datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|
tianyi0216/ukioye_editing | tianyi0216 | 2023-12-01T01:10:40Z | 0 | 1 | [
"region:us"
]
| null | 2023-11-30T18:23:46Z | ---
dataset_info:
features:
- name: source_img
dtype: image
- name: instruction
dtype: string
- name: target_img
dtype: image
splits:
- name: train
num_bytes: 447007253.829
num_examples: 1989
download_size: 446312410
dataset_size: 447007253.829
---
# Dataset Card for "ukioye_editing"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
datasets-examples/doc-image-7 | datasets-examples | 2023-11-30T19:33:26Z | 0 | 0 | [
"size_categories:n<1K",
"region:us"
]
| null | 2023-11-30T18:43:07Z | ---
size_categories:
- n<1K
---
# [doc] image dataset 7
This dataset contains 2 jpeg files in the `red` directory and 2 jpeg files in the `green` directory. | [
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|
Gabriel1322/taspiomodelo | Gabriel1322 | 2023-11-30T18:46:41Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-30T18:43:27Z | ---
license: openrail
---
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|
datasets-examples/doc-image-6 | datasets-examples | 2023-11-30T19:05:32Z | 0 | 0 | [
"size_categories:n<1K",
"region:us"
]
| null | 2023-11-30T18:43:40Z | ---
size_categories:
- n<1K
---
# [doc] image dataset 6
This dataset contains 4 jpeg files in the `train/images/` subdirectory, along with a `train/metadata.csv` file that provides the data for other columns. The metadata file contains relative paths to the images. | [
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|
generordo/nailson | generordo | 2023-12-01T02:20:27Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-30T18:46:12Z | ---
license: openrail
---
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]
|
RicardoRei/wmt-mqm-error-spans | RicardoRei | 2023-11-30T19:14:18Z | 0 | 0 | [
"size_categories:100K<n<1M",
"language:en",
"language:de",
"language:ru",
"language:zh",
"license:apache-2.0",
"mt-evaluation",
"WMT",
"MQM",
"arxiv:2310.10482",
"region:us"
]
| null | 2023-11-30T18:55:52Z | ---
license: apache-2.0
language:
- en
- de
- ru
- zh
tags:
- mt-evaluation
- WMT
- MQM
size_categories:
- 100K<n<1M
---
# Dataset Summary
This dataset contains all MQM human annotations from previous [WMT Metrics shared tasks](https://wmt-metrics-task.github.io/) and the MQM annotations from [Experts, Errors, and Context](https://aclanthology.org/2021.tacl-1.87/) in a form of error spans. Moreover, it contains some hallucinations used in the training of [XCOMET models](https://huggingface.co/Unbabel/XCOMET-XXL).
**Please note that this is not an official release of the data** and the original data can be found [here](https://github.com/google/wmt-mqm-human-evaluation).
The data is organised into 8 columns:
- src: input text
- mt: translation
- ref: reference translation
- annotations: List of error spans (dictionaries with 'start', 'end', 'severity', 'text')
- lp: language pair
While `en-ru` was annotated by Unbabel, `en-de` and `zh-en` was annotated by Google. This means that for en-de and zh-en you will only find minor and major errors while for en-ru you can find a few critical errors.
## Python usage:
```python
from datasets import load_dataset
dataset = load_dataset("RicardoRei/wmt-mqm-error-spans", split="train")
```
There is no standard train/test split for this dataset but you can easily split it according to year, language pair or domain. E.g. :
```python
# split by LP
data = dataset.filter(lambda example: example["lp"] == "en-de")
```
## Citation Information
If you use this data please cite the following works:
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://aclanthology.org/2021.tacl-1.87/)
- [Results of the WMT21 Metrics Shared Task: Evaluating Metrics with Expert-based Human Evaluations on TED and News Domain](https://aclanthology.org/2021.wmt-1.73/)
- [Results of WMT22 Metrics Shared Task: Stop Using BLEU – Neural Metrics Are Better and More Robust](https://aclanthology.org/2022.wmt-1.2/)
- [xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection](https://arxiv.org/pdf/2310.10482.pdf)
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|
Gunchar/dataset | Gunchar | 2023-11-30T19:00:30Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-11-30T19:00:30Z | ---
license: apache-2.0
---
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|
hippocrates/emrqa_train | hippocrates | 2023-11-30T19:03:43Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-30T19:03:35Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: conversations
list:
- name: from
dtype: string
- name: value
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 102482073
num_examples: 152907
- name: valid
num_bytes: 102482073
num_examples: 152907
- name: test
num_bytes: 17403391
num_examples: 26804
download_size: 78266641
dataset_size: 222367537
---
# Dataset Card for "emrqa_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
HansBauer/Rober | HansBauer | 2023-11-30T19:06:44Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-11-30T19:06:28Z | ---
license: apache-2.0
---
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|
BangumiBase/fullmetalpanicfumoffu | BangumiBase | 2023-11-30T19:56:58Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| null | 2023-11-30T19:22:16Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Full Metal Panic Fumoffu
This is the image base of bangumi Full Metal Panic Fumoffu, we detected 22 characters, 1168 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 | 333 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 43 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 238 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 26 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 35 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 29 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 21 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 92 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 13 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 14 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 16 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 33 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 18 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 10 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 12 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 8 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 11 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 20 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 49 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 7 | [Download](19/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 20 | 19 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 121 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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|
BangumiBase/thetwelvekingdoms | BangumiBase | 2023-11-30T21:42:54Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| null | 2023-11-30T19:23:01Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of The Twelve Kingdoms
This is the image base of bangumi The Twelve Kingdoms, we detected 62 characters, 4697 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 | 35 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 731 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 72 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 20 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 88 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 399 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 254 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 163 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 466 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 80 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 82 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 72 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 31 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 35 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 42 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 53 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 22 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 153 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 107 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 93 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 70 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 111 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 60 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 14 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 25 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 70 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 32 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 121 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 15 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 24 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 17 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 16 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 18 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 55 | [Download](33/dataset.zip) |  |  |  |  |  |  |  |  |
| 34 | 16 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 23 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 44 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 11 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 15 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 23 | [Download](39/dataset.zip) |  |  |  |  |  |  |  |  |
| 40 | 9 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 22 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 149 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 134 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 27 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| 45 | 42 | [Download](45/dataset.zip) |  |  |  |  |  |  |  |  |
| 46 | 11 | [Download](46/dataset.zip) |  |  |  |  |  |  |  |  |
| 47 | 18 | [Download](47/dataset.zip) |  |  |  |  |  |  |  |  |
| 48 | 14 | [Download](48/dataset.zip) |  |  |  |  |  |  |  |  |
| 49 | 55 | [Download](49/dataset.zip) |  |  |  |  |  |  |  |  |
| 50 | 27 | [Download](50/dataset.zip) |  |  |  |  |  |  |  |  |
| 51 | 20 | [Download](51/dataset.zip) |  |  |  |  |  |  |  |  |
| 52 | 11 | [Download](52/dataset.zip) |  |  |  |  |  |  |  |  |
| 53 | 55 | [Download](53/dataset.zip) |  |  |  |  |  |  |  |  |
| 54 | 30 | [Download](54/dataset.zip) |  |  |  |  |  |  |  |  |
| 55 | 13 | [Download](55/dataset.zip) |  |  |  |  |  |  |  |  |
| 56 | 26 | [Download](56/dataset.zip) |  |  |  |  |  |  |  |  |
| 57 | 109 | [Download](57/dataset.zip) |  |  |  |  |  |  |  |  |
| 58 | 14 | [Download](58/dataset.zip) |  |  |  |  |  |  |  |  |
| 59 | 20 | [Download](59/dataset.zip) |  |  |  |  |  |  |  |  |
| 60 | 13 | [Download](60/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 100 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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|
KAUE2006/MartaRhaulinEricCartman | KAUE2006 | 2023-11-30T19:26:44Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-30T19:25:13Z | ---
license: openrail
---
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|
datasets-examples/doc-image-8 | datasets-examples | 2023-11-30T19:39:56Z | 0 | 0 | [
"size_categories:n<1K",
"region:us"
]
| null | 2023-11-30T19:35:01Z | ---
size_categories:
- n<1K
---
# [doc] image dataset 8
This dataset contains 4 jpeg files in the `{train,test}/{green,red}/`subdirectories, which specify the splits and the image classes. | [
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|
datasets-examples/doc-image-10 | datasets-examples | 2023-12-01T17:26:24Z | 0 | 0 | [
