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LuisG07/es_corpora_parliament_processed | LuisG07 | 2022-02-17T23:10:16Z | 300 | 0 | null | [
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MickyMike/large_c_corpus | MickyMike | 2021-09-26T21:20:15Z | 300 | 0 | null | [
"region:us"
] | 2021-09-26T21:20:15Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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NTUYG/RAGTest | NTUYG | 2021-05-02T15:31:15Z | 300 | 0 | null | [
"region:us"
] | 2021-05-02T15:31:15Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
PaulLerner/viquae_wikipedia | PaulLerner | 2022-02-18T11:34:47Z | 300 | 0 | null | [
"region:us"
] | 2022-02-18T11:34:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | See https://github.com/PaulLerner/ViQuAE
---
license: cc-by-3.0
---
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PereLluis13/parla_text_corpus | PereLluis13 | 2022-01-29T02:59:00Z | 300 | 0 | null | [
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Plim/fr_corpora_parliament_processed | Plim | 2022-12-12T09:10:10Z | 300 | 0 | null | [
"language:fr",
"region:us"
] | 2022-12-12T09:10:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language: fr
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Plim/fr_wikipedia_processed | Plim | 2022-12-12T09:09:16Z | 300 | 0 | null | [
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SaulLu/Natural_Questions_HTML_reduced_all | SaulLu | 2021-10-04T15:25:50Z | 300 | 1 | null | [
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SetFit/hate_speech18 | SetFit | 2022-01-19T21:45:35Z | 300 | 0 | null | [
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abidlabs/test-audio-13 | abidlabs | 2022-01-21T16:42:41Z | 300 | 0 | null | [
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adalbertojunior/MININER | adalbertojunior | 2022-02-19T01:25:43Z | 300 | 0 | null | [
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addy88/sanskrit-asr-84 | addy88 | 2021-12-14T13:39:37Z | 300 | 0 | null | [
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albertvillanova/lm_en_dummy0 | albertvillanova | 2022-02-02T09:09:43Z | 300 | 0 | null | [
"region:us"
] | 2022-02-02T09:09:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
aseifert/pie-synthetic | aseifert | 2022-07-07T11:55:53Z | 300 | 1 | null | [
"multilinguality:translation",
"size_categories:unknown",
"language:en",
"region:us"
] | 2022-07-07T11:55:53Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators: []
language_creators: []
language:
- en
license: []
multilinguality:
- translation
pretty_name: pie-synthetic
size_categories:
- unknown
source_datasets: []
task_categories:
- conditional-text-generation
task_ids:
- machine-translation
---
# PIE synthetic dataset
Repo: https://github.com/awasthiabhijeet/PIE
Paper: https://aclanthology.org/D19-1435.pdf | [
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ashraq/dhivehi-corpus | ashraq | 2021-12-19T14:39:45Z | 300 | 2 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
caltonji/harrypotter_squad_v2_2 | caltonji | 2021-12-31T20:01:23Z | 300 | 0 | null | [
"region:us"
] | 2021-12-31T20:01:23Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ## Dataset Summary
Contains 15 Harry Potter trivia questions in Squadv2 format, 3 of which are unanswerable.
## Model Performance
[Test Notebook](https://colab.research.google.com/drive/1VFUJKV7eun68XgQDAHSHsbvoM_CGHzWA?usp=sharing)
| Model | exact | f1 |
| ----------- | ----------- | ----------- |
| Albert Base ([twmkn9/albert-base-v2-squad2](https://huggingface.co/twmkn9/albert-base-v2-squad2)) | 46.6667 | 46.6667 |
| Albert XXLarge ([ahotrod/albert_xxlargev1_squad2_512](https://huggingface.co/ahotrod/albert_xxlargev1_squad2_512)) | 66.6667 | 66.6667 |
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
castorini/msmarco_v2_doc_segmented_doc2query-t5_expansions | castorini | 2021-11-02T08:13:56Z | 300 | 0 | null | [
"region:us"
] | 2021-11-02T08:13:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- English
license: "Apache License 2.0"
---
# Dataset Summary
The repo provides queries generated for the MS MARCO v2 document segmented corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.
# Dataset Structure
All three folds (train, dev and test) share the same corpus.
An example data entry looks as follows:
```
{
'docid': '25#0',
'title': 'Autism',
'text': 'Autism is a developmental disorder characterized by difficulties with social interaction and communication, ...'
}
```
# Load Dataset
An example to load the dataset:
```
dataset = load_dataset('castorini/msmarco_v2_doc_segmented_doc2query-t5_expansions', data_files='d2q/d2q.jsonl???.gz')
```
# Citation Information
```
@article{docTTTTTquery,
title={From doc2query to {docTTTTTquery}},
author={Nogueira, Rodrigo and Lin, Jimmy},
year={2019}
}
@article{emdt5,
author = "Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin",
title = "The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models",
journal = "arXiv:2101.05667",
year = 2021,
}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
castorini/nq_gar-t5_expansions | castorini | 2023-10-10T18:58:22Z | 300 | 1 | null | [
"language:en",
"license:apache-2.0",
"region:us"
] | 2023-10-10T18:58:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- "en"
license: "apache-2.0"
---
# Dataset Summary
The repo provides answer, title and sentence expansions for the Natural Questions corpus with gar-T5.
# Dataset Structure
There are dev and test folds
An example data entry of the dev split looks as follows:
```
{
"id": "1",
"predicted_answers": ["312"], "predicted_titles": ["Invisible Man"], "predicted_sentences": ["The Invisible Man First edition Author Ralph Ellison Cover artist M."]
}
```
An example data entry of the test split looks as follows:
```
{
"id": "1",
"predicted_answers": ["May 18 , 2018"], "predicted_titles": ["Deadpool 2 *** Deadpool (film) *** Deadpool 2 (soundtrack) *** X-Men in other media"], "predicted_sentences": ["Deadpool 2 was released on May 18 , 2018 , with Leitch directing from a screenplay by Rhett Reese and Paul Wernick ."]
}
```
# Load Dataset
An example to load the dataset:
```python
data_files = {"dev":"dev/dev.jsonl", "test": "test/test.jsonl"}
dataset = load_dataset('castorini/nq_gar-t5_expansions')
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
castorini/triviaqa_gar-t5_expansions | castorini | 2022-02-17T00:58:32Z | 300 | 0 | null | [
"region:us"
] | 2022-02-17T00:58:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- English
license: "Apache License 2.0"
---
# Dataset Summary
The repo provides answer,title and sentence expansions for the Trivia QA corpus with gar-T5.
# Dataset Structure
There are dev and test folds
An example data entry of the dev split looks as follows:
```
{
"id": "1",
"predicted_answers": ["Bz"], "predicted_titles": ["Vehicle registration plates of Belize *** Vehicle registration plate"], "predicted_sentences": ["The international code for Belize is \"\"BZ\"\"."]
}
```
An example data entry of the test split looks as follows:
```
{
"id": "1",
"predicted_answers": ["Taurus"], "predicted_titles": ["Jamie Lee Curtis *** Under the Tuscan Sun *** Angels (Jamie Lee Curtis song) *** Under the Tuscan Sun (film) *** John Michael King *** Robert Earl *** Henry Jones, Sr. *** Jamie Lee (singer) *** Under the Tuscan Sun (1974 film) *** Richard Benjamin"], "predicted_sentences": ["In July 2007, several news outlets reported that the couple had quietly married in December 2007, and that Curtis had taken a liking to one another, sharing \"\"sweet nothings\"\" about their relationship."]
}
```
# Load Dataset
An example to load the dataset:
```python
data_files = {"dev":"dev/dev.jsonl", "test": "test/test.jsonl"}
dataset = load_dataset('castorini/triviaqa_gar-t5_expansions')
```
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cestwc/cnn_dailymail-test50 | cestwc | 2021-12-16T17:40:40Z | 300 | 0 | null | [
"region:us"
] | 2021-12-16T17:40:40Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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darkraipro/recipe-instructions | darkraipro | 2022-01-18T16:22:01Z | 300 | 0 | null | [
"region:us"
] | 2022-01-18T16:22:01Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
gigant/african_accented_french | gigant | 2022-10-24T17:39:03Z | 300 | 4 | null | [
"task_categories:automatic-speech-recognition",
"language:fr",
"license:cc",
"region:us"
] | 2022-10-24T17:39:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- fr
license: cc
size_categories:
fr:
- 10K<n<100K
task_categories:
- automatic-speech-recognition
task_ids: []
pretty_name: African Accented French
---
## Dataset Description
- **Homepage:** http://www.openslr.org/57/
### Dataset Summary
This corpus consists of approximately 22 hours of speech recordings. Transcripts are provided for all the recordings. The corpus can be divided into 3 parts:
1. Yaounde
Collected by a team from the U.S. Military Academy's Center for Technology Enhanced Language Learning (CTELL) in 2003 in Yaoundé, Cameroon. It has recordings from 84 speakers, 48 male and 36 female.
2. CA16
This part was collected by a RDECOM Science Team who participated in the United Nations exercise Central Accord 16 (CA16) in Libreville, Gabon in June 2016. The Science Team included DARPA's Dr. Boyan Onyshkevich and Dr. Aaron Lawson (SRI International), as well as RDECOM scientists. It has recordings from 125 speakers from Cameroon, Chad, Congo and Gabon.
3. Niger
This part was collected from 23 speakers in Niamey, Niger, Oct. 26-30 2015. These speakers were students in a course for officers and sergeants presented by Army trainers assigned to U.S. Army Africa. The data was collected by RDECOM Science & Technology Advisors Major Eddie Strimel and Mr. Bill Bergen.
### Languages
French
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, called audio and its sentence.
### Data Fields
- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- sentence: The sentence the user was prompted to speak
### Data Splits
The speech material has been subdivided into portions for train and test.
The train split consists of 9401 audio clips and the related sentences.
The test split consists of 1985 audio clips and the related sentences.
