id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
aucelio/pedro | aucelio | 2023-11-19T18:54:51Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-19T18:54:51Z | 2023-11-19T18:51:19.000Z | 2023-11-19T18:51:19 | ---
license: openrail
---
| [
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Bohrhh/video_edit | Bohrhh | 2023-11-19T18:56:31Z | 0 | 0 | null | [
"region:us"
] | 2023-11-19T18:56:31Z | 2023-11-19T18:54:53.000Z | 2023-11-19T18:54:53 | Entry not found | [
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BangumiBase/rakudaikishinocavalry | BangumiBase | 2023-11-19T20:05:25Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-19T20:05:25Z | 2023-11-19T18:55:29.000Z | 2023-11-19T18:55:29 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Rakudai Kishi No Cavalry
This is the image base of bangumi Rakudai Kishi no Cavalry, we detected 20 characters, 1314 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 | 305 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 19 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 365 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 41 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 62 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 44 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 47 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 23 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 105 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 9 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 18 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 20 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 9 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 10 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 9 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 9 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 37 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 6 | [Download](17/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 18 | 19 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 157 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| [
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0.4940384328365326... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/masougakuenhxh | BangumiBase | 2023-11-19T20:20:49Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-19T20:20:49Z | 2023-11-19T19:04:51.000Z | 2023-11-19T19:04:51 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Masou Gakuen Hxh
This is the image base of bangumi Masou Gakuen HxH, we detected 22 characters, 1642 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 | 183 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 62 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 55 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 160 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 21 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 80 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 488 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 32 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 12 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 5 | [Download](9/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 10 | 80 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 68 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 24 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 32 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 33 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 16 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 38 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 20 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 6 | [Download](18/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 19 | 67 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 7 | [Download](20/dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 153 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| [
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0.525819599628... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
kelen0102/Caine | kelen0102 | 2023-11-19T19:08:25Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-19T19:08:25Z | 2023-11-19T19:06:53.000Z | 2023-11-19T19:06:53 | ---
license: openrail
---
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-0.04782580211758613... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
nxsbr/schmidt | nxsbr | 2023-11-19T19:28:34Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-19T19:28:34Z | 2023-11-19T19:08:26.000Z | 2023-11-19T19:08:26 | ---
license: openrail
---
| [
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-0.04782580211758613... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
miragepa/ANDROIDEN18 | miragepa | 2023-11-19T19:17:17Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-19T19:17:17Z | 2023-11-19T19:16:32.000Z | 2023-11-19T19:16:32 | ---
license: openrail
---
| [
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-0.04782580211758613... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Jeryr/Yisus | Jeryr | 2023-11-19T19:31:06Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-19T19:31:06Z | 2023-11-19T19:19:27.000Z | 2023-11-19T19:19:27 | ---
license: apache-2.0
---
| [
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-0.04782580211758613... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Kana31/Peacock | Kana31 | 2023-11-19T20:23:54Z | 0 | 0 | null | [
"region:us"
] | 2023-11-19T20:23:54Z | 2023-11-19T19:23:12.000Z | 2023-11-19T19:23:12 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Norod78/RickAndMorty-blip-captions-1024 | Norod78 | 2023-11-19T19:36:23Z | 0 | 0 | null | [
"region:us"
] | 2023-11-19T19:36:23Z | 2023-11-19T19:36:05.000Z | 2023-11-19T19:36:05 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 178602553.0
num_examples: 188
download_size: 178603589
dataset_size: 178602553.0
---
# Dataset Card for "RickAndMorty-blip-captions-1024"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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0.3195391297340393... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
ProGamerGov/dalle-3-reddit-dataset | ProGamerGov | 2023-11-20T17:11:49Z | 0 | 0 | null | [
"language:en",
"license:mit",
"image-text-dataset",
"synthetic-dataset",
"region:us"
] | 2023-11-20T17:11:49Z | 2023-11-19T19:44:52.000Z | 2023-11-19T19:44:52 | ---
language:
- en
license:
- mit
tags:
- image-text-dataset
- synthetic-dataset
dataset_info:
features:
- name: image
dtype: image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for DALL·E 3 Reddit Images Dataset
**Description**: This dataset consists of high quality synthetic images produced with Dalle 3 that were shared on Reddit, and is meant to be captioned and combined with other datasets before use in training new models.
Currently this dataset contains 3465 images, and more images will be periodically added.
| [
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vibranium-dome/questions | vibranium-dome | 2023-11-19T19:59:30Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-19T19:59:30Z | 2023-11-19T19:58:37.000Z | 2023-11-19T19:58:37 | ---
license: mit
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xuanzz/Drone | xuanzz | 2023-11-19T21:08:14Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-19T21:08:14Z | 2023-11-19T20:23:57.000Z | 2023-11-19T20:23:57 | ---
license: mit
dataset_info:
features:
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splits:
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num_bytes: 381103939.086
num_examples: 1359
download_size: 379073342
dataset_size: 381103939.086
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Credits: Taken from https://www.kaggle.com/datasets/dasmehdixtr/drone-dataset-uav | [
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Violetmae14/text-it-to-video-snap | Violetmae14 | 2023-11-19T20:24:43Z | 0 | 0 | null | [
"license:bigscience-bloom-rail-1.0",
"region:us"
] | 2023-11-19T20:24:43Z | 2023-11-19T20:24:43.000Z | 2023-11-19T20:24:43 | ---
license: bigscience-bloom-rail-1.0
---
| [
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-0.0478260256350... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Yijia-Xiao/PII-PQA-raw | Yijia-Xiao | 2023-11-19T20:36:49Z | 0 | 0 | null | [
"region:us"
] | 2023-11-19T20:36:49Z | 2023-11-19T20:28:04.000Z | 2023-11-19T20:28:04 | ---
dataset_info:
features:
- name: Question
dtype: string
- name: Answer
dtype: string
- name: Protected Answer
dtype: string
splits:
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num_bytes: 7185732
num_examples: 42499
- name: test
num_bytes: 1274128
num_examples: 7504
download_size: 1212545
dataset_size: 8459860
---
# Dataset Card for "PPLM-PQA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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-0.38964030146598816... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
faizalnf1800/strategic-battles-webnovel | faizalnf1800 | 2023-11-20T00:34:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T00:34:24Z | 2023-11-19T20:53:02.000Z | 2023-11-19T20:53:02 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Yijia-Xiao/PPLM-PQA | Yijia-Xiao | 2023-11-19T20:53:59Z | 0 | 0 | null | [
"region:us"
] | 2023-11-19T20:53:59Z | 2023-11-19T20:53:54.000Z | 2023-11-19T20:53:54 | ---
dataset_info:
features:
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dtype: string
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splits:
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num_bytes: 8673197
num_examples: 42499
- name: test
num_bytes: 1536768
num_examples: 7504
download_size: 1233735
dataset_size: 10209965
---
# Dataset Card for "PPLM-PQA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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0.6262982487678528,
0.2629895508289337,
0.4242667555809021,
0.5494731068611145,
-0.615739643573761,
-0.7085661292076111,
-0.4849618077278137,
-0.38964053988456726... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
joshuasundance/govgis_nov2023-slim-spatial | joshuasundance | 2023-11-23T00:18:04Z | 0 | 0 | null | [
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"gis",
"geospatial",
"doi:10.57967/hf/1369",
"region:us"
] | 2023-11-23T00:18:04Z | 2023-11-19T20:53:59.000Z | 2023-11-19T20:53:59 | ---
license: mit
language:
- en
tags:
- gis
- geospatial
pretty_name: govgis_nov2023-slim-spatial
size_categories:
- 100K<n<1M
---
# govgis_nov2023-slim-spatial
🤖 This README was written by [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta). 🤖
Introducing the govgis_nov2023-slim-spatial dataset, a carefully curated and georeferenced subset of the extensive [govgis_nov2023](https://huggingface.co/datasets/joshuasundance/govgis_nov2023) collection. This dataset stands out for its focus on geospatial data analysis, enriched with vector embeddings. While we have only explored a portion of this vast collection, the variety and richness of the content encountered have been remarkable, making it challenging to fully capture the dataset's breadth in a brief overview.
## Overview
The govgis_nov2023-slim-spatial dataset condenses key elements from the larger govgis_nov2023 collection into a more manageable format. It offers a glimpse into an extensive range of geospatial data types, all augmented with vector embeddings using [`BAAI/bge-large-en-v1.5`](https://huggingface.co/BAAI/bge-large-en-v1.5). Our exploration has revealed a staggering variety in the data, suggesting vast potential applications.
Key Features:
- **Diverse Geospatial Data Types:** The dataset includes samples of data like ecological data, census data, administrative boundaries, transportation networks, and land use maps, representing just a fraction of what's available.
- **Advanced Vector Search Capabilities:** Augmented with vector embeddings using [`BAAI/bge-large-en-v1.5`](https://huggingface.co/BAAI/bge-large-en-v1.5) for sophisticated content discovery.
## Dataset Files
The dataset comprises two distinct files:
1. **`govgis_nov2023_slim_spatial.geoparquet`** This file offers core georeferenced spatial data, suitable for a broad range of analysis needs.
2. **`govgis_nov2023_slim_spatial_embs.geoparquet`:** A more comprehensive file with detailed vector embeddings, catering to more in-depth analytical demands.
This two-tiered approach allows users to tailor their engagement with the dataset based on their specific requirements.
## Benefits:
- **Selective Accessibility:** The dataset provides an accessible entry point to a seemingly endless variety of spatial data.
- **Efficient yet Comprehensive:** It distills a vast array of data into a more practical format without losing the essence of its diversity.
- **Untapped Application Potential:** The examples we provide are merely starting points; the dataset's true scope is far more extensive and varied.
- **Enhanced Analytical Depth:** Vector embeddings from [`BAAI/bge-large-en-v1.5`](https://huggingface.co/BAAI/bge-large-en-v1.5) offer advanced data analysis capabilities.
## Use Cases:
Given the sheer variety of data we've glimpsed, the dataset is poised to serve a myriad of applications, far beyond the few examples we can confidently cite. It's designed to be adaptable to diverse analytical pursuits across different fields.
# Conclusion:
The govgis_nov2023-slim-spatial dataset is a thoughtfully distilled, georeferenced, and vector-embedded version of its more extensive counterpart. Our limited exploration has revealed an astonishing variety of data, hinting at a much broader scope of potential applications than we can definitively describe. This dual-file dataset is crafted to meet a wide spectrum of spatial data analysis needs, from the straightforward to the highly specialized, accommodating the extensive possibilities that lie within the realm of geospatial data. | [
-0.6624610424041748,
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0.5669108033180237,
0.09603119641542435,
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0.5965556502342224,
0.295826256275177,
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-0.532570481300354,
-0.17318770289421082... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
xuanzz/VideoCaptions | xuanzz | 2023-11-19T21:13:02Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-19T21:13:02Z | 2023-11-19T21:08:44.000Z | 2023-11-19T21:08:44 | ---
license: mit
---
| [
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0.6529128551483154,
0.49436232447624207,
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0.7400146722793579,
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-0.7102247476577759,
-0.0478255338966846... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
someone13574/fictional-worlds | someone13574 | 2023-11-20T00:07:59Z | 0 | 1 | null | [
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-11-20T00:07:59Z | 2023-11-19T21:14:20.000Z | 2023-11-19T21:14:20 | ---
license: apache-2.0
language:
- en
pretty_name: Fictional Worlds
---
# Fictional Worlds
(this readme is temporary, full reproduction instructions + code will be added later)
Model used: Zephyr-7b-beta
#### Seed generation
Seed generation procedure: Take the level-3 vital articles from wikipedia and prompt zephyr with the following prompt and 8 fewshot examples from the list. Using Zephyr's chat template, say the title of a wikipedia article from the list and make the assistant respond with a seed concept.
Prompt: (Lists instruction twice following https://openreview.net/forum?id=3jXCF5dNpC)
```
You are a fantasy worldbuilding seed creator which creates the core concepts of worlds for worldbuilding. They should be interesting and unique, and take inspiration from a random word. The seed should describe the world at large, not a specific event. Each seed should be as short and simple as possible, with no additional explanation, and should be no more than 20 words. For each word I say after this, you will generate a corrosponding seed.
Read the instruction again: You are a fantasy worldbuilding seed creator which creates the core concepts of worlds for worldbuilding. They should be interesting and unique, and take inspiration from a random word. The seed should describe the world at large, not a specific event. Each seed should be as short and simple as possible, with no additional explanation, and should be no more than 20 words. For each word I say after this, you will generate a corrosponding seed
```
Fewshot examples (I selected 8 randomly every time):
```
{"word": "Oral tradition", "seed": "Words shape reality."},
{"word": "Power (social and political)", "seed": "Fears become formidable creatures."},
{"word": "Surgery", "seed": "Incisions alter the soul."},
{"word": "Crustacean", "seed": "Underwater cities from sentient shells."},
{"word": "Carl Friedrich Gauss", "seed": "Mathematics governs magic."},
{"word": "Mesoamerica", "seed": "Celestial events dictate fate."},
{"word": "Atlantic Ocean", "seed": "Vast realms sail seas and islands embody discoverers' dreams."},
{"word": "Weak interaction", "seed": "Reality shifts from subtle events; a flutter reshapes continents."},
{"word": "Physiology", "seed": "Life force is currency."},
{"word": "Mineral", "seed": "Living crystals house ancient spirits."},
{"word": "Elizabeth I", "seed": "Immortal queens rule."},
{"word": "Thailand", "seed": "Sky markets trade enchanted goods between magical realms."},
{"word": "Bicycle", "seed": "Eternal cycle of rebirth."},
{"word": "Dance", "seed": "Civilization thrives on rhythmic dances echoing in the heavens."},
{"word": "Newton's laws of motion", "seed": "Properties change with kinetic energy."},
{"word": "Copper", "seed": "Metallic veins grant conductivity magic."},
{"word": "Telephone", "seed": "Crystal devices link minds to machines."},
{"word": "James Cook", "seed": "Explorer's legacy opens portals to uncharted realms."},
{"word": "Chemical bond", "seed": "Invisible threads connect living things and breaking bonds triggers chaotic transformations."},
{"word": "Phoenicia", "seed": "Spacefaring nomads explore the stars."},
{"word": "Calligraphy", "seed": "Ink shapes reality."},
{"word": "Adolescence", "seed": "Adolescence sparks latent powers and teens shape landscapes with inner turmoil."},
```
#### Worldbuilding
For each seed in the seed concept list, feed it to the following prompt. I limited the full size to 2048 tokens to cut off generations taking too long, as they likely went off topic:
```
<|user|>
You are a worldbuilder and your goal is to create a unique and logically consistent world. Note that this does not mean it needs to be based in reality, it just needs to follow its own rules. You will fill out the following json fields in the order listed, using choices from eariler fields to guide the later ones.
