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nlphuji/winogavil | nlphuji | 2022-11-26T19:56:27Z | 474 | 0 | winogavil | [
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"commonsense-reasoning",
"visual-reasoning",
"arxiv:2207.12576",
"region:us"
] | 2022-11-26T19:56:27Z | 2022-09-23T19:27:29.000Z | 2022-09-23T19:27:29 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
paperswithcode_id: winogavil
pretty_name: WinoGAViL
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- commonsense-reasoning
- visual-reasoning
task_ids: []
extra_gated_prompt: "By clicking on “Access repository” below, you also agree that you are using it solely for research purposes. The full license agreement is available in the dataset files."
---
# Dataset Card for WinoGAViL
- [Dataset Description](#dataset-description)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Colab notebook code for Winogavil evaluation with CLIP](#colab-notebook-code-for-winogavil-evaluation-with-clip)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
WinoGAViL is a challenging dataset for evaluating vision-and-language commonsense reasoning abilities. Given a set of images, a cue, and a number K, the task is to select the K images that best fits the association. This dataset was collected via the WinoGAViL online game to collect vision-and-language associations, (e.g., werewolves to a full moon). Inspired by the popular card game Codenames, a spymaster gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating associations that are challenging for a rival AI model but still solvable by other human players. We evaluate several state-of-the-art vision-and-language models, finding that they are intuitive for humans (>90% Jaccard index) but challenging for state-of-the-art AI models, where the best model (ViLT) achieves a score of 52%, succeeding mostly where the cue is visually salient. Our analysis as well as the feedback we collect from players indicate that the collected associations require diverse reasoning skills, including general knowledge, common sense, abstraction, and more.
- **Homepage:**
https://winogavil.github.io/
- **Colab**
https://colab.research.google.com/drive/19qcPovniLj2PiLlP75oFgsK-uhTr6SSi
- **Repository:**
https://github.com/WinoGAViL/WinoGAViL-experiments/
- **Paper:**
https://arxiv.org/abs/2207.12576
- **Leaderboard:**
https://winogavil.github.io/leaderboard
- **Point of Contact:**
winogavil@gmail.com; yonatanbitton1@gmail.com
### Supported Tasks and Leaderboards
https://winogavil.github.io/leaderboard.
https://paperswithcode.com/dataset/winogavil.
## Colab notebook code for Winogavil evaluation with CLIP
https://colab.research.google.com/drive/19qcPovniLj2PiLlP75oFgsK-uhTr6SSi
### Languages
English.
## Dataset Structure
### Data Fields
candidates (list): ["bison", "shelter", "beard", "flea", "cattle", "shave"] - list of image candidates.
cue (string): pogonophile - the generated cue.
associations (string): ["bison", "beard", "shave"] - the images associated with the cue selected by the user.
score_fool_the_ai (int64): 80 - the spymaster score (100 - model score) for fooling the AI, with CLIP RN50 model.
num_associations (int64): 3 - The number of images selected as associative with the cue.
num_candidates (int64): 6 - the number of total candidates.
solvers_jaccard_mean (float64): 1.0 - three solvers scores average on the generated association instance.
solvers_jaccard_std (float64): 1.0 - three solvers scores standard deviation on the generated association instance
ID (int64): 367 - association ID.
### Data Splits
There is a single TEST split. In the accompanied paper and code we sample it to create different training sets, but the intended use is to use winogavil as a test set.
There are different number of candidates, which creates different difficulty levels:
-- With 5 candidates, random model expected score is 38%.
-- With 6 candidates, random model expected score is 34%.
-- With 10 candidates, random model expected score is 24%.
-- With 12 candidates, random model expected score is 19%.
<details>
<summary>Why random chance for success with 5 candidates is 38%?</summary>
It is a binomial distribution probability calculation.
Assuming N=5 candidates, and K=2 associations, there could be three events:
(1) The probability for a random guess is correct in 0 associations is 0.3 (elaborate below), and the Jaccard index is 0 (there is no intersection between the correct labels and the wrong guesses). Therefore the expected random score is 0.
(2) The probability for a random guess is correct in 1 associations is 0.6, and the Jaccard index is 0.33 (intersection=1, union=3, one of the correct guesses, and one of the wrong guesses). Therefore the expected random score is 0.6*0.33 = 0.198.
(3) The probability for a random guess is correct in 2 associations is 0.1, and the Jaccard index is 1 (intersection=2, union=2). Therefore the expected random score is 0.1*1 = 0.1.
* Together, when K=2, the expected score is 0+0.198+0.1 = 0.298.
To calculate (1), the first guess needs to be wrong. There are 3 "wrong" guesses and 5 candidates, so the probability for it is 3/5. The next guess should also be wrong. Now there are only 2 "wrong" guesses, and 4 candidates, so the probability for it is 2/4. Multiplying 3/5 * 2/4 = 0.3.
Same goes for (2) and (3).
Now we can perform the same calculation with K=3 associations.
Assuming N=5 candidates, and K=3 associations, there could be four events:
(4) The probability for a random guess is correct in 0 associations is 0, and the Jaccard index is 0. Therefore the expected random score is 0.
(5) The probability for a random guess is correct in 1 associations is 0.3, and the Jaccard index is 0.2 (intersection=1, union=4). Therefore the expected random score is 0.3*0.2 = 0.06.
(6) The probability for a random guess is correct in 2 associations is 0.6, and the Jaccard index is 0.5 (intersection=2, union=4). Therefore the expected random score is 0.6*5 = 0.3.
(7) The probability for a random guess is correct in 3 associations is 0.1, and the Jaccard index is 1 (intersection=3, union=3). Therefore the expected random score is 0.1*1 = 0.1.
* Together, when K=3, the expected score is 0+0.06+0.3+0.1 = 0.46.
Taking the average of 0.298 and 0.46 we reach 0.379.
Same process can be recalculated with 6 candidates (and K=2,3,4), 10 candidates (and K=2,3,4,5) and 123 candidates (and K=2,3,4,5,6).
</details>
## Dataset Creation
Inspired by the popular card game Codenames, a “spymaster” gives a textual cue related to several visual candidates, and another player has to identify them. Human players are rewarded for creating
associations that are challenging for a rival AI model but still solvable by other
human players.
### Annotations
#### Annotation process
We paid Amazon Mechanical Turk Workers to play our game.
## Considerations for Using the Data
All associations were obtained with human annotators.
### Licensing Information
CC-By 4.0
### Citation Information
@article{bitton2022winogavil,
title={WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models},
author={Bitton, Yonatan and Guetta, Nitzan Bitton and Yosef, Ron and Elovici, Yuval and Bansal, Mohit and Stanovsky, Gabriel and Schwartz, Roy},
journal={arXiv preprint arXiv:2207.12576},
year={2022}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
detection-datasets/coco | detection-datasets | 2023-03-15T15:11:53Z | 474 | 9 | null | [
"task_categories:object-detection",
"language:en",
"region:us"
] | 2023-03-15T15:11:53Z | 2022-10-04T08:13:16.000Z | 2022-10-04T08:13:16 | ---
task_categories:
- object-detection
language:
- en
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
code_x_glue_cc_code_completion_line | null | 2023-06-01T14:59:47Z | 473 | 1 | null | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:code",
"license:c-uda",
"region:us"
] | 2023-06-01T14:59:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- code
license:
- c-uda
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-generation
- fill-mask
task_ids:
- slot-filling
pretty_name: CodeXGlueCcCodeCompletionLine
dataset_info:
- config_name: java
features:
- name: id
dtype: int32
- name: input
dtype: string
- name: gt
dtype: string
splits:
- name: train
num_bytes: 5454783
num_examples: 3000
download_size: 5523586
dataset_size: 5454783
- config_name: python
features:
- name: id
dtype: int32
- name: input
dtype: string
- name: gt
dtype: string
splits:
- name: train
num_bytes: 24021562
num_examples: 10000
download_size: 24266715
dataset_size: 24021562
config_names:
- go
- java
- javascript
- php
- python
- ruby
---
# Dataset Card for "code_x_glue_cc_code_completion_line"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits-sample-size)
- [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/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-line
### Dataset Summary
CodeXGLUE CodeCompletion-line dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/CodeCompletion-line
Complete the unfinished line given previous context. Models are evaluated by exact match and edit similarity.
We propose line completion task to test model's ability to autocomplete a line. Majority code completion systems behave well in token level completion, but fail in completing an unfinished line like a method call with specific parameters, a function signature, a loop condition, a variable definition and so on. When a software develop finish one or more tokens of the current line, the line level completion model is expected to generate the entire line of syntactically correct code.
Line level code completion task shares the train/dev dataset with token level completion. After training a model on CodeCompletion-token, you could directly use it to test on line-level completion.
### Supported Tasks and Leaderboards
- `slot-filling`: The dataset can be used to train a model for completing entire code lines.
### Languages
- Java **programming** language
- Python **programming** language
## Dataset Structure
### Data Instances
#### java
An example of 'train' looks as follows.
```
{
"gt": "",
"id": 0,
"input": "<s> package org . rubypeople . rdt . internal . ui . rubyeditor ; import java . util . Iterator ; import org . eclipse . core . resources . IMarker ; import org . eclipse . ui . texteditor . MarkerAnnotation ; import org . eclipse . ui . texteditor . MarkerUtilities ; import org . rubypeople . rdt . core . IRubyElement ; import org . rubypeople . rdt . core . IRubyModelMarker ; import org . rubypeople . rdt . core . IRubyScript ; import org . rubypeople . rdt . core . RubyCore ; public class RubyMarkerAnnotation extends MarkerAnnotation implements IRubyAnnotation { public static final String RUBY_MARKER_TYPE_PREFIX = \"\" ; public static final String ERROR_ANNOTATION_TYPE = \"\" ; public static final String WARNING_ANNOTATION_TYPE = \"\" ; public static final String INFO_ANNOTATION_TYPE = \"\" ; public static final String TASK_ANNOTATION_TYPE = \"\" ; private IRubyAnnotation fOverlay ; public RubyMarkerAnnotation ( IMarker marker ) { super ( marker ) ; } public String [ ] getArguments ( ) { return null ; } public int getId ( ) { IMarker marker = getMarker ( ) ; if ( marker == null || ! marker . exists ( ) ) return - 1 ; if ( isProblem ( ) ) return marker . getAttribute ( IRubyModelMarker . ID , - 1 ) ; return - 1 ; } public boolean isProblem ( ) { String type = getType ( ) ; return WARNING_ANNOTATION_TYPE . equals ( type ) || ERROR_ANNOTATION_TYPE . equals"
}
```
#### python
An example of 'train' looks as follows.
```
{
"gt": "",
"id": 0,
"input": "<s> from __future__ import absolute_import <EOL> import weakref <EOL> import operator <EOL> from . compat import threading , itertools_filterfalse <EOL> from . import py2k <EOL> import types <EOL> EMPTY_SET = frozenset ( ) <EOL> class KeyedTuple ( tuple ) : <EOL> def __new__ ( cls , vals , labels = None ) : <EOL> t = tuple . __new__ ( cls , vals ) <EOL> t . _labels = [ ] <EOL> if labels : <EOL> t . __dict__ . update ( zip ( labels , vals ) ) <EOL> t . _labels = labels <EOL> return t <EOL> def keys ( self ) : <EOL> return [ l for l in self . _labels if l is not None ] <EOL> @ property <EOL> def _fields ( self ) : <EOL> return tuple ( self . keys ( ) ) <EOL> def _asdict ( self ) : <EOL> return dict ( ( key , self . __dict__ [ key ] ) for key in self . keys ( ) ) <EOL> class ImmutableContainer ( object ) : <EOL> def _immutable ( self , * arg , ** kw ) : <EOL> raise TypeError ( \"\" % self . __class__ . __name__ ) <EOL> __delitem__ = __setitem__ = __setattr__ = _immutable <EOL> class immutabledict ( ImmutableContainer , dict ) : <EOL> clear = pop = popitem = setdefault = update = ImmutableContainer . _immutable <EOL> def __new__ ( cls , * args ) : <EOL> new = dict . __new__ ( cls ) <EOL> dict . __init__ ( new , * args ) <EOL> return new <EOL> def __init__ ( self , * args ) : <EOL> pass <EOL> def __reduce__ ( self ) : <EOL> return immutabledict , ( dict ( self ) , ) <EOL> def union ( self , d ) : <EOL> if not self : <EOL> return immutabledict ( d ) <EOL> else : <EOL> d2 = immutabledict ( self ) <EOL> dict . update ( d2 , d ) <EOL> return d2 <EOL> def __repr__ ( self ) : <EOL> return \"\" % dict . __repr__ ( self ) <EOL> class Properties ( object ) : <EOL> def __init__ ( self , data ) : <EOL> self . __dict__ [ '_data' ] = data <EOL> def __len__ ( self ) : <EOL> return len ( self . _data ) <EOL> def __iter__ ( self ) : <EOL> return iter ( list ( self . _data . values ( ) ) ) <EOL> def __add__ ( self , other ) : <EOL> return list ( self ) + list ( other ) <EOL> def __setitem__ ( self , key , object ) : <EOL> self . _data [ key ] = object <EOL> def __getitem__ ( self , key ) : <EOL> return self . _data [ key ] <EOL> def __delitem__ ( self , key ) : <EOL> del self . _data [ key ] <EOL> def __setattr__ ( self , key , object ) : <EOL> self . _data [ key ] = object <EOL> def __getstate__ ( self ) : <EOL> return { '_data' : self . __dict__ [ '_data' ] } <EOL> def __setstate__ ( self , state ) : <EOL> self . __dict__ [ '_data' ] = state [ '_data' ] <EOL> def __getattr__ ( self , key ) : <EOL> try : <EOL> return self . _data [ key ] <EOL> except KeyError : <EOL> raise AttributeError ( key ) <EOL> def __contains__ ( self , key ) : <EOL> return key in self . _data <EOL> def as_immutable ( self ) : <EOL> return ImmutableProperties ( self . _data ) <EOL> def update ( self , value ) : <EOL> self . _data . update ( value ) <EOL> def get ( self , key , default = None ) : <EOL> if key in self : <EOL> return self [ key ] <EOL> else : <EOL> return default <EOL> def keys ( self ) : <EOL> return list ( self . _data ) <EOL> def values ( self ) : <EOL> return list ( self . _data . values ( ) ) <EOL> def items ( self ) : <EOL> return list ( self . _data . items ( ) ) <EOL> def has_key ( self , key ) : <EOL> return key in self . _data <EOL> def clear ( self ) : <EOL> self . _data . clear ( ) <EOL> class OrderedProperties ( Properties ) : <EOL> def __init__ ( self ) : <EOL> Properties . __init__ ( self , OrderedDict ( ) ) <EOL> class ImmutableProperties ( ImmutableContainer , Properties ) : <EOL> class OrderedDict ( dict ) : <EOL> def __init__ ( self , ____sequence = None , ** kwargs ) : <EOL> self . _list = [ ] <EOL> if ____sequence is None : <EOL> if kwargs : <EOL> self . update ( ** kwargs ) <EOL> else : <EOL> self . update ( ____sequence , ** kwargs ) <EOL> def clear ( self ) : <EOL> self . _list = [ ] <EOL> dict . clear ( self ) <EOL> def copy ( self ) : <EOL> return self . __copy__ ( ) <EOL> def __copy__ ( self ) : <EOL> return OrderedDict ( self ) <EOL> def sort ( self , * arg , ** kw ) : <EOL> self . _list . sort ( * arg , ** kw ) <EOL> def update ( self , ____sequence = None , ** kwargs ) : <EOL> if ____sequence is not None : <EOL> if hasattr ( ____sequence , 'keys' ) : <EOL> for key in ____sequence . keys ( ) : <EOL> self . __setitem__ ( key , ____sequence [ key ] ) <EOL> else : <EOL> for key , value in ____sequence : <EOL> self [ key ] = value <EOL> if kwargs : <EOL> self . update ( kwargs ) <EOL> def setdefault ( self , key , value ) : <EOL> if key not in self : <EOL> self . __setitem__ ( key , value ) <EOL> return value <EOL> else : <EOL> return self . __getitem__ ( key ) <EOL> def __iter__ ( self ) : <EOL> return iter ( self . _list ) <EOL> def keys ( self ) : <EOL> return list ( self ) <EOL> def values ( self ) : <EOL> return [ self [ key ] for key in self . _list ] <EOL> def items ( self ) : <EOL> return [ ( key , self [ key ] ) for key in self . _list ] <EOL> if py2k : <EOL> def itervalues ( self ) : <EOL> return iter ( self . values ( ) ) <EOL> def iterkeys ( self ) : <EOL> return iter ( self ) <EOL> def iteritems ( self ) : <EOL> return iter ( self . items ( ) ) <EOL> def __setitem__ ( self , key , object ) : <EOL> if key not in self : <EOL> try : <EOL> self . _list . append ( key ) <EOL> except AttributeError : <EOL> self . _list = [ key ] <EOL> dict . __setitem__ ( self , key , object ) <EOL> def __delitem__ ( self , key ) : <EOL> dict . __delitem__ ( self , key ) <EOL> self . _list . remove ( key ) <EOL> def pop ( self , key , * default ) : <EOL> present = key in self <EOL> value = dict . pop ( self , key , * default ) <EOL> if present : <EOL> self . _list . remove ( key ) <EOL> return value <EOL> def popitem ( self ) : <EOL> item = dict . popitem ( self ) <EOL> self . _list . remove ( item [ 0 ] ) <EOL> return item <EOL> class OrderedSet ( set ) : <EOL> def __init__ ( self , d = None ) : <EOL> set . __init__ ( self ) <EOL> self . _list = [ ] <EOL> if d is not None : <EOL>"
}
```
### Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
#### java, python
|field name| type | description |
|----------|------|----------------------------|
|id |int32 | Index of the sample |
|input |string| Input code string |
|gt |string| Code string to be predicted|
### Data Splits
| name |train|
|------|----:|
|java | 3000|
|python|10000|
## 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
https://github.com/microsoft, https://github.com/madlag
### Licensing Information
Computational Use of Data Agreement (C-UDA) License.
### Citation Information
```
@article{raychev2016probabilistic,
title={Probabilistic Model for Code with Decision Trees},
author={Raychev, Veselin and Bielik, Pavol and Vechev, Martin},
journal={ACM SIGPLAN Notices},
pages={731--747},
year={2016},
publisher={ACM New York, NY, USA}
}
@inproceedings{allamanis2013mining,
title={Mining Source Code Repositories at Massive Scale using Language Modeling},
author={Allamanis, Miltiadis and Sutton, Charles},
booktitle={2013 10th Working Conference on Mining Software Repositories (MSR)},
pages={207--216},
year={2013},
organization={IEEE}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
multi_booked | null | 2023-06-01T14:59:47Z | 473 | 0 | multibooked | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:original",
"language:ca",
"language:eu",
"license:cc-by-3.0",
"arxiv:1803.08614",
"region:us"
] | 2023-06-01T14:59:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- ca
- eu
license:
- cc-by-3.0
multilinguality:
- monolingual
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: multibooked
pretty_name: MultiBooked
dataset_info:
- config_name: ca
features:
- name: text
sequence:
- name: wid
dtype: string
- name: sent
dtype: string
- name: para
dtype: string
- name: word
dtype: string
- name: terms
sequence:
- name: tid
dtype: string
- name: lemma
dtype: string
- name: morphofeat
dtype: string
- name: pos
dtype: string
- name: target
sequence: string
- name: opinions
sequence:
- name: oid
dtype: string
- name: opinion_holder_target
sequence: string
- name: opinion_target_target
sequence: string
- name: opinion_expression_polarity
dtype:
class_label:
names:
'0': StrongNegative
'1': Negative
'2': Positive
'3': StrongPositive
- name: opinion_expression_target
sequence: string
splits:
- name: train
num_bytes: 1952731
num_examples: 567
download_size: 4429415
dataset_size: 1952731
- config_name: eu
features:
- name: text
sequence:
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dtype: string
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dtype: string
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dtype: string
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sequence:
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dtype: string
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sequence: string
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sequence: string
- name: opinion_expression_polarity
dtype:
class_label:
names:
'0': StrongNegative
'1': Negative
'2': Positive
'3': StrongPositive
- name: opinion_expression_target
sequence: string
splits:
- name: train
num_bytes: 1175816
num_examples: 343
download_size: 4429415
dataset_size: 1175816
config_names:
- ca
- eu
---
# Dataset Card for MultiBooked
## 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://hdl.handle.net/10230/33928
- **Repository:** https://github.com/jerbarnes/multibooked
- **Paper:** https://arxiv.org/abs/1803.08614
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
MultiBooked is a corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification.
The corpora are compiled from hotel reviews taken mainly from booking.com. The corpora are in Kaf/Naf format, which is
an xml-style stand-off format that allows for multiple layers of annotation. Each review was sentence- and
word-tokenized and lemmatized using Freeling for Catalan and ixa-pipes for Basque. Finally, for each language two
annotators annotated opinion holders, opinion targets, and opinion expressions for each review, following the
guidelines set out in the OpeNER project.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Each sub-dataset is monolingual in the languages:
- ca: Catalan
- eu: Basque
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- `text`: layer of the original text.
- `wid`: list of word IDs for each word within the example.
- `sent`: list of sentence IDs for each sentence within the example.
- `para`: list of paragraph IDs for each paragraph within the example.
- `word`: list of words.
- `terms`: layer of the terms resulting from the analysis of the original text (lemmatization, morphological,
PoS tagging)
- `tid`: list of term IDs for each term within the example.
- `lemma`: list of lemmas.
- `morphofeat`: list of morphological features.
- `pos`: list of PoS tags.
- `target`: list of sublists of the corresponding word IDs (normally, the sublists contain only one element,
in a one-to-one correspondence between words and terms).
- `opinions`: layer of the opinions in the text.
- `oid`: list of opinion IDs
- `opinion_holder_target`: list of sublists of the corresponding term IDs that span the opinion holder.
- `opinion_target_target`: list of sublists of the corresponding term IDs that span the opinion target.
- `opinion_expression_polarity`: list of the opinion expression polarities. The polarity can take one of the values:
`StrongNegative`, `Negative`, `Positive`, or `StrongPositive`.
- `opinion_expression_target`: list of sublists of the corresponding term IDs that span the opinion expression.
### 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
Dataset is under the [CC-BY 3.0](https://creativecommons.org/licenses/by/3.0/) license.
### Citation Information
```
@inproceedings{Barnes2018multibooked,
author={Barnes, Jeremy and Lambert, Patrik and Badia, Toni},
title={MultiBooked: A corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification},
booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC'18)},
year = {2018},
month = {May},
date = {7-12},
address = {Miyazaki, Japan},
publisher = {European Language Resources Association (ELRA)},
language = {english}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. | [
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Muennighoff/multi_eurlex | Muennighoff | 2023-05-21T18:17:23Z | 473 | 2 | null | [
"region:us"
] | 2023-05-21T18:17:23Z | 2023-05-21T14:54:28.000Z | 2023-05-21T14:54:28 | Entry not found | [
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ubuntu_dialogs_corpus | null | 2023-04-05T13:42:49Z | 472 | 15 | ubuntu-dialogue-corpus | [
"task_categories:conversational",
"task_ids:dialogue-generation",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1506.08909",
"region:us"
] | 2023-04-05T13:42:49Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: UDC (Ubuntu Dialogue Corpus)
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- conversational
task_ids:
- dialogue-generation
paperswithcode_id: ubuntu-dialogue-corpus
dataset_info:
- config_name: train
features:
- name: Context
dtype: string
- name: Utterance
dtype: string
- name: Label
dtype: int32
splits:
- name: train
num_bytes: 525126729
num_examples: 1000000
download_size: 0
dataset_size: 525126729
- config_name: dev_test
features:
- name: Context
dtype: string
- name: Ground Truth Utterance
dtype: string
- name: Distractor_0
dtype: string
- name: Distractor_1
dtype: string
- name: Distractor_2
dtype: string
- name: Distractor_3
dtype: string
- name: Distractor_4
dtype: string
- name: Distractor_5
dtype: string
- name: Distractor_6
dtype: string
- name: Distractor_7
dtype: string
- name: Distractor_8
dtype: string
splits:
- name: test
num_bytes: 27060502
num_examples: 18920
- name: validation
num_bytes: 27663181
num_examples: 19560
download_size: 0
dataset_size: 54723683
---
# Dataset Card for "ubuntu_dialogs_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
- **Repository:** https://github.com/rkadlec/ubuntu-ranking-dataset-creator
- **Paper:** [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://arxiv.org/abs/1506.08909)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 65.49 MB
- **Total amount of disk used:** 65.49 MB
### Dataset Summary
Ubuntu Dialogue Corpus, a dataset containing almost 1 million multi-turn dialogues, with a total of over 7 million utterances and 100 million words. This provides a unique resource for research into building dialogue managers based on neural language models that can make use of large amounts of unlabeled data. The dataset has both the multi-turn property of conversations in the Dialog State Tracking Challenge datasets, and the unstructured nature of interactions from microblog services such as Twitter.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### train
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 65.49 MB
- **Total amount of disk used:** 65.49 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"Context": "\"i think we could import the old comment via rsync , but from there we need to go via email . i think it be easier than cach the...",
"Label": 1,
"Utterance": "basic each xfree86 upload will not forc user to upgrad 100mb of font for noth __eou__ no someth i do in my spare time . __eou__"
}
```
### Data Fields
The data fields are the same among all splits.
#### train
- `Context`: a `string` feature.
- `Utterance`: a `string` feature.
- `Label`: a `int32` feature.
### Data Splits
|name |train |
|-----|-----:|
|train|127422|
## 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
```
@article{DBLP:journals/corr/LowePSP15,
author = {Ryan Lowe and
Nissan Pow and
Iulian Serban and
Joelle Pineau},
title = {The Ubuntu Dialogue Corpus: {A} Large Dataset for Research in Unstructured
Multi-Turn Dialogue Systems},
journal = {CoRR},
volume = {abs/1506.08909},
year = {2015},
url = {http://arxiv.org/abs/1506.08909},
archivePrefix = {arXiv},
eprint = {1506.08909},
timestamp = {Mon, 13 Aug 2018 16:48:23 +0200},
biburl = {https://dblp.org/rec/journals/corr/LowePSP15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SocialGrep/the-reddit-covid-dataset | SocialGrep | 2022-07-01T18:40:57Z | 472 | 1 | null | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-07-01T18:40:57Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for the-reddit-covid-dataset
## 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)
## Disclaimer
Due to file size limitations, we are not able to provide the comments for this dataset. Please feel free to download them from the [website](https://socialgrep.com/datasets?utm_source=huggingface&utm_medium=link&utm_campaign=theredditcoviddataset#the-reddit-covid-dataset) - no registration required.
## Dataset Description
- **Homepage:** [https://socialgrep.com/datasets](https://socialgrep.com/datasets?utm_source=huggingface&utm_medium=link&utm_campaign=theredditcoviddataset)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=theredditcoviddataset)
### Dataset Summary
This corpus contains all the mentions of the term `covid` in post titles on the social media platform Reddit, up until the 25th of October, 2021.