### Contributions
[@gigant](https://huggingface.co/gigant) added this dataset. | [
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huggingartists/enigma | huggingartists | 2022-10-25T09:29:13Z | 300 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:29:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/enigma"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.268668 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/4b5472082f220eb9c2ca6b22f4d12f45.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/enigma">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Enigma</div>
<a href="https://genius.com/artists/enigma">
<div style="text-align: center; font-size: 14px;">@enigma</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/enigma).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/enigma")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|277| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/enigma")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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huggingartists/enya | huggingartists | 2022-10-25T09:29:19Z | 300 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:29:19Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/enya"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.132575 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f43534295450e1b0a276620dffdc3740.379x379x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/enya">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Enya</div>
<a href="https://genius.com/artists/enya">
<div style="text-align: center; font-size: 14px;">@enya</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/enya).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/enya")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|172| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/enya")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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huggingartists/epic-rap-battles-of-history | huggingartists | 2022-10-25T09:29:25Z | 300 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:29:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/epic-rap-battles-of-history"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.252059 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/86da58e97d308e9127100e7954dc1d74.900x900x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/epic-rap-battles-of-history">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Epic Rap Battles of History</div>
<a href="https://genius.com/artists/epic-rap-battles-of-history">
<div style="text-align: center; font-size: 14px;">@epic-rap-battles-of-history</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/epic-rap-battles-of-history).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/epic-rap-battles-of-history")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|98| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/epic-rap-battles-of-history")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/freddie-dredd | huggingartists | 2022-10-25T09:29:57Z | 300 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:29:57Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/freddie-dredd"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.261399 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/f198be5e1dfd71285efa66c8b223ae6d.400x400x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/freddie-dredd">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Freddie Dredd</div>
<a href="https://genius.com/artists/freddie-dredd">
<div style="text-align: center; font-size: 14px;">@freddie-dredd</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/freddie-dredd).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/freddie-dredd")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|212| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/freddie-dredd")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/snoop-dogg | huggingartists | 2022-10-25T09:45:10Z | 300 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:45:10Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/snoop-dogg"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 4.603835 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/91bd22f5e53a3ea3cb1436de8f4a3722.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/snoop-dogg">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Snoop Dogg</div>
<a href="https://genius.com/artists/snoop-dogg">
<div style="text-align: center; font-size: 14px;">@snoop-dogg</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/snoop-dogg).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/snoop-dogg")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|1773| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/snoop-dogg")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/sqwore | huggingartists | 2022-10-25T09:45:16Z | 300 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:45:16Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/sqwore"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.067786 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/3557a234d4c5912569afbea078a23eff.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/sqwore">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sqwore</div>
<a href="https://genius.com/artists/sqwore">
<div style="text-align: center; font-size: 14px;">@sqwore</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/sqwore).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/sqwore")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|19| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/sqwore")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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huggingartists/suicideoscope | huggingartists | 2022-10-25T09:45:28Z | 300 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:45:28Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/suicideoscope"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.052767 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/86b0ba099a6797bab3deeba685f3dbc2.800x800x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/suicideoscope">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Suicideoscope</div>
<a href="https://genius.com/artists/suicideoscope">
<div style="text-align: center; font-size: 14px;">@suicideoscope</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/suicideoscope).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/suicideoscope")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|11| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/suicideoscope")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/the-beatles | huggingartists | 2022-10-25T09:46:31Z | 300 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:46:31Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/the-beatles"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 1.07072 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/df75ede64ffcf049727bfbb01d323081.400x400x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/the-beatles">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">The Beatles</div>
<a href="https://genius.com/artists/the-beatles">
<div style="text-align: center; font-size: 14px;">@the-beatles</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/the-beatles).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/the-beatles")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|878| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/the-beatles")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mteb/reddit-clustering-p2p | mteb | 2022-09-27T19:13:59Z | 300 | 0 | null | [
"language:en",
"region:us"
] | 2022-09-27T19:13:59Z | 2022-05-11T08:52:19.000Z | 2022-05-11T08:52:19 | ---
language:
- en
---
10 sets with the following stats:
1. 91 labels & 15592 samples
2. 64 labels & 79172 samples
3. 38 labels & 1942 samples
4. 11 labels & 13224 samples
5. 64 labels & 92303 samples
6. 87 labels & 28607 samples
7. 10 labels & 69146 samples
8. 48 labels & 67469 samples
9. 64 labels & 29683 samples
10. 31 labels & 62261 samples
Selected at random using the script available on the mteb github repository.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mac_morpho | null | 2023-01-25T14:34:31Z | 299 | 4 | null | [
"task_categories:token-classification",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:pt",
"license:cc-by-4.0",
"region:us"
] | 2023-01-25T14:34:31Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- part-of-speech
pretty_name: Mac-Morpho
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: pos_tags
sequence:
class_label:
names:
'0': PREP+PROADJ
'1': IN
'2': PREP+PRO-KS
'3': NPROP
'4': PREP+PROSUB
'5': KC
'6': PROPESS
'7': NUM
'8': PROADJ
'9': PREP+ART
'10': KS
'11': PRO-KS
'12': ADJ
'13': ADV-KS
'14': N
'15': PREP
'16': PROSUB
'17': PREP+PROPESS
'18': PDEN
'19': V
'20': PREP+ADV
'21': PCP
'22': CUR
'23': ADV
'24': PU
'25': ART
splits:
- name: train
num_bytes: 12635011
num_examples: 37948
- name: test
num_bytes: 3095292
num_examples: 9987
- name: validation
num_bytes: 671356
num_examples: 1997
download_size: 2463485
dataset_size: 16401659
---
# Dataset Card for Mac-Morpho
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Mac-Morpho homepage](http://nilc.icmc.usp.br/macmorpho/)
- **Repository:** [Mac-Morpho repository](http://nilc.icmc.usp.br/macmorpho/)
- **Paper:** [Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese](https://journal-bcs.springeropen.com/articles/10.1186/s13173-014-0020-x)
- **Point of Contact:** [Erick R Fonseca](mailto:erickrfonseca@gmail.com)
### Dataset Summary
Mac-Morpho is a corpus of Brazilian Portuguese texts annotated with part-of-speech tags.
Its first version was released in 2003 [1], and since then, two revisions have been made in order
to improve the quality of the resource [2, 3].
The corpus is available for download split into train, development and test sections.
These are 76%, 4% and 20% of the corpus total, respectively (the reason for the unusual numbers
is that the corpus was first split into 80%/20% train/test, and then 5% of the train section was
set aside for development). This split was used in [3], and new POS tagging research with Mac-Morpho
is encouraged to follow it in order to make consistent comparisons possible.
[1] Aluísio, S., Pelizzoni, J., Marchi, A.R., de Oliveira, L., Manenti, R., Marquiafável, V. 2003.
An account of the challenge of tagging a reference corpus for brazilian portuguese.
In: Proceedings of the 6th International Conference on Computational Processing of the Portuguese Language. PROPOR 2003
[2] Fonseca, E.R., Rosa, J.L.G. 2013. Mac-morpho revisited: Towards robust part-of-speech.
In: Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology – STIL
[3] Fonseca, E.R., Aluísio, Sandra Maria, Rosa, J.L.G. 2015.
Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese.
Journal of the Brazilian Computer Society.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Portuguese
## Dataset Structure
### Data Instances
An example from the Mac-Morpho dataset looks as follows:
```
{
"id": "0",
"pos_tags": [14, 19, 14, 15, 22, 7, 14, 9, 14, 9, 3, 15, 3, 3, 24],
"tokens": ["Jersei", "atinge", "média", "de", "Cr$", "1,4", "milhão", "na", "venda", "da", "Pinhal", "em", "São", "Paulo", "."]
}
```
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `pos`: the PoS tags of each token
The PoS tags correspond to this list:
```
"PREP+PROADJ", "IN", "PREP+PRO-KS", "NPROP", "PREP+PROSUB", "KC", "PROPESS", "NUM", "PROADJ", "PREP+ART", "KS",
"PRO-KS", "ADJ", "ADV-KS", "N", "PREP", "PROSUB", "PREP+PROPESS", "PDEN", "V", "PREP+ADV", "PCP", "CUR", "ADV", "PU", "ART"
```
### Data Splits
The data is split into train, validation and test set. The split sizes are as follow:
| Train | Val | Test |
| ------ | ----- | ----- |
| 37948 | 1997 | 9987 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{fonseca2015evaluating,
title={Evaluating word embeddings and a revised corpus for part-of-speech tagging in Portuguese},
author={Fonseca, Erick R and Rosa, Jo{\~a}o Lu{\'\i}s G and Alu{\'\i}sio, Sandra Maria},
journal={Journal of the Brazilian Computer Society},
volume={21},
number={1},
pages={2},
year={2015},
publisher={Springer}
}
```
### Contributions
Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset. | [
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polsum | null | 2022-11-03T16:07:56Z | 299 | 1 | null | [
"task_categories:summarization",
"task_ids:news-articles-summarization",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:pl",
"license:cc-by-3.0",
"region:us"
] | 2022-11-03T16:07:56Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- pl
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- summarization
task_ids:
- news-articles-summarization
paperswithcode_id: null
pretty_name: Polish Summaries Corpus
dataset_info:
features:
- name: id
dtype: string
- name: date
dtype: string
- name: title
dtype: string
- name: section
dtype: string
- name: authors
dtype: string
- name: body
dtype: string
- name: summaries
sequence:
- name: ratio
dtype: int32
- name: type
dtype: string
- name: author
dtype: string
- name: body
dtype: string
- name: spans
sequence:
- name: start
dtype: int32
- name: end
dtype: int32
- name: span_text
dtype: string
splits:
- name: train
num_bytes: 34787575
num_examples: 569
download_size: 6082812
dataset_size: 34787575
---
# Dataset Card for Polish Summaries Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://zil.ipipan.waw.pl/PolishSummariesCorpus
- **Repository:** http://zil.ipipan.waw.pl/PolishSummariesCorpus
- **Paper:** http://nlp.ipipan.waw.pl/Bib/ogro:kop:14:lrec.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Mateusz Kopeć](http://zil.ipipan.waw.pl/MateuszKopec)
### Dataset Summary
The Corpus contains a large number of manual summaries of news articles,
with many independently created summaries for a single text. Such approach is supposed to overcome the annotator bias, which is often described as a problem during the evaluation of the summarization algorithms against a single gold standard.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Polish
## Dataset Structure
### Data Instances
See below an example from the dataset. Detailed descriptions of the fields are provided in the following section.
```
{'authors': 'Krystyna Forowicz',