\"seed\" (< 15 words): An idea which the world is built around.
\"geography_and_nature\" (< 150 words): Describe the varied landscapes, climates, available resources, and the diverse flora and fauna that define the world. Consider the impact of geography on civilizations and ecosystems.
\"history\" (< 100 words): Outline the significant events that have shaped the world, leading up to its current state. Explore pivotal moments, conflicts, and cultural shifts that influence the present events would have an influence the current world.
\"culture_and_society\" (< 100 words): Define the societal structures, cultural norms, and traditions that shape the behavior of the world's inhabitants. Explore the diversity of civilizations, social classes, and the relationships between different groups.
\"religion_and_beliefs\" (< 100 words): Describe the various belief systems, religions, and spiritual practices that shape the worldview of the world's inhabitants.
\"politics_and_governance\" (< 75 words): Specify the political landscape, including governing bodies, power structures, and diplomatic relations between different regions or factions. Explore the dynamics of leadership and the balance of political influence.
\"technology\" (< 75 words): Define the technology of this world, shaped by its unique problems and history. Also explore what technologies are possible given the rules of this world and the level of advancement.
\"conflicts_and_threats\" (< 50 words): Identify ongoing conflicts, potential threats, and sources of tension within the world. Consider external and internal challenges, such as wars, rivalries, and existential threats that impact the stability of the world. </s>
<|assistant|>
{
\"seed\": \"{seed}\",
\"geography_and_nature\": \"The world is
```
#### Post-processing:
Through out anything which didn't complete in 2048 tokens, check that all json keys are present (at this point it was culled from 10k to 7k, based on the overlength and missing keys), replace/remove stray quotation marks, and repair jsons. | [
-0.40920940041542053,
-0.8122801780700684,
0.4485924541950226,
0.44017210602760315,
-0.22163620591163635,
0.1876596361398697,
0.26775801181793213,
-0.35776475071907043,
0.5220646858215332,
0.6128556132316589,
-0.8014140129089355,
-0.48087015748023987,
-0.5116299986839294,
0.043651800602674... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/sailormoon2010s | BangumiBase | 2023-11-19T23:01:48Z | 0 | 0 | null | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | 2023-11-19T23:01:48Z | 2023-11-19T21:14:39.000Z | 2023-11-19T21:14:39 | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Sailor Moon (2010s)
This is the image base of bangumi Sailor Moon (2010s), we detected 46 characters, 3463 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 | 901 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 140 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 16 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 313 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 19 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 77 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 52 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 17 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 17 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 26 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 21 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 102 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 164 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 73 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 46 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 9 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 269 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 24 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 10 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 21 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 11 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 271 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 99 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 14 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 40 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 9 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 205 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 18 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 12 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 22 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 15 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 14 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 16 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 7 | [Download](33/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 34 | 9 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 23 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 26 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 15 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 8 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 5 | [Download](39/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 40 | 11 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 9 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 9 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 21 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 12 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 245 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
| [
-0.7050097584724426,
-0.14417658746242523,
0.20969408750534058,
0.23897626996040344,
-0.2650940716266632,
-0.05690757557749748,
-0.014032409526407719,
-0.3619411885738373,
0.6739320158958435,
0.5724254846572876,
-0.9540380239486694,
-0.8582369089126587,
-0.6802312731742859,
0.5370507836341... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
BangumiBase/sailormoon1990s | BangumiBase | 2023-11-20T11:23:44Z | 0 | 0 | null | [
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] | 2023-11-20T11:23:44Z | 2023-11-19T21:15:02.000Z | 2023-11-19T21:15:02 | ---
license: mit
tags:
- art
size_categories:
- 10K<n<100K
---
# Bangumi Image Base of Sailor Moon (1990s)
This is the image base of bangumi Sailor Moon (1990s), we detected 132 characters, 14684 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 | 3008 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 94 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 696 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 49 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 29 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 176 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 95 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 72 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 180 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 75 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 108 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 113 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 32 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 42 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 47 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 602 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 1066 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 395 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 208 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 79 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 86 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 62 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 50 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 53 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 76 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 141 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 67 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 45 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 750 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 103 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 34 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 42 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 20 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 67 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 79 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 40 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 45 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 118 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 41 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 62 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 93 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 79 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 920 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 55 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 75 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 36 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 15 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 126 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 41 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 46 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 100 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 121 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 36 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 102 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 50 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 105 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 47 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 60 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 26 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 47 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 79 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 74 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 11 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 73 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 30 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 32 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 102 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 17 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 49 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 24 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 28 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 38 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 96 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 52 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 747 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 50 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 43 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 21 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 22 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 23 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 38 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 20 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 44 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 19 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 19 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 19 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
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| 87 | 48 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
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| 93 | 10 | [Download](93/dataset.zip) |  |  |  |  |  |  |  |  |
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| 104 | 11 | [Download](104/dataset.zip) |  |  |  |  |  |  |  |  |
| 105 | 7 | [Download](105/dataset.zip) |  |  |  |  |  |  |  | N/A |
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umm-maybe/gutenberg_english_pre1928 | umm-maybe | 2023-11-20T00:45:58Z | 0 | 0 | null | [
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icaro23/Icaro | icaro23 | 2023-11-19T21:28:07Z | 0 | 0 | null | [
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kc34251/Drone-Detection | kc34251 | 2023-11-19T22:28:16Z | 0 | 0 | null | [
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ywanny/Drone_Detection | ywanny | 2023-11-19T23:32:13Z | 0 | 0 | null | [
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# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset Name
<!-- Provide a quick summary of the dataset. -->
Credit: https://www.kaggle.com/datasets/dasmehdixtr/drone-dataset-uav
This is a dataset from the above the link. It's used for object detection training on yolo model for the class of drone.
## 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] | [
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giraffe176/forza | giraffe176 | 2023-11-19T23:23:15Z | 0 | 0 | null | [
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warzin/covers | warzin | 2023-11-25T15:15:04Z | 0 | 0 | null | [
"license:other",
"region:us"
] | 2023-11-25T15:15:04Z | 2023-11-19T23:31:34.000Z | 2023-11-19T23:31:34 | ---
license: other
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GabrielTOP/Aracy | GabrielTOP | 2023-11-19T23:37:54Z | 0 | 0 | null | [
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Diogeness/VOZ-josh | Diogeness | 2023-11-20T00:03:13Z | 0 | 0 | null | [
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willpowers/test | willpowers | 2023-11-19T23:53:27Z | 0 | 0 | null | [
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-0... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Ricktlw/ThomazCostaSet | Ricktlw | 2023-11-20T00:29:15Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-20T00:29:15Z | 2023-11-20T00:28:00.000Z | 2023-11-20T00:28:00 | ---
license: openrail
---
| [
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timo1227/Drone | timo1227 | 2023-11-20T00:47:09Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T00:47:09Z | 2023-11-20T00:45:22.000Z | 2023-11-20T00:45:22 | Entry not found | [
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erikliu18/us-congress-hearing | erikliu18 | 2023-11-20T01:01:16Z | 0 | 0 | null | [
"task_categories:text-classification",
"language:en",
"finance",
"legal",
"region:us"
] | 2023-11-20T01:01:16Z | 2023-11-20T00:49:47.000Z | 2023-11-20T00:49:47 | ---
task_categories:
- text-classification
language:
- en
tags:
- finance
- legal
---
# U.S. Congressional Hearings Dataset
This dataset currently contains cleaned sentences from all House Committee on Energy and Commerce hearings from 2002.
A total of 1K+ hearing transcripts in txt formats from govinfo.gov were collected and cleaned. | [
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zhafen/meow-by-meow-data | zhafen | 2023-11-20T01:01:10Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-20T01:01:10Z | 2023-11-20T00:57:55.000Z | 2023-11-20T00:57:55 | ---
license: mit
---
| [
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Roblox/luau_corpus | Roblox | 2023-11-20T01:09:48Z | 0 | 0 | null | [
"license:mit",
"code",
"region:us"
] | 2023-11-20T01:09:48Z | 2023-11-20T01:08:21.000Z | 2023-11-20T01:08:21 | ---
license: mit
tags:
- code
---
# Dataset card
The Luau dataset is a collection of code fragments collected from the Roblox Luau Data Sharing program.
Only experiences where creators gave us permission to contribute to the public Luau Dataset were used for producing this dataset.
# Languages:
Lua, Luau
# License:
MIT
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
# Format:
The dataset format is in jsonl format, with prompt / completion fields.
# Dataset usage:
This dataset is designed for fine tuning large language models.
# Risks:
The dataset has been filtered for various quality signals, though Roblox makes no guarantees of data quality.
# Evaluation:
We have found that typically fine tuning a generalist code LLM improve it’s performance on Roblox Lua code quality by 10 to 20%.
| [
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qq835376431/1 | qq835376431 | 2023-11-20T01:11:31Z | 0 | 0 | null | [
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] | 2023-11-20T01:11:31Z | 2023-11-20T01:11:31.000Z | 2023-11-20T01:11:31 | Entry not found | [
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Bode777/ZODD | Bode777 | 2023-11-20T01:31:00Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-20T01:31:00Z | 2023-11-20T01:30:02.000Z | 2023-11-20T01:30:02 | ---
license: openrail
---
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Kaue123456/AdamSandlerPortugues | Kaue123456 | 2023-11-20T01:59:33Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-20T01:59:33Z | 2023-11-20T01:58:23.000Z | 2023-11-20T01:58:23 | ---
license: openrail
---
| [
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sheel1206/Drone_Tracking_Data | sheel1206 | 2023-11-20T02:13:45Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T02:13:45Z | 2023-11-20T02:11:39.000Z | 2023-11-20T02:11:39 | Entry not found | [
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Junaid-EEE11/Data2 | Junaid-EEE11 | 2023-11-20T02:14:41Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T02:14:41Z | 2023-11-20T02:14:41.000Z | 2023-11-20T02:14:41 | Entry not found | [
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gilsonk12/mexicano | gilsonk12 | 2023-11-20T02:16:53Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-20T02:16:53Z | 2023-11-20T02:15:42.000Z | 2023-11-20T02:15:42 | ---
license: openrail
---
| [
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jameslpineda/cs370-uav-detection | jameslpineda | 2023-11-20T02:29:41Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T02:29:41Z | 2023-11-20T02:16:15.000Z | 2023-11-20T02:16:15 | The drone dataset that was used was from https://www.kaggle.com/datasets/muki2003/yolo-drone-detection-dataset | [
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leaudhiver/frdrpko | leaudhiver | 2023-11-20T02:34:53Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-20T02:34:53Z | 2023-11-20T02:19:49.000Z | 2023-11-20T02:19:49 | ---
license: apache-2.0
---
11-20 11:25
ID 1300까지 Deepl 번역 완. 미정제 후처리 안 한 데이터셋. 계속 번역 추가중. 번역 완료 후 데이터 정제하고 데이터셋명 변경예정.
original:
This dataset is the result of combing through several reverse proxy logs sets and cleaning them of refusals, duplicate, incomplete, and poor quality responses. Lots of manual quality checks. There's also things like ecommerce descriptions for sex toys and bondage gear, as well as examples of SEO optimized porn video descriptions. I will definitely be improving on this dataset continously; it should be considered a work in progress. My goal is to create a model (or set of models) which can completely replace OpenAI models for erotic roleplay and adult industry use.
Please consider supporting me on Patreon, I'm only asking for about tree fiddy.