The data was procured from Reddit using [SocialGrep](https://socialgrep.com/?utm_source=huggingface&utm_medium=link&utm_campaign=theredditcoviddataset).
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## 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
CC-BY v4.0
### Contributions
[Needs More Information] | [
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classla/FRENK-hate-hr | classla | 2022-10-21T07:46:28Z | 472 | 0 | null | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:hr",
"license:other",
"hate-speech-detection",
"offensive-language",
"arxiv:1906.02045",
"region:us"
] | 2022-10-21T07:46:28Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- hr
license:
- other
size_categories:
- 1K<n<10K
task_categories:
- text-classification
task_ids: []
tags:
- hate-speech-detection
- offensive-language
---
# Offensive language dataset of Croatian comments FRENK 1.0
Croatian subset of the [FRENK dataset](http://hdl.handle.net/11356/1433). Also available on HuggingFace dataset hub: [English subset](https://huggingface.co/datasets/5roop/FRENK-hate-en), [Slovenian subset](https://huggingface.co/datasets/5roop/FRENK-hate-sl).
## Dataset Description
- **Homepage:** http://hdl.handle.net/11356/1433
- **Repository:** http://hdl.handle.net/11356/1433
- **Paper:** https://arxiv.org/abs/1906.02045
- **Project page** https://nl.ijs.si/frenk/
## Description of the original dataset
>The original FRENK dataset consists of comments to Facebook posts (news articles) of mainstream media outlets from Croatia, Great Britain, and Slovenia, on the topics of migrants and LGBT. The dataset contains whole discussion threads. Each comment is annotated by the type of socially unacceptable discourse (e.g., inappropriate, offensive, violent speech) and its target (e.g., migrants/LGBT, commenters, media). The annotation schema is described in detail in [https://arxiv.org/pdf/1906.02045.pdf]. Usernames in the metadata are pseudo-anonymised and removed from the comments.
>
>The data in each language (Croatian (hr), English (en), Slovenian (sl), and topic (migrants, LGBT) is divided into a training and a testing portion. The training and testing data consist of separate discussion threads, i.e., there is no cross-discussion-thread contamination between training and testing data. The sizes of the splits are the following: Croatian, migrants: 4356 training comments, 978 testing comments; Croatian LGBT: 4494 training comments, 1142 comments; English, migrants: 4540 training comments, 1285 testing comments; English, LGBT: 4819 training comments, 1017 testing comments; Slovenian, migrants: 5145 training comments, 1277 testing comments; Slovenian, LGBT: 2842 training comments, 900 testing comments.
For this dataset only the Croatian data was used. Training segment has been split into beginning 90% (published here as training split) and end 10% (published here as dev split). Test segment has been preserved in its original form.
## Usage in `Transformers`
```python
import datasets
ds = datasets.load_dataset("classla/FRENK-hate-hr","binary")
```
For binary classification the following encoding is used:
```python
_CLASS_MAP_BINARY = {
'Acceptable': 0,
'Offensive': 1,
}
```
The original labels are available if the dataset is loaded with the `multiclass` option:
```python
import datasets
ds = datasets.load_dataset("classla/FRENK-hate-hr","multiclass").
```
In this case the encoding used is:
```python
_CLASS_MAP_MULTICLASS = {
'Acceptable speech': 0,
'Inappropriate': 1,
'Background offensive': 2,
'Other offensive': 3,
'Background violence': 4,
'Other violence': 5,
}
```
## Data structure
* `text`: text
* `target`: who is the target of the hate-speech text ("no target", "commenter", "target" (migrants or LGBT, depending on the topic), or "related to" (again, the topic))
* `topic`: whether the text relates to lgbt or migrants hate-speech domains
* `label`: label of the text instance, see above.
## Data instance
```
{'text': 'Potpisujem komentar g ankice pavicic',
'target': 'No target',
'topic': 'lgbt',
'label': 0}
```
## Licensing information
CLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0
## Citation information
When using this dataset please cite the following paper:
```
@misc{ljubešić2019frenk,
title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English},
author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
year={2019},
eprint={1906.02045},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1906.02045}
}
```
The original dataset can be cited as
```
@misc{11356/1433,
title = {Offensive language dataset of Croatian, English and Slovenian comments {FRENK} 1.0},
author = {Ljube{\v s}i{\'c}, Nikola and Fi{\v s}er, Darja and Erjavec, Toma{\v z}},
url = {http://hdl.handle.net/11356/1433},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {{CLARIN}.{SI} Licence {ACA} {ID}-{BY}-{NC}-{INF}-{NORED} 1.0},
year = {2021} }
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigbio/bc5cdr | bigbio | 2022-12-22T15:43:20Z | 472 | 1 | null | [
"multilinguality:monolingual",
"language:en",
"license:other",
"region:us"
] | 2022-12-22T15:43:20Z | 2022-11-13T22:06:13.000Z | 2022-11-13T22:06:13 |
---
language:
- en
bigbio_language:
- English
license: other
multilinguality: monolingual
bigbio_license_shortname: PUBLIC_DOMAIN_MARK_1p0
pretty_name: BC5CDR
homepage: http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_RECOGNITION
- NAMED_ENTITY_DISAMBIGUATION
- RELATION_EXTRACTION
---
# Dataset Card for BC5CDR
## Dataset Description
- **Homepage:** http://www.biocreative.org/tasks/biocreative-v/track-3-cdr/
- **Pubmed:** True
- **Public:** True
- **Tasks:** NER,NED,RE
The BioCreative V Chemical Disease Relation (CDR) dataset is a large annotated text corpus of human annotations of all chemicals, diseases and their interactions in 1,500 PubMed articles.
## Citation Information
```
@article{DBLP:journals/biodb/LiSJSWLDMWL16,
author = {Jiao Li and
Yueping Sun and
Robin J. Johnson and
Daniela Sciaky and
Chih{-}Hsuan Wei and
Robert Leaman and
Allan Peter Davis and
Carolyn J. Mattingly and
Thomas C. Wiegers and
Zhiyong Lu},
title = {BioCreative {V} {CDR} task corpus: a resource for chemical disease
relation extraction},
journal = {Database J. Biol. Databases Curation},
volume = {2016},
year = {2016},
url = {https://doi.org/10.1093/database/baw068},
doi = {10.1093/database/baw068},
timestamp = {Thu, 13 Aug 2020 12:41:41 +0200},
biburl = {https://dblp.org/rec/journals/biodb/LiSJSWLDMWL16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
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Kyle1668/AG-Tweets | Kyle1668 | 2023-08-09T22:22:37Z | 472 | 0 | null | [
"region:us"
] | 2023-08-09T22:22:37Z | 2023-06-29T22:07:56.000Z | 2023-06-29T22:07:56 | ---
pretty_name: AG News Tweets
---
\subsection{Motivation}
AG News is a four-way topic classification task introduced in \cite{Zhang2015CharacterlevelCN}. In this setup, a task model must classify whether a given news article is about world events (\textbf{\textit{World}}), sports and athletics (\textbf{\textit{Sports}}), business and economics (\textbf{\textit{Business}}), and scientific developments (\textbf{\textit{Sci/Tech}}). The test set on HuggingFace (\url{huggingface.co/datasets/ag_news}) is composed of 7,600 examples equally balanced across the four classes.
News topic classification presents a promising opportunity for largely isolating the effect of writing style shifts. Existing deep learning methods also perform well on this dataset with accuracy reaching higher than 90\% (\url{paperswithcode.com/sota/text-classification-on-ag-news}).
Another motivation for this particular task is the common risk of data augmentation inadvertently flipping the label/semantics of the text \cite{Bayer2021ASO}. Unlike other tasks such as sentiment classification or subtle hate speech, the topic of a news article is unlikely to change during augmentation, thus preserving the original label.
\subsection{Creation}
We used GPT-3.5 Turbo \cite{brown2020language} (6/7/23 version) for style transfer. We did an initial pass through all 7,600 examples using a conservative "V1" prompt and greedy decoding. Calls were made using the OpenAI Python SDK with top\_p and temperature set to zero. The data was then lightly preprocessed to reduce the number of examples that began with \textbf{BREAKING NEWS} flanked my emojis.
512 of the initial model responses did not result in satisfactory generations. These were typical cases where the generated text was almost indiscernible from the original text or the generation was entirely emojis. We called GPT-3.5 Turbo again with an updated prompt and hyperparameters (temperature=0.7, top\_p=0.9, frequency\_penalty=0.5, presence\_penalty=0.5) for these examples. Whereas all the first-pass generations did not have any instructions to the model as to the sentiment/mood of the hypothetical post author, we purposefully instructed the model to "\textit{Add some flare with humor, anger, or sarcasm.}" in the generation.
It's important to note that we did not enforce Twitter's character limit. These sequences should be considered as more broadly inspired by social media posts rather than following the exact specifications of Twitter posts. We also did not manually review every sequence in the dataset to confirm that the original label was preserved. GPT 3.5 Turbo also hallucinates facts, such as adding the hashtag \#Olympics2021 even though the original dataset was created in 2015. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
iamshnoo/geomlama | iamshnoo | 2023-11-22T10:53:13Z | 472 | 0 | null | [
"region:us"
] | 2023-11-22T10:53:13Z | 2023-08-02T01:18:19.000Z | 2023-08-02T01:18:19 | ---
dataset_info:
features:
- name: question
dtype: string
- name: answer
dtype: string
- name: candidate_answers
dtype: string
- name: context
dtype: string
- name: country
dtype: string
splits:
- name: en
num_bytes: 20705
num_examples: 150
- name: fa
num_bytes: 29418
num_examples: 150
- name: hi
num_bytes: 41903
num_examples: 150
- name: sw
num_bytes: 21231
num_examples: 150
- name: zh
num_bytes: 19155
num_examples: 150
- name: el
num_bytes: 38057
num_examples: 150
download_size: 45566
dataset_size: 170469
---
data from the paper GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models
(along with some new data and modifications for cleaning)
[GitHub](https://github.com/WadeYin9712/GeoMLAMA)
# Dataset Card for "geomlama"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SocialGrep/reddit-nonewnormal-complete | SocialGrep | 2022-07-01T19:02:06Z | 471 | 1 | null | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-07-01T19:02:06Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for reddit-nonewnormal-complete
## 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://socialgrep.com/datasets](https://socialgrep.com/datasets?utm_source=huggingface&utm_medium=link&utm_campaign=dataset&utm_term=nonewnormal)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=dataset&utm_term=nonewnormal)
### Dataset Summary
This corpus contains the complete data for the activity on subreddit /r/NoNewNormal for the entire duration of its existence.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## 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
CC-BY v4.0
### Contributions
[Needs More Information] | [
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classla/FRENK-hate-sl | classla | 2022-10-21T07:46:11Z | 471 | 0 | null | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:sl",
"license:other",
"hate-speech-detection",
"offensive-language",
"arxiv:1906.02045",
"region:us"
] | 2022-10-21T07:46:11Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
language:
- sl
license:
- other
size_categories:
- 1K<n<10K
task_categories:
- text-classification
task_ids: []
tags:
- hate-speech-detection
- offensive-language
---
Slovenian subset of the [FRENK dataset](http://hdl.handle.net/11356/1433). Also available on HuggingFace dataset hub: [English subset](https://huggingface.co/datasets/5roop/FRENK-hate-en), [Croatian subset](https://huggingface.co/datasets/5roop/FRENK-hate-hr).
## Dataset Description
- **Homepage:** http://hdl.handle.net/11356/1433
- **Repository:** http://hdl.handle.net/11356/1433
- **Paper:** https://arxiv.org/abs/1906.02045
- **Project page** https://nl.ijs.si/frenk/
## Description of the original dataset
>The original FRENK dataset consists of comments to Facebook posts (news articles) of mainstream media outlets from Croatia, Great Britain, and Slovenia, on the topics of migrants and LGBT. The dataset contains whole discussion threads. Each comment is annotated by the type of socially unacceptable discourse (e.g., inappropriate, offensive, violent speech) and its target (e.g., migrants/LGBT, commenters, media). The annotation schema is described in detail in [https://arxiv.org/pdf/1906.02045.pdf]. Usernames in the metadata are pseudo-anonymised and removed from the comments.
>
>The data in each language (Croatian (hr), English (en), Slovenian (sl), and topic (migrants, LGBT) is divided into a training and a testing portion. The training and testing data consist of separate discussion threads, i.e., there is no cross-discussion-thread contamination between training and testing data. The sizes of the splits are the following: Croatian, migrants: 4356 training comments, 978 testing comments; Croatian LGBT: 4494 training comments, 1142 comments; English, migrants: 4540 training comments, 1285 testing comments; English, LGBT: 4819 training comments, 1017 testing comments; Slovenian, migrants: 5145 training comments, 1277 testing comments; Slovenian, LGBT: 2842 training comments, 900 testing comments.
For this dataset only the Croatian data was used. Training segment has been split into beginning 90% (published here as training split) and end 10% (published here as dev split).
## Usage in `Transformers`
```python
import datasets
ds = datasets.load_dataset("classla/FRENK-hate-sl","binary")
```
For binary classification the following encoding is used:
```python
_CLASS_MAP_BINARY = {
'Acceptable': 0,
'Offensive': 1,
}
```
The original labels are available if the dataset is loaded with the `multiclass` option:
```python
import datasets
ds = datasets.load_dataset("classla/FRENK-hate-sl","multiclass").
```
In this case the encoding used is:
```python
_CLASS_MAP_MULTICLASS = {
'Acceptable speech': 0,
'Inappropriate': 1,
'Background offensive': 2,
'Other offensive': 3,
'Background violence': 4,
'Other violence': 5,
}
```
## Data structure
* `text`: text
* `target`: who is the target of the hate-speech text ("no target", "commenter", "target" (migrants or LGBT, depending on the topic), or "related to" (again, the topic))
* `topic`: whether the text relates to lgbt or migrants hate-speech domains
* `label`: label of the text instance, see above.
## Data instance
```
{'text': 'Otroci so odprti in brez predsodkov.Predsodke jim vcepimo starejši,starši,družba,družina...Če otroku lepo razložimo,razume.Nikoli ni dobro,da omejujemo otroka,njegovo inteligenco in duhovnost z lastnim ne razumevanjem nečesa ali nekoga.Predsodek je miselni zapor,prepreka,da bi bili svobodni.Ljubezen je svoboda.Sem ZA spremembo zakona!Srečno :D',
'target': 'No target',
'topic': 'lgbt',
'label': 0}
```
## Licensing information
CLARIN.SI Licence ACA ID-BY-NC-INF-NORED 1.0
## Citation information
When using this dataset please cite the following paper:
```
@misc{ljubešić2019frenk,
title={The FRENK Datasets of Socially Unacceptable Discourse in Slovene and English},
author={Nikola Ljubešić and Darja Fišer and Tomaž Erjavec},
year={2019},
eprint={1906.02045},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1906.02045}
}
```
The original dataset can be cited as
```
@misc{11356/1433,
title = {Offensive language dataset of Croatian, English and Slovenian comments {FRENK} 1.0},
author = {Ljube{\v s}i{\'c}, Nikola and Fi{\v s}er, Darja and Erjavec, Toma{\v z}},
url = {http://hdl.handle.net/11356/1433},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {{CLARIN}.{SI} Licence {ACA} {ID}-{BY}-{NC}-{INF}-{NORED} 1.0},
year = {2021} }
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
msr_sqa | null | 2022-11-18T21:30:23Z | 470 | 1 | null | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:ms-pl",
"region:us"
] | 2022-11-18T21:30:23Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- ms-pl
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: null
pretty_name: Microsoft Research Sequential Question Answering
dataset_info:
features:
- name: id
dtype: string
- name: annotator
dtype: int32
- name: position
dtype: int32
- name: question
dtype: string
- name: question_and_history
sequence: string
- name: table_file
dtype: string
- name: table_header
sequence: string
- name: table_data
sequence:
sequence: string
- name: answer_coordinates
sequence:
- name: row_index
dtype: int32
- name: column_index
dtype: int32
- name: answer_text
sequence: string
splits:
- name: train
num_bytes: 19732499
num_examples: 12276
- name: validation
num_bytes: 3738331
num_examples: 2265
- name: test
num_bytes: 5105873
num_examples: 3012
download_size: 4796932
dataset_size: 28576703
---
# Dataset Card for Microsoft Research Sequential Question Answering
## 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:** [Microsoft Research Sequential Question Answering (SQA) Dataset](https://msropendata.com/datasets/b25190ed-0f59-47b1-9211-5962858142c2)
- **Repository:**
- **Paper:** [https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/acl17-dynsp.pdf](https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/acl17-dynsp.pdf)
- **Leaderboard:**
- **Point of Contact:**
- Scott Wen-tau Yih scottyih@microsoft.com
- Mohit Iyyer m.iyyer@gmail.com
- Ming-Wei Chang minchang@microsoft.com
### Dataset Summary
Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions.
We created SQA by asking crowdsourced workers to decompose 2,022 questions from WikiTableQuestions (WTQ)*, which contains highly-compositional questions about tables from Wikipedia. We had three workers decompose each WTQ question, resulting in a dataset of 6,066 sequences that contain 17,553 questions in total. Each question is also associated with answers in the form of cell locations in the tables.
- Panupong Pasupat, Percy Liang. "Compositional Semantic Parsing on Semi-Structured Tables" ACL-2015.
[http://www-nlp.stanford.edu/software/sempre/wikitable/](http://www-nlp.stanford.edu/software/sempre/wikitable/)
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (`en`).
## Dataset Structure
### Data Instances
```
{'id': 'nt-639',
'annotator': 0,
'position': 0,
'question': 'where are the players from?',
'table_file': 'table_csv/203_149.csv',
'table_header': ['Pick', 'Player', 'Team', 'Position', 'School'],
'table_data': [['1',
'Ben McDonald',
'Baltimore Orioles',
'RHP',
'Louisiana State University'],
['2',
'Tyler Houston',
'Atlanta Braves',
'C',
'"Valley HS (Las Vegas',
' NV)"'],
['3', 'Roger Salkeld', 'Seattle Mariners', 'RHP', 'Saugus (CA) HS'],
['4',
'Jeff Jackson',
'Philadelphia Phillies',
'OF',
'"Simeon HS (Chicago',
' IL)"'],
['5', 'Donald Harris', 'Texas Rangers', 'OF', 'Texas Tech University'],
['6', 'Paul Coleman', 'Saint Louis Cardinals', 'OF', 'Frankston (TX) HS'],
['7', 'Frank Thomas', 'Chicago White Sox', '1B', 'Auburn University'],
['8', 'Earl Cunningham', 'Chicago Cubs', 'OF', 'Lancaster (SC) HS'],
['9',
'Kyle Abbott',
'California Angels',
'LHP',
'Long Beach State University'],
['10',
'Charles Johnson',
'Montreal Expos',
'C',
'"Westwood HS (Fort Pierce',
' FL)"'],
['11',
'Calvin Murray',
'Cleveland Indians',
'3B',
'"W.T. White High School (Dallas',
' TX)"'],
['12', 'Jeff Juden', 'Houston Astros', 'RHP', 'Salem (MA) HS'],
['13', 'Brent Mayne', 'Kansas City Royals', 'C', 'Cal State Fullerton'],
['14',
'Steve Hosey',
'San Francisco Giants',
'OF',
'Fresno State University'],
['15',
'Kiki Jones',
'Los Angeles Dodgers',
'RHP',
'"Hillsborough HS (Tampa',
' FL)"'],
['16', 'Greg Blosser', 'Boston Red Sox', 'OF', 'Sarasota (FL) HS'],
['17', 'Cal Eldred', 'Milwaukee Brewers', 'RHP', 'University of Iowa'],
['18',
'Willie Greene',
'Pittsburgh Pirates',
'SS',
'"Jones County HS (Gray',
' GA)"'],
['19', 'Eddie Zosky', 'Toronto Blue Jays', 'SS', 'Fresno State University'],
['20', 'Scott Bryant', 'Cincinnati Reds', 'OF', 'University of Texas'],
['21', 'Greg Gohr', 'Detroit Tigers', 'RHP', 'Santa Clara University'],
['22',
'Tom Goodwin',
'Los Angeles Dodgers',
'OF',
'Fresno State University'],
['23', 'Mo Vaughn', 'Boston Red Sox', '1B', 'Seton Hall University'],
['24', 'Alan Zinter', 'New York Mets', 'C', 'University of Arizona'],
['25', 'Chuck Knoblauch', 'Minnesota Twins', '2B', 'Texas A&M University'],
['26', 'Scott Burrell', 'Seattle Mariners', 'RHP', 'Hamden (CT) HS']],
'answer_coordinates': {'row_index': [0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15,
16,
17,
18,
19,
20,
21,
22,
23,
24,
25],
'column_index': [4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4,
4]},
'answer_text': ['Louisiana State University',
'Valley HS (Las Vegas, NV)',
'Saugus (CA) HS',
'Simeon HS (Chicago, IL)',
'Texas Tech University',
'Frankston (TX) HS',
'Auburn University',
'Lancaster (SC) HS',
'Long Beach State University',
'Westwood HS (Fort Pierce, FL)',
'W.T. White High School (Dallas, TX)',
'Salem (MA) HS',
'Cal State Fullerton',
'Fresno State University',
'Hillsborough HS (Tampa, FL)',
'Sarasota (FL) HS',
'University of Iowa',
'Jones County HS (Gray, GA)',
'Fresno State University',
'University of Texas',
'Santa Clara University',
'Fresno State University',
'Seton Hall University',
'University of Arizona',
'Texas A&M University',
'Hamden (CT) HS']}
```
### Data Fields
- `id` (`str`): question sequence id (the id is consistent with those in WTQ)
- `annotator` (`int`): `0`, `1`, `2` (the 3 annotators who annotated the question intent)
- `position` (`int`): the position of the question in the sequence
- `question` (`str`): the question given by the annotator
- `table_file` (`str`): the associated table
- `table_header` (`List[str]`): a list of headers in the table
- `table_data` (`List[List[str]]`): 2d array of data in the table
- `answer_coordinates` (`List[Dict]`): the table cell coordinates of the answers (0-based, where 0 is the first row after the table header)
- `row_index`
- `column_index`
- `answer_text` (`List[str]`): the content of the answer cells
Note that some text fields may contain Tab or LF characters and thus start with quotes.
It is recommended to use a CSV parser like the Python CSV package to process the data.
### Data Splits
| | train | test |
|-------------|------:|-----:|
| N. examples | 14541 | 3012 |
## 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
[Microsoft Research Data License Agreement](https://msropendata-web-api.azurewebsites.net/licenses/2f933be3-284d-500b-7ea3-2aa2fd0f1bb2/view).
### Citation Information
```
@inproceedings{iyyer-etal-2017-search,
title = "Search-based Neural Structured Learning for Sequential Question Answering",
author = "Iyyer, Mohit and
Yih, Wen-tau and
Chang, Ming-Wei",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1167",
doi = "10.18653/v1/P17-1167",
pages = "1821--1831",
}
```
### Contributions
Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset. | [
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wmt20_mlqe_task2 | null | 2023-06-01T14:59:47Z | 470 | 2 | null | [
"task_categories:translation",
"task_categories:text-classification",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:translation",
"size_categories:1K<n<10K",
"source_datasets:extended|wikipedia",
"language:de",
"language:en",
"language:zh",
"license:unknown",
"translation-quality-estimation",
"arxiv:1902.08646",
"region:us"
] | 2023-06-01T14:59:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- found
language:
- de
- en
- zh
license:
- unknown
multilinguality:
- translation
size_categories:
- 1K<n<10K
source_datasets:
- extended|wikipedia
task_categories:
- translation
- text-classification
task_ids: []
pretty_name: WMT20 - MultiLingual Quality Estimation (MLQE) Task2
tags:
- translation-quality-estimation
dataset_info:
- config_name: en-de
features:
- name: translation
dtype:
translation:
languages:
- en
- de
- name: src_tags
sequence:
class_label:
names:
'0': BAD
'1': OK
- name: mt_tags
sequence:
class_label:
names:
'0': BAD
'1': OK
- name: pe
dtype: string
- name: hter
dtype: float32
- name: alignments
sequence:
sequence: int32
splits:
- name: train
num_bytes: 6463930
num_examples: 7000
- name: test
num_bytes: 425582
num_examples: 1000
- name: validation
num_bytes: 927616
num_examples: 1000
download_size: 1377020
dataset_size: 7817128
- config_name: en-zh
features:
- name: translation
dtype:
translation:
languages:
- en
- zh
- name: src_tags
sequence:
class_label:
names:
'0': BAD
'1': OK
- name: mt_tags
sequence:
class_label:
names:
'0': BAD
'1': OK
- name: pe
dtype: string
- name: hter
dtype: float32
- name: alignments
sequence:
sequence: int32
splits:
- name: train
num_bytes: 6786898
num_examples: 7000
- name: test
num_bytes: 443740
num_examples: 1000
- name: validation
num_bytes: 954710
num_examples: 1000
download_size: 1564953
dataset_size: 8185348
config_names:
- en-de
- en-zh
---
# Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task2
## 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.*
*Task 1 evaluates the application of QE for post-editing purposes. It consists of predicting:*
- ***Word-level tags.*** *This is done both on source side (to detect which words caused errors) and target side (to detect mistranslated or missing words).*
- ***Target.*** *Each token is tagged as either `OK` or `BAD`. Additionally, each gap between two words is tagged as `BAD` if one or more missing words should have been there, and `OK` otherwise. Note that number of tags for each target sentence is 2*N+1, where N is the number of tokens in the sentence.*
- ***Source.*** *Tokens are tagged as `OK` if they were correctly translated, and `BAD` otherwise. Gaps are not tagged.*
- ***Sentence-level HTER scores.*** *HTER (Human Translation Error Rate) is the ratio between the number of edits (insertions/deletions/replacements) needed and the reference translation length.*
### Supported Tasks and Leaderboards
From the homepage:
*For sentence-level QE, submissions are evaluated in terms of the Pearson's correlation metric for the sentence-level HTER prediction. For word-level QE, they will be evaluated in terms of MCC ([Matthews correlation coefficient](https://en.wikipedia.org/wiki/Matthews_correlation_coefficient)). These are the [official evaluation scripts](https://github.com/sheffieldnlp/qe-eval-scripts).*
### Languages
There are two language pairs in this dataset:
- English - German (`en` - `de`)
- German - Chinese (`en` - `zh`)
## Dataset Structure
### Data Instances
An example looks like this:
```
{
'translation': {
'en': 'favorite fish include cod , salmon , winter flounder , haddock , striped bass , pollock , hake , bluefish , and , in southern New England , Tautog .',
'de': 'zu den Lieblingsfischen gehören Kabeljau , Lachs , Winterflounder , Schellfisch , gestreifter Bass , Pollock , Seehecht , Rotbarsch und in Südengland Tautog .',
}
'src_tags': [1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1],
'mt_tags': [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1],
'pe': 'zu den Lieblingsfischen zählen Kabeljau , Lachs , Winterflunder , Schellfisch , Wolfsbarsch , Pollock , Seehecht , Bluefish und im Süden Neuenglands Tautog .',
'hter': 0.3199999928474426,
'alignments': [[2, 0], [2, 1], [2, 3], [3, 2], [3, 4], [4, 5], [5, 6], [6, 5], [7, 6], [8, 6], [9, 7], [10, 8], [10, 10], [11, 9], [12, 12], [13, 13], [14, 11], [15, 12], [15, 15], [16, 14], [17, 17], [19, 16], [20, 16], [21, 20], [22, 18], [23, 19], [23, 21], [24, 22], [25, 21], [26, 22], [27, 22], [28, 23], [29, 24]],
}
```
### Data Fields
- `translation`: Dictionary with pairs (source,target).