'body': "ROZMOWA\n\nProf. Krzysztof Ernst, kierownik Zakładu Optyki Instytutu Fizyki Doświadczalnej Uniwersytetu Warszawskiego\n\nLidarowe oczy\n\nRYS. MAREK KONECKI\n\nJutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.\n\nCzy to kosztowne urządzenie będzie służyło tylko naukowcom?\n\nTego typu lidar jest rzeczywiście drogi, kosztuje około miliona marek niemieckich. Jest to najnowsza generacja tego typu lidarów. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem, staramy się m.in. rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Nad lidarem pracują specjaliści od laserów i od komputerów. Współpracujemy z doskonałym laboratorium prof. Ludgera Wöste z Freie Universitat Berlin rozwijającym m.in. problematykę lidarową. Pakiet software'u wzbogacamy o nowe algorytmy, które potrafią lepiej i dokładniej rozszyfrowywać sygnał lidarowy, a w konsekwencji skażenia. Żeby przetworzyć tzw. sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać rozsądne dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji. \n\nBadania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Zasadniczy koszt jego budowy pokryła uzyskana od Fundacji dotacja. Część pieniędzy przekazał też Narodowy Fundusz Ochrony Środowiska i Gospodarki Wodnej oraz Komitet Badań Naukowych.\n\nCzy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?\n\nNie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze łącznie z dostarczeniem informacji o ich rozkładzie. Ale np. obecnie prowadzimy badania mające na celu rozszerzenie możliwości lidaru o taką substancję jak fosgen. Tym szkodliwym gazem może być skażone powietrze w miastach, w których zlokalizowane są zakłady chemiczne, np. w Bydgoszczy pewne ilości fosgenu emitują Zakłady Chemiczne Organika- Zachem. \n\nLidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć. Cząsteczki, które wykrywamy mają pasma absorbcji w bliskim nadfiolecie. Możemy np. badać zawartość ozonu w troposferze. Okazuje się bowiem, że o ile brak tego gazu w wysokich warstwach atmosfery powoduje groźny efekt cieplarniany, to jego nadmiar tuż nad Ziemią jest szkodliwy. Groźne są też substancje gazowe, jak np. tlenki azotu, będące następstwem spalin samochodowych. A samochodów przybywa.\n\nCzy stać nas będzie na prowadzenie pomiarów ozonu w miastach? \n\nKoszt jednego dnia kampanii pomiarowej firmy zachodnie szacują na kilka tysięcy DM. Potrzebne są pieniądze na utrzymanie lidaru, na prowadzenie badań. Nasze przedsięwzięcie nie ma charakteru komercyjnego. Koszt pomiarów będzie znacznie niższy. Chcemy np. mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta. Chcielibyśmy rozwinąć tutaj współpracę z państwowymi i wojewódzkimi służbami ochrony środowiska. Tego typu badania były prowadzone np. w Lyonie. Okazało się, że najwięcej tlenków azotu występuje niekoniecznie tam gdzie są one produkowane, to znaczy nie przy najruchliwszych ulicach, jeśli są one dobrze wentylowane a gromadzą się one w małych uliczkach. Przede wszystkim jednak do końca tego roku zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu trzech granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie. Prowadziliśmy pomiary w samym Turowie, gdzie elektrownia Turoszowska jest głównym źródłem emisji. W planie mamy Bogatynię, zagłębie miedziowe. \n\nW Czarnym Trójkącie istnieje wiele stacjonarnych stacji monitoringowych.\n\nNasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów. Możemy zatem śledzić ewolucję rozprzestrzeniania się tych zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. Wyniki naszych pomiarów porównujemy z danymi uzyskanymi ze stacji monitoringowych. \n\nJak wypadł Czarny Trójkąt?\n\nKiedy występowaliśmy o finansowanie tego projektu do Fundacji Współpracy Polsko-Niemieckiej zanieczyszczenie powietrza w Czarnym Trójkącie było dużo większe niż obecnie i wszystko wskazuje na to, że będzie dalej spadać. Obecnie stężenie dwutlenku siarki jest na granicy naszych możliwości pomiarowych. Dla regionu Turoszowskiego to dobra wiadomość i dla stosunków polsko-niemieckich też.\n\nTypów lidarów jest wiele \n\nTen lidar pracuje w obszarze bliskiego nadfioletu i promieniowania widzialnego, które jest wynikiem wykorzystania drugiej lub trzeciej harmonicznej lasera szafirowego, pracującego na granicy czerwieni i podczerwieni. DIAL jest tym typem lidara, który dzisiaj ma zdecydowanie największe wzięcie w ochronie środowiska. Z lidarów korzysta meteorologia. W Stanach Zjednoczonych lidary umieszcza się na satelitach (program NASA). Określają na przestrzeni kilkudziesięciu kilometrów rozkłady temperatury, wilgotności, ciśnienia, a także prędkości wiatru. Wykrywają pojawianie się huraganów, a nawet mogą określać rozmiary oka tajfunu.\n\nIle takich urządzeń jest w Europie?\n\n- W Europie takich lidarów jak nasz jest zaledwie kilka. Większość z nich mierzy ozon, dwutlenek siarki i tlenek azotu. Wykrywanie toluenu i benzenu jest oryginalnym rozwiązaniem. Długość fali dla benzenu jest już na skraju możliwości widmowych. Nasz lidar typu DIAL jest najnowocześniejszym w Polsce. Ponadto jest lidarem ruchomym, zainstalowanym na samochodzie. Ale historia lidarów w naszym kraju jest dłuższa i zaczęła się na początku lat 60. Pierwsze próby prowadzone były w stacji geofizycznej PAN w Belsku, niedługo po skonstruowaniu pierwszego w świecie lasera rubinowego. Potem powstał lidar stacjonarny, również typu DIAL, w Gdańsku, a w Krakowie sodary - urządzenia oparte na falach akustycznych, wygodne np. do pomiarów szybkości wiatru. Lidar umieszczony na samochodzie i zbudowany w latach 80 na Politechnice Poznańskiej w perspektywie miał być lidarem typu DIAL.\n\nFizycy dotychczas nie zajmowali się ochroną środowiska?\n\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji (zdjęć satelitarnych) Instytutu Geofizyki i, co bardzo ważne, współpraca z Freie Universität Berlin. Mamy również na UW Międzywydziałowe Studia Ochrony Środowiska i studentom przekazujemy informacje o lidarze i fizycznych metodach badania środowiska. Nasze działania dydaktyczne bardzo efektywnie wspiera NFOŚ.\n\nRozmawiała Krystyna Forowicz",
'date': '1997-04-21',
'id': '199704210011',
'section': 'Nauka i Technika',
'summaries': {'author': ['I',
'I',
'I',
'C',
'C',
'C',
'K',
'K',
'K',
'G',
'G',
'G',
'J',
'J',
'J'],
'body': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.Czy to kosztowne urządzenie będzie służyło tylko naukowcom? Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad tym urządzeniem. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.Czy to kosztowne urządzenie będzie służyło tylko naukowcom? Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad tym urządzeniem. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Czy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?Nie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze łącznie z dostarczeniem informacji o ich rozkładzie. Możemy np. badać zawartość ozonu w troposferze. W Europie takich lidarów jak nasz jest zaledwie kilka. Większość z nich mierzy ozon, dwutlenek siarki i tlenek azotu. Fizycy dotychczas nie zajmowali się ochroną środowiska?Taka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji Instytutu Geofizyki i współpraca z Freie Universität Berlin.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. lidar Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, naukową I dydaktyczną. Żeby przetworzyć sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji. muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. lidar Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym. Jest to najnowsza generacja tego typu lidarów. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Żeby przetworzyć tzw. sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać rozsądne dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Zasadniczy koszt jego budowy pokryła uzyskana od Fundacji dotacja. Część pieniędzy przekazał też Narodowy Fundusz Ochrony Środowiska i Gospodarki Wodnej oraz Komitet Badań Naukowych. Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. lidar Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, naukową I dydaktyczną.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.Tego typu lidar jest drogi, kosztuje około miliona marek niemieckich. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem, staramy się m.in. rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć. Cząsteczki, które wykrywamy mają pasma absorbcji w bliskim nadfiolecie.Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów. Możemy zatem śledzić ewolucję rozprzestrzeniania się tych zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. Wyniki naszych pomiarów porównujemy z danymi uzyskanymi ze stacji monitoringowych.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową i dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.\nto najnowsza generacja tego typu lidarów. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. korzyść mamy potrójną: użyteczną, przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad urządzeniem I dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.\nNasze przedsięwzięcie nie ma charakteru komercyjnego. Chcemy np. mierzyć w Warszawie rozkłady koncentracji tlenków azotu. Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.\n\nto kosztowne urządzenie będzie służyło tylko naukowcom?\n\nlidar jest rzeczywiście drogi. to najnowsza generacja tego typu lidarów. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. korzyść mamy potrójną: użyteczną, przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad tym urządzeniem I dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.\n\nCzy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?\n\nNie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze. Ale prowadzimy badania mające na celu rozszerzenie możliwości lidaru o taką substancję jak fosgen.\n\nstać nas będzie na prowadzenie pomiarów ozonu w miastach? \n\nNasze przedsięwzięcie nie ma charakteru komercyjnego. Chcemy np. mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta. Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie. zanieczyszczenie było dużo większe niż obecnie i wszystko wskazuje na to, że będzie dalej spadać.\nDIAL dzisiaj ma zdecydowanie największe wzięcie w ochronie środowiska. \n\nFizycy dotychczas nie zajmowali się ochroną środowiska?\n\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu.',
'Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.\nto najnowsza generacja tego typu lidarów. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. korzyść mamy potrójną: użyteczną, wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad urządzeniem I dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. staramy się rozszerzyć jego zastosowanie na inne substancje występujące w atmosferze. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej. zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Nasz lidar ma większe możliwości niż stacje monitoringowe. Możemy śledzić ewolucję rozprzestrzeniania się zanieczyszczeń, ich kierunek i zmiany. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji Instytutu Geofizyki i współpraca z Freie Universität Berlin.',
"Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. DIAL - lidar absorbcji różnicowej potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. staramy się rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze. Pakiet software'u wzbogacamy o nowe algorytmy, które potrafią dokładniej rozszyfrowywać sygnał lidarowy, a w konsekwencji skażenia. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej. \n\nChcemy mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta. zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Nasz lidar ma większe możliwości niż stacje monitoringowe. Możemy śledzić ewolucję rozprzestrzeniania się zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. \n\nDIAL jest tym typem lidara, który dzisiaj ma największe wzięcie w ochronie środowiska. Z lidarów korzysta meteorologia. W Europie takich lidarów jak nasz jest zaledwie kilka. Nasz lidar jest najnowocześniejszym w Polsce. Ponadto jest lidarem ruchomym, zainstalowanym na samochodzie. \n\nFizycy dotychczas nie zajmowali się ochroną środowiska?\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji Instytutu Geofizyki i współpraca z Freie Universität Berlin.",
'Co to jest lidar? \nPROF. KRZYSZTOF ERNST: to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Nasz lidar ma większe możliwości niż stacje monitoringowe. Możemy śledzić ewolucję rozprzestrzeniania się zanieczyszczeń, ich kierunek i zmiany.'],
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'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.',
'Czy to kosztowne urządzenie będzie służyło tylko naukowcom?',
'Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad tym urządzeniem',
'.',
'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą.'],
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'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?',
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'Czy to kosztowne urządzenie będzie służyło tylko naukowcom?',
'Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad tym urządzeniem',
'.',
'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą.',
'Czy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?',
'Nie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze łącznie z dostarczeniem informacji o ich rozkładzie.',
'Możemy np. badać zawartość ozonu w troposferze.',
'W Europie takich lidarów jak nasz jest zaledwie kilka. Większość z nich mierzy ozon, dwutlenek siarki i tlenek azotu.',
'',
'Fizycy dotychczas nie zajmowali się ochroną środowiska?',
'Taka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji',
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'',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał',
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'span_text': ['Jutro',
'odbędzie sie pokaz nowego polskiego lidara typu DIAL.',
'lidar',
'Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną,',
'naukową',
'I',
'dydaktyczną',
'.',
'Żeby przetworzyć',
'sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać',
'dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji.',
'muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.'],
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'span_text': ['Jutro',
'odbędzie sie pokaz nowego polskiego lidara typu DIAL.',
'lidar',
'Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.',
'Jest to najnowsza generacja tego typu lidarów. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem',
'. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Żeby przetworzyć tzw. sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać rozsądne dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Zasadniczy koszt jego budowy pokryła uzyskana od Fundacji dotacja. Część pieniędzy przekazał też Narodowy Fundusz Ochrony Środowiska i Gospodarki Wodnej oraz Komitet Badań Naukowych.',
'',
'Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.'],
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'span_text': ['Jutro',
'odbędzie sie pokaz nowego polskiego lidara typu DIAL.',
'lidar',
'Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną,',
'naukową',
'I',
'dydaktyczną',
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'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.',
'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem',
'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów.'],
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'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.',
'Tego typu lidar jest',
'drogi, kosztuje około miliona marek niemieckich.',
'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem, staramy się m.in. rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze.',
'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć. Cząsteczki, które wykrywamy mają pasma absorbcji w bliskim nadfiolecie.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów. Możemy zatem śledzić ewolucję rozprzestrzeniania się tych zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. Wyniki naszych pomiarów porównujemy z danymi uzyskanymi ze stacji monitoringowych.'],
'start': [153, 247, 459, 493, 590, 1022, 2602, 4555]},
{'end': [246, 360, 626, 883, 920, 1102],
'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'',
'Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową',
'i',
'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.'],
'start': [153, 247, 625, 760, 919, 1032]},
{'end': [158,
262,
271,
359,
397,
590,
761,
803,
867,
907,
922,
1025,
1102,
3311,
3516,
3595,
3623,
3675,
4226,
4332],
'span_text': ['Jutro',
'odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF',
'ERNST:',
'urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'',
'to najnowsza generacja tego typu lidarów.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'korzyść mamy potrójną: użyteczną,',
'przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad',
'urządzeniem',
'I',
'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'',
'Nasze przedsięwzięcie nie ma charakteru komercyjnego.',
'Chcemy np. mierzyć w Warszawie rozkłady',
'koncentracji tlenków azotu',
'.',
'Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu',
'granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie.'],
'start': [153,
172,
263,
279,
396,
548,
699,
769,
806,
875,
911,
1022,
