datasetId
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117
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stringlengths 2
42
⌀ | last_modified
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int64 0
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int64 0
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gsynb/work1
|
gsynb
| 2023-11-21T05:05:25Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-21T05:05:04Z |
---
license: openrail
---
|
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yuyijiong/Chinese_Paper_Abstract
|
yuyijiong
| 2023-11-21T05:13:08Z | 0 | 0 |
[
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-11-21T05:10:41Z |
---
license: cc-by-nc-4.0
language:
- zh
size_categories:
- 10K<n<100K
---
* 包含 title、正文、中文摘要,可用于训练文本摘要任务
* 论文来自中国知网，版权受限，不能直接公开。下载后请勿上传到公开场合。
|
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] |
KaibaZax/MMCds
|
KaibaZax
| 2023-11-21T05:19:23Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-21T05:19:23Z |
---
license: unknown
---
|
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Chat-UniVi/Chat-UniVi-Eval
|
Chat-UniVi
| 2023-11-23T02:18:10Z | 0 | 0 |
[
"license:apache-2.0",
"arxiv:2311.08046",
"region:us"
] | null | 2023-11-21T05:43:46Z |
---
license: apache-2.0
---
# Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding
**Paper or resources for more information:**
[[Paper](https://huggingface.co/papers/2311.08046)] [[Code](https://github.com/PKU-YuanGroup/Chat-UniVi)]
|
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viethq188/translate-en2vi
|
viethq188
| 2023-11-21T06:41:09Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-21T06:41:09Z |
---
license: apache-2.0
---
|
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gnumanth/licenses
|
gnumanth
| 2023-11-21T06:52:10Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T06:47:28Z |
---
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- name: url
dtype: string
- name: superseded_by
dtype: string
splits:
- name: train
num_bytes: 20834.494382022473
num_examples: 66
- name: test
num_bytes: 7260.505617977528
num_examples: 23
download_size: 25625
dataset_size: 28095.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# Licenses
> Comprehensive Dataset of Open Source Licenses
|
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] |
Slient/Test
|
Slient
| 2023-11-22T06:34:51Z | 0 | 0 |
[
"task_categories:text-classification",
"size_categories:10M<n<100M",
"language:zh",
"license:apache-2.0",
"chemistry",
"region:us"
] |
[
"text-classification"
] | 2023-11-21T06:48:04Z |
---
license: apache-2.0
task_categories:
- text-classification
language:
- zh
tags:
- chemistry
size_categories:
- 10M<n<100M
---
|
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] |
nguyenthanhdo/Tiger-MathInstruct
|
nguyenthanhdo
| 2023-11-21T07:29:54Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T07:29:41Z |
---
configs:
- config_name: default
data_files:
- split: vi
path: data/vi-*
- split: en
path: data/en-*
dataset_info:
features:
- name: source
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: vi
num_bytes: 227116640
num_examples: 262040
- name: en
num_bytes: 188743056
num_examples: 262040
download_size: 207887300
dataset_size: 415859696
---
# Dataset Card for "Tiger-MathInstruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
nlp-vtcc/Tiger-MathInstruct
|
nlp-vtcc
| 2023-11-21T07:30:07Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T07:29:54Z |
---
configs:
- config_name: default
data_files:
- split: vi
path: data/vi-*
- split: en
path: data/en-*
dataset_info:
features:
- name: source
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: vi
num_bytes: 227116640
num_examples: 262040
- name: en
num_bytes: 188743056
num_examples: 262040
download_size: 207887300
dataset_size: 415859696
---
# Dataset Card for "Tiger-MathInstruct"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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BangumiBase/accelworld
|
BangumiBase
| 2023-11-21T09:52:30Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-21T07:50:52Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Accel World
This is the image base of bangumi Accel World, we detected 34 characters, 2098 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 | 146 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 8 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 614 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 140 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 55 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 27 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 8 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 58 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 47 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 16 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 21 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 99 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 13 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 8 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 23 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 10 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 27 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 429 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 14 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 17 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 14 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 28 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 6 | [Download](22/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 23 | 10 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 13 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 14 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 20 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 9 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 6 | [Download](28/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 29 | 5 | [Download](29/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 30 | 5 | [Download](30/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 31 | 10 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 7 | [Download](32/dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 171 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
[
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BangumiBase/tengentoppa
|
BangumiBase
| 2023-11-21T10:35:47Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-21T07:51:21Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Tengen Toppa
This is the image base of bangumi Tengen Toppa, we detected 40 characters, 3081 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 | 107 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 137 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 104 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 23 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 29 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 33 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 36 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 28 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 359 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 73 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 133 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 151 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 32 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 44 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 78 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 25 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 22 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 17 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 44 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 104 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 51 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 37 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 339 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 32 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 11 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 16 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 16 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 10 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 53 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 50 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 59 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 23 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 36 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 28 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 11 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 19 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 9 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 73 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 13 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 616 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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BangumiBase/haikyuu
|
BangumiBase
| 2023-11-21T17:33:41Z | 0 | 0 |
[
"size_categories:10K<n<100K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-21T07:51:49Z |
---
license: mit
tags:
- art
size_categories:
- 10K<n<100K
---
# Bangumi Image Base of Haikyuu!!
This is the image base of bangumi Haikyuu!!, we detected 63 characters, 19919 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 | 4856 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 1384 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 631 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 1893 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 585 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 1375 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 221 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 169 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 405 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 336 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 304 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 323 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 210 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 769 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 149 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 166 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 89 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 419 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 90 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 78 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 35 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 76 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 97 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 85 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 73 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 2198 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 611 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 340 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 163 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 84 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 22 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 46 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 27 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 102 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 41 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 52 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 29 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 28 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 110 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 20 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 18 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 37 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 87 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 10 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 15 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 11 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 13 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 34 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 34 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 50 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 17 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 285 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 107 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 299 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 11 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 19 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 24 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 14 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 20 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 20 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 34 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| 61 | 10 | [Download](61/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 59 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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smcproject/MSC
|
smcproject
| 2023-11-21T09:49:54Z | 0 | 0 |
[
"task_categories:automatic-speech-recognition",
"size_categories:1K<n<10K",
"language:ml",
"license:cc-by-4.0",
"doi:10.57967/hf/1373",
"region:us"
] |
[
"automatic-speech-recognition"
] | 2023-11-21T07:51:57Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: audio
dtype: audio
- name: speechid
dtype: string
- name: speaker_id
dtype: string
- name: review_score
dtype: int64
- name: transcript
dtype: string
- name: category
dtype: string
- name: speaker_gender
dtype: string
- name: speaker_age
dtype: string
splits:
- name: train
num_bytes: 579920220.506
num_examples: 1541
download_size: 422956016
dataset_size: 579920220.506
license: cc-by-4.0
task_categories:
- automatic-speech-recognition
language:
- ml
pretty_name: SMC Malayalam Speech Corpus
size_categories:
- 1K<n<10K
---
# Dataset Card for [msc]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **https://smc.org.in**
- **https://gitlab.com/smc/msc-reviewed-speech**
- **https://blog.smc.org.in/malayalam-speech-corpus/**
- **Point of Contact: Kavya Manohar**
### Dataset Summary
- 1541 speech samples
- 75 speech contributors
- 1:38:16 hours of speech
- 482 unique sentences
- 1400 unique words
- 553 unique syllables
- 48 unique phonemes
For more detailed analysis see the python notebook provided [here](https://gitlab.com/smc/msc-reviewed-speech/-/blob/master/analysis/EDA.ipynb)
### Supported Tasks and Leaderboards
Automatic Speech Recognition system development, gender and age identification of speakers
### Languages
Malayalam
## Dataset Structure
- file_name
- speechid
- speaker_id
- review_score
- transcript
- category (optional speech category)
- speaker_gender (optionally self declared)
- speaker_age (optionally self declared)
### Data Instances
### Data Fields
### Data Splits
So specific Splits
## Dataset Creation
The speech data is collected from volunteer users who read and record their speech through a [web application](https://msc.smc.org.in) using their personal devices. The recorded speech is reviewed (upvote and downvote gives a score of +1 and -1 respectively) by other users. The review score is also published.
### Curation Rationale
The recorded speech is reviewed (upvote and downvote gives a score of +1 and -1 respectively) by other users. The review score is also published.
### Curation Rationale
Those speech samples with at least three positive reviews are included in this dataset.
### Source Data
#### Initial Data Collection and Normalization
The speech data is collected from volunteer contributors who read and record their speech through a [web application](https://msc.smc.org.in). The users optionally provide name, age and gender. There is no further verification. Sentences to read out are curated by MSC Admin. The speech samples are reviewed by other users.
### Personal and Sensitive Information
Every speaker is identified by a unique alphanumeric id and age and gender are published if the speaker has voluntarily published them.
## Considerations for Using the Data
### Social Impact of Dataset
Read speech corpus, recorded in natural environments by the users.
### Dataset Curators
Kavya Manohar
### Licensing Information
CC-BY-SA 4.0
### Citation Information
### Contributions
http://msc.smc.org.in/
|
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doubledsbv/large-chat-and-instructions-german-32k-tokenized
|
doubledsbv
| 2023-11-21T11:11:41Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T08:02:01Z |
---
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: labels
sequence: int64
splits:
- name: train
num_bytes: 248520102456
num_examples: 583386
- name: test
num_bytes: 27613486716
num_examples: 64821
download_size: 7784448718
dataset_size: 276133589172
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
|
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] |
Lucas666/package_llava_665k_test
|
Lucas666
| 2023-11-21T08:26:29Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-21T08:26:29Z |
---
license: apache-2.0
---
|
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xiaozhou0822/dfdsfsdf
|
xiaozhou0822
| 2023-11-21T08:41:56Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-21T08:41:56Z |
---
license: mit
---
|
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] |
deepset/stackoverflow-survey-2023-text-sql
|
deepset
| 2023-11-24T09:38:44Z | 0 | 0 |
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:cc-by-4.0",
"region:us"
] |
[
"text-generation"
] | 2023-11-21T08:57:12Z |
---
license: cc-by-4.0
task_categories:
- text-generation
language:
- en
size_categories:
- n<1K
---
# BIQA Text-to-SQL Dataset
The data is from the [Stack Overflow Developer Survey 2023](https://survey.stackoverflow.co/2023/).
Created with this [Notebook](https://colab.research.google.com/drive/12NUeRMsld0toXMSXKFMaQVAv58XwOAT1?usp=sharing); uses [this spreadsheet](https://docs.google.com/spreadsheets/d/1Xh_TgMbyitvtw08g0byEmBpkwDGZDdBYenthOzcK6qI/edit?usp=sharing) defining manual adjustments.
- `data/eval_set_multi_answers_res.json`: Question and query pairs as list of `SQLSample`s with possibly more than one valid SQL for a question. Also results included.
- `data/survey_results_normalized_v2.db`: The main sqlite db file.
The json file contains a list of `SQLSample` objects as defined:
```python
@dataclass
class SQLQuery:
query: str
results: Optional[list[tuple]] = None
@dataclass
class SQLSample:
question: str
labels: list[SQLQuery]
prediction: Optional[SQLQuery] = None
pred_eval: str = ""
comment: str = ""
```
Can be read in through the code from the [related repository](https://github.com/deepset-ai/biqa-llm).
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] |
CNS-COVER/MAC
|
CNS-COVER
| 2023-11-21T11:58:09Z | 0 | 0 |
[
"task_categories:text-generation",
"size_categories:1K<n<10K",
"license:mit",
"biology",
"chemistry",
"medical",
"climate",
"region:us"
] |
[
"text-generation"
] | 2023-11-21T09:56:57Z |
---
dataset_info:
- config_name: MAC
features:
- name: file_name
dtype: string
- name: editorial
dtype: string
- name: journal
dtype: string
- name: abstracts
dtype: string
- name: img
dtype: image
splits:
- name: train
num_bytes: 3587064062.16
num_examples: 5872
download_size: 6434624578
dataset_size: 3587064062.16
- config_name: MAC-Medium
features:
- name: file_name
dtype: string
- name: editorial
dtype: string
- name: journal
dtype: string
- name: abstracts
dtype: string
- name: img
dtype: image
splits:
- name: train
num_bytes: 1120981649
num_examples: 940
download_size: 1120522431
dataset_size: 1120981649
- config_name: MAC-Small
features:
- name: file_name
dtype: string
- name: editorial
dtype: string
- name: journal
dtype: string
- name: abstracts
dtype: string
- name: img
dtype: image
splits:
- name: train
num_bytes: 71044560
num_examples: 50
download_size: 71026675
dataset_size: 71044560
configs:
- config_name: MAC
data_files:
- split: train
path: MAC/train-*
- config_name: MAC-Medium
data_files:
- split: train
path: MAC-Medium/train-*
- config_name: MAC-Small
data_files:
- split: train
path: MAC-Small/train-*
license: mit
task_categories:
- text-generation
tags:
- biology
- chemistry
- medical
- climate
size_categories:
- 1K<n<10K
---
# Dataset Card for MAC
<!-- Provide a quick summary of the dataset. -->
The Multimodal Academic Cover (MAC) is a benchmark, comprising a 5872 collection of cover images, cover stories, and relevant articles from leading academic journals, including Cell, Nature, Science, and their sub-publications.