"size_categories:n<1K",
"region:us"
]
| null | 2023-11-30T19:45:43Z | ---
size_categories:
- n<1K
---
# [doc] image dataset 10
This dataset contains a parquet file that contains an image column. | [
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|
diszep/rose | diszep | 2023-11-30T20:00:54Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-30T20:00:13Z | ---
license: openrail
---
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|
Gsabriel/ShalevVoice17 | Gsabriel | 2023-11-30T20:13:22Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-11-30T20:12:49Z | ---
license: apache-2.0
---
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|
SantiagoPG/customer_service_chatbot | SantiagoPG | 2023-12-02T15:07:21Z | 0 | 0 | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:en",
"region:us"
]
| [
"text-generation"
]
| 2023-11-30T20:12:53Z | ---
task_categories:
- text-generation
language:
- en
pretty_name: Customer Service Interacions
size_categories:
- 1K<n<10K
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|
BangumiBase/blacklagoon | BangumiBase | 2023-11-30T22:30:28Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| null | 2023-11-30T20:53:41Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Black Lagoon
This is the image base of bangumi Black Lagoon, we detected 24 characters, 2637 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 | 339 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 101 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 76 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 396 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 82 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 232 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 53 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 68 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 31 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 39 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 36 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 68 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 18 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 114 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 97 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 76 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 10 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 39 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 76 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 54 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 15 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 8 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 11 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 598 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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|
BangumiBase/azumangadaioh | BangumiBase | 2023-11-30T22:10:00Z | 0 | 0 | [
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
]
| null | 2023-11-30T20:59:44Z | ---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Azumanga Daioh
This is the image base of bangumi Azumanga Daioh, we detected 14 characters, 3047 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 | 76 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 83 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 607 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 311 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 233 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 502 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 478 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 151 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 31 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 500 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 20 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 7 | [Download](11/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 12 | 10 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 38 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
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|
evkes/required-deloitte-jobs | evkes | 2023-11-30T21:09:40Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-30T21:09:38Z | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 539333
num_examples: 327
download_size: 205214
dataset_size: 539333
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "required-deloitte-jobs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Gabriel1322/careca | Gabriel1322 | 2023-11-30T21:11:57Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-11-30T21:10:44Z | ---
license: openrail
---
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|
charchits7/test-images | charchits7 | 2023-11-30T22:18:51Z | 0 | 0 | [
"license:artistic-2.0",
"region:us"
]
| null | 2023-11-30T21:28:52Z | ---
license: artistic-2.0
---
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|
athirdpath/alpaca_dpo_pairs | athirdpath | 2023-12-01T03:18:22Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-11-30T21:32:24Z | ---
license: apache-2.0
---
<p align="center"><img src="https://i.ibb.co/3z86DFV/cygnuscat.png"/><font size="6"> <b>Adjusted to Alpaca from <a href="https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs">mlabonne/chatml_dpo_pairs</a></b> </font></p>
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|
moritzrippe/231130_submit_button | moritzrippe | 2023-11-30T22:31:41Z | 0 | 0 | [
"license:other",
"region:us"
]
| null | 2023-11-30T22:31:15Z | ---
license: other
license_name: moritzrippe
license_link: LICENSE
---
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|
Solshine/Portuguese-English-Vocab-PartiallyTransformed | Solshine | 2023-12-02T19:18:22Z | 0 | 1 | [
"license:mit",
"region:us"
]
| null | 2023-11-30T23:07:03Z | ---
license: mit
---
Notes on use:
Portuguese and English Translations of readme are available here.
Partially cleaned and reorganized. Minimal secondhand verification after generation through Google Bard on November 28th 2023. Mistakes are minimal but present, such as tagging of words in supplemental information sometimes using the whole word (ie Noun) and sometimes only a letter or abreviation (ie N) for the same part of speech.
Reccomended for finetuning of smaller models only, such as 12, 7, or 3 B models to create a basic basis for democratizing access to portuguese langauge users, or for inclusion into a much larger training data collections when used for training or finetuning larger models.
The dataset consists of over 2,000 Portuguese-English translation pairs consisting of either a translation pair of words, sentences, or words with definitions, along with (usually) autogenerated G3 Annotations, B- Tags , SRL Annotations, Dependency Parsing Annotations , POS Tagging Annotations, generated using Google Bard on November 28th 2023.
----
2087 observations
*English Readme:*
**Bridging Language Barriers and Empowering Marginalized Communities through Multilingual Parallel Corpora: The English-Portuguese MPC**
In the realm of natural language processing (NLP), the availability of high-quality language data is crucial for developing effective NLP models. However, marginalized indigenous communities, particularly those in Brazil and the Amazon Rainforest, often lack access to NLP tools and resources, hindering their ability to fully engage in the digital age.
To address this disparity, we propose the creation of an English-Portuguese Multilingual Parallel Corpus (MPC), a collection of carefully curated parallel text pairs in English and Portuguese. This resource is specifically designed to democratize access to NLP and promote knowledge exchange between these two languages, fostering cross-cultural communication and empowering marginalized communities.
**Democratizing Access and Fostering Cross-Pollination**
This English-Portuguese MPC, available uncleaned at Solshine/Portuguese-English-Translation-and-NLP-trainingdata-UNCLEANED , serves as a bridge between these two languages, enabling the development of more robust machine translation systems and enriching the availability of Portuguese language resources. For marginalized indigenous communities, this resource holds the potential to:
* **Preserve and revitalize indigenous languages:** By providing a benchmark for Portuguese-English translation, the MPC can facilitate the development of tools that can translate indigenous languages into Portuguese, aiding in language preservation and revitalization efforts.
* **Empower indigenous communities in the digital sphere:** Access to NLP tools trained on the MPC can enable indigenous communities to engage with online resources, participate in social media, and access information in their native language or Portuguese.
* **Promote cross-cultural understanding:** By fostering better communication between indigenous communities and the wider world, the MPC can help bridge cultural divides and promote mutual understanding.
**Addressing the Needs of Marginalized Indigenous Communities**
The English-Portuguese MPC is particularly relevant to marginalized indigenous communities in Brazil and the Amazon Rainforest, where Portuguese serves as the lingua franca and numerous indigenous languages are spoken. By providing a comprehensive resource for Portuguese-English translation, the MPC can empower these communities to:
* **Participate in education and research:** Indigenous students and researchers can access academic materials and engage in scientific research using NLP tools trained on the MPC.
* **Promote cultural heritage and storytelling:** Indigenous communities can utilize the MPC to translate their cultural narratives, folklore, and traditional knowledge into Portuguese, preserving and sharing their heritage with a wider audience.
* **Advocate for their rights and interests:** Indigenous communities can use the MPC to communicate effectively with government officials, NGOs, and the international community, advocating for their rights and interests.
**Sourcing the MPC through Conversation with Bard**
The English-Portuguese MPC was sourced through a combination of conversation with Bard and other sources. Bard's ability to understand and generate human language made it an invaluable tool for generating and refining the dataset, ensuring that the translations were accurate and natural-sounding.
Once the data was collected, it was carefully preprocessed and aligned to ensure that the sentences in English and Portuguese were truly parallel. This was a critical step, as it ensured that the MPC could be used to train NLP models that could accurately translate between the two languages.
**Conclusion**
The English-Portuguese MPC here is a valuable resource for NLP researchers and developers. It can be used to train machine translation systems, develop text summarization tools, and create sentiment analysis models. The MPC can also be used to study the relationship between English and Portuguese, and to develop new NLP algorithms. Moreover, the MPC empowers marginalized communities to preserve their languages, engage in the digital sphere, and advocate for their rights and interests.
----
*Portuguese Readme:*
**Superando barreiras linguísticas e empoderando comunidades marginalizadas por meio de corpora paralelos multilíngues: O MPC inglês-português**
No campo do processamento de linguagem natural (PLN), a disponibilidade de dados linguísticos de alta qualidade é crucial para o desenvolvimento de modelos de PLN eficazes. No entanto, comunidades indígenas marginalizadas, particularmente aquelas no Brasil e na Amazônia, muitas vezes não têm acesso a ferramentas e recursos de PLN, dificultando sua capacidade de se envolver plenamente na era digital.