1033,
3310,
3462,
3556,
3596,
3674,
4158,
4233]},
{'end': [158,
262,
271,
359,
398,
459,
498,
543,
590,
761,
803,
867,
922,
1025,
1102,
2242,
2300,
2406,
3247,
3311,
3516,
3595,
3675,
4226,
4333,
5130,
5241,
5439,
5661,
5756,
7113],
'span_text': ['Jutro',
'odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF',
'ERNST:',
'urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'',
'to kosztowne urządzenie będzie służyło tylko naukowcom?',
'lidar jest rzeczywiście drogi',
'.',
'to najnowsza generacja tego typu lidarów.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'korzyść mamy potrójną: użyteczną,',
'przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad tym urządzeniem',
'I',
'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Czy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?\n\nNie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze',
'. Ale',
'prowadzimy badania mające na celu rozszerzenie możliwości lidaru o taką substancję jak fosgen.',
'',
'stać nas będzie na prowadzenie pomiarów ozonu w miastach?',
'Nasze przedsięwzięcie nie ma charakteru komercyjnego.',
'Chcemy np. mierzyć w Warszawie rozkłady',
'koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta.',
'Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu',
'granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie.',
'zanieczyszczenie',
'było dużo większe niż obecnie i wszystko wskazuje na to, że będzie dalej spadać.',
'',
'DIAL',
'dzisiaj ma zdecydowanie największe wzięcie w ochronie środowiska.',
'Fizycy dotychczas nie zajmowali się ochroną środowiska?\n\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu.'],
'start': [153,
172,
263,
279,
396,
402,
469,
541,
548,
699,
769,
806,
875,
1022,
1033,
2062,
2294,
2312,
3245,
3251,
3462,
3556,
3596,
4158,
4233,
5114,
5160,
5438,
5656,
5690,
6990]},
{'end': [262, 271, 359, 397, 590, 761, 803, 807, 867, 907, 922, 1025, 1102],
'span_text': ['Co to jest lidar? \n\nPROF. KRZYSZTOF',
'ERNST:',
'urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'',
'to najnowsza generacja tego typu lidarów.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'korzyść mamy potrójną: użyteczną,',
'',
'wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad',
'urządzeniem',
'I',
'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.'],
'start': [227,
263,
279,
396,
548,
699,
769,
806,
824,
875,
911,
1022,
1033]},
{'end': [245,
360,
761,
936,
971,
1022,
1733,
1878,
4159,
4614,
4772,
4818,
4860,
4906,
7283,
7326,
7383],
'span_text': ['Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'staramy się',
'rozszerzyć jego zastosowanie',
'na inne substancje występujące w atmosferze.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej',
'.',
'zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe.',
'Możemy',
'śledzić ewolucję rozprzestrzeniania się',
'zanieczyszczeń, ich kierunek i zmiany',
'.',
'Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji',
'Instytutu Geofizyki i',
'współpraca z Freie Universität Berlin.'],
'start': [227,
246,
699,
924,
942,
977,
1631,
1876,
4076,
4555,
4765,
4778,
4823,
4904,
7114,
7305,
7344]},
{'end': [245,
360,
625,
761,
936,
1022,
1311,
1357,
1436,
1733,
1878,
3247,
3311,
3563,
3676,
4159,
4614,
4772,
4818,
4906,
5410,
5439,
5701,
5789,
6163,
6364,
6472,
7048,
7283,
7326,
7383],
'span_text': ['Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'DIAL - lidar absorbcji różnicowej',
'potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'staramy się',
'rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze.',
"Pakiet software'u",
'wzbogacamy o nowe algorytmy, które potrafią',
'dokładniej rozszyfrowywać sygnał lidarowy, a w konsekwencji skażenia.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej',
'.',
'',
'',
'Chcemy',
'mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta.',
'zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe.',
'Możemy',
'śledzić ewolucję rozprzestrzeniania się',
'zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi.',
'',
'',
'DIAL jest tym typem lidara, który dzisiaj ma',
'największe wzięcie w ochronie środowiska. Z lidarów korzysta meteorologia.',
'W Europie takich lidarów jak nasz jest zaledwie kilka.',
'Nasz lidar',
'jest najnowocześniejszym w Polsce. Ponadto jest lidarem ruchomym, zainstalowanym na samochodzie.',
'Fizycy dotychczas nie zajmowali się ochroną środowiska?',
'Taka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji',
'Instytutu Geofizyki i',
'współpraca z Freie Universität Berlin.'],
'start': [227,
246,
591,
668,
924,
942,
1293,
1313,
1366,
1631,
1876,
3246,
3310,
3556,
3567,
4076,
4555,
4765,
4778,
4823,
5409,
5438,
5656,
5714,
6108,
6353,
6374,
6990,
7049,
7305,
7344]},
{'end': [245, 271, 360, 761, 4159, 4614, 4772, 4818, 4860, 4905],
'span_text': ['Co to jest lidar?',
'PROF. KRZYSZTOF ERNST:',
'to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe.',
'Możemy',
'śledzić ewolucję rozprzestrzeniania się',
'zanieczyszczeń, ich kierunek i zmiany',
'.'],
'start': [227, 246, 276, 699, 4076, 4555, 4765, 4778, 4823, 4904]}],
'type': ['extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract']},
'title': 'Lidarowe oczy'}
```
### Data Fields
- `id`: a `string` example identifier
- `date`: date of the original article (`string`)
- `title`: title of the original article (`string`)
- `section`: the section of the newspaper the original article belonged to (`string`)
- `authors`: original article authors (`string`)
- `body`: original article body (list of `string`s)
- `summaries`: a dictionary feature containing summaries of the original article with the following attributes:
- `ratio`: ratio of summary - percentage of the original article (list of `int32`s)
- `type`: type of summary - extractive (`extract`) or abstractive (`abstract`) (list of `string`s)
- `author`: acronym of summary author (list of `string`)
- `body`: body of summary (list of `string`)
- `spans`: a list containing spans for extractive summaries (empty for abstractive summaries):
- `start`: start of span (`int32`)
- `end`: end of span (`int32`)
- `span_text`: span text (`string`)
### Data Splits
Single train split
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@inproceedings{
ogro:kop:14:lrec,
author = "Ogrodniczuk, Maciej and Kopeć, Mateusz",
pdf = "http://nlp.ipipan.waw.pl/Bib/ogro:kop:14:lrec.pdf",
title = "The {P}olish {S}ummaries {C}orpus",
pages = "3712--3715",
crossref = "lrec:14"
}
@proceedings{
lrec:14,
editor = "Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Loftsson, Hrafn and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios",
isbn = "978-2-9517408-8-4",
title = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014",
url = "http://www.lrec-conf.org/proceedings/lrec2014/index.html",
booktitle = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014",
address = "Reykjavík, Iceland",
key = "LREC",
year = "2014",
organization = "European Language Resources Association (ELRA)"
}
```
### Contributions
Thanks to [@kldarek](https://github.com/kldarek) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
AhmedSSoliman/CoNaLa | AhmedSSoliman | 2022-01-22T09:34:19Z | 299 | 0 | null | [
"region:us"
] | 2022-01-22T09:34:19Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
task_categories:
- Code Generation
- Translation
- Text2Text generation
---
# CoNaLa Dataset for Code Generation
## Table of content
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
## Dataset Descritpion
This dataset has been processed for Code Generation. CMU CoNaLa, the Code/Natural Language Challenge is a joint project of the Carnegie Mellon University NeuLab and STRUDEL Lab. This dataset was designed to test systems for generating program snippets from natural language. It is avilable at https://conala-corpus.github.io/ , and this is about 13k records from the full corpus of about 600k examples.
### Languages
English
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"intent": "convert a list to a dictionary in python",
"snippet": "b = dict(zip(a[0::2], a[1::2]))"
},
{
"intent": "python - sort a list of nested lists",
"snippet": "l.sort(key=sum_nested)"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"intent": "Value(dtype='string', id=None)",
"snippet": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train, validation and test split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 11125 |
| valid | 1237 |
| test | 500 |
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Crives/haha | Crives | 2022-02-22T09:40:35Z | 299 | 1 | null | [
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LeverageX/klue-mrc | LeverageX | 2022-01-12T05:01:00Z | 299 | 0 | null | [
"region:us"
] | 2022-01-12T05:01:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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Lucylulu/amazon | Lucylulu | 2021-12-12T16:21:23Z | 299 | 0 | null | [
"region:us"
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Mahalakshmi/ta_lm_processed | Mahalakshmi | 2022-02-06T06:38:15Z | 299 | 0 | null | [
"region:us"
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Mcy/random_uselesstestsequence | Mcy | 2021-12-09T08:54:02Z | 299 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Mulin/my_third_dataset | Mulin | 2021-09-19T01:36:15Z | 299 | 0 | null | [
"region:us"
] | 2021-09-19T01:36:15Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | My third Dataset
- for wolf classification | [
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Plim/language_model_fr | Plim | 2022-12-12T09:11:16Z | 299 | 0 | null | [
"language:fr",
"region:us"
] | 2022-12-12T09:11:16Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language: fr
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Recognai/ag_news_corrected_labels | Recognai | 2021-12-29T17:00:24Z | 299 | 0 | null | [
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Sabokou/qg_squad_modified_dev | Sabokou | 2021-12-30T10:35:48Z | 299 | 0 | null | [
"region:us"
] | 2021-12-30T10:35:48Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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Sam2021/Arguement_Mining_CL2017 | Sam2021 | 2021-08-30T23:11:05Z | 299 | 1 | null | [
"region:us"
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Shushant/nepali | Shushant | 2021-12-21T05:06:24Z | 299 | 0 | null | [
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adalbertojunior/punctuation-ptbr | adalbertojunior | 2021-12-19T15:02:20Z | 299 | 0 | null | [
"region:us"
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allegro/klej-allegro-reviews | allegro | 2021-11-29T18:25:32Z | 299 | 0 | null | [
"region:us"
] | 2021-11-29T18:25:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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allegro/klej-cdsc-r | allegro | 2021-11-29T19:14:36Z | 299 | 0 | null | [
"region:us"
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allegro/summarization-allegro-articles | allegro | 2021-09-24T14:10:17Z | 299 | 4 | null | [
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aminedjebbie/Multi-Arabic-dialects | aminedjebbie | 2022-02-10T20:28:50Z | 299 | 0 | null | [
"region:us"
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antoinegk/HealthChallenge_dataset | antoinegk | 2022-01-19T18:21:42Z | 299 | 0 | null | [
"region:us"
] | 2022-01-19T18:21:42Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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anuragshas/mr_cc100_processed | anuragshas | 2022-02-06T14:46:02Z | 299 | 0 | null | [
"region:us"
] | 2022-02-06T14:46:02Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
austin/rheum_abstracts | austin | 2022-01-04T05:10:23Z | 299 | 0 | null | [
"region:us"
] | 2022-01-04T05:10:23Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Dataset Card for Rheumatology Abstracts
## Data Source
This dataset comes from PubMed, derived from my fork of the pymed package (no longer maintained). My fork can be found at https://github.com/cmcmaster1/pymed
## Data Structure
The dataset is split into train (80%) and test (20%) files (CSV). Each file contains three columns:
- id
- abstract (minus conclusion)
- conclusion | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bhavnicksm/sentihood | bhavnicksm | 2022-10-25T09:07:23Z | 299 | 3 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"task_ids:multi-class-classification",
"task_ids:natural-language-inference",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:1610.03771",
"region:us"
] | 2022-10-25T09:07:23Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: SentiHood Dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
- multi-class-classification
- natural-language-inference
---
# Dataset Card for [SentiHood]
## 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
- **Paper:** https://arxiv.org/abs/1610.03771
- **Leaderboard:** https://paperswithcode.com/sota/aspect-based-sentiment-analysis-on-sentihood
### Dataset Summary
Created as a part of the paper "SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods" by Saeidi et al.
#### Abstract
In this paper, we introduce the task of targeted aspect-based sentiment analysis. The goal is to extract fine-grained information with respect to entities mentioned in user comments. This work extends both aspect-based sentiment analysis that assumes a single entity per document and targeted sentiment analysis that assumes a single sentiment towards a target entity. In particular, we identify the sentiment towards each aspect of one or more entities. As a testbed for this task, we introduce the SentiHood dataset, extracted from a question answering (QA) platform where urban neighborhoods are discussed by users. In this context units of text often mention several aspects of one or more neighborhoods. This is the first time that a generic social media platform in this case a QA platform, is used for fine-grained opinion mining. Text coming from QA platforms is far less constrained compared to text from review-specific platforms on which current datasets are based. We develop several strong baselines, relying on logistic regression and state-of-the-art recurrent neural networks.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Monolingual (only English)
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@Bhavnicksm](https://github.com/Bhavnicksm) for adding this dataset. | [
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bigscience-catalogue-data-dev/lm_code_github-eval_subset | bigscience-catalogue-data-dev | 2022-02-16T10:42:10Z | 299 | 0 | null | [
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bs-modeling-metadata/c4_newslike_url_only | bs-modeling-metadata | 2021-09-20T11:14:17Z | 299 | 0 | null | [
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bwu2018/anime-tagging-dataset | bwu2018 | 2021-12-08T17:20:46Z | 299 | 5 | null | [
"region:us"
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castorini/msmarco_v2_passage_doc2query-t5_expansions | castorini | 2021-11-02T06:37:36Z | 299 | 0 | null | [
"region:us"
] | 2021-11-02T06:37:36Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- English
license: "Apache License 2.0"
---
# Dataset Summary
The repo provides queries generated for the MS MARCO v2 passage corpus with docTTTTTquery (sometimes written as docT5query or doc2query-T5), the latest version of the doc2query family of document expansion models. The basic idea is to train a model, that when given an input document, generates questions that the document might answer (or more broadly, queries for which the document might be relevant). These predicted questions (or queries) are then appended to the original documents, which are then indexed as before. The docTTTTTquery model gets its name from the use of T5 as the expansion model.
# Dataset Structure
All three folds (train, dev and test) share the same corpus. The queries are generated from this corpus.