MAC is designed to test the ability of Multimodal models on the scientific visual understanding.
## Dataset Details
MAC consists of 5872 journal issues, each with a complete group of cover images, cover stories, and articles. Two subsets are also provided, MAC-Medium (940 issues) and MAC-Small (50 issues), to facilitate fast or qualitative evaluation.
- **Curated by:** Jin Gao, Jiahao Zhan, Chongxuan Li, Dequan Wang
- **Language(s) (NLP):** [English]
## Uses
<!-- Address questions around how the dataset is intended to be used. -->
```python
from datasets import load_dataset
dataset = load_dataset("JohnZhan/MAC","MAC-Small",split="train")
print(dataset["train"][0])
```
|
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HorcruxNo13/new_tool
|
HorcruxNo13
| 2023-11-21T10:26:30Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T10:25:39Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 328426121.0
num_examples: 16
download_size: 21122972
dataset_size: 328426121.0
---
# Dataset Card for "new_tool"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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argilla/notus-uf-dpo-full
|
argilla
| 2023-11-21T10:55:16Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T10:55:09Z |
---
dataset_info:
features:
- name: source
dtype: string
- name: instruction
dtype: string
- name: chosen_model
dtype: string
- name: chosen_rating
dtype: float64
- name: chosen_response
dtype: string
- name: rejected_responses
sequence: string
- name: rejected_ratings
sequence: float64
splits:
- name: train
num_bytes: 319830690
num_examples: 63966
download_size: 165861726
dataset_size: 319830690
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
gasp/french_rap_songs
|
gasp
| 2023-11-21T11:18:03Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-21T11:03:16Z |
---
license: mit
---
|
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BangumiBase/yuunaandthehauntedhotsprings
|
BangumiBase
| 2023-11-21T12:41:21Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-21T11:16:14Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Yuuna And The Haunted Hot Springs
This is the image base of bangumi Yuuna and the Haunted Hot Springs, we detected 28 characters, 2185 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 | 388 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 107 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 15 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 25 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 476 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 64 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 7 | [Download](6/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 7 | 21 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 11 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 202 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 152 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 22 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 14 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 9 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 94 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 128 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 48 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 6 | [Download](17/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 18 | 13 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 8 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 8 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 77 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 11 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 11 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 125 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 10 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 12 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 121 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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BangumiBase/gakusentoshiasterisk
|
BangumiBase
| 2023-11-21T13:21:20Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-21T11:18:05Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Gakusen Toshi Asterisk
This is the image base of bangumi Gakusen Toshi Asterisk, we detected 45 characters, 3325 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 | 851 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 25 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 54 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 36 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 80 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 45 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 31 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 490 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 22 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 37 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 33 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 15 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 270 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 39 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 20 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 18 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 90 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 26 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 41 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 20 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 31 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 16 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 40 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 27 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 6 | [Download](24/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 25 | 56 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 21 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 11 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 20 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 147 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 52 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 9 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 20 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 175 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 41 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 28 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 24 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 12 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 13 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 18 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 17 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 5 | [Download](41/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 42 | 18 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 22 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 253 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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BangumiBase/deadmanwonderland
|
BangumiBase
| 2023-11-21T12:24:45Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-21T11:19:37Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Deadman Wonderland
This is the image base of bangumi Deadman Wonderland, we detected 26 characters, 1386 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 | 11 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 9 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 27 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 140 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 27 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 458 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 40 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 45 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 20 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 49 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 40 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 20 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 8 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 37 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 14 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 70 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 42 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 25 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 48 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 9 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 9 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 7 | [Download](21/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 22 | 42 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 36 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 89 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 64 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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togakure/VozGeral1
|
togakure
| 2023-11-21T12:26:59Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-21T12:26:40Z |
---
license: openrail
---
|
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NicolasPre/Model_alpaca
|
NicolasPre
| 2023-11-21T12:59:23Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T12:59:20Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: autotrain_text
dtype: string
splits:
- name: train
num_bytes: 46221549
num_examples: 52002
- name: validation
num_bytes: 46221549
num_examples: 52002
download_size: 48492298
dataset_size: 92443098
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
# Dataset Card for "autotrain-data-q9ey-qe8x-tdod"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Pablao0948/Moto_Moto
|
Pablao0948
| 2023-11-21T13:10:16Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-21T13:07:20Z |
---
license: openrail
---
|
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] |
togakure/Eden
|
togakure
| 2023-11-21T13:49:19Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-21T13:48:58Z |
---
license: openrail
---
|
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hgbt/test
|
hgbt
| 2023-11-21T14:53:01Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-21T13:49:59Z |
---
license: unknown
---
|
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] |
ronnybehrens/mini-platypus_dc
|
ronnybehrens
| 2023-11-21T15:16:00Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T15:15:55Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4168526
num_examples: 1000
download_size: 2239555
dataset_size: 4168526
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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clt013/Obama-QA
|
clt013
| 2023-11-21T15:27:24Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-21T15:26:40Z |
---
license: mit
---
|
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] |
VanoInvestigations/BOE_with_BERTIN_for_tokenize_2048_2
|
VanoInvestigations
| 2023-11-21T15:33:05Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T15:32:48Z |
---
dataset_info:
features:
- name: boe_date_publication
dtype: string
- name: boe_previous
dtype: string
- name: boe_id
dtype: string
- name: boe_title
dtype: string
- name: boe_soup_xml
dtype: string
- name: tweet_date
dtype: string
- name: boe_text_cleaned
dtype: string
- name: tweet_original
dtype: string
- name: boe_alert
sequence: string
- name: boe_category
dtype: string
- name: boe_departament
dtype: string
- name: tweet_text_cleaned
