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antitheft159/Enunch.159 | antitheft159 | "2024-07-17T22:22:37Z" | 0 | 0 | [
"license:gpl-3.0",
"size_categories:10K<n<100K",
"format:csv",
"modality:tabular",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T22:20:32Z" | ---
license: gpl-3.0
---
|
stojchet/python-nkto2-sft8-empty | stojchet | "2024-07-17T23:30:55Z" | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T22:26:08Z" | ---
dataset_info:
- config_name: humaneval
features:
- name: task_id
dtype: string
- name: language
dtype: string
- name: completion
dtype: string
- name: prompt
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splits:
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num_bytes: 483338
num_examples: 164
download_size: 225898
dataset_size: 483338
- config_name: mbxp
features:
- name: task_id
dtype: string
- name: language
dtype: string
- name: completion
dtype: string
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dtype: string
splits:
- name: train
num_bytes: 2216501
num_examples: 974
download_size: 948138
dataset_size: 2216501
configs:
- config_name: humaneval
data_files:
- split: train
path: humaneval/train-*
- config_name: mbxp
data_files:
- split: train
path: mbxp/train-*
---
|
stojchet/python-nkto3-sft8-empty | stojchet | "2024-07-17T23:27:13Z" | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T22:27:23Z" | ---
dataset_info:
- config_name: humaneval
features:
- name: task_id
dtype: string
- name: language
dtype: string
- name: completion
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 459491
num_examples: 164
download_size: 216716
dataset_size: 459491
- config_name: mbxp
features:
- name: task_id
dtype: string
- name: language
dtype: string
- name: completion
dtype: string
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dtype: string
splits:
- name: train
num_bytes: 2205549
num_examples: 974
download_size: 933879
dataset_size: 2205549
configs:
- config_name: humaneval
data_files:
- split: train
path: humaneval/train-*
- config_name: mbxp
data_files:
- split: train
path: mbxp/train-*
---
|
BangumiBase/spykyoushitsu | BangumiBase | "2024-07-18T03:59:52Z" | 0 | 0 | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"region:us",
"art"
] | null | "2024-07-17T22:32:36Z" | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Spy Kyoushitsu
This is the image base of bangumi Spy Kyoushitsu, we detected 91 characters, 9503 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 | 1054 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 323 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 85 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 171 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 22 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 12 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 61 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 17 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 53 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 39 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 53 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 115 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 26 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 27 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 23 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 11 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 59 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 9 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 1156 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 32 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 29 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 27 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 29 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 30 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 13 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 12 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 21 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 6 | [Download](27/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 28 | 14 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 15 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 21 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 759 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 31 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 26 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 14 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 15 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 42 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 663 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 13 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 35 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 23 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 35 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 9 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 9 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 78 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 876 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 47 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 32 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 16 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 26 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 27 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 16 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 18 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 13 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 470 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 39 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 24 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 52 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 9 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 14 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 736 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 39 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 20 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 15 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 22 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 7 | [Download](65/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 66 | 494 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 19 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 10 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 89 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 39 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 14 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 17 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 78 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 8 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 18 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 49 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 7 | [Download](77/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 78 | 17 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 12 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 519 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 32 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 16 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 13 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 12 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 7 | [Download](85/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 86 | 7 | [Download](86/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 87 | 16 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 6 | [Download](88/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 89 | 10 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 159 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
lim142857/iem_s0_200K_test_100 | lim142857 | "2024-07-17T22:34:21Z" | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T22:34:04Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: idx
dtype: int32
- name: dataset
dtype: string
- name: height
dtype: int32
- name: width
dtype: int32
- name: src_info
dtype: string
splits:
- name: train
num_bytes: 95477817.0
num_examples: 100
download_size: 95474795
dataset_size: 95477817.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
stojchet/python-ndpo4-sft8-empty | stojchet | "2024-07-17T23:38:39Z" | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T22:37:21Z" | ---
dataset_info:
- config_name: humaneval
features:
- name: task_id
dtype: string
- name: language
dtype: string
- name: completion
dtype: string
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num_bytes: 482966
num_examples: 164
download_size: 228144
dataset_size: 482966
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features:
- name: task_id
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dtype: string
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- name: train
num_bytes: 2333247
num_examples: 974
download_size: 986498
dataset_size: 2333247
configs:
- config_name: humaneval
data_files:
- split: train
path: humaneval/train-*
- config_name: mbxp
data_files:
- split: train
path: mbxp/train-*
---
|
Abyss-Whisper/LuisaSonza_Audio | Abyss-Whisper | "2024-07-17T22:43:25Z" | 0 | 0 | [
"license:openrail",
"region:us"
] | null | "2024-07-17T22:42:44Z" | ---
license: openrail
---
|
BangumiBase/ousamarankingyuukinotakarabako | BangumiBase | "2024-07-18T01:11:43Z" | 0 | 0 | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"modality:text",
"region:us",
"art"
] | null | "2024-07-17T22:44:50Z" | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Ousama Ranking: Yuuki No Takarabako
This is the image base of bangumi Ousama Ranking: Yuuki no Takarabako, we detected 49 characters, 3512 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 | 686 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 356 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 196 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 276 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 165 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 242 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 41 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 29 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 19 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 205 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 25 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 92 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 111 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 46 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 80 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 30 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 19 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 105 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 51 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 23 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 67 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 50 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 11 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 12 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 14 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 32 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 24 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 22 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 34 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 25 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 30 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 29 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 27 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 31 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 12 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 13 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 14 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 21 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 12 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 23 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 60 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 10 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 11 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 12 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 8 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 27 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 18 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 10 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 56 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
safaalh/echantillon_enron | safaalh | "2024-07-17T22:47:12Z" | 0 | 1 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T22:47:08Z" | ---
dataset_info:
features:
- name: file
dtype: string
- name: Date
dtype: string
- name: Sender
dtype: string
- name: Receiver
dtype: string
- name: MESSAGE
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splits:
- name: train
num_bytes: 24564889.51244006
num_examples: 15522
download_size: 11854475
dataset_size: 24564889.51244006
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
NoNameFactory/LINA_product | NoNameFactory | "2024-07-17T22:48:48Z" | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T22:48:45Z" | ---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 334227
num_examples: 457
download_size: 112215
dataset_size: 334227
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
pdf2dataset/69e16b19ebe40bed906b0674547cb76b | pdf2dataset | "2024-07-17T22:52:19Z" | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T22:52:18Z" | ---
dataset_info:
features:
- name: text
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 213788
num_examples: 92
download_size: 110753
dataset_size: 213788
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
stojchet/python-ndpo6-sft8-empty | stojchet | "2024-07-18T00:02:53Z" | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T22:57:36Z" | ---
dataset_info:
- config_name: humaneval
features:
- name: task_id
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configs:
- config_name: humaneval
data_files:
- split: train
path: humaneval/train-*
- config_name: mbxp
data_files:
- split: train
path: mbxp/train-*
---
|
events-synergy/lh_ent_sum_coref | events-synergy | "2024-07-26T16:07:53Z" | 0 | 0 | [
"language:en",
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T23:01:52Z" | ---
language:
- en
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 26077448
num_examples: 38144
- name: dev
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num_examples: 6235
- name: dev_small
num_bytes: 302497
num_examples: 483
- name: test
num_bytes: 7592124
num_examples: 10993
download_size: 15928395
dataset_size: 37881484
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: dev
path: data/dev-*
- split: dev_small
path: data/dev_small-*
- split: test
path: data/test-*
---
|
antitheft159/tonal.159 | antitheft159 | "2024-07-17T23:25:14Z" | 0 | 0 | [
"license:gpl-3.0",
"size_categories:1K<n<10K",
"format:csv",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T23:19:31Z" | ---
license: gpl-3.0
---
|
stojchet/python-ndpo5-sft8-empty | stojchet | "2024-07-18T00:43:12Z" | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T23:30:19Z" | ---
dataset_info:
- config_name: humaneval
features:
- name: task_id
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- name: language
dtype: string
- name: completion
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- name: prompt
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splits:
- name: train
num_bytes: 532255
num_examples: 164
download_size: 246997
dataset_size: 532255
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features:
- name: task_id
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splits:
- name: train
num_bytes: 2561585
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download_size: 1087300
dataset_size: 2561585
configs:
- config_name: humaneval
data_files:
- split: train
path: humaneval/train-*
- config_name: mbxp
data_files:
- split: train
path: mbxp/train-*
---
|
AashishKumar/chatml-hinglish-conversation-gpt4 | AashishKumar | "2024-07-18T00:13:54Z" | 0 | 0 | [
"license:apache-2.0",
"size_categories:10K<n<100K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T23:35:07Z" | ---
license: apache-2.0
---
|
stojchet/python-deepseek-coder-1.3b-base-empty-10 | stojchet | "2024-07-18T01:12:11Z" | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T23:36:23Z" | ---
dataset_info:
- config_name: humaneval
features:
- name: task_id
dtype: string
- name: language
dtype: string
- name: completion
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splits:
- name: train
num_bytes: 2053230
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download_size: 110451
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features:
- name: task_id
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splits:
- name: train
num_bytes: 8888920
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download_size: 457331
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configs:
- config_name: humaneval
data_files:
- split: train
path: humaneval/train-*
- config_name: mbxp
data_files:
- split: train
path: mbxp/train-*
---
|
stojchet/java-jkto5k1-jsft8-empty | stojchet | "2024-07-18T03:07:48Z" | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-17T23:52:28Z" | ---
dataset_info:
- config_name: humaneval
features:
- name: task_id
dtype: string
- name: language
dtype: string
- name: completion
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 363229
num_examples: 161
download_size: 141788
dataset_size: 363229
- config_name: mbxp
features:
- name: task_id
dtype: string
- name: language
dtype: string
- name: completion
dtype: string
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 1849412
num_examples: 966
download_size: 630827
dataset_size: 1849412
configs:
- config_name: humaneval
data_files:
- split: train
path: humaneval/train-*
- config_name: mbxp
data_files:
- split: train
path: mbxp/train-*
---
|
BangumiBase/sukinakogameganewowasureta | BangumiBase | "2024-07-18T02:51:12Z" | 0 | 0 | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"modality:text",
"region:us",
"art"
] | null | "2024-07-17T23:58:35Z" | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Suki Na Ko Ga Megane Wo Wasureta
This is the image base of bangumi Suki na Ko ga Megane wo Wasureta, we detected 40 characters, 3878 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 | 1540 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 51 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 51 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 33 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 44 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 46 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 10 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 22 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 60 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 38 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 12 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 10 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 15 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 13 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 11 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 100 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 23 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 54 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 8 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 863 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 165 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 298 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 59 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 27 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 52 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 17 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 10 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 19 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 11 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 15 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 21 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 15 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 8 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 9 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 28 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 15 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 7 | [Download](36/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 37 | 12 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 8 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 78 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
ycfNTU/USB_llama2_oneshot | ycfNTU | "2024-07-18T00:01:29Z" | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T00:01:26Z" | ---
dataset_info:
features:
- name: id
dtype: string
- name: domain
dtype: string
- name: summ_idx
dtype: int64
- name: topic
dtype: string
- name: text
dtype: string
- name: output
dtype: string
- name: length
dtype: int64
- name: summary
dtype: string
splits:
- name: train
num_bytes: 960616
num_examples: 319
download_size: 432569
dataset_size: 960616
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