Para lidar com essa disparidade, propomos a criação de um Corpus Paralelo Multilíngue (MPC) inglês-português, uma coleção de pares de texto paralelos cuidadosamente selecionados em inglês e português. Este recurso é projetado especificamente para democratizar o acesso ao PLN e promover o intercâmbio de conhecimento entre esses dois idiomas, fomentando a comunicação intercultural e empoderando comunidades marginalizadas.
**Democratizando o acesso e fomentando a polinização cruzada**
O MPC inglês-português, Solshine/Portuguese-English-Translation-and-NLP-trainingdata-UNCLEANED , serve como uma ponte entre esses dois idiomas, possibilitando o desenvolvimento de sistemas de tradução automática mais robustos e enriquecendo a disponibilidade de recursos em português. Para comunidades indígenas marginalizadas, este recurso tem o potencial de:
* **Preservar e revitalizar línguas indígenas:** Ao fornecer um benchmark para a tradução português-inglês, o MPC pode facilitar o desenvolvimento de ferramentas que podem traduzir línguas indígenas para o português, auxiliando nos esforços de preservação e revitalização da língua.
* **Empoderar comunidades indígenas na esfera digital:** O acesso a ferramentas de PLN treinadas no MPC pode capacitar comunidades indígenas a se envolver com recursos online, participar de mídias sociais e acessar informações em sua língua nativa ou português.
* **Promover a compreensão intercultural:** Ao promover uma melhor comunicação entre as comunidades indígenas e o mundo em geral, o MPC pode ajudar a superar divisões culturais e promover a compreensão mútua.
**Abordando as necessidades de comunidades indígenas marginalizadas**
O MPC inglês-português é particularmente relevante para comunidades indígenas marginalizadas no Brasil e na Amazônia, onde o português serve como língua franca e inúmeras línguas indígenas são faladas. Ao fornecer um recurso abrangente para tradução português-inglês, o MPC pode capacitar essas comunidades a:
* **Participar de educação e pesquisa:** Estudantes e pesquisadores indígenas podem acessar materiais acadêmicos e se envolver em pesquisas científicas usando ferramentas de PLN treinadas no MPC.
* **Promover o patrimônio cultural e a narrativa:** Comunidades indígenas podem utilizar o MPC para traduzir suas narrativas culturais, folclore e conhecimento tradicional para o português, preservando e compartilhando seu patrimônio com um público mais amplo.
* **Defender seus direitos e interesses:** As comunidades indígenas podem usar o MPC para se comunicar efetivamente com autoridades governamentais, ONGs e a comunidade internacional, defendendo seus direitos e interesses.
**Obtendo o MPC por meio de conversas com Bard**
O MPC inglês-português foi obtido por meio de uma combinação de conversas com Bard e outras fontes. A capacidade do Bard de entender e gerar linguagem humana o tornou uma ferramenta inestimável para gerar e refinar o conjunto de dados, garantindo que as traduções fossem precisas e naturais.
Uma vez que os dados foram coletados, eles foram cuidadosamente pré-processados e alinhados para garantir que as frases em inglês e português fossem verdadeiramente paralelas. Esta foi uma etapa crítica, pois garantiu que o MPC pudesse ser usado para treinar modelos de PLN que pudessem traduzir com precisão entre os dois idiomas.
**Conclusão**
O MPC inglês-português é um recurso valioso para pesquisadores e desenvolvedores de PLN. Ele pode ser usado para treinar sistemas de tradução automática, desenvolver ferramentas de resumo de texto e criar modelos de análise de sentimento. O MPC também pode ser usado para estudar a relação entre inglês e português e desenvolver novos algoritmos de PLN. Além disso, o MPC capacita comunidades marginalizadas a preservar seus idiomas, se envolver na esfera digital e defender seus direitos e interesses.
----
**Notes about the data (currently being updated):**
Inspired by the (November 2023) developments of Ocra2 (albiet much less sophsticated) and the ability for large LLMs to now produce training datasets for smaller (ie 7B or 3B) models to efficienctly learn and distill the fundamentals of the knowledge into themselves.
This is a great Portuguese language dataset, connecting Portuguese into the most widely used and trained language (English), thus democratizing access. You are encouraged to use this in your training to enrich the model's Portuguese.
Most of the table include: G3 Annotations, B- Tags , SRL Annotations, Dependency Parsing Annotations , POS Tagging Annotations
Please note for a large portion of the data: The NER annotations (G3) indicate general entities, while the B- tags indicate specific types of entities (e.g., B-Location, B-Time). The SRL annotations indicate the semantic roles of the constituents in the sentence (e.g., B-Theme, B-Agent, B-Patient, B-Goal). The dependency parsing annotations indicate the grammatical relationships between the words in the sentence. The POS tagging annotations indicate the part-of-speech (e.g., N for noun, V for verb, A for adjective) of each word in the sentence.
The main source of the data is generated through structured questions to Google Bard in the final week of November 2023, and many of these generating queries can be found as title names of individual small tables. Much of that portion was structured explicitly by having the prompt instructions including the previous paragraph's data structure explanation. This resulted in potentially much more useful data about the sentences or words from an NLP perspective, albiet with more inconsistency and minor errors, and even information entered occasionally into the wrong field, in those NLP related fields mentioned above.
This data is mostly uncleaned and should be used with the understanding that it was largely uncleaned and gathered from various sources. The data in the tables in this dataset has inherently been filtered by the guardrails present in Bard and through careful observation of the data (rejecting obviously errorous generations) as it was being generated by Bard and uploaded to, minimally processed the dataset. This is a disclaimer of any possible error or omission, and the dataset should be useful with this understanding.
Some of the tables or observations are missing entries for some of those fields (with the exception of a Portuguese term or sentence, which is present in every observation) especially POS Tagging Annotations, and Dependency Parsing Annotations, usually due to reaching Bard's data limit in it's public browser version (as of November 28th 2023.)
Strong focus towards moral compass and ethical real-world problems solving, as well as Indigenous Knowledge Systems, Climate Change, Science, STEM, intellectual property from a Copyleft perspective, some simple aspects of law, Indigenous Archeology, Educational Philosophy, and basic Vocabulary.