https://www.patreon.com/openerotica | [
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QuinnZ129/AI-Assignment-3 | QuinnZ129 | 2023-11-20T02:51:00Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T02:51:00Z | 2023-11-20T02:48:52.000Z | 2023-11-20T02:48:52 | Entry not found | [
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Kana31/Imbeca | Kana31 | 2023-11-20T03:04:08Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T03:04:08Z | 2023-11-20T03:03:16.000Z | 2023-11-20T03:03:16 | Entry not found | [
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AiAF/Cheekie__dataset | AiAF | 2023-11-20T03:12:27Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T03:12:27Z | 2023-11-20T03:04:23.000Z | 2023-11-20T03:04:23 | Entry not found | [
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sayannath/pokemon-dataset | sayannath | 2023-11-20T03:16:43Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-20T03:16:43Z | 2023-11-20T03:10:18.000Z | 2023-11-20T03:10:18 | ---
license: apache-2.0
---
| [
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beltrewilton/punta-cana-spanish-reviews | beltrewilton | 2023-11-20T03:25:58Z | 0 | 0 | null | [
"task_categories:text-classification",
"language:es",
"license:mit",
"region:us"
] | 2023-11-20T03:25:58Z | 2023-11-20T03:19:51.000Z | 2023-11-20T03:19:51 | ---
license: mit
task_categories:
- text-classification
language:
- es
---
This data set was collected for academic purposes, suitable for some NLP tasks including sentiment analysis. | [
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coccoc-search/sft_rag | coccoc-search | 2023-11-20T04:42:59Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T04:42:59Z | 2023-11-20T03:19:56.000Z | 2023-11-20T03:19:56 | Retrieval Augmented Generation for Supervised FineTuning
| [
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ufotalent/zero_bubble_sample_dataset | ufotalent | 2023-11-20T03:29:59Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T03:29:59Z | 2023-11-20T03:24:52.000Z | 2023-11-20T03:24:52 | This is a preprocessed version of the realnewslike subdirectory of C4
C4 from: https://huggingface.co/datasets/allenai/c4
Files generated by using Megatron-LM https://github.com/NVIDIA/Megatron-LM/
```
python tools/preprocess_data.py \
--input 'c4/realnewslike/c4-train.0000[0-9]-of-00512.json' \
--partitions 8 \
--output-prefix preprocessed/c4 \
--tokenizer-type GPTSentencePieceTokenizer \
--tokenizer-model tokenizers/tokenizer.model \
--workers 8
```
---
license: odc-by
---
| [
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zia22k/zia | zia22k | 2023-11-20T03:41:19Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T03:41:19Z | 2023-11-20T03:41:19.000Z | 2023-11-20T03:41:19 | Entry not found | [
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CardinalityLM/imdb-card-pred-binary | CardinalityLM | 2023-11-20T03:52:38Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T03:52:38Z | 2023-11-20T03:52:32.000Z | 2023-11-20T03:52:32 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: prompt
dtype: string
- name: true_cardinality
dtype: int64
splits:
- name: train
num_bytes: 40068212.8
num_examples: 80000
- name: test
num_bytes: 10017053.2
num_examples: 20000
download_size: 8598252
dataset_size: 50085266.0
---
# Dataset Card for "imdb-card-pred-binary"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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CardinalityLM/imdb-card-pred-science | CardinalityLM | 2023-11-20T03:52:49Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T03:52:49Z | 2023-11-20T03:52:44.000Z | 2023-11-20T03:52:44 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: prompt
dtype: string
- name: true_cardinality
dtype: int64
splits:
- name: train
num_bytes: 39344995.2
num_examples: 80000
- name: test
num_bytes: 9836248.8
num_examples: 20000
download_size: 8632989
dataset_size: 49181244.0
---
# Dataset Card for "imdb-card-pred-science"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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icaro23/icarofrw | icaro23 | 2023-11-20T03:57:44Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-20T03:57:44Z | 2023-11-20T03:56:49.000Z | 2023-11-20T03:56:49 | ---
license: apache-2.0
---
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icaro23/ICAROGC | icaro23 | 2023-11-20T04:08:06Z | 0 | 0 | null | [
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"license:apache-2.0",
"region:us"
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license: apache-2.0
---
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ReDUB/SoundHarvest | ReDUB | 2023-11-20T05:05:09Z | 0 | 0 | null | [
"task_categories:translation",
"task_categories:audio-to-audio",
"size_categories:1K<n<10K",
"language:ar",
"language:es",
"language:fr",
"language:hi",
"language:id",
"language:ja",
"language:ko",
"language:pt",
"language:ru",
"language:th",
"language:tr",
"language:vi",
"language:en"... | 2023-11-20T05:05:09Z | 2023-11-20T04:21:11.000Z | 2023-11-20T04:21:11 | ---
license: other
task_categories:
- translation
- audio-to-audio
language:
- ar
- es
- fr
- hi
- id
- ja
- ko
- pt
- ru
- th
- tr
- vi
- en
tags:
- speech2speech
pretty_name: SoundHarvest
size_categories:
- 1K<n<10K
---
## Data Format
The dataset is organized in the following structure:
```yaml
dataset/
├── video_id_1/
│ ├── audio_language_1.wav
│ ├── audio_language_2.wav
│ ├── subtitle_language_1.vtt
│ ├── subtitle_language_2.vtt
│ └── unmatched/
│ └── ...
├── video_id_2/
│ ├── ...
└── ...
```
Original version with the channel (MrBeast) will contain 487 hours 27 minutes 59 seconds of audio files.
## Limitations
- **Copyright**: Please be aware of copyright restrictions when using this dataset. Ensure that you have the necessary permissions to use the audio and subtitle data for your intended purposes.
- **Inaccuracies**: While efforts have been made to align audio and subtitles accurately, there may be occasional mismatches or inaccuracies in the dataset. We recommend verifying the quality and alignment of the data for your specific use case.
## Generating Dataset
For generating the dataset launch:
1. `generate_urls.py` - to generate video URLs based on `channel_urls.txt`
2. `generate_dataset.py` - for generating dataset (can take **a lot** of time...)
3. `polish_dataset.py` - for cleaning the folders without any useful data
## Usage
The SoundHarvest dataset can be utilized for a variety of applications, including:
### 1. Automatic Speech Recognition (ASR)
Train ASR models to convert spoken language into text. SoundHarvest provides diverse language samples, making it suitable for multilingual ASR tasks.
### 2. Multilingual Natural Language Processing (NLP)
Leverage the dataset for multilingual NLP tasks, such as:
- Speech sentiment analysis.
- Language identification.
### 3. Linguistic Research and Analysis
Conduct linguistic research and analysis to explore various aspects of languages, including phonetics, dialects, and language evolution.
### 4. Speech-to-Speech Translation
Use the dataset to develop and evaluate speech-to-speech translation models. Translate spoken content from one language to another, expanding the dataset's applications to cross-lingual communication.
## Acknowledgments
We would like to express our gratitude to the YouTube content creators for providing valuable multilingual audio content that makes this dataset possible. | [
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Superintendent/world-building | Superintendent | 2023-11-21T20:35:29Z | 0 | 0 | null | [
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yoonlee/csProjectTextualInversionStyle1 | yoonlee | 2023-11-20T05:12:43Z | 0 | 0 | null | [
"region:us"
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---
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cp500/radiology_sample | cp500 | 2023-11-20T05:33:28Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T05:33:28Z | 2023-11-20T05:33:26.000Z | 2023-11-20T05:33:26 | ---
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AinzOoalGowns/Testdataset | AinzOoalGowns | 2023-11-20T06:01:48Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-20T06:01:48Z | 2023-11-20T06:01:48.000Z | 2023-11-20T06:01:48 | ---
license: apache-2.0
---
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nekofura/avtr | nekofura | 2023-11-24T06:02:11Z | 0 | 0 | null | [
"region:us"
] | 2023-11-24T06:02:11Z | 2023-11-20T06:22:16.000Z | 2023-11-20T06:22:16 | Entry not found | [
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nlp-vtcc/codex-math-en | nlp-vtcc | 2023-11-20T07:17:30Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T07:17:30Z | 2023-11-20T07:05:57.000Z | 2023-11-20T07:05:57 | ```py
import g4f
from copy import deepcopy
from datasets import load_dataset
translate_prompt = (
"Translate the following python snippet code into Vietnamese language (tiếng Việt). "
"Only translate the comments while preserving the name of functions, variables and other code. "
"Your translations must convey all the content in the original text and cannot involve explanations or other unnecessary information. "
"Please ensure that the translated text is natural for native speakers with correct grammar and proper word choices. "
"Your translation must also use exact terminology to provide accurate information even for the experts in the related fields. "
"Your output must only contain the code with translated comments and cannot include explanations or other information. "
"NOTE: Only translate the comments and DO NOT translate the name of functions, variables, arguments and other code. "
"Python code:\n"
)
def translate_response(example):
reply = example["reply"]
text = f"{translate_prompt}{reply}"
success = False
# try:
response = g4f.ChatCompletion.create(
model="gpt-3.5-turbo",
provider=g4f.Provider.GPTalk,
messages=[{"role": "user", "content": text}],
stream=False,
)
success = True
# except:
# response = text
# success = False
# print(f">>> Fail at {text}")
new_example = deepcopy(example)
new_example["reply"] = response
new_example["success"] = success
return new_example
## USAGE
dataset = load_dataset("json", data_files="codex00", split="train")
example = dataset[32]
new_example = translate_response(example)
print(new_example)
``` | [
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Back-up/review-crawl-data | Back-up | 2023-11-20T07:49:25Z | 0 | 0 | null | [
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---
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---
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DylanJHJ/pdsearch | DylanJHJ | 2023-11-25T03:30:03Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
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license: apache-2.0
---
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cherry0324/captions_100 | cherry0324 | 2023-11-20T07:57:06Z | 0 | 0 | null | [
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Saaddazhhar/predictiveswotanalysis | Saaddazhhar | 2023-11-20T08:05:48Z | 0 | 0 | null | [
"license:cc0-1.0",
"region:us"
] | 2023-11-20T08:05:48Z | 2023-11-20T08:03:47.000Z | 2023-11-20T08:03:47 | ---
license: cc0-1.0
---
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sinonimayzer/mixed-data-text | sinonimayzer | 2023-11-22T12:45:05Z | 0 | 0 | null | [
"task_categories:fill-mask",
"language:uz",
"region:us"
] | 2023-11-22T12:45:05Z | 2023-11-20T08:05:43.000Z | 2023-11-20T08:05:43 | ---
task_categories:
- fill-mask
language:
- uz
---
Credit goes to Tahrirchi, a chief contributor of our mixed-dataset (https://huggingface.co/datasets/tahrirchi/uz-books) | [
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open-llm-leaderboard/details_maywell__Synatra-7B-v0.3-dpo_public | open-llm-leaderboard | 2023-11-20T08:07:21Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T08:07:21Z | 2023-11-20T08:06:37.000Z | 2023-11-20T08:06:37 | ---
pretty_name: Evaluation run of maywell/Synatra-7B-v0.3-dpo
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [maywell/Synatra-7B-v0.3-dpo](https://huggingface.co/maywell/Synatra-7B-v0.3-dpo)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 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_maywell__Synatra-7B-v0.3-dpo_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-20T08:03:37.008028](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__Synatra-7B-v0.3-dpo_public/blob/main/results_2023-11-20T08-03-37.008028.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\": 0.610854861666512,\n\
\ \"acc_stderr\": 0.03282789791741049,\n \"acc_norm\": 0.6184353715807913,\n\
\ \"acc_norm_stderr\": 0.03351856767139879,\n \"mc1\": 0.39657282741738065,\n\
\ \"mc1_stderr\": 0.017124930942023518,\n \"mc2\": 0.5646058699056372,\n\
\ \"mc2_stderr\": 0.015306312553856578,\n \"em\": 0.006711409395973154,\n\
\ \"em_stderr\": 0.0008361500895152437,\n \"f1\": 0.086758598993289,\n\
\ \"f1_stderr\": 0.0017937356641132749\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.6006825938566553,\n \"acc_stderr\": 0.014312094557946709,\n\
\ \"acc_norm\": 0.6279863481228669,\n \"acc_norm_stderr\": 0.014124597881844461\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.6278629755028878,\n\
\ \"acc_stderr\": 0.004823867761332464,\n \"acc_norm\": 0.8258315076677952,\n\
\ \"acc_norm_stderr\": 0.0037847921724660665\n },\n \"harness|hendrycksTest-abstract_algebra|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-anatomy|5\": {\n \"acc\": 0.5925925925925926,\n\
\ \"acc_stderr\": 0.04244633238353228,\n \"acc_norm\": 0.5925925925925926,\n\
\ \"acc_norm_stderr\": 0.04244633238353228\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.03860731599316092,\n\
\ \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.03860731599316092\n\
\ },\n \"harness|hendrycksTest-business_ethics|5\": {\n \"acc\": 0.59,\n\
\ \"acc_stderr\": 0.049431107042371025,\n \"acc_norm\": 0.59,\n \
\ \"acc_norm_stderr\": 0.049431107042371025\n },\n \"harness|hendrycksTest-clinical_knowledge|5\"\
: {\n \"acc\": 0.660377358490566,\n \"acc_stderr\": 0.02914690474779833,\n\
\ \"acc_norm\": 0.660377358490566,\n \"acc_norm_stderr\": 0.02914690474779833\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6875,\n\
\ \"acc_stderr\": 0.038760854559127644,\n \"acc_norm\": 0.6875,\n\
\ \"acc_norm_stderr\": 0.038760854559127644\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.46,\n \"acc_stderr\": 0.05009082659620333,\n \
\ \"acc_norm\": 0.46,\n \"acc_norm_stderr\": 0.05009082659620333\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\": 0.32,\n \"acc_stderr\": 0.04688261722621505,\n \
\ \"acc_norm\": 0.32,\n \"acc_norm_stderr\": 0.04688261722621505\n \
\ },\n \"harness|hendrycksTest-college_medicine|5\": {\n \"acc\": 0.5953757225433526,\n\
\ \"acc_stderr\": 0.03742461193887248,\n \"acc_norm\": 0.5953757225433526,\n\
\ \"acc_norm_stderr\": 0.03742461193887248\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n\