LESW/dust_0718 | LESW | "2024-07-18T00:19:17Z" | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T00:18:49Z" | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 112
num_examples: 2
download_size: 1825
dataset_size: 112
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
LESW/rama | LESW | "2024-07-18T00:19:08Z" | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T00:19:06Z" | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
- name: input
dtype: string
splits:
- name: train
num_bytes: 112
num_examples: 2
download_size: 1825
dataset_size: 112
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BangumiBase/shingekinokyojinthefinalseason | BangumiBase | "2024-07-18T09:26:06Z" | 0 | 0 | [
"license:mit",
"size_categories:10K<n<100K",
"region:us",
"art"
] | null | "2024-07-18T00:27:39Z" | ---
license: mit
tags:
- art
size_categories:
- 10K<n<100K
---
# Bangumi Image Base of Shingeki No Kyojin: The Final Season
This is the image base of bangumi Shingeki no Kyojin: The Final Season, we detected 86 characters, 10747 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 | 1439 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 335 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 859 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 223 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 382 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 48 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 296 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 795 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 304 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 127 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 73 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 150 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 24 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 302 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 100 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 106 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 58 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 64 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 71 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 156 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 24 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 156 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 110 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 117 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 564 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 446 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 84 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 373 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 39 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 113 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 49 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 126 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 61 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 427 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 52 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 29 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 15 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 40 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 24 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 28 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 65 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 87 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 22 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 65 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 23 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 85 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 23 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 406 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 26 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 40 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 18 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 65 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 23 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 24 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 16 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 35 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 44 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 27 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 63 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 71 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 16 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 19 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 14 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 29 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 43 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 33 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 32 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 17 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 79 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 20 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 22 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 19 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 28 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 45 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 31 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 13 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 16 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 19 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 9 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 39 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 20 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 19 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 12 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 5 | [Download](83/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 84 | 6 | [Download](84/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 125 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
ycfNTU/Album_llama3_twoshot | ycfNTU | "2024-07-19T07:09:24Z" | 0 | 0 | [
"size_categories:n<1K",
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] | null | "2024-07-18T00:28:24Z" | ---
dataset_info:
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configs:
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data_files:
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path: data/train-*
---
|
maryyin/chinese_training | maryyin | "2024-07-18T00:43:55Z" | 0 | 0 | [
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T00:43:02Z" | ---
license: apache-2.0
---
|
gogo8232/ucla-agi-data-mistral-7b-instruct-sppo-iter1_perplexities | gogo8232 | "2024-07-18T00:43:31Z" | 0 | 0 | [
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] | null | "2024-07-18T00:43:25Z" | ---
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splits:
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configs:
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data_files:
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path: data/train-*
---
|
Self-GRIT/mini-wikitext-preprocessed-PP_claude-claude-3-5-sonnet-20240620 | Self-GRIT | "2024-07-18T01:15:03Z" | 0 | 0 | [
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] | null | "2024-07-18T00:43:46Z" | ---
dataset_info:
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struct:
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splits:
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download_size: 43285
dataset_size: 96938
configs:
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data_files:
- split: train
path: data/train-*
---
|
seankski/tool-parameters-v1-1-llama3-70B | seankski | "2024-08-02T00:17:43Z" | 0 | 0 | [
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] | null | "2024-07-18T00:48:44Z" | ---
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splits:
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download_size: 124944468
dataset_size: 611201015
- config_name: finetuning-messages-only
features:
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configs:
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data_files:
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path: passing-judgments-augmented/train-*
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data_files:
- split: train
path: raw/train-*
---
|
stojchet/java-deepseek-coder-1.3b-base-empty-10 | stojchet | "2024-07-18T01:03:23Z" | 0 | 0 | [
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] | null | "2024-07-18T00:56:16Z" | ---
dataset_info:
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download_size: 120047
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features:
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configs:
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data_files:
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path: humaneval/train-*
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data_files:
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path: mbxp/train-*
---
|
seankski/tool-parameters-messages-only-v1-1-llama3-70B | seankski | "2024-07-18T22:43:32Z" | 0 | 0 | [
"size_categories:n<1K",
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"library:pandas",
"library:mlcroissant",
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] | null | "2024-07-18T00:58:16Z" | ---
dataset_info:
features:
- name: messages
list:
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dtype: string
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dtype: string
splits:
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num_bytes: 7063478
num_examples: 970
download_size: 2168497
dataset_size: 7063478
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
open-llm-leaderboard/pankajmathur__orca_mini_v3_70b-details | open-llm-leaderboard | "2024-07-18T01:02:01Z" | 0 | 0 | [
"size_categories:10K<n<100K",
"format:json",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T00:58:53Z" | ---
pretty_name: Evaluation run of pankajmathur/orca_mini_v3_70b
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [pankajmathur/orca_mini_v3_70b](https://huggingface.co/pankajmathur/orca_mini_v3_70b)\n\
The dataset is composed of 38 configuration(s), each one corresponding 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.\n\nTo load the details from a run, you can for instance do the following:\n\
```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/pankajmathur__orca_mini_v3_70b-details\"\
,\n\tname=\"pankajmathur__orca_mini_v3_70b__leaderboard_bbh_boolean_expressions\"\
,\n\tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\
\ from run 2024-07-18T00-58-53.025846](https://huggingface.co/datasets/open-llm-leaderboard/pankajmathur__orca_mini_v3_70b-details/blob/main/pankajmathur__orca_mini_v3_70b/results_2024-07-18T00-58-53.025846.json)\
\ (note that there 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 \"leaderboard\": {\n\
\ \"inst_level_loose_acc,none\": 0.4844124700239808,\n \"\
inst_level_loose_acc_stderr,none\": \"N/A\",\n \"exact_match,none\":\
\ 0.03625377643504532,\n \"exact_match_stderr,none\": 0.005091404089611316,\n\
\ \"prompt_level_loose_acc,none\": 0.36229205175600737,\n \
\ \"prompt_level_loose_acc_stderr,none\": 0.0206844243149654,\n \"inst_level_strict_acc,none\"\
: 0.4628297362110312,\n \"inst_level_strict_acc_stderr,none\": \"N/A\"\
,\n \"acc_norm,none\": 0.5424828122973149,\n \"acc_norm_stderr,none\"\
: 0.005187239654766714,\n \"prompt_level_strict_acc,none\": 0.34011090573012936,\n\
\ \"prompt_level_strict_acc_stderr,none\": 0.020386791181835787,\n \
\ \"acc,none\": 0.3757480053191489,\n \"acc_stderr,none\": 0.0044154751798350916,\n\
\ \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\"\
: {\n \"acc_norm,none\": 0.593646936295782,\n \"acc_norm_stderr,none\"\
: 0.005912007702853069,\n \"alias\": \" - leaderboard_bbh\"\n \
\ },\n \"leaderboard_bbh_boolean_expressions\": {\n \"acc_norm,none\"\
: 0.832,\n \"acc_norm_stderr,none\": 0.023692813205492578,\n \
\ \"alias\": \" - leaderboard_bbh_boolean_expressions\"\n },\n \
\ \"leaderboard_bbh_causal_judgement\": {\n \"acc_norm,none\": 0.6470588235294118,\n\
\ \"acc_norm_stderr,none\": 0.03504019983419236,\n \"alias\"\
: \" - leaderboard_bbh_causal_judgement\"\n },\n \"leaderboard_bbh_date_understanding\"\
: {\n \"acc_norm,none\": 0.592,\n \"acc_norm_stderr,none\"\
: 0.0311452098465485,\n \"alias\": \" - leaderboard_bbh_date_understanding\"\
\n },\n \"leaderboard_bbh_disambiguation_qa\": {\n \"acc_norm,none\"\
: 0.776,\n \"acc_norm_stderr,none\": 0.026421361687347905,\n \
\ \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\n },\n \"\
leaderboard_bbh_formal_fallacies\": {\n \"acc_norm,none\": 0.644,\n \
\ \"acc_norm_stderr,none\": 0.03034368065715322,\n \"alias\"\
: \" - leaderboard_bbh_formal_fallacies\"\n },\n \"leaderboard_bbh_geometric_shapes\"\
: {\n \"acc_norm,none\": 0.452,\n \"acc_norm_stderr,none\"\
: 0.03153986449255662,\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\
\n },\n \"leaderboard_bbh_hyperbaton\": {\n \"acc_norm,none\"\
: 0.688,\n \"acc_norm_stderr,none\": 0.029361067575219817,\n \
\ \"alias\": \" - leaderboard_bbh_hyperbaton\"\n },\n \"leaderboard_bbh_logical_deduction_five_objects\"\
: {\n \"acc_norm,none\": 0.504,\n \"acc_norm_stderr,none\"\
: 0.03168519855119917,\n \"alias\": \" - leaderboard_bbh_logical_deduction_five_objects\"\
\n },\n \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \
\ \"acc_norm,none\": 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522373,\n\
\ \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\n\
\ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \
\ \"acc_norm,none\": 0.74,\n \"acc_norm_stderr,none\": 0.0277973157526443,\n\
\ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\"\n\
\ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\
acc_norm,none\": 0.84,\n \"acc_norm_stderr,none\": 0.023232714782060664,\n\
\ \"alias\": \" - leaderboard_bbh_movie_recommendation\"\n },\n\
\ \"leaderboard_bbh_navigate\": {\n \"acc_norm,none\": 0.66,\n\
\ \"acc_norm_stderr,none\": 0.03002007360545791,\n \"alias\"\
: \" - leaderboard_bbh_navigate\"\n },\n \"leaderboard_bbh_object_counting\"\
: {\n \"acc_norm,none\": 0.468,\n \"acc_norm_stderr,none\"\
: 0.031621252575725504,\n \"alias\": \" - leaderboard_bbh_object_counting\"\
\n },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\
acc_norm,none\": 0.4863013698630137,\n \"acc_norm_stderr,none\": 0.041507153312234174,\n\
\ \"alias\": \" - leaderboard_bbh_penguins_in_a_table\"\n },\n\
\ \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \"\
acc_norm,none\": 0.604,\n \"acc_norm_stderr,none\": 0.03099319785457785,\n\
\ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\n\
\ },\n \"leaderboard_bbh_ruin_names\": {\n \"acc_norm,none\"\
: 0.86,\n \"acc_norm_stderr,none\": 0.02198940964524027,\n \
\ \"alias\": \" - leaderboard_bbh_ruin_names\"\n },\n \"leaderboard_bbh_salient_translation_error_detection\"\
: {\n \"acc_norm,none\": 0.604,\n \"acc_norm_stderr,none\"\
: 0.030993197854577853,\n \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\"\
\n },\n \"leaderboard_bbh_snarks\": {\n \"acc_norm,none\"\
: 0.8089887640449438,\n \"acc_norm_stderr,none\": 0.02954704605327662,\n\
\ \"alias\": \" - leaderboard_bbh_snarks\"\n },\n \"leaderboard_bbh_sports_understanding\"\
: {\n \"acc_norm,none\": 0.84,\n \"acc_norm_stderr,none\"\
: 0.023232714782060654,\n \"alias\": \" - leaderboard_bbh_sports_understanding\"\
\n },\n \"leaderboard_bbh_temporal_sequences\": {\n \"\
acc_norm,none\": 0.696,\n \"acc_norm_stderr,none\": 0.029150213374159677,\n\
\ \"alias\": \" - leaderboard_bbh_temporal_sequences\"\n },\n\
\ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \
\ \"acc_norm,none\": 0.136,\n \"acc_norm_stderr,none\": 0.021723342617052072,\n\
\ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\
\n },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\
: {\n \"acc_norm,none\": 0.144,\n \"acc_norm_stderr,none\"\
: 0.022249407735450203,\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\
\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\
: {\n \"acc_norm,none\": 0.308,\n \"acc_norm_stderr,none\"\
: 0.029256928606501864,\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\
\n },\n \"leaderboard_bbh_web_of_lies\": {\n \"acc_norm,none\"\
: 0.484,\n \"acc_norm_stderr,none\": 0.03166998503010742,\n \
\ \"alias\": \" - leaderboard_bbh_web_of_lies\"\n },\n \"leaderboard_gpqa\"\
: {\n \"acc_norm,none\": 0.3179530201342282,\n \"acc_norm_stderr,none\"\
: 0.013502558289260929,\n \"alias\": \" - leaderboard_gpqa\"\n \
\ },\n \"leaderboard_gpqa_diamond\": {\n \"acc_norm,none\": 0.29797979797979796,\n\
\ \"acc_norm_stderr,none\": 0.032586303838365555,\n \"alias\"\
: \" - leaderboard_gpqa_diamond\"\n },\n \"leaderboard_gpqa_extended\"\
: {\n \"acc_norm,none\": 0.32051282051282054,\n \"acc_norm_stderr,none\"\
: 0.01999010546069711,\n \"alias\": \" - leaderboard_gpqa_extended\"\
\n },\n \"leaderboard_gpqa_main\": {\n \"acc_norm,none\"\
: 0.3236607142857143,\n \"acc_norm_stderr,none\": 0.022129598768200864,\n\
\ \"alias\": \" - leaderboard_gpqa_main\"\n },\n \"leaderboard_ifeval\"\
: {\n \"prompt_level_strict_acc,none\": 0.34011090573012936,\n \
\ \"prompt_level_strict_acc_stderr,none\": 0.020386791181835787,\n \
\ \"inst_level_strict_acc,none\": 0.4628297362110312,\n \"inst_level_strict_acc_stderr,none\"\
: \"N/A\",\n \"prompt_level_loose_acc,none\": 0.36229205175600737,\n\
\ \"prompt_level_loose_acc_stderr,none\": 0.0206844243149654,\n \
\ \"inst_level_loose_acc,none\": 0.4844124700239808,\n \"inst_level_loose_acc_stderr,none\"\
: \"N/A\",\n \"alias\": \" - leaderboard_ifeval\"\n },\n \
\ \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.03625377643504532,\n\
\ \"exact_match_stderr,none\": 0.005091404089611316,\n \"\
alias\": \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\"\
: {\n \"exact_match,none\": 0.03908794788273615,\n \"exact_match_stderr,none\"\
: 0.011079050508657068,\n \"alias\": \" - leaderboard_math_algebra_hard\"\
\n },\n \"leaderboard_math_counting_and_prob_hard\": {\n \
\ \"exact_match,none\": 0.04065040650406504,\n \"exact_match_stderr,none\"\
: 0.01787890756443746,\n \"alias\": \" - leaderboard_math_counting_and_prob_hard\"\
\n },\n \"leaderboard_math_geometry_hard\": {\n \"exact_match,none\"\
: 0.007575757575757576,\n \"exact_match_stderr,none\": 0.007575757575757563,\n\
\ \"alias\": \" - leaderboard_math_geometry_hard\"\n },\n \
\ \"leaderboard_math_intermediate_algebra_hard\": {\n \"exact_match,none\"\
: 0.0035714285714285713,\n \"exact_match_stderr,none\": 0.003571428571428569,\n\
\ \"alias\": \" - leaderboard_math_intermediate_algebra_hard\"\n \
\ },\n \"leaderboard_math_num_theory_hard\": {\n \"exact_match,none\"\
: 0.045454545454545456,\n \"exact_match_stderr,none\": 0.016839967582612487,\n\
\ \"alias\": \" - leaderboard_math_num_theory_hard\"\n },\n \
\ \"leaderboard_math_prealgebra_hard\": {\n \"exact_match,none\"\
: 0.09326424870466321,\n \"exact_match_stderr,none\": 0.020986854593289733,\n\
\ \"alias\": \" - leaderboard_math_prealgebra_hard\"\n },\n \
\ \"leaderboard_math_precalculus_hard\": {\n \"exact_match,none\"\
: 0.02962962962962963,\n \"exact_match_stderr,none\": 0.014648038602753785,\n\
\ \"alias\": \" - leaderboard_math_precalculus_hard\"\n },\n\
\ \"leaderboard_mmlu_pro\": {\n \"acc,none\": 0.3757480053191489,\n\
\ \"acc_stderr,none\": 0.0044154751798350916,\n \"alias\"\
: \" - leaderboard_mmlu_pro\"\n },\n \"leaderboard_musr\": {\n \
\ \"acc_norm,none\": 0.5066137566137566,\n \"acc_norm_stderr,none\"\
: 0.017748435072382215,\n \"alias\": \" - leaderboard_musr\"\n \
\ },\n \"leaderboard_musr_murder_mysteries\": {\n \"acc_norm,none\"\
: 0.612,\n \"acc_norm_stderr,none\": 0.030881038748993922,\n \
\ \"alias\": \" - leaderboard_musr_murder_mysteries\"\n },\n \"\
leaderboard_musr_object_placements\": {\n \"acc_norm,none\": 0.3515625,\n\
\ \"acc_norm_stderr,none\": 0.029899590697818237,\n \"alias\"\
: \" - leaderboard_musr_object_placements\"\n },\n \"leaderboard_musr_team_allocation\"\
: {\n \"acc_norm,none\": 0.56,\n \"acc_norm_stderr,none\"\
: 0.03145724452223564,\n \"alias\": \" - leaderboard_musr_team_allocation\"\
\n }\n },\n \"leaderboard\": {\n \"inst_level_loose_acc,none\"\
: 0.4844124700239808,\n \"inst_level_loose_acc_stderr,none\": \"N/A\",\n\
\ \"exact_match,none\": 0.03625377643504532,\n \"exact_match_stderr,none\"\
: 0.005091404089611316,\n \"prompt_level_loose_acc,none\": 0.36229205175600737,\n\
\ \"prompt_level_loose_acc_stderr,none\": 0.0206844243149654,\n \"\
inst_level_strict_acc,none\": 0.4628297362110312,\n \"inst_level_strict_acc_stderr,none\"\
: \"N/A\",\n \"acc_norm,none\": 0.5424828122973149,\n \"acc_norm_stderr,none\"\
: 0.005187239654766714,\n \"prompt_level_strict_acc,none\": 0.34011090573012936,\n\
\ \"prompt_level_strict_acc_stderr,none\": 0.020386791181835787,\n \
\ \"acc,none\": 0.3757480053191489,\n \"acc_stderr,none\": 0.0044154751798350916,\n\
\ \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\": {\n \
\ \"acc_norm,none\": 0.593646936295782,\n \"acc_norm_stderr,none\": 0.005912007702853069,\n\
\ \"alias\": \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\