dtype: string
- name: boe_subsequent
dtype: string
- name: boe_materials
sequence: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 179564833
num_examples: 2867
- name: validation
num_bytes: 19448449
num_examples: 392
- name: test
num_bytes: 22514673
num_examples: 389
download_size: 84281864
dataset_size: 221527955
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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] |
malteos/m_hellaswag
|
malteos
| 2023-12-04T11:25:10Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T15:50:26Z |
---
configs:
- config_name: ar
data_files:
- split: validation
path: ar_validation*
dataset_info:
features: # "endings": datasets.features.Sequence(datasets.Value("string")),
- name: ind
dtype: int32
- name: activity_label
dtype: string
- name: ctx_a
dtype: string
- name: ctx_a
dtype: string
- name: ctx
dtype: string
- name: endings
dtype: string
- name: source_id
dtype: string
- name: split
dtype: string
- name: split_type
dtype: string
- name: label
dtype: string
splits:
- name: validation
---
Mirror of https://github.com/nlp-uoregon/mlmm-evaluation
|
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] |
Sanjaykrishna/Leadknock_YellowPages
|
Sanjaykrishna
| 2023-11-23T18:23:36Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-21T16:28:58Z |
---
license: apache-2.0
dataset_info:
features:
- name: column_name
dtype: string
splits:
- name: train
num_bytes: 27
num_examples: 3
download_size: 813
dataset_size: 27
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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hf-internal-testing/etth1-hourly-batch
|
hf-internal-testing
| 2023-11-21T16:42:52Z | 0 | 0 |
[
"license:cc-by-nd-4.0",
"region:us"
] | null | 2023-11-21T16:36:42Z |
---
license: cc-by-nd-4.0
---
|
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] |
uatafaque/movemind2
|
uatafaque
| 2023-11-21T17:02:55Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-21T17:02:38Z |
---
license: openrail
---
|
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chrisgru/blended_skill_talk_chatml
|
chrisgru
| 2023-11-26T08:02:50Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T17:07:12Z |
---
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
splits:
- name: train
num_bytes: 1198146
num_examples: 980
download_size: 0
dataset_size: 1198146
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "blended_skill_talk_chatml"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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BangumiBase/chihayafuru
|
BangumiBase
| 2023-11-22T08:52:47Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-21T17:21:25Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Chihayafuru
This is the image base of bangumi Chihayafuru, we detected 58 characters, 8676 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 | 510 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 97 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 1030 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 509 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 459 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 172 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 183 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 84 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 287 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 60 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 26 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 18 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 177 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 182 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 71 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 26 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 32 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 27 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 106 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 423 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 74 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 59 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 81 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 92 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 36 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 149 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 1169 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 279 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 56 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 854 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 47 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 99 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 72 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 51 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 135 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 37 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 74 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 34 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 37 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 85 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 21 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 33 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 76 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 25 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 45 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 69 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 10 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 36 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 12 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 35 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 15 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 78 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 20 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 14 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 18 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 20 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 19 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 131 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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] |
joaobfm/ryan_ia_dataset_a
|
joaobfm
| 2023-11-21T17:34:27Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-21T17:33:51Z |
---
license: openrail
---
|
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PeterLawrence/inova8.schema.1
|
PeterLawrence
| 2023-12-03T17:25:52Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T17:38:13Z |
---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
splits:
- name: train
num_bytes: 91121
num_examples: 85
download_size: 13427
dataset_size: 91121
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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BangumiBase/sousounofrieren
|
BangumiBase
| 2023-11-21T19:10:10Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-21T17:56:03Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Sousou No Frieren
This is the image base of bangumi Sousou no Frieren, we detected 19 characters, 1826 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 | 31 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 20 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 85 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 25 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 10 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 14 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 78 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 56 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 39 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 218 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 8 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 6 | [Download](11/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 12 | 570 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 448 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 43 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 19 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 7 | [Download](16/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 17 | 24 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 125 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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] |
relhousieny/share_bike_test
|
relhousieny
| 2023-11-21T19:01:41Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T19:01:39Z |
---
dataset_info:
features:
- name: datetime
dtype: string
- name: season
dtype: int64
- name: holiday
dtype: int64
- name: workingday
dtype: int64
- name: weather
dtype: int64
- name: temp
dtype: float64
- name: atemp
dtype: float64
- name: humidity
dtype: int64
- name: windspeed
dtype: float64
splits:
- name: train
num_bytes: 564891
num_examples: 6493
download_size: 85156
dataset_size: 564891
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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Refic/RickSanchez
|
Refic
| 2023-11-21T19:22:57Z | 0 | 0 |
[
"license:unlicense",
"region:us"
] | null | 2023-11-21T19:22:57Z |
---
license: unlicense
---
|
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] |
datasets-examples/doc-image-1
|
datasets-examples
| 2023-11-21T21:29:35Z | 0 | 0 |
[
"size_categories:n<1K",
"region:us"
] | null | 2023-11-21T20:02:57Z |
---
size_categories:
- n<1K
---
# [doc] image dataset 1
This dataset contains 4 jpeg files at the root.
|
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Admin08077/oo
|
Admin08077
| 2023-11-21T20:37:29Z | 0 | 0 |
[
"license:other",
"region:us"
] | null | 2023-11-21T20:35:49Z |
---
license: other
license_name: citibankdemo
license_link: LICENSE
---
|
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Admin08077/oo1
|
Admin08077
| 2023-11-21T20:41:01Z | 0 | 0 |
[
"license:other",
"region:us"
] | null | 2023-11-21T20:40:14Z |
---
license: other
license_name: citibankdemo
license_link: LICENSE
---
|
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] |
Oumar199/Nalohou_climatic_time_series
|
Oumar199
| 2023-11-23T14:56:51Z | 0 | 0 |
[
"task_categories:time-series-forecasting",
"language:en",
"region:us"
] |
[
"time-series-forecasting"
] | 2023-11-21T20:49:51Z |
---
task_categories:
- time-series-forecasting
language:
- en
pretty_name: Sub-Saharan-Time-Series-Forecasting
---
|
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JACINTO223/ZORO
|
JACINTO223
| 2023-11-21T21:01:35Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-21T21:00:06Z |
---
license: openrail
---
|
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ctoraman/large-scale-hate-speech
|
ctoraman
| 2023-11-21T21:18:26Z | 0 | 0 |
[
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"language:tr",
"license:cc",
"hate-speech",
"hatespeech",
"hate-speech-detection",
"hatespeechdetection",
"region:us"
] |
[
"text-classification"
] | 2023-11-21T21:07:33Z |
---
license: cc
task_categories:
- text-classification
language:
- en
- tr
tags:
- hate-speech
- hatespeech
- hate-speech-detection
- hatespeechdetection
pretty_name: h
size_categories:
- 100K<n<1M
---
This repository contains the utilized dataset in the LREC 2022 paper "Large-Scale Hate Speech Detection with Cross-Domain Transfer". This study mainly focuses hate speech detection in Turkish and English. In addition, domain transfer success between hate domains is also examined.