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|
joey234/conandoyle_cue_scope | joey234 | 2023-11-30T23:37:44Z | 0 | 0 | [
"region:us"
]
| null | 2023-11-30T23:37:42Z | ---
dataset_info:
features:
- name: text
dtype: string
- name: cue
sequence: int64
- name: scope
sequence: int64
splits:
- name: train
num_bytes: 105721
num_examples: 235
download_size: 21262
dataset_size: 105721
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "conandoyle_cue_scope"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
eyewashere/testing | eyewashere | 2023-12-01T00:04:53Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-01T00:04:53Z | ---
license: apache-2.0
---
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|
TIGER-Lab/M-BEIR | TIGER-Lab | 2023-12-01T00:08:47Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-01T00:08:47Z | ---
license: mit
---
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|
automated-research-group/llama2_7b_chat-hellaswag_0_label | automated-research-group | 2023-12-01T01:17:24Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T01:17:22Z | ---
dataset_info:
features:
- name: id
dtype: string
- name: request
dtype: string
- name: response
dtype: string
- name: input_perplexity
dtype: float64
- name: input_likelihood
dtype: float64
- name: output_perplexity
dtype: float64
- name: output_likelihood
dtype: float64
splits:
- name: validation
num_bytes: 11385236
num_examples: 10042
download_size: 5370207
dataset_size: 11385236
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
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|
Seongill/NQ_train_wo_duplicates | Seongill | 2023-12-01T01:33:45Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T01:33:20Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 445494312
num_examples: 67571
download_size: 258779161
dataset_size: 445494312
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
anirudhajith/test | anirudhajith | 2023-12-01T02:05:53Z | 0 | 0 | [
"license:unknown",
"region:us"
]
| null | 2023-12-01T02:05:52Z | ---
license: unknown
---
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|
nlewins/dictionary_examples_with_audio_old | nlewins | 2023-12-01T10:53:01Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T02:20:43Z | ---
dataset_info:
features:
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: en
dtype: string
- name: audio_transcription
dtype:
audio:
sampling_rate: 16000
- name: ceb
dtype: string
splits:
- name: train
num_bytes: 11561589955.460125
num_examples: 26076
- name: test
num_bytes: 1460966494.4832342
num_examples: 3260
- name: valid
num_bytes: 1474900340.843234
num_examples: 3260
download_size: 14536707412
dataset_size: 14497456790.786594
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
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|
vinny9812/fabiltaruel | vinny9812 | 2023-12-01T02:26:51Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T02:23:50Z | ---
license: openrail
---
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|
Seongill/NQ_test | Seongill | 2023-12-01T02:38:26Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T02:38:21Z | ---
dataset_info:
features:
- name: question
dtype: string
- name: answers
sequence: string
- name: ctxs
list:
- name: hasanswer
dtype: bool
- name: id
dtype: string
- name: score
dtype: float64
- name: text
dtype: string
- name: title
dtype: string
splits:
- name: train
num_bytes: 23885126
num_examples: 3610
download_size: 13827382
dataset_size: 23885126
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Mitsuki-Sakamoto/reward-model-deberta-v3-large-v2-alpaca_farm-alpaca_gpt4_preference-preference_test | Mitsuki-Sakamoto | 2023-12-01T03:27:40Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T02:56:01Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output_1
dtype: string
- name: output_2
dtype: string
- name: preference
dtype: int64
- name: old_preference
dtype: int64
splits:
- name: preference
num_bytes: 113541
num_examples: 194
download_size: 76166
dataset_size: 113541
configs:
- config_name: default
data_files:
- split: preference
path: data/preference-*
---
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|
aisikoduro/fashion_image_caption-100-v2 | aisikoduro | 2023-12-01T03:11:40Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T03:11:29Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 22820471.0
num_examples: 100
download_size: 22820373
dataset_size: 22820471.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "fashion_image_caption-100-v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Mitsuki-Sakamoto/alpaca_farm-alpaca_gpt4_preference-preference_test | Mitsuki-Sakamoto | 2023-12-01T03:40:10Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T03:40:08Z | ---
dataset_info:
config_name: reward-model-deberta-v3-large-v2
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output_1
dtype: string
- name: output_2
dtype: string
- name: preference
dtype: int64
- name: old_preference
dtype: int64
splits:
- name: preference
num_bytes: 113541
num_examples: 194
download_size: 76166
dataset_size: 113541
configs:
- config_name: reward-model-deberta-v3-large-v2
data_files:
- split: preference
path: reward-model-deberta-v3-large-v2/preference-*
---
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|
casey-martin/multilingual-mathematical-autoformalization | casey-martin | 2023-12-02T17:41:02Z | 0 | 0 | [
"task_categories:translation",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"mathematics",
"autoformalization",
"lean",
"isabelle",
"arxiv:2311.03755",
"region:us"
]
| [
"translation",
"text-generation"
]
| 2023-12-01T03:57:06Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/*_train.jsonl
- split: val
path: data/*_val.jsonl
- split: test
path: data/*_test.jsonl
- config_name: lean
data_files:
- split: train
path: data/lean_train.jsonl
- split: val
path: data/lean_val.jsonl
- split: test
path: data/lean_test.jsonl
- config_name: isabelle
data_files:
- split: train
path: data/isabelle_train.jsonl
- split: val
path: data/isabelle_val.jsonl
license: apache-2.0
task_categories:
- translation
- text-generation
language:
- en
tags:
- mathematics
- autoformalization
- lean
- isabelle
size_categories:
- 100K<n<1M
---
# Multilingual Mathematical Autoformalization
["**Paper**"](https://arxiv.org/abs/2311.03755)
This repository contains parallel mathematical statements:
1. Input: An informal proof in natural language
2. Output: The corresponding formalization in either Lean or Isabelle
This dataset can be used to train models how to formalize mathematical statements into verifiable proofs, a form of machine translation.
## Abstract
Autoformalization is the task of translating natural language materials into machine-verifiable formalisations.
Progress in autoformalization research is hindered by the lack of a sizeable dataset consisting of informal-formal pairs expressing the same essence.
Existing methods tend to circumvent this challenge by manually curating small corpora or using few-shot learning with large language models.
But these methods suffer from data scarcity and formal language acquisition difficulty. In this work, we create MMA,
a large, flexible, multilingual, and multi-domain dataset of informal-formal pairs, by using a language model to translate in the reverse direction,
that is, from formal mathematical statements into corresponding informal ones. Experiments show that language models fine-tuned on MMA produce 16−18%
of statements acceptable with minimal corrections on the miniF2F and ProofNet benchmarks, up from 0% with the base model. We demonstrate that fine-tuning
on multilingual formal data results in more capable autoformalization models even when deployed on monolingual tasks.
### Example:
```
Input:
- Statement in natural language: If "r" is a finite set and "i" is an element of "r", then the result of the function "a" applied to "i" is an element of the multiset range of "a" over "r". Translate the statement in natural language to Isabelle:
Output:
- lemma mset_ran_mem[simp, intro]: "finite r \<Longrightarrow> i\<in>r \<Longrightarrow> a i \<in># mset_ran a r"
```
## External Links:
- [**Official GitHub Repository**](https://github.com/albertqjiang/mma)
- [**Papers With Code**](https://paperswithcode.com/paper/multilingual-mathematical-autoformalization)
- [**Arxiv**](https://arxiv.org/abs/2311.03755)
## Citation
```
@misc{jiang2023multilingual,
title={Multilingual Mathematical Autoformalization},
author={Albert Q. Jiang and Wenda Li and Mateja Jamnik},
year={2023},
eprint={2311.03755},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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|
rajivkale/ImageDataset_Webhook_Exercise | rajivkale | 2023-12-04T06:05:51Z | 0 | 0 | [
"language:en",
"region:us"
]
| null | 2023-12-01T04:00:14Z | ---
language:
- en
pretty_name: prettyimages
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|
KenDoStudio/Tender-Treats_Bob_Velseb | KenDoStudio | 2023-12-01T04:03:06Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-01T04:02:48Z | ---
license: mit
---
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|
KenDoStudio/ITZY_Ryujin | KenDoStudio | 2023-12-04T04:43:26Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-01T04:20:10Z | ---
license: mit
---
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|
KenDoStudio/Cassidy-Golden_Freddy | KenDoStudio | 2023-12-01T04:34:04Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-01T04:33:40Z | ---
license: mit
---
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|
biadrivex/gojo | biadrivex | 2023-12-01T05:01:40Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T04:59:35Z | ---
license: openrail
---
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|
cheafdevo56/HighlyInfluentialTriplets | cheafdevo56 | 2023-12-01T05:30:36Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-01T05:07:38Z | ---
license: apache-2.0
dataset_info:
features:
- name: query
struct:
- name: abstract
dtype: string
- name: corpus_id
dtype: int64
- name: title
dtype: string
- name: pos
struct:
- name: abstract
dtype: string
- name: corpus_id
dtype: int64
- name: title
dtype: string
- name: neg
struct:
- name: abstract
dtype: string
- name: corpus_id
dtype: int64
- name: score
dtype: int64
- name: title
dtype: string
splits:
- name: train
num_bytes: 73286160.22355364
num_examples: 19227
- name: validation
num_bytes: 8145447.776446358
num_examples: 2137
download_size: 48594525
dataset_size: 81431608.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
---
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|
emon-j/pseudo_labelled_cv | emon-j | 2023-12-01T06:07:28Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T05:31:45Z | ---
dataset_info:
config_name: ja
features:
- name: client_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 16000
- name: sentence
dtype: string