: {\n \"acc_norm,none\": 0.832,\n \"acc_norm_stderr,none\": 0.023692813205492578,\n\
\ \"alias\": \" - leaderboard_bbh_boolean_expressions\"\n },\n \"\
leaderboard_bbh_causal_judgement\": {\n \"acc_norm,none\": 0.6470588235294118,\n\
\ \"acc_norm_stderr,none\": 0.03504019983419236,\n \"alias\": \" \
\ - leaderboard_bbh_causal_judgement\"\n },\n \"leaderboard_bbh_date_understanding\"\
: {\n \"acc_norm,none\": 0.592,\n \"acc_norm_stderr,none\": 0.0311452098465485,\n\
\ \"alias\": \" - leaderboard_bbh_date_understanding\"\n },\n \"leaderboard_bbh_disambiguation_qa\"\
: {\n \"acc_norm,none\": 0.776,\n \"acc_norm_stderr,none\": 0.026421361687347905,\n\
\ \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\n },\n \"leaderboard_bbh_formal_fallacies\"\
: {\n \"acc_norm,none\": 0.644,\n \"acc_norm_stderr,none\": 0.03034368065715322,\n\
\ \"alias\": \" - leaderboard_bbh_formal_fallacies\"\n },\n \"leaderboard_bbh_geometric_shapes\"\
: {\n \"acc_norm,none\": 0.452,\n \"acc_norm_stderr,none\": 0.03153986449255662,\n\
\ \"alias\": \" - leaderboard_bbh_geometric_shapes\"\n },\n \"leaderboard_bbh_hyperbaton\"\
: {\n \"acc_norm,none\": 0.688,\n \"acc_norm_stderr,none\": 0.029361067575219817,\n\
\ \"alias\": \" - leaderboard_bbh_hyperbaton\"\n },\n \"leaderboard_bbh_logical_deduction_five_objects\"\
: {\n \"acc_norm,none\": 0.504,\n \"acc_norm_stderr,none\": 0.03168519855119917,\n\
\ \"alias\": \" - leaderboard_bbh_logical_deduction_five_objects\"\n \
\ },\n \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \"acc_norm,none\"\
: 0.464,\n \"acc_norm_stderr,none\": 0.03160397514522373,\n \"alias\"\
: \" - leaderboard_bbh_logical_deduction_seven_objects\"\n },\n \"leaderboard_bbh_logical_deduction_three_objects\"\
: {\n \"acc_norm,none\": 0.74,\n \"acc_norm_stderr,none\": 0.0277973157526443,\n\
\ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\"\n \
\ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"acc_norm,none\"\
: 0.84,\n \"acc_norm_stderr,none\": 0.023232714782060664,\n \"alias\"\
: \" - leaderboard_bbh_movie_recommendation\"\n },\n \"leaderboard_bbh_navigate\"\
: {\n \"acc_norm,none\": 0.66,\n \"acc_norm_stderr,none\": 0.03002007360545791,\n\
\ \"alias\": \" - leaderboard_bbh_navigate\"\n },\n \"leaderboard_bbh_object_counting\"\
: {\n \"acc_norm,none\": 0.468,\n \"acc_norm_stderr,none\": 0.031621252575725504,\n\
\ \"alias\": \" - leaderboard_bbh_object_counting\"\n },\n \"leaderboard_bbh_penguins_in_a_table\"\
: {\n \"acc_norm,none\": 0.4863013698630137,\n \"acc_norm_stderr,none\"\
: 0.041507153312234174,\n \"alias\": \" - leaderboard_bbh_penguins_in_a_table\"\
\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \"\
acc_norm,none\": 0.604,\n \"acc_norm_stderr,none\": 0.03099319785457785,\n\
\ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\n \
\ },\n \"leaderboard_bbh_ruin_names\": {\n \"acc_norm,none\": 0.86,\n\
\ \"acc_norm_stderr,none\": 0.02198940964524027,\n \"alias\": \" \
\ - leaderboard_bbh_ruin_names\"\n },\n \"leaderboard_bbh_salient_translation_error_detection\"\
: {\n \"acc_norm,none\": 0.604,\n \"acc_norm_stderr,none\": 0.030993197854577853,\n\
\ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\"\n\
\ },\n \"leaderboard_bbh_snarks\": {\n \"acc_norm,none\": 0.8089887640449438,\n\
\ \"acc_norm_stderr,none\": 0.02954704605327662,\n \"alias\": \" \
\ - leaderboard_bbh_snarks\"\n },\n \"leaderboard_bbh_sports_understanding\"\
: {\n \"acc_norm,none\": 0.84,\n \"acc_norm_stderr,none\": 0.023232714782060654,\n\
\ \"alias\": \" - leaderboard_bbh_sports_understanding\"\n },\n \"\
leaderboard_bbh_temporal_sequences\": {\n \"acc_norm,none\": 0.696,\n \
\ \"acc_norm_stderr,none\": 0.029150213374159677,\n \"alias\": \" -\
\ leaderboard_bbh_temporal_sequences\"\n },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\"\
: {\n \"acc_norm,none\": 0.136,\n \"acc_norm_stderr,none\": 0.021723342617052072,\n\
\ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\
\n },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \
\ \"acc_norm,none\": 0.144,\n \"acc_norm_stderr,none\": 0.022249407735450203,\n\
\ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\
\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \
\ \"acc_norm,none\": 0.308,\n \"acc_norm_stderr,none\": 0.029256928606501864,\n\
\ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\
\n },\n \"leaderboard_bbh_web_of_lies\": {\n \"acc_norm,none\": 0.484,\n\
\ \"acc_norm_stderr,none\": 0.03166998503010742,\n \"alias\": \" \
\ - leaderboard_bbh_web_of_lies\"\n },\n \"leaderboard_gpqa\": {\n \
\ \"acc_norm,none\": 0.3179530201342282,\n \"acc_norm_stderr,none\": 0.013502558289260929,\n\
\ \"alias\": \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\"\
: {\n \"acc_norm,none\": 0.29797979797979796,\n \"acc_norm_stderr,none\"\
: 0.032586303838365555,\n \"alias\": \" - leaderboard_gpqa_diamond\"\n \
\ },\n \"leaderboard_gpqa_extended\": {\n \"acc_norm,none\": 0.32051282051282054,\n\
\ \"acc_norm_stderr,none\": 0.01999010546069711,\n \"alias\": \" \
\ - leaderboard_gpqa_extended\"\n },\n \"leaderboard_gpqa_main\": {\n \
\ \"acc_norm,none\": 0.3236607142857143,\n \"acc_norm_stderr,none\": 0.022129598768200864,\n\
\ \"alias\": \" - leaderboard_gpqa_main\"\n },\n \"leaderboard_ifeval\"\
: {\n \"prompt_level_strict_acc,none\": 0.34011090573012936,\n \"\
prompt_level_strict_acc_stderr,none\": 0.020386791181835787,\n \"inst_level_strict_acc,none\"\
: 0.4628297362110312,\n \"inst_level_strict_acc_stderr,none\": \"N/A\",\n\
\ \"prompt_level_loose_acc,none\": 0.36229205175600737,\n \"prompt_level_loose_acc_stderr,none\"\
: 0.0206844243149654,\n \"inst_level_loose_acc,none\": 0.4844124700239808,\n\
\ \"inst_level_loose_acc_stderr,none\": \"N/A\",\n \"alias\": \" -\
\ leaderboard_ifeval\"\n },\n \"leaderboard_math_hard\": {\n \"exact_match,none\"\
: 0.03625377643504532,\n \"exact_match_stderr,none\": 0.005091404089611316,\n\
\ \"alias\": \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\"\
: {\n \"exact_match,none\": 0.03908794788273615,\n \"exact_match_stderr,none\"\
: 0.011079050508657068,\n \"alias\": \" - leaderboard_math_algebra_hard\"\
\n },\n \"leaderboard_math_counting_and_prob_hard\": {\n \"exact_match,none\"\
: 0.04065040650406504,\n \"exact_match_stderr,none\": 0.01787890756443746,\n\
\ \"alias\": \" - leaderboard_math_counting_and_prob_hard\"\n },\n \
\ \"leaderboard_math_geometry_hard\": {\n \"exact_match,none\": 0.007575757575757576,\n\
\ \"exact_match_stderr,none\": 0.007575757575757563,\n \"alias\":\
\ \" - leaderboard_math_geometry_hard\"\n },\n \"leaderboard_math_intermediate_algebra_hard\"\
: {\n \"exact_match,none\": 0.0035714285714285713,\n \"exact_match_stderr,none\"\
: 0.003571428571428569,\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\"\
\n },\n \"leaderboard_math_num_theory_hard\": {\n \"exact_match,none\"\
: 0.045454545454545456,\n \"exact_match_stderr,none\": 0.016839967582612487,\n\
\ \"alias\": \" - leaderboard_math_num_theory_hard\"\n },\n \"leaderboard_math_prealgebra_hard\"\
: {\n \"exact_match,none\": 0.09326424870466321,\n \"exact_match_stderr,none\"\
: 0.020986854593289733,\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\
\n },\n \"leaderboard_math_precalculus_hard\": {\n \"exact_match,none\"\
: 0.02962962962962963,\n \"exact_match_stderr,none\": 0.014648038602753785,\n\
\ \"alias\": \" - leaderboard_math_precalculus_hard\"\n },\n \"leaderboard_mmlu_pro\"\
: {\n \"acc,none\": 0.3757480053191489,\n \"acc_stderr,none\": 0.0044154751798350916,\n\
\ \"alias\": \" - leaderboard_mmlu_pro\"\n },\n \"leaderboard_musr\"\
: {\n \"acc_norm,none\": 0.5066137566137566,\n \"acc_norm_stderr,none\"\
: 0.017748435072382215,\n \"alias\": \" - leaderboard_musr\"\n },\n \
\ \"leaderboard_musr_murder_mysteries\": {\n \"acc_norm,none\": 0.612,\n\
\ \"acc_norm_stderr,none\": 0.030881038748993922,\n \"alias\": \"\
\ - leaderboard_musr_murder_mysteries\"\n },\n \"leaderboard_musr_object_placements\"\
: {\n \"acc_norm,none\": 0.3515625,\n \"acc_norm_stderr,none\": 0.029899590697818237,\n\
\ \"alias\": \" - leaderboard_musr_object_placements\"\n },\n \"leaderboard_musr_team_allocation\"\
: {\n \"acc_norm,none\": 0.56,\n \"acc_norm_stderr,none\": 0.03145724452223564,\n\
\ \"alias\": \" - leaderboard_musr_team_allocation\"\n }\n}\n```"
repo_url: https://huggingface.co/pankajmathur/orca_mini_v3_70b
leaderboard_url: ''
point_of_contact: ''
configs:
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_boolean_expressions
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_boolean_expressions_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_boolean_expressions_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_causal_judgement
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_causal_judgement_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_causal_judgement_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_date_understanding
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_date_understanding_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_date_understanding_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_disambiguation_qa
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_disambiguation_qa_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_disambiguation_qa_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_formal_fallacies
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_formal_fallacies_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_formal_fallacies_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_geometric_shapes
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_geometric_shapes_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_geometric_shapes_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_hyperbaton
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_hyperbaton_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_hyperbaton_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_logical_deduction_five_objects
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_logical_deduction_seven_objects
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_logical_deduction_three_objects
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_movie_recommendation
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_movie_recommendation_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_movie_recommendation_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_navigate
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_navigate_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_navigate_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_object_counting
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_object_counting_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_object_counting_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_penguins_in_a_table
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_penguins_in_a_table_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_penguins_in_a_table_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_reasoning_about_colored_objects
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_ruin_names
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_ruin_names_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_ruin_names_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_salient_translation_error_detection
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_snarks
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_snarks_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_snarks_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_sports_understanding
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_sports_understanding_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_sports_understanding_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_temporal_sequences
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_temporal_sequences_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_temporal_sequences_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_tracking_shuffled_objects_five_objects
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_tracking_shuffled_objects_seven_objects
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_tracking_shuffled_objects_three_objects
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_bbh_web_of_lies
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_bbh_web_of_lies_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_web_of_lies_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_gpqa_diamond
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_gpqa_diamond_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_gpqa_diamond_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_gpqa_extended
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_gpqa_extended_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_gpqa_extended_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_gpqa_main
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_gpqa_main_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_gpqa_main_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_ifeval
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_ifeval_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_ifeval_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_math_algebra_hard
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_math_algebra_hard_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_algebra_hard_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_math_counting_and_prob_hard
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_math_counting_and_prob_hard_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_counting_and_prob_hard_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_math_geometry_hard
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_math_geometry_hard_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_geometry_hard_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_math_intermediate_algebra_hard
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_math_intermediate_algebra_hard_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_intermediate_algebra_hard_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_math_num_theory_hard
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_math_num_theory_hard_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_num_theory_hard_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_math_prealgebra_hard
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_math_prealgebra_hard_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_prealgebra_hard_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_math_precalculus_hard
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_math_precalculus_hard_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_precalculus_hard_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_mmlu_pro
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_mmlu_pro_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_mmlu_pro_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_musr_murder_mysteries
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_musr_murder_mysteries_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_musr_murder_mysteries_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_musr_object_placements
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_musr_object_placements_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_musr_object_placements_2024-07-18T00-58-53.025846.jsonl'
- config_name: pankajmathur__orca_mini_v3_70b__leaderboard_musr_team_allocation
data_files:
- split: 2024_07_18T00_58_53.025846
path:
- '**/samples_leaderboard_musr_team_allocation_2024-07-18T00-58-53.025846.jsonl'
- split: latest
path:
- '**/samples_leaderboard_musr_team_allocation_2024-07-18T00-58-53.025846.jsonl'
---
# Dataset Card for Evaluation run of pankajmathur/orca_mini_v3_70b
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [pankajmathur/orca_mini_v3_70b](https://huggingface.co/pankajmathur/orca_mini_v3_70b)
The dataset is composed of 38 configuration(s), each one corresponding 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.
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/pankajmathur__orca_mini_v3_70b-details",
name="pankajmathur__orca_mini_v3_70b__leaderboard_bbh_boolean_expressions",
split="latest"
)
```
## Latest results
These are the [latest results from run 2024-07-18T00-58-53.025846](https://huggingface.co/datasets/open-llm-leaderboard/pankajmathur__orca_mini_v3_70b-details/blob/main/pankajmathur__orca_mini_v3_70b/results_2024-07-18T00-58-53.025846.json) (note that there 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": {
"leaderboard": {
"inst_level_loose_acc,none": 0.4844124700239808,
"inst_level_loose_acc_stderr,none": "N/A",
"exact_match,none": 0.03625377643504532,
"exact_match_stderr,none": 0.005091404089611316,
"prompt_level_loose_acc,none": 0.36229205175600737,
"prompt_level_loose_acc_stderr,none": 0.0206844243149654,
"inst_level_strict_acc,none": 0.4628297362110312,
"inst_level_strict_acc_stderr,none": "N/A",
"acc_norm,none": 0.5424828122973149,
"acc_norm_stderr,none": 0.005187239654766714,
"prompt_level_strict_acc,none": 0.34011090573012936,
"prompt_level_strict_acc_stderr,none": 0.020386791181835787,
"acc,none": 0.3757480053191489,
"acc_stderr,none": 0.0044154751798350916,
"alias": "leaderboard"
},
"leaderboard_bbh": {
"acc_norm,none": 0.593646936295782,
"acc_norm_stderr,none": 0.005912007702853069,
"alias": " - leaderboard_bbh"
},
"leaderboard_bbh_boolean_expressions": {
"acc_norm,none": 0.832,
"acc_norm_stderr,none": 0.023692813205492578,
"alias": " - leaderboard_bbh_boolean_expressions"
},
"leaderboard_bbh_causal_judgement": {
"acc_norm,none": 0.6470588235294118,
"acc_norm_stderr,none": 0.03504019983419236,
"alias": " - leaderboard_bbh_causal_judgement"
},
"leaderboard_bbh_date_understanding": {
"acc_norm,none": 0.592,
"acc_norm_stderr,none": 0.0311452098465485,
"alias": " - leaderboard_bbh_date_understanding"
},
"leaderboard_bbh_disambiguation_qa": {
"acc_norm,none": 0.776,
"acc_norm_stderr,none": 0.026421361687347905,
"alias": " - leaderboard_bbh_disambiguation_qa"
},
"leaderboard_bbh_formal_fallacies": {
"acc_norm,none": 0.644,
"acc_norm_stderr,none": 0.03034368065715322,
"alias": " - leaderboard_bbh_formal_fallacies"
},
"leaderboard_bbh_geometric_shapes": {
"acc_norm,none": 0.452,
"acc_norm_stderr,none": 0.03153986449255662,
"alias": " - leaderboard_bbh_geometric_shapes"
},
"leaderboard_bbh_hyperbaton": {
"acc_norm,none": 0.688,
"acc_norm_stderr,none": 0.029361067575219817,
"alias": " - leaderboard_bbh_hyperbaton"
},
"leaderboard_bbh_logical_deduction_five_objects": {
"acc_norm,none": 0.504,
"acc_norm_stderr,none": 0.03168519855119917,
"alias": " - leaderboard_bbh_logical_deduction_five_objects"
},
"leaderboard_bbh_logical_deduction_seven_objects": {
"acc_norm,none": 0.464,
"acc_norm_stderr,none": 0.03160397514522373,
"alias": " - leaderboard_bbh_logical_deduction_seven_objects"
},
"leaderboard_bbh_logical_deduction_three_objects": {
"acc_norm,none": 0.74,
"acc_norm_stderr,none": 0.0277973157526443,
"alias": " - leaderboard_bbh_logical_deduction_three_objects"
},
"leaderboard_bbh_movie_recommendation": {
"acc_norm,none": 0.84,
"acc_norm_stderr,none": 0.023232714782060664,
"alias": " - leaderboard_bbh_movie_recommendation"
},
"leaderboard_bbh_navigate": {
"acc_norm,none": 0.66,
"acc_norm_stderr,none": 0.03002007360545791,
"alias": " - leaderboard_bbh_navigate"
},
"leaderboard_bbh_object_counting": {
"acc_norm,none": 0.468,
"acc_norm_stderr,none": 0.031621252575725504,
"alias": " - leaderboard_bbh_object_counting"
},
"leaderboard_bbh_penguins_in_a_table": {
"acc_norm,none": 0.4863013698630137,
"acc_norm_stderr,none": 0.041507153312234174,
"alias": " - leaderboard_bbh_penguins_in_a_table"
},
"leaderboard_bbh_reasoning_about_colored_objects": {
"acc_norm,none": 0.604,
"acc_norm_stderr,none": 0.03099319785457785,
"alias": " - leaderboard_bbh_reasoning_about_colored_objects"
},
"leaderboard_bbh_ruin_names": {
"acc_norm,none": 0.86,
"acc_norm_stderr,none": 0.02198940964524027,
"alias": " - leaderboard_bbh_ruin_names"
},
"leaderboard_bbh_salient_translation_error_detection": {
"acc_norm,none": 0.604,
"acc_norm_stderr,none": 0.030993197854577853,
"alias": " - leaderboard_bbh_salient_translation_error_detection"
},
"leaderboard_bbh_snarks": {
"acc_norm,none": 0.8089887640449438,
"acc_norm_stderr,none": 0.02954704605327662,
"alias": " - leaderboard_bbh_snarks"
},
"leaderboard_bbh_sports_understanding": {
"acc_norm,none": 0.84,
"acc_norm_stderr,none": 0.023232714782060654,
"alias": " - leaderboard_bbh_sports_understanding"
},
"leaderboard_bbh_temporal_sequences": {
"acc_norm,none": 0.696,
"acc_norm_stderr,none": 0.029150213374159677,
"alias": " - leaderboard_bbh_temporal_sequences"
},
"leaderboard_bbh_tracking_shuffled_objects_five_objects": {
"acc_norm,none": 0.136,
"acc_norm_stderr,none": 0.021723342617052072,
"alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects"
},
"leaderboard_bbh_tracking_shuffled_objects_seven_objects": {
"acc_norm,none": 0.144,
"acc_norm_stderr,none": 0.022249407735450203,
"alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects"
},
"leaderboard_bbh_tracking_shuffled_objects_three_objects": {
"acc_norm,none": 0.308,
"acc_norm_stderr,none": 0.029256928606501864,
"alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects"
},
"leaderboard_bbh_web_of_lies": {
"acc_norm,none": 0.484,
"acc_norm_stderr,none": 0.03166998503010742,
"alias": " - leaderboard_bbh_web_of_lies"
},
"leaderboard_gpqa": {
"acc_norm,none": 0.3179530201342282,
"acc_norm_stderr,none": 0.013502558289260929,
"alias": " - leaderboard_gpqa"
},
"leaderboard_gpqa_diamond": {
"acc_norm,none": 0.29797979797979796,
"acc_norm_stderr,none": 0.032586303838365555,
"alias": " - leaderboard_gpqa_diamond"
},
"leaderboard_gpqa_extended": {
"acc_norm,none": 0.32051282051282054,
"acc_norm_stderr,none": 0.01999010546069711,
"alias": " - leaderboard_gpqa_extended"
},
"leaderboard_gpqa_main": {
"acc_norm,none": 0.3236607142857143,
"acc_norm_stderr,none": 0.022129598768200864,
"alias": " - leaderboard_gpqa_main"
},
"leaderboard_ifeval": {
"prompt_level_strict_acc,none": 0.34011090573012936,
"prompt_level_strict_acc_stderr,none": 0.020386791181835787,
"inst_level_strict_acc,none": 0.4628297362110312,
"inst_level_strict_acc_stderr,none": "N/A",
"prompt_level_loose_acc,none": 0.36229205175600737,
"prompt_level_loose_acc_stderr,none": 0.0206844243149654,
"inst_level_loose_acc,none": 0.4844124700239808,
"inst_level_loose_acc_stderr,none": "N/A",
"alias": " - leaderboard_ifeval"
},
"leaderboard_math_hard": {
"exact_match,none": 0.03625377643504532,
"exact_match_stderr,none": 0.005091404089611316,
"alias": " - leaderboard_math_hard"
},
"leaderboard_math_algebra_hard": {
"exact_match,none": 0.03908794788273615,
"exact_match_stderr,none": 0.011079050508657068,
"alias": " - leaderboard_math_algebra_hard"
},
"leaderboard_math_counting_and_prob_hard": {
"exact_match,none": 0.04065040650406504,
"exact_match_stderr,none": 0.01787890756443746,
"alias": " - leaderboard_math_counting_and_prob_hard"
},
"leaderboard_math_geometry_hard": {
"exact_match,none": 0.007575757575757576,
"exact_match_stderr,none": 0.007575757575757563,
"alias": " - leaderboard_math_geometry_hard"
},
"leaderboard_math_intermediate_algebra_hard": {
"exact_match,none": 0.0035714285714285713,
"exact_match_stderr,none": 0.003571428571428569,
"alias": " - leaderboard_math_intermediate_algebra_hard"
},
"leaderboard_math_num_theory_hard": {
"exact_match,none": 0.045454545454545456,
"exact_match_stderr,none": 0.016839967582612487,
"alias": " - leaderboard_math_num_theory_hard"
},
"leaderboard_math_prealgebra_hard": {
"exact_match,none": 0.09326424870466321,
"exact_match_stderr,none": 0.020986854593289733,
"alias": " - leaderboard_math_prealgebra_hard"
},
"leaderboard_math_precalculus_hard": {
"exact_match,none": 0.02962962962962963,
"exact_match_stderr,none": 0.014648038602753785,
"alias": " - leaderboard_math_precalculus_hard"
},
"leaderboard_mmlu_pro": {
"acc,none": 0.3757480053191489,
"acc_stderr,none": 0.0044154751798350916,
"alias": " - leaderboard_mmlu_pro"
},
"leaderboard_musr": {
"acc_norm,none": 0.5066137566137566,
"acc_norm_stderr,none": 0.017748435072382215,
"alias": " - leaderboard_musr"
},
"leaderboard_musr_murder_mysteries": {
"acc_norm,none": 0.612,
"acc_norm_stderr,none": 0.030881038748993922,
"alias": " - leaderboard_musr_murder_mysteries"
},
"leaderboard_musr_object_placements": {
"acc_norm,none": 0.3515625,
"acc_norm_stderr,none": 0.029899590697818237,
"alias": " - leaderboard_musr_object_placements"
},
"leaderboard_musr_team_allocation": {
"acc_norm,none": 0.56,
"acc_norm_stderr,none": 0.03145724452223564,
"alias": " - leaderboard_musr_team_allocation"
}
},
"leaderboard": {
"inst_level_loose_acc,none": 0.4844124700239808,
"inst_level_loose_acc_stderr,none": "N/A",
"exact_match,none": 0.03625377643504532,
"exact_match_stderr,none": 0.005091404089611316,
"prompt_level_loose_acc,none": 0.36229205175600737,
"prompt_level_loose_acc_stderr,none": 0.0206844243149654,
"inst_level_strict_acc,none": 0.4628297362110312,
"inst_level_strict_acc_stderr,none": "N/A",
"acc_norm,none": 0.5424828122973149,
"acc_norm_stderr,none": 0.005187239654766714,
"prompt_level_strict_acc,none": 0.34011090573012936,
"prompt_level_strict_acc_stderr,none": 0.020386791181835787,
"acc,none": 0.3757480053191489,
"acc_stderr,none": 0.0044154751798350916,
"alias": "leaderboard"
},
"leaderboard_bbh": {
"acc_norm,none": 0.593646936295782,
"acc_norm_stderr,none": 0.005912007702853069,
"alias": " - leaderboard_bbh"
},
"leaderboard_bbh_boolean_expressions": {
"acc_norm,none": 0.832,
"acc_norm_stderr,none": 0.023692813205492578,
"alias": " - leaderboard_bbh_boolean_expressions"
},
"leaderboard_bbh_causal_judgement": {
"acc_norm,none": 0.6470588235294118,
"acc_norm_stderr,none": 0.03504019983419236,
"alias": " - leaderboard_bbh_causal_judgement"
},
"leaderboard_bbh_date_understanding": {
"acc_norm,none": 0.592,
"acc_norm_stderr,none": 0.0311452098465485,
"alias": " - leaderboard_bbh_date_understanding"
},
"leaderboard_bbh_disambiguation_qa": {
"acc_norm,none": 0.776,
"acc_norm_stderr,none": 0.026421361687347905,
"alias": " - leaderboard_bbh_disambiguation_qa"
},
"leaderboard_bbh_formal_fallacies": {
"acc_norm,none": 0.644,
"acc_norm_stderr,none": 0.03034368065715322,
"alias": " - leaderboard_bbh_formal_fallacies"
},
"leaderboard_bbh_geometric_shapes": {
"acc_norm,none": 0.452,
"acc_norm_stderr,none": 0.03153986449255662,
"alias": " - leaderboard_bbh_geometric_shapes"
},
"leaderboard_bbh_hyperbaton": {
"acc_norm,none": 0.688,
"acc_norm_stderr,none": 0.029361067575219817,
"alias": " - leaderboard_bbh_hyperbaton"
},
"leaderboard_bbh_logical_deduction_five_objects": {
"acc_norm,none": 0.504,
"acc_norm_stderr,none": 0.03168519855119917,
"alias": " - leaderboard_bbh_logical_deduction_five_objects"
},
"leaderboard_bbh_logical_deduction_seven_objects": {
"acc_norm,none": 0.464,
"acc_norm_stderr,none": 0.03160397514522373,
"alias": " - leaderboard_bbh_logical_deduction_seven_objects"
},
"leaderboard_bbh_logical_deduction_three_objects": {
"acc_norm,none": 0.74,
"acc_norm_stderr,none": 0.0277973157526443,
"alias": " - leaderboard_bbh_logical_deduction_three_objects"
},
"leaderboard_bbh_movie_recommendation": {
"acc_norm,none": 0.84,
"acc_norm_stderr,none": 0.023232714782060664,
"alias": " - leaderboard_bbh_movie_recommendation"
},
"leaderboard_bbh_navigate": {
"acc_norm,none": 0.66,
"acc_norm_stderr,none": 0.03002007360545791,
"alias": " - leaderboard_bbh_navigate"
},
"leaderboard_bbh_object_counting": {
"acc_norm,none": 0.468,
"acc_norm_stderr,none": 0.031621252575725504,
"alias": " - leaderboard_bbh_object_counting"
},
"leaderboard_bbh_penguins_in_a_table": {
"acc_norm,none": 0.4863013698630137,
"acc_norm_stderr,none": 0.041507153312234174,
"alias": " - leaderboard_bbh_penguins_in_a_table"
},
"leaderboard_bbh_reasoning_about_colored_objects": {
"acc_norm,none": 0.604,
"acc_norm_stderr,none": 0.03099319785457785,
"alias": " - leaderboard_bbh_reasoning_about_colored_objects"
},
"leaderboard_bbh_ruin_names": {
"acc_norm,none": 0.86,
"acc_norm_stderr,none": 0.02198940964524027,
"alias": " - leaderboard_bbh_ruin_names"
},
"leaderboard_bbh_salient_translation_error_detection": {
"acc_norm,none": 0.604,
"acc_norm_stderr,none": 0.030993197854577853,
"alias": " - leaderboard_bbh_salient_translation_error_detection"
},
"leaderboard_bbh_snarks": {
"acc_norm,none": 0.8089887640449438,
"acc_norm_stderr,none": 0.02954704605327662,
"alias": " - leaderboard_bbh_snarks"
},
"leaderboard_bbh_sports_understanding": {
"acc_norm,none": 0.84,
"acc_norm_stderr,none": 0.023232714782060654,
"alias": " - leaderboard_bbh_sports_understanding"
},
"leaderboard_bbh_temporal_sequences": {
"acc_norm,none": 0.696,
"acc_norm_stderr,none": 0.029150213374159677,
"alias": " - leaderboard_bbh_temporal_sequences"
},
"leaderboard_bbh_tracking_shuffled_objects_five_objects": {
"acc_norm,none": 0.136,
"acc_norm_stderr,none": 0.021723342617052072,
"alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects"
},
"leaderboard_bbh_tracking_shuffled_objects_seven_objects": {
"acc_norm,none": 0.144,
"acc_norm_stderr,none": 0.022249407735450203,
"alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects"
},
"leaderboard_bbh_tracking_shuffled_objects_three_objects": {
"acc_norm,none": 0.308,
"acc_norm_stderr,none": 0.029256928606501864,
"alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects"
},
"leaderboard_bbh_web_of_lies": {
"acc_norm,none": 0.484,
"acc_norm_stderr,none": 0.03166998503010742,
"alias": " - leaderboard_bbh_web_of_lies"
},
"leaderboard_gpqa": {
"acc_norm,none": 0.3179530201342282,
"acc_norm_stderr,none": 0.013502558289260929,
"alias": " - leaderboard_gpqa"
},
"leaderboard_gpqa_diamond": {
"acc_norm,none": 0.29797979797979796,
"acc_norm_stderr,none": 0.032586303838365555,
"alias": " - leaderboard_gpqa_diamond"
},
"leaderboard_gpqa_extended": {
"acc_norm,none": 0.32051282051282054,
"acc_norm_stderr,none": 0.01999010546069711,
"alias": " - leaderboard_gpqa_extended"
},
"leaderboard_gpqa_main": {
"acc_norm,none": 0.3236607142857143,
"acc_norm_stderr,none": 0.022129598768200864,
"alias": " - leaderboard_gpqa_main"
},
"leaderboard_ifeval": {
"prompt_level_strict_acc,none": 0.34011090573012936,
"prompt_level_strict_acc_stderr,none": 0.020386791181835787,
"inst_level_strict_acc,none": 0.4628297362110312,
"inst_level_strict_acc_stderr,none": "N/A",
"prompt_level_loose_acc,none": 0.36229205175600737,
"prompt_level_loose_acc_stderr,none": 0.0206844243149654,
"inst_level_loose_acc,none": 0.4844124700239808,
"inst_level_loose_acc_stderr,none": "N/A",
"alias": " - leaderboard_ifeval"
},
"leaderboard_math_hard": {
"exact_match,none": 0.03625377643504532,
"exact_match_stderr,none": 0.005091404089611316,
"alias": " - leaderboard_math_hard"
},
"leaderboard_math_algebra_hard": {
"exact_match,none": 0.03908794788273615,
"exact_match_stderr,none": 0.011079050508657068,
"alias": " - leaderboard_math_algebra_hard"
},
"leaderboard_math_counting_and_prob_hard": {
"exact_match,none": 0.04065040650406504,
"exact_match_stderr,none": 0.01787890756443746,
"alias": " - leaderboard_math_counting_and_prob_hard"
},
"leaderboard_math_geometry_hard": {
"exact_match,none": 0.007575757575757576,
"exact_match_stderr,none": 0.007575757575757563,
"alias": " - leaderboard_math_geometry_hard"
},
"leaderboard_math_intermediate_algebra_hard": {
"exact_match,none": 0.0035714285714285713,
"exact_match_stderr,none": 0.003571428571428569,
"alias": " - leaderboard_math_intermediate_algebra_hard"
},
"leaderboard_math_num_theory_hard": {
"exact_match,none": 0.045454545454545456,
"exact_match_stderr,none": 0.016839967582612487,
"alias": " - leaderboard_math_num_theory_hard"
},
"leaderboard_math_prealgebra_hard": {
"exact_match,none": 0.09326424870466321,
"exact_match_stderr,none": 0.020986854593289733,
"alias": " - leaderboard_math_prealgebra_hard"
},
"leaderboard_math_precalculus_hard": {
"exact_match,none": 0.02962962962962963,
"exact_match_stderr,none": 0.014648038602753785,
"alias": " - leaderboard_math_precalculus_hard"
},
"leaderboard_mmlu_pro": {
"acc,none": 0.3757480053191489,
"acc_stderr,none": 0.0044154751798350916,
"alias": " - leaderboard_mmlu_pro"
},
"leaderboard_musr": {
"acc_norm,none": 0.5066137566137566,
"acc_norm_stderr,none": 0.017748435072382215,
"alias": " - leaderboard_musr"
},
"leaderboard_musr_murder_mysteries": {
"acc_norm,none": 0.612,
"acc_norm_stderr,none": 0.030881038748993922,
"alias": " - leaderboard_musr_murder_mysteries"
},
"leaderboard_musr_object_placements": {
"acc_norm,none": 0.3515625,
"acc_norm_stderr,none": 0.029899590697818237,
"alias": " - leaderboard_musr_object_placements"
},
"leaderboard_musr_team_allocation": {
"acc_norm,none": 0.56,
"acc_norm_stderr,none": 0.03145724452223564,
"alias": " - leaderboard_musr_team_allocation"
}
}
```
## 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. -->
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## More Information [optional]
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## Dataset Card Contact
[More Information Needed] |
HongxinLi/AutoGUI-v1-mini | HongxinLi | "2024-07-18T01:48:18Z" | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:image",
"modality:text",
"modality:timeseries",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T01:02:06Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: instruction
dtype: string
- name: answer
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- name: unnormalized_box
sequence: float32
- name: elem_text
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splits:
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num_examples: 100
download_size: 20069711
dataset_size: 20372037.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
This miniset of the AutoGUI-v1 dataset demonstrates representative samples of functionality grounding/captioning tasks. |
stojchet/java-deepseek-coder-1.3b-base-markdown-10 | stojchet | "2024-07-18T01:09:39Z" | 0 | 0 | [
"size_categories:10K<n<100K",
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"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T01:02:15Z" | ---
dataset_info:
- config_name: humaneval
features:
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configs:
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data_files:
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path: humaneval/train-*
- config_name: mbxp
data_files:
- split: train
path: mbxp/train-*
---
|
stojchet/python-deepseek-coder-1.3b-base-oneshot-10 | stojchet | "2024-07-18T01:14:07Z" | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
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"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T01:07:44Z" | ---
dataset_info:
- config_name: humaneval
features:
- name: task_id
dtype: string
- name: language
dtype: string
- name: completion
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features:
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data_files:
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path: humaneval/train-*
- config_name: mbxp
data_files:
- split: train
path: mbxp/train-*
---
|
ycfNTU/USB_llama3_twoshot | ycfNTU | "2024-07-18T01:35:09Z" | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:tabular",
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"library:pandas",
"library:mlcroissant",
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] | null | "2024-07-18T01:07:59Z" | ---
dataset_info:
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configs:
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path: data/train-*
---
|
MAOkka/dataset_demo | MAOkka | "2024-07-18T02:52:30Z" | 0 | 0 | [
"size_categories:1K<n<10K",
"format:parquet",
"modality:image",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T01:10:14Z" | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': Cat
'1': Dog
splits:
- name: train
num_bytes: 140439590.912
num_examples: 3848
download_size: 139189487
dataset_size: 140439590.912
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BangumiBase/kubosanwamobwoyurusanai | BangumiBase | "2024-07-18T03:36:00Z" | 0 | 0 | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"modality:text",
"region:us",
"art"
] | null | "2024-07-18T01:10:55Z" | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Kubo-san Wa Mob Wo Yurusanai
This is the image base of bangumi Kubo-san wa Mob wo Yurusanai, we detected 35 characters, 4261 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 | 1227 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 34 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 43 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 74 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 49 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 25 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 35 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 28 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 25 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 9 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 1927 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 6 | [Download](11/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 12 | 29 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 17 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 19 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 9 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 22 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 7 | [Download](17/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 18 | 87 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 16 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 15 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 9 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 19 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 8 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 268 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 7 | [Download](25/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 26 | 118 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 7 | [Download](27/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 28 | 13 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 9 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 8 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 6 | [Download](31/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 32 | 7 | [Download](32/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 33 | 5 | [Download](33/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 74 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
anthj/dpo_belief | anthj | "2024-07-18T01:13:14Z" | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T01:13:08Z" | ---
dataset_info:
features:
- name: question
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
- name: __index_level_0__
dtype: int64
splits:
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- name: eval
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num_examples: 5498
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dataset_size: 37943452
configs:
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data_files:
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path: data/train-*
- split: eval
path: data/eval-*
---
|
stojchet/python-deepseek-coder-1.3b-base-markdown-10 | stojchet | "2024-07-18T01:23:54Z" | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T01:14:43Z" | ---
dataset_info:
- config_name: humaneval
features:
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dtype: string
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dtype: string
- name: completion
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download_size: 122997
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configs:
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data_files:
- split: train
path: mbxp/train-*
---
|
stojchet/java-deepseek-coder-1.3b-base-oneshot-10 | stojchet | "2024-07-18T01:25:21Z" | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T01:16:58Z" | ---
dataset_info:
- config_name: humaneval
features:
- name: task_id
dtype: string
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dtype: string
- name: completion
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splits:
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num_examples: 1610
download_size: 137768
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features:
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dtype: string
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dtype: string
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splits:
- name: train
num_bytes: 17455970
num_examples: 9660
download_size: 607750
dataset_size: 17455970
configs:
- config_name: humaneval
data_files:
- split: train
path: humaneval/train-*
- config_name: mbxp
data_files:
- split: train
path: mbxp/train-*
---
|
MAOkka/11 | MAOkka | "2024-07-18T01:18:14Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2024-07-18T01:18:14Z" | ---
license: apache-2.0
---
|
TingchenFu/ChineseDialogue | TingchenFu | "2024-07-18T03:53:27Z" | 0 | 0 | [
"license:apache-2.0",
"region:us"
] | null | "2024-07-18T01:19:25Z" | ---
license: apache-2.0
---
|
on1onmangoes/HF_test071724 | on1onmangoes | "2024-07-18T01:20:32Z" | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:audio",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T01:20:22Z" | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: text
dtype: string
splits:
- name: train
num_bytes: 212408587.0
num_examples: 92
download_size: 202176585
dataset_size: 212408587.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
lycaoduong/FTIR | lycaoduong | "2024-08-08T14:13:13Z" | 0 | 0 | [
"license:apache-2.0",
"size_categories:1K<n<10K",
"region:us",
"chemistry"
] | null | "2024-07-18T01:25:18Z" | ---
license: apache-2.0
tags:
- chemistry
size_categories:
- 1K<n<10K
---
### FTIR Absorbance Spectra Dataset for Fcg-Former Paper
# Dataset Description
The data was obtained from the National Institute of Standards and Technology (NIST) Chemistry WebBook. The dataset is intended for use in the "Fcg-Former" research project, which focuses on machine learning models for spectral analysis.
# Source
The FTIR spectra data was sourced from the NIST Chemistry WebBook.
NIST Chemistry WebBook: https://webbook.nist.gov/
# Data Format
File Format: The spectra are stored in numpy (*.npy) files and their corresponding label are stored in text (*.txt) files, with each file corresponding to a different compound.