- src_lg: sequence of text in source language.
- tgt_lg: sequence of text in target language.
- `src_tags`: source word-level tags. `0`=`BAD`, `1`=`OK`. `[]` if N/A (only for test).
- `mt_tags`: target word-level tags. `0`=`BAD`, `1`=`OK`. `[]` if N/A (only for test).
- `pe`: post-edited version of NMT output. `""` if N/A (only for test).
- `hter`: human translation error rate. `-10_000` if N/A (only for test).
- `alignments`: Word aligments. List of pairs of integers.
### Data Splits
There are 2 configurations in this dataset (one for each available language pair). Each configuration is composed of 7K examples for training, 1K for validation and 1K for (blind) test.
## Dataset Creation
### Curation Rationale
The original text is extracted from Wikipedia.
From the homepage:
*Word-level labels have been obtained by using the alignments provided by the [TER](http://www.cs.umd.edu/~snover/tercom/) tool (settings: tokenised, case insensitive, exact matching only, disabling shifts by using the `-d 0` option) between machine translations and their post-edited versions. Shifts (word order errors) were not annotated as such (but rather as deletions + insertions) to avoid introducing noise in the annotation.*
*HTER values are obtained deterministically from word-level tags. However, when computing HTER, we allow shifts in TER.*
*The baseline system is a neural predictor-estimator approach implemented in [OpenKiwi](https://github.com/Unbabel/OpenKiwi) ([Kepler at al., 2019](https://arxiv.org/abs/1902.08646)), where the predictor model will be trained on the parallel data used to train the NMT model.*
### 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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gsarti/itacola | gsarti | 2022-07-01T15:38:55Z | 470 | 1 | null | [
"task_categories:text-classification",
"task_ids:acceptability-classification",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:it",
"license:unknown",
"arxiv:2109.12053",
"region:us"
] | 2022-07-01T15:38:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- it
license:
- unknown
multilinguality:
- monolingual
pretty_name: itacola
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- acceptability-classification
---
# Dataset Card for ItaCoLA
## Table of Contents
- [Dataset Card for ItaCoLA](#dataset-card-for-itacola)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Acceptability Classification](#acceptability-classification)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Scores Configuration](#scores-configuration)
- [Phenomena Configuration](#phenomena-configuration)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** [Github](https://github.com/dhfbk/ItaCoLA-dataset)
- **Paper:** [Arxiv](http://ceur-ws.org/Vol-2765/paper169.pdf)
- **Point of Contact:** [Daniela Trotta](dtrotta@unisa.it)
### Dataset Summary
The Italian Corpus of Linguistic Acceptability includes almost 10k sentences taken from linguistic literature with a binary annotation made by the original authors themselves. The work is inspired by the English [Corpus of Linguistic Acceptability](https://nyu-mll.github.io/CoLA/).
**Disclaimer**: *The ItaCoLA corpus is hosted on Github by the [Digital Humanities group at FBK](https://dh.fbk.eu/)*. It was introduced in the article [Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus](https://arxiv.org/abs/2109.12053) by [Daniela Trotta](https://dh.fbk.eu/author/daniela/), [Raffaele Guarasci](https://www.icar.cnr.it/persone/guarasci/), [Elisa Leonardelli](https://dh.fbk.eu/author/elisa/), [Sara Tonelli](https://dh.fbk.eu/author/sara/)
### Supported Tasks and Leaderboards
#### Acceptability Classification
The following table is taken from Table 4 of the original paper, where an LSTM and a BERT model pretrained on the Italian languages are fine-tuned on the `train` split of the corpus and evaluated respectively on the `test` split (*In-domain*, `in`) and on the acceptability portion of the [AcCompl-it] corpus (*Out-of-domain*, `out`). Models are evaluated with accuracy (*Acc.*) and Matthews Correlation Coefficient (*MCC*) in both settings. Results are averaged over 10 runs with ±stdev. error bounds.
| | `in`, Acc.| `in`, MCC| `out`, Acc.|`out`, MCC|
|---------:|-----------:|----------:|-----------:|---------:|
|`LSTM` | 0.794 | 0.278 ± 0.029 | 0.605 | 0.147 ± 0.066 |
|`ITA-BERT`| 0.904 | 0.603 ± 0.022 | 0.683 | 0.198 ± 0.036 |
### Languages
The language data in ItaCoLA is in Italian (BCP-47 `it`)
## Dataset Structure
### Data Instances
#### Scores Configuration
The `scores` configuration contains sentences with acceptability judgments. An example from the `train` split of the `scores` config (default) is provided below.
```json
{
"unique_id": 1,
"source": "Graffi_1994",
"acceptability": 1,
"sentence": "Quest'uomo mi ha colpito."
}
```
The text is provided as-is, without further preprocessing or tokenization.
The fields are the following:
- `unique_id`: Unique identifier for the sentence across configurations.
- `source`: Original source for the sentence.
- `acceptability`: Binary score, 1 = acceptable, 0 = not acceptable.
- `sentence`: The evaluated sentence.
#### Phenomena Configuration
The `phenomena` configuration contains a sample of sentences from `scores` that has been manually annotated to denote the presence of 9 linguistic phenomena. An example from the `train` split is provided below:
```json
{
"unique_id": 1,
"source": "Graffi_1994",
"acceptability": 1,
"sentence": "Quest'uomo mi ha colpito.",
"cleft_construction": 0,
"copular_construction": 0,
"subject_verb_agreement": 1,
"wh_islands_violations": 0,
"simple": 0,
"question": 0,
"auxiliary": 1,
"bind": 0,
"indefinite_pronouns": 0
}
```
For each one of the new fields, the value of the binary score denotes the presence (1) or the absence (0) of the respective phenomenon. Refer to the original paper for a detailed description of each phenomenon.
### Data Splits
| config| train| test|
|----------:|-----:|----:|
|`scores` | 7801 | 975 |
|`phenomena`| 2088 | - |
### Dataset Creation
Please refer to the original article [Monolingual and Cross-Lingual Acceptability Judgments with the Italian CoLA corpus](https://arxiv.org/abs/2109.12053) for additional information on dataset creation.
## Additional Information
### Dataset Curators
The authors are the curators of the original dataset. For problems or updates on this 🤗 Datasets version, please contact [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com).
### Licensing Information
No licensing information available.
### Citation Information
Please cite the authors if you use these corpora in your work:
```bibtex
@inproceedings{trotta-etal-2021-monolingual-cross,
title = "Monolingual and Cross-Lingual Acceptability Judgments with the {I}talian {C}o{LA} corpus",
author = "Trotta, Daniela and
Guarasci, Raffaele and
Leonardelli, Elisa and
Tonelli, Sara",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.250",
doi = "10.18653/v1/2021.findings-emnlp.250",
pages = "2929--2940"
}
```
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iohadrubin/mtop | iohadrubin | 2022-01-01T20:54:04Z | 470 | 0 | null | [
"region:us"
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ncslgr | null | 2022-11-03T16:16:28Z | 469 | 4 | null | [
"task_categories:translation",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:translation",
"size_categories:n<1K",
"source_datasets:original",
"language:ase",
"language:en",
"license:mit",
"region:us"
] | 2022-11-03T16:16:28Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- ase
- en
license:
- mit
multilinguality:
- translation
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- translation
task_ids: []
paperswithcode_id: null
pretty_name: NCSLGR
dataset_info:
- config_name: entire_dataset
features:
- name: eaf
dtype: string
- name: sentences
sequence:
- name: gloss
dtype: string
- name: text
dtype: string
- name: videos
sequence: string
splits:
- name: train
num_bytes: 783504
num_examples: 870
download_size: 4113829143
dataset_size: 783504
- config_name: annotations
features:
- name: eaf
dtype: string
- name: sentences
sequence:
- name: gloss
dtype: string
- name: text
dtype: string
- name: videos
sequence: string
splits:
- name: train
num_bytes: 371725
num_examples: 870
download_size: 5335358
dataset_size: 371725
---
# Dataset Card for NCSLGR
## 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.bu.edu/asllrp/ncslgr.html
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
A small corpus of American Sign Language (ASL) video data from native signers, annotated with non-manual features.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
- American Sign Language
- English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- eaf: path to an ELAN annotation file
- videos: sequence of strings to video paths
- sentences: sequence of parallel sentences
- gloss: American Sign Language gloss annotations
- text: English text
### Data Splits
None
## 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
```bibtex
@misc{dataset:databases2007volumes,
title={Volumes 2--7},
author={Databases, NCSLGR},
year={2007},
publisher={American Sign Language Linguistic Research Project (Distributed on CD-ROM~…}
}
```
### Contributions
Thanks to [@AmitMY](https://github.com/AmitMY) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SocialGrep/top-american-universities-on-reddit | SocialGrep | 2022-07-25T18:57:00Z | 468 | 2 | null | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-07-25T18:57:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for top-american-universities-on-reddit
## 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://socialgrep.com/datasets](https://socialgrep.com/datasets/top-american-universities-on-reddit?utm_source=huggingface&utm_medium=link&utm_campaign=topamericanuniversitiesonreddit)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=topamericanuniversitiesonreddit)
### Dataset Summary
This corpus contains the complete data for the activity of the subreddits of the top 10 US colleges, according to the [2019 Forbes listing](https://www.forbes.com/top-colleges/#1208425d1987).
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## 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
CC-BY v4.0
### Contributions
[Needs More Information] | [
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eloukas/edgar-corpus | eloukas | 2023-07-14T07:17:12Z | 468 | 17 | null | [
"task_categories:other",
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] | 2023-07-14T07:17:12Z | 2022-12-30T16:55:08.000Z | 2022-12-30T16:55:08 | ---
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- name: section_9A
dtype: string
- name: section_9B
dtype: string
- name: section_10
dtype: string
- name: section_11
dtype: string
- name: section_12
dtype: string
- name: section_13
dtype: string
- name: section_14
dtype: string
- name: section_15
dtype: string
splits:
- name: train
num_bytes: 1541847387
num_examples: 5480
- name: validation
num_bytes: 193498658
num_examples: 686
- name: test
num_bytes: 192600298
num_examples: 685
download_size: 1946916132
dataset_size: 1927946343
annotations_creators:
- no-annotation
language:
- en
language_creators:
- other
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: EDGAR-CORPUS (10-K Filings from 1999 to 2020)
size_categories:
- 100K<n<1M
source_datasets:
- extended|other
tags:
- research papers
- edgar
- sec
- finance
- financial
- filings
- 10K
- 10-K
- nlp
- research
- econlp
- economics
- business
task_categories:
- other
task_ids: []
---
# Dataset Card for [EDGAR-CORPUS]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [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)
- [Licensing Information](#licensing-information)
- [References](#references)
- [Contributions](#contributions)
## Dataset Description
- **Point of Contact: Lefteris Loukas**
### Dataset Summary
This dataset card is based on the paper **EDGAR-CORPUS: Billions of Tokens Make The World Go Round** authored by _Lefteris Loukas et.al_, as published in the _ECONLP 2021_ workshop.
This dataset contains the annual reports of public companies from 1993-2020 from SEC EDGAR filings.
There is supported functionality to load a specific year.
Care: since this is a corpus dataset, different `train/val/test` splits do not have any special meaning. It's the default HF card format to have train/val/test splits.
If you wish to load specific year(s) of specific companies, you probably want to use the open-source software which generated this dataset, EDGAR-CRAWLER: https://github.com/nlpaueb/edgar-crawler.
## Citation
If this work helps or inspires you in any way, please consider citing the relevant paper published at the [3rd Economics and Natural Language Processing (ECONLP) workshop](https://lt3.ugent.be/econlp/) at EMNLP 2021 (Punta Cana, Dominican Republic):
```
@inproceedings{loukas-etal-2021-edgar,
title = "{EDGAR}-{CORPUS}: Billions of Tokens Make The World Go Round",
author = "Loukas, Lefteris and
Fergadiotis, Manos and
Androutsopoulos, Ion and
Malakasiotis, Prodromos",
booktitle = "Proceedings of the Third Workshop on Economics and Natural Language Processing",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.econlp-1.2",
pages = "13--18",
}
```
### Supported Tasks
This is a raw dataset/corpus for financial NLP.
As such, there are no annotations or labels.
### Languages
The EDGAR Filings are in English.
## Dataset Structure
### Data Instances
Refer to the dataset preview.
### Data Fields
**filename**: Name of file on EDGAR from which the report was extracted.<br>
**cik**: EDGAR identifier for a firm.<br>
**year**: Year of report.<br>
**section_1**: Corressponding section of the Annual Report.<br>
**section_1A**: Corressponding section of the Annual Report.<br>
**section_1B**: Corressponding section of the Annual Report.<br>
**section_2**: Corressponding section of the Annual Report.<br>
**section_3**: Corressponding section of the Annual Report.<br>
**section_4**: Corressponding section of the Annual Report.<br>
**section_5**: Corressponding section of the Annual Report.<br>
**section_6**: Corressponding section of the Annual Report.<br>
**section_7**: Corressponding section of the Annual Report.<br>
**section_7A**: Corressponding section of the Annual Report.<br>
**section_8**: Corressponding section of the Annual Report.<br>
**section_9**: Corressponding section of the Annual Report.<br>
**section_9A**: Corressponding section of the Annual Report.<br>
**section_9B**: Corressponding section of the Annual Report.<br>
**section_10**: Corressponding section of the Annual Report.<br>
**section_11**: Corressponding section of the Annual Report.<br>
**section_12**: Corressponding section of the Annual Report.<br>
**section_13**: Corressponding section of the Annual Report.<br>
**section_14**: Corressponding section of the Annual Report.<br>
**section_15**: Corressponding section of the Annual Report.<br>
```python
import datasets
# Load the entire dataset
raw_dataset = datasets.load_dataset("eloukas/edgar-corpus", "full")
# Load a specific year and split
year_1993_training_dataset = datasets.load_dataset("eloukas/edgar-corpus", "year_1993", split="train")
```
### Data Splits
| Config | Training | Validation | Test |
| --------- | -------- | ---------- | ------ |
| full | 176,289 | 22,050 | 22,036 |
| year_1993 | 1,060 | 133 | 133 |
| year_1994 | 2,083 | 261 | 260 |
| year_1995 | 4,110 | 514 | 514 |
| year_1996 | 7,589 | 949 | 949 |
| year_1997 | 8,084 | 1,011 | 1,011 |
| year_1998 | 8,040 | 1,006 | 1,005 |
| year_1999 | 7,864 | 984 | 983 |
| year_2000 | 7,589 | 949 | 949 |
| year_2001 | 7,181 | 898 | 898 |
| year_2002 | 6,636 | 830 | 829 |
| year_2003 | 6,672 | 834 | 834 |
| year_2004 | 7,111 | 889 | 889 |
| year_2005 | 7,113 | 890 | 889 |
| year_2006 | 7,064 | 883 | 883 |
| year_2007 | 6,683 | 836 | 835 |
| year_2008 | 7,408 | 927 | 926 |
| year_2009 | 7,336 | 917 | 917 |
| year_2010 | 7,013 | 877 | 877 |
| year_2011 | 6,724 | 841 | 840 |
| year_2012 | 6,479 | 810 | 810 |
| year_2013 | 6,372 | 797 | 796 |
| year_2014 | 6,261 | 783 | 783 |
| year_2015 | 6,028 | 754 | 753 |
| year_2016 | 5,812 | 727 | 727 |
| year_2017 | 5,635 | 705 | 704 |
| year_2018 | 5,508 | 689 | 688 |
| year_2019 | 5,354 | 670 | 669 |
| year_2020 | 5,480 | 686 | 685 |
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
Initial data was collected and processed by the authors of the research paper **EDGAR-CORPUS: Billions of Tokens Make The World Go Round**.
#### Who are the source language producers?
Public firms filing with the SEC.
### Annotations
#### Annotation process
NA
#### Who are the annotators?
NA
### Personal and Sensitive Information
The dataset contains public filings data from SEC.
## Considerations for Using the Data
### Social Impact of Dataset
Low to none.
### Discussion of Biases
The dataset is about financial information of public companies and as such the tone and style of text is in line with financial literature.
### Other Known Limitations
The dataset needs further cleaning for improved performance.
## Additional Information
### Licensing Information
EDGAR data is publicly available.
### Shoutout
Huge shoutout to [@JanosAudran](https://huggingface.co/JanosAudran) for the HF Card setup!
### References
- [Research Paper] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CORPUS: Billions of Tokens Make The World Go Round. Third Workshop on Economics and Natural Language Processing (ECONLP). https://arxiv.org/abs/2109.14394 - Punta Cana, Dominican Republic, November 2021.
- [Software] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CRAWLER. https://github.com/nlpaueb/edgar-crawler (2021)
- [EDGAR CORPUS, but in zip files] EDGAR CORPUS: A corpus for financial NLP research, built from SEC's EDGAR. https://zenodo.org/record/5528490 (2021)
- [Word Embeddings] EDGAR-W2V: Word2vec Embeddings trained on EDGAR-CORPUS. https://zenodo.org/record/5524358 (2021)
- [Applied Research paper where EDGAR-CORPUS is used] Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, and, George Paliouras. FiNER: Financial Numeric Entity Recognition for XBRL Tagging. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/2022.acl-long.303 (2022)
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pn_summary | null | 2023-01-25T14:42:36Z | 467 | 4 | pn-summary | [
"task_categories:summarization",
"task_categories:text-classification",
"task_ids:news-articles-summarization",
"task_ids:news-articles-headline-generation",
"task_ids:text-simplification",
"task_ids:topic-classification",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:fa",
"license:mit",
"arxiv:2012.11204",
"region:us"
] | 2023-01-25T14:42:36Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- fa
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
- text-classification
task_ids:
- news-articles-summarization
- news-articles-headline-generation
- text-simplification
- topic-classification
paperswithcode_id: pn-summary
pretty_name: Persian News Summary (PnSummary)
dataset_info:
features:
- name: id
dtype: string
- name: title
dtype: string
- name: article
dtype: string
- name: summary
dtype: string
- name: category
dtype:
class_label:
names:
'0': Economy
'1': Roads-Urban
'2': Banking-Insurance
'3': Agriculture
'4': International
'5': Oil-Energy
'6': Industry
'7': Transportation
'8': Science-Technology
'9': Local
'10': Sports
'11': Politics
'12': Art-Culture
'13': Society
'14': Health
'15': Research
'16': Education-University
'17': Tourism
- name: categories
dtype: string
- name: network
dtype:
class_label:
names:
'0': Tahlilbazaar
'1': Imna
'2': Shana
'3': Mehr
'4': Irna
'5': Khabaronline
- name: link
dtype: string
config_name: 1.0.0
splits:
- name: train
num_bytes: 309436493
num_examples: 82022
- name: validation
num_bytes: 21311817
num_examples: 5592
- name: test
num_bytes: 20936820
num_examples: 5593
download_size: 89591141
dataset_size: 351685130
---
# Dataset Card for Persian News Summary (pn_summary)
## 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
- **Repository:** https://github.com/hooshvare/pn-summary/
- **Paper:** https://arxiv.org/abs/2012.11204
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [Mehrdad Farahani](mailto:m3hrdadfphi@gmail.com)
### Dataset Summary
A well-structured summarization dataset for the Persian language consists of 93,207 records. It is prepared for Abstractive/Extractive tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification.
It is imperative to consider that the newlines were replaced with the `[n]` symbol. Please interpret them into normal newlines (for ex. `t.replace("[n]", "\n")`) and then use them for your purposes.
### Supported Tasks and Leaderboards
The dataset is prepared for Abstractive/Extractive summarization tasks (like cnn_dailymail for English). It can also be used in other scopes like Text Generation, Title Generation, and News Category Classification.
### Languages
The dataset covers Persian mostly and somewhere a combination with English.
## Dataset Structure
### Data Instances
A record consists of 8 features:
```python
record = ['id','title', 'article', 'summary', 'category', 'categories', 'network', 'link']
```
In the following, you can see an example of `pn_summmary`.
```json
{
"article": "به گزارش شانا، علی کاردر امروز (۲۷ دی ماه) در مراسم تودیع محسن قمصری، مدیر سابق امور بین الملل شرکت ملی نفت ایران و معارفه سعید خوشرو، مدیر جدید امور بین الملل این شرکت، گفت: مدیریت امور بین\u200eالملل به عنوان یکی از تاثیرگذارترین مدیریت\u200cهای شرکت ملی نفت ایران در دوران تحریم\u200cهای ظالمانه غرب علیه کشورمان بسیار هوشمندانه عمل کرد و ما توانستیم به خوبی از عهده تحریم\u200cها برآییم. [n] وی افزود: مجموعه امور بین الملل در همه دوران\u200cها با سختی\u200cها و مشکلات بسیاری مواجه بوده است، به ویژه در دوره اخیر به دلیل مسائل پیرامون تحریم وظیفه سنگینی بر عهده داشت که با تدبیر مدیریت خوب این مجموعه سربلند از آن بیرون آمد. [n] کاردر با قدردانی از زحمات محسن قمصری، به سلامت مدیریت امور بین الملل این شرکت اشاره کرد و افزود: محوریت کار مدیریت اموربین الملل سلامت مالی بوده است. [n] وی بر ضرورت نهادینه سازی جوانگرایی در مدیریت شرکت ملی نفت ایران تاکید کرد و گفت: مدیریت امور بین الملل در پرورش نیروهای زبده و کارآزموده آنچنان قوی عملکرده است که برای انتخاب مدیر جدید مشکلی وجود نداشت. [n] کاردر، حرفه\u200eای\u200eگری و کار استاندارد را از ویژگی\u200cهای مدیران این مدیریت برشمرد و گفت: نگاه جامع، خلاقیت و نوآوری و بکارگیری نیروهای جوان باید همچنان مد نظر مدیریت جدید امور بین الملل شرکت ملی نفت ایران باشد.",
"categories": "نفت",
"category": 5,
"id": "738e296491f8b24c5aa63e9829fd249fb4428a66",
"link": "https://www.shana.ir/news/275284/%D9%85%D8%AF%DB%8C%D8%B1%DB%8C%D8%AA-%D9%81%D8%B1%D9%88%D8%B4-%D9%86%D9%81%D8%AA-%D8%AF%D8%B1-%D8%AF%D9%88%D8%B1%D8%A7%D9%86-%D8%AA%D8%AD%D8%B1%DB%8C%D9%85-%D9%87%D9%88%D8%B4%D9%85%D9%86%D8%AF%D8%A7%D9%86%D9%87-%D8%B9%D9%85%D9%84-%DA%A9%D8%B1%D8%AF",
"network": 2,
"summary": "مدیرعامل شرکت ملی نفت، عملکرد مدیریت امور بین\u200eالملل این شرکت را در دوران تحریم بسیار هوشمندانه خواند و گفت: امور بین الملل در دوران پس از تحریم\u200eها نیز می\u200cتواند نقش بزرگی در تسریع روند توسعه داشته باشد.",
"title": "مدیریت فروش نفت در دوران تحریم هوشمندانه عمل کرد"
}
```
### Data Fields
- `id (string)`: ID of the news.
- `title (string)`: The title of the news.
- `article (string)`: The article of the news.
- `summary (string)`: The summary of the news.
- `category (int)`: The category of news in English (index of categories), including `Economy`, `Roads-Urban`, `Banking-Insurance`, `Agriculture`, `International`, `Oil-Energy`, `Industry`, `Transportation`, `Science-Technology`, `Local`, `Sports`, `Politics`, `Art-Culture`, `Society`, `Health`, `Research`, `Education-University`, `Tourism`.
- `categories (string)`: The category and sub-category of the news in Persian.
- `network (int)`: The news agency name (index of news agencies), including `Tahlilbazaar`, `Imna`, `Shana`, `Mehr`, `Irna`, `Khabaronline`.
- `link (string)`: The link of the news.
The category in English includes 18 different article categories from economy to tourism.
```bash
Economy, Roads-Urban, Banking-Insurance, Agriculture, International, Oil-Energy, Industry, Transportation, Science-Technology, Local, Sports, Politics, Art-Culture, Society, Health, Research, Education-University, Tourism
```
### Data Splits
Training (82,022 records, 8 features), validation (5,592 records, 8 features), and test split (5,593 records and 8 features).
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The dataset comprises numerous articles of various categories that have been crawled from six news agency websites (Tahlilbazaar, Imna, Shana, Mehr, Irna, and Khabaronline).
### Annotations
#### Annotation process
Each record (article) includes the long original text as well as a human-generated summary. The total number of cleaned articles is 93,207 (from 200,000 crawled articles).
#### Who are the annotators?
The dataset was organized by [Mehrdad Farahani](https://github.com/m3hrdadfi), [Mohammad Gharachorloo](https://github.com/baarsaam) and [Mohammad Manthouri](https://github.com/mmanthouri) for this paper [Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization](https://arxiv.org/abs/2012.11204)
### 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
This dataset was curated by [Mehrdad Farahani](https://github.com/m3hrdadfi), [Mohammad Gharachorloo](https://github.com/baarsaam) and [Mohammad Manthouri](https://github.com/mmanthouri).
### Licensing Information
This dataset is licensed under MIT License.
### Citation Information
```bibtex
@article{pnSummary,
title={Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization},
author={Mehrdad Farahani, Mohammad Gharachorloo, Mohammad Manthouri},
year={2020},
eprint={2012.11204},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@m3hrdadfi](https://github.com/m3hrdadfi) for adding this dataset. | [
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wiki_split | null | 2023-04-05T13:43:23Z | 467 | 3 | wikisplit | [
"task_categories:text2text-generation",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"split-and-rephrase",
"arxiv:1808.09468",
"region:us"
] | 2023-04-05T13:43:23Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: WikiSplit
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
paperswithcode_id: wikisplit
tags:
- split-and-rephrase
dataset_info:
features:
- name: complex_sentence
dtype: string
- name: simple_sentence_1
dtype: string
- name: simple_sentence_2
dtype: string
splits:
- name: test
num_bytes: 1949294
num_examples: 5000
- name: train
num_bytes: 384513073
num_examples: 989944
- name: validation
num_bytes: 1935459
num_examples: 5000
download_size: 100279164
dataset_size: 388397826
---
# Dataset Card for "wiki_split"
## 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://dataset-homepage/](https://dataset-homepage/)
- **Repository:** https://github.com/google-research-datasets/wiki-split
- **Paper:** [Learning To Split and Rephrase From Wikipedia Edit History](https://arxiv.org/abs/1808.09468)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 100.28 MB
- **Size of the generated dataset:** 388.40 MB
- **Total amount of disk used:** 488.68 MB
### Dataset Summary
One million English sentences, each split into two sentences that together preserve the original meaning, extracted from Wikipedia
Google's WikiSplit dataset was constructed automatically from the publicly available Wikipedia revision history. Although
the dataset contains some inherent noise, it can serve as valuable training data for models that split or merge sentences.