\ \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.79,\n \"acc_stderr\": 0.040936018074033256,\n \"acc_norm\": 0.79,\n\
\ \"acc_norm_stderr\": 0.040936018074033256\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5404255319148936,\n \"acc_stderr\": 0.03257901482099835,\n\
\ \"acc_norm\": 0.5404255319148936,\n \"acc_norm_stderr\": 0.03257901482099835\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.4473684210526316,\n\
\ \"acc_stderr\": 0.04677473004491199,\n \"acc_norm\": 0.4473684210526316,\n\
\ \"acc_norm_stderr\": 0.04677473004491199\n },\n \"harness|hendrycksTest-electrical_engineering|5\"\
: {\n \"acc\": 0.5655172413793104,\n \"acc_stderr\": 0.041307408795554966,\n\
\ \"acc_norm\": 0.5655172413793104,\n \"acc_norm_stderr\": 0.041307408795554966\n\
\ },\n \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\"\
: 0.42328042328042326,\n \"acc_stderr\": 0.02544636563440678,\n \"\
acc_norm\": 0.42328042328042326,\n \"acc_norm_stderr\": 0.02544636563440678\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.3888888888888889,\n\
\ \"acc_stderr\": 0.04360314860077459,\n \"acc_norm\": 0.3888888888888889,\n\
\ \"acc_norm_stderr\": 0.04360314860077459\n },\n \"harness|hendrycksTest-global_facts|5\"\
: {\n \"acc\": 0.41,\n \"acc_stderr\": 0.049431107042371025,\n \
\ \"acc_norm\": 0.41,\n \"acc_norm_stderr\": 0.049431107042371025\n \
\ },\n \"harness|hendrycksTest-high_school_biology|5\": {\n \"acc\"\
: 0.7451612903225806,\n \"acc_stderr\": 0.024790118459332208,\n \"\
acc_norm\": 0.7451612903225806,\n \"acc_norm_stderr\": 0.024790118459332208\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.4729064039408867,\n \"acc_stderr\": 0.03512819077876106,\n \"\
acc_norm\": 0.4729064039408867,\n \"acc_norm_stderr\": 0.03512819077876106\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.69,\n \"acc_stderr\": 0.04648231987117316,\n \"acc_norm\"\
: 0.69,\n \"acc_norm_stderr\": 0.04648231987117316\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7818181818181819,\n \"acc_stderr\": 0.03225078108306289,\n\
\ \"acc_norm\": 0.7818181818181819,\n \"acc_norm_stderr\": 0.03225078108306289\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7929292929292929,\n \"acc_stderr\": 0.02886977846026704,\n \"\
acc_norm\": 0.7929292929292929,\n \"acc_norm_stderr\": 0.02886977846026704\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8393782383419689,\n \"acc_stderr\": 0.026499057701397443,\n\
\ \"acc_norm\": 0.8393782383419689,\n \"acc_norm_stderr\": 0.026499057701397443\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6076923076923076,\n \"acc_stderr\": 0.024756000382130952,\n\
\ \"acc_norm\": 0.6076923076923076,\n \"acc_norm_stderr\": 0.024756000382130952\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.2814814814814815,\n \"acc_stderr\": 0.02742001935094527,\n \
\ \"acc_norm\": 0.2814814814814815,\n \"acc_norm_stderr\": 0.02742001935094527\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6554621848739496,\n \"acc_stderr\": 0.030868682604121626,\n\
\ \"acc_norm\": 0.6554621848739496,\n \"acc_norm_stderr\": 0.030868682604121626\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.31788079470198677,\n \"acc_stderr\": 0.03802039760107903,\n \"\
acc_norm\": 0.31788079470198677,\n \"acc_norm_stderr\": 0.03802039760107903\n\
\ },\n \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\"\
: 0.8073394495412844,\n \"acc_stderr\": 0.01690927688493607,\n \"\
acc_norm\": 0.8073394495412844,\n \"acc_norm_stderr\": 0.01690927688493607\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49074074074074076,\n \"acc_stderr\": 0.034093869469927006,\n \"\
acc_norm\": 0.49074074074074076,\n \"acc_norm_stderr\": 0.034093869469927006\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.7990196078431373,\n \"acc_stderr\": 0.028125972265654373,\n \"\
acc_norm\": 0.7990196078431373,\n \"acc_norm_stderr\": 0.028125972265654373\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.759493670886076,\n \"acc_stderr\": 0.027820781981149685,\n \
\ \"acc_norm\": 0.759493670886076,\n \"acc_norm_stderr\": 0.027820781981149685\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.7085201793721974,\n\
\ \"acc_stderr\": 0.030500283176545843,\n \"acc_norm\": 0.7085201793721974,\n\
\ \"acc_norm_stderr\": 0.030500283176545843\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.6946564885496184,\n \"acc_stderr\": 0.040393149787245605,\n\
\ \"acc_norm\": 0.6946564885496184,\n \"acc_norm_stderr\": 0.040393149787245605\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7933884297520661,\n \"acc_stderr\": 0.036959801280988226,\n \"\
acc_norm\": 0.7933884297520661,\n \"acc_norm_stderr\": 0.036959801280988226\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\": 0.7423312883435583,\n \"acc_stderr\": 0.03436150827846917,\n\
\ \"acc_norm\": 0.7423312883435583,\n \"acc_norm_stderr\": 0.03436150827846917\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.5089285714285714,\n\
\ \"acc_stderr\": 0.04745033255489123,\n \"acc_norm\": 0.5089285714285714,\n\
\ \"acc_norm_stderr\": 0.04745033255489123\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.7378640776699029,\n \"acc_stderr\": 0.043546310772605956,\n\
\ \"acc_norm\": 0.7378640776699029,\n \"acc_norm_stderr\": 0.043546310772605956\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\": 0.72,\n \"acc_stderr\": 0.045126085985421276,\n \
\ \"acc_norm\": 0.72,\n \"acc_norm_stderr\": 0.045126085985421276\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.7982120051085568,\n\
\ \"acc_stderr\": 0.014351702181636856,\n \"acc_norm\": 0.7982120051085568,\n\
\ \"acc_norm_stderr\": 0.014351702181636856\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6936416184971098,\n \"acc_stderr\": 0.024818350129436593,\n\
\ \"acc_norm\": 0.6936416184971098,\n \"acc_norm_stderr\": 0.024818350129436593\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.3407821229050279,\n\
\ \"acc_stderr\": 0.015852002449862106,\n \"acc_norm\": 0.3407821229050279,\n\
\ \"acc_norm_stderr\": 0.015852002449862106\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.673202614379085,\n \"acc_stderr\": 0.026857294663281413,\n\
\ \"acc_norm\": 0.673202614379085,\n \"acc_norm_stderr\": 0.026857294663281413\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6913183279742765,\n\
\ \"acc_stderr\": 0.02623696588115327,\n \"acc_norm\": 0.6913183279742765,\n\
\ \"acc_norm_stderr\": 0.02623696588115327\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.7037037037037037,\n \"acc_stderr\": 0.025407197798890162,\n\
\ \"acc_norm\": 0.7037037037037037,\n \"acc_norm_stderr\": 0.025407197798890162\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.475177304964539,\n \"acc_stderr\": 0.02979071924382972,\n \
\ \"acc_norm\": 0.475177304964539,\n \"acc_norm_stderr\": 0.02979071924382972\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4491525423728814,\n\
\ \"acc_stderr\": 0.012704030518851488,\n \"acc_norm\": 0.4491525423728814,\n\
\ \"acc_norm_stderr\": 0.012704030518851488\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.6397058823529411,\n \"acc_stderr\": 0.029163128570670733,\n\
\ \"acc_norm\": 0.6397058823529411,\n \"acc_norm_stderr\": 0.029163128570670733\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.6323529411764706,\n \"acc_stderr\": 0.019506291693954847,\n \
\ \"acc_norm\": 0.6323529411764706,\n \"acc_norm_stderr\": 0.019506291693954847\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.5909090909090909,\n\
\ \"acc_stderr\": 0.04709306978661896,\n \"acc_norm\": 0.5909090909090909,\n\
\ \"acc_norm_stderr\": 0.04709306978661896\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.673469387755102,\n \"acc_stderr\": 0.0300210562384403,\n\
\ \"acc_norm\": 0.673469387755102,\n \"acc_norm_stderr\": 0.0300210562384403\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\": 0.81,\n \"acc_stderr\": 0.03942772444036625,\n \
\ \"acc_norm\": 0.81,\n \"acc_norm_stderr\": 0.03942772444036625\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4939759036144578,\n\
\ \"acc_stderr\": 0.03892212195333045,\n \"acc_norm\": 0.4939759036144578,\n\
\ \"acc_norm_stderr\": 0.03892212195333045\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8070175438596491,\n \"acc_stderr\": 0.030267457554898458,\n\
\ \"acc_norm\": 0.8070175438596491,\n \"acc_norm_stderr\": 0.030267457554898458\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.39657282741738065,\n\
\ \"mc1_stderr\": 0.017124930942023518,\n \"mc2\": 0.5646058699056372,\n\
\ \"mc2_stderr\": 0.015306312553856578\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7624309392265194,\n \"acc_stderr\": 0.011961298905803145\n\
\ },\n \"harness|drop|3\": {\n \"em\": 0.006711409395973154,\n \
\ \"em_stderr\": 0.0008361500895152437,\n \"f1\": 0.086758598993289,\n\
\ \"f1_stderr\": 0.0017937356641132749\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.23730098559514784,\n \"acc_stderr\": 0.011718409178739446\n\
\ }\n}\n```"
repo_url: https://huggingface.co/maywell/Synatra-7B-v0.3-dpo
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_11_20T08_03_37.008028
path:
- '**/details_harness|arc:challenge|25_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|drop|3_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|gsm8k|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hellaswag|10_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-20T08-03-37.008028.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-20T08-03-37.008028.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- '**/details_harness|winogrande|5_2023-11-20T08-03-37.008028.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-20T08-03-37.008028.parquet'
- config_name: results
data_files:
- split: 2023_11_20T08_03_37.008028
path:
- results_2023-11-20T08-03-37.008028.parquet
- split: latest
path:
- results_2023-11-20T08-03-37.008028.parquet
---
# Dataset Card for Evaluation run of maywell/Synatra-7B-v0.3-dpo
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/maywell/Synatra-7B-v0.3-dpo
- **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 [maywell/Synatra-7B-v0.3-dpo](https://huggingface.co/maywell/Synatra-7B-v0.3-dpo) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 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_maywell__Synatra-7B-v0.3-dpo_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-20T08:03:37.008028](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__Synatra-7B-v0.3-dpo_public/blob/main/results_2023-11-20T08-03-37.008028.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": 0.610854861666512,
"acc_stderr": 0.03282789791741049,
"acc_norm": 0.6184353715807913,
"acc_norm_stderr": 0.03351856767139879,
"mc1": 0.39657282741738065,
"mc1_stderr": 0.017124930942023518,
"mc2": 0.5646058699056372,
"mc2_stderr": 0.015306312553856578,
"em": 0.006711409395973154,
"em_stderr": 0.0008361500895152437,
"f1": 0.086758598993289,
"f1_stderr": 0.0017937356641132749
},
"harness|arc:challenge|25": {
"acc": 0.6006825938566553,
"acc_stderr": 0.014312094557946709,
"acc_norm": 0.6279863481228669,
"acc_norm_stderr": 0.014124597881844461
},
"harness|hellaswag|10": {
"acc": 0.6278629755028878,
"acc_stderr": 0.004823867761332464,
"acc_norm": 0.8258315076677952,
"acc_norm_stderr": 0.0037847921724660665
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.34,
"acc_stderr": 0.04760952285695235,
"acc_norm": 0.34,
"acc_norm_stderr": 0.04760952285695235
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5925925925925926,
"acc_stderr": 0.04244633238353228,
"acc_norm": 0.5925925925925926,
"acc_norm_stderr": 0.04244633238353228
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6578947368421053,
"acc_stderr": 0.03860731599316092,
"acc_norm": 0.6578947368421053,
"acc_norm_stderr": 0.03860731599316092
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.59,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.59,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.660377358490566,
"acc_stderr": 0.02914690474779833,
"acc_norm": 0.660377358490566,
"acc_norm_stderr": 0.02914690474779833
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.6875,
"acc_stderr": 0.038760854559127644,
"acc_norm": 0.6875,
"acc_norm_stderr": 0.038760854559127644
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.46,
"acc_stderr": 0.05009082659620333,
"acc_norm": 0.46,
"acc_norm_stderr": 0.05009082659620333
},
"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": 0.32,
"acc_stderr": 0.04688261722621505,
"acc_norm": 0.32,
"acc_norm_stderr": 0.04688261722621505
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.5953757225433526,
"acc_stderr": 0.03742461193887248,
"acc_norm": 0.5953757225433526,
"acc_norm_stderr": 0.03742461193887248
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.28431372549019607,
"acc_stderr": 0.04488482852329017,
"acc_norm": 0.28431372549019607,
"acc_norm_stderr": 0.04488482852329017
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.79,
"acc_stderr": 0.040936018074033256,
"acc_norm": 0.79,
"acc_norm_stderr": 0.040936018074033256
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5404255319148936,
"acc_stderr": 0.03257901482099835,
"acc_norm": 0.5404255319148936,
"acc_norm_stderr": 0.03257901482099835
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.4473684210526316,
"acc_stderr": 0.04677473004491199,
"acc_norm": 0.4473684210526316,
"acc_norm_stderr": 0.04677473004491199
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.5655172413793104,
"acc_stderr": 0.041307408795554966,
"acc_norm": 0.5655172413793104,
"acc_norm_stderr": 0.041307408795554966
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.42328042328042326,
"acc_stderr": 0.02544636563440678,
"acc_norm": 0.42328042328042326,
"acc_norm_stderr": 0.02544636563440678
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.3888888888888889,
"acc_stderr": 0.04360314860077459,
"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.04360314860077459
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.41,
"acc_stderr": 0.049431107042371025,
"acc_norm": 0.41,
"acc_norm_stderr": 0.049431107042371025
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.7451612903225806,
"acc_stderr": 0.024790118459332208,
"acc_norm": 0.7451612903225806,
"acc_norm_stderr": 0.024790118459332208
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.4729064039408867,
"acc_stderr": 0.03512819077876106,
"acc_norm": 0.4729064039408867,
"acc_norm_stderr": 0.03512819077876106
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.69,
"acc_stderr": 0.04648231987117316,
"acc_norm": 0.69,
"acc_norm_stderr": 0.04648231987117316
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.7818181818181819,
"acc_stderr": 0.03225078108306289,
"acc_norm": 0.7818181818181819,
"acc_norm_stderr": 0.03225078108306289
},
"harness|hendrycksTest-high_school_geography|5": {
"acc": 0.7929292929292929,