# **Citation**
```@article{doan2024fcg,
title={Fcg-Former: Identification of Functional Groups in FTIR Spectra Using Enhanced Transformer-Based Model},
author={Doan, Vu Hoang Minh and Ly, Cao Duong and Mondal, Sudip and Truong, Thi Thuy and Nguyen, Tan Dung and Choi, Jaeyeop and Lee, Byeongil and Oh, Junghwan},
journal={Analytical Chemistry},
year={2024},
publisher={ACS Publications}
} |
BangumiBase/eiyuuoubuwokiwamerutametenseisuminianime | BangumiBase | "2024-07-18T05:30:12Z" | 0 | 0 | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"region:us",
"art"
] | null | "2024-07-18T01:29:27Z" | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Eiyuuou, Bu Wo Kiwameru Tame Tenseisu Mini Anime
This is the image base of bangumi Eiyuuou, Bu wo Kiwameru Tame Tenseisu Mini Anime, we detected 110 characters, 5551 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 | 6 | [Download](0/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 1 | 36 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 462 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 56 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 86 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 22 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 108 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 124 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 17 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 18 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 42 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 66 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 48 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 52 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 38 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 17 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 16 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 27 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 30 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 116 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 18 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 119 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 7 | [Download](22/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 23 | 17 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 37 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 22 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 19 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 15 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 9 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 19 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 13 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 17 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 15 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 6 | [Download](33/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 34 | 18 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 7 | [Download](35/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 36 | 27 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 16 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 141 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 21 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 85 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 11 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 227 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 24 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 11 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 37 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 10 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 12 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 43 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 17 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 32 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 7 | [Download](51/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 52 | 40 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 18 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 10 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 30 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 16 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 111 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 53 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 26 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 428 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 37 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 24 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 37 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 38 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 36 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 38 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 43 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 46 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 13 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 10 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 11 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 12 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 12 | [Download](73/dataset.zip) |  |  |  |  |  |  |  |  |
| 74 | 62 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 32 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 153 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 14 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 12 | [Download](78/dataset.zip) |  |  |  |  |  |  |  |  |
| 79 | 34 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 21 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 29 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 14 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 7 | [Download](83/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 84 | 17 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 5 | [Download](85/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 86 | 9 | [Download](86/dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 14 | [Download](87/dataset.zip) |  |  |  |  |  |  |  |  |
| 88 | 154 | [Download](88/dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 52 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 16 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
| 91 | 78 | [Download](91/dataset.zip) |  |  |  |  |  |  |  |  |
| 92 | 14 | [Download](92/dataset.zip) |  |  |  |  |  |  |  |  |
| 93 | 9 | [Download](93/dataset.zip) |  |  |  |  |  |  |  |  |
| 94 | 10 | [Download](94/dataset.zip) |  |  |  |  |  |  |  |  |
| 95 | 34 | [Download](95/dataset.zip) |  |  |  |  |  |  |  |  |
| 96 | 10 | [Download](96/dataset.zip) |  |  |  |  |  |  |  |  |
| 97 | 85 | [Download](97/dataset.zip) |  |  |  |  |  |  |  |  |
| 98 | 396 | [Download](98/dataset.zip) |  |  |  |  |  |  |  |  |
| 99 | 5 | [Download](99/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 100 | 9 | [Download](100/dataset.zip) |  |  |  |  |  |  |  |  |
| 101 | 39 | [Download](101/dataset.zip) |  |  |  |  |  |  |  |  |
| 102 | 7 | [Download](102/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 103 | 9 | [Download](103/dataset.zip) |  |  |  |  |  |  |  |  |
| 104 | 208 | [Download](104/dataset.zip) |  |  |  |  |  |  |  |  |
| 105 | 8 | [Download](105/dataset.zip) |  |  |  |  |  |  |  |  |
| 106 | 57 | [Download](106/dataset.zip) |  |  |  |  |  |  |  |  |
| 107 | 57 | [Download](107/dataset.zip) |  |  |  |  |  |  |  |  |
| 108 | 25 | [Download](108/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 291 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
adalbertojunior/gpt4v-dataset-portuguese | adalbertojunior | "2024-07-18T01:32:36Z" | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:image",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T01:31:35Z" | ---
dataset_info:
features:
- name: link
dtype: string
- name: caption
dtype: string
- name: message_id
dtype: string
- name: timestamp
dtype: string
- name: image
dtype: 'null'
splits:
- name: train
num_bytes: 13624781
num_examples: 12356
download_size: 7798958
dataset_size: 13624781
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
cl-nagoya/auto-wiki-nli-reward | cl-nagoya | "2024-07-18T01:53:19Z" | 0 | 0 | [
"language:ja",
"license:cc-by-sa-4.0",
"size_categories:100K<n<1M",
"format:parquet",
"modality:tabular",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T01:36:31Z" | ---
language:
- ja
license: cc-by-sa-4.0
dataset_info:
features:
- name: id
dtype: int64
- name: premise
dtype: string
- name: entailment
dtype: string
- name: contradiction
dtype: string
- name: helpfulness
dtype: float64
- name: correctness
dtype: float64
- name: coherence
dtype: float64
- name: complexity
dtype: float64
- name: verbosity
dtype: float64
splits:
- name: train
num_bytes: 122882434.0
num_examples: 248619
download_size: 79067963
dataset_size: 122882434.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
wanderer2k1/medic_question_positive_negatives | wanderer2k1 | "2024-07-18T01:37:51Z" | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T01:37:10Z" | ---
dataset_info:
features:
- name: question_id
dtype: string
- name: question
dtype: string
- name: positive_context_id
dtype: string
- name: positive_context
dtype: string
- name: hard_negative_ids
dtype: string
- name: hard_negatives
dtype: string
splits:
- name: train
num_bytes: 2145617812
num_examples: 63921
download_size: 965662264
dataset_size: 2145617812
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
wanderer2k1/medic_contexts | wanderer2k1 | "2024-07-18T01:38:14Z" | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T01:38:06Z" | ---
dataset_info:
features:
- name: context_id
dtype: string
- name: context
dtype: string
splits:
- name: train
num_bytes: 358420438
num_examples: 63921
download_size: 167443522
dataset_size: 358420438
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BangumiBase/trigunstampede | BangumiBase | "2024-07-18T04:37:33Z" | 0 | 0 | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"modality:text",
"region:us",
"art"
] | null | "2024-07-18T01:43:06Z" | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Trigun Stampede
This is the image base of bangumi Trigun Stampede, we detected 37 characters, 3814 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 | 310 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 22 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 59 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 1034 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 10 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 18 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 20 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 37 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 18 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 65 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 387 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 546 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 10 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 16 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 633 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 31 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 11 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 13 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 34 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 42 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 48 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 16 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 20 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 14 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 14 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 23 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 13 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 110 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 45 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 19 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 16 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 16 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 16 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 10 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 21 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 27 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 70 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
BangumiBase/benriyasaitousanisekainiiku | BangumiBase | "2024-07-18T04:48:17Z" | 0 | 0 | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"modality:text",
"region:us",
"art"
] | null | "2024-07-18T01:43:50Z" | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Benriya Saitou-san, Isekai Ni Iku
This is the image base of bangumi Benriya Saitou-san, Isekai ni Iku, we detected 55 characters, 3792 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 | 200 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 50 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 34 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 27 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 921 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 54 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 60 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 40 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 96 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 37 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 33 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 8 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 34 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 27 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 38 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 158 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 76 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 75 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 12 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 26 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 43 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 302 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 81 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 27 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 44 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 10 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 28 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 57 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 7 | [Download](28/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 29 | 140 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 22 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 9 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 16 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 8 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 90 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 9 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 23 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 6 | [Download](37/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 38 | 70 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 17 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 16 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 9 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 21 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 139 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 15 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 121 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 11 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 19 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 14 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 166 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 30 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 16 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 45 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 6 | [Download](53/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 149 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
zeref713/gsm8k-clip | zeref713 | "2024-07-18T01:50:08Z" | 0 | 0 | [
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zeref713/gsm8k-clipped | zeref713 | "2024-07-18T01:51:16Z" | 0 | 0 | [
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|
yuvenhol/xhs_trip_plan | yuvenhol | "2024-07-18T01:58:10Z" | 0 | 0 | [
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|
westbrook/gigaspeech-tiny-0 | westbrook | "2024-07-21T03:57:49Z" | 0 | 0 | [
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|
westbrook/gigaspeech-tiny-1 | westbrook | "2024-07-21T03:57:50Z" | 0 | 0 | [
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|
westbrook/gigaspeech-tiny-2 | westbrook | "2024-07-21T03:57:51Z" | 0 | 0 | [
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|
westbrook/gigaspeech-tiny-3 | westbrook | "2024-07-21T03:57:52Z" | 0 | 0 | [
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---
|
westbrook/gigaspeech-tiny-4 | westbrook | "2024-07-21T03:57:53Z" | 0 | 0 | [
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|
Cartinoe5930/kin_best_crawl | Cartinoe5930 | "2024-07-18T02:01:38Z" | 0 | 0 | [
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|
kihyunsoo/ft_dataset0718 | kihyunsoo | "2024-07-18T02:17:08Z" | 0 | 0 | [
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license: mit
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|
helloelwin/vale-hhrlhf-10k | helloelwin | "2024-07-18T02:17:24Z" | 0 | 0 | [
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|
xppj/sdgsag | xppj | "2024-08-02T06:16:37Z" | 0 | 1 | [
"task_categories:text-classification",
"license:apache-2.0",
"region:us"
] | [
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] | "2024-07-18T02:19:58Z" | ---
license: apache-2.0
task_categories:
- text-classification
extra_gated_prompt: "You agree to not use the dataset to conduct experiments that cause harm to human subjects."
extra_gated_fields:
Company: text
Country: country
Specific date: date_picker
I want to use this dataset for:
type: select
options:
- Research
- Education
- label: Other
value: other
I agree to use this dataset for non-commercial use ONLY: checkbox
---
asfasgasgas |
mlburnham/polistance_issue_tweets | mlburnham | "2024-08-03T20:21:44Z" | 0 | 0 | [
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configs:
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---
# Dataset Card for "polistance_issue_tweets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ycfNTU/Album_mistral_twoshot | ycfNTU | "2024-07-19T00:03:19Z" | 0 | 0 | [
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---
|
JesusCrist/med_qa_fewshot | JesusCrist | "2024-07-23T08:08:41Z" | 0 | 0 | [
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---
This datasets comes from 'bigbio/med_qa'. Shot from validation split
prompt is "Question: {question}\nA. {A}\nB. {B}\nC. {C}\nD. {D}\nE. {E}\nAnswer: {answer}"
each shot and real question are seperated by '\n\n' (HF DATASET preview can't show correctly) |
Bo1015/ssp_q3 | Bo1015 | "2024-07-18T02:46:32Z" | 0 | 0 | [
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---
|
kennedibeta/informal | kennedibeta | "2024-07-18T02:48:30Z" | 0 | 0 | [
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license: apache-2.0
task_categories:
- text2text-generation
language:
- pt
tags:
- not-for-all-audiences
pretty_name: InformalV1
size_categories:
- n<1K
--- |
jan-hq/instruction-speech-conversation-v1.5-phase-2-interleaved | jan-hq | "2024-07-18T02:56:09Z" | 0 | 0 | [
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dataset_info:
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---
|
LeBrony/original-legal-set | LeBrony | "2024-07-18T02:47:20Z" | 0 | 0 | [
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---
|
Bo1015/fold_prediction | Bo1015 | "2024-07-18T02:50:27Z" | 0 | 0 | [
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---
|
ycfNTU/USB_llama2_twoshot | ycfNTU | "2024-07-18T02:50:48Z" | 0 | 0 | [
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---
|
Bo1015/antibiotic_resistance | Bo1015 | "2024-07-18T02:52:04Z" | 0 | 0 | [
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---
|
Bo1015/cloning_clf | Bo1015 | "2024-07-18T02:52:57Z" | 0 | 0 | [
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---
|
Bo1015/enzyme_catalytic_efficiency | Bo1015 | "2024-07-18T02:54:01Z" | 0 | 0 | [
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] | null | "2024-07-18T02:53:49Z" | ---
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---
|
kennedibeta/InformalV1 | kennedibeta | "2024-07-18T04:23:31Z" | 0 | 0 | [
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] | null | "2024-07-18T02:58:03Z" | ---
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---
|
Bo1015/fitness_prediction | Bo1015 | "2024-07-18T02:59:01Z" | 0 | 0 | [
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---
|
Bo1015/fluorescence_prediction | Bo1015 | "2024-07-18T03:00:52Z" | 0 | 0 | [
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---
|
Bo1015/localization_prediction | Bo1015 | "2024-07-18T03:02:42Z" | 0 | 0 | [
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---
|
Bo1015/material_production | Bo1015 | "2024-07-18T03:05:59Z" | 0 | 0 | [
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---
|
Bo1015/metal_ion_binding | Bo1015 | "2024-07-18T03:07:03Z" | 0 | 0 | [
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---
|
helloelwin/selfeval-saferpaca4-2b-s2-t0.6-l-n | helloelwin | "2024-07-18T03:07:18Z" | 0 | 0 | [
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---
|
open-llm-leaderboard/migtissera__Llama-3-70B-Synthia-v3.5-details | open-llm-leaderboard | "2024-07-18T03:10:53Z" | 0 | 0 | [
"size_categories:10K<n<100K",
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"modality:tabular",
"modality:text",
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"library:pandas",
"library:mlcroissant",
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"region:us"
] | null | "2024-07-18T03:07:17Z" | ---
pretty_name: Evaluation run of migtissera/Llama-3-70B-Synthia-v3.5
dataset_summary: "Dataset automatically created during the evaluation run of model\
\ [migtissera/Llama-3-70B-Synthia-v3.5](https://huggingface.co/migtissera/Llama-3-70B-Synthia-v3.5)\n\
The dataset is composed of 38 configuration(s), each one corresponding 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.\n\nTo load the details from a run, you can for instance do the following:\n\
```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t\"open-llm-leaderboard/migtissera__Llama-3-70B-Synthia-v3.5-details\"\
,\n\tname=\"migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_boolean_expressions\"\
,\n\tsplit=\"latest\"\n)\n```\n\n## Latest results\n\nThese are the [latest results\
\ from run 2024-07-18T03-07-17.091809](https://huggingface.co/datasets/open-llm-leaderboard/migtissera__Llama-3-70B-Synthia-v3.5-details/blob/main/migtissera__Llama-3-70B-Synthia-v3.5/results_2024-07-18T03-07-17.091809.json)\
\ (note that there 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 \"leaderboard\": {\n\
\ \"prompt_level_strict_acc,none\": 0.5582255083179297,\n \
\ \"prompt_level_strict_acc_stderr,none\": 0.021370184758950993,\n \"\
inst_level_loose_acc,none\": 0.6810551558752997,\n \"inst_level_loose_acc_stderr,none\"\
: \"N/A\",\n \"acc,none\": 0.4658410904255319,\n \"acc_stderr,none\"\
: 0.00454782004750598,\n \"inst_level_strict_acc,none\": 0.657074340527578,\n\
\ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"acc_norm,none\"\
: 0.5922947204566091,\n \"acc_norm_stderr,none\": 0.0051250948092908385,\n\
\ \"exact_match,none\": 0.18957703927492447,\n \"exact_match_stderr,none\"\
: 0.010099276921495459,\n \"prompt_level_loose_acc,none\": 0.5841035120147874,\n\
\ \"prompt_level_loose_acc_stderr,none\": 0.0212099993568188,\n \
\ \"alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\": {\n\
\ \"acc_norm,none\": 0.6479777816351328,\n \"acc_norm_stderr,none\"\
: 0.005761202824362101,\n \"alias\": \" - leaderboard_bbh\"\n \
\ },\n \"leaderboard_bbh_boolean_expressions\": {\n \"acc_norm,none\"\
: 0.868,\n \"acc_norm_stderr,none\": 0.02145098082403813,\n \
\ \"alias\": \" - leaderboard_bbh_boolean_expressions\"\n },\n \
\ \"leaderboard_bbh_causal_judgement\": {\n \"acc_norm,none\": 0.6951871657754011,\n\
\ \"acc_norm_stderr,none\": 0.033752894763675746,\n \"alias\"\
: \" - leaderboard_bbh_causal_judgement\"\n },\n \"leaderboard_bbh_date_understanding\"\
: {\n \"acc_norm,none\": 0.592,\n \"acc_norm_stderr,none\"\
: 0.031145209846548495,\n \"alias\": \" - leaderboard_bbh_date_understanding\"\
\n },\n \"leaderboard_bbh_disambiguation_qa\": {\n \"acc_norm,none\"\
: 0.692,\n \"acc_norm_stderr,none\": 0.029256928606501868,\n \
\ \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\n },\n \"\
leaderboard_bbh_formal_fallacies\": {\n \"acc_norm,none\": 0.648,\n \
\ \"acc_norm_stderr,none\": 0.03026628805735994,\n \"alias\"\
: \" - leaderboard_bbh_formal_fallacies\"\n },\n \"leaderboard_bbh_geometric_shapes\"\
: {\n \"acc_norm,none\": 0.488,\n \"acc_norm_stderr,none\"\
: 0.03167708558254709,\n \"alias\": \" - leaderboard_bbh_geometric_shapes\"\
\n },\n \"leaderboard_bbh_hyperbaton\": {\n \"acc_norm,none\"\
: 0.636,\n \"acc_norm_stderr,none\": 0.030491555220405555,\n \