An example data entry looks as follows:
```
{ "id": "msmarco_passage_22_0",
"predicted_queries": ["in drug combat does a zombie take more damage or die", "is the health bar the same as smash bros", "is brawlhalla health bar", "icpri league brawlhalla", "what is a battle brawlhalla", "is smash bros minecraft brawlhalla zombies", "what are the health bars on brawlhalla", "does smash bros have health bars", "is brawlhalla a health bar", "what is brawlhalla", "what is brwlhalla", "how many health bars is in brawlhalla", "is there health bar in brawlhalla", "what is boiledhalla?", "what is a good health bar in brawlhalla", "what is skills brawlhalla", "how many gobs in a brawlhalla", "is smash bros. an nsb game", "how many health bars are there in the brawlhalla", "what is brawlhalla"]
}
```
# Load Dataset
An example to load the dataset:
```
dataset = load_dataset('castorini/msmarco_v2_passage_doc2query-t5_expansions', data_files='d2q/d2q.jsonl???.gz')
```
# Citation Information
```
@article{docTTTTTquery,
title={From doc2query to {docTTTTTquery}},
author={Nogueira, Rodrigo and Lin, Jimmy},
year={2019}
}
@article{emdt5,
author={Ronak Pradeep and Rodrigo Nogueira and Jimmy Lin},
title={The Expando-Mono-Duo Design Pattern for Text Ranking with Pretrained Sequence-to-Sequence Models},
journal={arXiv:2101.05667},
year={2021},
}
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cestwc/cnn_dailymail-snippets | cestwc | 2022-02-15T06:09:43Z | 299 | 0 | null | [
"region:us"
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chmanoj/ai4bharat__samanantar_processed_te | chmanoj | 2022-02-05T04:02:51Z | 299 | 0 | null | [
"region:us"
] | 2022-02-05T04:02:51Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | This is extracted from telugu subset from https://huggingface.co/datasets/ai4bharat/samanantar - used to create telugu kenLM models for ASR decoding. | [
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dataset/wikipedia_bn | dataset | 2021-06-04T16:22:44Z | 299 | 1 | null | [
"region:us"
] | 2021-06-04T16:22:44Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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davanstrien/crowdsourced-keywords | davanstrien | 2022-03-01T14:07:11Z | 299 | 0 | null | [
"region:us"
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huggingartists/face | huggingartists | 2022-10-25T09:29:31Z | 299 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:29:31Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/face"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.563916 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/1dcb4e1dc4242207c27fe5cd0d4090e8.1000x1000x1.jpg')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/face">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">FACE</div>
<a href="https://genius.com/artists/face">
<div style="text-align: center; font-size: 14px;">@face</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/face).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/face")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|213| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/face")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
huggingartists/fascinoma | huggingartists | 2022-10-25T09:29:38Z | 299 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:29:38Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/fascinoma"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 0.028316 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://assets.genius.com/images/default_avatar_300.png?1627659427')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/fascinoma">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Fascinoma</div>
<a href="https://genius.com/artists/fascinoma">
<div style="text-align: center; font-size: 14px;">@fascinoma</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/fascinoma).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/fascinoma")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|1| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/fascinoma")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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huggingartists/young-thug | huggingartists | 2022-10-25T09:50:19Z | 299 | 0 | null | [
"language:en",
"huggingartists",
"lyrics",
"region:us"
] | 2022-10-25T09:50:19Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- en
tags:
- huggingartists
- lyrics
---
# Dataset Card for "huggingartists/young-thug"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [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)
- [About](#about)
## Dataset Description
- **Homepage:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Repository:** [https://github.com/AlekseyKorshuk/huggingartists](https://github.com/AlekseyKorshuk/huggingartists)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of the generated dataset:** 4.254273 MB
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/b08755976e2dcad78a75ee47059adcbc.777x777x1.png')">
</div>
</div>
<a href="https://huggingface.co/huggingartists/young-thug">
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
</a>
<div style="text-align: center; font-size: 16px; font-weight: 800">Young Thug</div>
<a href="https://genius.com/artists/young-thug">
<div style="text-align: center; font-size: 14px;">@young-thug</div>
</a>
</div>
### Dataset Summary
The Lyrics dataset parsed from Genius. This dataset is designed to generate lyrics with HuggingArtists.
Model is available [here](https://huggingface.co/huggingartists/young-thug).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
en
## How to use
How to load this dataset directly with the datasets library:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/young-thug")
```
## Dataset Structure
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"text": "Look, I was gonna go easy on you\nNot to hurt your feelings\nBut I'm only going to get this one chance\nSomething's wrong, I can feel it..."
}
```
### Data Fields
The data fields are the same among all splits.
- `text`: a `string` feature.
### Data Splits
| train |validation|test|
|------:|---------:|---:|
|1656| -| -|
'Train' can be easily divided into 'train' & 'validation' & 'test' with few lines of code:
```python
from datasets import load_dataset, Dataset, DatasetDict
import numpy as np
datasets = load_dataset("huggingartists/young-thug")
train_percentage = 0.9
validation_percentage = 0.07
test_percentage = 0.03
train, validation, test = np.split(datasets['train']['text'], [int(len(datasets['train']['text'])*train_percentage), int(len(datasets['train']['text'])*(train_percentage + validation_percentage))])
datasets = DatasetDict(
{
'train': Dataset.from_dict({'text': list(train)}),
'validation': Dataset.from_dict({'text': list(validation)}),
'test': Dataset.from_dict({'text': list(test)})
}
)
```
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{huggingartists,
author={Aleksey Korshuk}
year=2021
}
```
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jgammack/SAE-door-abstracts | jgammack | 2022-10-22T08:23:24Z | 299 | 0 | null | [
"task_ids:language-modeling",
"annotations_creators:no-annotation",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2022-10-22T08:23:24Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- expert-generated
language:
- en
language_bcp47:
- en-US
license:
- unknown
multilinguality:
- monolingual
pretty_name: SAE-door-abstracts
size_categories:
- unknown
source_datasets:
- original
task_categories:
- sequence-modeling
task_ids:
- language-modeling
---
# SAE-door-abstracts
This dataset includes ~1,550 texts of abstracts of technical papers and journal articles from the SAE Mobilus database that cover the topics of automotive or aerospace doors, noise, acoustics, and vibrations. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
keremberke/license-plate-object-detection | keremberke | 2023-01-18T20:37:51Z | 299 | 8 | null | [
"task_categories:object-detection",
"roboflow",
"roboflow2huggingface",
"Self Driving",
"Anpr",
"region:us"
] | 2023-01-18T20:37:51Z | 2023-01-01T02:32:07.000Z | 2023-01-01T02:32:07 | ---
task_categories:
- object-detection
tags:
- roboflow
- roboflow2huggingface
- Self Driving
- Anpr
---
<div align="center">
<img width="640" alt="keremberke/license-plate-object-detection" src="https://huggingface.co/datasets/keremberke/license-plate-object-detection/resolve/main/thumbnail.jpg">
</div>
### Dataset Labels
```
['license_plate']
```
### Number of Images
```json
{'train': 6176, 'valid': 1765, 'test': 882}
```
### How to Use
- Install [datasets](https://pypi.org/project/datasets/):
```bash
pip install datasets
```
- Load the dataset:
```python
from datasets import load_dataset
ds = load_dataset("keremberke/license-plate-object-detection", name="full")
example = ds['train'][0]
```
### Roboflow Dataset Page
[https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk/dataset/1](https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk/dataset/1?ref=roboflow2huggingface)
### Citation
```
@misc{ vehicle-registration-plates-trudk_dataset,
title = { Vehicle Registration Plates Dataset },
type = { Open Source Dataset },
author = { Augmented Startups },
howpublished = { \\url{ https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk } },
url = { https://universe.roboflow.com/augmented-startups/vehicle-registration-plates-trudk },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { jun },
note = { visited on 2023-01-18 },
}
```
### License
CC BY 4.0
### Dataset Summary
This dataset was exported via roboflow.ai on January 13, 2022 at 5:20 PM GMT
It includes 8823 images.
VRP are annotated in COCO format.
The following pre-processing was applied to each image:
* Auto-orientation of pixel data (with EXIF-orientation stripping)
No image augmentation techniques were applied.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
jphme/ger_micro_benchmark | jphme | 2023-09-18T21:15:10Z | 299 | 1 | null | [
"region:us"
] | 2023-09-18T21:15:10Z | 2023-09-18T20:55:08.000Z | 2023-09-18T20:55:08 | ---
configs:
- config_name: default
data_files:
- split: eval
path: data/eval-*
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: subject
dtype: string
splits:
- name: eval
num_bytes: 69430
num_examples: 200
download_size: 39957
dataset_size: 69430
---
# Dataset Card for "ger_micro_benchmark"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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eduge | null | 2023-01-25T14:29:42Z | 298 | 3 | null | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:mn",
"license:unknown",
"region:us"
] | 2023-01-25T14:29:42Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- mn
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
pretty_name: Eduge
dataset_info:
features:
- name: news
dtype: string
- name: label
dtype:
class_label:
names:
'0': урлаг соёл
'1': эдийн засаг
'2': эрүүл мэнд
'3': хууль
'4': улс төр
'5': спорт
'6': технологи
'7': боловсрол
'8': байгал орчин
splits:
- name: train
num_bytes: 255275842
num_examples: 60528
- name: test
num_bytes: 64451731
num_examples: 15133
download_size: 320395067
dataset_size: 319727573
---
# Dataset Card for Eduge
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://eduge.mn/
- **Repository:** https://github.com/tugstugi/mongolian-nlp
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Eduge news classification dataset provided by Bolorsoft LLC. Used to train the Eduge.mn production news classifier
75K news articles in 9 categories: урлаг соёл, эдийн засаг, эрүүл мэнд, хууль, улс төр, спорт, технологи, боловсрол and байгал орчин
### Supported Tasks and Leaderboards
- `text-classification`: We can transform the above into a 9-class classification task.
### Languages
The text in the dataset is in Mongolian
## Dataset Structure
### Data Instances
For the `default` configuration:
```
{
'label': 0, # 'урлаг соёл'
'news': 'Шударга өрсөлдөөн, хэрэглэгчийн төлөө газар 2013 оны дөрөвдүгээр сараас эхлэн Монгол киноны ашиг орлогын мэдээллийг олон нийтэд хүргэж байгаа. Ингэснээр Монголын кино үйлдвэрлэгчид улсад ашиг орлогоо шударгаар төлөх, мөн чанартай уран бүтээлийн тоо өсөх боломж бүрдэж байгаа юм.',
}
```
### Data Fields
- `news`: a complete news article on a specific topic as a string
- `label`: the single class of the topic, among these values: "урлаг соёл" (0), "эдийн засаг" (1), "эрүүл мэнд" (2), "хууль" (3), "улс төр" (4), "спорт" (5), "технологи" (6), "боловсрол" (7), "байгал орчин" (8).
### Data Splits
The set of complete articles is split into a training and test set.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
Eduge.mn which is a combination from shuud.mn, ikon.mn, olloo.mn, news.gogo.mn, montsame.mn, zaluu.com, sonin.mn, medee.mn, bloombergtv.mn.
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
No citation available for this dataset.
### Contributions
Thanks to [@enod](https://github.com/enod) for adding this dataset. | [
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opus_fiskmo | null | 2022-11-03T16:08:01Z | 298 | 0 | null | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:fi",
"language:sv",
"license:unknown",
"region:us"
] | 2022-11-03T16:08:01Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- fi
- sv
license:
- unknown
multilinguality:
- translation
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusFiskmo
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- fi
- sv
config_name: fi-sv
splits:
- name: train
num_bytes: 326528834
num_examples: 2100001
download_size: 144858927
dataset_size: 326528834
---
# Dataset Card for [opus_fiskmo]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[fiskmo](http://opus.nlpl.eu/fiskmo.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
fiskmo, a massive parallel corpus for Finnish and Swedish.
### Supported Tasks and Leaderboards
The underlying task is machine translation for language pair Finnish and Swedish.
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
opus_memat | null | 2022-11-03T16:08:11Z | 298 | 1 | null | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"language:xh",
"license:unknown",
"region:us"
] | 2022-11-03T16:08:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
- xh
license:
- unknown
multilinguality:
- translation
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusMemat
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- xh
- en
config_name: xh-en
splits:
- name: train
num_bytes: 25400570
num_examples: 154764
download_size: 8382865
dataset_size: 25400570
---
# Dataset Card for [opus_memat]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[memat](http://opus.nlpl.eu/memat.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Xhosa-English parallel corpora, funded by EPSRC, the Medical Machine Translation project worked on machine translation between ixiXhosa and English, with a focus on the medical domain.
