, anole, Anolis carolinensis
41: whiptail, whiptail lizard
42: agama
43: frilled lizard, Chlamydosaurus kingi
44: alligator lizard
45: Gila monster, Heloderma suspectum
46: green lizard, Lacerta viridis
47: African chameleon, Chamaeleo chamaeleon
48: Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
49: African crocodile, Nile crocodile, Crocodylus niloticus
50: American alligator, Alligator mississipiensis
51: triceratops
52: thunder snake, worm snake, Carphophis amoenus
53: ringneck snake, ring-necked snake, ring snake
54: hognose snake, puff adder, sand viper
55: green snake, grass snake
56: king snake, kingsnake
57: garter snake, grass snake
58: water snake
59: vine snake
60: night snake, Hypsiglena torquata
61: boa constrictor, Constrictor constrictor
62: rock python, rock snake, Python sebae
63: Indian cobra, Naja naja
64: green mamba
65: sea snake
66: horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
67: diamondback, diamondback rattlesnake, Crotalus adamanteus
68: sidewinder, horned rattlesnake, Crotalus cerastes
69: trilobite
70: harvestman, daddy longlegs, Phalangium opilio
71: scorpion
72: black and gold garden spider, Argiope aurantia
73: barn spider, Araneus cavaticus
74: garden spider, Aranea diademata
75: black widow, Latrodectus mactans
76: tarantula
77: wolf spider, hunting spider
78: tick
79: centipede
80: black grouse
81: ptarmigan
82: ruffed grouse, partridge, Bonasa umbellus
83: prairie chicken, prairie grouse, prairie fowl
84: peacock
85: quail
86: partridge
87: African grey, African gray, Psittacus erithacus
88: macaw
89: sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
90: lorikeet
91: coucal
92: bee eater
93: hornbill
94: hummingbird
95: jacamar
96: toucan
97: drake
98: red-breasted merganser, Mergus serrator
99: goose
100: black swan, Cygnus atratus
101: tusker
102: echidna, spiny anteater, anteater
103: platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus
anatinus
104: wallaby, brush kangaroo
105: koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
106: wombat
107: jellyfish
108: sea anemone, anemone
109: brain coral
110: flatworm, platyhelminth
111: nematode, nematode worm, roundworm
112: conch
113: snail
114: slug
115: sea slug, nudibranch
116: chiton, coat-of-mail shell, sea cradle, polyplacophore
117: chambered nautilus, pearly nautilus, nautilus
118: Dungeness crab, Cancer magister
119: rock crab, Cancer irroratus
120: fiddler crab
121: king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes
camtschatica
122: American lobster, Northern lobster, Maine lobster, Homarus americanus
123: spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
124: crayfish, crawfish, crawdad, crawdaddy
125: hermit crab
126: isopod
127: white stork, Ciconia ciconia
128: black stork, Ciconia nigra
129: spoonbill
130: flamingo
131: little blue heron, Egretta caerulea
132: American egret, great white heron, Egretta albus
133: bittern
134: crane
135: limpkin, Aramus pictus
136: European gallinule, Porphyrio porphyrio
137: American coot, marsh hen, mud hen, water hen, Fulica americana
138: bustard
139: ruddy turnstone, Arenaria interpres
140: red-backed sandpiper, dunlin, Erolia alpina
141: redshank, Tringa totanus
142: dowitcher
143: oystercatcher, oyster catcher
144: pelican
145: king penguin, Aptenodytes patagonica
146: albatross, mollymawk
147: grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius
robustus
148: killer whale, killer, orca, grampus, sea wolf, Orcinus orca
149: dugong, Dugong dugon
150: sea lion
151: Chihuahua
152: Japanese spaniel
153: Maltese dog, Maltese terrier, Maltese
154: Pekinese, Pekingese, Peke
155: Shih-Tzu
156: Blenheim spaniel
157: papillon
158: toy terrier
159: Rhodesian ridgeback
160: Afghan hound, Afghan
161: basset, basset hound
162: beagle
163: bloodhound, sleuthhound
164: bluetick
165: black-and-tan coonhound
166: Walker hound, Walker foxhound
167: English foxhound
168: redbone
169: borzoi, Russian wolfhound
170: Irish wolfhound
171: Italian greyhound
172: whippet
173: Ibizan hound, Ibizan Podenco
174: Norwegian elkhound, elkhound
175: otterhound, otter hound
176: Saluki, gazelle hound
177: Scottish deerhound, deerhound
178: Weimaraner
179: Staffordshire bullterrier, Staffordshire bull terrier
180: American Staffordshire terrier, Staffordshire terrier, American pit
bull terrier, pit bull terrier
181: Bedlington terrier
182: Border terrier
183: Kerry blue terrier
184: Irish terrier
185: Norfolk terrier
186: Norwich terrier
187: Yorkshire terrier
188: wire-haired fox terrier
189: Lakeland terrier
190: Sealyham terrier, Sealyham
191: Airedale, Airedale terrier
192: cairn, cairn terrier
193: Australian terrier
194: Dandie Dinmont, Dandie Dinmont terrier
195: Boston bull, Boston terrier
196: miniature schnauzer
197: giant schnauzer
198: standard schnauzer
199: Scotch terrier, Scottish terrier, Scottie
200: Tibetan terrier, chrysanthemum dog
201: silky terrier, Sydney silky
202: soft-coated wheaten terrier
203: West Highland white terrier
204: Lhasa, Lhasa apso
205: flat-coated retriever
206: curly-coated retriever
207: golden retriever
208: Labrador retriever
209: Chesapeake Bay retriever
210: German short-haired pointer
211: vizsla, Hungarian pointer
212: English setter
213: Irish setter, red setter
214: Gordon setter
215: Brittany spaniel
216: clumber, clumber spaniel
217: English springer, English springer spaniel
218: Welsh springer spaniel
219: cocker spaniel, English cocker spaniel, cocker
220: Sussex spaniel
221: Irish water spaniel
222: kuvasz
223: schipperke
224: groenendael
225: malinois
226: briard
227: kelpie
228: komondor
229: Old English sheepdog, bobtail
230: Shetland sheepdog, Shetland sheep dog, Shetland
231: collie
232: Border collie
233: Bouvier des Flandres, Bouviers des Flandres
234: Rottweiler
235: German shepherd, German shepherd dog, German police dog, alsatian
236: Doberman, Doberman pinscher
237: miniature pinscher
238: Greater Swiss Mountain dog
239: Bernese mountain dog
240: Appenzeller
241: EntleBucher
242: boxer
243: bull mastiff
244: Tibetan mastiff
245: French bulldog
246: Great Dane
247: Saint Bernard, St Bernard
248: Eskimo dog, husky
249: malamute, malemute, Alaskan malamute
250: Siberian husky
251: dalmatian, coach dog, carriage dog
252: affenpinscher, monkey pinscher, monkey dog
253: basenji
254: pug, pug-dog
255: Leonberg
256: Newfoundland, Newfoundland dog
257: Great Pyrenees
258: Samoyed, Samoyede
259: Pomeranian
260: chow, chow chow
261: keeshond
262: Brabancon griffon
263: Pembroke, Pembroke Welsh corgi
264: Cardigan, Cardigan Welsh corgi
265: toy poodle
266: miniature poodle
267: standard poodle
268: Mexican hairless
269: timber wolf, grey wolf, gray wolf, Canis lupus
270: white wolf, Arctic wolf, Canis lupus tundrarum
271: red wolf, maned wolf, Canis rufus, Canis niger
272: coyote, prairie wolf, brush wolf, Canis latrans
273: dingo, warrigal, warragal, Canis dingo
274: dhole, Cuon alpinus
275: African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
276: hyena, hyaena
277: red fox, Vulpes vulpes
278: kit fox, Vulpes macrotis
279: Arctic fox, white fox, Alopex lagopus
280: grey fox, gray fox, Urocyon cinereoargenteus
281: tabby, tabby cat
282: tiger cat
283: Persian cat
284: Siamese cat, Siamese
285: Egyptian cat
286: cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
287: lynx, catamount
288: leopard, Panthera pardus
289: snow leopard, ounce, Panthera uncia
290: jaguar, panther, Panthera onca, Felis onca
291: lion, king of beasts, Panthera leo
292: tiger, Panthera tigris
293: cheetah, chetah, Acinonyx jubatus
294: brown bear, bruin, Ursus arctos
295: American black bear, black bear, Ursus americanus, Euarctos americanus
296: ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
297: sloth bear, Melursus ursinus, Ursus ursinus
298: mongoose
299: meerkat, mierkat
300: tiger beetle
301: ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
302: ground beetle, carabid beetle