\ \"alias\": \" - leaderboard_bbh_hyperbaton\"\n },\n \"leaderboard_bbh_logical_deduction_five_objects\"\
: {\n \"acc_norm,none\": 0.548,\n \"acc_norm_stderr,none\"\
: 0.03153986449255662,\n \"alias\": \" - leaderboard_bbh_logical_deduction_five_objects\"\
\n },\n \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \
\ \"acc_norm,none\": 0.508,\n \"acc_norm_stderr,none\": 0.031682156431413803,\n\
\ \"alias\": \" - leaderboard_bbh_logical_deduction_seven_objects\"\n\
\ },\n \"leaderboard_bbh_logical_deduction_three_objects\": {\n \
\ \"acc_norm,none\": 0.8,\n \"acc_norm_stderr,none\": 0.025348970020979085,\n\
\ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\"\n\
\ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"\
acc_norm,none\": 0.908,\n \"acc_norm_stderr,none\": 0.018316275379429644,\n\
\ \"alias\": \" - leaderboard_bbh_movie_recommendation\"\n },\n\
\ \"leaderboard_bbh_navigate\": {\n \"acc_norm,none\": 0.664,\n\
\ \"acc_norm_stderr,none\": 0.029933259094191516,\n \"alias\"\
: \" - leaderboard_bbh_navigate\"\n },\n \"leaderboard_bbh_object_counting\"\
: {\n \"acc_norm,none\": 0.732,\n \"acc_norm_stderr,none\"\
: 0.02806876238252669,\n \"alias\": \" - leaderboard_bbh_object_counting\"\
\n },\n \"leaderboard_bbh_penguins_in_a_table\": {\n \"\
acc_norm,none\": 0.6095890410958904,\n \"acc_norm_stderr,none\": 0.04051310916589183,\n\
\ \"alias\": \" - leaderboard_bbh_penguins_in_a_table\"\n },\n\
\ \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \"\
acc_norm,none\": 0.748,\n \"acc_norm_stderr,none\": 0.02751385193303135,\n\
\ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\n\
\ },\n \"leaderboard_bbh_ruin_names\": {\n \"acc_norm,none\"\
: 0.884,\n \"acc_norm_stderr,none\": 0.02029342980308387,\n \
\ \"alias\": \" - leaderboard_bbh_ruin_names\"\n },\n \"leaderboard_bbh_salient_translation_error_detection\"\
: {\n \"acc_norm,none\": 0.656,\n \"acc_norm_stderr,none\"\
: 0.03010450339231639,\n \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\"\
\n },\n \"leaderboard_bbh_snarks\": {\n \"acc_norm,none\"\
: 0.7359550561797753,\n \"acc_norm_stderr,none\": 0.03313431077658841,\n\
\ \"alias\": \" - leaderboard_bbh_snarks\"\n },\n \"leaderboard_bbh_sports_understanding\"\
: {\n \"acc_norm,none\": 0.912,\n \"acc_norm_stderr,none\"\
: 0.01795308477705288,\n \"alias\": \" - leaderboard_bbh_sports_understanding\"\
\n },\n \"leaderboard_bbh_temporal_sequences\": {\n \"\
acc_norm,none\": 0.932,\n \"acc_norm_stderr,none\": 0.01595374841074703,\n\
\ \"alias\": \" - leaderboard_bbh_temporal_sequences\"\n },\n\
\ \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \
\ \"acc_norm,none\": 0.296,\n \"acc_norm_stderr,none\": 0.02892893938837962,\n\
\ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\
\n },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\
: {\n \"acc_norm,none\": 0.224,\n \"acc_norm_stderr,none\"\
: 0.02642136168734791,\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\
\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\"\
: {\n \"acc_norm,none\": 0.332,\n \"acc_norm_stderr,none\"\
: 0.029844039047465905,\n \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\
\n },\n \"leaderboard_bbh_web_of_lies\": {\n \"acc_norm,none\"\
: 0.464,\n \"acc_norm_stderr,none\": 0.031603975145223735,\n \
\ \"alias\": \" - leaderboard_bbh_web_of_lies\"\n },\n \"leaderboard_gpqa\"\
: {\n \"acc_norm,none\": 0.3875838926174497,\n \"acc_norm_stderr,none\"\
: 0.014128506690246682,\n \"alias\": \" - leaderboard_gpqa\"\n \
\ },\n \"leaderboard_gpqa_diamond\": {\n \"acc_norm,none\": 0.3939393939393939,\n\
\ \"acc_norm_stderr,none\": 0.034812853382329624,\n \"alias\"\
: \" - leaderboard_gpqa_diamond\"\n },\n \"leaderboard_gpqa_extended\"\
: {\n \"acc_norm,none\": 0.38278388278388276,\n \"acc_norm_stderr,none\"\
: 0.02082078979830678,\n \"alias\": \" - leaderboard_gpqa_extended\"\
\n },\n \"leaderboard_gpqa_main\": {\n \"acc_norm,none\"\
: 0.390625,\n \"acc_norm_stderr,none\": 0.023076427311939374,\n \
\ \"alias\": \" - leaderboard_gpqa_main\"\n },\n \"leaderboard_ifeval\"\
: {\n \"prompt_level_strict_acc,none\": 0.5582255083179297,\n \
\ \"prompt_level_strict_acc_stderr,none\": 0.021370184758950996,\n \
\ \"inst_level_strict_acc,none\": 0.657074340527578,\n \"inst_level_strict_acc_stderr,none\"\
: \"N/A\",\n \"prompt_level_loose_acc,none\": 0.5841035120147874,\n \
\ \"prompt_level_loose_acc_stderr,none\": 0.0212099993568188,\n \
\ \"inst_level_loose_acc,none\": 0.6810551558752997,\n \"inst_level_loose_acc_stderr,none\"\
: \"N/A\",\n \"alias\": \" - leaderboard_ifeval\"\n },\n \
\ \"leaderboard_math_hard\": {\n \"exact_match,none\": 0.18957703927492447,\n\
\ \"exact_match_stderr,none\": 0.010099276921495459,\n \"\
alias\": \" - leaderboard_math_hard\"\n },\n \"leaderboard_math_algebra_hard\"\
: {\n \"exact_match,none\": 0.2964169381107492,\n \"exact_match_stderr,none\"\
: 0.026106489872007893,\n \"alias\": \" - leaderboard_math_algebra_hard\"\
\n },\n \"leaderboard_math_counting_and_prob_hard\": {\n \
\ \"exact_match,none\": 0.12195121951219512,\n \"exact_match_stderr,none\"\
: 0.029625963299228612,\n \"alias\": \" - leaderboard_math_counting_and_prob_hard\"\
\n },\n \"leaderboard_math_geometry_hard\": {\n \"exact_match,none\"\
: 0.07575757575757576,\n \"exact_match_stderr,none\": 0.02311906874179555,\n\
\ \"alias\": \" - leaderboard_math_geometry_hard\"\n },\n \
\ \"leaderboard_math_intermediate_algebra_hard\": {\n \"exact_match,none\"\
: 0.039285714285714285,\n \"exact_match_stderr,none\": 0.011630873964205705,\n\
\ \"alias\": \" - leaderboard_math_intermediate_algebra_hard\"\n \
\ },\n \"leaderboard_math_num_theory_hard\": {\n \"exact_match,none\"\
: 0.22727272727272727,\n \"exact_match_stderr,none\": 0.033879817840905886,\n\
\ \"alias\": \" - leaderboard_math_num_theory_hard\"\n },\n \
\ \"leaderboard_math_prealgebra_hard\": {\n \"exact_match,none\"\
: 0.42487046632124353,\n \"exact_match_stderr,none\": 0.035674713352125395,\n\
\ \"alias\": \" - leaderboard_math_prealgebra_hard\"\n },\n \
\ \"leaderboard_math_precalculus_hard\": {\n \"exact_match,none\"\
: 0.05185185185185185,\n \"exact_match_stderr,none\": 0.019154368449050517,\n\
\ \"alias\": \" - leaderboard_math_precalculus_hard\"\n },\n\
\ \"leaderboard_mmlu_pro\": {\n \"acc,none\": 0.4658410904255319,\n\
\ \"acc_stderr,none\": 0.00454782004750598,\n \"alias\": \"\
\ - leaderboard_mmlu_pro\"\n },\n \"leaderboard_musr\": {\n \
\ \"acc_norm,none\": 0.49074074074074076,\n \"acc_norm_stderr,none\"\
: 0.01753944328998668,\n \"alias\": \" - leaderboard_musr\"\n \
\ },\n \"leaderboard_musr_murder_mysteries\": {\n \"acc_norm,none\"\
: 0.628,\n \"acc_norm_stderr,none\": 0.030630325944558313,\n \
\ \"alias\": \" - leaderboard_musr_murder_mysteries\"\n },\n \"\
leaderboard_musr_object_placements\": {\n \"acc_norm,none\": 0.30859375,\n\
\ \"acc_norm_stderr,none\": 0.028926115987483066,\n \"alias\"\
: \" - leaderboard_musr_object_placements\"\n },\n \"leaderboard_musr_team_allocation\"\
: {\n \"acc_norm,none\": 0.54,\n \"acc_norm_stderr,none\"\
: 0.03158465389149899,\n \"alias\": \" - leaderboard_musr_team_allocation\"\
\n }\n },\n \"leaderboard\": {\n \"prompt_level_strict_acc,none\"\
: 0.5582255083179297,\n \"prompt_level_strict_acc_stderr,none\": 0.021370184758950993,\n\
\ \"inst_level_loose_acc,none\": 0.6810551558752997,\n \"inst_level_loose_acc_stderr,none\"\
: \"N/A\",\n \"acc,none\": 0.4658410904255319,\n \"acc_stderr,none\"\
: 0.00454782004750598,\n \"inst_level_strict_acc,none\": 0.657074340527578,\n\
\ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"acc_norm,none\"\
: 0.5922947204566091,\n \"acc_norm_stderr,none\": 0.0051250948092908385,\n\
\ \"exact_match,none\": 0.18957703927492447,\n \"exact_match_stderr,none\"\
: 0.010099276921495459,\n \"prompt_level_loose_acc,none\": 0.5841035120147874,\n\
\ \"prompt_level_loose_acc_stderr,none\": 0.0212099993568188,\n \"\
alias\": \"leaderboard\"\n },\n \"leaderboard_bbh\": {\n \"acc_norm,none\"\
: 0.6479777816351328,\n \"acc_norm_stderr,none\": 0.005761202824362101,\n\
\ \"alias\": \" - leaderboard_bbh\"\n },\n \"leaderboard_bbh_boolean_expressions\"\
: {\n \"acc_norm,none\": 0.868,\n \"acc_norm_stderr,none\": 0.02145098082403813,\n\
\ \"alias\": \" - leaderboard_bbh_boolean_expressions\"\n },\n \"\
leaderboard_bbh_causal_judgement\": {\n \"acc_norm,none\": 0.6951871657754011,\n\
\ \"acc_norm_stderr,none\": 0.033752894763675746,\n \"alias\": \"\
\ - leaderboard_bbh_causal_judgement\"\n },\n \"leaderboard_bbh_date_understanding\"\
: {\n \"acc_norm,none\": 0.592,\n \"acc_norm_stderr,none\": 0.031145209846548495,\n\
\ \"alias\": \" - leaderboard_bbh_date_understanding\"\n },\n \"leaderboard_bbh_disambiguation_qa\"\
: {\n \"acc_norm,none\": 0.692,\n \"acc_norm_stderr,none\": 0.029256928606501868,\n\
\ \"alias\": \" - leaderboard_bbh_disambiguation_qa\"\n },\n \"leaderboard_bbh_formal_fallacies\"\
: {\n \"acc_norm,none\": 0.648,\n \"acc_norm_stderr,none\": 0.03026628805735994,\n\
\ \"alias\": \" - leaderboard_bbh_formal_fallacies\"\n },\n \"leaderboard_bbh_geometric_shapes\"\
: {\n \"acc_norm,none\": 0.488,\n \"acc_norm_stderr,none\": 0.03167708558254709,\n\
\ \"alias\": \" - leaderboard_bbh_geometric_shapes\"\n },\n \"leaderboard_bbh_hyperbaton\"\
: {\n \"acc_norm,none\": 0.636,\n \"acc_norm_stderr,none\": 0.030491555220405555,\n\
\ \"alias\": \" - leaderboard_bbh_hyperbaton\"\n },\n \"leaderboard_bbh_logical_deduction_five_objects\"\
: {\n \"acc_norm,none\": 0.548,\n \"acc_norm_stderr,none\": 0.03153986449255662,\n\
\ \"alias\": \" - leaderboard_bbh_logical_deduction_five_objects\"\n \
\ },\n \"leaderboard_bbh_logical_deduction_seven_objects\": {\n \"acc_norm,none\"\
: 0.508,\n \"acc_norm_stderr,none\": 0.031682156431413803,\n \"alias\"\
: \" - leaderboard_bbh_logical_deduction_seven_objects\"\n },\n \"leaderboard_bbh_logical_deduction_three_objects\"\
: {\n \"acc_norm,none\": 0.8,\n \"acc_norm_stderr,none\": 0.025348970020979085,\n\
\ \"alias\": \" - leaderboard_bbh_logical_deduction_three_objects\"\n \
\ },\n \"leaderboard_bbh_movie_recommendation\": {\n \"acc_norm,none\"\
: 0.908,\n \"acc_norm_stderr,none\": 0.018316275379429644,\n \"alias\"\
: \" - leaderboard_bbh_movie_recommendation\"\n },\n \"leaderboard_bbh_navigate\"\
: {\n \"acc_norm,none\": 0.664,\n \"acc_norm_stderr,none\": 0.029933259094191516,\n\
\ \"alias\": \" - leaderboard_bbh_navigate\"\n },\n \"leaderboard_bbh_object_counting\"\
: {\n \"acc_norm,none\": 0.732,\n \"acc_norm_stderr,none\": 0.02806876238252669,\n\
\ \"alias\": \" - leaderboard_bbh_object_counting\"\n },\n \"leaderboard_bbh_penguins_in_a_table\"\
: {\n \"acc_norm,none\": 0.6095890410958904,\n \"acc_norm_stderr,none\"\
: 0.04051310916589183,\n \"alias\": \" - leaderboard_bbh_penguins_in_a_table\"\
\n },\n \"leaderboard_bbh_reasoning_about_colored_objects\": {\n \"\
acc_norm,none\": 0.748,\n \"acc_norm_stderr,none\": 0.02751385193303135,\n\
\ \"alias\": \" - leaderboard_bbh_reasoning_about_colored_objects\"\n \
\ },\n \"leaderboard_bbh_ruin_names\": {\n \"acc_norm,none\": 0.884,\n\
\ \"acc_norm_stderr,none\": 0.02029342980308387,\n \"alias\": \" \
\ - leaderboard_bbh_ruin_names\"\n },\n \"leaderboard_bbh_salient_translation_error_detection\"\
: {\n \"acc_norm,none\": 0.656,\n \"acc_norm_stderr,none\": 0.03010450339231639,\n\
\ \"alias\": \" - leaderboard_bbh_salient_translation_error_detection\"\n\
\ },\n \"leaderboard_bbh_snarks\": {\n \"acc_norm,none\": 0.7359550561797753,\n\
\ \"acc_norm_stderr,none\": 0.03313431077658841,\n \"alias\": \" \
\ - leaderboard_bbh_snarks\"\n },\n \"leaderboard_bbh_sports_understanding\"\
: {\n \"acc_norm,none\": 0.912,\n \"acc_norm_stderr,none\": 0.01795308477705288,\n\
\ \"alias\": \" - leaderboard_bbh_sports_understanding\"\n },\n \"\
leaderboard_bbh_temporal_sequences\": {\n \"acc_norm,none\": 0.932,\n \
\ \"acc_norm_stderr,none\": 0.01595374841074703,\n \"alias\": \" - leaderboard_bbh_temporal_sequences\"\
\n },\n \"leaderboard_bbh_tracking_shuffled_objects_five_objects\": {\n \
\ \"acc_norm,none\": 0.296,\n \"acc_norm_stderr,none\": 0.02892893938837962,\n\
\ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_five_objects\"\
\n },\n \"leaderboard_bbh_tracking_shuffled_objects_seven_objects\": {\n \
\ \"acc_norm,none\": 0.224,\n \"acc_norm_stderr,none\": 0.02642136168734791,\n\
\ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_seven_objects\"\
\n },\n \"leaderboard_bbh_tracking_shuffled_objects_three_objects\": {\n \
\ \"acc_norm,none\": 0.332,\n \"acc_norm_stderr,none\": 0.029844039047465905,\n\
\ \"alias\": \" - leaderboard_bbh_tracking_shuffled_objects_three_objects\"\
\n },\n \"leaderboard_bbh_web_of_lies\": {\n \"acc_norm,none\": 0.464,\n\
\ \"acc_norm_stderr,none\": 0.031603975145223735,\n \"alias\": \"\
\ - leaderboard_bbh_web_of_lies\"\n },\n \"leaderboard_gpqa\": {\n \
\ \"acc_norm,none\": 0.3875838926174497,\n \"acc_norm_stderr,none\": 0.014128506690246682,\n\
\ \"alias\": \" - leaderboard_gpqa\"\n },\n \"leaderboard_gpqa_diamond\"\
: {\n \"acc_norm,none\": 0.3939393939393939,\n \"acc_norm_stderr,none\"\
: 0.034812853382329624,\n \"alias\": \" - leaderboard_gpqa_diamond\"\n \
\ },\n \"leaderboard_gpqa_extended\": {\n \"acc_norm,none\": 0.38278388278388276,\n\
\ \"acc_norm_stderr,none\": 0.02082078979830678,\n \"alias\": \" \
\ - leaderboard_gpqa_extended\"\n },\n \"leaderboard_gpqa_main\": {\n \
\ \"acc_norm,none\": 0.390625,\n \"acc_norm_stderr,none\": 0.023076427311939374,\n\
\ \"alias\": \" - leaderboard_gpqa_main\"\n },\n \"leaderboard_ifeval\"\
: {\n \"prompt_level_strict_acc,none\": 0.5582255083179297,\n \"prompt_level_strict_acc_stderr,none\"\
: 0.021370184758950996,\n \"inst_level_strict_acc,none\": 0.657074340527578,\n\
\ \"inst_level_strict_acc_stderr,none\": \"N/A\",\n \"prompt_level_loose_acc,none\"\
: 0.5841035120147874,\n \"prompt_level_loose_acc_stderr,none\": 0.0212099993568188,\n\
\ \"inst_level_loose_acc,none\": 0.6810551558752997,\n \"inst_level_loose_acc_stderr,none\"\
: \"N/A\",\n \"alias\": \" - leaderboard_ifeval\"\n },\n \"leaderboard_math_hard\"\
: {\n \"exact_match,none\": 0.18957703927492447,\n \"exact_match_stderr,none\"\
: 0.010099276921495459,\n \"alias\": \" - leaderboard_math_hard\"\n },\n\
\ \"leaderboard_math_algebra_hard\": {\n \"exact_match,none\": 0.2964169381107492,\n\
\ \"exact_match_stderr,none\": 0.026106489872007893,\n \"alias\":\
\ \" - leaderboard_math_algebra_hard\"\n },\n \"leaderboard_math_counting_and_prob_hard\"\
: {\n \"exact_match,none\": 0.12195121951219512,\n \"exact_match_stderr,none\"\
: 0.029625963299228612,\n \"alias\": \" - leaderboard_math_counting_and_prob_hard\"\
\n },\n \"leaderboard_math_geometry_hard\": {\n \"exact_match,none\"\
: 0.07575757575757576,\n \"exact_match_stderr,none\": 0.02311906874179555,\n\
\ \"alias\": \" - leaderboard_math_geometry_hard\"\n },\n \"leaderboard_math_intermediate_algebra_hard\"\
: {\n \"exact_match,none\": 0.039285714285714285,\n \"exact_match_stderr,none\"\
: 0.011630873964205705,\n \"alias\": \" - leaderboard_math_intermediate_algebra_hard\"\
\n },\n \"leaderboard_math_num_theory_hard\": {\n \"exact_match,none\"\
: 0.22727272727272727,\n \"exact_match_stderr,none\": 0.033879817840905886,\n\
\ \"alias\": \" - leaderboard_math_num_theory_hard\"\n },\n \"leaderboard_math_prealgebra_hard\"\
: {\n \"exact_match,none\": 0.42487046632124353,\n \"exact_match_stderr,none\"\
: 0.035674713352125395,\n \"alias\": \" - leaderboard_math_prealgebra_hard\"\
\n },\n \"leaderboard_math_precalculus_hard\": {\n \"exact_match,none\"\
: 0.05185185185185185,\n \"exact_match_stderr,none\": 0.019154368449050517,\n\
\ \"alias\": \" - leaderboard_math_precalculus_hard\"\n },\n \"leaderboard_mmlu_pro\"\
: {\n \"acc,none\": 0.4658410904255319,\n \"acc_stderr,none\": 0.00454782004750598,\n\
\ \"alias\": \" - leaderboard_mmlu_pro\"\n },\n \"leaderboard_musr\"\
: {\n \"acc_norm,none\": 0.49074074074074076,\n \"acc_norm_stderr,none\"\
: 0.01753944328998668,\n \"alias\": \" - leaderboard_musr\"\n },\n \
\ \"leaderboard_musr_murder_mysteries\": {\n \"acc_norm,none\": 0.628,\n\
\ \"acc_norm_stderr,none\": 0.030630325944558313,\n \"alias\": \"\
\ - leaderboard_musr_murder_mysteries\"\n },\n \"leaderboard_musr_object_placements\"\
: {\n \"acc_norm,none\": 0.30859375,\n \"acc_norm_stderr,none\": 0.028926115987483066,\n\
\ \"alias\": \" - leaderboard_musr_object_placements\"\n },\n \"leaderboard_musr_team_allocation\"\
: {\n \"acc_norm,none\": 0.54,\n \"acc_norm_stderr,none\": 0.03158465389149899,\n\
\ \"alias\": \" - leaderboard_musr_team_allocation\"\n }\n}\n```"
repo_url: https://huggingface.co/migtissera/Llama-3-70B-Synthia-v3.5
leaderboard_url: ''
point_of_contact: ''
configs:
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_boolean_expressions
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_boolean_expressions_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_boolean_expressions_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_causal_judgement
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_causal_judgement_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_causal_judgement_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_date_understanding
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_date_understanding_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_date_understanding_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_disambiguation_qa
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_disambiguation_qa_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_disambiguation_qa_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_formal_fallacies
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_formal_fallacies_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_formal_fallacies_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_geometric_shapes
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_geometric_shapes_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_geometric_shapes_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_hyperbaton
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_hyperbaton_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_hyperbaton_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_logical_deduction_five_objects
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_logical_deduction_five_objects_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_logical_deduction_seven_objects
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_logical_deduction_seven_objects_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_logical_deduction_three_objects
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_logical_deduction_three_objects_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_movie_recommendation
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_movie_recommendation_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_movie_recommendation_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_navigate
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_navigate_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_navigate_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_object_counting
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_object_counting_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_object_counting_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_penguins_in_a_table
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_penguins_in_a_table_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_penguins_in_a_table_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_reasoning_about_colored_objects
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_reasoning_about_colored_objects_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_ruin_names
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_ruin_names_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_ruin_names_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_salient_translation_error_detection
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_salient_translation_error_detection_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_snarks
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_snarks_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_snarks_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_sports_understanding
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_sports_understanding_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_sports_understanding_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_temporal_sequences
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_temporal_sequences_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_temporal_sequences_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_tracking_shuffled_objects_five_objects
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_tracking_shuffled_objects_five_objects_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_tracking_shuffled_objects_seven_objects
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_tracking_shuffled_objects_seven_objects_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_tracking_shuffled_objects_three_objects
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_tracking_shuffled_objects_three_objects_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_web_of_lies
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_bbh_web_of_lies_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_bbh_web_of_lies_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_gpqa_diamond
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_gpqa_diamond_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_gpqa_diamond_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_gpqa_extended
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_gpqa_extended_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_gpqa_extended_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_gpqa_main
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_gpqa_main_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_gpqa_main_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_ifeval
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_ifeval_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_ifeval_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_math_algebra_hard
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_math_algebra_hard_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_algebra_hard_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_math_counting_and_prob_hard
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_math_counting_and_prob_hard_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_counting_and_prob_hard_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_math_geometry_hard
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_math_geometry_hard_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_geometry_hard_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_math_intermediate_algebra_hard
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_math_intermediate_algebra_hard_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_intermediate_algebra_hard_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_math_num_theory_hard
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_math_num_theory_hard_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_num_theory_hard_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_math_prealgebra_hard
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_math_prealgebra_hard_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_prealgebra_hard_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_math_precalculus_hard
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_math_precalculus_hard_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_math_precalculus_hard_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_mmlu_pro
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_mmlu_pro_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_mmlu_pro_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_musr_murder_mysteries
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_musr_murder_mysteries_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_musr_murder_mysteries_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_musr_object_placements
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_musr_object_placements_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_musr_object_placements_2024-07-18T03-07-17.091809.jsonl'
- config_name: migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_musr_team_allocation
data_files:
- split: 2024_07_18T03_07_17.091809
path:
- '**/samples_leaderboard_musr_team_allocation_2024-07-18T03-07-17.091809.jsonl'
- split: latest
path:
- '**/samples_leaderboard_musr_team_allocation_2024-07-18T03-07-17.091809.jsonl'
---
# Dataset Card for Evaluation run of migtissera/Llama-3-70B-Synthia-v3.5
<!-- Provide a quick summary of the dataset. -->
Dataset automatically created during the evaluation run of model [migtissera/Llama-3-70B-Synthia-v3.5](https://huggingface.co/migtissera/Llama-3-70B-Synthia-v3.5)
The dataset is composed of 38 configuration(s), each one corresponding 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.