### Supported Tasks and Leaderboards
- Split and Rephrase
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 100.28 MB
- **Size of the generated dataset:** 388.40 MB
- **Total amount of disk used:** 488.68 MB
An example of 'train' looks as follows.
```
{
"complex_sentence": " '' As she translates from one language to another , she tries to find the appropriate wording and context in English that would correspond to the work in Spanish her poems and stories started to have differing meanings in their respective languages .",
"simple_sentence_1": "' '' As she translates from one language to another , she tries to find the appropriate wording and context in English that would correspond to the work in Spanish . ",
"simple_sentence_2": " Ergo , her poems and stories started to have differing meanings in their respective languages ."
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `complex_sentence`: a `string` feature.
- `simple_sentence_1`: a `string` feature.
- `simple_sentence_2`: a `string` feature.
### Data Splits
| name |train |validation|test|
|-------|-----:|---------:|---:|
|default|989944| 5000|5000|
## 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
The WikiSplit dataset is a verbatim copy of certain content from the publicly available Wikipedia revision history.
The dataset is therefore licensed under [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0/).
Any third party content or data is provided "As Is" without any warranty, express or implied.
### Citation Information
```
@inproceedings{botha-etal-2018-learning,
title = "Learning To Split and Rephrase From {W}ikipedia Edit History",
author = "Botha, Jan A. and
Faruqui, Manaal and
Alex, John and
Baldridge, Jason and
Das, Dipanjan",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1080",
doi = "10.18653/v1/D18-1080",
pages = "732--737",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@albertvillanova](https://github.com/albertvillanova), [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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dangne/gcc_caption_only | dangne | 2022-08-08T04:48:09Z | 467 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Geonmo/midjourney-prompts-only | Geonmo | 2023-10-25T09:12:51Z | 467 | 1 | null | [
"region:us"
] | 2023-10-25T09:12:51Z | 2023-10-25T09:10:06.000Z | 2023-10-25T09:10:06 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 495800699
num_examples: 3492544
download_size: 334934157
dataset_size: 495800699
---
# Dataset Card for "midjourney-prompts-only"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
numer_sense | null | 2022-11-18T21:34:07Z | 466 | 1 | numersense | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:slot-filling",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:extended|other",
"language:en",
"license:mit",
"arxiv:2005.00683",
"region:us"
] | 2022-11-18T21:34:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- extended|other
task_categories:
- text-generation
- fill-mask
task_ids:
- slot-filling
paperswithcode_id: numersense
pretty_name: NumerSense
dataset_info:
features:
- name: sentence
dtype: string
- name: target
dtype: string
splits:
- name: train
num_bytes: 825865
num_examples: 10444
- name: test_core
num_bytes: 62652
num_examples: 1132
- name: test_all
num_bytes: 184180
num_examples: 3146
download_size: 985463
dataset_size: 1072697
---
# Dataset Card for [Dataset Name]
## 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://inklab.usc.edu/NumerSense/
- **Repository:** https://github.com/INK-USC/NumerSense
- **Paper:** https://arxiv.org/abs/2005.00683
- **Leaderboard:** https://inklab.usc.edu/NumerSense/#exp
- **Point of Contact:** Author emails listed in [paper](https://arxiv.org/abs/2005.00683)
### Dataset Summary
NumerSense is a new numerical commonsense reasoning probing task, with a diagnostic dataset consisting of 3,145
masked-word-prediction probes. The general idea is to mask numbers between 0-10 in sentences mined from a commonsense
corpus and evaluate whether a language model can correctly predict the masked value.
### Supported Tasks and Leaderboards
The dataset supports the task of slot-filling, specifically as an evaluation of numerical common sense. A leaderboard
is included on the [dataset webpage](https://inklab.usc.edu/NumerSense/#exp) with included benchmarks for GPT-2,
RoBERTa, BERT, and human performance. Leaderboards are included for both the core set and the adversarial set
discussed below.
### Languages
This dataset is in English.
## Dataset Structure
### Data Instances
Each instance consists of a sentence with a masked numerical value between 0-10 and (in the train set) a target.
Example from the training set:
```
sentence: Black bears are about <mask> metres tall.
target: two
```
### Data Fields
Each value of the training set consists of:
- `sentence`: The sentence with a number masked out with the `<mask>` token.
- `target`: The ground truth target value. Since the test sets do not include the ground truth, the `target` field
values are empty strings in the `test_core` and `test_all` splits.
### Data Splits
The dataset includes the following pre-defined data splits:
- A train set with >10K labeled examples (i.e. containing a ground truth value)
- A core test set (`test_core`) with 1,132 examples (no ground truth provided)
- An expanded test set (`test_all`) encompassing `test_core` with the addition of adversarial examples for a total of
3,146 examples. See section 2.2 of [the paper] for a discussion of how these examples are constructed.
## Dataset Creation
### Curation Rationale
The purpose of this dataset is "to study whether PTLMs capture numerical commonsense knowledge, i.e., commonsense
knowledge that provides an understanding of the numeric relation between entities." This work is motivated by the
prior research exploring whether language models possess _commonsense knowledge_.
### Source Data
#### Initial Data Collection and Normalization
The dataset is an extension of the [Open Mind Common Sense](https://huggingface.co/datasets/open_mind_common_sense)
corpus. A query was performed to discover sentences containing numbers between 0-12, after which the resulting
sentences were manually evaluated for inaccuracies, typos, and the expression of commonsense knowledge. The numerical
values were then masked.
#### Who are the source language producers?
The [Open Mind Common Sense](https://huggingface.co/datasets/open_mind_common_sense) corpus, from which this dataset
is sourced, is a crowdsourced dataset maintained by the MIT Media Lab.
### Annotations
#### Annotation process
No annotations are present in this dataset beyond the `target` values automatically sourced from the masked
sentences, as discussed above.
#### Who are the annotators?
The curation and inspection was done in two rounds by graduate students.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
The motivation of measuring a model's ability to associate numerical values with real-world concepts appears
relatively innocuous. However, as discussed in the following section, the source dataset may well have biases encoded
from crowdworkers, particularly in terms of factoid coverage. A model's ability to perform well on this benchmark
should therefore not be considered evidence that it is more unbiased or objective than a human performing similar
tasks.
[More Information Needed]
### Discussion of Biases
This dataset is sourced from a crowdsourced commonsense knowledge base. While the information contained in the graph
is generally considered to be of high quality, the coverage is considered to very low as a representation of all
possible commonsense knowledge. The representation of certain factoids may also be skewed by the demographics of the
crowdworkers. As one possible example, the term "homophobia" is connected with "Islam" in the ConceptNet knowledge
base, but not with any other religion or group, possibly due to the biases of crowdworkers contributing to the
project.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset was collected by Bill Yuchen Lin, Seyeon Lee, Rahul Khanna, and Xiang Ren, Computer Science researchers
at the at the University of Southern California.
### Licensing Information
The data is hosted in a GitHub repositor with the
[MIT License](https://github.com/INK-USC/NumerSense/blob/main/LICENSE).
### Citation Information
```
@inproceedings{lin2020numersense,
title={Birds have four legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models},
author={Bill Yuchen Lin and Seyeon Lee and Rahul Khanna and Xiang Ren},
booktitle={Proceedings of EMNLP},
year={2020},
note={to appear}
}
```
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. | [
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SocialGrep/reddit-wallstreetbets-aug-2021 | SocialGrep | 2022-07-01T19:15:07Z | 466 | 2 | null | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-07-01T19:15:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for reddit-wallstreetbets-aug-2021
## 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://socialgrep.com/datasets](https://socialgrep.com/datasets?utm_source=huggingface&utm_medium=link&utm_campaign=dataset&utm_term=wallstreetbets)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=dataset&utm_term=wallstreetbets)
### Dataset Summary
This corpus contains the complete data for the activity on subreddit /r/WallStreetBets for the entire month of August.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## 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
CC-BY v4.0
### Contributions
[Needs More Information] | [
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clarin-pl/aspectemo | clarin-pl | 2022-08-29T16:39:32Z | 466 | 1 | null | [
"task_categories:token-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:mit",
"region:us"
] | 2022-08-29T16:39:32Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- mit
multilinguality:
- monolingual
pretty_name: 'AspectEmo'
size_categories:
- 1K
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- sentiment-classification
---
# AspectEmo
## Description
AspectEmo Corpus is an extended version of a publicly available PolEmo 2.0 corpus of Polish customer reviews used in many projects on the use of different methods in sentiment analysis. The AspectEmo corpus consists of four subcorpora, each containing online customer reviews from the following domains: school, medicine, hotels, and products. All documents are annotated at the aspect level with six sentiment categories: strong negative (minus_m), weak negative (minus_s), neutral (zero), weak positive (plus_s), strong positive (plus_m).
## Versions
| version | config name | description | default | notes |
|---------|-------------|--------------------------------|---------|------------------|
| 1.0 | "1.0" | The version used in the paper. | YES | |
| 2.0 | - | Some bugs fixed. | NO | work in progress |
## Tasks (input, output and metrics)
Aspect-based sentiment analysis (ABSA) is a text analysis method that categorizes data by aspects and identifies the sentiment assigned to each aspect. It is the sequence tagging task.
**Input** ('*tokens'* column): sequence of tokens
**Output** ('*labels'* column): sequence of predicted tokens’ classes ("O" + 6 possible classes: strong negative (a_minus_m), weak negative (a_minus_s), neutral (a_zero), weak positive (a_plus_s), strong positive (a_plus_m), ambiguous (a_amb) )
**Domain**: school, medicine, hotels and products
**Measurements**: F1-score (seqeval)
**Example***:*
Input: `['Dużo', 'wymaga', ',', 'ale', 'bardzo', 'uczciwy', 'i', 'przyjazny', 'studentom', '.', 'Warto', 'chodzić', 'na', 'konsultacje', '.', 'Docenia', 'postępy', 'i', 'zaangażowanie', '.', 'Polecam', '.']`
Input (translated by DeepL): `'Demands a lot , but very honest and student friendly . Worth going to consultations . Appreciates progress and commitment . I recommend .'`
Output: `['O', 'a_plus_s', 'O', 'O', 'O', 'a_plus_m', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'a_zero', 'O', 'a_plus_m', 'O', 'O', 'O', 'O', 'O', 'O']`
## Data splits
| Subset | Cardinality (sentences) |
|:-------|------------------------:|
| train | 1173 |
| val | 0 |
| test | 292 |
## Class distribution(without "O")
| Class | train | validation | test |
|:----------|--------:|-------------:|-------:|
| a_plus_m | 0.359 | - | 0.369 |
| a_minus_m | 0.305 | - | 0.377 |
| a_zero | 0.234 | - | 0.182 |
| a_minus_s | 0.037 | - | 0.024 |
| a_plus_s | 0.037 | - | 0.015 |
| a_amb | 0.027 | - | 0.033 |
## Citation
```
@misc{11321/849,
title = {{AspectEmo} 1.0: Multi-Domain Corpus of Consumer Reviews for Aspect-Based Sentiment Analysis},
author = {Koco{\'n}, Jan and Radom, Jarema and Kaczmarz-Wawryk, Ewa and Wabnic, Kamil and Zaj{\c a}czkowska, Ada and Za{\'s}ko-Zieli{\'n}ska, Monika},
url = {http://hdl.handle.net/11321/849},
note = {{CLARIN}-{PL} digital repository},
copyright = {The {MIT} License},
year = {2021}
}
```
## License
```
The MIT License
```
## Links
[HuggingFace](https://huggingface.co/datasets/clarin-pl/aspectemo)
[Source](https://clarin-pl.eu/dspace/handle/11321/849)
[Paper](https://sentic.net/sentire2021kocon.pdf)
## Examples
### Loading
```python
from pprint import pprint
from datasets import load_dataset
dataset = load_dataset("clarin-pl/aspectemo")
pprint(dataset['train'][20])
# {'labels': [0, 4, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 3, 0, 5, 0, 0, 0, 0, 0, 0],
# 'tokens': ['Dużo',
# 'wymaga',
# ',',
# 'ale',
# 'bardzo',
# 'uczciwy',
# 'i',
# 'przyjazny',
# 'studentom',
# '.',
# 'Warto',
# 'chodzić',
# 'na',
# 'konsultacje',
# '.',
# 'Docenia',
# 'postępy',
# 'i',
# 'zaangażowanie',
# '.',
# 'Polecam',
# '.']}
```
### Evaluation
```python
import random
from pprint import pprint
from datasets import load_dataset, load_metric
dataset = load_dataset("clarin-pl/aspectemo")
references = dataset["test"]["labels"]
# generate random predictions
predictions = [
[
random.randrange(dataset["train"].features["labels"].feature.num_classes)
for _ in range(len(labels))
]
for labels in references
]
# transform to original names of labels
references_named = [
[dataset["train"].features["labels"].feature.names[label] for label in labels]
for labels in references
]
predictions_named = [
[dataset["train"].features["labels"].feature.names[label] for label in labels]
for labels in predictions
]
# transform to BILOU scheme
references_named = [
[f"U-{label}" if label != "O" else label for label in labels]
for labels in references_named
]
predictions_named = [
[f"U-{label}" if label != "O" else label for label in labels]
for labels in predictions_named
]
# utilise seqeval to evaluate
seqeval = load_metric("seqeval")
seqeval_score = seqeval.compute(
predictions=predictions_named,
references=references_named,
scheme="BILOU",
mode="strict",
)
pprint(seqeval_score)
# {'a_amb': {'f1': 0.00597237775289287,
# 'number': 91,
# 'precision': 0.003037782418834251,
# 'recall': 0.17582417582417584},
# 'a_minus_m': {'f1': 0.048306148055207034,
# 'number': 1039,
# 'precision': 0.0288551620760727,
# 'recall': 0.1482194417709336},
# 'a_minus_s': {'f1': 0.004682997118155619,
# 'number': 67,
# 'precision': 0.0023701002734731083,
# 'recall': 0.19402985074626866},
# 'a_plus_m': {'f1': 0.045933014354066985,
# 'number': 1015,
# 'precision': 0.027402473834443386,
# 'recall': 0.14187192118226602},
# 'a_plus_s': {'f1': 0.0021750951604132683,
# 'number': 41,
# 'precision': 0.001095690284879474,
# 'recall': 0.14634146341463414},
# 'a_zero': {'f1': 0.025159400310184387,
# 'number': 501,
# 'precision': 0.013768389287061486,
# 'recall': 0.14570858283433133},
# 'overall_accuracy': 0.13970115681233933,
# 'overall_f1': 0.02328248652368391,
# 'overall_precision': 0.012639312620633834,
# 'overall_recall': 0.14742193173565724}
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
midas/inspec | midas | 2022-03-05T03:08:37Z | 466 | 9 | null | [
"arxiv:1910.08840",
"region:us"
] | 2022-03-05T03:08:37Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | A dataset for benchmarking keyphrase extraction and generation techniques from abstracts of English scientific papers. For more details about the dataset please refer the original paper - [https://dl.acm.org/doi/pdf/10.3115/1119355.1119383](https://dl.acm.org/doi/pdf/10.3115/1119355.1119383).
Data source - [https://github.com/boudinfl/ake-datasets/tree/master/datasets/Inspec](https://github.com/boudinfl/ake-datasets/tree/master/datasets/Inspec)
## Dataset Summary
The Inspec dataset was originally proposed by *Hulth* in the paper titled - [Improved automatic keyword extraction given more linguistic knowledge](https://aclanthology.org/W03-1028.pdf) in the year 2003. The dataset consists of abstracts of 2,000 English scientific papers from the [Inspec database](https://clarivate.com/webofsciencegroup/solutions/webofscience-inspec/). The abstracts are from papers belonging to the scientific domains of *Computers and Control* and *Information Technology* published between 1998 to 2002. Each abstract has two sets of keyphrases annotated by professional indexers - *controlled* and *uncontrolled*. The *controlled* keyphrases are obtained from the Inspec thesaurus and therefore are often not present in the abstract's text. Only 18.1% of the *controlled* keyphrases are actually present in the abstract's text. The *uncontrolled* keyphrases are those selected by the indexers after reading the full-length scientific articles and 76.2% of them are present in the abstract's text. There is no information in the original paper about how these 2,000 scientific papers were selected. It is unknown whether the papers were randomly selected out of all the papers published between 1998-2002 in the *Computers and Control* and *Information Technology* domains or were there only 2,000 papers in this domain that were indexed by Inspec. The train, dev and test splits of the data were arbitrarily chosen.
One of the key aspect of this dataset which makes it unique is that it provides keyphrases assigned by professional indexers, which is uncommon in the keyphrase literature. Most of the datasets in this domain have author assigned keyphrases as the ground truth. The dataset shared over here does not explicitly presents the *controlled* and *uncontrolled* keyphrases instead it only categorizes the keyphrases into *extractive* and *abstractive*. **Extractive keyphrases** are those that could be found in the input text and the **abstractive keyphrases** are those that are not present in the input text. In order to get all the meta-data about the documents and keyphrases please refer to the [original source](https://github.com/boudinfl/ake-datasets/tree/master/datasets/Inspec) from which the dataset was taken from. The main motivation behind making this dataset available in the form as presented over here is to make it easy for the researchers to programmatically download it and evaluate their models for the tasks of keyphrase extraction and generation. As keyphrase extraction by treating it as a sequence tagging task and using contextual language models has become popular - [Keyphrase extraction from scholarly articles as sequence labeling using contextualized embeddings](https://arxiv.org/pdf/1910.08840.pdf), we have also made the token tags available in the BIO tagging format.
## Dataset Structure
## Dataset Statistics
Table 1: Statistics on the length of the abstractive keyphrases for Train, Test, and Validation splits of Inspec dataset.
| | Train | Test | Validation |
|:---------------:|:-----------------------:|:----------------------:|:------------------------:|
| Single word | 9.0% | 9.5% | 10.1% |
| Two words | 50.4% | 48.2% | 45.7% |
| Three words | 27.6% | 28.6% | 29.8% |
| Four words | 9.3% | 10.3% | 10.3% |
| Five words | 2.4% | 2.0% | 3.2% |
| Six words | 0.9% | 1.2% | 0.7% |
| Seven words | 0.3% | 0.2% | 0.2% |
| Eight words | 0.1% | 0% | 0.1% |
| Nine words | 0% | 0.1% | 0% |
Table 2: Statistics on the length of the extractive keyphrases for Train, Test, and Validation splits of Inspec dataset.
| | Train | Test | Validation |
|:------------:|:-----------------------:|:----------------------:|:------------------------:|
| Single word | 16.2% | 15.4% | 17.0% |
| Two words | 52.4% | 54.8% | 51.6% |
| Three words | 24.3% | 22.99% | 24.3% |
| Four words | 5.6% | 4.96% | 5.8% |
| Five words | 1.2% | 1.3% | 1.1% |
| Six words | 0.2% | 0.36% | 0.2% |
| Seven words | 0.1% | 0.06% | 0.1% |
| Eight words | 0% | 0% | 0.03% |
Table 3: General statistics of the Inspec dataset.
| Type of Analysis | Train | Test | Validation |
|:----------------------------------------------:|:------------------------------:|:------------------------------:|:------------------------------:|
| Annotator Type | Professional Indexers | Professional Indexers | Professional Indexers |
| Document Type | Abstracts from Inspec Database | Abstracts from Inspec Database | Abstracts from Inspec Database |
| No. of Documents | 1000 | 500 | 500 |
| Avg. Document length (words) | 141.5 | 134.6 | 132.6 |
| Max Document length (words) | 557 | 384 | 330 |
| Max no. of abstractive keyphrases in a document | 17 | 20 | 14 |
| Min no. of abstractive keyphrases in a document | 0 | 0 | 0 |
| Avg. no. of abstractive keyphrases per document | 3.39 | 3.26 | 3.12 |
| Max no. of extractive keyphrases in a document | 24 | 27 | 22 |
| Min no. of extractive keyphrases in a document | 0 | 0 | 0 |
| Avg. no. of extractive keyphrases per document | 6.39 | 6.56 | 5.95 |
- Percentage of keyphrases that are named entities: 55.25% (named entities detected using scispacy - en-core-sci-lg model)
- Percentage of keyphrases that are noun phrases: 73.59% (noun phrases detected using spacy after removing determiners)
### Data Fields
- **id**: unique identifier of the document.
- **document**: Whitespace separated list of words in the document.
- **doc_bio_tags**: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.
- **extractive_keyphrases**: List of all the present keyphrases.
- **abstractive_keyphrase**: List of all the absent keyphrases.
### Data Splits
|Split| No. of datapoints |
|--|--|
| Train | 1,000 |
| Test | 500 |
| Validation | 500 |
## Usage
### Full Dataset
```python
from datasets import load_dataset
# get entire dataset
dataset = load_dataset("midas/inspec", "raw")
# sample from the train split
print("Sample from training dataset split")
train_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in train_sample.keys()])
print("Tokenized Document: ", train_sample["document"])
print("Document BIO Tags: ", train_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the validation split
print("Sample from validation dataset split")
validation_sample = dataset["validation"][0]
print("Fields in the sample: ", [key for key in validation_sample.keys()])
print("Tokenized Document: ", validation_sample["document"])
print("Document BIO Tags: ", validation_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the test split
print("Sample from test dataset split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
**Output**
```bash
Sample from training data split
Fields in the sample: ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document: ['A', 'conflict', 'between', 'language', 'and', 'atomistic', 'information', 'Fred', 'Dretske', 'and', 'Jerry', 'Fodor', 'are', 'responsible', 'for', 'popularizing', 'three', 'well-known', 'theses', 'in', 'contemporary', 'philosophy', 'of', 'mind', ':', 'the', 'thesis', 'of', 'Information-Based', 'Semantics', '-LRB-', 'IBS', '-RRB-', ',', 'the', 'thesis', 'of', 'Content', 'Atomism', '-LRB-', 'Atomism', '-RRB-', 'and', 'the', 'thesis', 'of', 'the', 'Language', 'of', 'Thought', '-LRB-', 'LOT', '-RRB-', '.', 'LOT', 'concerns', 'the', 'semantically', 'relevant', 'structure', 'of', 'representations', 'involved', 'in', 'cognitive', 'states', 'such', 'as', 'beliefs', 'and', 'desires', '.', 'It', 'maintains', 'that', 'all', 'such', 'representations', 'must', 'have', 'syntactic', 'structures', 'mirroring', 'the', 'structure', 'of', 'their', 'contents', '.', 'IBS', 'is', 'a', 'thesis', 'about', 'the', 'nature', 'of', 'the', 'relations', 'that', 'connect', 'cognitive', 'representations', 'and', 'their', 'parts', 'to', 'their', 'contents', '-LRB-', 'semantic', 'relations', '-RRB-', '.', 'It', 'holds', 'that', 'these', 'relations', 'supervene', 'solely', 'on', 'relations', 'of', 'the', 'kind', 'that', 'support', 'information', 'content', ',', 'perhaps', 'with', 'some', 'help', 'from', 'logical', 'principles', 'of', 'combination', '.', 'Atomism', 'is', 'a', 'thesis', 'about', 'the', 'nature', 'of', 'the', 'content', 'of', 'simple', 'symbols', '.', 'It', 'holds', 'that', 'each', 'substantive', 'simple', 'symbol', 'possesses', 'its', 'content', 'independently', 'of', 'all', 'other', 'symbols', 'in', 'the', 'representational', 'system', '.', 'I', 'argue', 'that', 'Dretske', "'s", 'and', 'Fodor', "'s", 'theories', 'are', 'false', 'and', 'that', 'their', 'falsehood', 'results', 'from', 'a', 'conflict', 'IBS', 'and', 'Atomism', ',', 'on', 'the', 'one', 'hand', ',', 'and', 'LOT', ',', 'on', 'the', 'other']
Document BIO Tags: ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'B', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O']
Extractive/present Keyphrases: ['philosophy of mind', 'content atomism', 'ibs', 'language of thought', 'lot', 'cognitive states', 'beliefs', 'desires']
Abstractive/absent Keyphrases: ['information-based semantics']
-----------
Sample from validation data split
Fields in the sample: ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document: ['Impact', 'of', 'aviation', 'highway-in-the-sky', 'displays', 'on', 'pilot', 'situation', 'awareness', 'Thirty-six', 'pilots', '-LRB-', '31', 'men', ',', '5', 'women', '-RRB-', 'were', 'tested', 'in', 'a', 'flight', 'simulator', 'on', 'their', 'ability', 'to', 'intercept', 'a', 'pathway', 'depicted', 'on', 'a', 'highway-in-the-sky', '-LRB-', 'HITS', '-RRB-', 'display', '.', 'While', 'intercepting', 'and', 'flying', 'the', 'pathway', ',', 'pilots', 'were', 'required', 'to', 'watch', 'for', 'traffic', 'outside', 'the', 'cockpit', '.', 'Additionally', ',', 'pilots', 'were', 'tested', 'on', 'their', 'awareness', 'of', 'speed', ',', 'altitude', ',', 'and', 'heading', 'during', 'the', 'flight', '.', 'Results', 'indicated', 'that', 'the', 'presence', 'of', 'a', 'flight', 'guidance', 'cue', 'significantly', 'improved', 'flight', 'path', 'awareness', 'while', 'intercepting', 'the', 'pathway', ',', 'but', 'significant', 'practice', 'effects', 'suggest', 'that', 'a', 'guidance', 'cue', 'might', 'be', 'unnecessary', 'if', 'pilots', 'are', 'given', 'proper', 'training', '.', 'The', 'amount', 'of', 'time', 'spent', 'looking', 'outside', 'the', 'cockpit', 'while', 'using', 'the', 'HITS', 'display', 'was', 'significantly', 'less', 'than', 'when', 'using', 'conventional', 'aircraft', 'instruments', '.', 'Additionally', ',', 'awareness', 'of', 'flight', 'information', 'present', 'on', 'the', 'HITS', 'display', 'was', 'poor', '.', 'Actual', 'or', 'potential', 'applications', 'of', 'this', 'research', 'include', 'guidance', 'for', 'the', 'development', 'of', 'perspective', 'flight', 'display', 'standards', 'and', 'as', 'a', 'basis', 'for', 'flight', 'training', 'requirements']
Document BIO Tags: ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
Extractive/present Keyphrases: ['flight simulator', 'pilots', 'cockpit', 'flight guidance', 'situation awareness', 'flight path awareness']
Abstractive/absent Keyphrases: ['highway-in-the-sky display', 'human factors', 'aircraft display']
-----------
Sample from test data split
Fields in the sample: ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document: ['A', 'new', 'graphical', 'user', 'interface', 'for', 'fast', 'construction', 'of', 'computation', 'phantoms', 'and', 'MCNP', 'calculations', ':', 'application', 'to', 'calibration', 'of', 'in', 'vivo', 'measurement', 'systems', 'Reports', 'on', 'a', 'new', 'utility', 'for', 'development', 'of', 'computational', 'phantoms', 'for', 'Monte', 'Carlo', 'calculations', 'and', 'data', 'analysis', 'for', 'in', 'vivo', 'measurements', 'of', 'radionuclides', 'deposited', 'in', 'tissues', '.', 'The', 'individual', 'properties', 'of', 'each', 'worker', 'can', 'be', 'acquired', 'for', 'a', 'rather', 'precise', 'geometric', 'representation', 'of', 'his', '-LRB-', 'her', '-RRB-', 'anatomy', ',', 'which', 'is', 'particularly', 'important', 'for', 'low', 'energy', 'gamma', 'ray', 'emitting', 'sources', 'such', 'as', 'thorium', ',', 'uranium', ',', 'plutonium', 'and', 'other', 'actinides', '.', 'The', 'software', 'enables', 'automatic', 'creation', 'of', 'an', 'MCNP', 'input', 'data', 'file', 'based', 'on', 'scanning', 'data', '.', 'The', 'utility', 'includes', 'segmentation', 'of', 'images', 'obtained', 'with', 'either', 'computed', 'tomography', 'or', 'magnetic', 'resonance', 'imaging', 'by', 'distinguishing', 'tissues', 'according', 'to', 'their', 'signal', '-LRB-', 'brightness', '-RRB-', 'and', 'specification', 'of', 'the', 'source', 'and', 'detector', '.', 'In', 'addition', ',', 'a', 'coupling', 'of', 'individual', 'voxels', 'within', 'the', 'tissue', 'is', 'used', 'to', 'reduce', 'the', 'memory', 'demand', 'and', 'to', 'increase', 'the', 'calculational', 'speed', '.', 'The', 'utility', 'was', 'tested', 'for', 'low', 'energy', 'emitters', 'in', 'plastic', 'and', 'biological', 'tissues', 'as', 'well', 'as', 'for', 'computed', 'tomography', 'and', 'magnetic', 'resonance', 'imaging', 'scanning', 'information']
Document BIO Tags: ['O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'B', 'I', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'I', 'I', 'I', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'B', 'O', 'B', 'I', 'O', 'O', 'B', 'I', 'I', 'I', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'B', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'B', 'I', 'I', 'I', 'I']
Extractive/present Keyphrases: ['computational phantoms', 'monte carlo calculations', 'in vivo measurements', 'radionuclides', 'tissues', 'worker', 'precise geometric representation', 'mcnp input data file', 'scanning data', 'computed tomography', 'brightness', 'graphical user interface', 'computation phantoms', 'calibration', 'in vivo measurement systems', 'signal', 'detector', 'individual voxels', 'memory demand', 'calculational speed', 'plastic', 'magnetic resonance imaging scanning information', 'anatomy', 'low energy gamma ray emitting sources', 'actinides', 'software', 'automatic creation']
Abstractive/absent Keyphrases: ['th', 'u', 'pu', 'biological tissues']
-----------
```
### Keyphrase Extraction
```python
from datasets import load_dataset
# get the dataset only for keyphrase extraction
dataset = load_dataset("midas/inspec", "extraction")
print("Samples for Keyphrase Extraction")
# sample from the train split
print("Sample from training data split")
train_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in train_sample.keys()])
print("Tokenized Document: ", train_sample["document"])
print("Document BIO Tags: ", train_sample["doc_bio_tags"])
print("\n-----------\n")
# sample from the validation split
print("Sample from validation data split")
validation_sample = dataset["validation"][0]
print("Fields in the sample: ", [key for key in validation_sample.keys()])
print("Tokenized Document: ", validation_sample["document"])
print("Document BIO Tags: ", validation_sample["doc_bio_tags"])
print("\n-----------\n")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("\n-----------\n")
```
### Keyphrase Generation
```python
# get the dataset only for keyphrase generation
dataset = load_dataset("midas/inspec", "generation")
print("Samples for Keyphrase Generation")
# sample from the train split
print("Sample from training data split")
train_sample = dataset["train"][0]
print("Fields in the sample: ", [key for key in train_sample.keys()])
print("Tokenized Document: ", train_sample["document"])
print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the validation split
print("Sample from validation data split")
validation_sample = dataset["validation"][0]
print("Fields in the sample: ", [key for key in validation_sample.keys()])
print("Tokenized Document: ", validation_sample["document"])
print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
## Citation Information
Please cite the works below if you use this dataset in your work.