303: long-horned beetle, longicorn, longicorn beetle
304: leaf beetle, chrysomelid
305: dung beetle
306: rhinoceros beetle
307: weevil
308: fly
309: bee
310: ant, emmet, pismire
311: grasshopper, hopper
312: cricket
313: walking stick, walkingstick, stick insect
314: cockroach, roach
315: mantis, mantid
316: cicada, cicala
317: leafhopper
318: lacewing, lacewing fly
319: dragonfly, darning needle, devil's darning needle, sewing needle, snake
feeder, snake doctor, mosquito hawk, skeeter hawk
320: damselfly
321: admiral
322: ringlet, ringlet butterfly
323: monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
324: cabbage butterfly
325: sulphur butterfly, sulfur butterfly
326: lycaenid, lycaenid butterfly
327: starfish, sea star
328: sea urchin
329: sea cucumber, holothurian
330: wood rabbit, cottontail, cottontail rabbit
331: hare
332: Angora, Angora rabbit
333: hamster
334: porcupine, hedgehog
335: fox squirrel, eastern fox squirrel, Sciurus niger
336: marmot
337: beaver
338: guinea pig, Cavia cobaya
339: sorrel
340: zebra
341: hog, pig, grunter, squealer, Sus scrofa
342: wild boar, boar, Sus scrofa
343: warthog
344: hippopotamus, hippo, river horse, Hippopotamus amphibius
345: ox
346: water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
347: bison
348: ram, tup
349: bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain
sheep, Ovis canadensis
350: ibex, Capra ibex
351: hartebeest
352: impala, Aepyceros melampus
353: gazelle
354: Arabian camel, dromedary, Camelus dromedarius
355: llama
356: weasel
357: mink
358: polecat, fitch, foulmart, foumart, Mustela putorius
359: black-footed ferret, ferret, Mustela nigripes
360: otter
361: skunk, polecat, wood pussy
362: badger
363: armadillo
364: three-toed sloth, ai, Bradypus tridactylus
365: orangutan, orang, orangutang, Pongo pygmaeus
366: gorilla, Gorilla gorilla
367: chimpanzee, chimp, Pan troglodytes
368: gibbon, Hylobates lar
369: siamang, Hylobates syndactylus, Symphalangus syndactylus
370: guenon, guenon monkey
371: patas, hussar monkey, Erythrocebus patas
372: baboon
373: macaque
374: langur
375: colobus, colobus monkey
376: proboscis monkey, Nasalis larvatus
377: marmoset
378: capuchin, ringtail, Cebus capucinus
379: howler monkey, howler
380: titi, titi monkey
381: spider monkey, Ateles geoffroyi
382: squirrel monkey, Saimiri sciureus
383: Madagascar cat, ring-tailed lemur, Lemur catta
384: indri, indris, Indri indri, Indri brevicaudatus
385: Indian elephant, Elephas maximus
386: African elephant, Loxodonta africana
387: lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
388: giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
389: barracouta, snoek
390: eel
391: coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
392: rock beauty, Holocanthus tricolor
393: anemone fish
394: sturgeon
395: gar, garfish, garpike, billfish, Lepisosteus osseus
396: lionfish
397: puffer, pufferfish, blowfish, globefish
398: abacus
399: abaya
400: academic gown, academic robe, judge's robe
401: accordion, piano accordion, squeeze box
402: acoustic guitar
403: aircraft carrier, carrier, flattop, attack aircraft carrier
404: airliner
405: airship, dirigible
406: altar
407: ambulance
408: amphibian, amphibious vehicle
409: analog clock
410: apiary, bee house
411: apron
412: ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin,
dustbin, trash barrel, trash bin
413: assault rifle, assault gun
414: backpack, back pack, knapsack, packsack, rucksack, haversack
415: bakery, bakeshop, bakehouse
416: balance beam, beam
417: balloon
418: ballpoint, ballpoint pen, ballpen, Biro
419: Band Aid
420: banjo
421: bannister, banister, balustrade, balusters, handrail
422: barbell
423: barber chair
424: barbershop
425: barn
426: barometer
427: barrel, cask
428: barrow, garden cart, lawn cart, wheelbarrow
429: baseball
430: basketball
431: bassinet
432: bassoon
433: bathing cap, swimming cap
434: bath towel
435: bathtub, bathing tub, bath, tub
436: beach wagon, station wagon, wagon, estate car, beach waggon, station
waggon, waggon
437: beacon, lighthouse, beacon light, pharos
438: beaker
439: bearskin, busby, shako
440: beer bottle
441: beer glass
442: bell cote, bell cot
443: bib
444: bicycle-built-for-two, tandem bicycle, tandem
445: bikini, two-piece
446: binder, ring-binder
447: binoculars, field glasses, opera glasses
448: birdhouse
449: boathouse
450: bobsled, bobsleigh, bob
451: bolo tie, bolo, bola tie, bola
452: bonnet, poke bonnet
453: bookcase
454: bookshop, bookstore, bookstall
455: bottlecap
456: bow
457: bow tie, bow-tie, bowtie
458: brass, memorial tablet, plaque
459: brassiere, bra, bandeau
460: breakwater, groin, groyne, mole, bulwark, seawall, jetty
461: breastplate, aegis, egis
462: broom
463: bucket, pail
464: buckle
465: bulletproof vest
466: bullet train, bullet
467: butcher shop, meat market
468: cab, hack, taxi, taxicab
469: caldron, cauldron
470: candle, taper, wax light
471: cannon
472: canoe
473: can opener, tin opener
474: cardigan
475: car mirror
476: carousel, carrousel, merry-go-round, roundabout, whirligig
477: carpenter's kit, tool kit
478: carton
479: car wheel
480: cash machine, cash dispenser, automated teller machine, automatic teller
machine, automated teller, automatic teller, ATM
481: cassette
482: cassette player
483: castle
484: catamaran
485: CD player
486: cello, violoncello
487: cellular telephone, cellular phone, cellphone, cell, mobile phone
488: chain
489: chainlink fence
490: chain mail, ring mail, mail, chain armor, chain armour, ring armor,
ring armour
491: chain saw, chainsaw
492: chest
493: chiffonier, commode
494: chime, bell, gong
495: china cabinet, china closet
496: Christmas stocking
497: church, church building
498: cinema, movie theater, movie theatre, movie house, picture palace
499: cleaver, meat cleaver, chopper
500: cliff dwelling
501: cloak
502: clog, geta, patten, sabot
503: cocktail shaker
504: coffee mug
505: coffeepot
506: coil, spiral, volute, whorl, helix
507: combination lock
508: computer keyboard, keypad
509: confectionery, confectionary, candy store
510: container ship, containership, container vessel
511: convertible
512: corkscrew, bottle screw
513: cornet, horn, trumpet, trump
514: cowboy boot
515: cowboy hat, ten-gallon hat
516: cradle
517: crane2
518: crash helmet
519: crate
520: crib, cot
521: Crock Pot
522: croquet ball
523: crutch
524: cuirass
525: dam, dike, dyke
526: desk
527: desktop computer
528: dial telephone, dial phone
529: diaper, nappy, napkin
530: digital clock
531: digital watch
532: dining table, board
533: dishrag, dishcloth
534: dishwasher, dish washer, dishwashing machine
535: disk brake, disc brake
536: dock, dockage, docking facility
537: dogsled, dog sled, dog sleigh
538: dome
539: doormat, welcome mat
540: drilling platform, offshore rig
541: drum, membranophone, tympan
542: drumstick
543: dumbbell
544: Dutch oven
545: electric fan, blower
546: electric guitar
547: electric locomotive
548: entertainment center
549: envelope
550: espresso maker
551: face powder
552: feather boa, boa
553: file, file cabinet, filing cabinet
554: fireboat
555: fire engine, fire truck
556: fire screen, fireguard
557: flagpole, flagstaff
558: flute, transverse flute
559: folding chair
560: football helmet
561: forklift
562: fountain
563: fountain pen
564: four-poster
565: freight car
566: French horn, horn
567: frying pan, frypan, skillet
568: fur coat
569: garbage truck, dustcart
570: gasmask, respirator, gas helmet
571: gas pump, gasoline pump, petrol pump, island dispenser
572: goblet
573: go-kart
574: golf ball
575: golfcart, golf cart
576: gondola
577: gong, tam-tam
578: gown
579: grand piano, grand
580: greenhouse, nursery, glasshouse
581: grille, radiator grille
582: grocery store, grocery, food market, market
583: guillotine
584: hair slide
585: hair spray
586: half track
587: hammer
588: hamper
589: hand blower, blow dryer, blow drier, hair dryer, hair drier