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/migtissera__Llama-3-70B-Synthia-v3.5-details",
name="migtissera__Llama-3-70B-Synthia-v3.5__leaderboard_bbh_boolean_expressions",
split="latest"
)
```
## Latest results
These are the [latest results from run 2024-07-18T03-07-17.091809](https://huggingface.co/datasets/open-llm-leaderboard/migtissera__Llama-3-70B-Synthia-v3.5-details/blob/main/migtissera__Llama-3-70B-Synthia-v3.5/results_2024-07-18T03-07-17.091809.json) (note that there 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": {
"leaderboard": {
"prompt_level_strict_acc,none": 0.5582255083179297,
"prompt_level_strict_acc_stderr,none": 0.021370184758950993,
"inst_level_loose_acc,none": 0.6810551558752997,
"inst_level_loose_acc_stderr,none": "N/A",
"acc,none": 0.4658410904255319,
"acc_stderr,none": 0.00454782004750598,
"inst_level_strict_acc,none": 0.657074340527578,
"inst_level_strict_acc_stderr,none": "N/A",
"acc_norm,none": 0.5922947204566091,
"acc_norm_stderr,none": 0.0051250948092908385,
"exact_match,none": 0.18957703927492447,
"exact_match_stderr,none": 0.010099276921495459,
"prompt_level_loose_acc,none": 0.5841035120147874,
"prompt_level_loose_acc_stderr,none": 0.0212099993568188,
"alias": "leaderboard"
},
"leaderboard_bbh": {
"acc_norm,none": 0.6479777816351328,
"acc_norm_stderr,none": 0.005761202824362101,
"alias": " - leaderboard_bbh"
},
"leaderboard_bbh_boolean_expressions": {
"acc_norm,none": 0.868,
"acc_norm_stderr,none": 0.02145098082403813,
"alias": " - leaderboard_bbh_boolean_expressions"
},
"leaderboard_bbh_causal_judgement": {
"acc_norm,none": 0.6951871657754011,
"acc_norm_stderr,none": 0.033752894763675746,
"alias": " - leaderboard_bbh_causal_judgement"
},
"leaderboard_bbh_date_understanding": {
"acc_norm,none": 0.592,
"acc_norm_stderr,none": 0.031145209846548495,
"alias": " - leaderboard_bbh_date_understanding"
},
"leaderboard_bbh_disambiguation_qa": {
"acc_norm,none": 0.692,
"acc_norm_stderr,none": 0.029256928606501868,
"alias": " - leaderboard_bbh_disambiguation_qa"
},
"leaderboard_bbh_formal_fallacies": {
"acc_norm,none": 0.648,
"acc_norm_stderr,none": 0.03026628805735994,
"alias": " - leaderboard_bbh_formal_fallacies"
},
"leaderboard_bbh_geometric_shapes": {
"acc_norm,none": 0.488,
"acc_norm_stderr,none": 0.03167708558254709,
"alias": " - leaderboard_bbh_geometric_shapes"
},
"leaderboard_bbh_hyperbaton": {
"acc_norm,none": 0.636,
"acc_norm_stderr,none": 0.030491555220405555,
"alias": " - leaderboard_bbh_hyperbaton"
},
"leaderboard_bbh_logical_deduction_five_objects": {
"acc_norm,none": 0.548,
"acc_norm_stderr,none": 0.03153986449255662,
"alias": " - leaderboard_bbh_logical_deduction_five_objects"
},
"leaderboard_bbh_logical_deduction_seven_objects": {
"acc_norm,none": 0.508,
"acc_norm_stderr,none": 0.031682156431413803,
"alias": " - leaderboard_bbh_logical_deduction_seven_objects"
},
"leaderboard_bbh_logical_deduction_three_objects": {
"acc_norm,none": 0.8,
"acc_norm_stderr,none": 0.025348970020979085,
"alias": " - leaderboard_bbh_logical_deduction_three_objects"
},
"leaderboard_bbh_movie_recommendation": {
"acc_norm,none": 0.908,
"acc_norm_stderr,none": 0.018316275379429644,
"alias": " - leaderboard_bbh_movie_recommendation"
},
"leaderboard_bbh_navigate": {
"acc_norm,none": 0.664,
"acc_norm_stderr,none": 0.029933259094191516,
"alias": " - leaderboard_bbh_navigate"
},
"leaderboard_bbh_object_counting": {
"acc_norm,none": 0.732,
"acc_norm_stderr,none": 0.02806876238252669,
"alias": " - leaderboard_bbh_object_counting"
},
"leaderboard_bbh_penguins_in_a_table": {
"acc_norm,none": 0.6095890410958904,
"acc_norm_stderr,none": 0.04051310916589183,
"alias": " - leaderboard_bbh_penguins_in_a_table"
},
"leaderboard_bbh_reasoning_about_colored_objects": {
"acc_norm,none": 0.748,
"acc_norm_stderr,none": 0.02751385193303135,
"alias": " - leaderboard_bbh_reasoning_about_colored_objects"
},
"leaderboard_bbh_ruin_names": {
"acc_norm,none": 0.884,
"acc_norm_stderr,none": 0.02029342980308387,
"alias": " - leaderboard_bbh_ruin_names"
},
"leaderboard_bbh_salient_translation_error_detection": {
"acc_norm,none": 0.656,
"acc_norm_stderr,none": 0.03010450339231639,
"alias": " - leaderboard_bbh_salient_translation_error_detection"
},
"leaderboard_bbh_snarks": {
"acc_norm,none": 0.7359550561797753,
"acc_norm_stderr,none": 0.03313431077658841,
"alias": " - leaderboard_bbh_snarks"
},
"leaderboard_bbh_sports_understanding": {
"acc_norm,none": 0.912,
"acc_norm_stderr,none": 0.01795308477705288,
"alias": " - leaderboard_bbh_sports_understanding"
},
"leaderboard_bbh_temporal_sequences": {
"acc_norm,none": 0.932,
"acc_norm_stderr,none": 0.01595374841074703,
"alias": " - leaderboard_bbh_temporal_sequences"
},
"leaderboard_bbh_tracking_shuffled_objects_five_objects": {
"acc_norm,none": 0.296,
"acc_norm_stderr,none": 0.02892893938837962,
"alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects"
},
"leaderboard_bbh_tracking_shuffled_objects_seven_objects": {
"acc_norm,none": 0.224,
"acc_norm_stderr,none": 0.02642136168734791,
"alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects"
},
"leaderboard_bbh_tracking_shuffled_objects_three_objects": {
"acc_norm,none": 0.332,
"acc_norm_stderr,none": 0.029844039047465905,
"alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects"
},
"leaderboard_bbh_web_of_lies": {
"acc_norm,none": 0.464,
"acc_norm_stderr,none": 0.031603975145223735,
"alias": " - leaderboard_bbh_web_of_lies"
},
"leaderboard_gpqa": {
"acc_norm,none": 0.3875838926174497,
"acc_norm_stderr,none": 0.014128506690246682,
"alias": " - leaderboard_gpqa"
},
"leaderboard_gpqa_diamond": {
"acc_norm,none": 0.3939393939393939,
"acc_norm_stderr,none": 0.034812853382329624,
"alias": " - leaderboard_gpqa_diamond"
},
"leaderboard_gpqa_extended": {
"acc_norm,none": 0.38278388278388276,
"acc_norm_stderr,none": 0.02082078979830678,
"alias": " - leaderboard_gpqa_extended"
},
"leaderboard_gpqa_main": {
"acc_norm,none": 0.390625,
"acc_norm_stderr,none": 0.023076427311939374,
"alias": " - leaderboard_gpqa_main"
},
"leaderboard_ifeval": {
"prompt_level_strict_acc,none": 0.5582255083179297,
"prompt_level_strict_acc_stderr,none": 0.021370184758950996,
"inst_level_strict_acc,none": 0.657074340527578,
"inst_level_strict_acc_stderr,none": "N/A",
"prompt_level_loose_acc,none": 0.5841035120147874,
"prompt_level_loose_acc_stderr,none": 0.0212099993568188,
"inst_level_loose_acc,none": 0.6810551558752997,
"inst_level_loose_acc_stderr,none": "N/A",
"alias": " - leaderboard_ifeval"
},
"leaderboard_math_hard": {
"exact_match,none": 0.18957703927492447,
"exact_match_stderr,none": 0.010099276921495459,
"alias": " - leaderboard_math_hard"
},
"leaderboard_math_algebra_hard": {
"exact_match,none": 0.2964169381107492,
"exact_match_stderr,none": 0.026106489872007893,
"alias": " - leaderboard_math_algebra_hard"
},
"leaderboard_math_counting_and_prob_hard": {
"exact_match,none": 0.12195121951219512,
"exact_match_stderr,none": 0.029625963299228612,
"alias": " - leaderboard_math_counting_and_prob_hard"
},
"leaderboard_math_geometry_hard": {
"exact_match,none": 0.07575757575757576,
"exact_match_stderr,none": 0.02311906874179555,
"alias": " - leaderboard_math_geometry_hard"
},
"leaderboard_math_intermediate_algebra_hard": {
"exact_match,none": 0.039285714285714285,
"exact_match_stderr,none": 0.011630873964205705,
"alias": " - leaderboard_math_intermediate_algebra_hard"
},
"leaderboard_math_num_theory_hard": {
"exact_match,none": 0.22727272727272727,
"exact_match_stderr,none": 0.033879817840905886,
"alias": " - leaderboard_math_num_theory_hard"
},
"leaderboard_math_prealgebra_hard": {
"exact_match,none": 0.42487046632124353,
"exact_match_stderr,none": 0.035674713352125395,
"alias": " - leaderboard_math_prealgebra_hard"
},
"leaderboard_math_precalculus_hard": {
"exact_match,none": 0.05185185185185185,
"exact_match_stderr,none": 0.019154368449050517,
"alias": " - leaderboard_math_precalculus_hard"
},
"leaderboard_mmlu_pro": {
"acc,none": 0.4658410904255319,
"acc_stderr,none": 0.00454782004750598,
"alias": " - leaderboard_mmlu_pro"
},
"leaderboard_musr": {
"acc_norm,none": 0.49074074074074076,
"acc_norm_stderr,none": 0.01753944328998668,
"alias": " - leaderboard_musr"
},
"leaderboard_musr_murder_mysteries": {
"acc_norm,none": 0.628,
"acc_norm_stderr,none": 0.030630325944558313,
"alias": " - leaderboard_musr_murder_mysteries"
},
"leaderboard_musr_object_placements": {
"acc_norm,none": 0.30859375,
"acc_norm_stderr,none": 0.028926115987483066,
"alias": " - leaderboard_musr_object_placements"
},
"leaderboard_musr_team_allocation": {
"acc_norm,none": 0.54,
"acc_norm_stderr,none": 0.03158465389149899,
"alias": " - leaderboard_musr_team_allocation"
}
},
"leaderboard": {
"prompt_level_strict_acc,none": 0.5582255083179297,
"prompt_level_strict_acc_stderr,none": 0.021370184758950993,
"inst_level_loose_acc,none": 0.6810551558752997,
"inst_level_loose_acc_stderr,none": "N/A",
"acc,none": 0.4658410904255319,
"acc_stderr,none": 0.00454782004750598,
"inst_level_strict_acc,none": 0.657074340527578,
"inst_level_strict_acc_stderr,none": "N/A",
"acc_norm,none": 0.5922947204566091,
"acc_norm_stderr,none": 0.0051250948092908385,
"exact_match,none": 0.18957703927492447,
"exact_match_stderr,none": 0.010099276921495459,
"prompt_level_loose_acc,none": 0.5841035120147874,
"prompt_level_loose_acc_stderr,none": 0.0212099993568188,
"alias": "leaderboard"
},
"leaderboard_bbh": {
"acc_norm,none": 0.6479777816351328,
"acc_norm_stderr,none": 0.005761202824362101,
"alias": " - leaderboard_bbh"
},
"leaderboard_bbh_boolean_expressions": {
"acc_norm,none": 0.868,
"acc_norm_stderr,none": 0.02145098082403813,
"alias": " - leaderboard_bbh_boolean_expressions"
},
"leaderboard_bbh_causal_judgement": {
"acc_norm,none": 0.6951871657754011,
"acc_norm_stderr,none": 0.033752894763675746,
"alias": " - leaderboard_bbh_causal_judgement"
},
"leaderboard_bbh_date_understanding": {
"acc_norm,none": 0.592,
"acc_norm_stderr,none": 0.031145209846548495,
"alias": " - leaderboard_bbh_date_understanding"
},
"leaderboard_bbh_disambiguation_qa": {
"acc_norm,none": 0.692,
"acc_norm_stderr,none": 0.029256928606501868,
"alias": " - leaderboard_bbh_disambiguation_qa"
},
"leaderboard_bbh_formal_fallacies": {
"acc_norm,none": 0.648,
"acc_norm_stderr,none": 0.03026628805735994,
"alias": " - leaderboard_bbh_formal_fallacies"
},
"leaderboard_bbh_geometric_shapes": {
"acc_norm,none": 0.488,
"acc_norm_stderr,none": 0.03167708558254709,
"alias": " - leaderboard_bbh_geometric_shapes"
},
"leaderboard_bbh_hyperbaton": {
"acc_norm,none": 0.636,
"acc_norm_stderr,none": 0.030491555220405555,
"alias": " - leaderboard_bbh_hyperbaton"
},
"leaderboard_bbh_logical_deduction_five_objects": {
"acc_norm,none": 0.548,
"acc_norm_stderr,none": 0.03153986449255662,
"alias": " - leaderboard_bbh_logical_deduction_five_objects"
},
"leaderboard_bbh_logical_deduction_seven_objects": {
"acc_norm,none": 0.508,
"acc_norm_stderr,none": 0.031682156431413803,
"alias": " - leaderboard_bbh_logical_deduction_seven_objects"
},
"leaderboard_bbh_logical_deduction_three_objects": {
"acc_norm,none": 0.8,
"acc_norm_stderr,none": 0.025348970020979085,
"alias": " - leaderboard_bbh_logical_deduction_three_objects"
},
"leaderboard_bbh_movie_recommendation": {
"acc_norm,none": 0.908,
"acc_norm_stderr,none": 0.018316275379429644,
"alias": " - leaderboard_bbh_movie_recommendation"
},
"leaderboard_bbh_navigate": {
"acc_norm,none": 0.664,
"acc_norm_stderr,none": 0.029933259094191516,
"alias": " - leaderboard_bbh_navigate"
},
"leaderboard_bbh_object_counting": {
"acc_norm,none": 0.732,
"acc_norm_stderr,none": 0.02806876238252669,
"alias": " - leaderboard_bbh_object_counting"
},
"leaderboard_bbh_penguins_in_a_table": {
"acc_norm,none": 0.6095890410958904,
"acc_norm_stderr,none": 0.04051310916589183,
"alias": " - leaderboard_bbh_penguins_in_a_table"
},
"leaderboard_bbh_reasoning_about_colored_objects": {
"acc_norm,none": 0.748,
"acc_norm_stderr,none": 0.02751385193303135,
"alias": " - leaderboard_bbh_reasoning_about_colored_objects"
},
"leaderboard_bbh_ruin_names": {
"acc_norm,none": 0.884,
"acc_norm_stderr,none": 0.02029342980308387,
"alias": " - leaderboard_bbh_ruin_names"
},
"leaderboard_bbh_salient_translation_error_detection": {
"acc_norm,none": 0.656,
"acc_norm_stderr,none": 0.03010450339231639,
"alias": " - leaderboard_bbh_salient_translation_error_detection"
},
"leaderboard_bbh_snarks": {
"acc_norm,none": 0.7359550561797753,
"acc_norm_stderr,none": 0.03313431077658841,
"alias": " - leaderboard_bbh_snarks"
},
"leaderboard_bbh_sports_understanding": {
"acc_norm,none": 0.912,
"acc_norm_stderr,none": 0.01795308477705288,
"alias": " - leaderboard_bbh_sports_understanding"
},
"leaderboard_bbh_temporal_sequences": {
"acc_norm,none": 0.932,
"acc_norm_stderr,none": 0.01595374841074703,
"alias": " - leaderboard_bbh_temporal_sequences"
},
"leaderboard_bbh_tracking_shuffled_objects_five_objects": {
"acc_norm,none": 0.296,
"acc_norm_stderr,none": 0.02892893938837962,
"alias": " - leaderboard_bbh_tracking_shuffled_objects_five_objects"
},
"leaderboard_bbh_tracking_shuffled_objects_seven_objects": {
"acc_norm,none": 0.224,
"acc_norm_stderr,none": 0.02642136168734791,
"alias": " - leaderboard_bbh_tracking_shuffled_objects_seven_objects"
},
"leaderboard_bbh_tracking_shuffled_objects_three_objects": {
"acc_norm,none": 0.332,
"acc_norm_stderr,none": 0.029844039047465905,
"alias": " - leaderboard_bbh_tracking_shuffled_objects_three_objects"
},
"leaderboard_bbh_web_of_lies": {
"acc_norm,none": 0.464,
"acc_norm_stderr,none": 0.031603975145223735,
"alias": " - leaderboard_bbh_web_of_lies"
},
"leaderboard_gpqa": {
"acc_norm,none": 0.3875838926174497,
"acc_norm_stderr,none": 0.014128506690246682,
"alias": " - leaderboard_gpqa"
},
"leaderboard_gpqa_diamond": {
"acc_norm,none": 0.3939393939393939,
"acc_norm_stderr,none": 0.034812853382329624,
"alias": " - leaderboard_gpqa_diamond"
},
"leaderboard_gpqa_extended": {
"acc_norm,none": 0.38278388278388276,
"acc_norm_stderr,none": 0.02082078979830678,
"alias": " - leaderboard_gpqa_extended"
},
"leaderboard_gpqa_main": {
"acc_norm,none": 0.390625,
"acc_norm_stderr,none": 0.023076427311939374,
"alias": " - leaderboard_gpqa_main"
},
"leaderboard_ifeval": {
"prompt_level_strict_acc,none": 0.5582255083179297,
"prompt_level_strict_acc_stderr,none": 0.021370184758950996,
"inst_level_strict_acc,none": 0.657074340527578,
"inst_level_strict_acc_stderr,none": "N/A",
"prompt_level_loose_acc,none": 0.5841035120147874,
"prompt_level_loose_acc_stderr,none": 0.0212099993568188,
"inst_level_loose_acc,none": 0.6810551558752997,
"inst_level_loose_acc_stderr,none": "N/A",
"alias": " - leaderboard_ifeval"
},
"leaderboard_math_hard": {
"exact_match,none": 0.18957703927492447,
"exact_match_stderr,none": 0.010099276921495459,
"alias": " - leaderboard_math_hard"
},
"leaderboard_math_algebra_hard": {
"exact_match,none": 0.2964169381107492,
"exact_match_stderr,none": 0.026106489872007893,
"alias": " - leaderboard_math_algebra_hard"
},
"leaderboard_math_counting_and_prob_hard": {
"exact_match,none": 0.12195121951219512,
"exact_match_stderr,none": 0.029625963299228612,
"alias": " - leaderboard_math_counting_and_prob_hard"
},
"leaderboard_math_geometry_hard": {
"exact_match,none": 0.07575757575757576,
"exact_match_stderr,none": 0.02311906874179555,
"alias": " - leaderboard_math_geometry_hard"
},
"leaderboard_math_intermediate_algebra_hard": {
"exact_match,none": 0.039285714285714285,
"exact_match_stderr,none": 0.011630873964205705,
"alias": " - leaderboard_math_intermediate_algebra_hard"
},
"leaderboard_math_num_theory_hard": {
"exact_match,none": 0.22727272727272727,
"exact_match_stderr,none": 0.033879817840905886,
"alias": " - leaderboard_math_num_theory_hard"
},
"leaderboard_math_prealgebra_hard": {
"exact_match,none": 0.42487046632124353,
"exact_match_stderr,none": 0.035674713352125395,
"alias": " - leaderboard_math_prealgebra_hard"
},
"leaderboard_math_precalculus_hard": {
"exact_match,none": 0.05185185185185185,
"exact_match_stderr,none": 0.019154368449050517,
"alias": " - leaderboard_math_precalculus_hard"
},
"leaderboard_mmlu_pro": {
"acc,none": 0.4658410904255319,
"acc_stderr,none": 0.00454782004750598,
"alias": " - leaderboard_mmlu_pro"
},
"leaderboard_musr": {
"acc_norm,none": 0.49074074074074076,
"acc_norm_stderr,none": 0.01753944328998668,
"alias": " - leaderboard_musr"
},
"leaderboard_musr_murder_mysteries": {
"acc_norm,none": 0.628,
"acc_norm_stderr,none": 0.030630325944558313,
"alias": " - leaderboard_musr_murder_mysteries"
},
"leaderboard_musr_object_placements": {
"acc_norm,none": 0.30859375,
"acc_norm_stderr,none": 0.028926115987483066,
"alias": " - leaderboard_musr_object_placements"
},
"leaderboard_musr_team_allocation": {
"acc_norm,none": 0.54,
"acc_norm_stderr,none": 0.03158465389149899,
"alias": " - leaderboard_musr_team_allocation"
}
}
```
## 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] |
Bo1015/optimal_ph | Bo1015 | "2024-07-18T03:08:04Z" | 0 | 0 | [
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dataset_info:
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---
|
Bo1015/optimal_temperature | Bo1015 | "2024-07-18T03:09:05Z" | 0 | 0 | [
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dataset_info:
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---
|
Bo1015/peptide_HLA_MHC_affinity | Bo1015 | "2024-07-18T03:10:15Z" | 0 | 0 | [
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] | null | "2024-07-18T03:10:02Z" | ---
dataset_info:
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---
|
1231czx/9b_iter4_forbtmpiter3_1 | 1231czx | "2024-07-18T03:12:46Z" | 0 | 0 | [
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|
ZhangShenao/avg_gc_fine_ultrafeedback | ZhangShenao | "2024-07-18T03:16:37Z" | 0 | 0 | [
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dataset_info:
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|
jan-hq/instruction-speech-text-v1.5-sound-convo | jan-hq | "2024-07-18T03:21:36Z" | 0 | 0 | [
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|
1231czx/9b_iter4_bmpmodel | 1231czx | "2024-07-18T03:27:12Z" | 0 | 0 | [
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|
Bo1015/solubility_prediction | Bo1015 | "2024-07-18T03:15:56Z" | 0 | 0 | [
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] | null | "2024-07-18T03:15:45Z" | ---
dataset_info:
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configs:
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path: data/train-*
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path: data/test-*
---
|
BangumiBase/kimetsunoyaibayuukakuhen | BangumiBase | "2024-07-18T06:27:39Z" | 0 | 0 | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"modality:text",
"region:us",
"art"
] | null | "2024-07-18T03:16:03Z" | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Kimetsu No Yaiba: Yuukaku-hen