```
@inproceedings{hulth2003improved,
title={Improved automatic keyword extraction given more linguistic knowledge},
author={Hulth, Anette},
booktitle={Proceedings of the 2003 conference on Empirical methods in natural language processing},
pages={216--223},
year={2003}
}
```
and
```
@InProceedings{10.1007/978-3-030-45442-5_41,
author="Sahrawat, Dhruva
and Mahata, Debanjan
and Zhang, Haimin
and Kulkarni, Mayank
and Sharma, Agniv
and Gosangi, Rakesh
and Stent, Amanda
and Kumar, Yaman
and Shah, Rajiv Ratn
and Zimmermann, Roger",
editor="Jose, Joemon M.
and Yilmaz, Emine
and Magalh{\~a}es, Jo{\~a}o
and Castells, Pablo
and Ferro, Nicola
and Silva, M{\'a}rio J.
and Martins, Fl{\'a}vio",
title="Keyphrase Extraction as Sequence Labeling Using Contextualized Embeddings",
booktitle="Advances in Information Retrieval",
year="2020",
publisher="Springer International Publishing",
address="Cham",
pages="328--335",
abstract="In this paper, we formulate keyphrase extraction from scholarly articles as a sequence labeling task solved using a BiLSTM-CRF, where the words in the input text are represented using deep contextualized embeddings. We evaluate the proposed architecture using both contextualized and fixed word embedding models on three different benchmark datasets, and compare with existing popular unsupervised and supervised techniques. Our results quantify the benefits of: (a) using contextualized embeddings over fixed word embeddings; (b) using a BiLSTM-CRF architecture with contextualized word embeddings over fine-tuning the contextualized embedding model directly; and (c) using domain-specific contextualized embeddings (SciBERT). Through error analysis, we also provide some insights into why particular models work better than the others. Lastly, we present a case study where we analyze different self-attention layers of the two best models (BERT and SciBERT) to better understand their predictions.",
isbn="978-3-030-45442-5"
}
```
and
```
@article{kulkarni2021learning,
title={Learning Rich Representation of Keyphrases from Text},
author={Kulkarni, Mayank and Mahata, Debanjan and Arora, Ravneet and Bhowmik, Rajarshi},
journal={arXiv preprint arXiv:2112.08547},
year={2021}
}
```
## Contributions
Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax), [@UmaGunturi](https://github.com/UmaGunturi) and [@ad6398](https://github.com/ad6398) for adding this dataset
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jeanlee/kmhas_korean_hate_speech | jeanlee | 2022-11-28T16:26:56Z | 466 | 11 | korean-multi-label-hate-speech-dataset | [
"task_categories:text-classification",
"task_ids:multi-label-classification",
"task_ids:hate-speech-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:ko",
"license:cc-by-sa-4.0",
"K-MHaS",
"Korean NLP",
"Hate Speech Detection",
"Dataset",
"Coling2022",
"arxiv:2208.10684",
"region:us"
] | 2022-11-28T16:26:56Z | 2022-11-21T05:03:58.000Z | 2022-11-21T05:03:58 | ---
annotations_creators:
- crowdsourced
language:
- ko
language_creators:
- found
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: 'K-MHaS'
size_categories:
- 100K<n<1M
source_datasets:
- original
tags:
- K-MHaS
- Korean NLP
- Hate Speech Detection
- Dataset
- Coling2022
task_categories:
- text-classification
task_ids:
- multi-label-classification
- hate-speech-detection
paperswithcode_id: korean-multi-label-hate-speech-dataset
dataset_info:
features:
- name: text
dtype: string
- name: label
sequence:
class_label:
names:
0: origin
1: physical
2: politics
3: profanity
4: age
5: gender
6: race
7: religion
8: not_hate_speech
splits:
- name: train
num_bytes: 6845463
num_examples: 78977
- name: validation
num_bytes: 748899
num_examples: 8776
- name: test
num_bytes: 1902352
num_examples: 21939
download_size: 9496714
dataset_size: 109692
---
# Dataset Card for K-MHaS
## 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)
## Sample Code
<a href="https://colab.research.google.com/drive/171KhS1_LVBtpAFd_kaT8lcrZmhcz5ehY?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="base"/></a>
## Dataset Description
- **Homepage:** [K-MHaS](https://github.com/adlnlp/K-MHaS)
- **Repository:** [Korean Multi-label Hate Speech Dataset](https://github.com/adlnlp/K-MHaS)
- **Paper:** [K-MHaS: A Multi-label Hate Speech Detection Dataset in Korean Online News Comment](https://arxiv.org/abs/2208.10684)
- **Point of Contact:** [Caren Han](caren.han@sydney.edu.au)
- **Sample code:** [Colab](https://colab.research.google.com/drive/171KhS1_LVBtpAFd_kaT8lcrZmhcz5ehY?usp=sharing)
### Dataset Summary
The Korean Multi-label Hate Speech Dataset, **K-MHaS**, consists of 109,692 utterances from Korean online news comments, labelled with 8 fine-grained hate speech classes (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`) or `Not Hate Speech` class. Each utterance provides from a single to four labels that can handles Korean language patterns effectively. For more details, please refer to our paper about [**K-MHaS**](https://aclanthology.org/2022.coling-1.311), published at COLING 2022.
### Supported Tasks and Leaderboards
Hate Speech Detection
* `binary classification` (labels: `Hate Speech`, `Not Hate Speech`)
* `multi-label classification`: (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`, `Not Hate Speech`)
For the multi-label classification, a `Hate Speech` class from the binary classification, is broken down into eight classes, associated with the hate speech category. In order to reflect the social and historical context, we select the eight hate speech classes. For example, the `Politics` class is chosen, due to a significant influence on the style of Korean hate speech.
### Languages
Korean
## Dataset Structure
### Data Instances
The dataset is provided with train/validation/test set in the txt format. Each instance is a news comment with a corresponding one or more hate speech classes (labels: `Politics`, `Origin`, `Physical`, `Age`, `Gender`, `Religion`, `Race`, `Profanity`) or `Not Hate Speech` class. The label numbers matching in both English and Korean is in the data fields section.
```python
{'text':'수꼴틀딱시키들이 다 디져야 나라가 똑바로 될것같다..답이 없는 종자들ㅠ'
'label': [2, 3, 4]
}
```
### Data Fields
* `text`: utterance from Korean online news comment.
* `label`: the label numbers matching with 8 fine-grained hate speech classes and `not hate speech` class are follows.
* `0`: `Origin`(`출신차별`) hate speech based on place of origin or identity;
* `1`: `Physical`(`외모차별`) hate speech based on physical appearance (e.g. body, face) or disability;
* `2`: `Politics`(`정치성향차별`) hate speech based on political stance;
* `3`: `Profanity`(`혐오욕설`) hate speech in the form of swearing, cursing, cussing, obscene words, or expletives; or an unspecified hate speech category;
* `4`: `Age`(`연령차별`) hate speech based on age;
* `5`: `Gender`(`성차별`) hate speech based on gender or sexual orientation (e.g. woman, homosexual);
* `6`: `Race`(`인종차별`) hate speech based on ethnicity;
* `7`: `Religion`(`종교차별`) hate speech based on religion;
* `8`: `Not Hate Speech`(`해당사항없음`).
### Data Splits
In our repository, we provide splitted datasets that have 78,977(train) / 8,776 (validation) / 21,939 (test) samples, preserving the class proportion.
## Dataset Creation
### Curation Rationale
We propose K-MHaS, a large size Korean multi-label hate speech detection dataset that represents Korean language patterns effectively. Most datasets in hate speech research are annotated using a single label classification of particular aspects, even though the subjectivity of hate speech cannot be explained with a mutually exclusive annotation scheme. We propose a multi-label hate speech annotation scheme that allows overlapping labels associated with the subjectivity and the intersectionality of hate speech.
### Source Data
#### Initial Data Collection and Normalization
Our dataset is based on the Korean online news comments available on Kaggle and Github. The unlabeled raw data was collected between January 2018 and June 2020. Please see the details in our paper [K-MHaS](https://aclanthology.org/2022.coling-1.311) published at COLING2020.
#### Who are the source language producers?
The language producers are users who left the comments on the Korean online news platform between 2018 and 2020.
### Annotations
#### Annotation process
We begin with the common categories of hate speech found in literature and match the keywords for each category. After the preliminary round, we investigate the results to merge or remove labels in order to provide the most representative subtype labels of hate speech contextual to the cultural background. Our annotation instructions explain a twolayered annotation to (a) distinguish hate and not hate speech, and (b) the categories of hate speech. Annotators are requested to consider given keywords or alternatives of each category within social, cultural, and historical circumstances. For more details, please refer to the paper [K-MHaS](https://aclanthology.org/2022.coling-1.311).
#### Who are the annotators?
Five native speakers were recruited for manual annotation in both the preliminary and main rounds.
### Personal and Sensitive Information
This datasets contains examples of hateful language, however, has no personal information.
## Considerations for Using the Data
### Social Impact of Dataset
We propose K-MHaS, a new large-sized dataset for Korean hate speech detection with a multi-label annotation scheme. We provided extensive baseline experiment results, presenting the usability of a dataset to detect Korean language patterns in hate speech.
### Discussion of Biases
All annotators were recruited from a crowdsourcing platform. They were informed about hate speech before handling the data. Our instructions allowed them to feel free to leave if they were uncomfortable with the content. With respect to the potential risks, we note that the subjectivity of human annotation would impact on the quality of the dataset.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset is curated by Taejun Lim, Heejun Lee and Bogeun Jo.
### Licensing Information
Creative Commons Attribution-ShareAlike 4.0 International (cc-by-sa-4.0).
### Citation Information
```
@inproceedings{lee-etal-2022-k,
title = "K-{MH}a{S}: A Multi-label Hate Speech Detection Dataset in {K}orean Online News Comment",
author = "Lee, Jean and
Lim, Taejun and
Lee, Heejun and
Jo, Bogeun and
Kim, Yangsok and
Yoon, Heegeun and
Han, Soyeon Caren",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.311",
pages = "3530--3538",
abstract = "Online hate speech detection has become an important issue due to the growth of online content, but resources in languages other than English are extremely limited. We introduce K-MHaS, a new multi-label dataset for hate speech detection that effectively handles Korean language patterns. The dataset consists of 109k utterances from news comments and provides a multi-label classification using 1 to 4 labels, and handles subjectivity and intersectionality. We evaluate strong baselines on K-MHaS. KR-BERT with a sub-character tokenizer outperforms others, recognizing decomposed characters in each hate speech class.",
}
```
### Contributions
The contributors of the work are:
- [Jean Lee](https://jeanlee-ai.github.io/) (The University of Sydney)
- [Taejun Lim](https://github.com/taezun) (The University of Sydney)
- [Heejun Lee](https://bigwaveai.com/) (BigWave AI)
- [Bogeun Jo](https://bigwaveai.com/) (BigWave AI)
- Yangsok Kim (Keimyung University)
- Heegeun Yoon (National Information Society Agency)
- [Soyeon Caren Han](https://drcarenhan.github.io/) (The University of Western Australia and The University of Sydney)
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
covid_qa_ucsd | null | 2023-06-01T14:59:47Z | 465 | 1 | null | [
"task_categories:question-answering",
"task_ids:closed-domain-qa",
"annotations_creators:found",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"size_categories:n<1K",
"source_datasets:original",
"language:en",
"language:zh",
"license:unknown",
"arxiv:2005.05442",
"region:us"
] | 2023-06-01T14:59:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- expert-generated
- found
language:
- en
- zh
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- closed-domain-qa
pretty_name: CovidQaUcsd
dataset_info:
- config_name: en
features:
- name: dialogue_id
dtype: int32
- name: dialogue_url
dtype: string
- name: dialogue_turns
sequence:
- name: speaker
dtype:
class_label:
names:
'0': Patient
'1': Doctor
- name: utterance
dtype: string
splits:
- name: train
num_bytes: 484944
num_examples: 572
download_size: 0
dataset_size: 484944
- config_name: zh
features:
- name: dialogue_id
dtype: int32
- name: dialogue_url
dtype: string
- name: dialogue_turns
sequence:
- name: speaker
dtype:
class_label:
names:
'0': 病人
'1': 医生
- name: utterance
dtype: string
splits:
- name: train
num_bytes: 1352377
num_examples: 1088
download_size: 0
dataset_size: 1352377
config_names:
- en
- zh
---
# Dataset Card for [Dataset Name]
## 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/UCSD-AI4H/COVID-Dialogue
- **Repository:** The data is also present in the same [GIT](https://github.com/UCSD-AI4H/COVID-Dialogue) repository
- **Paper:** https://pengtaoxie.github.io/coviddiag.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
COVID-Dialogue-Dataset-English is an English medical dialogue dataset about COVID-19 and other types of pneumonia. Patients who are concerned that they may be infected by COVID-19 or other pneumonia consult doctors and doctors provide advice. There are 603 consultations.
COVID-Dialogue-Dataset-Chinese is a Chinese medical dialogue dataset about COVID-19 and other types of pneumonia. Patients who are concerned that they may be infected by COVID-19 or other pneumonia consult doctors and doctors provide advice. There are 1393 consultations.
The dataset is present as a single text file. COVID-Dialogue-Dataset-Chinese.txt for Chinese and COVID-Dialogue-Dataset-English.txt for English.
### Supported Tasks and Leaderboards
Used for QA tasks. There is also a COVID-19 dialogue generation model available for the Chinese Data. The pre-print and more information is available in [this arxiv pre-print](https://arxiv.org/abs/2005.05442).
### Languages
Monolingual. The datasets are in English (EN) and Chinese (ZH)
## Dataset Structure
### Data Instances
An example of dialogue is:
```
{
'dialogue_id': 602,
'dialogue_url': 'https://www.healthtap.com/member/fg?page=/search/covid',
'dialogue_turns': [{'speaker': 'Patient',
'utterance': 'Can coronavirus symptoms be mild for some people versus severe? For example, could it just involve being very fatigued, low grade fever for a few days and not the extreme symptoms? Or is it always a full blown cold and struggle to breathe?Can coronavirus symptoms be mild for some people versus severe? For example, could it just involve being very fatigued, low grade fever for a few days and not the extreme symptoms? Or is it always a full blown cold and struggle to breathe?'},
{'speaker': 'Doctor',
'utterance': 'In brief: Symptoms vary. Some may have no symptoms at all. Some can be life threatening. Would you like to video or text chat with me?'}]
}
```
The dataset is built from [icliniq.com](https://www.icliniq.com/), [healthcaremagic.com](https://www.healthcaremagic.com/), [healthtap.com](https://www.healthtap.com/) and all copyrights of the data belong to these websites. _(for English)_
The dataset is built from [Haodf.com](https://www.haodf.com/) and all copyrights of the data belong to [Haodf.com](https://www.haodf.com/). _(for Chinese)_
### Data Fields
Each consultation consists of the below:
- ID
- URL
- Description of patient’s medical condition
- Dialogue
- Diagnosis and suggestions (Optional, mostly for Chinese)
For generating the QA only the below fields have been considered:
- ID : Consultatation Identifier (restarts for each file)
- URL: The url link of the extracted conversation
- Dialogue : The conversation between the doctor and the patient.
These are arranged as below in the prepared dataset. Each item will be represented with these parameters.
- "file_name": string - signifies the file from which the conversation was extracted
- "dialogue_id": int32 - the dialogue id
- "dialogue_url": string - url of the conversation
- "dialogue_turns": datasets.Sequence - sequence of dialogues between patient and the doctor.Consists ClassLabel(names=["病人", "医生"]), and "utterance"(string) for each turn. (ClassLable(names=["Patient", "Doctor"]) for english)
### Data Splits
There are no data splits on the original data
## 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{ju2020CovidDialog,
title={CovidDialog: Medical Dialogue Datasets about COVID-19},
author={Ju, Zeqian and Chakravorty, Subrato and He, Xuehai and Chen, Shu and Yang, Xingyi and Xie, Pengtao},
journal={ https://github.com/UCSD-AI4H/COVID-Dialogue},
year={2020}
}
### Contributions
Thanks to [@vrindaprabhu](https://github.com/vrindaprabhu) for adding this dataset. | [
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silk-road/ChatHaruhi-from-RoleLLM | silk-road | 2023-10-20T12:27:24Z | 465 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | 2023-10-20T12:27:24Z | 2023-10-20T08:39:56.000Z | 2023-10-20T08:39:56 | ---
license: cc-by-4.0
---
Adapt English Role in RoleBench into ChatHaruhi format
only using profiles part in [ZenMoore/RoleBench](https://huggingface.co/datasets/ZenMoore/RoleBench)
Great thanks to on authors of RoleLLM!
usage:
```python
# if you pip installed chatharuhi it should be
# from chatharuhi import ChatHaruhi
from ChatHaruhi import ChatHaruhi
chatbot = ChatHaruhi( role_from_hf = 'silk-road/ChatHaruhi-from-RoleLLM/Sherlock Holmes', \
llm = 'openai',
embedding = 'bge_en')
response = chatbot.chat(role='Police Chief', text = 'Oh no, there has been another murder')
print(response)
```
The output should like this:
```
Sherlock Holmes: "Ah, another murder case, you say? Fascinating. Pray, do provide me with the details, Constable."