590: hand-held computer, hand-held microcomputer
591: handkerchief, hankie, hanky, hankey
592: hard disc, hard disk, fixed disk
593: harmonica, mouth organ, harp, mouth harp
594: harp
595: harvester, reaper
596: hatchet
597: holster
598: home theater, home theatre
599: honeycomb
600: hook, claw
601: hoopskirt, crinoline
602: horizontal bar, high bar
603: horse cart, horse-cart
604: hourglass
605: iPod
606: iron, smoothing iron
607: jack-o'-lantern
608: jean, blue jean, denim
609: jeep, landrover
610: jersey, T-shirt, tee shirt
611: jigsaw puzzle
612: jinrikisha, ricksha, rickshaw
613: joystick
614: kimono
615: knee pad
616: knot
617: lab coat, laboratory coat
618: ladle
619: lampshade, lamp shade
620: laptop, laptop computer
621: lawn mower, mower
622: lens cap, lens cover
623: letter opener, paper knife, paperknife
624: library
625: lifeboat
626: lighter, light, igniter, ignitor
627: limousine, limo
628: liner, ocean liner
629: lipstick, lip rouge
630: Loafer
631: lotion
632: loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
633: loupe, jeweler's loupe
634: lumbermill, sawmill
635: magnetic compass
636: mailbag, postbag
637: mailbox, letter box
638: maillot
639: maillot, tank suit
640: manhole cover
641: maraca
642: marimba, xylophone
643: mask
644: matchstick
645: maypole
646: maze, labyrinth
647: measuring cup
648: medicine chest, medicine cabinet
649: megalith, megalithic structure
650: microphone, mike
651: microwave, microwave oven
652: military uniform
653: milk can
654: minibus
655: miniskirt, mini
656: minivan
657: missile
658: mitten
659: mixing bowl
660: mobile home, manufactured home
661: Model T
662: modem
663: monastery
664: monitor
665: moped
666: mortar
667: mortarboard
668: mosque
669: mosquito net
670: motor scooter, scooter
671: mountain bike, all-terrain bike, off-roader
672: mountain tent
673: mouse, computer mouse
674: mousetrap
675: moving van
676: muzzle
677: nail
678: neck brace
679: necklace
680: nipple
681: notebook, notebook computer
682: obelisk
683: oboe, hautboy, hautbois
684: ocarina, sweet potato
685: odometer, hodometer, mileometer, milometer
686: oil filter
687: organ, pipe organ
688: oscilloscope, scope, cathode-ray oscilloscope, CRO
689: overskirt
690: oxcart
691: oxygen mask
692: packet
693: paddle, boat paddle
694: paddlewheel, paddle wheel
695: padlock
696: paintbrush
697: pajama, pyjama, pj's, jammies
698: palace
699: panpipe, pandean pipe, syrinx
700: paper towel
701: parachute, chute
702: parallel bars, bars
703: park bench
704: parking meter
705: passenger car, coach, carriage
706: patio, terrace
707: pay-phone, pay-station
708: pedestal, plinth, footstall
709: pencil box, pencil case
710: pencil sharpener
711: perfume, essence
712: Petri dish
713: photocopier
714: pick, plectrum, plectron
715: pickelhaube
716: picket fence, paling
717: pickup, pickup truck
718: pier
719: piggy bank, penny bank
720: pill bottle
721: pillow
722: ping-pong ball
723: pinwheel
724: pirate, pirate ship
725: pitcher, ewer
726: plane, carpenter's plane, woodworking plane
727: planetarium
728: plastic bag
729: plate rack
730: plow, plough
731: plunger, plumber's helper
732: Polaroid camera, Polaroid Land camera
733: pole
734: police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
735: poncho
736: pool table, billiard table, snooker table
737: pop bottle, soda bottle
738: pot, flowerpot
739: potter's wheel
740: power drill
741: prayer rug, prayer mat
742: printer
743: prison, prison house
744: projectile, missile
745: projector
746: puck, hockey puck
747: punching bag, punch bag, punching ball, punchball
748: purse
749: quill, quill pen
750: quilt, comforter, comfort, puff
751: racer, race car, racing car
752: racket, racquet
753: radiator
754: radio, wireless
755: radio telescope, radio reflector
756: rain barrel
757: recreational vehicle, RV, R.V.
758: reel
759: reflex camera
760: refrigerator, icebox
761: remote control, remote
762: restaurant, eating house, eating place, eatery
763: revolver, six-gun, six-shooter
764: rifle
765: rocking chair, rocker
766: rotisserie
767: rubber eraser, rubber, pencil eraser
768: rugby ball
769: rule, ruler
770: running shoe
771: safe
772: safety pin
773: saltshaker, salt shaker
774: sandal
775: sarong
776: sax, saxophone
777: scabbard
778: scale, weighing machine
779: school bus
780: schooner
781: scoreboard
782: screen, CRT screen
783: screw
784: screwdriver
785: seat belt, seatbelt
786: sewing machine
787: shield, buckler
788: shoe shop, shoe-shop, shoe store
789: shoji
790: shopping basket
791: shopping cart
792: shovel
793: shower cap
794: shower curtain
795: ski
796: ski mask
797: sleeping bag
798: slide rule, slipstick
799: sliding door
800: slot, one-armed bandit
801: snorkel
802: snowmobile
803: snowplow, snowplough
804: soap dispenser
805: soccer ball
806: sock
807: solar dish, solar collector, solar furnace
808: sombrero
809: soup bowl
810: space bar
811: space heater
812: space shuttle
813: spatula
814: speedboat
815: spider web, spider's web
816: spindle
817: sports car, sport car
818: spotlight, spot
819: stage
820: steam locomotive
821: steel arch bridge
822: steel drum
823: stethoscope
824: stole
825: stone wall
826: stopwatch, stop watch
827: stove
828: strainer
829: streetcar, tram, tramcar, trolley, trolley car
830: stretcher
831: studio couch, day bed
832: stupa, tope
833: submarine, pigboat, sub, U-boat
834: suit, suit of clothes
835: sundial
836: sunglass
837: sunglasses, dark glasses, shades
838: sunscreen, sunblock, sun blocker
839: suspension bridge
840: swab, swob, mop
841: sweatshirt
842: swimming trunks, bathing trunks
843: swing
844: switch, electric switch, electrical switch
845: syringe
846: table lamp
847: tank, army tank, armored combat vehicle, armoured combat vehicle
848: tape player
849: teapot
850: teddy, teddy bear
851: television, television system
852: tennis ball
853: thatch, thatched roof
854: theater curtain, theatre curtain
855: thimble
856: thresher, thrasher, threshing machine
857: throne
858: tile roof
859: toaster
860: tobacco shop, tobacconist shop, tobacconist
861: toilet seat
862: torch
863: totem pole
864: tow truck, tow car, wrecker
865: toyshop
866: tractor
867: trailer truck, tractor trailer, trucking rig, rig, articulated lorry,
semi
868: tray
869: trench coat
870: tricycle, trike, velocipede
871: trimaran
872: tripod
873: triumphal arch
874: trolleybus, trolley coach, trackless trolley
875: trombone
876: tub, vat
877: turnstile
878: typewriter keyboard
879: umbrella
880: unicycle, monocycle
881: upright, upright piano
882: vacuum, vacuum cleaner
883: vase
884: vault
885: velvet
886: vending machine
887: vestment
888: viaduct
889: violin, fiddle
890: volleyball
891: waffle iron
892: wall clock
893: wallet, billfold, notecase, pocketbook
894: wardrobe, closet, press
895: warplane, military plane
896: washbasin, handbasin, washbowl, lavabo, wash-hand basin
897: washer, automatic washer, washing machine
898: water bottle
899: water jug
900: water tower
901: whiskey jug
902: whistle
903: wig
904: window screen
905: window shade
906: Windsor tie
907: wine bottle
908: wing
909: wok
910: wooden spoon
911: wool, woolen, woollen
912: worm fence, snake fence, snake-rail fence, Virginia fence
913: wreck
914: yawl
915: yurt
916: web site, website, internet site, site
917: comic book
918: crossword puzzle, crossword
919: street sign
920: traffic light, traffic signal, stoplight
921: book jacket, dust cover, dust jacket, dust wrapper
922: menu
923: plate
924: guacamole
925: consomme
926: hot pot, hotpot
927: trifle
928: ice cream, icecream
929: ice lolly, lolly, lollipop, popsicle
930: French loaf
931: bagel, beigel
932: pretzel
933: cheeseburger
934: hotdog, hot dog, red hot
935: mashed potato
936: head cabbage
937: broccoli
938: cauliflower
939: zucchini, courgette
940: spaghetti squash
941: acorn squash
942: butternut squash
943: cucumber, cuke
944: artichoke, globe artichoke