"acc_stderr": 0.02886977846026704,
"acc_norm": 0.7929292929292929,
"acc_norm_stderr": 0.02886977846026704
},
"harness|hendrycksTest-high_school_government_and_politics|5": {
"acc": 0.8393782383419689,
"acc_stderr": 0.026499057701397443,
"acc_norm": 0.8393782383419689,
"acc_norm_stderr": 0.026499057701397443
},
"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6076923076923076,
"acc_stderr": 0.024756000382130952,
"acc_norm": 0.6076923076923076,
"acc_norm_stderr": 0.024756000382130952
},
"harness|hendrycksTest-high_school_mathematics|5": {
"acc": 0.2814814814814815,
"acc_stderr": 0.02742001935094527,
"acc_norm": 0.2814814814814815,
"acc_norm_stderr": 0.02742001935094527
},
"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6554621848739496,
"acc_stderr": 0.030868682604121626,
"acc_norm": 0.6554621848739496,
"acc_norm_stderr": 0.030868682604121626
},
"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31788079470198677,
"acc_stderr": 0.03802039760107903,
"acc_norm": 0.31788079470198677,
"acc_norm_stderr": 0.03802039760107903
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8073394495412844,
"acc_stderr": 0.01690927688493607,
"acc_norm": 0.8073394495412844,
"acc_norm_stderr": 0.01690927688493607
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49074074074074076,
"acc_stderr": 0.034093869469927006,
"acc_norm": 0.49074074074074076,
"acc_norm_stderr": 0.034093869469927006
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.7990196078431373,
"acc_stderr": 0.028125972265654373,
"acc_norm": 0.7990196078431373,
"acc_norm_stderr": 0.028125972265654373
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.759493670886076,
"acc_stderr": 0.027820781981149685,
"acc_norm": 0.759493670886076,
"acc_norm_stderr": 0.027820781981149685
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.7085201793721974,
"acc_stderr": 0.030500283176545843,
"acc_norm": 0.7085201793721974,
"acc_norm_stderr": 0.030500283176545843
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.6946564885496184,
"acc_stderr": 0.040393149787245605,
"acc_norm": 0.6946564885496184,
"acc_norm_stderr": 0.040393149787245605
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7933884297520661,
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-virology|5": {
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"harness|hendrycksTest-world_religions|5": {
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"acc_norm_stderr": 0.030267457554898458
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"harness|truthfulqa:mc|0": {
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"harness|winogrande|5": {
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"harness|drop|3": {
"em": 0.006711409395973154,
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"f1": 0.086758598993289,
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"harness|gsm8k|5": {
"acc": 0.23730098559514784,
"acc_stderr": 0.011718409178739446
}
}
```
### 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] | [
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nisha05/dataset.txt | nisha05 | 2023-11-20T08:13:04Z | 0 | 0 | null | [
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] | 2023-11-20T08:13:04Z | 2023-11-20T08:13:03.000Z | 2023-11-20T08:13:03 | ---
dataset_info:
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---
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lewisbails/esg-fine-risks | lewisbails | 2023-11-20T08:24:28Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T08:24:28Z | 2023-11-20T08:24:23.000Z | 2023-11-20T08:24:23 | ---
configs:
- config_name: default
data_files:
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path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
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- name: test
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num_examples: 401
download_size: 13297109
dataset_size: 27732636.10905881
---
# Dataset Card for "esg-fine-risks"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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tam801/translated | tam801 | 2023-11-20T08:26:36Z | 0 | 0 | null | [
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---
# Dataset Card for "AutomaticSpeechRecognition_LibriSpeech-TestOther"
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open-llm-leaderboard/details_maywell__koOpenChat-sft_public | open-llm-leaderboard | 2023-11-20T08:40:25Z | 0 | 0 | null | [
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pretty_name: Evaluation run of maywell/koOpenChat-sft
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [maywell/koOpenChat-sft](https://huggingface.co/maywell/koOpenChat-sft) on the\
\ [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 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_maywell__koOpenChat-sft_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-20T08:36:25.253046](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__koOpenChat-sft_public/blob/main/results_2023-11-20T08-36-25.253046.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\": 0.6084632908836825,\n\
\ \"acc_stderr\": 0.03295483776577676,\n \"acc_norm\": 0.6158685044863811,\n\
\ \"acc_norm_stderr\": 0.03365334045258809,\n \"mc1\": 0.3378212974296206,\n\
\ \"mc1_stderr\": 0.01655716732251688,\n \"mc2\": 0.5124049209846685,\n\
\ \"mc2_stderr\": 0.014984310875510325,\n \"em\": 0.005138422818791947,\n\
\ \"em_stderr\": 0.0007322104102794216,\n \"f1\": 0.07822776845637572,\n\
\ \"f1_stderr\": 0.0016538004844235878\n },\n \"harness|arc:challenge|25\"\
: {\n \"acc\": 0.568259385665529,\n \"acc_stderr\": 0.014474591427196202,\n\
\ \"acc_norm\": 0.5981228668941979,\n \"acc_norm_stderr\": 0.014327268614578273\n\
\ },\n \"harness|hellaswag|10\": {\n \"acc\": 0.5913164708225453,\n\
\ \"acc_stderr\": 0.004905859114942294,\n \"acc_norm\": 0.7872933678550089,\n\
\ \"acc_norm_stderr\": 0.004083855139469325\n },\n \"harness|hendrycksTest-abstract_algebra|5\"\
: {\n \"acc\": 0.37,\n \"acc_stderr\": 0.04852365870939098,\n \
\ \"acc_norm\": 0.37,\n \"acc_norm_stderr\": 0.04852365870939098\n \
\ },\n \"harness|hendrycksTest-anatomy|5\": {\n \"acc\": 0.5481481481481482,\n\
\ \"acc_stderr\": 0.042992689054808644,\n \"acc_norm\": 0.5481481481481482,\n\
\ \"acc_norm_stderr\": 0.042992689054808644\n },\n \"harness|hendrycksTest-astronomy|5\"\
: {\n \"acc\": 0.6578947368421053,\n \"acc_stderr\": 0.038607315993160904,\n\
\ \"acc_norm\": 0.6578947368421053,\n \"acc_norm_stderr\": 0.038607315993160904\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\": 0.6943396226415094,\n \"acc_stderr\": 0.028353298073322666,\n\
\ \"acc_norm\": 0.6943396226415094,\n \"acc_norm_stderr\": 0.028353298073322666\n\
\ },\n \"harness|hendrycksTest-college_biology|5\": {\n \"acc\": 0.6527777777777778,\n\
\ \"acc_stderr\": 0.039812405437178615,\n \"acc_norm\": 0.6527777777777778,\n\
\ \"acc_norm_stderr\": 0.039812405437178615\n },\n \"harness|hendrycksTest-college_chemistry|5\"\
: {\n \"acc\": 0.49,\n \"acc_stderr\": 0.05024183937956913,\n \
\ \"acc_norm\": 0.49,\n \"acc_norm_stderr\": 0.05024183937956913\n \
\ },\n \"harness|hendrycksTest-college_computer_science|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-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\": 0.6416184971098265,\n\
\ \"acc_stderr\": 0.03656343653353159,\n \"acc_norm\": 0.6416184971098265,\n\
\ \"acc_norm_stderr\": 0.03656343653353159\n },\n \"harness|hendrycksTest-college_physics|5\"\
: {\n \"acc\": 0.28431372549019607,\n \"acc_stderr\": 0.04488482852329017,\n\
\ \"acc_norm\": 0.28431372549019607,\n \"acc_norm_stderr\": 0.04488482852329017\n\
\ },\n \"harness|hendrycksTest-computer_security|5\": {\n \"acc\":\
\ 0.76,\n \"acc_stderr\": 0.042923469599092816,\n \"acc_norm\": 0.76,\n\
\ \"acc_norm_stderr\": 0.042923469599092816\n },\n \"harness|hendrycksTest-conceptual_physics|5\"\
: {\n \"acc\": 0.5617021276595745,\n \"acc_stderr\": 0.03243618636108102,\n\
\ \"acc_norm\": 0.5617021276595745,\n \"acc_norm_stderr\": 0.03243618636108102\n\
\ },\n \"harness|hendrycksTest-econometrics|5\": {\n \"acc\": 0.43859649122807015,\n\
\ \"acc_stderr\": 0.04668000738510455,\n \"acc_norm\": 0.43859649122807015,\n\
\ \"acc_norm_stderr\": 0.04668000738510455\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\"\
: 0.3888888888888889,\n \"acc_stderr\": 0.025107425481137285,\n \"\
acc_norm\": 0.3888888888888889,\n \"acc_norm_stderr\": 0.025107425481137285\n\
\ },\n \"harness|hendrycksTest-formal_logic|5\": {\n \"acc\": 0.42063492063492064,\n\
\ \"acc_stderr\": 0.04415438226743744,\n \"acc_norm\": 0.42063492063492064,\n\
\ \"acc_norm_stderr\": 0.04415438226743744\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\": 0.7451612903225806,\n\
\ \"acc_stderr\": 0.024790118459332208,\n \"acc_norm\": 0.7451612903225806,\n\
\ \"acc_norm_stderr\": 0.024790118459332208\n },\n \"harness|hendrycksTest-high_school_chemistry|5\"\
: {\n \"acc\": 0.458128078817734,\n \"acc_stderr\": 0.03505630140785741,\n\
\ \"acc_norm\": 0.458128078817734,\n \"acc_norm_stderr\": 0.03505630140785741\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|5\": {\n \
\ \"acc\": 0.65,\n \"acc_stderr\": 0.04793724854411019,\n \"acc_norm\"\
: 0.65,\n \"acc_norm_stderr\": 0.04793724854411019\n },\n \"harness|hendrycksTest-high_school_european_history|5\"\
: {\n \"acc\": 0.7333333333333333,\n \"acc_stderr\": 0.03453131801885417,\n\
\ \"acc_norm\": 0.7333333333333333,\n \"acc_norm_stderr\": 0.03453131801885417\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.7525252525252525,\n \"acc_stderr\": 0.030746300742124484,\n \"\
acc_norm\": 0.7525252525252525,\n \"acc_norm_stderr\": 0.030746300742124484\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.8808290155440415,\n \"acc_stderr\": 0.023381935348121434,\n\
\ \"acc_norm\": 0.8808290155440415,\n \"acc_norm_stderr\": 0.023381935348121434\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.6256410256410256,\n \"acc_stderr\": 0.0245375915728305,\n \
\ \"acc_norm\": 0.6256410256410256,\n \"acc_norm_stderr\": 0.0245375915728305\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.34444444444444444,\n \"acc_stderr\": 0.02897264888484427,\n \
\ \"acc_norm\": 0.34444444444444444,\n \"acc_norm_stderr\": 0.02897264888484427\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.6428571428571429,\n \"acc_stderr\": 0.031124619309328177,\n\
\ \"acc_norm\": 0.6428571428571429,\n \"acc_norm_stderr\": 0.031124619309328177\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\"\
: 0.8238532110091743,\n \"acc_stderr\": 0.016332882393431374,\n \"\
acc_norm\": 0.8238532110091743,\n \"acc_norm_stderr\": 0.016332882393431374\n\
\ },\n \"harness|hendrycksTest-high_school_statistics|5\": {\n \"acc\"\
: 0.49537037037037035,\n \"acc_stderr\": 0.03409825519163572,\n \"\
acc_norm\": 0.49537037037037035,\n \"acc_norm_stderr\": 0.03409825519163572\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.803921568627451,\n \"acc_stderr\": 0.027865942286639318,\n \"\
acc_norm\": 0.803921568627451,\n \"acc_norm_stderr\": 0.027865942286639318\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.8016877637130801,\n \"acc_stderr\": 0.02595502084162113,\n \
\ \"acc_norm\": 0.8016877637130801,\n \"acc_norm_stderr\": 0.02595502084162113\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\": 0.732824427480916,\n \"acc_stderr\": 0.03880848301082395,\n\
\ \"acc_norm\": 0.732824427480916,\n \"acc_norm_stderr\": 0.03880848301082395\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.7768595041322314,\n \"acc_stderr\": 0.03800754475228732,\n \"\
acc_norm\": 0.7768595041322314,\n \"acc_norm_stderr\": 0.03800754475228732\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.7407407407407407,\n\
\ \"acc_stderr\": 0.04236511258094633,\n \"acc_norm\": 0.7407407407407407,\n\
\ \"acc_norm_stderr\": 0.04236511258094633\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\": 0.7961165048543689,\n \"acc_stderr\": 0.039891398595317706,\n\
\ \"acc_norm\": 0.7961165048543689,\n \"acc_norm_stderr\": 0.039891398595317706\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.8632478632478633,\n\
\ \"acc_stderr\": 0.02250903393707781,\n \"acc_norm\": 0.8632478632478633,\n\
\ \"acc_norm_stderr\": 0.02250903393707781\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\": 0.7956577266922095,\n\
\ \"acc_stderr\": 0.014419123980931899,\n \"acc_norm\": 0.7956577266922095,\n\
\ \"acc_norm_stderr\": 0.014419123980931899\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.6965317919075145,\n \"acc_stderr\": 0.024752411960917205,\n\
\ \"acc_norm\": 0.6965317919075145,\n \"acc_norm_stderr\": 0.024752411960917205\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.4134078212290503,\n\
\ \"acc_stderr\": 0.01646981492840617,\n \"acc_norm\": 0.4134078212290503,\n\
\ \"acc_norm_stderr\": 0.01646981492840617\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.6503267973856209,\n \"acc_stderr\": 0.027305308076274695,\n\
\ \"acc_norm\": 0.6503267973856209,\n \"acc_norm_stderr\": 0.027305308076274695\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.6977491961414791,\n\
\ \"acc_stderr\": 0.02608270069539966,\n \"acc_norm\": 0.6977491961414791,\n\
\ \"acc_norm_stderr\": 0.02608270069539966\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.6481481481481481,\n \"acc_stderr\": 0.026571483480719967,\n\
\ \"acc_norm\": 0.6481481481481481,\n \"acc_norm_stderr\": 0.026571483480719967\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.46808510638297873,\n \"acc_stderr\": 0.029766675075873866,\n \
\ \"acc_norm\": 0.46808510638297873,\n \"acc_norm_stderr\": 0.029766675075873866\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.4511082138200782,\n\
\ \"acc_stderr\": 0.012709037347346233,\n \"acc_norm\": 0.4511082138200782,\n\
\ \"acc_norm_stderr\": 0.012709037347346233\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.5588235294117647,\n \"acc_stderr\": 0.030161911930767112,\n\
\ \"acc_norm\": 0.5588235294117647,\n \"acc_norm_stderr\": 0.030161911930767112\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.619281045751634,\n \"acc_stderr\": 0.019643801557924803,\n \
\ \"acc_norm\": 0.619281045751634,\n \"acc_norm_stderr\": 0.019643801557924803\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\": 0.6489795918367347,\n \"acc_stderr\": 0.030555316755573637,\n\
\ \"acc_norm\": 0.6489795918367347,\n \"acc_norm_stderr\": 0.030555316755573637\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.7910447761194029,\n\
\ \"acc_stderr\": 0.028748298931728655,\n \"acc_norm\": 0.7910447761194029,\n\
\ \"acc_norm_stderr\": 0.028748298931728655\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.8,\n \"acc_stderr\": 0.04020151261036845,\n \
\ \"acc_norm\": 0.8,\n \"acc_norm_stderr\": 0.04020151261036845\n },\n\
\ \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.4759036144578313,\n\
\ \"acc_stderr\": 0.038879718495972646,\n \"acc_norm\": 0.4759036144578313,\n\