### Supported Tasks and Leaderboards
The underlying task is machine translation from Xhosa to English
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
opus_montenegrinsubs | null | 2022-11-03T16:08:11Z | 298 | 0 | null | [
"task_categories:translation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:cnr",
"language:en",
"license:unknown",
"region:us"
] | 2022-11-03T16:08:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- cnr
- en
license:
- unknown
multilinguality:
- translation
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: OpusMontenegrinsubs
dataset_info:
features:
- name: translation
dtype:
translation:
languages:
- en
- me
config_name: en-me
splits:
- name: train
num_bytes: 4896403
num_examples: 65043
download_size: 1990570
dataset_size: 4896403
---
# Dataset Card for [opus_montenegrinsubs]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[opus MontenegrinSubs ](http://opus.nlpl.eu/MontenegrinSubs.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Opus MontenegrinSubs dataset for machine translation task, for language pair en-me: english and montenegrin
### Supported Tasks and Leaderboards
The underlying task is machine translation from en to me
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
py_ast | null | 2022-11-18T21:40:05Z | 298 | 3 | null | [
"task_categories:text2text-generation",
"task_categories:text-generation",
"task_categories:fill-mask",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:code",
"license:bsd-2-clause",
"license:mit",
"code-modeling",
"code-generation",
"region:us"
] | 2022-11-18T21:40:05Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: PyAst
annotations_creators:
- machine-generated
language_creators:
- found
language:
- code
license:
- bsd-2-clause
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
- text-generation
- fill-mask
task_ids: []
paperswithcode_id: null
tags:
- code-modeling
- code-generation
dataset_info:
features:
- name: ast
sequence:
- name: type
dtype: string
- name: value
dtype: string
- name: children
sequence: int32
config_name: ast
splits:
- name: train
num_bytes: 1870790180
num_examples: 100000
- name: test
num_bytes: 907514993
num_examples: 50000
download_size: 526642289
dataset_size: 2778305173
---
# Dataset Card for [py_ast]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **homepage**: [py150](https://www.sri.inf.ethz.ch/py150)
- **Paper**: [Probabilistic Model for Code with Decision Trees](https://www.semanticscholar.org/paper/Probabilistic-model-for-code-with-decision-trees-Raychev-Bielik/62e176977d439aac2e2d7eca834a7a99016dfcaf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset consists of parsed ASTs that were used to train and evaluate the DeepSyn tool.
The Python programs are collected from GitHub repositories
by removing duplicate files, removing project forks (copy of another existing repository),
keeping only programs that parse and have at most 30'000 nodes in the AST and
we aim to remove obfuscated files
### Supported Tasks and Leaderboards
Code Representation, Unsupervised Learning
### Languages
Python
## Dataset Structure
### Data Instances
A typical datapoint contains an AST of a python program, parsed.
The main key is `ast` wherein every program's AST is stored.
Each children would have,
`type` which will formulate the type of the node.
`children` which enumerates if a given node has children(non-empty list).
`value`, if the given node has any hardcoded value(else "N/A").
An example would be,
'''
[ {"type":"Module","children":[1,4]},{"type":"Assign","children":[2,3]},{"type":"NameStore","value":"x"},{"type":"Num","value":"7"}, {"type":"Print","children":[5]}, {"type":"BinOpAdd","children":[6,7]}, {"type":"NameLoad","value":"x"}, {"type":"Num","value":"1"} ]
'''
### Data Fields
- `ast`: a list of dictionaries, wherein every dictionary is a node in the Abstract Syntax Tree.
- `type`: explains the type of the node.
- `children`: list of nodes which are children under the given
- `value`: hardcoded value, if the node holds an hardcoded value.
### Data Splits
The data is split into a training and test set.
The final split sizes are as follows:
| | train | validation |
|------------------|--------:|------------:|
| py_ast examples | 100000 | 50000 |
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Raychev, V., Bielik, P., and Vechev, M
### Licensing Information
MIT, BSD and Apache
### Citation Information
@InProceedings{OOPSLA ’16, ACM,
title = {Probabilistic Model for Code with Decision Trees.},
authors={Raychev, V., Bielik, P., and Vechev, M.},
year={2016}
}
```
@inproceedings{10.1145/2983990.2984041,
author = {Raychev, Veselin and Bielik, Pavol and Vechev, Martin},
title = {Probabilistic Model for Code with Decision Trees},
year = {2016},
isbn = {9781450344449},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2983990.2984041},
doi = {10.1145/2983990.2984041},
booktitle = {Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications},
pages = {731–747},
numpages = {17},
keywords = {Code Completion, Decision Trees, Probabilistic Models of Code},
location = {Amsterdam, Netherlands},
series = {OOPSLA 2016}
}
```
### Contributions
Thanks to [@reshinthadithyan](https://github.com/reshinthadithyan) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
smartdata | null | 2023-01-25T14:44:26Z | 298 | 1 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:de",
"license:cc-by-4.0",
"region:us"
] | 2023-01-25T14:44:26Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- de
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: SmartData
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': O
'1': B-DATE
'2': I-DATE
'3': B-DISASTER_TYPE
'4': I-DISASTER_TYPE
'5': B-DISTANCE
'6': I-DISTANCE
'7': B-DURATION
'8': I-DURATION
'9': B-LOCATION
'10': I-LOCATION
'11': B-LOCATION_CITY
'12': I-LOCATION_CITY
'13': B-LOCATION_ROUTE
'14': I-LOCATION_ROUTE
'15': B-LOCATION_STOP
'16': I-LOCATION_STOP
'17': B-LOCATION_STREET
'18': I-LOCATION_STREET
'19': B-NUMBER
'20': I-NUMBER
'21': B-ORGANIZATION
'22': I-ORGANIZATION
'23': B-ORGANIZATION_COMPANY
'24': I-ORGANIZATION_COMPANY
'25': B-ORG_POSITION
'26': I-ORG_POSITION
'27': B-PERSON
'28': I-PERSON
'29': B-TIME
'30': I-TIME
'31': B-TRIGGER
'32': I-TRIGGER
config_name: smartdata-v3_20200302
splits:
- name: train
num_bytes: 2124312
num_examples: 1861
- name: test
num_bytes: 266529
num_examples: 230
- name: validation
num_bytes: 258681
num_examples: 228
download_size: 18880782
dataset_size: 2649522
---
# Dataset Card for SmartData
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www.dfki.de/web/forschung/projekte-publikationen/publikationen-uebersicht/publikation/9427/
- **Repository:** https://github.com/DFKI-NLP/smartdata-corpus
- **Paper:** https://www.dfki.de/fileadmin/user_upload/import/9427_lrec_smartdata_corpus.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
DFKI SmartData Corpus is a dataset of 2598 German-language documents
which has been annotated with fine-grained geo-entities, such as streets,
stops and routes, as well as standard named entity types. It has also
been annotated with a set of 15 traffic- and industry-related n-ary
relations and events, such as Accidents, Traffic jams, Acquisitions,
and Strikes. The corpus consists of newswire texts, Twitter messages,
and traffic reports from radio stations, police and railway companies.
It allows for training and evaluating both named entity recognition
algorithms that aim for fine-grained typing of geo-entities, as well
as n-ary relation extraction systems.
### Supported Tasks and Leaderboards
NER
### Languages
German
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- id: an identifier for the article the text came from
- tokens: a list of string tokens for the text of the article
- ner_tags: a corresponding list of NER tags in the BIO format
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CC-BY 4.0
### Citation Information
```
@InProceedings{SCHIERSCH18.85,
author = {Martin Schiersch and Veselina Mironova and Maximilian Schmitt and Philippe Thomas and Aleksandra Gabryszak and Leonhard Hennig},
title = "{A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events}",
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
year = {2018},
month = {May 7-12, 2018},
address = {Miyazaki, Japan},
editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga},
publisher = {European Language Resources Association (ELRA)},
isbn = {979-10-95546-00-9},
language = {english}
}
```
### Contributions
Thanks to [@aseifert](https://github.com/aseifert) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
wmt20_mlqe_task3 | null | 2023-01-25T15:02:49Z | 298 | 0 | null | [
"task_categories:translation",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|amazon_us_reviews",
"language:en",
"language:fr",
"license:unknown",
"region:us"
] | 2023-01-25T15:02:49Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- found
language:
- en
- fr
license:
- unknown
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|amazon_us_reviews
task_categories:
- translation
task_ids: []
pretty_name: WMT20 - MultiLingual Quality Estimation (MLQE) Task3
dataset_info:
features:
- name: document_id
dtype: string
- name: source_segments
sequence: string
- name: source_tokenized
sequence: string
- name: mt_segments
sequence: string
- name: mt_tokenized
sequence: string
- name: annotations
sequence:
- name: segment_id
sequence: int32
- name: annotation_start
sequence: int32
- name: annotation_length
sequence: int32
- name: severity
dtype:
class_label:
names:
'0': minor
'1': major
'2': critical
- name: severity_weight
dtype: float32
- name: category
dtype:
class_label:
names:
'0': Addition
'1': Agreement
'2': Ambiguous Translation
'3': Capitalization
'4': Character Encoding
'5': Company Terminology
'6': Date/Time
'7': Diacritics
'8': Duplication
'9': False Friend
'10': Grammatical Register
'11': Hyphenation
'12': Inconsistency
'13': Lexical Register
'14': Lexical Selection
'15': Named Entity
'16': Number
'17': Omitted Auxiliary Verb
'18': Omitted Conjunction
'19': Omitted Determiner
'20': Omitted Preposition
'21': Omitted Pronoun
'22': Orthography
'23': Other POS Omitted
'24': Over-translation
'25': Overly Literal
'26': POS
'27': Punctuation
'28': Shouldn't Have Been Translated
'29': Shouldn't have been translated
'30': Spelling
'31': Tense/Mood/Aspect
'32': Under-translation
'33': Unidiomatic
'34': Unintelligible
'35': Unit Conversion
'36': Untranslated
'37': Whitespace
'38': Word Order
'39': Wrong Auxiliary Verb
'40': Wrong Conjunction
'41': Wrong Determiner
'42': Wrong Language Variety
'43': Wrong Preposition
'44': Wrong Pronoun
- name: token_annotations
sequence:
- name: segment_id
sequence: int32
- name: first_token
sequence: int32
- name: last_token
sequence: int32
- name: token_after_gap
sequence: int32
- name: severity
dtype:
class_label:
names:
'0': minor
'1': major
'2': critical
- name: category
dtype:
class_label:
names:
'0': Addition
'1': Agreement
'2': Ambiguous Translation
'3': Capitalization
'4': Character Encoding
'5': Company Terminology
'6': Date/Time
'7': Diacritics
'8': Duplication
'9': False Friend
'10': Grammatical Register
'11': Hyphenation
'12': Inconsistency
'13': Lexical Register
'14': Lexical Selection
'15': Named Entity
'16': Number
'17': Omitted Auxiliary Verb
'18': Omitted Conjunction
'19': Omitted Determiner
'20': Omitted Preposition
'21': Omitted Pronoun
'22': Orthography
'23': Other POS Omitted
'24': Over-translation
'25': Overly Literal
'26': POS
'27': Punctuation
'28': Shouldn't Have Been Translated
'29': Shouldn't have been translated
'30': Spelling
'31': Tense/Mood/Aspect
'32': Under-translation
'33': Unidiomatic
'34': Unintelligible
'35': Unit Conversion
'36': Untranslated
'37': Whitespace
'38': Word Order
'39': Wrong Auxiliary Verb
'40': Wrong Conjunction
'41': Wrong Determiner
'42': Wrong Language Variety
'43': Wrong Preposition
'44': Wrong Pronoun
- name: token_index
sequence:
sequence:
sequence: int32
- name: total_words
dtype: int32
config_name: plain_text
splits:
- name: train
num_bytes: 10762355
num_examples: 1448
- name: test
num_bytes: 745260
num_examples: 180
- name: validation
num_bytes: 1646596
num_examples: 200
download_size: 3534634
dataset_size: 13154211
---
# Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task3
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [WMT20 Quality Estimation Shared Task](http://www.statmt.org/wmt20/quality-estimation-task.html)
- **Repository**: [Github repository](https://github.com/deep-spin/deep-spin.github.io/tree/master/docs/data/wmt2020_qe)
- **Paper:** *Not available*
### Dataset Summary
From the homepage:
*This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.*
*The goal of this task 3 is to predict document-level quality scores as well as fine-grained annotations.*
*Each document has a product title and its description, and is annotated for translation errors according to the MQM framework. Each error annotation has:*
- ***Word span(s).*** *Errors may consist of one or more words, not necessarily contiguous.*
- ***Severity.*** *An error can be minor (if it doesn't lead to a loss of meaning and it doesn't confuse or mislead the user), major (if it changes the meaning) or critical (if it changes the meaning and carry any type of implication, or could be seen as offensive).*
- ***Type.*** *A label specifying the error type, such as wrong word order, missing words, agreement, etc. They may provide additional information, but systems don't need to predict them.*
### Supported Tasks and Leaderboards
From the homepage:
*Submissions will be evaluated as in Task 1, in terms of Pearson's correlation between the true and predicted MQM document-level scores. Additionally, the predicted annotations will be evaluated in terms of their F1 scores with respect to the gold annotations. The [official evaluation scripts](https://github.com/sheffieldnlp/qe-eval-scripts) are available.*
### Languages
There is a single language pair in the dataset: English (`en`) - French (`fr`).