945: bell pepper
946: cardoon
947: mushroom
948: Granny Smith
949: strawberry
950: orange
951: lemon
952: fig
953: pineapple, ananas
954: banana
955: jackfruit, jak, jack
956: custard apple
957: pomegranate
958: hay
959: carbonara
960: chocolate sauce, chocolate syrup
961: dough
962: meat loaf, meatloaf
963: pizza, pizza pie
964: potpie
965: burrito
966: red wine
967: espresso
968: cup
969: eggnog
970: alp
971: bubble
972: cliff, drop, drop-off
973: coral reef
974: geyser
975: lakeside, lakeshore
976: promontory, headland, head, foreland
977: sandbar, sand bar
978: seashore, coast, seacoast, sea-coast
979: valley, vale
980: volcano
981: ballplayer, baseball player
982: groom, bridegroom
983: scuba diver
984: rapeseed
985: daisy
986: yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus,
Cypripedium parviflorum
987: corn
988: acorn
989: hip, rose hip, rosehip
990: buckeye, horse chestnut, conker
991: coral fungus
992: agaric
993: gyromitra
994: stinkhorn, carrion fungus
995: earthstar
996: hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
997: bolete
998: ear, spike, capitulum
999: toilet tissue, toilet paper, bathroom tissue
splits:
- name: validation
num_bytes: 1523906.0
num_examples: 40
download_size: 1524396
dataset_size: 1523906.0
configs:
- config_name: default
data_files:
- split: validation
path: data/val-*
--- |
CyberHarem/blanc_azurlane | ---
license: mit
task_categories:
- text-to-image
tags:
- art
- not-for-all-audiences
size_categories:
- n<1K
---
# Dataset of blanc/ブラン/布兰 (Azur Lane)
This is the dataset of blanc/ブラン/布兰 (Azur Lane), containing 500 images and their tags.
The core tags of this character are `short_hair, brown_hair, blue_eyes, hat, hair_between_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs)).
## List of Packages
| Name | Images | Size | Download | Type | Description |
|:-----------------|---------:|:-----------|:----------------------------------------------------------------------------------------------------------------|:-----------|:---------------------------------------------------------------------|
| raw | 500 | 606.40 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blanc_azurlane/resolve/main/dataset-raw.zip) | Waifuc-Raw | Raw data with meta information (min edge aligned to 1400 if larger). |
| 800 | 500 | 340.52 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blanc_azurlane/resolve/main/dataset-800.zip) | IMG+TXT | dataset with the shorter side not exceeding 800 pixels. |
| stage3-p480-800 | 1180 | 728.91 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blanc_azurlane/resolve/main/dataset-stage3-p480-800.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
| 1200 | 500 | 532.75 MiB | [Download](https://huggingface.co/datasets/CyberHarem/blanc_azurlane/resolve/main/dataset-1200.zip) | IMG+TXT | dataset with the shorter side not exceeding 1200 pixels. |
| stage3-p480-1200 | 1180 | 1.03 GiB | [Download](https://huggingface.co/datasets/CyberHarem/blanc_azurlane/resolve/main/dataset-stage3-p480-1200.zip) | IMG+TXT | 3-stage cropped dataset with the area not less than 480x480 pixels. |
### Load Raw Dataset with Waifuc
We provide raw dataset (including tagged images) for [waifuc](https://deepghs.github.io/waifuc/main/tutorials/installation/index.html) loading. If you need this, just run the following code
```python
import os
import zipfile
from huggingface_hub import hf_hub_download
from waifuc.source import LocalSource
# download raw archive file
zip_file = hf_hub_download(
repo_id='CyberHarem/blanc_azurlane',
repo_type='dataset',
filename='dataset-raw.zip',
)
# extract files to your directory
dataset_dir = 'dataset_dir'
os.makedirs(dataset_dir, exist_ok=True)
with zipfile.ZipFile(zip_file, 'r') as zf:
zf.extractall(dataset_dir)
# load the dataset with waifuc
source = LocalSource(dataset_dir)
for item in source:
print(item.image, item.meta['filename'], item.meta['tags'])
```
## List of Clusters
List of tag clustering result, maybe some outfits can be mined here.
### Raw Text Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | Tags |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 0 | 8 |  |  |  |  |  | 1girl, blush, hetero, nipples, sex, solo_focus, vaginal, navel, penis, spread_legs, 1boy, cum_in_pussy, looking_at_viewer, nude, small_breasts, bar_censor, on_back, open_mouth, straddling, thighhighs |
| 1 | 5 |  |  |  |  |  | 1girl, blush, looking_at_viewer, nude, small_breasts, solo, nipples, wet, navel, collarbone, flat_chest, smile, towel, upper_body |
| 2 | 12 |  |  |  |  |  | 1girl, bare_shoulders, blush, solo, looking_at_viewer, book, fur_trim, open_mouth |
| 3 | 35 |  |  |  |  |  | 1girl, bare_shoulders, solo, white_dress, looking_at_viewer, blush, fur_trim, white_background, coat, simple_background, spaghetti_strap |
| 4 | 13 |  |  |  |  |  | 1girl, bare_shoulders, looking_at_viewer, solo, white_dress, blush, holding_book, simple_background, spaghetti_strap, white_background, white_coat, off_shoulder, long_sleeves, expressionless, fur-trimmed_coat, wide_sleeves |
| 5 | 9 |  |  |  |  |  | 2girls, looking_at_viewer, bare_shoulders, blush, 1girl |
| 6 | 5 |  |  |  |  |  | 1girl, bare_shoulders, blush, dress_lift, lifted_by_self, looking_at_viewer, solo, white_dress, small_breasts, white_panties, navel, open_mouth, spaghetti_strap, simple_background |
| 7 | 5 |  |  |  |  |  | 1girl, looking_at_viewer, solo, blush, serafuku, pleated_skirt, ribbon, sitting |
| 8 | 9 |  |  |  |  |  | 1girl, looking_at_viewer, navel, solo, blush, small_breasts, simple_background, bikini_skirt, white_background, white_bikini |
| 9 | 5 |  |  |  |  |  | 1girl, beret, school_uniform, solo, black_thighhighs, blazer, bowtie, long_sleeves, looking_at_viewer, plaid_skirt, pleated_skirt, blush, zettai_ryouiki, alternate_costume, black_headwear, brown_skirt, expressionless, loafers, plaid_bow, shirt, simple_background, white_background |
| 10 | 7 |  |  |  |  |  | 1girl, bare_shoulders, blush, looking_at_viewer, playboy_bunny, rabbit_ears, solo, wrist_cuffs, fake_animal_ears, leotard, detached_collar, small_breasts, fishnet_pantyhose, rabbit_tail, simple_background, white_background, alternate_costume, blue_bowtie, strapless, thigh_gap |
### Table Version
| # | Samples | Img-1 | Img-2 | Img-3 | Img-4 | Img-5 | 1girl | blush | hetero | nipples | sex | solo_focus | vaginal | navel | penis | spread_legs | 1boy | cum_in_pussy | looking_at_viewer | nude | small_breasts | bar_censor | on_back | open_mouth | straddling | thighhighs | solo | wet | collarbone | flat_chest | smile | towel | upper_body | bare_shoulders | book | fur_trim | white_dress | white_background | coat | simple_background | spaghetti_strap | holding_book | white_coat | off_shoulder | long_sleeves | expressionless | fur-trimmed_coat | wide_sleeves | 2girls | dress_lift | lifted_by_self | white_panties | serafuku | pleated_skirt | ribbon | sitting | bikini_skirt | white_bikini | beret | school_uniform | black_thighhighs | blazer | bowtie | plaid_skirt | zettai_ryouiki | alternate_costume | black_headwear | brown_skirt | loafers | plaid_bow | shirt | playboy_bunny | rabbit_ears | wrist_cuffs | fake_animal_ears | leotard | detached_collar | fishnet_pantyhose | rabbit_tail | blue_bowtie | strapless | thigh_gap |
|----:|----------:|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:----------------------------------|:--------|:--------|:---------|:----------|:------|:-------------|:----------|:--------|:--------|:--------------|:-------|:---------------|:--------------------|:-------|:----------------|:-------------|:----------|:-------------|:-------------|:-------------|:-------|:------|:-------------|:-------------|:--------|:--------|:-------------|:-----------------|:-------|:-----------|:--------------|:-------------------|:-------|:--------------------|:------------------|:---------------|:-------------|:---------------|:---------------|:-----------------|:-------------------|:---------------|:---------|:-------------|:-----------------|:----------------|:-----------|:----------------|:---------|:----------|:---------------|:---------------|:--------|:-----------------|:-------------------|:---------|:---------|:--------------|:-----------------|:--------------------|:-----------------|:--------------|:----------|:------------|:--------|:----------------|:--------------|:--------------|:-------------------|:----------|:------------------|:--------------------|:--------------|:--------------|:------------|:------------|