There are two dataset versions.
Dataset v1: The original dataset that includes 100,000 tweets per English and Turkish, published in LREC 2022. The annotations with more than 60% agreement are included.
Dataset v2: A more reliable dataset version that includes 68,597 tweets for English and 60,310 for Turkish. The annotations with more than 80% agreement are included.
For more details: https://github.com/avaapm/hatespeech/
|
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] |
severo/doc-image-2
|
severo
| 2023-11-21T21:26:13Z | 0 | 0 |
[
"size_categories:n<1K",
"region:us"
] | null | 2023-11-21T21:11:42Z |
---
size_categories:
- n<1K
---
# [doc] image dataset 2
This dataset contains 4 image files at the root, using 4 different image formats: jpeg, png, tiff, webp.
|
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sujanevo/human-body
|
sujanevo
| 2023-11-21T21:15:19Z | 0 | 0 |
[
"size_categories:n<1K",
"language:en",
"health",
"body parts",
"region:us"
] | null | 2023-11-21T21:14:20Z |
---
language:
- en
tags:
- health
- body parts
size_categories:
- n<1K
---
|
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TheGreatP/minhavoz2
|
TheGreatP
| 2023-11-21T21:17:12Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-21T21:16:03Z |
---
license: openrail
---
|
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] |
ctoraman/BilCat-news-classification
|
ctoraman
| 2023-11-21T21:46:22Z | 0 | 0 |
[
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:tr",
"license:cc",
"news-classification",
"text-classification",
"news-categorization",
"text-categorization",
"news-articles",
"region:us"
] |
[
"text-classification"
] | 2023-11-21T21:25:00Z |
---
license: cc
task_categories:
- text-classification
language:
- tr
tags:
- news-classification
- text-classification
- news-categorization
- text-categorization
- news-articles
size_categories:
- 1K<n<10K
---
BilCat: Bilkent Text Classification (News Categorization) Dataset
7540 Turkish news articles (Milliyet and TRT merged) with category labels (Dunya, Ekonomi, Politika, KulturSanat, Saglik, Spor, Turkiye, Yazarlar).
Column header is the first line.
Other details are at https://github.com/BilkentInformationRetrievalGroup/BilCat/
Citation:
C. Toraman, F. Can and S. Koçberber. Developing a text categorization template for Turkish news portals. 2011 International Symposium on Innovations in Intelligent Systems and Applications, Istanbul, 2011, pp. 379-383. DOI: 10.1109/INISTA.2011.5946096
|
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NoobPROBR/JoaoStephanini
|
NoobPROBR
| 2023-11-21T21:27:29Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-21T21:26:16Z |
---
license: openrail
---
|
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] |
ajmangus/qm_alice_mixture_1.0e
|
ajmangus
| 2023-11-28T02:42:37Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T21:26:27Z |
---
dataset_info:
features:
- name: alice_label
dtype: bool
- name: bob_label
dtype: bool
- name: charlie_label
dtype: bool
- name: difficulty
dtype: int64
- name: statement
dtype: string
- name: choices
sequence: string
- name: character
dtype: string
- name: label
dtype:
class_label:
names:
'0': 'False'
'1': 'True'
splits:
- name: train
num_bytes: 24063561.333333332
num_examples: 199999
- name: validation
num_bytes: 2403257.0
num_examples: 19999
- name: test
num_bytes: 2407978.6666666665
num_examples: 19999
download_size: 7642902
dataset_size: 28874797.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
|
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DeliberatorArchiver/movie_binaries_0012
|
DeliberatorArchiver
| 2023-11-22T22:08:58Z | 0 | 0 |
[
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | 2023-11-21T21:35:04Z |
---
license: cc-by-nc-nd-4.0
---
|
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ctoraman/deprem-tweet-dataset
|
ctoraman
| 2023-11-21T21:55:37Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:token-classification",
"size_categories:1K<n<10K",
"language:tr",
"license:cc",
"disaster-relief",
"disaster",
"earthquake",
"tweets",
"deprem",
"tweet-classification",
"ner",
"arxiv:2302.13403",
"region:us"
] |
[
"text-classification",
"token-classification"
] | 2023-11-21T21:52:08Z |
---
license: cc
task_categories:
- text-classification
- token-classification
language:
- tr
tags:
- disaster-relief
- disaster
- earthquake
- tweets
- deprem
- tweet-classification
- ner
size_categories:
- 1K<n<10K
---
Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster
The annotated dataset is given at dataset.tsv. We annotate 1,000 tweets in Turkish if tweets call for help (i.e. request rescue, supply, or donation), and their entity tags (person, city, address, status).
Column Name Description
label Human annotation if tweet calls for help (binary classification)
entities Human annotation of entity tags (i.e. person, city, address, and status). The format is [START_INDEX]:[END_INDEX]%[TAG_TYPE].
tweet_id Tweet ID from Twitter API.
Other details can be found at https://github.com/avaapm/deprem
Citation
If you make use of this dataset, please cite following paper.
@misc{toraman2023earthquake,
doi = {10.48550/ARXIV.2302.13403},
url = {https://arxiv.org/abs/2302.13403},
author = {Toraman, Cagri and Kucukkaya, Izzet Emre and Ozcelik, Oguzhan and Sahin, Umitcan},
keywords = {Social and Information Networks (cs.SI), Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Tweets Under the Rubble: Detection of Messages Calling for Help in Earthquake Disaster},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
|
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] |
ctoraman/misinformation-detection-tweets
|
ctoraman
| 2023-11-21T22:01:28Z | 0 | 0 |
[
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"language:tr",
"license:cc",
"misinformation-detection",
"misinformation",
"disinformation",
"disinformation-detection",
"misinfo",
"fakenews",
"fake-news",
"tweets",
"arxiv:2210.05401",
"region:us"
] |
[
"text-classification"
] | 2023-11-21T21:56:31Z |
---
license: cc
language:
- en
- tr
task_categories:
- text-classification
tags:
- misinformation-detection
- misinformation
- disinformation
- disinformation-detection
- misinfo
- fakenews
- fake-news
- tweets
size_categories:
- 10K<n<100K
---
Mide22 Dataset published at "Not Good Times for Lies: Misinformation Detection on the Russia-Ukraine War, COVID-19, and Refugees"
The dataset is composed of 10,348 tweets: 5,284 for English and 5,064 for Turkish. Tweets in the dataset cover different topics: the Russia-Ukraine war, COVID-19 pandemic, Refugees, and additional miscellaneous events. Three misinformation label of the tweet are also given. Since we follow Twitter's Terms and Conditions, we publish tweet IDs not the tweet content directly. Explanations of the columns of the file are as follows:
Column Name Description
Topic Topic of the tweet: Ukraine, Covid, Refugees or Misc
Event Event of the tweet: EN01-EN40 in English and TR01-TR40 in Turkish
Label Label of the tweet: True, False, or Other
Tweet_id Twitter ID of the tweet
Other details are at https://github.com/avaapm/mide22/
Citation
If you make use of this dataset, please cite following paper.