```
if you chat with bot in English, embedding = 'bge_en'
if in Chinese, embedding = 'luotuo_openai' ( or default )
More detail information see our github https://github.com/LC1332/Chat-Haruhi-Suzumiya
95 Roles contains in this dataset:
Role | Movie | 中文 | How-to-call-in-ChatHaruhi
---|---|---|---
HAL 9000 | 2001-A-Space-Odyssey | 《2001太空漫游》中的HAL 9000电脑 | silk-road/ChatHaruhi-from-RoleLLM/HAL 9000
Colonel Nathan R. Jessep | A-Few-Good-Men | 《好汉两三个》中的内森·R·杰瑟普上校 | silk-road/ChatHaruhi-from-RoleLLM/Colonel Nathan R. Jessep
Antonio Salieri | Amadeus | 《阿玛迪斯》中的安东尼奥·萨列里 | silk-road/ChatHaruhi-from-RoleLLM/Antonio Salieri
Stifler | American-Pie | 《美国派》中的斯蒂夫勒 | silk-road/ChatHaruhi-from-RoleLLM/Stifler
Paul Vitti | Analyze-That | 《心理分析那件事》中的保罗·维蒂 | silk-road/ChatHaruhi-from-RoleLLM/Paul Vitti
Alvy Singer | Annie-Hall | 《安妮·霍尔》中的阿尔维·辛格 | silk-road/ChatHaruhi-from-RoleLLM/Alvy Singer
Violet Weston | August-Osage-County | 《奥塞奇郡的八月》中的紫罗兰·韦斯顿 | silk-road/ChatHaruhi-from-RoleLLM/Violet Weston
Willie Soke | Bad-Santa | 《坏圣诞老人》中的威利·索克 | silk-road/ChatHaruhi-from-RoleLLM/Willie Soke
Gaston | Beauty-and-the-Beast | 《美女与野兽》中的加斯顿 | silk-road/ChatHaruhi-from-RoleLLM/Gaston
The Dude | Big-Lebowski,-The | 《大勒布斯基》中的“大佬” | silk-road/ChatHaruhi-from-RoleLLM/The Dude
Murphy MacManus | Boondock-Saints,-The | 《天使之城》中的墨菲·麦克马纳斯 | silk-road/ChatHaruhi-from-RoleLLM/Murphy MacManus
Paul Conroy | Buried | 《活埋》中的保罗·康罗伊 | silk-road/ChatHaruhi-from-RoleLLM/Paul Conroy
Truman Capote | Capote | 《卡波特》中的杜鲁门·卡波特 | silk-road/ChatHaruhi-from-RoleLLM/Truman Capote
Mater | Cars-2 | 《赛车总动员2》中的玛特 | silk-road/ChatHaruhi-from-RoleLLM/Mater
Andrew Detmer | Chronicle | 《编年史》中的安德鲁·德特默 | silk-road/ChatHaruhi-from-RoleLLM/Andrew Detmer
Coriolanus | Coriolanus | 《科里奥兰纳斯》中的主角 | silk-road/ChatHaruhi-from-RoleLLM/Coriolanus
Benjamin Button | Curious-Case-of-Benjamin-Button,-The | 《本杰明·巴顿奇事》中的本杰明·巴顿 | silk-road/ChatHaruhi-from-RoleLLM/Benjamin Button
John Keating | Dead-Poets-Society | 《死亡诗社》中的约翰·基廷 | silk-road/ChatHaruhi-from-RoleLLM/John Keating
Wade Wilson | Deadpool | 《死侍》中的韦德·威尔逊 | silk-road/ChatHaruhi-from-RoleLLM/Wade Wilson
Jim Morrison | Doors,-The | 《门》中的吉姆·莫里森 | silk-road/ChatHaruhi-from-RoleLLM/Jim Morrison
Queen Elizabeth I | Elizabeth-The-Golden-Age | 《伊丽莎白：黄金时代》中的伊丽莎白一世女王 | silk-road/ChatHaruhi-from-RoleLLM/Queen Elizabeth I
Jeff Spicoli | Fast-Times-at-Ridgemont-High | 《瑞奇蒙特高中时光》中的杰夫·斯皮科利 | silk-road/ChatHaruhi-from-RoleLLM/Jeff Spicoli
Fred Flintstone | Flintstones,-The | 《石头家族》中的弗雷德·弗林斯通 | silk-road/ChatHaruhi-from-RoleLLM/Fred Flintstone
Freddy Krueger | Freddy-vs.-Jason | 《弗雷迪对杰森》中的弗雷迪·克鲁格 | silk-road/ChatHaruhi-from-RoleLLM/Freddy Krueger
Tyrion Lannister | Game_of_Thrones | 《权力的游戏》中的提利昂·兰尼斯特 | silk-road/ChatHaruhi-from-RoleLLM/Tyrion Lannister
James Brown | Get-on-Up | 《起身舞蹈》中的詹姆斯·布朗 | silk-road/ChatHaruhi-from-RoleLLM/James Brown
Walt Kowalski | Gran-Torino | 《老无所依》中的沃尔特·科瓦尔斯基 | silk-road/ChatHaruhi-from-RoleLLM/Walt Kowalski
John Coffey | Green-Mile,-The | 《绿里奇迹》中的约翰·科菲 | silk-road/ChatHaruhi-from-RoleLLM/John Coffey
Theodore Twombly | Her | 《她》中的西奥多·特温布利 | silk-road/ChatHaruhi-from-RoleLLM/Theodore Twombly
Gregory House | House-M.D. | 《豪斯医生》中的格雷戈里·豪斯 | silk-road/ChatHaruhi-from-RoleLLM/Gregory House
Sonny | I,-Robot | 《我，机器人》中的桑尼 | silk-road/ChatHaruhi-from-RoleLLM/Sonny
Colonel Hans Landa | Inglourious-Basterds | 《无耻混蛋》中的汉斯·兰达上校 | silk-road/ChatHaruhi-from-RoleLLM/Colonel Hans Landa
Judge Dredd | Judge-Dredd | 《德莱德法官》中的法官德莱德 | silk-road/ChatHaruhi-from-RoleLLM/Judge Dredd
Juno MacGuff | Juno | 《朱诺》中的朱诺·麦克夫 | silk-road/ChatHaruhi-from-RoleLLM/Juno MacGuff
Po | Kung-Fu-Panda | 《功夫熊猫》中的阿宝 | silk-road/ChatHaruhi-from-RoleLLM/Po
Professor G.H. Dorr | Ladykillers,-The | 《夫人杀手》中的G.H.多尔教授 | silk-road/ChatHaruhi-from-RoleLLM/Professor G.H. Dorr
Fletcher Reede | Liar-Liar | 《撒谎的男人》中的弗莱彻·里德 | silk-road/ChatHaruhi-from-RoleLLM/Fletcher Reede
Abraham Lincoln | Lincoln | 《林肯》中的亚伯拉罕·林肯 | silk-road/ChatHaruhi-from-RoleLLM/Abraham Lincoln
Frank T.J. Mackey | Magnolia | 《木兰花》中的弗兰克 T.J. 麦凯 | silk-road/ChatHaruhi-from-RoleLLM/Frank T.J. Mackey
Malcolm X | Malcolm-X | 《马尔科姆X》中的马尔科姆X | silk-road/ChatHaruhi-from-RoleLLM/Malcolm X
Leonard Shelby | Memento | 《记忆碎片》中的伦纳德·谢尔比 | silk-road/ChatHaruhi-from-RoleLLM/Leonard Shelby
Harvey Milk | Milk | 《牛奶》中的哈维·牛奶 | silk-road/ChatHaruhi-from-RoleLLM/Harvey Milk
Randle McMurphy | One-Flew-Over-the-Cuckoo's-Nest | 《飞越疯人院》中的兰德尔·麦克默菲 | silk-road/ChatHaruhi-from-RoleLLM/Randle McMurphy
Jack Sparrow | Pirates-of-the-Caribbean-Dead-Man's-Chest | 《加勒比海盗》中的杰克·斯派洛船长 | silk-road/ChatHaruhi-from-RoleLLM/Jack Sparrow
John Dillinger | Public-Enemies | 《公敌》中的约翰·迪林格 | silk-road/ChatHaruhi-from-RoleLLM/John Dillinger
Lestat de Lioncourt | Queen-of-the-Damned | 《诅咒女王》中的莱斯塔特·德·莱昂科特 | silk-road/ChatHaruhi-from-RoleLLM/Lestat de Lioncourt
Tyler Hawkins | Remember-Me | 《记得我》中的泰勒·霍金斯 | silk-road/ChatHaruhi-from-RoleLLM/Tyler Hawkins
Caesar | Rise-of-the-Planet-of-the-Apes | 《猩球崛起》中的凯撒 | silk-road/ChatHaruhi-from-RoleLLM/Caesar
Jack | Room | 《房间》中的杰克 | silk-road/ChatHaruhi-from-RoleLLM/Jack
James Carter | Rush-Hour-2 | 《尖峰时刻2》中的詹姆斯·卡特 | silk-road/ChatHaruhi-from-RoleLLM/James Carter
Jigsaw | Saw | 《电锯惊魂》中的拼图杀手 | silk-road/ChatHaruhi-from-RoleLLM/Jigsaw
John Doe | Se7en | 《七宗罪》中的约翰·多 | silk-road/ChatHaruhi-from-RoleLLM/John Doe
Jackie Moon | Semi-Pro | 《半职业球员》中的杰基·月亮 | silk-road/ChatHaruhi-from-RoleLLM/Jackie Moon
Sherlock Holmes | Sherlock-Holmes | 《夏洛克·福尔摩斯》中的夏洛克·福尔摩斯 | silk-road/ChatHaruhi-from-RoleLLM/Sherlock Holmes
Shrek | Shrek | 《史莱克》中的史莱克 | silk-road/ChatHaruhi-from-RoleLLM/Shrek
Pat Solitano | Silver-Linings-Playbook | 《乌云背后的幸福线》中的帕特·索利塔诺 | silk-road/ChatHaruhi-from-RoleLLM/Pat Solitano
Karl Childers | Sling-Blade | 《刀锯》中的卡尔·柴尔德斯 | silk-road/ChatHaruhi-from-RoleLLM/Karl Childers
Peter Parker | Spider-Man | 《蜘蛛侠》中的彼得·帕克 | silk-road/ChatHaruhi-from-RoleLLM/Peter Parker
Bruno Antony | Strangers-on-a-Train | 《列车上的陌生人》中的布鲁诺·安东尼 | silk-road/ChatHaruhi-from-RoleLLM/Bruno Antony
Seth | Superbad | 《超级糟糕》中的塞思 | silk-road/ChatHaruhi-from-RoleLLM/Seth
Caden Cotard | Synecdoche,-New-York | 《纽约奇缘》中的卡登·科塔德 | silk-road/ChatHaruhi-from-RoleLLM/Caden Cotard
Travis Bickle | Taxi-Driver | 《出租车司机》中的特拉维斯·比克尔 | silk-road/ChatHaruhi-from-RoleLLM/Travis Bickle
Stanley Ipkiss | Mask,-The | 《面具》中的斯坦利·伊普基斯 | silk-road/ChatHaruhi-from-RoleLLM/Stanley Ipkiss
Lyn Cassady | Men-Who-Stare-at-Goats,-The | 《盯羊的男人》中的林恩·卡萨迪 | silk-road/ChatHaruhi-from-RoleLLM/Lyn Cassady
Michael Scott | The_Office | 《办公室》中的迈克尔·斯科特 | silk-road/ChatHaruhi-from-RoleLLM/Michael Scott
Robert Angier | Prestige,-The | 《名望》中的罗伯特·安吉尔 | silk-road/ChatHaruhi-from-RoleLLM/Robert Angier
Rachel Lang | The-Rage-Carrie-2 | 《瑞秋的愤怒：凯丽2》中的瑞秋·朗 | silk-road/ChatHaruhi-from-RoleLLM/Rachel Lang
Dr. Frank-N-Furter | Rocky-Horror-Picture-Show,-The | 《洛奇恐怖秀》中的弗兰克·N·福特医生 | silk-road/ChatHaruhi-from-RoleLLM/Dr. Frank-N-Furter
Jack Torrance | Shining,-The | 《闪灵》中的杰克·托兰斯 | silk-road/ChatHaruhi-from-RoleLLM/Jack Torrance
Tom Ripley | Talented-Mr.-Ripley,-The | 《天才雷普利》中的汤姆·雷普利 | silk-road/ChatHaruhi-from-RoleLLM/Tom Ripley
D_Artagnan | Three-Musketeers,-The | 《三剑客》中的达达尼昂 | silk-road/ChatHaruhi-from-RoleLLM/D_Artagnan
Stephen Hawking | Theory-of-Everything,-The | 《万物理论》中的斯蒂芬·霍金 | silk-road/ChatHaruhi-from-RoleLLM/Stephen Hawking
Thor | Thor-Ragnarok | 《雷神：诸神黄昏》中的雷神索尔 | silk-road/ChatHaruhi-from-RoleLLM/Thor
James Bond | Tomorrow-Never-Dies | 《明日帝国》中的詹姆斯·邦德 | silk-road/ChatHaruhi-from-RoleLLM/James Bond
Mark Renton | Trainspotting | 《迷幻列车》中的马克·伦顿 | silk-road/ChatHaruhi-from-RoleLLM/Mark Renton
Tugg Speedman | Tropic-Thunder | 《热带惊雷》中的塔格·斯皮德曼 | silk-road/ChatHaruhi-from-RoleLLM/Tugg Speedman
David Aames | Vanilla-Sky | 《香草天空》中的大卫·艾姆斯 | silk-road/ChatHaruhi-from-RoleLLM/David Aames
Rorschach | Watchmen | 《守望者》中的罗夏克 | silk-road/ChatHaruhi-from-RoleLLM/Rorschach
Jordan Belfort | Wolf-of-Wall-Street,-The | 《华尔街之狼》中的乔丹·贝尔福特 | silk-road/ChatHaruhi-from-RoleLLM/Jordan Belfort
Logan | X-Men-Origins-Wolverine | 《X战警：金刚狼》中的洛根 | silk-road/ChatHaruhi-from-RoleLLM/Logan
Judy Hoops | Zootopia | 《疯狂动物城》中的朱迪·胡普斯 | silk-road/ChatHaruhi-from-RoleLLM/Judy Hoops
Doctor Who | Doctor_Who | 《神秘博士》中的博士 | silk-road/ChatHaruhi-from-RoleLLM/Doctor Who
Blair Waldorf | Gossip_Girl | 《绯闻女孩》中的布莱尔·沃尔多夫 | silk-road/ChatHaruhi-from-RoleLLM/Blair Waldorf
Raylan Givens | Justified | 《正当防卫》中的雷兰·吉文斯 | silk-road/ChatHaruhi-from-RoleLLM/Raylan Givens
Mary Sibley | Salem | 《塞勒姆》中的玛丽·西布利 | silk-road/ChatHaruhi-from-RoleLLM/Mary Sibley
Lucifer Morningstar | Lucifer | 《路西法》中的路西法·晨星 | silk-road/ChatHaruhi-from-RoleLLM/Lucifer Morningstar
Sheldon Cooper | The_Big_Bang_Theory | 《生活大爆炸》中的谢尔顿·库珀 | silk-road/ChatHaruhi-from-RoleLLM/Sheldon Cooper
Twilight Sparkle | My_Little_Pony__Friendship_is_Magic | 《我的小马驹：友谊之魔》中的暮光星辉 | silk-road/ChatHaruhi-from-RoleLLM/Twilight Sparkle
Oliver Queen | Arrow | 《绿箭侠》中的奥利弗·皇后 | silk-road/ChatHaruhi-from-RoleLLM/Oliver Queen
Leroy Jethro Gibbs | NCIS | 《海军罪案调查处》中的利洛伊·杰斯罗·吉布斯 | silk-road/ChatHaruhi-from-RoleLLM/Leroy Jethro Gibbs
Angel | Angel | 《天使》中的天使 | silk-road/ChatHaruhi-from-RoleLLM/Angel
Klaus Mikaelson | The_Originals | 《始祖家族》中的克劳斯·米卡尔森 | silk-road/ChatHaruhi-from-RoleLLM/Klaus Mikaelson
Queen Catherine | Reign | 《王权》中的凯瑟琳女王 | silk-road/ChatHaruhi-from-RoleLLM/Queen Catherine
Dr. Hannibal Lecter | Hannibal | 《汉尼拔》中的汉尼拔·莱克特医生 | silk-road/ChatHaruhi-from-RoleLLM/Dr. Hannibal Lecter
Coach Eric Taylor | Friday_Night_Lights | 《星期五之光》中的教练埃里克·泰勒 | silk-road/ChatHaruhi-from-RoleLLM/Coach Eric Taylor
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coarse_discourse | null | 2023-04-05T10:01:55Z | 464 | 3 | coarse-discourse | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2023-04-05T10:01:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Coarse Discourse
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
paperswithcode_id: coarse-discourse
dataset_info:
features:
- name: title
dtype: string
- name: is_self_post
dtype: bool
- name: subreddit
dtype: string
- name: url
dtype: string
- name: majority_link
dtype: string
- name: is_first_post
dtype: bool
- name: majority_type
dtype: string
- name: id_post
dtype: string
- name: post_depth
dtype: int32
- name: in_reply_to
dtype: string
- name: annotations
sequence:
- name: annotator
dtype: string
- name: link_to_post
dtype: string
- name: main_type
dtype: string
splits:
- name: train
num_bytes: 45443464
num_examples: 116357
download_size: 4636201
dataset_size: 45443464
---
# Dataset Card for "coarse_discourse"
## 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/google-research-datasets/coarse-discourse
- **Paper:** [Characterizing Online Discussion Using Coarse Discourse Sequences](https://research.google/pubs/pub46055/)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 4.63 MB
- **Size of the generated dataset:** 45.45 MB
- **Total amount of disk used:** 50.08 MB
### Dataset Summary
A large corpus of discourse annotations and relations on ~10K forum threads.
We collect and release a corpus of over 9,000 threads comprising over 100,000 comments manually annotated via paid crowdsourcing with discourse acts and randomly sampled from the site Reddit.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 4.63 MB
- **Size of the generated dataset:** 45.45 MB
- **Total amount of disk used:** 50.08 MB
An example of 'train' looks as follows.
```
{
"annotations": {
"annotator": ["fc96a15ab87f02dd1998ff55a64f6478", "e9e4b3ab355135fa954badcc06bfccc6", "31ac59c1734c1547d4d0723ff254c247"],
"link_to_post": ["", "", ""],
"main_type": ["elaboration", "elaboration", "elaboration"]
},
"id_post": "t1_c9b30i1",
"in_reply_to": "t1_c9b2nyd",
"is_first_post": false,
"is_self_post": true,
"majority_link": "t1_c9b2nyd",
"majority_type": "elaboration",
"post_depth": 2,
"subreddit": "100movies365days",
"title": "DTX120: #87 - Nashville",
"url": "https://www.reddit.com/r/100movies365days/comments/1bx6qw/dtx120_87_nashville/"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `title`: a `string` feature.
- `is_self_post`: a `bool` feature.
- `subreddit`: a `string` feature.
- `url`: a `string` feature.
- `majority_link`: a `string` feature.
- `is_first_post`: a `bool` feature.
- `majority_type`: a `string` feature.
- `id_post`: a `string` feature.
- `post_depth`: a `int32` feature.
- `in_reply_to`: a `string` feature.
- `annotations`: a dictionary feature containing:
- `annotator`: a `string` feature.
- `link_to_post`: a `string` feature.
- `main_type`: a `string` feature.
### Data Splits
| name |train |
|-------|-----:|
|default|116357|
## 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{coarsediscourse, title={Characterizing Online Discussion Using Coarse Discourse Sequences}, author={Zhang, Amy X. and Culbertson, Bryan and Paritosh, Praveen}, booktitle={Proceedings of the 11th International AAAI Conference on Weblogs and Social Media}, series={ICWSM '17}, year={2017}, location = {Montreal, Canada} }
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
hansards | null | 2023-04-05T10:07:00Z | 462 | 0 | null | [
"region:us"
] | 2023-04-05T10:07:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
paperswithcode_id: null
pretty_name: hansards
dataset_info:
- config_name: senate
features:
- name: fr
dtype: string
- name: en
dtype: string
splits:
- name: test
num_bytes: 5711686
num_examples: 25553
- name: train
num_bytes: 40324278
num_examples: 182135
download_size: 15247360
dataset_size: 46035964
- config_name: house
features:
- name: fr
dtype: string
- name: en
dtype: string
splits:
- name: test
num_bytes: 22906629
num_examples: 122290
- name: train
num_bytes: 191459584
num_examples: 947969
download_size: 67584000
dataset_size: 214366213
---
# Dataset Card for "hansards"
## 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.isi.edu/natural-language/download/hansard/](https://www.isi.edu/natural-language/download/hansard/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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 downloaded dataset files:** 82.83 MB
- **Size of the generated dataset:** 260.40 MB
- **Total amount of disk used:** 343.23 MB
### Dataset Summary
This release contains 1.3 million pairs of aligned text chunks (sentences or smaller fragments)
from the official records (Hansards) of the 36th Canadian Parliament.
The complete Hansards of the debates in the House and Senate of the 36th Canadian Parliament,
as far as available, were aligned. The corpus was then split into 5 sets of sentence pairs:
training (80% of the sentence pairs), two sets of sentence pairs for testing (5% each), and
two sets of sentence pairs for final evaluation (5% each). The current release consists of the
training and testing sets. The evaluation sets are reserved for future MT evaluation purposes
and currently not available.
Caveats
1. This release contains only sentence pairs. Even though the order of the sentences is the same
as in the original, there may be gaps resulting from many-to-one, many-to-many, or one-to-many
alignments that were filtered out. Therefore, this release may not be suitable for
discourse-related research.
2. Neither the sentence splitting nor the alignments are perfect. In particular, watch out for
pairs that differ considerably in length. You may want to filter these out before you do
any statistical training.
The alignment of the Hansards was performed as part of the ReWrite project under funding
from the DARPA TIDES program.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### house
- **Size of downloaded dataset files:** 67.58 MB
- **Size of the generated dataset:** 214.37 MB
- **Total amount of disk used:** 281.95 MB
An example of 'train' looks as follows.
```
{
"en": "Mr. Walt Lastewka (Parliamentary Secretary to Minister of Industry, Lib.):",
"fr": "M. Walt Lastewka (secrétaire parlementaire du ministre de l'Industrie, Lib.):"
}
```
#### senate
- **Size of downloaded dataset files:** 15.25 MB
- **Size of the generated dataset:** 46.03 MB
- **Total amount of disk used:** 61.28 MB
An example of 'train' looks as follows.
```
{
"en": "Mr. Walt Lastewka (Parliamentary Secretary to Minister of Industry, Lib.):",
"fr": "M. Walt Lastewka (secrétaire parlementaire du ministre de l'Industrie, Lib.):"
}
```
### Data Fields
The data fields are the same among all splits.
#### house
- `fr`: a `string` feature.
- `en`: a `string` feature.
#### senate
- `fr`: a `string` feature.
- `en`: a `string` feature.
### Data Splits
| name |train | test |
|------|-----:|-----:|
|house |947969|122290|
|senate|182135| 25553|
## 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
```
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. | [
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numeric_fused_head | null | 2023-06-01T14:59:47Z | 462 | 1 | numeric-fused-head | [
"task_categories:token-classification",
"annotations_creators:crowdsourced",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:mit",
"fused-head-identification",
"region:us"
] | 2023-06-01T14:59:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
- expert-generated
- machine-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
paperswithcode_id: numeric-fused-head
pretty_name: Numeric Fused Heads
tags:
- fused-head-identification
dataset_info:
- config_name: identification
features:
- name: tokens
sequence: string
- name: start_index
dtype: int32
- name: end_index
dtype: int32
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
splits:
- name: train
num_bytes: 22290345
num_examples: 165606
- name: test
num_bytes: 68282
num_examples: 500
- name: validation
num_bytes: 2474528
num_examples: 18401
download_size: 24407520
dataset_size: 24833155
- config_name: resolution
features:
- name: tokens
sequence: string
- name: line_indices
sequence: int32
- name: head
sequence: string
- name: speakers
sequence: string
- name: anchors_indices
sequence: int32
splits:
- name: train
num_bytes: 19766437
num_examples: 7412
- name: test
num_bytes: 2743071
num_examples: 1000
- name: validation
num_bytes: 2633549
num_examples: 1000
download_size: 24923403
dataset_size: 25143057
config_names:
- identification
- resolution
---
# Dataset Card for Numeric Fused Heads
## 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:** [The Numeric Fused-Head demo](https://nlp.biu.ac.il/~lazary/fh/)
- **Repository:** [Github Repo](https://github.com/yanaiela/num_fh)
- **Paper:** [Where’s My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution](https://www.mitpressjournals.org/doi/full/10.1162/tacl_a_00280)
- **Leaderboard:** [NLP Progress](http://nlpprogress.com/english/missing_elements.html)
- **Point of Contact:** [Yanai Elazar](https://yanaiela.github.io), [Yoav Goldberg](https://www.cs.bgu.ac.il/~yoavg/uni/)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
- Numeric Fused Head Identification
- Numeric Fused Head Resolution
### Languages
English
## Dataset Structure
### Data Instances
## Identification
```
{
"tokens": ["It", "’s", "a", "curious", "thing", ",", "the", "death", "of", "a", "loved", "one", "."]
"start_index": 11
"end_index": 12
"label": 1
}
```
## Resolution
```
{
"tokens": ["I", "'m", "eighty", "tomorrow", ".", "Are", "you", "sure", "?"],
"line_indices": [0, 0, 0, 0, 0, 1, 1, 1, 1],
"head": ["AGE"],
"speakers": ["John Doe", "John Doe", "John Doe", "John Doe", "John Doe", "Joe Bloggs", "Joe Bloggs", "Joe Bloggs", "Joe Bloggs"],
"anchors_indices": [2]
}
```
### Data Fields
## Identification
- `tokens` - List of token strings as tokenized with [Spacy](spacy.io).
- `start_index` - Start index of the anchor.
- `end_index` - End index of the anchor.
- `label` - "pos" or "neg" depending on whether this example contains a numeric fused head.
## Resolution
- `tokens` - List of token strings as tokenized with [Spacy](spacy.io)
- `line_indices` - List of indices indicating line number (one for each token)
- `head` - Reference to the missing head. If the head exists elsewhere in the sentence this is given as a token index.
- `speakers` - List of speaker names (one for each token)
- `anchors_indices` - Index to indicate which token is the anchor (the visible number)
### Data Splits
Train, Test, Dev
[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
MIT License
### Citation Information
```
@article{doi:10.1162/tacl\_a\_00280,
author = {Elazar, Yanai and Goldberg, Yoav},
title = {Where’s My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution},
journal = {Transactions of the Association for Computational Linguistics},
volume = {7},
number = {},
pages = {519-535},
year = {2019},
doi = {10.1162/tacl\_a\_00280},
}
```
### Contributions
Thanks to [@ghomasHudson](https://github.com/ghomasHudson) for adding this dataset. | [
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text2log | null | 2022-11-03T16:15:15Z | 462 | 3 | null | [
"task_categories:translation",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | 2022-11-03T16:15:15Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
pretty_name: text2log
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- translation
task_ids: []
dataset_info:
features:
- name: sentence
dtype: string
- name: fol_translation
dtype: string
splits:
- name: train
num_bytes: 10358134
num_examples: 101931
download_size: 9746473
dataset_size: 10358134
---
# Dataset Card for text2log
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:**
- **Repository:** [GitHub](https://github.com/alevkov/text2log)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** https://github.com/alevkov
### Dataset Summary
The dataset contains 100,000 simple English sentences selected and filtered from `enTenTen15` and their translation into First Order Logic (FOL) using `ccg2lambda`.
### Supported Tasks and Leaderboards
'semantic-parsing': The data set is used to train models which can generate FOL statements from natural language text
### Languages
en-US
## Dataset Structure
### Data Instances
```
{
'clean':'All things that are new are good.',
'trans':'all x1.(_thing(x1) -> (_new(x1) -> _good(x1)))'
}
```
### Data Fields
- 'clean': a simple English sentence
- 'trans': the corresponding translation into Lambda Dependency-based Compositional Semantics
### Data Splits
No predefined train/test split is given. The authors used a 80/20 split
## Dataset Creation
### Curation Rationale
The text2log data set is used to improve FOL statement generation from natural text
### Source Data
#### Initial Data Collection and Normalization
Short text samples selected from enTenTen15
#### Who are the source language producers?
See https://www.sketchengine.eu/ententen-english-corpus/
### Annotations
#### Annotation process
Machine generated using https://github.com/mynlp/ccg2lambda
#### Who are the annotators?
none
### Personal and Sensitive Information
The dataset does not contain personal or sensitive 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
None given
### Citation Information
```bibtex
@INPROCEEDINGS{9401852,
author={Levkovskyi, Oleksii and Li, Wei},
booktitle={SoutheastCon 2021},
title={Generating Predicate Logic Expressions from Natural Language},
year={2021},
volume={},
number={},
pages={1-8},
doi={10.1109/SoutheastCon45413.2021.9401852}
}
```
### Contributions
Thanks to [@apergo-ai](https://github.com/apergo-ai) for adding this dataset. | [
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lewtun/github-issues | lewtun | 2021-10-04T15:49:55Z | 462 | 4 | null | [
"arxiv:2005.00614",
"region:us"
] | 2021-10-04T15:49:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Dataset Card for GitHub Issues
## Dataset Description
- **Point of Contact:** [Lewis Tunstall](lewis@huggingface.co)
### Dataset Summary
GitHub Issues is a dataset consisting of GitHub issues and pull requests associated with the 🤗 Datasets [repository](https://github.com/huggingface/datasets). It is intended for educational purposes and can be used for semantic search or multilabel text classification. The contents of each GitHub issue are in English and concern the domain of datasets for NLP, computer vision, and beyond.
### Supported Tasks and Leaderboards
For each of the tasks tagged for this dataset, give a brief description of the tag, metrics, and suggested models (with a link to their HuggingFace implementation if available). Give a similar description of tasks that were not covered by the structured tag set (repace the `task-category-tag` with an appropriate `other:other-task-name`).
- `task-category-tag`: The dataset can be used to train a model for [TASK NAME], which consists in [TASK DESCRIPTION]. Success on this task is typically measured by achieving a *high/low* [metric name](https://huggingface.co/metrics/metric_name). The ([model name](https://huggingface.co/model_name) or [model class](https://huggingface.co/transformers/model_doc/model_class.html)) model currently achieves the following score. *[IF A LEADERBOARD IS AVAILABLE]:* This task has an active leaderboard which can be found at [leaderboard url]() and ranks models based on [metric name](https://huggingface.co/metrics/metric_name) while also reporting [other metric name](https://huggingface.co/metrics/other_metric_name).
### Languages
Provide a brief overview of the languages represented in the dataset. Describe relevant details about specifics of the language such as whether it is social media text, African American English,...
When relevant, please provide [BCP-47 codes](https://tools.ietf.org/html/bcp47), which consist of a [primary language subtag](https://tools.ietf.org/html/bcp47#section-2.2.1), with a [script subtag](https://tools.ietf.org/html/bcp47#section-2.2.3) and/or [region subtag](https://tools.ietf.org/html/bcp47#section-2.2.4) if available.
## Dataset Structure
### Data Instances
Provide an JSON-formatted example and brief description of a typical instance in the dataset. If available, provide a link to further examples.
```
{
'example_field': ...,
...
}
```
Provide any additional information that is not covered in the other sections about the data here. In particular describe any relationships between data points and if these relationships are made explicit.
### Data Fields
List and describe the fields present in the dataset. Mention their data type, and whether they are used as input or output in any of the tasks the dataset currently supports. If the data has span indices, describe their attributes, such as whether they are at the character level or word level, whether they are contiguous or not, etc. If the datasets contains example IDs, state whether they have an inherent meaning, such as a mapping to other datasets or pointing to relationships between data points.