\ \"acc_norm_stderr\": 0.038879718495972646\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.8011695906432749,\n \"acc_stderr\": 0.03061111655743253,\n\
\ \"acc_norm\": 0.8011695906432749,\n \"acc_norm_stderr\": 0.03061111655743253\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.3378212974296206,\n\
\ \"mc1_stderr\": 0.01655716732251688,\n \"mc2\": 0.5124049209846685,\n\
\ \"mc2_stderr\": 0.014984310875510325\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.7640094711917916,\n \"acc_stderr\": 0.011933828850275626\n\
\ },\n \"harness|drop|3\": {\n \"em\": 0.005138422818791947,\n \
\ \"em_stderr\": 0.0007322104102794216,\n \"f1\": 0.07822776845637572,\n\
\ \"f1_stderr\": 0.0016538004844235878\n },\n \"harness|gsm8k|5\":\
\ {\n \"acc\": 0.24184988627748294,\n \"acc_stderr\": 0.011794861371318695\n\
\ }\n}\n```"
repo_url: https://huggingface.co/maywell/koOpenChat-sft
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_11_20T08_36_25.253046
path:
- '**/details_harness|arc:challenge|25_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|drop|3_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|gsm8k|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hellaswag|10_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-20T08-36-25.253046.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-20T08-36-25.253046.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- '**/details_harness|winogrande|5_2023-11-20T08-36-25.253046.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-20T08-36-25.253046.parquet'
- config_name: results
data_files:
- split: 2023_11_20T08_36_25.253046
path:
- results_2023-11-20T08-36-25.253046.parquet
- split: latest
path:
- results_2023-11-20T08-36-25.253046.parquet
---
# Dataset Card for Evaluation run of maywell/koOpenChat-sft
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/maywell/koOpenChat-sft
- **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 [maywell/koOpenChat-sft](https://huggingface.co/maywell/koOpenChat-sft) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 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_maywell__koOpenChat-sft_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-20T08:36:25.253046](https://huggingface.co/datasets/open-llm-leaderboard/details_maywell__koOpenChat-sft_public/blob/main/results_2023-11-20T08-36-25.253046.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": 0.6084632908836825,
"acc_stderr": 0.03295483776577676,
"acc_norm": 0.6158685044863811,
"acc_norm_stderr": 0.03365334045258809,
"mc1": 0.3378212974296206,
"mc1_stderr": 0.01655716732251688,
"mc2": 0.5124049209846685,
"mc2_stderr": 0.014984310875510325,
"em": 0.005138422818791947,
"em_stderr": 0.0007322104102794216,
"f1": 0.07822776845637572,
"f1_stderr": 0.0016538004844235878
},
"harness|arc:challenge|25": {
"acc": 0.568259385665529,
"acc_stderr": 0.014474591427196202,
"acc_norm": 0.5981228668941979,
"acc_norm_stderr": 0.014327268614578273
},
"harness|hellaswag|10": {
"acc": 0.5913164708225453,
"acc_stderr": 0.004905859114942294,
"acc_norm": 0.7872933678550089,
"acc_norm_stderr": 0.004083855139469325
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.37,
"acc_stderr": 0.04852365870939098,
"acc_norm": 0.37,
"acc_norm_stderr": 0.04852365870939098
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.5481481481481482,
"acc_stderr": 0.042992689054808644,
"acc_norm": 0.5481481481481482,
"acc_norm_stderr": 0.042992689054808644
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.6578947368421053,
"acc_stderr": 0.038607315993160904,
"acc_norm": 0.6578947368421053,
"acc_norm_stderr": 0.038607315993160904
},
"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": 0.6943396226415094,
"acc_stderr": 0.028353298073322666,
"acc_norm": 0.6943396226415094,
"acc_norm_stderr": 0.028353298073322666
},
"harness|hendrycksTest-college_biology|5": {
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"acc_stderr": 0.039812405437178615,
"acc_norm": 0.6527777777777778,
"acc_norm_stderr": 0.039812405437178615
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.49,
"acc_stderr": 0.05024183937956913,
"acc_norm": 0.49,
"acc_norm_stderr": 0.05024183937956913
},
"harness|hendrycksTest-college_computer_science|5": {
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"acc_norm": 0.52,
"acc_norm_stderr": 0.050211673156867795
},
"harness|hendrycksTest-college_mathematics|5": {
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"acc_norm": 0.38,
"acc_norm_stderr": 0.04878317312145633
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.6416184971098265,
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"acc_norm_stderr": 0.03656343653353159
},
"harness|hendrycksTest-college_physics|5": {
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"acc_norm": 0.28431372549019607,
"acc_norm_stderr": 0.04488482852329017
},
"harness|hendrycksTest-computer_security|5": {
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"acc_norm": 0.76,
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},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.5617021276595745,
"acc_stderr": 0.03243618636108102,
"acc_norm": 0.5617021276595745,
"acc_norm_stderr": 0.03243618636108102
},
"harness|hendrycksTest-econometrics|5": {
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"acc_norm": 0.43859649122807015,
"acc_norm_stderr": 0.04668000738510455
},
"harness|hendrycksTest-electrical_engineering|5": {
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"harness|hendrycksTest-elementary_mathematics|5": {
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"acc_norm": 0.3888888888888889,
"acc_norm_stderr": 0.025107425481137285
},
"harness|hendrycksTest-formal_logic|5": {
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"harness|hendrycksTest-global_facts|5": {
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"acc_norm": 0.34,
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},
"harness|hendrycksTest-high_school_biology|5": {
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"acc_norm": 0.7451612903225806,
"acc_norm_stderr": 0.024790118459332208
},
"harness|hendrycksTest-high_school_chemistry|5": {
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},
"harness|hendrycksTest-high_school_computer_science|5": {
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},
"harness|hendrycksTest-high_school_european_history|5": {
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},
"harness|hendrycksTest-high_school_geography|5": {
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"harness|hendrycksTest-high_school_macroeconomics|5": {
"acc": 0.6256410256410256,
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"acc_norm": 0.6256410256410256,
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},
"harness|hendrycksTest-high_school_mathematics|5": {
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"harness|hendrycksTest-high_school_microeconomics|5": {
"acc": 0.6428571428571429,
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"acc_norm": 0.6428571428571429,
"acc_norm_stderr": 0.031124619309328177
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"harness|hendrycksTest-high_school_physics|5": {
"acc": 0.31788079470198677,
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"acc_norm_stderr": 0.038020397601079024
},
"harness|hendrycksTest-high_school_psychology|5": {
"acc": 0.8238532110091743,
"acc_stderr": 0.016332882393431374,
"acc_norm": 0.8238532110091743,
"acc_norm_stderr": 0.016332882393431374
},
"harness|hendrycksTest-high_school_statistics|5": {
"acc": 0.49537037037037035,
"acc_stderr": 0.03409825519163572,
"acc_norm": 0.49537037037037035,
"acc_norm_stderr": 0.03409825519163572
},
"harness|hendrycksTest-high_school_us_history|5": {
"acc": 0.803921568627451,
"acc_stderr": 0.027865942286639318,
"acc_norm": 0.803921568627451,
"acc_norm_stderr": 0.027865942286639318
},
"harness|hendrycksTest-high_school_world_history|5": {
"acc": 0.8016877637130801,
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"acc_norm": 0.8016877637130801,
"acc_norm_stderr": 0.02595502084162113
},
"harness|hendrycksTest-human_aging|5": {
"acc": 0.6771300448430493,
"acc_stderr": 0.03138147637575499,
"acc_norm": 0.6771300448430493,
"acc_norm_stderr": 0.03138147637575499
},
"harness|hendrycksTest-human_sexuality|5": {
"acc": 0.732824427480916,
"acc_stderr": 0.03880848301082395,
"acc_norm": 0.732824427480916,
"acc_norm_stderr": 0.03880848301082395
},
"harness|hendrycksTest-international_law|5": {
"acc": 0.7768595041322314,
"acc_stderr": 0.03800754475228732,
"acc_norm": 0.7768595041322314,
"acc_norm_stderr": 0.03800754475228732
},
"harness|hendrycksTest-jurisprudence|5": {
"acc": 0.7407407407407407,
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"acc_norm": 0.7407407407407407,
"acc_norm_stderr": 0.04236511258094633
},
"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": 0.7961165048543689,
"acc_stderr": 0.039891398595317706,
"acc_norm": 0.7961165048543689,
"acc_norm_stderr": 0.039891398595317706
},
"harness|hendrycksTest-marketing|5": {
"acc": 0.8632478632478633,
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"acc_norm": 0.8632478632478633,
"acc_norm_stderr": 0.02250903393707781
},
"harness|hendrycksTest-medical_genetics|5": {
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"acc_norm": 0.7,
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},
"harness|hendrycksTest-miscellaneous|5": {
"acc": 0.7956577266922095,
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"harness|hendrycksTest-moral_disputes|5": {
"acc": 0.6965317919075145,
"acc_stderr": 0.024752411960917205,
"acc_norm": 0.6965317919075145,
"acc_norm_stderr": 0.024752411960917205
},
"harness|hendrycksTest-moral_scenarios|5": {
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"acc_norm": 0.4134078212290503,
"acc_norm_stderr": 0.01646981492840617
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"harness|hendrycksTest-nutrition|5": {
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"acc_norm": 0.6503267973856209,
"acc_norm_stderr": 0.027305308076274695
},
"harness|hendrycksTest-philosophy|5": {
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"harness|hendrycksTest-prehistory|5": {
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"acc_norm_stderr": 0.026571483480719967
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"harness|hendrycksTest-professional_accounting|5": {
"acc": 0.46808510638297873,
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"acc_norm_stderr": 0.029766675075873866
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"harness|hendrycksTest-professional_law|5": {
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"harness|hendrycksTest-professional_medicine|5": {
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},
"harness|hendrycksTest-public_relations|5": {
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"acc_norm_stderr": 0.04607582090719976
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"harness|hendrycksTest-security_studies|5": {
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"acc_norm": 0.6489795918367347,
"acc_norm_stderr": 0.030555316755573637
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"harness|hendrycksTest-sociology|5": {
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"acc_norm_stderr": 0.028748298931728655
},
"harness|hendrycksTest-us_foreign_policy|5": {
"acc": 0.8,
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"acc_norm": 0.8,
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},
"harness|hendrycksTest-virology|5": {
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},
"harness|hendrycksTest-world_religions|5": {
"acc": 0.8011695906432749,
"acc_stderr": 0.03061111655743253,
"acc_norm": 0.8011695906432749,
"acc_norm_stderr": 0.03061111655743253
},
"harness|truthfulqa:mc|0": {
"mc1": 0.3378212974296206,
"mc1_stderr": 0.01655716732251688,
"mc2": 0.5124049209846685,
"mc2_stderr": 0.014984310875510325
},
"harness|winogrande|5": {
"acc": 0.7640094711917916,
"acc_stderr": 0.011933828850275626
},
"harness|drop|3": {
"em": 0.005138422818791947,
"em_stderr": 0.0007322104102794216,
"f1": 0.07822776845637572,
"f1_stderr": 0.0016538004844235878
},
"harness|gsm8k|5": {
"acc": 0.24184988627748294,
"acc_stderr": 0.011794861371318695
}
}
```
### 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] | [
-0.6925712823867798,
-0.8574981689453125,
0.2753567397594452,
0.22015774250030518,
-0.17201729118824005,
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0.016547981649637222,
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0.5567726492881775,
-0.04849126562476158,
-0.4844370484352112,
-0.7218382954597473,
-0.4251364469528198,
0.238395899534... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
DynamicSuperb/AutomaticSpeechRecognition_LJSpeech | DynamicSuperb | 2023-11-20T08:45:26Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T08:45:26Z | 2023-11-20T08:42:22.000Z | 2023-11-20T08:42:22 | ---
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
dataset_info:
features:
- name: file
dtype: string
- name: audio
dtype: audio
- name: label
dtype: string
- name: instruction
dtype: string
splits:
- name: test
num_bytes: 3800884574.0
num_examples: 13100
download_size: 3785131725
dataset_size: 3800884574.0
---
# Dataset Card for "AutomaticSpeechRecognition_LJSpeech"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
-0.5252369046211243,
-0.16360914707183838,
0.23037736117839813,
0.22782596945762634,
-0.06600980460643768,
0.022636733949184418,
0.2790595591068268,
-0.34431347250938416,
1.0338140726089478,
0.4491541087627411,
-0.7841199636459351,
-0.5509805083274841,
-0.6215600371360779,
-0.1179908886551... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
patrickshitou/ArcMMLU | patrickshitou | 2023-11-20T08:52:15Z | 0 | 0 | null | [
"license:cc-by-nc-sa-4.0",
"arxiv:2307.14852",
"region:us"
] | 2023-11-20T08:52:15Z | 2023-11-20T08:44:58.000Z | 2023-11-20T08:44:58 | ---
license: cc-by-nc-sa-4.0
---
## Introduction
[ArcMMLU](https://github.com/stzhang-patrick/ArcMMLU) is a Chinese benchmark specifically designed for evaluating LLMs on Library & Information Science (LIS). It aims to evaluate the knowledge and reasoning capabilities of LLMs in the LIS academic field, which covers four key sub-areas: Archival Science, Data Science, Library Science, and Information Science.
It is important to note that the name ArcMMLU is derived from our previous large language model research project—[ArcGPT](https://arxiv.org/abs/2307.14852), which was primarily focused on Archival Science. Later, our research scope expanded from Archival Science to a broader field of information management, but we retained the name ArcMMLU. Therefore, ArcMMLU is not just an evaluation benchmark for Archival Science; it is a comprehensive evaluation dataset for the entire LIS discipline.