@misc{toraman2022good,
title={Not Good Times for Lies: Misinformation Detection on the Russia-Ukraine War, COVID-19, and Refugees},
author={Cagri Toraman and Oguzhan Ozcelik and Furkan Şahinuç and Fazli Can},
year={2022},
eprint={2210.05401},
archivePrefix={arXiv},
primaryClass={cs.SI}
}
|
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hotal/emergency_combined_prompt
|
hotal
| 2023-11-21T22:04:00Z | 0 | 1 |
[
"region:us"
] | null | 2023-11-21T22:03:57Z |
---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 27433336.0
num_examples: 26488
download_size: 4827292
dataset_size: 27433336.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "emergency_combined_prompt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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ctoraman/tweet-topic-detection
|
ctoraman
| 2023-11-21T22:21:04Z | 0 | 0 |
[
"task_categories:text-classification",
"language:en",
"license:cc",
"tweet-classification",
"topic-detection",
"topic-classification",
"topics",
"tweets",
"tweet-length",
"region:us"
] |
[
"text-classification"
] | 2023-11-21T22:11:11Z |
---
license: cc
task_categories:
- text-classification
language:
- en
tags:
- tweet-classification
- topic-detection
- topic-classification
- topics
- tweets
- tweet-length
---
Published tweet dataset used in "Tweet Length Matters: A Comparative Analysis on Topic Detection in Microblogs" includes tweet id and corresponding topic number.
Topic numbers encoded as follows:
Topic Topic Number
BLM Movement 0
Covid-19 1
K-Pop 2
Bollywood 3
Gaming 4
U.S. Politics 5
Out-of-Topic 6
In total, there are 354,310 tweet instances.
More details can be found at https://github.com/avaapm/ECIR2021/
Citation
If you make use of these tools, please cite following paper.
@inproceedings{DBLP:conf/ecir/SahinucT21,
author = {Furkan {\c{S}}ahinu{\c{c}} and Cagri Toraman},
title = {Tweet Length Matters: {A} Comparative Analysis on Topic Detection in Microblogs},
booktitle = {Advances in Information Retrieval - 43rd European Conference on {IR} Research, {ECIR} 2021, Virtual Event, March 28 - April 1, 2021, Proceedings, Part {II}},
series = {Lecture Notes in Computer Science},
volume = {12657},
pages = {471--478},
publisher = {Springer},
year = {2021},
url = {https://doi.org/10.1007/978-3-030-72240-1\_50},
doi = {10.1007/978-3-030-72240-1\_50},
}
|
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] |
Kaue123456/PopeyeOrlandoDrummond
|
Kaue123456
| 2023-11-21T22:46:46Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-21T22:45:53Z |
---
license: openrail
---
|
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] |
someone13574/fictional-worlds-v2-seeds
|
someone13574
| 2023-11-21T23:14:11Z | 0 | 0 |
[
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
] | null | 2023-11-21T23:04:00Z |
---
license: apache-2.0
size_categories:
- 10K<n<100K
language:
- en
---
# Worldbuilding Seeds
30k (three per topic) seed phrases for worldbuilding or synthetic datasets. Filtering for quality and format (should be free of anything but the seeds).
#### Details
Generated with Zephyr-beta-7b, mirostat sampling with tau=8.0.
#### Prompt
Fewshot prompting + an instruction was used for generation. Topics are from wikipedia vital article titles (level 4). Some topics were removed due to inability to generate good seeds. Three seeds are generated at the same time per topic because it makes more diverse outputs than prompting for one.
```
<|user|>
You are fantasy world seed creator. You create short sentences which describe the core concept which a fictional fantasy world is built around. They should be unique and varied, taking inspiration from a random topic. The seed should describe what makes the world at large special in the simplest way possible, and shouldn't meantion real world proper nouns. For each topic I say after this, create 3 seeds which take different approaches. Make the worlds different from our own.
Topic: Surgery </s>
<|assistant|>
Ether blades sculpt fate.
Bloodline tattoos reveal ancestral memories.
Healing crystals forge immortal connections. </s>
<|user|>
Topic: Carl Friedrich Gauss </s>
<|assistant|>
Equations reshape the fabric of reality.
Arcane symbols unlock interdimensional gateways.
Numerical patterns summon celestial guardians. </s>
<|user|>
Topic: Thailand </s>
<|assistant|>
Elemental monks maintain cosmic balance.
Celestial elephants carry realms on their tusks.
Floating temples bridge mortal and divine. </s>
<|user|>
Topic: Phoenicia </s>
<|assistant|>
Winged ships navigate skies of aether.
Elemental sails harness storms for propulsion.
Stellar traders weave constellations into tales. </s>
<|user|>
Topic: Calligraphy </s>
<|assistant|>
Inked verses rewrite the past.
Tattooed scrolls reveal hidden destinies.
Glyphs on skin awaken dormant powers. </s>
<|user|>
Topic: Al-Khwarizmi </s>
<|assistant|>
Ciphered scripts unlock ancient gateways.
Algorithmic gardens grow sentient equations.
Time travel guided by numeric formulas. </s>
<|user|>
Topic: {topic} </s>
<|assistant|>
```
|
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] |
gnumanth/git-tips
|
gnumanth
| 2023-11-21T23:23:30Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-21T23:15:23Z |
---
dataset_info:
features:
- name: tip
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 7206.0898203592815
num_examples: 83
- name: test
num_bytes: 2951.8922155688624
num_examples: 34
download_size: 10514
dataset_size: 10157.982035928144
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
# git-tips
> Most commonly used git tips and tricks.