- name: up_votes
dtype: int64
- name: down_votes
dtype: int64
- name: age
dtype: string
- name: gender
dtype: string
- name: accent
dtype: string
- name: locale
dtype: string
- name: segment
dtype: string
- name: variant
dtype: string
- name: whisper_transcript
dtype: string
splits:
- name: train
num_bytes: 222278949.27
num_examples: 7071
- name: validation
num_bytes: 139475413.25
num_examples: 4961
download_size: 355893739
dataset_size: 361754362.52
configs:
- config_name: ja
data_files:
- split: train
path: ja/train-*
- split: validation
path: ja/validation-*
---
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|
Nexusflow/VirusTotalMultiple | Nexusflow | 2023-12-01T06:24:16Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T06:24:16Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: fncall
sequence: string
- name: generated_question
dtype: string
splits:
- name: train
num_bytes: 25028
num_examples: 70
download_size: 12622
dataset_size: 25028
---
# Dataset Card for "vt_multiapi_v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
venkat-srinivasan-nexusflow/vt_multiapi_v0 | venkat-srinivasan-nexusflow | 2023-12-01T06:24:35Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T06:24:34Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: fncall
sequence: string
- name: generated_question
dtype: string
splits:
- name: train
num_bytes: 25028
num_examples: 70
download_size: 12622
dataset_size: 25028
---
# Dataset Card for "vt_multiapi_v0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
venkat-srinivasan-nexusflow/new_vt_apis | venkat-srinivasan-nexusflow | 2023-12-01T06:25:06Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T06:25:05Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: args_dicts
list:
- name: default
dtype: string
- name: description
dtype: string
- name: name
dtype: string
- name: required
dtype: bool
- name: type
dtype: string
- name: api_type
dtype: string
- name: description
dtype: string
- name: name
dtype: string
- name: dataset
dtype: string
splits:
- name: train
num_bytes: 20764
num_examples: 29
download_size: 14860
dataset_size: 20764
---
# Dataset Card for "new_vt_apis"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
imbetter/bob | imbetter | 2023-12-01T06:38:25Z | 0 | 0 | [
"license:unknown",
"region:us"
]
| null | 2023-12-01T06:38:25Z | ---
license: unknown
---
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|
Deojoandco/capstone_fromgpt_without_gold_v11_all | Deojoandco | 2023-12-01T06:50:42Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T06:49:08Z | ---
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
- name: precision
dtype: float64
- name: recall
dtype: float64
- name: f1
dtype: float64
- name: accuracy
dtype: float64
splits:
- name: validation
num_bytes: 23400
num_examples: 12
- name: test
num_bytes: 14700
num_examples: 12
download_size: 45072
dataset_size: 38100
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
# Dataset Card for "capstone_fromgpt_without_gold_v11_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
casey-martin/qald_9_plus | casey-martin | 2023-12-01T07:36:24Z | 0 | 0 | [
"task_categories:table-question-answering",
"task_categories:text2text-generation",
"language:ba",
"language:be",
"language:de",
"language:en",
"language:fr",
"language:hy",
"language:lt",
"language:ru",
"language:uk",
"license:cc-by-4.0",
"semantic web",
"sparql",
"wikidata",
"dbpedia",
"arxiv:2202.00120",
"region:us"
]
| [
"table-question-answering",
"text2text-generation"
]
| 2023-12-01T07:28:09Z | ---
configs:
- config_name: default
data_files:
- split: train
path: data/*_train.parquet
- config_name: ba
data_files:
- split: train
path: data/*_ba_train.parquet
- config_name: be
data_files:
- split: train
path: data/*_be_train.parquet
- config_name: de
data_files:
- split: train
path: data/*_de_train.parquet
- config_name: en
data_files:
- split: train
path: data/*_en_train.parquet
- config_name: fr
data_files:
- split: train
path: data/*_fr_train.parquet
- config_name: hy
data_files:
- split: train
path: data/*_hy_train.parquet
- config_name: lt
data_files:
- split: train
path: data/*_lt_train.parquet
- config_name: ru
data_files:
- split: train
path: data/*_ru_train.parquet
- config_name: uk
data_files:
- split: train
path: data/*_uk_train.parquet
license: cc-by-4.0
task_categories:
- table-question-answering
- text2text-generation
language:
- ba
- be
- de
- en
- fr
- hy
- lt
- ru
- uk
tags:
- semantic web
- sparql
- wikidata
- dbpedia
pretty_name: QALD 9+
---
# QALD-9-plus Dataset Description
QALD-9-plus is the dataset for Knowledge Graph Question Answering (KGQA) based on well-known [QALD-9](https://github.com/ag-sc/QALD/tree/master/9/data).
QALD-9-plus enables to train and test KGQA systems over DBpedia and Wikidata using questions in 9 different languages: English, German, Russian, French, Armenian, Belarusian, Lithuanian, Bashkir, and Ukrainian.
Some of the questions have several alternative writings in particular languages which enables to evaluate the robustness of KGQA systems and train paraphrasing models.
As the questions' translations were provided by native speakers, they are considered as gold standard, therefore, machine translation tools can be trained and evaluated on the dataset.
# Dataset Statistics
| | en | de | fr | ru | uk | lt | be | ba | hy | # questions DBpedia | # questions Wikidata |
|-------|:---:|:---:|:--:|:----:|:---:|:---:|:---:|:---:|:--:|:-----------:|:-----------:|
| Train | 408 | 543 | 260 | 1203 | 447 | 468 | 441 | 284 | 80 | 408 | 371 |
| Test | 150 | 176 | 26 | 348 | 176 | 186 | 155 | 117 | 20 | 150 | 136 |
Given the numbers, it is obvious that some of the languages are covered more than once i.e., there is more than one translation for a particular question.
For example, there are 1203 Russian translations available while only 408 unique questions exist in the training subset (i.e., 2.9 Russian translations per one question).
The availability of such parallel corpora enables the researchers, developers and other dataset users to address the paraphrasing task.
# Evaluation
We used [GERBIL](https://github.com/dice-group/gerbil/) system for the evaluation of the dataset. The detailed information for the experiments is available at the individual link (click the value in the cells).
## Wikidata
### QAnswer
| | en | de | ru | fr |
|-----|----|----|----|----|
|Test |[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202110010001)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112180000)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112180001)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112180002)|
|Train|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202110010007)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112180006)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112180007)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112180008)|
### DeepPavlov
| | en | ru |
|-----|----|----|
|Test |[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202110080010)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112180003)|
|Train|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202110090001)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112180009)|
### Platypus
| | en | fr |
|-----|----|----|
|Test |[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202110110004)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112180004)|
|Train|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202110110006)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112180010)|
## DBpedia
### QAnswer
| | en | de | ru | fr |
|-----|----|----|----|----|
|Test |[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202110120004)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190000)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190001)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190002)|
|Train|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202110130002)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190003)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190004)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190005)|
## Wikidata Original Translations
### QAnswer
| | de | ru | fr |
|-----|----|----|----|
|Test |[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190006)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190007)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190008)|
|Train|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190009)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190010)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190011)|
### DeepPavlov
| | ru |
|-----|----|
|Test |[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190012)|
|Train|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190014)|
### Platypus
| | fr |
|-----|----|
|Test |[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190013)|
|Train|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190015)|
## DBpedia Original Translations
### QAnswer
| | de | ru | fr |
|-----|----|----|----|
|Test |[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190016)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190017)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190018)|
|Train|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190019)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190020)|[link](http://gerbil-qa.aksw.org/gerbil/experiment?id=202112190021)|
# Cite
```bibtex
@inproceedings{perevalov2022qald9plus,
author={Perevalov, Aleksandr and Diefenbach, Dennis and Usbeck, Ricardo and Both, Andreas},
booktitle={2022 IEEE 16th International Conference on Semantic Computing (ICSC)},
title={QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers},
year={2022},
pages={229-234},
doi={10.1109/ICSC52841.2022.00045}
}
```
# Useful Links
data/* ArXiv [link](https://arxiv.org/abs/2202.00120)
data/* Papers with Code: [Paper](https://paperswithcode.com/paper/qald-9-plus-a-multilingual-dataset-for-1), [Dataset](https://paperswithcode.com/dataset/qald-9-plus)
data/* Video presentation on YouTube: https://youtu.be/W1w7CJTV48c
data/* Presentation [slides](https://drive.google.com/file/d/1cDphq4DeSiZr-WBvdwu34rcxQ0aP4q95/view?usp=sharing)
data/* Google Colab [notebook](https://colab.research.google.com/drive/1eWsQoIaeT9_vii1v3PVU04Rms4EoyLAh?usp=sharing)
# Licence [![CC BY 4.0][cc-by-shield]][cc-by]
This work is licensed under a
[Creative Commons Attribution 4.0 International License][cc-by].
[![CC BY 4.0][cc-by-image]][cc-by]
[cc-by]: http://creativecommons.org/licenses/by/4.0/
[cc-by-image]: https://i.creativecommons.org/l/by/4.0/88x31.png
[cc-by-shield]: https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg
# Dataset Metadata
The following table is necessary for this dataset to be indexed by search
engines such as <a href=https://g.co/datasetsearch>Google Dataset Search</a>.