| 0 | 8 |  |  |  |  |  | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 1 | 5 |  |  |  |  |  | X | X | | X | | | | X | | | | | X | X | X | | | | | | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 2 | 12 |  |  |  |  |  | X | X | | | | | | | | | | | X | | | | | X | | | X | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 3 | 35 |  |  |  |  |  | X | X | | | | | | | | | | | X | | | | | | | | X | | | | | | | X | | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 4 | 13 |  |  |  |  |  | X | X | | | | | | | | | | | X | | | | | | | | X | | | | | | | X | | | X | X | | X | X | X | X | X | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 5 | 9 |  |  |  |  |  | X | X | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 6 | 5 |  |  |  |  |  | X | X | | | | | | X | | | | | X | | X | | | X | | | X | | | | | | | X | | | X | | | X | X | | | | | | | | | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 7 | 5 |  |  |  |  |  | X | X | | | | | | | | | | | X | | | | | | | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | X | X | X | | | | | | | | | | | | | | | | | | | | | | | | | | |
| 8 | 9 |  |  |  |  |  | X | X | | | | | | X | | | | | X | | X | | | | | | X | | | | | | | | | | | X | | X | | | | | | | | | | | | | | | | | X | X | | | | | | | | | | | | | | | | | | | | | | | | |
| 9 | 5 |  |  |  |  |  | X | X | | | | | | | | | | | X | | | | | | | | X | | | | | | | | | | | X | | X | | | | | X | X | | | | | | | | X | | | | | X | X | X | X | X | X | X | X | X | X | X | X | X | | | | | | | | | | | |
| 10 | 7 |  |  |  |  |  | X | X | | | | | | | | | | | X | | X | | | | | | X | | | | | | | X | | | | X | | X | | | | | | | | | | | | | | | | | | | | | | | | | | X | | | | | | X | X | X | X | X | X | X | X | X | X | X |
|
raygx/NepaliTweets | ---
dataset_info:
features:
- name: Sentiment
dtype: int64
- name: Sentences
dtype: string
splits:
- name: train
num_bytes: 14803386
num_examples: 44269
download_size: 5521101
dataset_size: 14803386
---
# Dataset Card for "NepaliTweets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yangzhou321/ImageNet1k_Corrupt | ---
license: mit
---
|
patent/AIPD_nlp_granted_claims | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
dataset_info:
features:
- name: patent_num
dtype: int64
- name: claim_num1
dtype: int64
- name: claim_num2
dtype: int64
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1311491690.1458514
num_examples: 906087
- name: test
num_bytes: 72861855.63886473
num_examples: 50339
- name: valid
num_bytes: 72860408.21528383
num_examples: 50338
download_size: 562077206
dataset_size: 1457213954.0
---
# Dataset Card for "AIPD_nlp_granted_claims"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pittawat/uppercase-english-characters | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': A
'1': B
'2': C
'3': D
'4': E
'5': F
'6': G
'7': H
'8': I
'9': J
'10': K
'11': L
'12': M
'13': N
'14': O
'15': P
'16': Q
'17': R
'18': S
'19': T
'20': U
'21': V
'22': W
'23': X
'24': Y
'25': Z
splits:
- name: train
num_bytes: 6855359.12
num_examples: 2340
- name: test
num_bytes: 710358.0
num_examples: 234
download_size: 4821164
dataset_size: 7565717.12
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
Emm9625/chain-of-density-full | ---
dataset_info:
features:
- name: article
dtype: string
- name: highlights
dtype: string
- name: id
dtype: string
- name: prediction
sequence: string
- name: missing
sequence: string
- name: model
dtype: string
splits:
- name: train
num_bytes: 6332744
num_examples: 1000
- name: test
num_bytes: 662471
num_examples: 100
download_size: 3946951
dataset_size: 6995215
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
presencesw/dataset_995_decompose_question | ---
dataset_info:
features:
- name: entities
sequence: 'null'
- name: triplets
list:
- name: question
dtype: string
- name: answer
dtype: string
- name: complex_question
dtype: string
splits:
- name: train
num_bytes: 319598
num_examples: 995
download_size: 120850
dataset_size: 319598
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dataset_995_decompose_question"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kelmy0/teste | ---
license: openrail
---
|
open-llm-leaderboard/details_yeontaek__llama-2-13B-ensemble-v3 | ---
pretty_name: Evaluation run of yeontaek/llama-2-13B-ensemble-v3
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [yeontaek/llama-2-13B-ensemble-v3](https://huggingface.co/yeontaek/llama-2-13B-ensemble-v3)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 61 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_yeontaek__llama-2-13B-ensemble-v3\"\
,\n\t\"harness_truthfulqa_mc_0\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\
\nThese are the [latest results from run 2023-08-26T00:04:59.687493](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-13B-ensemble-v3/blob/main/results_2023-08-26T00%3A04%3A59.687493.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 \"acc\": 0.5779190308106052,\n\
\ \"acc_stderr\": 0.03411621449047892,\n \"acc_norm\": 0.5817150329939268,\n\
\ \"acc_norm_stderr\": 0.03409598894931763,\n \"mc1\": 0.35495716034271724,\n\
\ \"mc1_stderr\": 0.0167508623813759,\n \"mc2\": 0.49782296764839973,\n\
\ \"mc2_stderr\": 0.015206569782538341\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.5981228668941979,\n \"acc_stderr\": 0.014327268614578274,\n\
\ \"acc_norm\": 0.6237201365187713,\n \"acc_norm_stderr\": 0.014157022555407161\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6245767775343557,\n\
\ \"acc_stderr\": 0.004832423630593182,\n \"acc_norm\": 0.8229436367257519,\n\
\ \"acc_norm_stderr\": 0.0038093627612481094\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.33,\n \"acc_stderr\": 0.04725815626252606,\n \
\ \"acc_norm\": 0.33,\n \"acc_norm_stderr\": 0.04725815626252606\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.45925925925925926,\n\
\ \"acc_stderr\": 0.04304979692464243,\n \"acc_norm\": 0.45925925925925926,\n\
\ \"acc_norm_stderr\": 0.04304979692464243\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.5263157894736842,\n \"acc_stderr\": 0.04063302731486671,\n\
\ \"acc_norm\": 0.5263157894736842,\n \"acc_norm_stderr\": 0.04063302731486671\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.04943110704237102,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.04943110704237102\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.6113207547169811,\n \"acc_stderr\": 0.030000485448675986,\n\
\ \"acc_norm\": 0.6113207547169811,\n \"acc_norm_stderr\": 0.030000485448675986\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6180555555555556,\n\
\ \"acc_stderr\": 0.040629907841466674,\n \"acc_norm\": 0.6180555555555556,\n\
\ \"acc_norm_stderr\": 0.040629907841466674\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.048523658709391,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.048523658709391\n },\n\
\ \"harness|hendrycksTest-college_computer_science|5\": {\n \"acc\": 0.46,\n\
\ \"acc_stderr\": 0.05009082659620332,\n \"acc_norm\": 0.46,\n \
\ \"acc_norm_stderr\": 0.05009082659620332\n },\n \"harness|hendrycksTest-college_mathematics|5\"\
: {\n \"acc\": 0.34,\n \"acc_stderr\": 0.04760952285695236,\n \
\ \"acc_norm\": 0.34,\n \"acc_norm_stderr\": 0.04760952285695236\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5317919075144508,\n\
\ \"acc_stderr\": 0.038047497443647646,\n \"acc_norm\": 0.5317919075144508,\n\