## Dataset Structure
### Data Instances
An example looks like this:
```
{
'document_id': 'B0000568SY',
'source_segments': ['Razor Scooter Replacement Wheels Set with Bearings', 'Scooter Wheels w/Bearings-Blue'],
'source_tokenized': ['Razor Scooter Replacement Wheels Set with Bearings', 'Scooter Wheels w / Bearings-Blue'],
'mt_segments': ['Roues de rechange Razor Scooter sertie de roulements', 'Roues de scooter w/roulements-bleu'],
'mt_tokenized': ['Roues de rechange Razor Scooter sertie de roulements', 'Roues de scooter w / roulements-bleu'],
'annotations': {
'segment_id': [[0], [1], [1], [0, 0], [0], [1], [1]],
'annotation_start': [[42], [19], [9], [0, 32], [9], [17], [30]],
'annotation_length': [[10], [10], [7], [5, 6], [8], [1], [4]],
'severity': [0, 0, 0, 0, 0, 1, 0],
'severity_weight': [1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0]
'category': [3, 3, 3, 1, 3, 36, 3],
},
'token_annotations': {
'category': [3, 3, 3, 1, 3, 36, 3],
'first_token': [[7], [5], [2], [0, 5], [2], [3], [5]],
'last_token': [[7], [5], [2], [0, 5], [2], [3], [5]],
'segment_id': [[0], [1], [1], [0, 0], [0], [1], [1]],
'severity': [0, 0, 0, 0, 0, 1, 0],
'token_after_gap': [[-1], [-1], [-1], [-1, -1], [-1], [-1], [-1]]
},
'token_index': [[[0, 5], [6, 2], [9, 8], [18, 5], [24, 7], [32, 6], [39, 2], [42, 10]], [[0, 5], [6, 2], [9, 7], [17, 1], [18, 1], [19, 15]]],
'total_words': 16
}
```
### Data Fields
- `document_id`: the document id (name of the folder).
- `source_segments`: the original source text, one sentence per line (i.e. per element of the list).
- `source_tokenized`: a tokenized version of `source_segments`.
- `mt_segments`: the original machine-translated text, one sentence per line (i.e. per element of the list).
- `mt_tokenized`: a tokenized version of `mt_segments`. Default value is `[]` when this information is not available (it happens 3 times in the train set: `B0001BW0PQ`, `B0001GS19U` and `B000A6SMJ0`).
- `annotations`: error annotations for the document. Each item of the list corresponds to an error annotation, which in turn may contain one or more error spans. Error fields are encoded in a dictionary. In the case of a multi-span error, multiple starting positions and lengths are encoded in the list. Note that these positions points to `mt.segments`, not `mt_tokenized`.
- `segment_id`: List of list of integers. Id of each error.
- `annotation_start`: List of list of integers. Start of each error.
- `annotation_length`: List of list of intergers. Length of each error.
- `severity`: List of one hot. Severity category of each error.
- `severity_weight`: List of floats. Severity weight of each error.
- `category`: List of one hot. Category of each error. See the 45 categories in `_ANNOTATION_CATEGORIES_MAPPING`.
- `token_annotations`: tokenized version of `annotations`. Each error span that contains one or more tokens has a "first token" and "last token". Again, multi-span errors have their first and last tokens encoded in a list. When a span is over a gap between two tokens, the "first" and "last" positions are `-1` (encoded as `-` in the original data), and instead the `token_after_gap` column points to the token immediately after the gap. In case of a gap occurring at the end of the sentence, this value will be equal to the number of tokens.
- `segment_id`: List of list of integers. Id of each error.
- `first_token`: List of list of integers. Start of each error.
- `last_token`: List of list of intergers. End of each error.
- `token_after_gap`: List of list of integers. Token after gap of each error.
- `severity`: List of one hot. Severity category of each error.
- `category`: List of one hot. Category of each error. See the 45 categories in `_ANNOTATION_CATEGORIES_MAPPING`.
- `token_index`: a mapping of tokens to their start and ending positions in `mt_segments`. For each token, a start and end value are encoded in a list of length 2, and all tokens represent one item in the list.
- `total_words`: total number of words in the document
```
_ANNOTATION_CATEGORIES_MAPPING = {
0: 'Addition',
1: 'Agreement',
2: 'Ambiguous Translation',
3: 'Capitalization',
4: 'Character Encoding',
5: 'Company Terminology',
6: 'Date/Time',
7: 'Diacritics',
8: 'Duplication',
9: 'False Friend',
10: 'Grammatical Register',
11: 'Hyphenation',
12: 'Inconsistency',
13: 'Lexical Register',
14: 'Lexical Selection',
15: 'Named Entity',
16: 'Number',
17: 'Omitted Auxiliary Verb',
18: 'Omitted Conjunction',
19: 'Omitted Determiner',
20: 'Omitted Preposition',
21: 'Omitted Pronoun',
22: 'Orthography',
23: 'Other POS Omitted',
24: 'Over-translation',
25: 'Overly Literal',
26: 'POS',
27: 'Punctuation',
28: "Shouldn't Have Been Translated",
29: "Shouldn't have been translated",
30: 'Spelling',
31: 'Tense/Mood/Aspect',
32: 'Under-translation',
33: 'Unidiomatic',
34: 'Unintelligible',
35: 'Unit Conversion',
36: 'Untranslated',
37: 'Whitespace',
38: 'Word Order',
39: 'Wrong Auxiliary Verb',
40: 'Wrong Conjunction',
41: 'Wrong Determiner',
42: 'Wrong Language Variety',
43: 'Wrong Preposition',
44: 'Wrong Pronoun'
}
```
### Data Splits
The dataset contains 1,448 documents for training, 200 documents for validation and 180 for (blind) test (all English-French).
## Dataset Creation
### Curation Rationale
The data is dervied from the [Amazon Product Reviews dataset](http://jmcauley.ucsd.edu/data/amazon/).
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Unknown
### Citation Information
```
Not available.
```
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. | [
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youtube_caption_corrections | null | 2023-01-25T15:03:42Z | 298 | 4 | null | [
"task_categories:other",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"token-classification-of-text-errors",
"region:us"
] | 2023-01-25T15:03:42Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- other
- text-generation
- fill-mask
task_ids:
- slot-filling
pretty_name: YouTube Caption Corrections
tags:
- token-classification-of-text-errors
dataset_info:
features:
- name: video_ids
dtype: string
- name: default_seq
sequence: string
- name: correction_seq
sequence: string
- name: diff_type
sequence:
class_label:
names:
'0': NO_DIFF
'1': CASE_DIFF
'2': PUNCUATION_DIFF
'3': CASE_AND_PUNCUATION_DIFF
'4': STEM_BASED_DIFF
'5': DIGIT_DIFF
'6': INTRAWORD_PUNC_DIFF
'7': UNKNOWN_TYPE_DIFF
'8': RESERVED_DIFF
splits:
- name: train
num_bytes: 355978939
num_examples: 10769
download_size: 222479455
dataset_size: 355978939
---
# Dataset Card for YouTube Caption Corrections
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/2dot71mily/youtube_captions_corrections
- **Repository:** https://github.com/2dot71mily/youtube_captions_corrections
- **Paper:** [N/A]
- **Leaderboard:** [N/A]
- **Point of Contact:** Emily McMilin
### Dataset Summary
This dataset is built from pairs of YouTube captions where both an auto-generated and a manually-corrected caption are available for a single specified language. It currently only in English, but scripts at repo support other languages. The motivation for creating it was from viewing errors in auto-generated captions at a recent virtual conference, with the hope that there could be some way to help correct those errors.
The dataset in the repo at https://github.com/2dot71mily/youtube_captions_corrections records in a non-destructive manner all the differences between an auto-generated and a manually-corrected caption for thousands of videos. The dataset here focuses on the subset of those differences which are mutual and have the same size in token length difference, which means it excludes token insertion or deletion differences between the two captions. Therefore dataset here remains a non-destructive representation of the original auto-generated captions, but excludes some of the differences that are found in the manually-corrected captions.
### Supported Tasks and Leaderboards
- `token-classification`: The tokens in `default_seq` are from the auto-generated YouTube captions. If `diff_type` is labeled greater than `0` at a given index, then the associated token in same index in the `default_seq` was found to be different to the token in the manually-corrected YouTube caption, and therefore we assume it is an error. A model can be trained to learn when there are errors in the auto-generated captions.
- `slot-filling`: The `correction_seq` is sparsely populated with tokens from the manually-corrected YouTube captions in the locations where there was found to be a difference to the token in the auto-generated YouTube captions. These 'incorrect' tokens in the `default_seq` can be masked in the locations where `diff_type` is labeled greater than `0`, so that a model can be trained to hopefully find a better word to fill in, rather than the 'incorrect' one.
End to end, the models could maybe first identify and then replace (with suitable alternatives) errors in YouTube and other auto-generated captions that are lacking manual corrections
### Languages
English
## Dataset Structure
### Data Instances
If `diff_type` is labeled greater than `0` at a given index, then the associated token in same index in the `default_seq` was found to have a difference to the token in the manually-corrected YouTube caption. The `correction_seq` is sparsely populated with tokens from the manually-corrected YouTube captions at those locations of differences.
`diff_type` labels for tokens are as follows:
0: No difference
1: Case based difference, e.g. `hello` vs `Hello`
2: Punctuation difference, e.g. `hello` vs `hello`
3: Case and punctuation difference, e.g. `hello` vs `Hello,`
4: Word difference with same stem, e.g. `thank` vs `thanked`
5: Digit difference, e.g. `2` vs `two`
6: Intra-word punctuation difference, e.g. `autogenerated` vs `auto-generated`
7: Unknown type of difference, e.g. `laughter` vs `draft`
8: Reserved for unspecified difference
{
'video_titles': '_QUEXsHfsA0',
'default_seq': ['you', 'see', "it's", 'a', 'laughter', 'but', 'by', 'the', 'time', 'you', 'see', 'this', 'it', "won't", 'be', 'so', 'we', 'have', 'a', 'big']
'correction_seq': ['', 'see,', '', '', 'draft,', '', '', '', '', '', 'read', 'this,', '', '', 'be.', 'So', '', '', '', '']
'diff_type': [0, 2, 0, 0, 7, 0, 0, 0, 0, 0, 7, 2, 0, 0, 2, 1, 0, 0, 0, 0]
}
### Data Fields
- 'video_ids': Unique ID used by YouTube for each video. Can paste into `https://www.youtube.com/watch?v=<{video_ids}` to see video
- 'default_seq': Tokenized auto-generated YouTube captions for the video
- 'correction_seq': Tokenized manually-corrected YouTube captions only at those locations, where there is a difference between the auto-generated and manually-corrected captions
- 'diff_type': A value greater than `0` at every token where there is a difference between the auto-generated and manually-corrected captions
### Data Splits
No data splits
## Dataset Creation
### Curation Rationale
It was created after viewing errors in auto-generated captions at a recent virtual conference, with the hope that there could be some way to help correct those errors.
### Source Data
#### Initial Data Collection and Normalization
All captions are requested via `googleapiclient` and `youtube_transcript_api` at the `channel_id` and language granularity, using scripts written at https://github.com/2dot71mily/youtube_captions_corrections.
The captions are tokenized on spaces and the manually-corrected sequence has here been reduced to only include differences between it and the auto-generated sequence.
#### Who are the source language producers?
Auto-generated scripts are from YouTube and the manually-corrected scripts are from creators, and any support they may have (e.g. community or software support)
### Annotations
#### Annotation process
Scripts at repo, https://github.com/2dot71mily/youtube_captions_corrections take a diff of the two captions and use this to create annotations.
#### Who are the annotators?
YouTube creators, and any support they may have (e.g. community or software support)
### Personal and Sensitive Information
All content publicly available on YouTube
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Emily McMilin
### Licensing Information
MIT License
### Citation Information
https://github.com/2dot71mily/youtube_captions_corrections
### Contributions
Thanks to [@2dot71mily](https://github.com/2dot71mily) for adding this dataset. | [
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Cyberfish/text_error_correction | Cyberfish | 2021-08-19T13:07:16Z | 298 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Daniele/dante-corpus | Daniele | 2021-11-12T11:44:16Z | 298 | 1 | null | [
"region:us"
] | 2021-11-12T11:44:16Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
YAML tags:
- copy-paste the tags obtained with the online tagging app: https://huggingface.co/spaces/huggingface/datasets-tagging
---
# Dataset Card for [Dataset Name]
## 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)
- [Additional Information](#additional-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
All literary production of great poet Dante Alighieri.
### Supported Tasks and Leaderboards
Fill Mask task.
### Languages
(Ancient) Italian.
### Contributions
Thanks to [@danielekp](https://github.com/danielekp) for adding this dataset.