This is a dataset from [git-tips](https://github.com/git-tips/tips)
|
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cointegrated/taiga_stripped_stihi
|
cointegrated
| 2023-11-23T09:48:44Z | 0 | 1 |
[
"task_categories:text-generation",
"task_categories:fill-mask",
"size_categories:1M<n<10M",
"language:ru",
"license:cc-by-sa-3.0",
"taiga",
"tayga",
"region:us"
] |
[
"text-generation",
"fill-mask"
] | 2023-11-21T23:37:19Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: file
dtype: string
splits:
- name: train
num_bytes: 14185482821
num_examples: 9157973
download_size: 7745419481
dataset_size: 14185482821
license: cc-by-sa-3.0
language:
- ru
tags:
- taiga
- tayga
size_categories:
- 1M<n<10M
task_categories:
- text-generation
- fill-mask
---
# Dataset Card for "taiga_stripped_stihi"
This is a subset of the Taiga corpus (https://tatianashavrina.github.io/taiga_site), derived from the `stihi` source (a.k.a. "Poetry").
The dataset consists of plain texts, without morphological and syntactic annotation or metainformation. Apart from stripping the annotations, the texts were not modified.
For more details and analysis, and for the texts with annotation or metadata, please refer to website of the corpus.
Other subsets of Taiga: [proza](https://huggingface.co/datasets/cointegrated/taiga_stripped_proza) (fiction)
and [other sources](https://huggingface.co/datasets/cointegrated/taiga_stripped_rest) (news, subtitles, and social media).
License: [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/).
|
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brantfetter/primary
|
brantfetter
| 2023-11-21T23:41:30Z | 0 | 0 |
[
"license:cc-by-nd-4.0",
"region:us"
] | null | 2023-11-21T23:41:30Z |
---
license: cc-by-nd-4.0
---
|
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Bluebomber182/Branch-From-Trolls
|
Bluebomber182
| 2023-11-22T00:13:09Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-22T00:10:17Z |
---
license: unknown
---
|
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Bluebomber182/Poppy-From-Trolls
|
Bluebomber182
| 2023-11-22T00:16:07Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-22T00:13:30Z |
---
license: unknown
---
|
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] |
Kevv17/Chster
|
Kevv17
| 2023-11-22T00:58:27Z | 0 | 0 |
[
"license:cc",
"region:us"
] | null | 2023-11-22T00:58:27Z |
---
license: cc
---
|
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] |
m111styd4y/marininhasena
|
m111styd4y
| 2023-11-22T01:00:29Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-22T00:59:10Z |
---
license: openrail
---
|
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deboramachadoandrade/sft_dataset_rlaif
|
deboramachadoandrade
| 2023-11-22T01:08:21Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-22T01:08:20Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 8091
num_examples: 5
download_size: 16567
dataset_size: 8091
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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jseims/sft_dataset_rlaif
|
jseims
| 2023-11-22T01:10:13Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-22T01:10:11Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 6493
num_examples: 5
download_size: 13374
dataset_size: 6493
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
JuanKO/sft_dataset_rlaif
|
JuanKO
| 2023-11-22T01:20:19Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-22T01:20:16Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 9271
num_examples: 5
download_size: 19196
dataset_size: 9271
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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someone13574/fictional-worlds-v2-geography
|
someone13574
| 2023-11-23T01:43:58Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-22T01:23:36Z |
---
license: apache-2.0
---
Intermediate step for the fictional-worlds-v2 dataset. Under construction.
|
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] |
Vezora/test2x
|
Vezora
| 2023-11-22T01:59:50Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-22T01:58:18Z |
---
license: apache-2.0
---
|
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mediabiasgroup/bias_lexicon
|
mediabiasgroup
| 2023-11-22T02:08:25Z | 0 | 0 |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-11-22T02:00:30Z |
---
license: cc-by-nc-4.0
---
# Description:
The bias_lexicon file is a comprehensive dictionary of biased words. This lexicon is designed to assist in identifying and analyzing biased language in various texts. The dictionary encompasses a wide range of words that are often associated with biased expressions, including those related to gender, race, age, and other social categories.
# Usage:
This resource can be pivotal for crafting features in natural language processing (NLP) tasks, sentiment analysis, and in developing models that aim to detect or mitigate biased language. It's particularly useful in research and applications focusing on ethical AI and fair representation in language models.
|
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] |
kamakani/sft_dataset_rlaif
|
kamakani
| 2023-11-22T02:02:56Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-22T02:02:35Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 8349
num_examples: 5
download_size: 18125
dataset_size: 8349
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
lillybak/sft_dataset_rlaif
|
lillybak
| 2023-11-22T02:19:14Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-22T02:19:13Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
splits:
- name: train
num_bytes: 3126
num_examples: 5
download_size: 6861
dataset_size: 3126
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
Nickkkkkk/Beatrix
|
Nickkkkkk
| 2023-11-22T02:39:19Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-22T02:37:42Z |
---
license: openrail
---
|
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Fael123/ModelMCPOZErodo
|
Fael123
| 2023-11-22T02:58:01Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-22T02:58:01Z |
---
license: openrail
---
|
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] |
Mzh666/DONG1
|
Mzh666
| 2023-11-22T05:25:02Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-22T03:14:53Z |
---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 27951045.0
num_examples: 96
download_size: 22243607
dataset_size: 27951045.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
Alucard1681/Vozdoazir
|
Alucard1681
| 2023-11-22T03:19:11Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-22T03:18:07Z |
---
license: openrail
---
|
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] |
Praghxx/Litlegiela
|
Praghxx
| 2023-11-22T03:22:53Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-22T03:22:06Z |
---
license: openrail
---
|
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0.26214760541915894,
0.04487833380699158,
-0.6869441866874695
] |
gagan3012/finner
|
gagan3012
| 2023-11-22T03:26:59Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-22T03:26:56Z |
---
dataset_info:
features:
- name: label
sequence: string
- name: answer
dtype: string
- name: text
dtype: string
- name: query
dtype: string
splits:
- name: train
num_bytes: 29189680
num_examples: 8100
download_size: 9009979
dataset_size: 29189680
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
caiosoares26/freefiri
|
caiosoares26
| 2023-11-22T04:08:27Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-22T04:07:25Z |
---
license: openrail
---
|
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Am4nu3l/amharic-language-voices
|
Am4nu3l
| 2023-11-22T04:40:09Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-22T04:36:44Z |
---
license: mit
---
|
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DollyDayko/RetriBooru
|
DollyDayko
| 2023-11-22T06:10:00Z | 0 | 0 |
[
"task_categories:image-to-image",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"region:us"
] |
[
"image-to-image"
] | 2023-11-22T04:47:37Z |
---
license: mit
task_categories:
- image-to-image
language:
- en
size_categories:
- 100K<n<1M
---
# Retrieving Conditions from Reference Images for Diffusion Models
This is the HuggingFace Dataset repo for RetriBooru dataset, containing json metadata (coming soon!). Please navigate to our [GitHub Project Page](https://haorantang.github.io/retribooru/) for details about the proposed dataset and method.