\ \"acc_norm_stderr\": 0.038047497443647646\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.3235294117647059,\n \"acc_stderr\": 0.046550104113196177,\n\
\ \"acc_norm\": 0.3235294117647059,\n \"acc_norm_stderr\": 0.046550104113196177\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.75,\n \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.75,\n\
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.46382978723404256,\n \"acc_stderr\": 0.032600385118357715,\n\
\ \"acc_norm\": 0.46382978723404256,\n \"acc_norm_stderr\": 0.032600385118357715\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.2982456140350877,\n\
\ \"acc_stderr\": 0.04303684033537315,\n \"acc_norm\": 0.2982456140350877,\n\
\ \"acc_norm_stderr\": 0.04303684033537315\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5448275862068965,\n \"acc_stderr\": 0.04149886942192118,\n\
\ \"acc_norm\": 0.5448275862068965,\n \"acc_norm_stderr\": 0.04149886942192118\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.3386243386243386,\n \"acc_stderr\": 0.02437319786798307,\n \"\
acc_norm\": 0.3386243386243386,\n \"acc_norm_stderr\": 0.02437319786798307\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.36507936507936506,\n\
\ \"acc_stderr\": 0.04306241259127153,\n \"acc_norm\": 0.36507936507936506,\n\
\ \"acc_norm_stderr\": 0.04306241259127153\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.38,\n \"acc_stderr\": 0.04878317312145632,\n \
\ \"acc_norm\": 0.38,\n \"acc_norm_stderr\": 0.04878317312145632\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\": 0.667741935483871,\n\
\ \"acc_stderr\": 0.0267955608481228,\n \"acc_norm\": 0.667741935483871,\n\
\ \"acc_norm_stderr\": 0.0267955608481228\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.43349753694581283,\n \"acc_stderr\": 0.03486731727419872,\n\
\ \"acc_norm\": 0.43349753694581283,\n \"acc_norm_stderr\": 0.03486731727419872\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.6,\n \"acc_stderr\": 0.049236596391733084,\n \"acc_norm\"\
: 0.6,\n \"acc_norm_stderr\": 0.049236596391733084\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.703030303030303,\n \"acc_stderr\": 0.03567969772268049,\n\
\ \"acc_norm\": 0.703030303030303,\n \"acc_norm_stderr\": 0.03567969772268049\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7474747474747475,\n \"acc_stderr\": 0.030954055470365897,\n \"\
acc_norm\": 0.7474747474747475,\n \"acc_norm_stderr\": 0.030954055470365897\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8238341968911918,\n \"acc_stderr\": 0.02749350424454806,\n\
\ \"acc_norm\": 0.8238341968911918,\n \"acc_norm_stderr\": 0.02749350424454806\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.5717948717948718,\n \"acc_stderr\": 0.025088301454694838,\n\
\ \"acc_norm\": 0.5717948717948718,\n \"acc_norm_stderr\": 0.025088301454694838\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.28888888888888886,\n \"acc_stderr\": 0.02763490726417854,\n \
\ \"acc_norm\": 0.28888888888888886,\n \"acc_norm_stderr\": 0.02763490726417854\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.5966386554621849,\n \"acc_stderr\": 0.031866081214088314,\n\
\ \"acc_norm\": 0.5966386554621849,\n \"acc_norm_stderr\": 0.031866081214088314\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31788079470198677,\n \"acc_stderr\": 0.038020397601079024,\n \"\
acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.038020397601079024\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.7651376146788991,\n \"acc_stderr\": 0.018175110510343567,\n \"\
acc_norm\": 0.7651376146788991,\n \"acc_norm_stderr\": 0.018175110510343567\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.4444444444444444,\n \"acc_stderr\": 0.03388857118502326,\n \"\
acc_norm\": 0.4444444444444444,\n \"acc_norm_stderr\": 0.03388857118502326\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7549019607843137,\n \"acc_stderr\": 0.030190282453501947,\n \"\
acc_norm\": 0.7549019607843137,\n \"acc_norm_stderr\": 0.030190282453501947\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.7552742616033755,\n \"acc_stderr\": 0.027985699387036423,\n \
\ \"acc_norm\": 0.7552742616033755,\n \"acc_norm_stderr\": 0.027985699387036423\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.672645739910314,\n\
\ \"acc_stderr\": 0.03149384670994131,\n \"acc_norm\": 0.672645739910314,\n\
\ \"acc_norm_stderr\": 0.03149384670994131\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6793893129770993,\n \"acc_stderr\": 0.04093329229834278,\n\
\ \"acc_norm\": 0.6793893129770993,\n \"acc_norm_stderr\": 0.04093329229834278\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.768595041322314,\n \"acc_stderr\": 0.03849856098794088,\n \"acc_norm\"\
: 0.768595041322314,\n \"acc_norm_stderr\": 0.03849856098794088\n },\n\
\ \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.75,\n \
\ \"acc_stderr\": 0.04186091791394607,\n \"acc_norm\": 0.75,\n \
\ \"acc_norm_stderr\": 0.04186091791394607\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.7055214723926381,\n \"acc_stderr\": 0.03581165790474082,\n\
\ \"acc_norm\": 0.7055214723926381,\n \"acc_norm_stderr\": 0.03581165790474082\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.44642857142857145,\n\
\ \"acc_stderr\": 0.04718471485219588,\n \"acc_norm\": 0.44642857142857145,\n\
\ \"acc_norm_stderr\": 0.04718471485219588\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7669902912621359,\n \"acc_stderr\": 0.04185832598928315,\n\
\ \"acc_norm\": 0.7669902912621359,\n \"acc_norm_stderr\": 0.04185832598928315\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8290598290598291,\n\
\ \"acc_stderr\": 0.02466249684520982,\n \"acc_norm\": 0.8290598290598291,\n\
\ \"acc_norm_stderr\": 0.02466249684520982\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.67,\n \"acc_stderr\": 0.04725815626252609,\n \
\ \"acc_norm\": 0.67,\n \"acc_norm_stderr\": 0.04725815626252609\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7739463601532567,\n\
\ \"acc_stderr\": 0.014957458504335837,\n \"acc_norm\": 0.7739463601532567,\n\
\ \"acc_norm_stderr\": 0.014957458504335837\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6445086705202312,\n \"acc_stderr\": 0.025770292082977257,\n\
\ \"acc_norm\": 0.6445086705202312,\n \"acc_norm_stderr\": 0.025770292082977257\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4324022346368715,\n\
\ \"acc_stderr\": 0.01656897123354861,\n \"acc_norm\": 0.4324022346368715,\n\
\ \"acc_norm_stderr\": 0.01656897123354861\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6372549019607843,\n \"acc_stderr\": 0.027530078447110307,\n\
\ \"acc_norm\": 0.6372549019607843,\n \"acc_norm_stderr\": 0.027530078447110307\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6495176848874598,\n\
\ \"acc_stderr\": 0.027098652621301754,\n \"acc_norm\": 0.6495176848874598,\n\
\ \"acc_norm_stderr\": 0.027098652621301754\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6327160493827161,\n \"acc_stderr\": 0.026822801759507894,\n\
\ \"acc_norm\": 0.6327160493827161,\n \"acc_norm_stderr\": 0.026822801759507894\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.42907801418439717,\n \"acc_stderr\": 0.02952591430255856,\n \
\ \"acc_norm\": 0.42907801418439717,\n \"acc_norm_stderr\": 0.02952591430255856\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.41395045632333766,\n\
\ \"acc_stderr\": 0.012579699631289262,\n \"acc_norm\": 0.41395045632333766,\n\
\ \"acc_norm_stderr\": 0.012579699631289262\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5294117647058824,\n \"acc_stderr\": 0.03032024326500413,\n\
\ \"acc_norm\": 0.5294117647058824,\n \"acc_norm_stderr\": 0.03032024326500413\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.5833333333333334,\n \"acc_stderr\": 0.01994491413687358,\n \
\ \"acc_norm\": 0.5833333333333334,\n \"acc_norm_stderr\": 0.01994491413687358\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.6454545454545455,\n\