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GEM-submissions/Leo__mbart-large-cc25__1645802644 | GEM-submissions | 2022-02-25T15:24:07Z | 298 | 0 | null | [
"benchmark:gem",
"region:us"
] | 2022-02-25T15:24:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: mbart-large-cc25
---
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GEM-submissions/lewtun__hugging-face-test-t5-base.outputs.json-36bf2a59__1645558682 | GEM-submissions | 2022-02-22T19:38:08Z | 298 | 0 | null | [
"benchmark:gem",
"region:us"
] | 2022-02-22T19:38:08Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
benchmark: gem
type: prediction
submission_name: Hugging Face test T5-base.outputs.json 36bf2a59
---
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Jack0508/test | Jack0508 | 2021-11-07T18:03:21Z | 298 | 0 | null | [
"region:us"
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Khanoooo/autonlp-data-Corona | Khanoooo | 2021-08-08T19:59:14Z | 298 | 0 | null | [
"region:us"
] | 2021-08-08T19:59:14Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | It's all about Corona | [
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NYTK/HuRC | NYTK | 2022-07-07T13:03:49Z | 298 | 1 | null | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|other",
"language:hu",
"license:cc-by-4.0",
"region:us"
] | 2022-07-07T13:03:49Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
YAML tags:
annotations_creators:
- crowdsourced
language_creators:
- found
- expert-generated
language:
- hu
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: HuRC
size_categories:
- unknown
source_datasets:
- extended|other
task_categories:
- question-answering
task_ids:
- extractive-qa
- abstractive-qa
---
# Dataset Card for HuRC
## 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
- **Homepage:**
- **Repository:**
[HuRC dataset](https://github.com/nytud/HuRC)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
[lnnoemi](mailto:ligeti-nagy.noemi@nytud.hu)
### Dataset Summary
This is the dataset card for the Hungarian Corpus for Reading Comprehension with Commonsense Reasoning (HuRC), which is also part of the Hungarian Language Understanding Evaluation Benchmark Kit HuLU.
The dataset contains 80 614 instances. Each instance is composed of a lead, a passage and a cloze-style query with a masked entity. The task is to select the named entity that is being masked in the query.
The data was automatically collected from the online news of Népszabadság online (nol.hu).
### Languages
The BCP-47 code for Hungarian, the only represented language in this dataset, is hu-HU.
## Dataset Structure
### Data Instances
For each instance, there is an id, a lead, a passage, a query and a MASK.
An example:
```
{
"id": "1",
"lead": ["A Közigazgatási és Igazságügyi Minisztérium szerint a Bárka Színház esetében felmerült a felelőtlen gazdálkodás gyanúja, egyes értesülések szerint pedig ebben \"a színház igazgatójának és gazdasági vezetőjének felelőssége is felmerül\""],
"passage": [
"A teátrumnak Navracsics Tibor közigazgatási és igazságügyi miniszterhez és Kocsis Máté VIII. kerületi polgármesterhez",
"reagálva a tárca azt írta, hogy a felelőtlen gazdálkodás gyanújában \"egyes értesülések szerint a színház igazgatójának és gazdasági vezetőjének felelőssége is felmerül\". A KIM \"éppen ezért nagyon várja az Állami Számvevőszék készülő jelentését, hogy tiszta képet kaphasson a színház működéséről\".",
"A minisztérium hangsúlyozta, hogy az elmúlt évben is mindent elkövetett azért, hogy a Bárka Színház \"valós, rangos művészeti térként\" működjön, és a továbbiakban is ez a szándéka, de jelenleg a társulat működtetését a minisztérium fenntartói támogatás formájában jogszerűen még nem tudja megoldani.",
"A teátrum az átadás-átvétel elhúzódásának okát keresve tette közzé nyílt levelét, amelyben elmaradó fizetésekre, előadásokra és bemutatókra hívta fel a figyelmet, és jelezte, hogy várja a helyzet megoldását.",
"A színház átadás-átvétele jelenleg zajlik, a folyamat végeztével a Bárka a józsefvárosi önkormányzattól állami tulajdonba, a tervek szerint a Közigazgatási és Igazságügyi Minisztérium fenntartásába kerül."
],
"query": "A KIM 2014-es költségvetésében szerepel a Bárka Színház, de amíg nem a minisztérium a [MASK] fenntartója, addig ez a költségvetési keret nem nyitható meg.",
"MASK": "Bárka",
}
```
### Data Fields
- id: unique id of the instances;
- lead: a short summary of the article as it was extracted from the source texts;
- passage: 3-6 paragraphs of texts as the body of the article;
- query: the last paragraph of an article, some kind of summary or conclusion, with a named entity masked (with [MASK]) in it;
- MASK: the masked named entity.
### Data Splits
HuRC has 3 splits: *train*, *validation* and *test*.
| Dataset split | Number of instances in the split | Proportion of the split
|---------------|----------------------------------| ---------|
| train | 64614 | 80%|
| validation | 8000 |10%|
| test | 8000 |10%|
The test data is distributed without the MASK fields. To evaluate your model, please [contact us](mailto:ligeti-nagy.noemi@nytud.hu), or check [HuLU's website](hulu.nlp.nytud.hu) for an automatic evaluation (this feature is under construction at the moment).
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
To produce the Hungarian material, we used the daily articles from Népszabadság Online which had titles and summaries as well. We selected 3-6 paragraphs from each article from the ones which contain proper nouns both in the main part and the summary as well. We trained a NER model using huBERT (Nemeskey 2021) for recognizing proper nouns. NerKor (Simon és Vadász 2021) and Huggingface’s token-level classification library were used to fine-tune the model. Our model achieved an F-score of 90.18 on the test material. As a final step, we found pairs of proper names which are present both in the main article and the summary. Multiple articles contained more than one such pairs so we used those more than once. This resulted in a database of 88655 instances (from 49782 articles).
The quantitative properties of our corpus are as follows: Number of articles: 88655 Number of different articles (type): 49782 Token: 27703631 Type: 1115.260 Average length of text (token): 249.42 (median: 229) Average question length (token): 63.07 (median: 56). We fine-tuned the corpus by hand.
One annotator per 100 unit checked and validated the dataset for which we provided our own demo interface. Automatic masking and the previous occurrence of the entity was checked. This resulted in a database of 80 614 validated entries.
## Additional Information
### Licensing Information
HuRC is released under the cc-by-4.0 license.
### Citation Information
If you use this resource or any part of its documentation, please refer to:
Ligeti-Nagy, N., Ferenczi, G., Héja, E., Jelencsik-Mátyus, K., Laki, L. J., Vadász, N., Yang, Z. Gy. and Váradi, T. (2022) HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából [HuLU: Hungarian benchmark dataset to evaluate neural language models]. XVIII. Magyar Számítógépes Nyelvészeti Konferencia. (in press)
```
@inproceedings{ligetinagy2022hulu,
title={HuLU: magyar nyelvű benchmark adatbázis kiépítése a neurális nyelvmodellek kiértékelése céljából},
author={Ligeti-Nagy, N. and Ferenczi, G. and Héja, E. and Jelencsik-Mátyus, K. and Laki, L. J. and Vadász, N. and Yang, Z. Gy. and Váradi, T.},
booktitle={XVIII. Magyar Számítógépes Nyelvészeti Konferencia},
year={2022}
}
```
### Contributions
Thanks to [lnnoemi](https://github.com/lnnoemi) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
PaulLerner/viquae_dataset | PaulLerner | 2022-02-15T17:43:12Z | 298 | 2 | null | [
"region:us"
] | 2022-02-15T17:43:12Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | See https://github.com/PaulLerner/ViQuAE
---
license: cc-by-4.0
---
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Recognai/corrected_labels_ag_news | Recognai | 2021-12-29T16:57:56Z | 298 | 0 | null | [
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Recognai/news | Recognai | 2022-02-17T14:33:02Z | 298 | 0 | null | [
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Recognai/veganuary | Recognai | 2022-02-04T10:07:21Z | 298 | 0 | null | [
"region:us"
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RollingMuffin/test_scripts | RollingMuffin | 2022-02-18T13:58:25Z | 298 | 0 | null | [
"region:us"
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Sabokou/qg_squad_modified | Sabokou | 2021-12-29T20:46:02Z | 298 | 0 | null | [
"region:us"
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Shushant/ContaminationQA | Shushant | 2022-01-14T15:46:12Z | 298 | 0 | null | [
"region:us"
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Smiling/webnovels-en | Smiling | 2021-11-04T05:51:23Z | 298 | 0 | null | [
"region:us"
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Valahaar/wsdmt | Valahaar | 2021-05-15T12:42:12Z | 298 | 0 | null | [
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abidlabs/crowdsourced-speech4 | abidlabs | 2022-01-21T16:26:22Z | 298 | 0 | null | [
"region:us"
] | 2022-01-21T16:26:22Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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abidlabs/crowdsourced-speech5 | abidlabs | 2022-01-21T16:38:34Z | 298 | 0 | null | [
"region:us"
] | 2022-01-21T16:38:34Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Entry not found | [
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addy88/sanskrit-asr-84-eval | addy88 | 2021-12-14T13:42:29Z | 298 | 1 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
albertvillanova/carbon_24 | albertvillanova | 2022-10-24T15:25:03Z | 298 | 0 | null | [
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:other-crystallography",
"size_categories:unknown",
"language:cif",
"license:mit",
"material-property-optimization",
"material-reconstruction",
"material-generation",
"arxiv:2110.06197",
"region:us"
] | 2022-10-24T15:25:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- cif
license:
- mit
multilinguality:
- other-crystallography
size_categories:
- unknown
source_datasets: []
task_categories:
- other
task_ids: []
pretty_name: Carbon-24
tags:
- material-property-optimization
- material-reconstruction
- material-generation
---
# Dataset Card for Carbon-24
## 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
- **Homepage:**
- **Repository:** https://github.com/txie-93/cdvae/tree/main/data/carbon_24
- **Paper:** [Crystal Diffusion Variational Autoencoder for Periodic Material Generation](https://arxiv.org/abs/2110.06197)
- **Leaderboard:**
- **Point of Contact:** [Tian Xie](mailto:txie@csail.mit.edu)
### Dataset Summary
Carbon-24 contains 10k carbon materials, which share the same composition, but have different structures. There is 1 element and the materials have 6 - 24 atoms in the unit cells.
Carbon-24 includes various carbon structures obtained via ab initio random structure searching (AIRSS) (Pickard & Needs, 2006; 2011) performed at 10 GPa.
The original dataset includes 101529 carbon structures, and we selected the 10% of the carbon structure with the lowest energy per atom to create Carbon-24. All 10153 structures are at local energy minimum after DFT relaxation. The most stable structure is diamond at 10 GPa. All remaining structures are thermodynamically unstable but may be kinetically stable.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
Please consider citing the following papers:
```
@article{xie2021crystal,
title={Crystal Diffusion Variational Autoencoder for Periodic Material Generation},
author={Tian Xie and Xiang Fu and Octavian-Eugen Ganea and Regina Barzilay and Tommi Jaakkola},
year={2021},
eprint={2110.06197},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
and
```
@misc{carbon2020data,
doi = {10.24435/MATERIALSCLOUD:2020.0026/V1},
url = {https://archive.materialscloud.org/record/2020.0026/v1},
author = {Pickard, Chris J.},
keywords = {DFT, ab initio random structure searching, carbon},
language = {en},
title = {AIRSS data for carbon at 10GPa and the C+N+H+O system at 1GPa},
publisher = {Materials Cloud},
year = {2020},
copyright = {info:eu-repo/semantics/openAccess}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
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albertvillanova/datasets-tests-compression | albertvillanova | 2021-08-16T13:22:32Z | 298 | 0 | null | [
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albertvillanova/lm_en_dummy1 | albertvillanova | 2022-02-02T09:10:11Z | 298 | 0 | null | [
"region:us"
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albertvillanova/lm_en_dummy2 | albertvillanova | 2022-02-02T08:51:44Z | 298 | 0 | null | [
"region:us"
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1.1796802282333374,
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0.07975788414478302,
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0.6594987511634827,
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-0.4864429533481598,
0.3832802176475525,
0.3523699641227722,
0.44048118591308594,
0.6148120164871216,
0.14084689319133759,
0.8338428735733032,
0.31260576844215393,
-0.17026856541633606,
0.2698982357978821,
-0.4559198319911957,
-0.02893332578241825,
-0.05796259641647339,
0.3101596236228943,
-1.026215672492981
] | null | null | null | null | null | null | null | null | null | null | null | null | null |
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