|
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] |
ppxscal/wikigraph_pairs
|
ppxscal
| 2023-11-22T05:12:28Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-22T04:50:25Z |
---
dataset_info:
features:
- name: Query Alias
dtype: string
- name: Query
dtype: string
- name: Relation
dtype: string
- name: Content
dtype: string
- name: Content Alias
dtype: string
splits:
- name: train
num_bytes: 16467352698
num_examples: 4553783
download_size: 4844940237
dataset_size: 16467352698
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
pairs extracted from https://deepgraphlearning.github.io/project/wikidata5m
|
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] |
sankovic/shozz
|
sankovic
| 2023-11-22T04:52:20Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-22T04:51:46Z |
---
license: openrail
---
|
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dangkhoadl/ICASSP2024-Acoustic_Scattering_AI-Noninvasive_Object_Classifications
|
dangkhoadl
| 2023-11-22T10:40:22Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-22T05:12:08Z |
---
license: apache-2.0
---
|
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Deojoandco/capstone_fromgpt_without_gold_v3
|
Deojoandco
| 2023-11-22T05:19:13Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-22T05:19:10Z |
---
dataset_info:
features:
- name: dialog_id
dtype: int64
- name: dialogue
dtype: string
- name: summary
dtype: string
- name: gold_tags
dtype: string
- name: gpt_success
dtype: bool
- name: gpt_response
dtype: string
- name: gold_tags_tokens_count
dtype: int64
- name: GPT_TAGS_FOUND
dtype: bool
- name: gpt_output_tags
dtype: string
- name: gpt_output_tag_tokens_count
dtype: int64
- name: GPT_MI_FOUND
dtype: bool
- name: gpt_tags_token_count
dtype: int64
- name: gpt_tags
dtype: string
- name: tag_token_count_match
dtype: bool
splits:
- name: test
num_bytes: 20712
num_examples: 12
download_size: 22423
dataset_size: 20712
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
---
# Dataset Card for "capstone_fromgpt_without_gold_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
deepghs/quality_rlhf
|
deepghs
| 2023-11-24T08:59:25Z | 0 | 0 |
[
"task_categories:reinforcement-learning",
"license:openrail",
"art",
"not-for-all-audiences",
"region:us"
] |
[
"reinforcement-learning"
] | 2023-11-22T05:28:57Z |
---
license: openrail
task_categories:
- reinforcement-learning
tags:
- art
- not-for-all-audiences
---
|
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HuskyAI/game_icon_diablo_style
|
HuskyAI
| 2023-11-22T05:41:30Z | 0 | 1 |
[
"task_categories:text-to-image",
"license:apache-2.0",
"game icon",
"diablo",
"style",
"lora",
"region:us"
] |
[
"text-to-image"
] | 2023-11-22T05:34:02Z |
---
license: apache-2.0
task_categories:
- text-to-image
tags:
- game icon
- diablo
- style
- lora
---
|
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BangumiBase/rwbyhyousetsuteikoku
|
BangumiBase
| 2023-11-22T07:20:23Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-22T05:34:44Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Rwby - Hyousetsu Teikoku
This is the image base of bangumi RWBY - Hyousetsu Teikoku, we detected 29 characters, 2529 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 | 229 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 49 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 34 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 13 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 38 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 76 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 18 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 14 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 10 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 550 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 19 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 9 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 322 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 25 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 55 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 33 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 177 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 27 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 376 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 16 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 19 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 114 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 10 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 6 | [Download](23/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 24 | 72 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 23 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 7 | [Download](26/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 27 | 14 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 174 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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BangumiBase/goldenkamuy
|
BangumiBase
| 2023-11-22T12:02:49Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-22T05:35:28Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Golden Kamuy
This is the image base of bangumi Golden Kamuy, we detected 44 characters, 8914 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 | 2560 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 737 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 50 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 1259 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 95 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 250 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 227 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 379 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 178 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 243 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 39 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 69 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 110 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 63 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 219 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 24 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 36 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 1180 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 54 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 45 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 185 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 151 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 27 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 31 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 16 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 42 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 42 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 55 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 14 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 58 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 33 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 24 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 53 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 49 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 11 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 15 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 49 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 38 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 15 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 19 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 53 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 10 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 24 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 83 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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KADUZADA/MANIVELA
|
KADUZADA
| 2023-11-22T06:03:25Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-22T06:02:56Z |
---
license: openrail
---
|
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] |
Subhadeep/English_IITM_Check_dataset_en_pseudo_labelled
|
Subhadeep
| 2023-11-22T10:57:00Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-22T06:19:25Z |
---
dataset_info:
config_name: en
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: path
dtype: string
- name: sentence
dtype: string
- name: length
dtype: float64
- name: whisper_transcript
sequence: int64
splits:
- name: train
num_bytes: 618348530.525
num_examples: 3009
download_size: 606619139
dataset_size: 618348530.525
configs:
- config_name: en
data_files:
- split: train
path: en/train-*
---
|
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] |
sabasazad/sft_dataset_rlaif
|
sabasazad
| 2023-11-22T07:17:41Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-22T06:54:22Z |
---
dataset_info:
features:
- name: prompt
dtype: string
- name: response
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 7953
num_examples: 5
download_size: 16723
dataset_size: 7953
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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deepghs/anime_style_ages
|
deepghs
| 2023-11-29T08:23:40Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-22T06:58:00Z |
---
license: openrail
---
|
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collabora/indic-superb
|
collabora
| 2023-11-22T08:13:35Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-22T07:35:07Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
- name: duration
dtype: float64
splits:
- name: train
num_bytes: 46776721194.64
num_examples: 24872
- name: test
num_bytes: 1592137067.0
num_examples: 872
download_size: 46065024050
dataset_size: 48368858261.64
---
# Dataset Card for "indic-superb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
dipudl/ms-marco-llama-gptq-prompts
|
dipudl
| 2023-11-30T22:34:18Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-22T07:41:35Z |
---
dataset_info:
features:
- name: query_id
dtype: int32
- name: answers
sequence: string
- name: passages
struct:
- name: is_selected
sequence: int32
- name: passage_text
sequence: string
- name: url
sequence: string
- name: query
dtype: string
- name: query_type
dtype: string
- name: wellFormedAnswers
sequence: 'null'
- name: ai_answers
dtype: string
- name: query_len
dtype: int64
- name: prompt
dtype: string
splits:
- name: train
num_bytes: 85426944
num_examples: 20000
download_size: 41558640
dataset_size: 85426944
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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