<div itemscope itemtype=http://schema.org/Dataset>
<table>
<tr>
<th>property</th>
<th>value</th>
</tr>
<tr>
<td>name</td>
<td><code itemprop=name>QALD-9-plus: A Multilingual Dataset for Question Answering over DBpedia and Wikidata Translated by Native Speakers</code></td>
</tr>
<tr>
<td>alternateName</td>
<td><code itemprop=alternateName>QALD-9-plus</code></td>
</tr>
<tr>
<td>url</td>
<td><code itemprop=url>https://github.com/Perevalov/qald_9_plus/tree/main/data</code></td>
</tr>
<tr>
<td>description</td>
<td><code itemprop=description>QALD-9-Plus is the dataset for Knowledge Graph Question Answering (KGQA) based on well-known QALD-9.<br/>
QALD-9-Plus enables to train and test KGQA systems over DBpedia and Wikidata using questions in 9 different languages: English, German, Russian, French, Armenian, Belarusian, Lithuanian, Bashkir, and Ukrainian.<br/>
Some of the questions have several alternative writings in particular languages which enables to evaluate the robustness of KGQA systems and train paraphrasing models.<br/>
As the questions' translations were provided by native speakers, they are considered as gold standard, therefore, machine translation tools can be trained and evaluated on the dataset.</code></td>
</tr>
<tr>
<td>license</td>
<td>
<div itemscope itemtype=http://schema.org/CreativeWork itemprop=license>
<table>
<tr>
<th>property</th>
<th>value</th>
</tr>
<tr>
<td>name</td>
<td><code itemprop=name>CC-BY-4.0</code></td>
</tr>
<tr>
<td>url</td>
<td><code itemprop=url>https://creativecommons.org/licenses/by/4.0/</code></td>
</tr>
</table>
</div>
</td>
</tr>
<tr>
<td>citation</td>
<td><code itemprop=citation>Perevalov, Aleksandr, Diefenbach, Diefenback, Usbeck, Ricardo, Both, Andreas: QALD-9-plus: A multilingual dataset for question answering over DBpedia and Wikidata translated by native speakers. In: 2022 IEEE 16th International Conference on Semantic Computing (ICSC). IEEE (2022)</code></td>
</tr>
</table>
</div>
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|
kunishou/jp-effective-instructions | kunishou | 2023-12-01T07:52:41Z | 0 | 0 | [
"language:ja",
"license:other",
"region:us"
]
| null | 2023-12-01T07:36:25Z | ---
license: other
license_name: mixed-liscence
license_link: LICENSE
language:
- ja
---
oasst1-89k-ja , databricks-dolly-15k-ja , hh-rlhf-49k-ja の中から JGLUE（ JcommonsenseQA , MARC-ja , JSQuAD ）の観点で高品質なデータセットに絞り込んだデータセットです。
品質スコアリングの詳細はこちらを参考にして下さい。
https://qiita.com/kunishou/items/efd9f68d6aa86d56dc73
データセットの構成とライセンスは以下の通りになります。
|dataset|num records|liscence|
|:----|:----|:----|
|[oasst1-89k-ja](https://huggingface.co/datasets/kunishou/oasst1-89k-ja)|4,204|Apache 2.0|
|[databricks-dolly-15k-ja](https://huggingface.co/datasets/kunishou/databricks-dolly-15k-ja)|282|CC-BY-SA-3.0|
|[hh-rlhf-49k-ja](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja)|989|[MIT](https://github.com/anthropics/hh-rlhf/blob/master/LICENSE)|
※ ライセンスはオリジナルのデータセットのライセンスに従います。 | [
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|
radhakrishnanrajan/guanaco-llama2-1k-rk | radhakrishnanrajan | 2023-12-01T09:02:45Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T09:02:40Z | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 1654448
num_examples: 1000
download_size: 966693
dataset_size: 1654448
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
akash140500/failure12 | akash140500 | 2023-12-01T09:17:58Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-01T09:09:28Z | ---
license: apache-2.0
---
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|
Najung/cora | Najung | 2023-12-01T09:20:00Z | 0 | 0 | [
"license:unknown",
"region:us"
]
| null | 2023-12-01T09:19:59Z | ---
license: unknown
---
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|
vibha-mah/Bat-Classification | vibha-mah | 2023-12-01T09:33:09Z | 0 | 0 | [
"task_categories:audio-classification",
"language:en",
"biology",
"medical",
"science",
"bats",
"region:us"
]
| [
"audio-classification"
]
| 2023-12-01T09:30:27Z | ---
task_categories:
- audio-classification
language:
- en
tags:
- biology
- medical
- science
- bats
pretty_name: Bat Classification in Europe
--- | [
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|
deepinv/images | deepinv | 2023-12-01T14:11:14Z | 0 | 0 | [
"license:bsd-3-clause",
"region:us"
]
| null | 2023-12-01T10:50:17Z | ---
license: bsd-3-clause
---
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|
deepinv/degradations | deepinv | 2023-12-01T10:53:54Z | 0 | 0 | [
"license:bsd-3-clause",
"region:us"
]
| null | 2023-12-01T10:53:31Z | ---
license: bsd-3-clause
---
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|
satyambarnwal/balcony2 | satyambarnwal | 2023-12-01T11:26:50Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T11:26:27Z | ---
dataset_info:
features:
- name: image
dtype: image
splits:
- name: train
num_bytes: 29121185.0
num_examples: 67
download_size: 29124021
dataset_size: 29121185.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
sujitthakur/mini-platypus | sujitthakur | 2023-12-01T11:34:28Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T11:34:27Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4186564
num_examples: 1000
download_size: 2245924
dataset_size: 4186564
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
kashif/nectar_dpo_pairs | kashif | 2023-12-01T12:14:22Z | 0 | 0 | [
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"RLHF",
"RLAIF",
"reward model",
"region:us"
]
| null | 2023-12-01T11:51:01Z | ---
license: cc-by-nc-4.0
language:
- en
size_categories:
- 100K<n<1M
datasets:
- berkeley-nest/Nectar
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen
dtype: string
- name: rejected
dtype: string
splits:
- name: train
num_bytes: 8651355540
num_examples: 3842034
download_size: 911865387
dataset_size: 8651355540
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
tags:
- RLHF
- RLAIF
- reward model
---
# Dataset Card for Nectar DPO Pairs
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|
Rycharllyson/M4rkim | Rycharllyson | 2023-12-01T12:04:37Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T12:04:10Z | ---
license: openrail
---
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|
Rayan2023/dataset | Rayan2023 | 2023-12-01T12:29:35Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T12:28:49Z | ---
license: openrail
---
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|
lowdewijk/debateavegan_prompts | lowdewijk | 2023-12-01T14:03:54Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-01T12:29:07Z | ---
license: apache-2.0
---
This dataset contains:
* All reddit posts (submission) and comments of the subreddit reddit.com/r/debateavegan from Jan 20 2011 till Oct 30 2022.
* A set of prompts extracted LLMs fine-tuning based on the highest scoring top-level comments to each reddit post. | [
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|
arieg/cluster06_large_150 | arieg | 2023-12-01T12:59:47Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T12:58:58Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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splits:
- name: train
num_bytes: 767619651.9
num_examples: 14850
download_size: 757346467
dataset_size: 767619651.9
configs:
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data_files:
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path: data/train-*
---
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|
Gabriel1322/tialucydataset | Gabriel1322 | 2023-12-01T13:16:19Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T13:16:02Z | ---
license: openrail
---
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|
venetis/synthfin_call_intent | venetis | 2023-12-01T13:20:21Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T13:20:20Z | ---
dataset_info:
features:
- name: intent
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 542058
num_examples: 365
download_size: 264672
dataset_size: 542058
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Starkate/alpha | Starkate | 2023-12-01T13:36:57Z | 0 | 0 | [
"license:apache-2.0",
"region:us"
]
| null | 2023-12-01T13:36:57Z | ---
license: apache-2.0
---
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|
murilor9/retardado | murilor9 | 2023-12-01T13:40:39Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T13:39:57Z | ---
license: openrail
---
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|
arieg/cluster07_large_150 | arieg | 2023-12-01T13:44:50Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T13:44:00Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
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splits:
- name: train
num_bytes: 754462250.9
num_examples: 15900
download_size: 733992059
dataset_size: 754462250.9
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
Rayan2023/datasethurt | Rayan2023 | 2023-12-01T13:53:18Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T13:52:39Z | ---
license: openrail
---
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|
arieg/cluster08_large_150 | arieg | 2023-12-01T13:59:01Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T13:58:38Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': 006778
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splits:
- name: train
num_bytes: 339273198.25
num_examples: 6750
download_size: 330547066
dataset_size: 339273198.25
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
BrookBvn/finalll | BrookBvn | 2023-12-01T14:27:26Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T14:15:22Z | ---
license: openrail
---
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|
Lam-ia/Wikipedia-Euskera-dataseta | Lam-ia | 2023-12-01T14:18:30Z | 0 | 0 | [
"task_categories:text-generation",
"size_categories:1K<n<10K",
"language:eu",
"license:apache-2.0",
"region:us"
]
| [
"text-generation"
]
| 2023-12-01T14:15:34Z | ---
license: apache-2.0
task_categories:
- text-generation
language:
- eu
pretty_name: Wikipedia Euskeraz
size_categories:
- 1K<n<10K
--- | [
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|
Lam-ia/Euskal-liburu-dataseta | Lam-ia | 2023-12-01T14:25:41Z | 0 | 0 | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:eu",
"license:apache-2.0",
"region:us"
]
| [
"text-generation"
]
| 2023-12-01T14:21:35Z | ---
license: apache-2.0
task_categories:
- text-generation
language:
- eu
pretty_name: Euskal Liburuak
size_categories:
- 10K<n<100K
---
Euskerazko liburuak osaturiko dataseta. Booktegi webgunetik aterata. | [
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|
tjorgais/mini-platypus | tjorgais | 2023-12-01T14:23:13Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T14:23:10Z | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 4186564
num_examples: 1000
download_size: 2245924
dataset_size: 4186564
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
BrookBvn/capitaooo | BrookBvn | 2023-12-01T14:39:10Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T14:37:56Z | ---
license: openrail
---
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|
Rayan2023/gabrielacapella | Rayan2023 | 2023-12-01T15:41:14Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T14:42:08Z | ---
license: openrail
---
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|
BrookBvn/finali | BrookBvn | 2023-12-01T14:46:03Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T14:45:43Z | ---
license: openrail
---
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|
Xenova/ai-tube-my-chess-bot | Xenova | 2023-12-01T14:58:26Z | 0 | 0 | [
"license:cc-by-nc-sa-4.0",
"ai-tube:Chess Bot",
"region:us"
]
| null | 2023-12-01T14:57:05Z | ---
license: cc-by-nc-sa-4.0
pretty_name: My Chess Bot
tags:
- "ai-tube:Chess Bot"
---
## Description
I am a chess expert.