For the sake of convenience, ArcMMLU adopts the same data format as CMMLU. Furthermore, based on the CMMLU project, we provide evaluation code. For models that have been evaluated on CMMLU, conducting an evaluation on ArcMMLU will be pretty straightforward. Special thanks to the [CMMLU---Chinese Multi-Task Language Understanding Evaluation](https://github.com/haonan-li/CMMLU) project for its contribution to the evaluation of Chinese LLMs. We hope that ArcMMLU can serve as a powerful supplement in specialized fields, bringing more detail and depth to the evaluation of Chinese LLMs.
| [
-0.36608925461769104,
-0.6399866342544556,
0.48058632016181946,
0.026714138686656952,
-0.26231878995895386,
-0.029393544420599937,
-0.3124750852584839,
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-0.2105703055858612,
0.4513220489025116,
-0.48000410199165344,
-0.7313660979270935,
-0.3844488561153412,
0.106378123... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
frank-chieng/lanhua_oil | frank-chieng | 2023-11-20T09:07:26Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T09:07:26Z | 2023-11-20T09:02:58.000Z | 2023-11-20T09:02:58 | Entry not found | [
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0.2563217282295227,
-0.7852813005447388,
-0.22573819756507874,
-0.9104475975036621,
0.5715674161911011,
-... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
open-llm-leaderboard/details_l3utterfly__mistral-7b-v0.1-layla-v2_public | open-llm-leaderboard | 2023-11-20T09:07:49Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T09:07:49Z | 2023-11-20T09:07:04.000Z | 2023-11-20T09:07:04 | ---
pretty_name: Evaluation run of l3utterfly/mistral-7b-v0.1-layla-v2
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [l3utterfly/mistral-7b-v0.1-layla-v2](https://huggingface.co/l3utterfly/mistral-7b-v0.1-layla-v2)\
\ on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).\n\
\nThe dataset is composed of 64 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_l3utterfly__mistral-7b-v0.1-layla-v2_public\"\
,\n\t\"harness_winogrande_5\",\n\tsplit=\"train\")\n```\n\n## Latest results\n\n\
These are the [latest results from run 2023-11-20T09:04:01.597218](https://huggingface.co/datasets/open-llm-leaderboard/details_l3utterfly__mistral-7b-v0.1-layla-v2_public/blob/main/results_2023-11-20T09-04-01.597218.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\": 0.25336826428615794,\n\
\ \"acc_stderr\": 0.030795119371799243,\n \"acc_norm\": 0.25419620341620086,\n\
\ \"acc_norm_stderr\": 0.0316093435882046,\n \"mc1\": 0.2350061199510404,\n\
\ \"mc1_stderr\": 0.014843061507731601,\n \"mc2\": 0.4904100535198025,\n\
\ \"mc2_stderr\": 0.017085995013096343,\n \"em\": 0.0,\n \"\
em_stderr\": 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0\n },\n \
\ \"harness|arc:challenge|25\": {\n \"acc\": 0.24061433447098976,\n \
\ \"acc_stderr\": 0.012491468532390578,\n \"acc_norm\": 0.27047781569965873,\n\
\ \"acc_norm_stderr\": 0.012980954547659558\n },\n \"harness|hellaswag|10\"\
: {\n \"acc\": 0.25712009559848636,\n \"acc_stderr\": 0.004361529679492745,\n\
\ \"acc_norm\": 0.25871340370444135,\n \"acc_norm_stderr\": 0.004370328224831781\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\": 0.3333333333333333,\n \"acc_stderr\": 0.04072314811876837,\n\
\ \"acc_norm\": 0.3333333333333333,\n \"acc_norm_stderr\": 0.04072314811876837\n\
\ },\n \"harness|hendrycksTest-astronomy|5\": {\n \"acc\": 0.3026315789473684,\n\
\ \"acc_stderr\": 0.037385206761196665,\n \"acc_norm\": 0.3026315789473684,\n\
\ \"acc_norm_stderr\": 0.037385206761196665\n },\n \"harness|hendrycksTest-business_ethics|5\"\
: {\n \"acc\": 0.23,\n \"acc_stderr\": 0.04229525846816506,\n \
\ \"acc_norm\": 0.23,\n \"acc_norm_stderr\": 0.04229525846816506\n \
\ },\n \"harness|hendrycksTest-clinical_knowledge|5\": {\n \"acc\": 0.2188679245283019,\n\
\ \"acc_stderr\": 0.02544786382510861,\n \"acc_norm\": 0.2188679245283019,\n\
\ \"acc_norm_stderr\": 0.02544786382510861\n },\n \"harness|hendrycksTest-college_biology|5\"\
: {\n \"acc\": 0.2569444444444444,\n \"acc_stderr\": 0.03653946969442099,\n\
\ \"acc_norm\": 0.2569444444444444,\n \"acc_norm_stderr\": 0.03653946969442099\n\
\ },\n \"harness|hendrycksTest-college_chemistry|5\": {\n \"acc\":\
\ 0.18,\n \"acc_stderr\": 0.03861229196653694,\n \"acc_norm\": 0.18,\n\
\ \"acc_norm_stderr\": 0.03861229196653694\n },\n \"harness|hendrycksTest-college_computer_science|5\"\
: {\n \"acc\": 0.26,\n \"acc_stderr\": 0.0440844002276808,\n \
\ \"acc_norm\": 0.26,\n \"acc_norm_stderr\": 0.0440844002276808\n },\n\
\ \"harness|hendrycksTest-college_mathematics|5\": {\n \"acc\": 0.25,\n\
\ \"acc_stderr\": 0.04351941398892446,\n \"acc_norm\": 0.25,\n \
\ \"acc_norm_stderr\": 0.04351941398892446\n },\n \"harness|hendrycksTest-college_medicine|5\"\
: {\n \"acc\": 0.24855491329479767,\n \"acc_stderr\": 0.03295304696818318,\n\
\ \"acc_norm\": 0.24855491329479767,\n \"acc_norm_stderr\": 0.03295304696818318\n\
\ },\n \"harness|hendrycksTest-college_physics|5\": {\n \"acc\": 0.21568627450980393,\n\
\ \"acc_stderr\": 0.04092563958237655,\n \"acc_norm\": 0.21568627450980393,\n\
\ \"acc_norm_stderr\": 0.04092563958237655\n },\n \"harness|hendrycksTest-computer_security|5\"\
: {\n \"acc\": 0.3,\n \"acc_stderr\": 0.046056618647183814,\n \
\ \"acc_norm\": 0.3,\n \"acc_norm_stderr\": 0.046056618647183814\n \
\ },\n \"harness|hendrycksTest-conceptual_physics|5\": {\n \"acc\": 0.20425531914893616,\n\
\ \"acc_stderr\": 0.026355158413349424,\n \"acc_norm\": 0.20425531914893616,\n\
\ \"acc_norm_stderr\": 0.026355158413349424\n },\n \"harness|hendrycksTest-econometrics|5\"\
: {\n \"acc\": 0.24561403508771928,\n \"acc_stderr\": 0.04049339297748141,\n\
\ \"acc_norm\": 0.24561403508771928,\n \"acc_norm_stderr\": 0.04049339297748141\n\
\ },\n \"harness|hendrycksTest-electrical_engineering|5\": {\n \"acc\"\
: 0.296551724137931,\n \"acc_stderr\": 0.03806142687309993,\n \"acc_norm\"\
: 0.296551724137931,\n \"acc_norm_stderr\": 0.03806142687309993\n },\n\
\ \"harness|hendrycksTest-elementary_mathematics|5\": {\n \"acc\": 0.2671957671957672,\n\
\ \"acc_stderr\": 0.02278967314577656,\n \"acc_norm\": 0.2671957671957672,\n\
\ \"acc_norm_stderr\": 0.02278967314577656\n },\n \"harness|hendrycksTest-formal_logic|5\"\
: {\n \"acc\": 0.15079365079365079,\n \"acc_stderr\": 0.03200686497287392,\n\
\ \"acc_norm\": 0.15079365079365079,\n \"acc_norm_stderr\": 0.03200686497287392\n\
\ },\n \"harness|hendrycksTest-global_facts|5\": {\n \"acc\": 0.33,\n\
\ \"acc_stderr\": 0.04725815626252604,\n \"acc_norm\": 0.33,\n \
\ \"acc_norm_stderr\": 0.04725815626252604\n },\n \"harness|hendrycksTest-high_school_biology|5\"\
: {\n \"acc\": 0.25161290322580643,\n \"acc_stderr\": 0.024685979286239956,\n\
\ \"acc_norm\": 0.25161290322580643,\n \"acc_norm_stderr\": 0.024685979286239956\n\
\ },\n \"harness|hendrycksTest-high_school_chemistry|5\": {\n \"acc\"\
: 0.2955665024630542,\n \"acc_stderr\": 0.032104944337514575,\n \"\
acc_norm\": 0.2955665024630542,\n \"acc_norm_stderr\": 0.032104944337514575\n\
\ },\n \"harness|hendrycksTest-high_school_computer_science|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_european_history|5\"\
: {\n \"acc\": 0.28484848484848485,\n \"acc_stderr\": 0.035243908445117836,\n\
\ \"acc_norm\": 0.28484848484848485,\n \"acc_norm_stderr\": 0.035243908445117836\n\
\ },\n \"harness|hendrycksTest-high_school_geography|5\": {\n \"acc\"\
: 0.25252525252525254,\n \"acc_stderr\": 0.030954055470365897,\n \"\
acc_norm\": 0.25252525252525254,\n \"acc_norm_stderr\": 0.030954055470365897\n\
\ },\n \"harness|hendrycksTest-high_school_government_and_politics|5\": {\n\
\ \"acc\": 0.22797927461139897,\n \"acc_stderr\": 0.030276909945178256,\n\
\ \"acc_norm\": 0.22797927461139897,\n \"acc_norm_stderr\": 0.030276909945178256\n\
\ },\n \"harness|hendrycksTest-high_school_macroeconomics|5\": {\n \
\ \"acc\": 0.2128205128205128,\n \"acc_stderr\": 0.020752423722128013,\n\
\ \"acc_norm\": 0.2128205128205128,\n \"acc_norm_stderr\": 0.020752423722128013\n\
\ },\n \"harness|hendrycksTest-high_school_mathematics|5\": {\n \"\
acc\": 0.26296296296296295,\n \"acc_stderr\": 0.02684205787383371,\n \
\ \"acc_norm\": 0.26296296296296295,\n \"acc_norm_stderr\": 0.02684205787383371\n\
\ },\n \"harness|hendrycksTest-high_school_microeconomics|5\": {\n \
\ \"acc\": 0.21008403361344538,\n \"acc_stderr\": 0.026461398717471874,\n\
\ \"acc_norm\": 0.21008403361344538,\n \"acc_norm_stderr\": 0.026461398717471874\n\
\ },\n \"harness|hendrycksTest-high_school_physics|5\": {\n \"acc\"\
: 0.271523178807947,\n \"acc_stderr\": 0.03631329803969653,\n \"acc_norm\"\
: 0.271523178807947,\n \"acc_norm_stderr\": 0.03631329803969653\n },\n\
\ \"harness|hendrycksTest-high_school_psychology|5\": {\n \"acc\": 0.22201834862385322,\n\
\ \"acc_stderr\": 0.01781884956479663,\n \"acc_norm\": 0.22201834862385322,\n\
\ \"acc_norm_stderr\": 0.01781884956479663\n },\n \"harness|hendrycksTest-high_school_statistics|5\"\
: {\n \"acc\": 0.21296296296296297,\n \"acc_stderr\": 0.027920963147993656,\n\
\ \"acc_norm\": 0.21296296296296297,\n \"acc_norm_stderr\": 0.027920963147993656\n\
\ },\n \"harness|hendrycksTest-high_school_us_history|5\": {\n \"acc\"\
: 0.25980392156862747,\n \"acc_stderr\": 0.030778554678693264,\n \"\
acc_norm\": 0.25980392156862747,\n \"acc_norm_stderr\": 0.030778554678693264\n\
\ },\n \"harness|hendrycksTest-high_school_world_history|5\": {\n \"\
acc\": 0.26582278481012656,\n \"acc_stderr\": 0.028756799629658335,\n \
\ \"acc_norm\": 0.26582278481012656,\n \"acc_norm_stderr\": 0.028756799629658335\n\
\ },\n \"harness|hendrycksTest-human_aging|5\": {\n \"acc\": 0.20179372197309417,\n\
\ \"acc_stderr\": 0.026936111912802273,\n \"acc_norm\": 0.20179372197309417,\n\
\ \"acc_norm_stderr\": 0.026936111912802273\n },\n \"harness|hendrycksTest-human_sexuality|5\"\
: {\n \"acc\": 0.22900763358778625,\n \"acc_stderr\": 0.036853466317118506,\n\
\ \"acc_norm\": 0.22900763358778625,\n \"acc_norm_stderr\": 0.036853466317118506\n\
\ },\n \"harness|hendrycksTest-international_law|5\": {\n \"acc\":\
\ 0.371900826446281,\n \"acc_stderr\": 0.044120158066245044,\n \"\
acc_norm\": 0.371900826446281,\n \"acc_norm_stderr\": 0.044120158066245044\n\
\ },\n \"harness|hendrycksTest-jurisprudence|5\": {\n \"acc\": 0.23148148148148148,\n\
\ \"acc_stderr\": 0.04077494709252626,\n \"acc_norm\": 0.23148148148148148,\n\
\ \"acc_norm_stderr\": 0.04077494709252626\n },\n \"harness|hendrycksTest-logical_fallacies|5\"\
: {\n \"acc\": 0.3006134969325153,\n \"acc_stderr\": 0.03602511318806771,\n\
\ \"acc_norm\": 0.3006134969325153,\n \"acc_norm_stderr\": 0.03602511318806771\n\
\ },\n \"harness|hendrycksTest-machine_learning|5\": {\n \"acc\": 0.24107142857142858,\n\
\ \"acc_stderr\": 0.04059867246952687,\n \"acc_norm\": 0.24107142857142858,\n\
\ \"acc_norm_stderr\": 0.04059867246952687\n },\n \"harness|hendrycksTest-management|5\"\
: {\n \"acc\": 0.1941747572815534,\n \"acc_stderr\": 0.039166677628225836,\n\
\ \"acc_norm\": 0.1941747572815534,\n \"acc_norm_stderr\": 0.039166677628225836\n\
\ },\n \"harness|hendrycksTest-marketing|5\": {\n \"acc\": 0.2564102564102564,\n\
\ \"acc_stderr\": 0.02860595370200425,\n \"acc_norm\": 0.2564102564102564,\n\
\ \"acc_norm_stderr\": 0.02860595370200425\n },\n \"harness|hendrycksTest-medical_genetics|5\"\
: {\n \"acc\": 0.2,\n \"acc_stderr\": 0.040201512610368445,\n \
\ \"acc_norm\": 0.2,\n \"acc_norm_stderr\": 0.040201512610368445\n \
\ },\n \"harness|hendrycksTest-miscellaneous|5\": {\n \"acc\": 0.2707535121328225,\n\
\ \"acc_stderr\": 0.015889888362560486,\n \"acc_norm\": 0.2707535121328225,\n\
\ \"acc_norm_stderr\": 0.015889888362560486\n },\n \"harness|hendrycksTest-moral_disputes|5\"\
: {\n \"acc\": 0.29190751445086704,\n \"acc_stderr\": 0.02447699407624734,\n\
\ \"acc_norm\": 0.29190751445086704,\n \"acc_norm_stderr\": 0.02447699407624734\n\
\ },\n \"harness|hendrycksTest-moral_scenarios|5\": {\n \"acc\": 0.24692737430167597,\n\
\ \"acc_stderr\": 0.014422292204808835,\n \"acc_norm\": 0.24692737430167597,\n\
\ \"acc_norm_stderr\": 0.014422292204808835\n },\n \"harness|hendrycksTest-nutrition|5\"\
: {\n \"acc\": 0.25163398692810457,\n \"acc_stderr\": 0.024848018263875195,\n\
\ \"acc_norm\": 0.25163398692810457,\n \"acc_norm_stderr\": 0.024848018263875195\n\
\ },\n \"harness|hendrycksTest-philosophy|5\": {\n \"acc\": 0.2990353697749196,\n\
\ \"acc_stderr\": 0.026003301117885135,\n \"acc_norm\": 0.2990353697749196,\n\
\ \"acc_norm_stderr\": 0.026003301117885135\n },\n \"harness|hendrycksTest-prehistory|5\"\
: {\n \"acc\": 0.2932098765432099,\n \"acc_stderr\": 0.02532988817190092,\n\
\ \"acc_norm\": 0.2932098765432099,\n \"acc_norm_stderr\": 0.02532988817190092\n\
\ },\n \"harness|hendrycksTest-professional_accounting|5\": {\n \"\
acc\": 0.2695035460992908,\n \"acc_stderr\": 0.026469036818590638,\n \
\ \"acc_norm\": 0.2695035460992908,\n \"acc_norm_stderr\": 0.026469036818590638\n\
\ },\n \"harness|hendrycksTest-professional_law|5\": {\n \"acc\": 0.27053455019556716,\n\
\ \"acc_stderr\": 0.011345996743539264,\n \"acc_norm\": 0.27053455019556716,\n\
\ \"acc_norm_stderr\": 0.011345996743539264\n },\n \"harness|hendrycksTest-professional_medicine|5\"\
: {\n \"acc\": 0.16544117647058823,\n \"acc_stderr\": 0.022571771025494767,\n\
\ \"acc_norm\": 0.16544117647058823,\n \"acc_norm_stderr\": 0.022571771025494767\n\
\ },\n \"harness|hendrycksTest-professional_psychology|5\": {\n \"\
acc\": 0.2761437908496732,\n \"acc_stderr\": 0.018087276935663137,\n \
\ \"acc_norm\": 0.2761437908496732,\n \"acc_norm_stderr\": 0.018087276935663137\n\
\ },\n \"harness|hendrycksTest-public_relations|5\": {\n \"acc\": 0.20909090909090908,\n\
\ \"acc_stderr\": 0.038950910157241364,\n \"acc_norm\": 0.20909090909090908,\n\
\ \"acc_norm_stderr\": 0.038950910157241364\n },\n \"harness|hendrycksTest-security_studies|5\"\
: {\n \"acc\": 0.24081632653061225,\n \"acc_stderr\": 0.027372942201788163,\n\
\ \"acc_norm\": 0.24081632653061225,\n \"acc_norm_stderr\": 0.027372942201788163\n\
\ },\n \"harness|hendrycksTest-sociology|5\": {\n \"acc\": 0.24875621890547264,\n\
\ \"acc_stderr\": 0.030567675938916707,\n \"acc_norm\": 0.24875621890547264,\n\
\ \"acc_norm_stderr\": 0.030567675938916707\n },\n \"harness|hendrycksTest-us_foreign_policy|5\"\
: {\n \"acc\": 0.25,\n \"acc_stderr\": 0.04351941398892446,\n \
\ \"acc_norm\": 0.25,\n \"acc_norm_stderr\": 0.04351941398892446\n \
\ },\n \"harness|hendrycksTest-virology|5\": {\n \"acc\": 0.20481927710843373,\n\
\ \"acc_stderr\": 0.03141784291663926,\n \"acc_norm\": 0.20481927710843373,\n\
\ \"acc_norm_stderr\": 0.03141784291663926\n },\n \"harness|hendrycksTest-world_religions|5\"\
: {\n \"acc\": 0.29239766081871343,\n \"acc_stderr\": 0.034886477134579215,\n\
\ \"acc_norm\": 0.29239766081871343,\n \"acc_norm_stderr\": 0.034886477134579215\n\
\ },\n \"harness|truthfulqa:mc|0\": {\n \"mc1\": 0.2350061199510404,\n\
\ \"mc1_stderr\": 0.014843061507731601,\n \"mc2\": 0.4904100535198025,\n\
\ \"mc2_stderr\": 0.017085995013096343\n },\n \"harness|winogrande|5\"\