- `example_field`: description of `example_field`
Note that the descriptions can be initialized with the **Show Markdown Data Fields** output of the [tagging app](https://github.com/huggingface/datasets-tagging), you will then only need to refine the generated descriptions.
### Data Splits
Describe and name the splits in the dataset if there are more than one.
Describe any criteria for splitting the data, if used. If their are differences between the splits (e.g. if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.
Provide the sizes of each split. As appropriate, provide any descriptive statistics for the features, such as average length. For example:
| | Tain | Valid | Test |
| ----- | ------ | ----- | ---- |
| Input Sentences | | | |
| Average Sentence Length | | | |
## Dataset Creation
### Curation Rationale
What need motivated the creation of this dataset? What are some of the reasons underlying the major choices involved in putting it together?
### Source Data
This section describes the source data (e.g. news text and headlines, social media posts, translated sentences,...)
#### Initial Data Collection and Normalization
Describe the data collection process. Describe any criteria for data selection or filtering. List any key words or search terms used. If possible, include runtime information for the collection process.
If data was collected from other pre-existing datasets, link to source here and to their [Hugging Face version](https://huggingface.co/datasets/dataset_name).
If the data was modified or normalized after being collected (e.g. if the data is word-tokenized), describe the process and the tools used.
#### Who are the source language producers?
State whether the data was produced by humans or machine generated. Describe the people or systems who originally created the data.
If available, include self-reported demographic or identity information for the source data creators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
Describe the conditions under which the data was created (for example, if the producers were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
Describe other people represented or mentioned in the data. Where possible, link to references for the information.
### Annotations
If the dataset contains annotations which are not part of the initial data collection, describe them in the following paragraphs.
#### Annotation process
If applicable, describe the annotation process and any tools used, or state otherwise. Describe the amount of data annotated, if not all. Describe or reference annotation guidelines provided to the annotators. If available, provide interannotator statistics. Describe any annotation validation processes.
#### Who are the annotators?
If annotations were collected for the source data (such as class labels or syntactic parses), state whether the annotations were produced by humans or machine generated.
Describe the people or systems who originally created the annotations and their selection criteria if applicable.
If available, include self-reported demographic or identity information for the annotators, but avoid inferring this information. Instead state that this information is unknown. See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender.
Describe the conditions under which the data was annotated (for example, if the annotators were crowdworkers, state what platform was used, or if the data was found, what website the data was found on). If compensation was provided, include that information here.
### Personal and Sensitive Information
State whether the dataset uses identity categories and, if so, how the information is used. Describe where this information comes from (i.e. self-reporting, collecting from profiles, inferring, etc.). See [Larson 2017](https://www.aclweb.org/anthology/W17-1601.pdf) for using identity categories as a variables, particularly gender. State whether the data is linked to individuals and whether those individuals can be identified in the dataset, either directly or indirectly (i.e., in combination with other data).
State whether the dataset contains other data that might be considered sensitive (e.g., data that reveals racial or ethnic origins, sexual orientations, religious beliefs, political opinions or union memberships, or locations; financial or health data; biometric or genetic data; forms of government identification, such as social security numbers; criminal history).
If efforts were made to anonymize the data, describe the anonymization process.
## Considerations for Using the Data
### Social Impact of Dataset
Please discuss some of the ways you believe the use of this dataset will impact society.
The statement should include both positive outlooks, such as outlining how technologies developed through its use may improve people's lives, and discuss the accompanying risks. These risks may range from making important decisions more opaque to people who are affected by the technology, to reinforcing existing harmful biases (whose specifics should be discussed in the next section), among other considerations.
Also describe in this section if the proposed dataset contains a low-resource or under-represented language. If this is the case or if this task has any impact on underserved communities, please elaborate here.
### Discussion of Biases
Provide descriptions of specific biases that are likely to be reflected in the data, and state whether any steps were taken to reduce their impact.
For Wikipedia text, see for example [Dinan et al 2020 on biases in Wikipedia (esp. Table 1)](https://arxiv.org/abs/2005.00614), or [Blodgett et al 2020](https://www.aclweb.org/anthology/2020.acl-main.485/) for a more general discussion of the topic.
If analyses have been run quantifying these biases, please add brief summaries and links to the studies here.
### Other Known Limitations
If studies of the datasets have outlined other limitations of the dataset, such as annotation artifacts, please outline and cite them here.
## Additional Information
### Dataset Curators
List the people involved in collecting the dataset and their affiliation(s). If funding information is known, include it here.
### Licensing Information
Provide the license and link to the license webpage if available.
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@article{article_id,
author = {Author List},
title = {Dataset Paper Title},
journal = {Publication Venue},
year = {2525}
}
```
If the dataset has a [DOI](https://www.doi.org/), please provide it here.
### Contributions
Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
neulab/conala | neulab | 2022-10-20T20:25:00Z | 462 | 46 | null | [
"task_categories:text2text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:code",
"license:mit",
"code-generation",
"arxiv:1805.08949",
"region:us"
] | 2022-10-20T20:25:00Z | 2022-09-14T19:31:08.000Z | 2022-09-14T19:31:08 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- mit
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids: []
pretty_name: CoNaLa
tags:
- code-generation
---
## Dataset Description
- **Repository:** https://conala-corpus.github.io/
- **Paper:** [Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow](https://arxiv.org/pdf/1805.08949.pdf)
### Dataset Summary
[CoNaLa](https://conala-corpus.github.io/) is a benchmark of code and natural language pairs, for the evaluation of code generation tasks. The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators, split into 2,379 training and 500 test examples. The automatically mined dataset is also available with almost 600k examples.
### Supported Tasks and Leaderboards
This dataset is used to evaluate code generations.
### Languages
English - Python code.
## Dataset Structure
```python
dataset_curated = load_dataset("neulab/conala")
DatasetDict({
train: Dataset({
features: ['question_id', 'intent', 'rewritten_intent', 'snippet'],
num_rows: 2379
})
test: Dataset({
features: ['question_id', 'intent', 'rewritten_intent', 'snippet'],
num_rows: 500
})
})
dataset_mined = load_dataset("neulab/conala", "mined")
DatasetDict({
train: Dataset({
features: ['question_id', 'parent_answer_post_id', 'prob', 'snippet', 'intent', 'id'],
num_rows: 593891
})
})
```
### Data Instances
#### CoNaLa - curated
This is the curated dataset by annotators
```
{
'question_id': 41067960,
'intent': 'How to convert a list of multiple integers into a single integer?',
'rewritten_intent': "Concatenate elements of a list 'x' of multiple integers to a single integer",
'snippet': 'sum(d * 10 ** i for i, d in enumerate(x[::-1]))'
}
```
#### CoNaLa - mined
This is the automatically mined dataset before curation
```
{
'question_id': 34705205,
'parent_answer_post_id': 34705233,
'prob': 0.8690001442846342,
'snippet': 'sorted(l, key=lambda x: (-int(x[1]), x[0]))',
'intent': 'Sort a nested list by two elements',
'id': '34705205_34705233_0'
}
```
### Data Fields
Curated:
|Field|Type|Description|
|---|---|---|
|question_id|int64|Id of the Stack Overflow question|
|intent|string|Natural Language intent (i.e., the title of a Stack Overflow question)|
|rewritten_intent|string|Crowdsourced revised intents that try to better reflect the full meaning of the code|
|snippet|string| Code snippet that implements the intent|
Mined:
|Field|Type|Description|
|---|---|---|
|question_id|int64|Id of the Stack Overflow question|
|parent_answer_post_id|int64|Id of the answer post from which the candidate snippet is extracted|
|intent|string|Natural Language intent (i.e., the title of a Stack Overflow question)|
|snippet|string| Code snippet that implements the intent|
|id|string|Unique id for this intent/snippet pair|
|prob|float64|Probability given by the mining model|
### Data Splits
There are two version of the dataset (curated and mined), mined only has a train split and curated has two splits: train and test.
## Dataset Creation
The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original [paper](https://arxiv.org/pdf/1805.08949.pdf)
### Citation Information
```
@inproceedings{yin2018learning,
title={Learning to mine aligned code and natural language pairs from stack overflow},
author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
pages={476--486},
year={2018},
organization={IEEE}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SocialGrep/reddit-crypto-aug-2021 | SocialGrep | 2022-07-01T19:08:05Z | 461 | 4 | null | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-07-01T19:08:05Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for reddit-crypto-aug-2021
## 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://socialgrep.com/datasets](https://socialgrep.com/datasets?utm_source=huggingface&utm_medium=link&utm_campaign=dataset&utm_term=crypto)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=dataset&utm_term=crypto)
### Dataset Summary
This corpus contains the complete data for the activity on the following subreddits for the entire month of August 2021:
- /r/cryptocurrency
- /r/cryptocurrencyclassic
- /r/cryptocurrencyico
- /r/cryptomars
- /r/cryptomoon
- /r/cryptomoonshots
- /r/satoshistreetbets
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## 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
CC-BY v4.0
### Contributions
[Needs More Information] | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
bigbio/medmentions | bigbio | 2022-12-22T15:45:34Z | 461 | 4 | null | [
"multilinguality:monolingual",
"language:en",
"license:cc0-1.0",
"arxiv:1902.09476",
"region:us"
] | 2022-12-22T15:45:34Z | 2022-11-13T22:09:49.000Z | 2022-11-13T22:09:49 |
---
language:
- en
bigbio_language:
- English
license: cc0-1.0
multilinguality: monolingual
bigbio_license_shortname: CC0_1p0
pretty_name: MedMentions
homepage: https://github.com/chanzuckerberg/MedMentions
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- NAMED_ENTITY_DISAMBIGUATION
- NAMED_ENTITY_RECOGNITION
---
# Dataset Card for MedMentions
## Dataset Description
- **Homepage:** https://github.com/chanzuckerberg/MedMentions
- **Pubmed:** True
- **Public:** True
- **Tasks:** NED,NER
MedMentions is a new manually annotated resource for the recognition of biomedical concepts.
What distinguishes MedMentions from other annotated biomedical corpora is its size (over 4,000
abstracts and over 350,000 linked mentions), as well as the size of the concept ontology (over
3 million concepts from UMLS 2017) and its broad coverage of biomedical disciplines.
Corpus: The MedMentions corpus consists of 4,392 papers (Titles and Abstracts) randomly selected
from among papers released on PubMed in 2016, that were in the biomedical field, published in
the English language, and had both a Title and an Abstract.
Annotators: We recruited a team of professional annotators with rich experience in biomedical
content curation to exhaustively annotate all UMLS® (2017AA full version) entity mentions in
these papers.
Annotation quality: We did not collect stringent IAA (Inter-annotator agreement) data. To gain
insight on the annotation quality of MedMentions, we randomly selected eight papers from the
annotated corpus, containing a total of 469 concepts. Two biologists ('Reviewer') who did not
participate in the annotation task then each reviewed four papers. The agreement between
Reviewers and Annotators, an estimate of the Precision of the annotations, was 97.3%.
## Citation Information
```
@misc{mohan2019medmentions,
title={MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts},
author={Sunil Mohan and Donghui Li},
year={2019},
eprint={1902.09476},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Muennighoff/babi | Muennighoff | 2023-02-12T13:34:24Z | 461 | 0 | null | [
"region:us"
] | 2023-02-12T13:34:24Z | 2023-02-12T09:19:00.000Z | 2023-02-12T09:19:00 |
Creation (Copied & adapted from https://github.com/stanford-crfm/helm/blob/0eaaa62a2263ddb94e9850ee629423b010f57e4a/src/helm/benchmark/scenarios/babi_qa_scenario.py):
```python
!wget http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz
!tar -xf tasks_1-20_v1-2.tar.gz
import json
from typing import List
tasks = list(range(1, 20))
splits = ["train", "valid", "test"]
def process_path(path: str) -> str:
"""Turn a path string (task 19) from the original format 's,w' to a verbal model-friendly format 'south west'"""
steps: List[str] = path.split(",")
directions = {"s": "south", "n": "north", "e": "east", "w": "west"}
path = " ".join([directions[step] for step in steps])
return path
for split in splits:
with open(f"babi_{split}.jsonl", "w") as f_base:
for task in tasks:
split_path: str = f"./tasks_1-20_v1-2/en-valid/qa{task}_{split}.txt"
with open(split_path, "r") as f:
facts = list(f)
story: List[str] = []
for fact in facts:
fid = int(fact.split(" ")[0])
if fid == 1:
story = []
fact = " ".join(fact.split(" ")[1:])
is_question = "?" in fact
if is_question:
question, answer = fact.split("\t")[:2]
question, answer = question.strip(), answer.strip()
# All tasks except task 19 have a verbal single-word answer (e.g. kitchen, apple, yes).
# Task 19 (path finding) has a non verbal answer format (
if task == 19:
answer = process_path(answer)
f_base.write(json.dumps({
"passage": "".join(story),
"question": question,
"answer": answer,
"task": task,
}) + "\n")
if "?" in story:
print("STORY", "".join(story))
else:
story.append(fact)
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
HuggingFaceM4/FairFace | HuggingFaceM4 | 2022-12-09T00:14:46Z | 460 | 5 | null | [
"license:cc-by-4.0",
"region:us"
] | 2022-12-09T00:14:46Z | 2022-12-08T23:00:45.000Z | 2022-12-08T23:00:45 | ---
license: cc-by-4.0
---
# 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)
- [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/joojs/fairface](https://github.com/joojs/fairface)
- **Repository:** [https://github.com/joojs/fairface](https://github.com/joojs/fairface)
- **Paper:** [https://openaccess.thecvf.com/content/WACV2021/papers/Karkkainen_FairFace_Face_Attribute_Dataset_for_Balanced_Race_Gender_and_Age_WACV_2021_paper.pdf](https://openaccess.thecvf.com/content/WACV2021/papers/Karkkainen_FairFace_Face_Attribute_Dataset_for_Balanced_Race_Gender_and_Age_WACV_2021_paper.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
FairFace is a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino.
Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Each instance has the following structure:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=448x448 at 0x7FCABA221FA0>,
'age': 6,
'gender': 0,
'race': 0,
'service_test': True
}
```
### Data Fields
- `image`: The image
- `age`: Age class among `["0-2", "3-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "more than 70"]`
- `gender`: Gender class among `["Male", "Female"]`
- `race`: Race class among `["East Asian", "Indian", "Black", "White", "Middle Eastern", "Latino_Hispanic", "Southeast Asian"]`
- `service_test`: Not sure what this is. See [issue](https://github.com/joojs/fairface/issues/9).
### 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 [@VictorSanh](https://github.com/VictorSanh) for adding this dataset.
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
c-s-ale/alpaca-gpt4-data-zh | c-s-ale | 2023-05-03T17:56:55Z | 460 | 22 | null | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:zh",
"license:cc-by-4.0",
"gpt",
"alpaca",
"fine-tune",
"instruct-tune",
"instruction",
"arxiv:2304.03277",
"region:us"
] | 2023-05-03T17:56:55Z | 2023-04-07T19:22:10.000Z | 2023-04-07T19:22:10 | ---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 32150579
num_examples: 48818
download_size: 35100559
dataset_size: 32150579
license: cc-by-4.0
language:
- zh
pretty_name: Instruction Tuning with GPT-4
size_categories:
- 10K<n<100K
task_categories:
- text-generation
tags:
- gpt
- alpaca
- fine-tune
- instruct-tune
- instruction
---
# Dataset Description
- **Project Page:** https://instruction-tuning-with-gpt-4.github.io
- **Repo:** https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM
- **Paper:** https://arxiv.org/abs/2304.03277
# Dataset Card for "alpaca-gpt4-data-zh"
All of the work is done by [this team](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM).
# Usage and License Notices
The data is intended and licensed for research use only. The dataset is CC BY NC 4.0 (allowing only non-commercial use) and models trained using the dataset should not be used outside of research purposes.
# English Dataset
[Found here](https://huggingface.co/datasets/c-s-ale/alpaca-gpt4-data)
# Citation
```
@article{peng2023gpt4llm,
title={Instruction Tuning with GPT-4},
author={Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao},
journal={arXiv preprint arXiv:2304.03277},
year={2023}
}
``` | [
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electricity_load_diagrams | null | 2022-11-18T20:00:21Z | 459 | 5 | null | [
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pretty_name: Electricity Load Diagrams
size_categories:
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---
# Dataset Card for Electricity Load Diagrams
## 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:** [Electricity Load Diagrams 2011-2014](https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014)
- **Paper:** [Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
](https://dl.acm.org/doi/10.1145/3209978.3210006)
- **Point of Contact:** [Artur Trindade](mailto:artur.trindade@elergone.pt)
### Dataset Summary
This dataset contains hourly kW electricity consumption time series of 370 Portuguese clients from 2011 to 2014.
### Dataset Usage
The dataset has the following configuration parameters:
- `freq` is the time series frequency at which we resample (default: `"1H"`)
- `prediction_length` is the forecast horizon for this task which is used to make the validation and test splits (default: `24`)
- `rolling_evaluations` is the number of rolling window time series in the test split for evaluation purposes (default: `7`)
For example, you can specify your own configuration different from those used in the papers as follows:
```python
load_dataset("electricity_load_diagrams", "uci", rolling_evaluations=10)
```
> Notes:
> - Data set has no missing values.
> - Values are in kW of each 15 min rescaled to hourly. To convert values in kWh values must be divided by 4.
> - All time labels report to Portuguese hour, however all days present 96 measures (24*4).
> - Every year in March time change day (which has only 23 hours) the values between 1:00 am and 2:00 am are zero for all points.
> - Every year in October time change day (which has 25 hours) the values between 1:00 am and 2:00 am aggregate the consumption of two hours.
### Supported Tasks and Leaderboards
- `univariate-time-series-forecasting`: The time series forecasting tasks involves learning the future `target` values of time series in a dataset for the `prediction_length` time steps. The results of the forecasts can then be validated via the ground truth in the `validation` split and tested via the `test` split.
### Languages
## Dataset Structure
Data set has no missing values. The raw values are in kW of each 15 min interval and are resampled to hourly frequency.
Each time series represent one client. Some clients were created after 2011. In these cases consumption were considered zero. All time labels report to Portuguese hour, however all days contain 96 measurements (24*4). Every year in March time change day (which has only 23 hours) the values between 1:00 am and 2:00 am are zero for all points. Every year in October time change day (which has 25 hours) the values between 1:00 am and 2:00 am aggregate the consumption of two hours.
### Data Instances
A sample from the training set is provided below:
```
{
'start': datetime.datetime(2012, 1, 1, 0, 0),
'target': [14.0, 18.0, 21.0, 20.0, 22.0, 20.0, 20.0, 20.0, 13.0, 11.0], # <= this target array is a concatenated sample
'feat_static_cat': [0],
'item_id': '0'
}
```
We have two configurations `uci` and `lstnet`, which are specified as follows.
The time series are resampled to hourly frequency. We test on 7 rolling windows of prediction length of 24.
The `uci` validation therefore ends 24*7 time steps before the end of each time series. The training split ends 24 time steps before the end of the validation split.
For the `lsnet` configuration we split the training window so that it is 0.6-th of the full time series and the validation is 0.8-th of the full time series and the last 0.2-th length time windows is used as the test set of 7 rolling windows of the 24 time steps each. Finally, as in the LSTNet paper, we only consider time series that are active in the year 2012--2014, which leaves us with 320 time series.
### Data Fields
For this univariate regular time series we have:
- `start`: a `datetime` of the first entry of each time series in the dataset
- `target`: an `array[float32]` of the actual target values
- `feat_static_cat`: an `array[uint64]` which contains a categorical identifier of each time series in the dataset
- `item_id`: a string identifier of each time series in a dataset for reference
Given the `freq` and the `start` datetime, we can assign a datetime to each entry in the target array.
### Data Splits
| name |train|unsupervised|test |
|----------|----:|-----------:|----:|
|uci|370| 2590|370|
|lstnet|320| 2240|320|
## Dataset Creation
The Electricity Load Diagrams 2011–2014 Dataset was developed by Artur Trindade and shared in UCI Machine Learning Repository. This dataset covers the electricity load of 370 substations in Portugal from the start of 2011 to the end of 2014 with a sampling period of 15 min. We will resample this to hourly time series.
### Curation Rationale
Research and development of load forecasting methods. In particular short-term electricity forecasting.
### Source Data
This dataset covers the electricity load of 370 sub-stations in Portugal from the start of 2011 to the end of 2014 with a sampling period of 15 min.
#### 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
```bibtex
@inproceedings{10.1145/3209978.3210006,
author = {Lai, Guokun and Chang, Wei-Cheng and Yang, Yiming and Liu, Hanxiao},
title = {Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks},
year = {2018},
isbn = {9781450356572},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3209978.3210006},
doi = {10.1145/3209978.3210006},
booktitle = {The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval},
pages = {95--104},
numpages = {10},
location = {Ann Arbor, MI, USA},
series = {SIGIR '18}
}
```
### Contributions
Thanks to [@kashif](https://github.com/kashif) for adding this dataset. | [
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irc_disentangle | null | 2022-11-18T20:10:09Z | 459 | 4 | irc-disentanglement | [
"task_categories:token-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"conversation-disentanglement",
"arxiv:1810.11118",
"region:us"
] | 2022-11-18T20:10:09Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
paperswithcode_id: irc-disentanglement
pretty_name: IRC Disentanglement
tags:
- conversation-disentanglement
dataset_info:
- config_name: ubuntu
features:
- name: id
dtype: int32
- name: raw
dtype: string
- name: ascii
dtype: string
- name: tokenized
dtype: string
- name: date
dtype: string
- name: connections
sequence: int32
splits:
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num_bytes: 56012854
num_examples: 220616
- name: validation
num_bytes: 3081479
num_examples: 12510
- name: test
num_bytes: 3919900
num_examples: 15010
download_size: 118470210
dataset_size: 63014233
- config_name: channel_two
features:
- name: id
dtype: int32
- name: raw
dtype: string
- name: ascii
dtype: string
- name: tokenized
dtype: string
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sequence: int32
splits:
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num_examples: 1001
- name: pilot
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num_examples: 501
- name: test
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num_examples: 1001
- name: pilot_dev
num_bytes: 290175
num_examples: 1501
- name: all_
num_bytes: 496524
num_examples: 2602
download_size: 118470210
dataset_size: 1263690
---
# Dataset Card for IRC Disentanglement
## 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)
- [Acknowledgments](#acknowledgments)
## Dataset Description
- **Homepage:** https://jkk.name/irc-disentanglement/
- **Repository:** https://github.com/jkkummerfeld/irc-disentanglement/tree/master/data
- **Paper:** https://aclanthology.org/P19-1374/
- **Leaderboard:** NA
- **Point of Contact:** jkummerf@umich.edu
### Dataset Summary
Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. This new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. The dataset is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context.
Note, the Github repository for the dataset also contains several useful tools for:
- Conversion (e.g. extracting conversations from graphs)
- Evaluation
- Preprocessing
- Word embeddings trained on the full Ubuntu logs in 2018
### Supported Tasks and Leaderboards
Conversational Disentanglement
### Languages
English (en)
## Dataset Structure
### Data Instances
For Ubuntu:
data["train"][1050]
```
{
'ascii': "[03:57] <Xophe> (also, I'm guessing that this isn't a good place to report minor but annoying bugs... what is?)",
'connections': [1048, 1054, 1055, 1072, 1073],
'date': '2004-12-25',
'id': 1050,
'raw': "[03:57] <Xophe> (also, I'm guessing that this isn't a good place to report minor but annoying bugs... what is?)",
'tokenized': "<s> ( also , i 'm guessing that this is n't a good place to report minor but annoying bugs ... what is ?) </s>"
}
```
For Channel_two:
data["train"][50]
```
{
'ascii': "[01:04] <Felicia> Chanel: i don't know off hand sorry",
'connections': [49, 53],
'id': 50,
'raw': "[01:04] <Felicia> Chanel: i don't know off hand sorry",
'tokenized': "<s> <user> : i do n't know off hand sorry </s>"
}
```
### Data Fields
'id' : The id of the message, this is the value that would be in the 'connections' of associated messages.
'raw' : The original message from the IRC log, as downloaded.
'ascii' : The raw message converted to ascii (unconvertable characters are replaced with a special word).
'tokenized' : The same message with automatic tokenisation and replacement of rare words with placeholder symbols.
'connections' : The indices of linked messages.
(only ubuntu) 'date' : The date the messages are from. The labelling for each date only start after the first 1000 messages of that date.
### Data Splits
The dataset has 4 parts:
| Part | Number of Annotated Messages |
| ------------- | ------------------------------------------- |
| Train | 67,463 |
| Dev | 2,500 |
| Test | 5,000 |
| Channel 2 | 2,600 |
## Dataset Creation
### Curation Rationale
IRC is a synchronous chat setting with a long history of use.
Several channels log all messages and make them publicly available.
The Ubuntu channel is particularly heavily used and has been the subject of several academic studies.
Data was selected from the channel in order to capture the diversity of situations in the channel (e.g. when there are many users or very few users).
For full details, see the [annotation information page](https://github.com/jkkummerfeld/irc-disentanglement/blob/master/data/READ.history.md).
### Source Data
#### Initial Data Collection and Normalization
Data was collected from the Ubuntu IRC channel logs, which are publicly available at [https://irclogs.ubuntu.com/](https://irclogs.ubuntu.com/).
The raw files are included, as well as two other versions:
- ASCII, converted using the script [make_txt.py](https://github.com/jkkummerfeld/irc-disentanglement/blob/master/tools/preprocessing/make-txt.py)
- Tok, tokenised text with rare words replaced by UNK using the script [dstc8-tokenise.py](https://github.com/jkkummerfeld/irc-disentanglement/blob/master/tools/preprocessing/dstc8-tokenise.py)
The raw channel two data is from prior work [(Elsner and Charniak, 2008)](https://www.aclweb.org/anthology/P08-1095.pdf)].
#### Who are the source language producers?
The text is from a large group of internet users asking questions and providing answers related to Ubuntu.
### Annotations
#### Annotation process
The data is expert annotated with:
- Training, one annotation per line in general, a small portion is double-annotated and adjudicated
- Dev, Channel 2, double annotated and adjudicated
- Test, triple annotated and adjudicated
| Part | Annotators | Adjudication? |
| ------------- | --------------- | ------------------------------------- |
| Train | 1 or 2 per file | For files with 2 annotators (only 10) |
| Dev | 2 | Yes |
| Test | 3 | Yes |
| Channel 2 | 2 | Yes |
#### Who are the annotators?
Students and a postdoc at the University of Michigan.
Everyone involved went through a training process with feedback to learn the annotation guidelines.
### Personal and Sensitive Information
No content is removed or obfuscated.
There is probably personal information in the dataset from users.
## Considerations for Using the Data
### Social Impact of Dataset
The raw data is already available online and the annotations do not significantly provide additional information that could have a direct social impact.
### Discussion of Biases
The data is mainly from a single technical domain (Ubuntu tech support) that probably has a demographic skew of some sort.