\ \"acc_stderr\": 0.045820048415054174,\n \"acc_norm\": 0.6454545454545455,\n\
\ \"acc_norm_stderr\": 0.045820048415054174\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.6448979591836734,\n \"acc_stderr\": 0.030635655150387638,\n\
\ \"acc_norm\": 0.6448979591836734,\n \"acc_norm_stderr\": 0.030635655150387638\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7263681592039801,\n\
\ \"acc_stderr\": 0.03152439186555401,\n \"acc_norm\": 0.7263681592039801,\n\
\ \"acc_norm_stderr\": 0.03152439186555401\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.83,\n \"acc_stderr\": 0.03775251680686371,\n \
\ \"acc_norm\": 0.83,\n \"acc_norm_stderr\": 0.03775251680686371\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.463855421686747,\n\
\ \"acc_stderr\": 0.03882310850890593,\n \"acc_norm\": 0.463855421686747,\n\
\ \"acc_norm_stderr\": 0.03882310850890593\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.7777777777777778,\n \"acc_stderr\": 0.03188578017686398,\n\
\ \"acc_norm\": 0.7777777777777778,\n \"acc_norm_stderr\": 0.03188578017686398\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.35495716034271724,\n\
\ \"mc1_stderr\": 0.0167508623813759,\n \"mc2\": 0.49782296764839973,\n\
\ \"mc2_stderr\": 0.015206569782538341\n }\n}\n```"
repo_url: https://huggingface.co/yeontaek/llama-2-13B-ensemble-v3
leaderboard_url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
point_of_contact: clementine@hf.co
configs:
- config_name: harness_arc_challenge_25
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|arc:challenge|25_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hellaswag|10_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T00:04:59.687493.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-08-26T00:04:59.687493.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-26T00:04:59.687493.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-08-26T00:04:59.687493.parquet'
- config_name: results
data_files:
- split: 2023_08_26T00_04_59.687493
path:
- results_2023-08-26T00:04:59.687493.parquet
- split: latest
path:
- results_2023-08-26T00:04:59.687493.parquet
---
# Dataset Card for Evaluation run of yeontaek/llama-2-13B-ensemble-v3
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/yeontaek/llama-2-13B-ensemble-v3
- **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 [yeontaek/llama-2-13B-ensemble-v3](https://huggingface.co/yeontaek/llama-2-13B-ensemble-v3) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 61 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_yeontaek__llama-2-13B-ensemble-v3",
"harness_truthfulqa_mc_0",
split="train")
```
## Latest results
These are the [latest results from run 2023-08-26T00:04:59.687493](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-13B-ensemble-v3/blob/main/results_2023-08-26T00%3A04%3A59.687493.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": {
"acc": 0.5779190308106052,
"acc_stderr": 0.03411621449047892,
"acc_norm": 0.5817150329939268,
"acc_norm_stderr": 0.03409598894931763,
"mc1": 0.35495716034271724,
"mc1_stderr": 0.0167508623813759,
"mc2": 0.49782296764839973,
"mc2_stderr": 0.015206569782538341
},
"harness|arc:challenge|25": {
"acc": 0.5981228668941979,
"acc_stderr": 0.014327268614578274,
"acc_norm": 0.6237201365187713,
"acc_norm_stderr": 0.014157022555407161
},
"harness|hellaswag|10": {
"acc": 0.6245767775343557,
"acc_stderr": 0.004832423630593182,
"acc_norm": 0.8229436367257519,
"acc_norm_stderr": 0.0038093627612481094
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.33,
"acc_stderr": 0.04725815626252606,
"acc_norm": 0.33,
"acc_norm_stderr": 0.04725815626252606
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.45925925925925926,
"acc_stderr": 0.04304979692464243,
"acc_norm": 0.45925925925925926,
"acc_norm_stderr": 0.04304979692464243
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.5263157894736842,
"acc_stderr": 0.04063302731486671,
"acc_norm": 0.5263157894736842,
"acc_norm_stderr": 0.04063302731486671
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.04943110704237102,
"acc_norm": 0.59,
"acc_norm_stderr": 0.04943110704237102
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.6113207547169811,
"acc_stderr": 0.030000485448675986,
"acc_norm": 0.6113207547169811,
"acc_norm_stderr": 0.030000485448675986
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6180555555555556,
"acc_stderr": 0.040629907841466674,
"acc_norm": 0.6180555555555556,
"acc_norm_stderr": 0.040629907841466674
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.37,
"acc_stderr": 0.048523658709391,
"acc_norm": 0.37,
"acc_norm_stderr": 0.048523658709391
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620332,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620332
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695236,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695236
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5317919075144508,
"acc_stderr": 0.038047497443647646,
"acc_norm": 0.5317919075144508,
"acc_norm_stderr": 0.038047497443647646
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.3235294117647059,
"acc_stderr": 0.046550104113196177,
"acc_norm": 0.3235294117647059,
"acc_norm_stderr": 0.046550104113196177
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.75,
"acc_stderr": 0.04351941398892446,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04351941398892446
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.46382978723404256,
"acc_stderr": 0.032600385118357715,
"acc_norm": 0.46382978723404256,
"acc_norm_stderr": 0.032600385118357715
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.2982456140350877,
"acc_stderr": 0.04303684033537315,
"acc_norm": 0.2982456140350877,
"acc_norm_stderr": 0.04303684033537315
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5448275862068965,
"acc_stderr": 0.04149886942192118,
"acc_norm": 0.5448275862068965,
"acc_norm_stderr": 0.04149886942192118
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.3386243386243386,
"acc_stderr": 0.02437319786798307,
"acc_norm": 0.3386243386243386,
"acc_norm_stderr": 0.02437319786798307
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.36507936507936506,
"acc_stderr": 0.04306241259127153,
"acc_norm": 0.36507936507936506,
"acc_norm_stderr": 0.04306241259127153
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.38,
"acc_stderr": 0.04878317312145632,
"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145632
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.667741935483871,
"acc_stderr": 0.0267955608481228,
"acc_norm": 0.667741935483871,
"acc_norm_stderr": 0.0267955608481228
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.43349753694581283,
"acc_stderr": 0.03486731727419872,
"acc_norm": 0.43349753694581283,
"acc_norm_stderr": 0.03486731727419872
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.6,
"acc_stderr": 0.049236596391733084,
"acc_norm": 0.6,
"acc_norm_stderr": 0.049236596391733084
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.703030303030303,
"acc_stderr": 0.03567969772268049,
"acc_norm": 0.703030303030303,
"acc_norm_stderr": 0.03567969772268049
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7474747474747475,
"acc_stderr": 0.030954055470365897,
"acc_norm": 0.7474747474747475,
"acc_norm_stderr": 0.030954055470365897
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8238341968911918,
"acc_stderr": 0.02749350424454806,
"acc_norm": 0.8238341968911918,
"acc_norm_stderr": 0.02749350424454806
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.5717948717948718,
"acc_stderr": 0.025088301454694838,
"acc_norm": 0.5717948717948718,
"acc_norm_stderr": 0.025088301454694838
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.28888888888888886,
"acc_stderr": 0.02763490726417854,
"acc_norm": 0.28888888888888886,
"acc_norm_stderr": 0.02763490726417854
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.5966386554621849,
"acc_stderr": 0.031866081214088314,
"acc_norm": 0.5966386554621849,