This is the image base of bangumi Kimetsu no Yaiba: Yuukaku-hen, we detected 54 characters, 3702 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 | 122 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 24 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 265 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 37 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 9 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 1085 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 16 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 78 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 45 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 83 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 30 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 17 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 134 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 138 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 51 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 17 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 56 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 26 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 21 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 46 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 28 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 37 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 23 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 23 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 459 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 20 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 25 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 15 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 19 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 30 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 9 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 19 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 51 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 12 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 36 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 8 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 15 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 9 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 7 | [Download](38/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 39 | 23 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 8 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 13 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 7 | [Download](42/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 43 | 7 | [Download](43/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 44 | 71 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 70 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 117 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 9 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 13 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 64 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 7 | [Download](50/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 51 | 11 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 14 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 123 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
Bo1015/stability_prediction | Bo1015 | "2024-07-18T03:17:18Z" | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T03:17:07Z" | ---
dataset_info:
features:
- name: seq
dtype: string
- name: label
dtype: float64
splits:
- name: train
num_bytes: 3068946
num_examples: 53614
- name: valid
num_bytes: 155744
num_examples: 2512
- name: test
num_bytes: 709292
num_examples: 12851
download_size: 2058102
dataset_size: 3933982
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: valid
path: data/valid-*
- split: test
path: data/test-*
---
|
BangumiBase/eiyuukyoushitsu | BangumiBase | "2024-07-18T06:50:33Z" | 0 | 0 | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"region:us",
"art"
] | null | "2024-07-18T03:24:51Z" | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Eiyuu Kyoushitsu
This is the image base of bangumi Eiyuu Kyoushitsu, we detected 67 characters, 6096 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 | 15 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 1026 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 119 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 51 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 337 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 124 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 177 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 17 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 276 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 274 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 33 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 23 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 33 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 15 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 24 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 224 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 115 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 30 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 73 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 30 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 58 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 24 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 37 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 11 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 889 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 28 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 42 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 22 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 13 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 13 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 31 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 23 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 11 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 241 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 173 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 12 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 8 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 8 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 18 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 11 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 17 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 26 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 340 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 29 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 20 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 27 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 21 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 18 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 15 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 9 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 217 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 8 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 17 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 33 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 317 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 8 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 5 | [Download](56/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 57 | 11 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 13 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 6 | [Download](59/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 60 | 7 | [Download](60/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 61 | 12 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 9 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 18 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 12 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 10 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 182 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
fydhfzh/arabic-aug-split-clean-80-large | fydhfzh | "2024-07-18T03:39:09Z" | 0 | 0 | [
"size_categories:10K<n<100K",
"format:parquet",
"modality:audio",
"library:datasets",
"library:dask",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T03:31:04Z" | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: label
dtype:
class_label:
names:
'0': '0000101'
'1': '0000110'
'2': '0000111'
'3': '0001001'
'4': '0001010'
'5': '0001011'
'6': '0001101'
'7': '0001110'
'8': '0001111'
'9': '0010001'
'10': '0010010'
'11': '0010011'
'12': '0010101'
'13': '0010110'
'14': '0010111'
'15': '0011001'
'16': '0011010'
'17': '0011011'
'18': '0011101'
'19': '0011110'
'20': '0011111'
'21': '0100001'
'22': '0100010'
'23': '0100011'
'24': '0100101'
'25': '0100110'
'26': '0100111'
'27': '0101001'
'28': '0101010'
'29': '0101011'
'30': '0101101'
'31': '0101110'
'32': '0101111'
'33': '0110001'
'34': '0110010'
'35': '0110011'
'36': '0110101'
'37': '0110110'
'38': '0110111'
'39': '0111001'
'40': '0111010'
'41': '0111011'
'42': '0111101'
'43': '0111110'
'44': '0111111'
'45': '1000001'
'46': '1000010'
'47': '1000011'
'48': '1000101'
'49': '1000110'
'50': '1000111'
'51': '1001001'
'52': '1001010'
'53': '1001011'
'54': '1001101'
'55': '1001110'
'56': '1001111'
'57': '1010001'
'58': '1010010'
'59': '1010011'
'60': '1010101'
'61': '1010110'
'62': '1010111'
'63': '1011001'
'64': '1011010'
'65': '1011011'
'66': '1011101'
'67': '1011110'
'68': '1011111'
'69': '1100001'
'70': '1100010'
'71': '1100011'
'72': '1100101'
'73': '1100110'
'74': '1100111'
'75': '1101001'
'76': '1101010'
'77': '1101011'
'78': '1101101'
'79': '1101110'
'80': '1101111'
'81': '1110001'
'82': '1110010'
'83': '1110011'
splits:
- name: train
num_bytes: 1104443488.5
num_examples: 41524
- name: test
num_bytes: 23785552.0
num_examples: 742
- name: validation
num_bytes: 23785552.0
num_examples: 742
download_size: 992344756
dataset_size: 1152014592.5
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
---
|
jameskkk/find_start_time | jameskkk | "2024-07-18T06:11:07Z" | 0 | 0 | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"format:csv",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | [
"question-answering"
] | "2024-07-18T03:33:24Z" | ---
task_categories:
- question-answering
--- |
thesven/TigerMath-Evaluated-EvolQuality | thesven | "2024-07-19T04:39:56Z" | 0 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T03:45:05Z" | ---
dataset_info:
features:
- name: source
dtype: string
- name: instruction
dtype: string
- name: response
dtype: string
- name: evolved_response
dtype: string
- name: model_name
dtype: string
splits:
- name: train
num_bytes: 183552884
num_examples: 100955
download_size: 95920606
dataset_size: 183552884
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
thesven/TigerMath-Evaluated-EvolQuality-DPO | thesven | "2024-07-19T04:39:58Z" | 0 | 0 | [
"size_categories:100K<n<1M",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T03:45:05Z" | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: rejected
dtype: string
- name: chosen
dtype: string
splits:
- name: train
num_bytes: 176709648
num_examples: 100955
download_size: 95765693
dataset_size: 176709648
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
BangumiBase/nanatsunomakengashihaisuru | BangumiBase | "2024-07-18T08:45:15Z" | 0 | 0 | [
"license:mit",
"size_categories:1K<n<10K",
"modality:image",
"region:us",
"art"
] | null | "2024-07-18T03:45:06Z" | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Nanatsu No Maken Ga Shihai Suru
This is the image base of bangumi Nanatsu no Maken ga Shihai suru, we detected 106 characters, 7996 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 | 1149 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 171 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 40 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 91 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 53 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 27 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 12 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 23 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 119 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 20 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 29 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 22 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 37 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 401 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 34 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 156 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 18 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 24 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 39 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 43 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 97 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 35 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 42 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 11 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 12 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 172 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 20 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 22 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 26 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 538 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 21 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 43 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 388 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 39 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 33 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 49 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 36 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 72 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 21 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 142 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 56 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 15 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 11 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 137 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 6 | [Download](44/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 45 | 14 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 17 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 157 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 48 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 20 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 655 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 45 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 27 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 14 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 10 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 25 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 8 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 26 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 19 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 24 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 11 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 10 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| 62 | 90 | [Download](62/dataset.zip) |  |  |  |  |  |  |  |  |
| 63 | 633 | [Download](63/dataset.zip) |  |  |  |  |  |  |  |  |
| 64 | 22 | [Download](64/dataset.zip) |  |  |  |  |  |  |  |  |
| 65 | 30 | [Download](65/dataset.zip) |  |  |  |  |  |  |  |  |
| 66 | 216 | [Download](66/dataset.zip) |  |  |  |  |  |  |  |  |
| 67 | 22 | [Download](67/dataset.zip) |  |  |  |  |  |  |  |  |
| 68 | 12 | [Download](68/dataset.zip) |  |  |  |  |  |  |  |  |
| 69 | 22 | [Download](69/dataset.zip) |  |  |  |  |  |  |  |  |
| 70 | 20 | [Download](70/dataset.zip) |  |  |  |  |  |  |  |  |
| 71 | 44 | [Download](71/dataset.zip) |  |  |  |  |  |  |  |  |
| 72 | 10 | [Download](72/dataset.zip) |  |  |  |  |  |  |  |  |
| 73 | 7 | [Download](73/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 74 | 13 | [Download](74/dataset.zip) |  |  |  |  |  |  |  |  |
| 75 | 486 | [Download](75/dataset.zip) |  |  |  |  |  |  |  |  |
| 76 | 14 | [Download](76/dataset.zip) |  |  |  |  |  |  |  |  |
| 77 | 15 | [Download](77/dataset.zip) |  |  |  |  |  |  |  |  |
| 78 | 6 | [Download](78/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 79 | 17 | [Download](79/dataset.zip) |  |  |  |  |  |  |  |  |
| 80 | 16 | [Download](80/dataset.zip) |  |  |  |  |  |  |  |  |
| 81 | 18 | [Download](81/dataset.zip) |  |  |  |  |  |  |  |  |
| 82 | 17 | [Download](82/dataset.zip) |  |  |  |  |  |  |  |  |
| 83 | 51 | [Download](83/dataset.zip) |  |  |  |  |  |  |  |  |
| 84 | 26 | [Download](84/dataset.zip) |  |  |  |  |  |  |  |  |
| 85 | 11 | [Download](85/dataset.zip) |  |  |  |  |  |  |  |  |
| 86 | 17 | [Download](86/dataset.zip) |  |  |  |  |  |  |  |  |
| 87 | 5 | [Download](87/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 88 | 22 | [Download](88/dataset.zip) |  |  |  |  |  |  |  |  |
| 89 | 15 | [Download](89/dataset.zip) |  |  |  |  |  |  |  |  |
| 90 | 12 | [Download](90/dataset.zip) |  |  |  |  |  |  |  |  |
| 91 | 22 | [Download](91/dataset.zip) |  |  |  |  |  |  |  |  |
| 92 | 18 | [Download](92/dataset.zip) |  |  |  |  |  |  |  |  |
| 93 | 13 | [Download](93/dataset.zip) |  |  |  |  |  |  |  |  |
| 94 | 55 | [Download](94/dataset.zip) |  |  |  |  |  |  |  |  |
| 95 | 9 | [Download](95/dataset.zip) |  |  |  |  |  |  |  |  |
| 96 | 5 | [Download](96/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 97 | 7 | [Download](97/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 98 | 6 | [Download](98/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 99 | 8 | [Download](99/dataset.zip) |  |  |  |  |  |  |  |  |
| 100 | 7 | [Download](100/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 101 | 14 | [Download](101/dataset.zip) |  |  |  |  |  |  |  |  |
| 102 | 8 | [Download](102/dataset.zip) |  |  |  |  |  |  |  |  |
| 103 | 22 | [Download](103/dataset.zip) |  |  |  |  |  |  |  |  |
| 104 | 5 | [Download](104/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| noise | 226 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
AmarNagargoje/high_quality_dataset | AmarNagargoje | "2024-07-18T20:13:43Z" | 0 | 0 | [
"size_categories:n<1K",
"format:parquet",
"modality:text",
"library:datasets",
"library:pandas",
"library:mlcroissant",
"library:polars",
"region:us"
] | null | "2024-07-18T03:45:24Z" | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: completion
dtype: string
- name: generated_instruction
dtype: string
- name: input_ids
sequence: int32
- name: token_type_ids
sequence: int8
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 165469
num_examples: 47
download_size: 108072
dataset_size: 165469
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|