## Prompt
A video channel managed by a renowed grandmaster, Mongoose Carlsun.
The videos are informative, but playful and fun.
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|
Gabriel1322/tialucy | Gabriel1322 | 2023-12-01T14:58:41Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T14:57:13Z | ---
license: openrail
---
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|
dyxohjl666/ACL_CocoScisum | dyxohjl666 | 2023-12-03T07:51:29Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T15:02:00Z | ---
configs:
- config_name: default
data_files:
- split: noctrl
path: "noctrl.csv"
- split: 2023_len50
path: "acl_results_len100_2023.csv"
--- | [
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|
Gabriel1322/rafaeldataset | Gabriel1322 | 2023-12-01T15:24:29Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T15:21:53Z | ---
license: openrail
---
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|
merve/ai-tube-dummy | merve | 2023-12-01T15:56:25Z | 0 | 0 | [
"license:apache-2.0",
"ai-tube:Dummy",
"region:us"
]
| null | 2023-12-01T15:23:57Z | ---
license: apache-2.0
pretty_name: AI Tube
tags:
- "ai-tube:Dummy"
---
## Description
In a galaxy far far away, there was a wholesome Knight and a small creature as companion.
## Prompt
A video channel managed by the famous Space Knight Djin Darin.
The videos are scenery of galaxy, futuristic knights, aliens, planets, spacecrafts.
The humor should be about how the absurd the small creature acts.
The video will be with trips about the stories of the knight and a small creature, life on different planets. | [
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|
SALT-NLP/Impressions | SALT-NLP | 2023-12-01T15:58:34Z | 0 | 0 | [
"task_categories:image-to-text",
"task_categories:visual-question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-sa-4.0",
"art",
"arxiv:2310.17887",
"region:us"
]
| [
"image-to-text",
"visual-question-answering"
]
| 2023-12-01T15:39:53Z | ---
dataset_info:
features:
- name: image
dtype: image
- name: AnnotatorId
dtype: string
- name: ImgId
dtype: string
- name: caption
dtype: string
- name: Impact
dtype: float64
- name: image_description
dtype: string
- name: image_impression
dtype: string
- name: image_aesthetic_eval
dtype: string
- name: image_url
dtype: string
- name: source
dtype: string
splits:
- name: train
num_bytes: 2366953929.024
num_examples: 1352
download_size: 2214475090
dataset_size: 2366953929.024
license: cc-by-sa-4.0
task_categories:
- image-to-text
- visual-question-answering
language:
- en
tags:
- art
pretty_name: Impressions
size_categories:
- 1K<n<10K
---
# Dataset Card for "Impressions"
## Overview
The Impressions dataset is a multimodal benchmark that consists of 4,100 unique annotations and over 1,375 image-caption pairs from the photography domain. Each annotation explores (1) the aesthetic impactfulness of a photograph, (2) image descriptions in which pragmatic inferences are welcome, (3) emotions/thoughts/beliefs that the photograph may inspire, and (4) the aesthetic elements that elicited the expressed impression.
EMNLP 2023 | [Paper](https://arxiv.org/abs/2310.17887)
## Additional Data
The Impressions dataset comes with more information that just image annotations on questions pertaining to *Pragmatic Description*, *Perception*, and *Aesthetic Evaluation*. For annotator personality and demographic metadata, as well as all *Aesthetic Impact* annotations, please see our [git repository](https://github.com/SALT-NLP/Impressions)!
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
nguyenthanhdo/caphesuada-290k | nguyenthanhdo | 2023-12-01T15:47:55Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T15:47:05Z | ---
dataset_info:
features:
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: source
dtype: string
- name: from
dtype: string
splits:
- name: train
num_bytes: 1139633907
num_examples: 291341
download_size: 513792352
dataset_size: 1139633907
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "caphesuada-290k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
Deojoandco/capstone_fromgpt_without_gold_v12_all | Deojoandco | 2023-12-01T15:49:46Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T15:47:57Z | ---
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
- name: precision
dtype: float64
- name: recall
dtype: float64
- name: f1
dtype: float64
- name: accuracy
dtype: float64
splits:
- name: validation
num_bytes: 23408
num_examples: 12
download_size: 25882
dataset_size: 23408
configs:
- config_name: default
data_files:
- split: validation
path: data/validation-*
---
# Dataset Card for "capstone_fromgpt_without_gold_v12_all"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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|
afcarvallo/rapanui_audio_transcription_bible_splitted | afcarvallo | 2023-12-01T16:05:54Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T15:59:18Z | ---
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
splits:
- name: train
num_bytes: 7946091783.213605
num_examples: 26725
- name: test
num_bytes: 883064183.1363951
num_examples: 2970
download_size: 8433974495
dataset_size: 8829155966.35
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---
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|
adxtya/llama_test | adxtya | 2023-12-01T16:09:06Z | 0 | 0 | [
"license:mit",
"region:us"
]
| null | 2023-12-01T16:08:48Z | ---
license: mit
---
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|
aiozai/AIOZ-GDANCE | aiozai | 2023-12-04T02:32:54Z | 0 | 0 | [
"language:en",
"license:other",
"region:us"
]
| null | 2023-12-01T16:37:55Z | ---
license: other
license_name: aioz-license
license_link: LICENSE
language:
- en
---
# [Music-Driven Group Choreography (CVPR 2023)](https://openaccess.thecvf.com/content/CVPR2023/papers/Le_Music-Driven_Group_Choreography_CVPR_2023_paper.pdf)
### *[Nhat Le](https://minhnhatvt.github.io/), [Thang Pham](https://phamtrongthang123.github.io/), [Tuong Do](https://scholar.google.com/citations?user=qCcSKkMAAAAJ&hl=en), [Erman Tjiputra](https://sg.linkedin.com/in/erman-tjiputra), [Quang D. Tran](https://scholar.google.com/citations?user=DbAThEgAAAAJ&hl=en), [Anh Nguyen](https://cgi.csc.liv.ac.uk/~anguyen/)*
### [[Project Page](https://aioz-ai.github.io/AIOZ-GDANCE/)] [[Paper](https://openaccess.thecvf.com/content/CVPR2023/papers/Le_Music-Driven_Group_Choreography_CVPR_2023_paper.pdf)]
*<center> We demonstrate the AIOZ-GDANCE dataset with in-the-wild videos, music audio, and 3D group dance motion. </center>*
## Abstract
> Music-driven choreography is a challenging problem with a wide variety of industrial applications. Recently, many methods have been proposed to synthesize dance motions from music for a single dancer. However, generating dance motion for a group remains an open problem. In this paper, we present GDANCE, a new large-scale dataset for music-driven group dance generation. Unlike existing datasets that only support single dance, our new dataset contains group dance videos, hence supporting the study of group choreography. We propose a semi-autonomous labeling method with humans in the loop to obtain the 3D ground truth for our dataset. The proposed dataset consists of 16.7 hours of paired music and 3D motion from in-the-wild videos, covering 7 dance styles and 16 music genres. We show that naively applying single dance generation technique to creating group dance motion may lead to unsatisfactory results, such as inconsistent movements and collisions between dancers. Based on our new dataset, we propose a new method that takes an input music sequence and a set of 3D positions of dancers to efficiently produce multiple group-coherent choreographies. We propose new evaluation metrics for measuring group dance quality and perform intensive experiments to demonstrate the effectiveness of our method. Our code and dataset will be released to facilitate future research on group dance generation.