"acc_norm_stderr": 0.031866081214088314
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31788079470198677,
"acc_stderr": 0.038020397601079024,
"acc_norm": 0.31788079470198677,
"acc_norm_stderr": 0.038020397601079024
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.7651376146788991,
"acc_stderr": 0.018175110510343567,
"acc_norm": 0.7651376146788991,
"acc_norm_stderr": 0.018175110510343567
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.4444444444444444,
"acc_stderr": 0.03388857118502326,
"acc_norm": 0.4444444444444444,
"acc_norm_stderr": 0.03388857118502326
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7549019607843137,
"acc_stderr": 0.030190282453501947,
"acc_norm": 0.7549019607843137,
"acc_norm_stderr": 0.030190282453501947
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.7552742616033755,
"acc_stderr": 0.027985699387036423,
"acc_norm": 0.7552742616033755,
"acc_norm_stderr": 0.027985699387036423
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.672645739910314,
"acc_stderr": 0.03149384670994131,
"acc_norm": 0.672645739910314,
"acc_norm_stderr": 0.03149384670994131
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6793893129770993,
"acc_stderr": 0.04093329229834278,
"acc_norm": 0.6793893129770993,
"acc_norm_stderr": 0.04093329229834278
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.768595041322314,
"acc_stderr": 0.03849856098794088,
"acc_norm": 0.768595041322314,
"acc_norm_stderr": 0.03849856098794088
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.75,
"acc_stderr": 0.04186091791394607,
"acc_norm": 0.75,
"acc_norm_stderr": 0.04186091791394607
},
"harness|hendrycksTest-logical_fallacies|5": {
"acc": 0.7055214723926381,
"acc_stderr": 0.03581165790474082,
"acc_norm": 0.7055214723926381,
"acc_norm_stderr": 0.03581165790474082
},
"harness|hendrycksTest-machine_learning|5": {
"acc": 0.44642857142857145,
"acc_stderr": 0.04718471485219588,
"acc_norm": 0.44642857142857145,
"acc_norm_stderr": 0.04718471485219588
},
"harness|hendrycksTest-management|5": {
"acc": 0.7669902912621359,
"acc_stderr": 0.04185832598928315,
"acc_norm": 0.7669902912621359,
"acc_norm_stderr": 0.04185832598928315
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8290598290598291,
"acc_stderr": 0.02466249684520982,
"acc_norm": 0.8290598290598291,
"acc_norm_stderr": 0.02466249684520982
},
"harness|hendrycksTest-medical_genetics|5": {
"acc": 0.67,
"acc_stderr": 0.04725815626252609,
"acc_norm": 0.67,
"acc_norm_stderr": 0.04725815626252609
},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7739463601532567,
"acc_stderr": 0.014957458504335837,
"acc_norm": 0.7739463601532567,
"acc_norm_stderr": 0.014957458504335837
},
"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6445086705202312,
"acc_stderr": 0.025770292082977257,
"acc_norm": 0.6445086705202312,
"acc_norm_stderr": 0.025770292082977257
},
"harness|hendrycksTest-moral_scenarios|5": {
"acc": 0.4324022346368715,
"acc_stderr": 0.01656897123354861,
"acc_norm": 0.4324022346368715,
"acc_norm_stderr": 0.01656897123354861
},
"harness|hendrycksTest-nutrition|5": {
"acc": 0.6372549019607843,
"acc_stderr": 0.027530078447110307,
"acc_norm": 0.6372549019607843,
"acc_norm_stderr": 0.027530078447110307
},
"harness|hendrycksTest-philosophy|5": {
"acc": 0.6495176848874598,
"acc_stderr": 0.027098652621301754,
"acc_norm": 0.6495176848874598,
"acc_norm_stderr": 0.027098652621301754
},
"harness|hendrycksTest-prehistory|5": {
"acc": 0.6327160493827161,
"acc_stderr": 0.026822801759507894,
"acc_norm": 0.6327160493827161,
"acc_norm_stderr": 0.026822801759507894
},
"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.42907801418439717,
"acc_stderr": 0.02952591430255856,
"acc_norm": 0.42907801418439717,
"acc_norm_stderr": 0.02952591430255856
},
"harness|hendrycksTest-professional_law|5": {
"acc": 0.41395045632333766,
"acc_stderr": 0.012579699631289262,
"acc_norm": 0.41395045632333766,
"acc_norm_stderr": 0.012579699631289262
},
"harness|hendrycksTest-professional_medicine|5": {
"acc": 0.5294117647058824,
"acc_stderr": 0.03032024326500413,
"acc_norm": 0.5294117647058824,
"acc_norm_stderr": 0.03032024326500413
},
"harness|hendrycksTest-professional_psychology|5": {
"acc": 0.5833333333333334,
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"acc_norm": 0.5833333333333334,
"acc_norm_stderr": 0.01994491413687358
},
"harness|hendrycksTest-public_relations|5": {
"acc": 0.6454545454545455,
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"acc_norm": 0.6454545454545455,
"acc_norm_stderr": 0.045820048415054174
},
"harness|hendrycksTest-security_studies|5": {
"acc": 0.6448979591836734,
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"acc_norm": 0.6448979591836734,
"acc_norm_stderr": 0.030635655150387638
},
"harness|hendrycksTest-sociology|5": {
"acc": 0.7263681592039801,
"acc_stderr": 0.03152439186555401,
"acc_norm": 0.7263681592039801,
"acc_norm_stderr": 0.03152439186555401
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.83,
"acc_stderr": 0.03775251680686371,
"acc_norm": 0.83,
"acc_norm_stderr": 0.03775251680686371
},
"harness|hendrycksTest-virology|5": {
"acc": 0.463855421686747,
"acc_stderr": 0.03882310850890593,
"acc_norm": 0.463855421686747,
"acc_norm_stderr": 0.03882310850890593
},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.7777777777777778,
"acc_stderr": 0.03188578017686398,
"acc_norm": 0.7777777777777778,
"acc_norm_stderr": 0.03188578017686398
},
"harness|truthfulqa:mc|0": {
"mc1": 0.35495716034271724,
"mc1_stderr": 0.0167508623813759,
"mc2": 0.49782296764839973,
"mc2_stderr": 0.015206569782538341
}
}
```
### 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] |
beverlym/product-info | ---
license: openrail
---
|
carnival13/massive_5_lang_DA2_tokenized | ---
dataset_info:
features:
- name: pass_label
dtype: int64
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 424287645
num_examples: 552890
download_size: 127805722
dataset_size: 424287645
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "massive_5_lang_DA2_tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ismaelbrt/Drauzio1 | ---
license: openrail
---
|
FarReelAILab/law_examples | ---
license: apache-2.0
---
## 1000 examples of law items
law_item.jsonl contains 1000 samples of current and effective Chinese laws. e.g.
```python
{"title": "《中华人民共和国劳动合同法(2012修正)》",
"classification": "类别 : 劳动合同营商环境优化 ",
"num": "第十九条",
"contents": "第十九条【试用期】劳动合同期限三个月以上不满一年的，试用期不得超过一个月；劳动合同期限一年以上不满三年的，试用期不得超过二个月；三年以上固定期限和无固定期限的劳动合同，试用期不得超过六个月。同一用人单位与同一劳动者只能约定一次试用期。以完成一定工作任务为期限的劳动合同或者劳动合同期限不满三个月的，不得约定试用期。试用期包含在劳动合同期限内。劳动合同仅约定试用期的，试用期不成立，该期限为劳动合同期限。"}
```
## Using BGE Embedding to compute similarity between query and law item
```python
from FlagEmbedding import FlagModel
from datasets import load_dataset
dataset = load_dataset("FarReelAILab/law_examples")
model = FlagModel('BAAI/bge-large-zh-v1.5',
query_instruction_for_retrieval="为这个句子生成表示以用于检索相关文章：",
use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
queries = ['我是中职生，在一家酒店工作，试用期内上班7天了，现在辞职工资还会有吗？', '工作时间每天12个小时，早上十点上班中间基本不休息，然后到晚上11-12点下班有时候说加班就加班想辞职走人酒店压着工资不让走怎么办？']
passages = [f"法律：{dataset['train'][11]['title']}，法条：{dataset['train'][11]['contents']}",
f"法律：{dataset['train'][173]['title']}，法条：{dataset['train'][173]['contents']}"]
print(f"法律：{dataset['train'][11]['title']}，法条：{dataset['train'][11]['contents']}")
print(f"法律：{dataset['train'][173]['title']}，法条：{dataset['train'][173]['contents']}")
q_embeddings = model.encode_queries(queries)
p_embeddings = model.encode(passages)
scores = q_embeddings @ p_embeddings.T
print(scores)
```
output:
```python
法律：《中华人民共和国劳动合同法(2012修正)》，法条：第十九条【试用期】劳动合同期限三个月以上不满一年的，试用期不得超过一个月；劳动合同期限一年以上不满三年的，试用期不得超过二个月；三年以上固定期限和无固定期限的劳动合同，试用期不得超过六个月。同一用人单位与同一劳动者只能约定一次试用期。以完成一定工作任务为期限的劳动合同或者劳动合同期限不满三个月的，不得约定试用期。试用期包含在劳动合同期限内。劳动合同仅约定试用期的，试用期不成立，该期限为劳动合同期限。
法律：《中华人民共和国劳动法(2018修正)》，法条：第四十三条用人单位不得违反本法规定延长劳动者的工作时间。
[[0.7573 0.72 ]
[0.7417 0.7734]]
``` |
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