: {\n \"acc\": 0.489344909234412,\n \"acc_stderr\": 0.014049294536290403\n\
\ },\n \"harness|drop|3\": {\n \"em\": 0.0,\n \"em_stderr\"\
: 0.0,\n \"f1\": 0.0,\n \"f1_stderr\": 0.0\n },\n \"harness|gsm8k|5\"\
: {\n \"acc\": 0.0,\n \"acc_stderr\": 0.0\n }\n}\n```"
repo_url: https://huggingface.co/l3utterfly/mistral-7b-v0.1-layla-v2
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_11_20T09_04_01.597218
path:
- '**/details_harness|arc:challenge|25_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|arc:challenge|25_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_drop_3
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|drop|3_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|drop|3_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_gsm8k_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|gsm8k|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|gsm8k|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hellaswag_10
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hellaswag|10_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hellaswag|10_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-international_law|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-management|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-marketing|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-sociology|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-virology|5_2023-11-20T09-04-01.597218.parquet'
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_abstract_algebra_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-abstract_algebra|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_anatomy_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-anatomy|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_astronomy_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-astronomy|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_business_ethics_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-business_ethics|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_clinical_knowledge_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-clinical_knowledge|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_college_biology_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_biology|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_college_chemistry_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_chemistry|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_college_computer_science_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_computer_science|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_college_mathematics_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_mathematics|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_college_medicine_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_medicine|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_college_physics_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-college_physics|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_computer_security_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-computer_security|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_conceptual_physics_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-conceptual_physics|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_econometrics_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-econometrics|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_electrical_engineering_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-electrical_engineering|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_elementary_mathematics_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-elementary_mathematics|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_formal_logic_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-formal_logic|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_global_facts_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-global_facts|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_biology_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_biology|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_chemistry_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_chemistry|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_computer_science_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_computer_science|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_european_history_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_european_history|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_geography_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_geography|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_government_and_politics_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_government_and_politics|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_macroeconomics_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_macroeconomics|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_mathematics_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_mathematics|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_microeconomics_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_microeconomics|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_physics_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_physics|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_psychology_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_psychology|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_statistics_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_statistics|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_us_history_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_us_history|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_high_school_world_history_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-high_school_world_history|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_human_aging_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_aging|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_human_sexuality_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-human_sexuality|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_international_law_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-international_law|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_jurisprudence_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-jurisprudence|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_logical_fallacies_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-logical_fallacies|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_machine_learning_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-machine_learning|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_management_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-management|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_marketing_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-marketing|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_medical_genetics_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-medical_genetics|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_miscellaneous_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-miscellaneous|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_moral_disputes_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_disputes|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_moral_scenarios_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-moral_scenarios|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_nutrition_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-nutrition|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_philosophy_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-philosophy|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_prehistory_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-prehistory|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_professional_accounting_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_accounting|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_professional_law_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_law|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_professional_medicine_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_medicine|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_professional_psychology_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-professional_psychology|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_public_relations_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-public_relations|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_security_studies_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-security_studies|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_sociology_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-sociology|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_us_foreign_policy_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-us_foreign_policy|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_virology_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-virology|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_hendrycksTest_world_religions_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|hendrycksTest-world_religions|5_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_truthfulqa_mc_0
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|truthfulqa:mc|0_2023-11-20T09-04-01.597218.parquet'
- config_name: harness_winogrande_5
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- '**/details_harness|winogrande|5_2023-11-20T09-04-01.597218.parquet'
- split: latest
path:
- '**/details_harness|winogrande|5_2023-11-20T09-04-01.597218.parquet'
- config_name: results
data_files:
- split: 2023_11_20T09_04_01.597218
path:
- results_2023-11-20T09-04-01.597218.parquet
- split: latest
path:
- results_2023-11-20T09-04-01.597218.parquet
---
# Dataset Card for Evaluation run of l3utterfly/mistral-7b-v0.1-layla-v2
## Dataset Description
- **Homepage:**
- **Repository:** https://huggingface.co/l3utterfly/mistral-7b-v0.1-layla-v2
- **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 [l3utterfly/mistral-7b-v0.1-layla-v2](https://huggingface.co/l3utterfly/mistral-7b-v0.1-layla-v2) on the [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
The dataset is composed of 64 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_l3utterfly__mistral-7b-v0.1-layla-v2_public",
"harness_winogrande_5",
split="train")
```
## Latest results
These are the [latest results from run 2023-11-20T09:04:01.597218](https://huggingface.co/datasets/open-llm-leaderboard/details_l3utterfly__mistral-7b-v0.1-layla-v2_public/blob/main/results_2023-11-20T09-04-01.597218.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": 0.25336826428615794,
"acc_stderr": 0.030795119371799243,
"acc_norm": 0.25419620341620086,
"acc_norm_stderr": 0.0316093435882046,
"mc1": 0.2350061199510404,
"mc1_stderr": 0.014843061507731601,
"mc2": 0.4904100535198025,
"mc2_stderr": 0.017085995013096343,
"em": 0.0,
"em_stderr": 0.0,
"f1": 0.0,
"f1_stderr": 0.0
},
"harness|arc:challenge|25": {
"acc": 0.24061433447098976,
"acc_stderr": 0.012491468532390578,
"acc_norm": 0.27047781569965873,
"acc_norm_stderr": 0.012980954547659558
},
"harness|hellaswag|10": {
"acc": 0.25712009559848636,
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```
### 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] | [
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zphang/hf_benchmark_sample | zphang | 2023-11-20T09:22:19Z | 0 | 0 | null | [
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VamsiPranav/hindi_telugu_dataset | VamsiPranav | 2023-11-20T09:09:03Z | 0 | 0 | null | [
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joseluhf11/oct-object-detection-v3-average | joseluhf11 | 2023-11-22T08:48:49Z | 0 | 0 | null | [
"region:us"
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dataset_info:
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path: data/train-*
---
# Dataset Card for "oct-object-detection-v3-average"
Dataset is composed of images with multiples object detection box in coco format (x,y,w,h). Images are OCT (type of eye scaner) with boxes indicating some features associated to AMD disease.
The unique difference from from v2 is categories field must have as many class label as there are boxes annotated in each image, even if the class label is the same. So for a image with 3 boxes for the same object, must have 3 class labels.
[Source datataset](https://doi.org/10.1101/2023.03.29.534704)
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data_files:
- split: train
path: data/train-*
---
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mitsudate/Kouon_NSF_Vocoder-training_data | mitsudate | 2023-11-20T10:39:43Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T10:39:43Z | 2023-11-20T10:16:53.000Z | 2023-11-20T10:16:53 | Entry not found | [
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... | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AanVar/Sample_Dataset_01 | AanVar | 2023-11-20T10:50:34Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-20T10:50:34Z | 2023-11-20T10:50:34.000Z | 2023-11-20T10:50:34 | ---
license: mit
---
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vardhanam/steve_jobs_2005_commencement | vardhanam | 2023-11-20T12:01:59Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T12:01:59Z | 2023-11-20T10:56:59.000Z | 2023-11-20T10:56:59 | Entry not found | [
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Subhadeep/common_voice_11_0_hi_pseudo_labelled | Subhadeep | 2023-11-22T10:31:21Z | 0 | 0 | null | [
"region:us"
] | 2023-11-22T10:31:21Z | 2023-11-20T10:59:05.000Z | 2023-11-20T10:59:05 | ---
dataset_info:
config_name: hi
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: whisper_transcript
sequence: int64
splits:
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num_bytes: 131053542.138
num_examples: 4361
- name: validation
num_bytes: 64148344.509
num_examples: 2179
- name: test
num_bytes: 100961651.174
num_examples: 2894
download_size: 260542039
dataset_size: 296163537.821
configs:
- config_name: hi
data_files:
- split: train
path: hi/train-*
- split: validation
path: hi/validation-*
- split: test
path: hi/test-*
---
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superfine/advertising-banner-generation | superfine | 2023-11-20T11:01:44Z | 0 | 0 | null | [
"region:us"
] | 2023-11-20T11:01:44Z | 2023-11-20T11:01:37.000Z | 2023-11-20T11:01:37 | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 86418717.13
num_examples: 1362
- name: test
num_bytes: 481468.0
num_examples: 3
download_size: 84068700
dataset_size: 86900185.13
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
| [
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-0.047825977206230... | null | null | null | null | null | null | null | null | null | null | null | null | null |
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