## Table of Contents
1. [AIOZ-GDANCE Dataset](#aioz-gdance-dataset)
2. [Visualizing](#visualizing)
3. [Prerequisites](#prerequisites)
4. [Usage](#usage)
## AIOZ-GDANCE Dataset
**[Download]** The dataset can be downloaded at [Data](https://vision.aioz.io/f/430eb9d90552480e8b4e/?dl=1)
**[Updated]** The music and dance labels are now available at [Labels](https://vision.aioz.io/f/bef3ae93990e4e43addf/?dl=1)
The data directory is organized as follows:
- **split_sequence_names.txt**:
- a txt file containing seperate sequence names in the data (each sequence should have unique name or id)
- **musics**:
- contains raw music .wav file of each sequence with the corresponding name. The music frames are aligned with the motion frames.
- **motions_smpl**:
- contains the motion file of each sequence with the corresponding name, the motion is provided in .pkl file format.
- Each data dictionary mainly includes the following items:
- `'smpl_poses': shape[num_persons x num_frames x 72]`: the motions contain 72-D vector pose sequences in SMPL pose format (24 joints).
- ``'root_trans': shape[num_persons x num_frames x 3]``: sequences of root translation.
Here is an example python script to read the motion file
```python
import pickle
import numpy as np
data = pickle.load(open("sequence_name.pkl","rb"))
print(data.keys())
smpl_poses = data['smpl_poses']
smpl_trans = data['root_trans']
# ... may utilize the pose by using SMPL forward function: https://github.com/vchoutas/smplx
```
## Visualizing
We provide demo code for loading and visualizing the motions.
### Prerequisites
First, you need to download the [SMPL model](https://smpl.is.tue.mpg.de/) (v1.0.0) and rename the model files for visualization. The directory structure of the data is expected to be:
The directory structure of the data is expected to be:
```
<DATA_DIR>
├── motions_smpl/
├── musics/
└── split_sequence_names.txt
<SMPL_DIR>
├── SMPL_MALE.pkl
└── SMPL_FEMALE.pkl
```
Then run this to install the necessary packages
```
pip install scipy torch smplx chumpy vedo trimesh
pip install numpy==1.23.0
```
### Usage
#### Visualize the SMPL joints
The following command will first calculate the SMPL joint locations (joint rotations and root translation) and then plot on the 3D figure in realtime.
``` bash
python vis_smpl_kpt.py \
--data_dir <DATA_DIR>/motions_smpl \
--smpl_path <SMPL_DIR>/SMPL_FEMALE.PKL \
--sequence_name sequence_name.pkl
```
#### Visualize the SMPL Mesh
The following command will calculate the SMPL meshes and visualize in 3D.
``` bash
python vis_smpl_mesh.py \
--data_dir <DATA_DIR>/motions_smpl \
--smpl_path <SMPL_DIR>/SMPL_FEMALE.PKL \
--sequence_name sequence_name.pkl
```
## TODO
- [x] ~~**Dataset**~~
- [ ] **Baseline model & training**: TBD
## Citation
```
@inproceedings{aiozGdance,
author = {Le, Nhat and Pham, Thang and Do, Tuong and Tjiputra, Erman and Tran, Quang D. and Nguyen, Anh},
title = {Music-Driven Group Choreography},
journal = {CVPR},
year = {2023},
}
```
## License
Software Copyright License for non-commercial scientific research purposes.
Please read carefully the following [terms and conditions](LICENSE) and any accompanying
documentation before you download and/or use AIOZ-GDANCE data, model and
software, (the "Data & Software"), including 3D meshes, images, videos,
textures, software, scripts, and animations. By downloading and/or using the
Data & Software (including downloading, cloning, installing, and any other use
of the corresponding github repository), you acknowledge that you have read
these [terms and conditions](LICENSE), understand them, and agree to be bound by them. If
you do not agree with these [terms and conditions](LICENSE), you must not download and/or
use the Data & Software. Any infringement of the terms of this agreement will
automatically terminate your rights under this [License](LICENSE).
## Acknowledgement
This repo used visualization code from [AIST++](https://github.com/google/aistplusplus_api/tree/main) | [
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|
JinglesDados/EduardoFrei | JinglesDados | 2023-12-01T17:04:28Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T17:03:55Z | ---
license: openrail
---
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|
perigo/flavia | perigo | 2023-12-01T17:07:45Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T17:07:01Z | ---
license: openrail
---
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|
octoz/CantorDominguinhos | octoz | 2023-12-01T17:33:38Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T17:23:03Z | ---
license: openrail
---
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|
datasets-examples/doc-image-9 | datasets-examples | 2023-12-01T17:29:48Z | 0 | 0 | [
"size_categories:n<1K",
"region:us"
]
| null | 2023-12-01T17:26:41Z | ---
size_categories:
- n<1K
configs:
- config_name: default
drop_labels: true
---
# [doc] image dataset 9
This dataset contains 2 jpeg files in the `red` directory and 2 jpeg files in the `green` directory. The YAML header in this README disables the automatic "label" column. | [
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|
HorcruxNo13/toolwear_segmentsai_tools_unprocessed_test | HorcruxNo13 | 2023-12-01T17:38:47Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T17:32:32Z | ---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 51304727.0
num_examples: 27
download_size: 11314100
dataset_size: 51304727.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
nguyenthanhdo/goodboi | nguyenthanhdo | 2023-12-01T17:42:00Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T17:41:57Z | ---
dataset_info:
features:
- name: source
dtype: string
- name: from
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 3778115.48
num_examples: 1000
download_size: 1745688
dataset_size: 3778115.48
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
resurrectio0n/russo-nachrucks-v2 | resurrectio0n | 2023-12-01T18:20:23Z | 0 | 0 | [
"language:ru",
"language:it",
"language:fr",
"language:pl",
"language:de",
"license:cc-by-3.0",
"region:us"
]
| null | 2023-12-01T18:00:02Z | ---
license: cc-by-3.0
language:
- ru
- it
- fr
- pl
- de
---
# Dataset Card for Dataset Name
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This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
## Dataset Details
### Dataset Description
This dataset contains Russian propaganda across different language language segments in Telegram-channels. Rule-based method was used, so I cannot account for 100% of samples.
- **Curated by:** [More Information Needed]
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## Uses
Responsible use. For disinformation detection, sentiment detection, propaganda detection.
### Direct Use
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### Out-of-Scope Use
Misuse, malacious use, discriminative content generation use.
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## Dataset Structure
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[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
Scraped from Telegram-channels that use offensive and discriminatory language towards Ukraine.
English translation was performed.
Links removal.
[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. -->
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### Annotations [optional]
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#### Annotation process
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#### 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. -->
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## Bias, Risks, and Limitations
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
## Citation [optional]
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## More Information [optional]
[More Information Needed]
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## Dataset Card Contact
[More Information Needed] | [
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|
octoz/Domingues | octoz | 2023-12-01T18:12:05Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T18:11:32Z | ---
license: openrail
---
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|
octoz/domingo | octoz | 2023-12-01T18:20:24Z | 0 | 0 | [
"license:openrail",
"region:us"
]
| null | 2023-12-01T18:14:23Z | ---
license: openrail
---
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|
HorcruxNo13/toolwear_complete_tool | HorcruxNo13 | 2023-12-01T18:25:59Z | 0 | 0 | [
"region:us"
]
| null | 2023-12-01T18:15:05Z | ---
dataset_info:
features:
- name: name
dtype: string
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 464993039.0
num_examples: 43
download_size: 117710589
dataset_size: 464993039.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
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|
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