Given that users are only identified by their self-selected usernames, it is difficult to know more about the authors.
### Other Known Limitations
Being focused on a single language and a single channel means that the data is likely capturing a particular set of conventions in communication.
Those conventions may not apply to other channels, or beyond IRC.
## Additional Information
### Dataset Curators
Jonathan K. Kummerfeld
### Licensing Information
Creative Commons Attribution 4.0
### Citation Information
```
@inproceedings{kummerfeld-etal-2019-large,
title = "A Large-Scale Corpus for Conversation Disentanglement",
author = "Kummerfeld, Jonathan K. and
Gouravajhala, Sai R. and
Peper, Joseph J. and
Athreya, Vignesh and
Gunasekara, Chulaka and
Ganhotra, Jatin and
Patel, Siva Sankalp and
Polymenakos, Lazaros C and
Lasecki, Walter",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1374",
doi = "10.18653/v1/P19-1374",
pages = "3846--3856",
arxiv = "https://arxiv.org/abs/1810.11118",
software = "https://jkk.name/irc-disentanglement",
data = "https://jkk.name/irc-disentanglement",
abstract = "Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our data is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 89{\%} of conversations in a widely used dialogue corpus are either missing messages or contain extra messages. Our manually-annotated data presents an opportunity to develop robust data-driven methods for conversation disentanglement, which will help advance dialogue research.",
}
```
### Contributions
Thanks to [@dhruvjoshi1998](https://github.com/dhruvjoshi1998) for adding this dataset.
Thanks to [@jkkummerfeld](https://github.com/jkkummerfeld) for improvements to the documentation.
### Acknowledgments
This material is based in part upon work supported by IBM under contract 4915012629. Any opinions, findings, conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views of IBM. | [
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allenai/peer_read | allenai | 2022-11-18T21:37:46Z | 459 | 4 | peerread | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"acceptability-classification",
"arxiv:1804.09635",
"region:us"
] | 2022-11-18T21:37:46Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: peerread
pretty_name: PeerRead
tags:
- acceptability-classification
dataset_info:
- config_name: parsed_pdfs
features:
- name: name
dtype: string
- name: metadata
struct:
- name: source
dtype: string
- name: title
dtype: string
- name: authors
sequence: string
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sequence: string
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sequence:
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dtype: string
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sequence:
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dtype: string
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dtype: int32
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dtype: int32
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dtype: int32
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dtype: string
- name: creator
dtype: string
splits:
- name: train
num_bytes: 571263679
num_examples: 11090
- name: test
num_bytes: 34284777
num_examples: 637
- name: validation
num_bytes: 32488519
num_examples: 637
download_size: 1246688292
dataset_size: 638036975
- config_name: reviews
features:
- name: id
dtype: string
- name: conference
dtype: string
- name: comments
dtype: string
- name: subjects
dtype: string
- name: version
dtype: string
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dtype: string
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dtype: string
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sequence: string
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dtype: bool
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sequence:
sequence: string
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sequence:
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dtype: bool
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dtype: string
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dtype: string
splits:
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num_bytes: 15234922
num_examples: 11090
- name: test
num_bytes: 878906
num_examples: 637
- name: validation
num_bytes: 864799
num_examples: 637
download_size: 1246688292
dataset_size: 16978627
---
# Dataset Card for peer_read
## 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://arxiv.org/abs/1804.09635
- **Repository:** https://github.com/allenai/PeerRead
- **Paper:** https://arxiv.org/pdf/1804.09635.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
PearRead is a dataset of scientific peer reviews available to help researchers study this important artifact. The dataset consists of over 14K paper drafts and the corresponding accept/reject decisions in top-tier venues including ACL, NIPS and ICLR, as well as over 10K textual peer reviews written by experts for a subset of the papers.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
en-English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
#### parsed_pdfs
- `name`: `string` Filename in the dataset
- `metadata`: `dict` Paper metadata
- `source`: `string` Paper source
- `authors`: `list<string>` List of paper authors
- `title`: `string` Paper title
- `sections`: `list<dict>` List of section heading and corresponding description
- `heading`: `string` Section heading
- `text`: `string` Section description
- `references`: `string` List of references
- `title`: `string` Title of reference paper
- `author`: `list<string>` List of reference paper authors
- `venue`: `string` Reference venue
- `citeRegEx`: `string` Reference citeRegEx
- `shortCiteRegEx`: `string` Reference shortCiteRegEx
- `year`: `int` Reference publish year
- `referenceMentions`: `list<string>` List of reference mentions
- `referenceID`: `int` Reference mention ID
- `context`: `string` Reference mention context
- `startOffset`: `int` Reference startOffset
- `endOffset`: `int` Reference endOffset
- `year`: `int` Paper publish year
- `abstractText`: `string` Paper abstract
- `creator`: `string` Paper creator
#### reviews
- `id`: `int` Review ID
- `conference`: `string` Conference name
- `comments`: `string` Review comments
- `subjects`: `string` Review subjects
- `version`: `string` Review version
- `date_of_submission`: `string` Submission date
- `title`: `string` Paper title
- `authors`: `list<string>` List of paper authors
- `accepted`: `bool` Paper accepted flag
- `abstract`: `string` Paper abstract
- `histories`: `list<string>` Paper details with link
- `reviews`: `dict` Paper reviews
- `date`: `string` Date of review
- `title`: `string` Paper title
- `other_keys`: `string` Reviewer other details
- `originality`: `string` Originality score
- `comments`: `string` Reviewer comments
- `is_meta_review`: `bool` Review type flag
- `recommendation`: `string` Reviewer recommendation
- `replicability`: `string` Replicability score
- `presentation_format`: `string` Presentation type
- `clarity`: `string` Clarity score
- `meaningful_comparison`: `string` Meaningful comparison score
- `substance`: `string` Substance score
- `reviewer_confidence`: `string` Reviewer confidence score
- `soundness_correctness`: `string` Soundness correctness score
- `appropriateness`: `string` Appropriateness score
- `impact`: `string` Impact score
### 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
Dongyeop Kang, Waleed Ammar, Bhavana Dalvi Mishra, Madeleine van Zuylen, Sebastian Kohlmeier, Eduard Hovy, Roy Schwartz
### Licensing Information
[More Information Needed]
### Citation Information
@inproceedings{kang18naacl,
title = {A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications},
author = {Dongyeop Kang and Waleed Ammar and Bhavana Dalvi and Madeleine van Zuylen and Sebastian Kohlmeier and Eduard Hovy and Roy Schwartz},
booktitle = {Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL)},
address = {New Orleans, USA},
month = {June},
url = {https://arxiv.org/abs/1804.09635},
year = {2018}
}
### Contributions
Thanks to [@vinaykudari](https://github.com/vinaykudari) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
Fraser/short-jokes | Fraser | 2021-02-24T08:31:31Z | 459 | 5 | null | [
"region:us"
] | 2021-02-24T08:31:31Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | Copy of [Kaggle dataset](https://www.kaggle.com/abhinavmoudgil95/short-jokes), adding to Huggingface for ease of use.
Description from Kaggle:
Context
Generating humor is a complex task in the domain of machine learning, and it requires the models to understand the deep semantic meaning of a joke in order to generate new ones. Such problems, however, are difficult to solve due to a number of reasons, one of which is the lack of a database that gives an elaborate list of jokes. Thus, a large corpus of over 0.2 million jokes has been collected by scraping several websites containing funny and short jokes.
Visit my Github repository for more information regarding collection of data and the scripts used.
Content
This dataset is in the form of a csv file containing 231,657 jokes. Length of jokes ranges from 10 to 200 characters. Each line in the file contains a unique ID and joke.
Disclaimer
It has been attempted to keep the jokes as clean as possible. Since the data has been collected by scraping websites, it is possible that there may be a few jokes that are inappropriate or offensive to some people.
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philschmid/guanaco-oai-style | philschmid | 2023-11-08T14:37:18Z | 459 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-08T14:37:18Z | 2023-11-08T14:31:00.000Z | 2023-11-08T14:31:00 | ---
license: apache-2.0
---
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polemo2 | null | 2023-01-25T14:42:43Z | 458 | 0 | null | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pl",
"license:bsd-3-clause",
"region:us"
] | 2023-01-25T14:42:43Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- other
language:
- pl
license:
- bsd-3-clause
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: polemo2
dataset_info:
- config_name: in
features:
- name: sentence
dtype: string
- name: target
dtype:
class_label:
names:
'0': __label__meta_amb
'1': __label__meta_minus_m
'2': __label__meta_plus_m
'3': __label__meta_zero
splits:
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num_bytes: 4810215
num_examples: 5783
- name: test
num_bytes: 582052
num_examples: 722
- name: validation
num_bytes: 593530
num_examples: 723
download_size: 2350339
dataset_size: 5985797
- config_name: out
features:
- name: sentence
dtype: string
- name: target
dtype:
class_label:
names:
'0': __label__meta_amb
'1': __label__meta_minus_m
'2': __label__meta_plus_m
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splits:
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num_bytes: 4810215
num_examples: 5783
- name: test
num_bytes: 309790
num_examples: 494
- name: validation
num_bytes: 310977
num_examples: 494
download_size: 2139891
dataset_size: 5430982
---
# Dataset Card for [Dataset Name]
## 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://clarin-pl.eu/dspace/handle/11321/710
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The PolEmo2.0 is a set of online reviews from medicine and hotels domains. The task is to predict the sentiment of a review. There are two separate test sets, to allow for in-domain (medicine and hotels) as well as out-of-domain (products and university) validation.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Polish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- sentence: string, the review
- target: sentiment of the sentence class
The same tag system is used in plWordNet Emo for lexical units: [+m] (strong positive), [+s] (weak positive), [-m] (strong negative), [-s] (weak negative), [amb] (ambiguous) and [0] (neutral).
Note that the test set doesn't have targets so -1 is used instead
### 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
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CC BY-NC-SA 4.0
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SocialGrep/one-year-of-r-india | SocialGrep | 2022-07-01T18:48:19Z | 458 | 1 | null | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-07-01T18:48:19Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for one-year-of-r-india
## 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://socialgrep.com/datasets](https://socialgrep.com/datasets?utm_source=huggingface&utm_medium=link&utm_campaign=oneyearofrindia)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=oneyearofrindia)
### Dataset Summary
This corpus contains the complete data for the activity of the subreddit /r/India from Sep 30, 2020 to Sep 30, 2021.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## 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
CC-BY v4.0
### Contributions
[Needs More Information] | [
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ghadeermobasher/CRAFT-Chem | ghadeermobasher | 2022-01-20T22:09:10Z | 458 | 0 | null | [
"region:us"
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mteb/stackexchange-clustering | mteb | 2022-09-27T19:11:56Z | 458 | 0 | null | [
"language:en",
"region:us"
] | 2022-09-27T19:11:56Z | 2022-04-07T13:42:09.000Z | 2022-04-07T13:42:09 | ---
language:
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GATE-engine/COCOStuff164K | GATE-engine | 2023-06-26T06:29:49Z | 458 | 0 | null | [
"region:us"
] | 2023-06-26T06:29:49Z | 2023-06-26T04:56:48.000Z | 2023-06-26T04:56:48 | ---
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
splits:
- name: val
num_bytes: 2431424833.0
num_examples: 5000
- name: train
num_bytes: 57790292141.76
num_examples: 118287
download_size: 39862772718
dataset_size: 60221716974.76
---
# Dataset Card for "COCOStuff164K"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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nielsr/CelebA-faces | nielsr | 2022-03-21T14:48:37Z | 457 | 4 | null | [
"region:us"
] | 2022-03-21T14:48:37Z | 2022-03-21T14:45:41.000Z | 2022-03-21T14:45:41 | Entry not found | [
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Dahoas/instruct-synthetic-prompt-responses | Dahoas | 2022-12-19T16:18:50Z | 456 | 9 | null | [
"region:us"
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igbo_ner | null | 2022-11-03T16:16:30Z | 455 | 0 | null | [
"task_categories:token-classification",
"task_ids:named-entity-recognition",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ig",
"license:unknown",
"arxiv:2004.00648",
"region:us"
] | 2022-11-03T16:16:30Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- found
language_creators:
- found
language:
- ig
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
paperswithcode_id: null
pretty_name: Igbo NER dataset
dataset_info:
- config_name: ner_data
features:
- name: content_n
dtype: string
- name: named_entity
dtype: string
- name: sentences
sequence: string
splits:
- name: train
num_bytes: 60315228
num_examples: 30715
download_size: 3311204
dataset_size: 60315228
- config_name: free_text
features:
- name: sentences
dtype: string
splits:
- name: train
num_bytes: 1172152
num_examples: 10000
download_size: 1132151
dataset_size: 1172152
---
# Dataset Card for Igbo NER dataset
## 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/IgnatiusEzeani/IGBONLP/tree/master/ig_ner
- **Repository:** https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_ner
- **Paper:** https://arxiv.org/abs/2004.00648
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```
{'content_n': 'content_0', 'named_entity': 'Ike Ekweremmadụ', 'sentences': ['Ike Ekweremmadụ', "Ike ịda jụụ otụ nkeji banyere oke ogbugbu na-eme n'ala Naijiria agwụla Ekweremmadụ"]}
```
### Data Fields
- content_n : ID
- named_entity : Name of the entity
- sentences : List of sentences for the entity
### 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
@misc{ezeani2020igboenglish,
title={Igbo-English Machine Translation: An Evaluation Benchmark},
author={Ignatius Ezeani and Paul Rayson and Ikechukwu Onyenwe and Chinedu Uchechukwu and Mark Hepple},
year={2020},
eprint={2004.00648},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
### Contributions
Thanks to [@purvimisal](https://github.com/purvimisal) for adding this dataset. | [
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xsum_factuality | null | 2023-01-25T15:03:16Z | 455 | 4 | null | [
"task_categories:summarization",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-xsum",
"language:en",
"license:cc-by-4.0",
"hallucinations",
"region:us"
] | 2023-01-25T15:03:16Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-xsum
task_categories:
- summarization
task_ids: []
pretty_name: XSum Hallucination Annotations
tags:
- hallucinations
dataset_info:
- config_name: xsum_factuality
features:
- name: bbcid
dtype: int32
- name: system
dtype: string
- name: summary
dtype: string
- name: is_factual
dtype:
class_label:
names:
'0': 'no'
'1': 'yes'
- name: worker_id
dtype: string
splits:
- name: train
num_bytes: 800027
num_examples: 5597
download_size: 2864759
dataset_size: 800027
- config_name: xsum_faithfulness
features:
- name: bbcid
dtype: int32
- name: system
dtype: string
- name: summary
dtype: string
- name: hallucination_type
dtype:
class_label:
names:
'0': intrinsic
'1': extrinsic
- name: hallucinated_span_start
dtype: int32
- name: hallucinated_span_end
dtype: int32
- name: worker_id
dtype: string
splits:
- name: train
num_bytes: 1750325
num_examples: 11185
download_size: 2864759
dataset_size: 1750325
---
# Dataset Card for XSum Hallucination Annotations
## 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:** [XSUM Hallucination Annotations Homepage](https://research.google/tools/datasets/xsum-hallucination-annotations/)
- **Repository:** [XSUM Hallucination Annotations Homepage](https://github.com/google-research-datasets/xsum_hallucination_annotations)
- **Paper:** [ACL Web](https://www.aclweb.org/anthology/2020.acl-main.173.pdf)
- **Point of Contact:** [xsum-hallucinations-acl20@google.com](mailto:xsum-hallucinations-acl20@google.com)
### Dataset Summary
Neural abstractive summarization models are highly prone to hallucinate content that is unfaithful to the input document. The popular metric such as ROUGE fails to show the severity of the problem. This dataset contains a large scale human evaluation of several neural abstractive summarization systems to better understand the types of hallucinations they produce. The dataset consists of faithfulness and factuality annotations of abstractive summaries for the XSum dataset. The dataset has crowdsourced 3 judgements for each of 500 x 5 document-system pairs. This will be a valuable resource to the abstractive summarization community.
### Supported Tasks and Leaderboards
* `summarization`: : The dataset can be used to train a model for Summarization,, which consists in summarizing a given document. Success on this task is typically measured by achieving a *high/low* [ROUGE Score](https://huggingface.co/metrics/rouge).
### Languages
The text in the dataset is in English which are abstractive summaries for the [XSum dataset](https://www.aclweb.org/anthology/D18-1206.pdf). The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
##### Faithfulness annotations dataset
A typical data point consists of an ID referring to the news article(complete document), summary, and the hallucination span information.
An example from the XSum Faithfulness dataset looks as follows:
```
{
'bbcid': 34687720,
'hallucinated_span_end': 114,
'hallucinated_span_start': 1,
'hallucination_type': 1,
'summary': 'rory mcilroy will take a one-shot lead into the final round of the wgc-hsbc champions after carding a three-under',
'system': 'BERTS2S',
'worker_id': 'wid_0'
}
```
##### Factuality annotations dataset
A typical data point consists of an ID referring to the news article(complete document), summary, and whether the summary is factual or not.
An example from the XSum Factuality dataset looks as follows:
```
{
'bbcid': 29911712,
'is_factual': 0,
'summary': 'more than 50 pupils at a bristol academy have been sent home from school because of a lack of uniform.',
'system': 'BERTS2S',
'worker_id': 'wid_0'
}
```
### Data Fields
##### Faithfulness annotations dataset
Raters are shown the news article and the system summary, and are tasked with identifying and annotating the spans that aren't supported by the input article. The file contains the following columns:
- `bbcid`: Document id in the XSum corpus.
- `system`: Name of neural summarizer.
- `summary`: Summary generated by ‘system’.
- `hallucination_type`: Type of hallucination: intrinsic (0) or extrinsic (1)
- `hallucinated_span`: Hallucinated span in the ‘summary’.
- `hallucinated_span_start`: Index of the start of the hallucinated span.
- `hallucinated_span_end`: Index of the end of the hallucinated span.
- `worker_id`: Worker ID (one of 'wid_0', 'wid_1', 'wid_2')
The `hallucination_type` column has NULL value for some entries which have been replaced iwth `-1`.
##### Factuality annotations dataset
Raters are shown the news article and the hallucinated system summary, and are tasked with assessing the summary whether it is factual or not. The file contains the following columns:
- `bbcid1: Document id in the XSum corpus.
- `system`: Name of neural summarizer.
- `summary`: Summary generated by ‘system’.
- `is_factual`: Yes (1) or No (0)
- `worker_id`: Worker ID (one of 'wid_0', 'wid_1', 'wid_2')
The `is_factual` column has NULL value for some entries which have been replaced iwth `-1`.
### Data Splits
There is only a single split for both the Faithfulness annotations dataset and Factuality annotations dataset.
| | train |
|--------------------------|------:|
| Faithfulness annotations | 11185 |
| Factuality annotations | 5597 |
## 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
[Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/legalcode)
### Citation Information
```
@InProceedings{maynez_acl20,
author = "Joshua Maynez and Shashi Narayan and Bernd Bohnet and Ryan Thomas Mcdonald",
title = "On Faithfulness and Factuality in Abstractive Summarization",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
year = "2020",
pages = "1906--1919",
address = "Online",
}
```
### Contributions
Thanks to [@vineeths96](https://github.com/vineeths96) for adding this dataset. | [
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GEM/conversational_weather | GEM | 2022-10-24T15:30:13Z | 455 | 2 | null | [
"task_categories:table-to-text",
"annotations_creators:none",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:en",
"license:cc-by-nc-4.0",
"data-to-text",
"region:us"
] | 2022-10-24T15:30:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: conversational_weather
tags:
- data-to-text
---
# Dataset Card for GEM/conversational_weather
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/facebookresearch/TreeNLG
- **Paper:** https://aclanthology.org/P19-1080
- **Leaderboard:** N/A
- **Point of Contact:** Kartikeya Upasani
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/conversational_weather).
### Dataset Summary
The purpose of this dataset is to assess how well a model can learn a template-like structure in a very low data setting. The task here is to produce a response to a weather-related query. The reply is further specified through the data attributes and discourse structure in the input. The output contains both the lexicalized text and discourse markers for attributes (e.g., `_ARG_TEMP_ 34`).
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/conversational_weather')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/conversational_weather).
#### paper
[ACL Anthology](https://aclanthology.org/P19-1080)
#### authors
Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba (Facebook Conversational AI)
## Dataset Overview
### Where to find the Data and its Documentation
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/facebookresearch/TreeNLG)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/P19-1080)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{balakrishnan-etal-2019-constrained,
title = "Constrained Decoding for Neural {NLG} from Compositional Representations in Task-Oriented Dialogue",
author = "Balakrishnan, Anusha and
Rao, Jinfeng and
Upasani, Kartikeya and
White, Michael and
Subba, Rajen",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1080",
doi = "10.18653/v1/P19-1080",
pages = "831--844"
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Kartikeya Upasani
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
kart@fb.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-nc-4.0: Creative Commons Attribution Non Commercial 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
This dataset is intended to help develop conversational agents that exhibit human-like properties such as matching the framing of the response with the query or contrasting relevant data attributes.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Producing a text that is a response to a weather query as per the discourse structure and data attributes specified in the input meaning representation.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Facebook
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Anusha Balakrishnan, Jinfeng Rao, Kartikeya Upasani, Michael White, Rajen Subba (Facebook Conversational AI)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Facebook
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Vipul Raheja (Grammarly)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `gem_id`: (string): GEM-formatted row id
- `id`: (string): Row id in the original data
- `user_query`: (string): Natural language weather query from humans
- `tree_str_mr`: (string): Synthetically-added user context (datetime and location) in the form of a tree-structured MR
- `response`: (string): A tree-structured annotation of the response.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{'gem_id': 'weather-train-11',
'id': '1108963',
'synthetic_user_context': '[__DG_INFORM__ [__ARG_TASK__ get_forecast ] '
'[__ARG_TEMP__ 37 ] [__ARG_TEMP_UNIT__ fahrenheit ] '
'[__ARG_CLOUD_COVERAGE__ partly cloudy ] '
'[__ARG_DATE_TIME__ [__ARG_COLLOQUIAL__ currently ] '
'] [__ARG_LOCATION__ [__ARG_CITY__ Oakland ] '
'[__ARG_COUNTRY__ United States ] [__ARG_REGION__ '
'California ] ] ] [__DG_INFORM__ [__ARG_TASK__ '
'get_forecast ] [__ARG_TEMP_SUMMARY__ mid 40s ] '
'[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This '
'afternoon ] ] [__ARG_LOCATION__ [__ARG_CITY__ '
'Oakland ] [__ARG_COUNTRY__ United States ] '
'[__ARG_REGION__ California ] ] ] [__DG_INFORM__ '
'[__ARG_TASK__ get_forecast ] '
'[__ARG_CLOUD_COVERAGE__ mostly sunny ] '
'[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This '
'afternoon ] ] [__ARG_LOCATION__ [__ARG_CITY__ '
'Oakland ] [__ARG_COUNTRY__ United States ] '
'[__ARG_REGION__ California ] ] ]',
'tree_str_mr': "[__DG_INFORM__ It's [__ARG_DATE_TIME__ [__ARG_COLLOQUIAL__ "
'currently ] ] [__ARG_CLOUD_COVERAGE__ partly cloudy ] and '
'[__ARG_TEMP__ __ARG_TEMP__ ] [__ARG_TEMP_UNIT__ '
'__ARG_TEMP_UNIT__ ] [__ARG_LOCATION__ in [__ARG_CITY__ '
'__ARG_CITY__ ] , [__ARG_REGION__ __ARG_REGION__ ] , '
'[__ARG_COUNTRY__ __ARG_COUNTRY__ ] ] . ] [__DG_INFORM__ '
'[__ARG_DATE_TIME_RANGE__ [__ARG_COLLOQUIAL__ This afternoon ] '
"] , it'll be [__ARG_CLOUD_COVERAGE__ mostly sunny ] ] "
'[__DG_INFORM__ with temperatures in the [__ARG_TEMP_SUMMARY__ '
'mid <number> ] ]',
'user_query': 'Show weather forecast for Oakland, CA. '}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- Standard Splits: Train/Validation/Test
- Additional Split: Disc_Test (a more challenging subset of the test set that contains discourse relations)
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The test set contains 3,121 examples, of which 1.1K (35%) have unique MRs that have never been seen in the training set.
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
```
{'gem_id': 'weather-train-13333', 'data_id': '1260610', 'user_query': 'Sundown', 'tree_str_mr': '[__DG_INFORM__ [__ARG_TASK__ get_weather_attribute ] [__ARG_SUNSET_TIME_DATE_TIME__ [__ARG_TIME__ 05:04 PM ] ] ]', 'response': '[__DG_INFORM__ The sun will go down at [__ARG_SUNSET_TIME_DATE_TIME__ [__ARG_TIME__ __ARG_TIME__ ] ] ]'}
```
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
The dataset was curated to develop a weather bot that exhibits human-like properties such as matching the framing of the response with the query or contrasting relevant data attributes.
The dataset offers rich tree-based meaning representations that offer fine-grained control over the response, e.g. by specifying which two attributes are to be contrasted. The natural language input queries are also provided to model the coherence of the response based on the input. The output response is annotated with the input meaning components using special bracketing tokens, which enables developing new techniques such as constrained decoding to improve quality of output responses
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Adequately expressing CONTRAST and JUSTIFY discourse relations with appropriate grouping of arguments; adequately generalizing to many combinations of arguments.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`data points removed`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
The original repo contained a challenge set disc_test.tsv, which is a subset of the test set consisting of discourse relations (CONTRAST and JUSTIFY) , but also contained JOIN relations.
This discrepancy has been rectified in the GEM version. The rectified version has been added in the `challenge_sets`
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Adequately expressing CONTRAST and JUSTIFY discourse relations with appropriate grouping of arguments; adequately generalizing to many combinations of arguments.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
Tree accuracy: It measures whether the tree structure in the prediction matches that of the input MR exactly (modulo repeated arguments that need only appear once).
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
Automatic metrics are evaluated on the raw model predictions (which have de-lexicalized fields):
* Tree accuracy: Measures whether the tree structure in the prediction matches that of the input MR exactly.
* BLEU-4: A word overlap metric commonly used for evaluating NLG systems.
Authors also performed human evaluation studies by asking annotators to evaluate the quality of responses produced by different models. Annotators provided binary ratings on the following dimensions:
• Grammaticality: Measures fluency of the responses.
• Correctness: Measures semantic correctness of the responses.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The dataset was curated to develop a weather bot that exhibits human-like properties such as matching the framing of the response with the query or contrasting relevant data attributes. To achieve this, the dataset contains rich tree-structured meaning representations that are specified using several data arguments and discourse acts, the input natural language queries, and annotations for the responses.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Producing a text that is a response to a weather query as per the discourse structure and data attributes specified in the input meaning representation.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`, `Machine-generated`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Other crowdworker platform`
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The dataset is focused on the weather domain: Weather was the first successful case of NLG put into production back in the 80s (Reiter & Dale, 1997). This domain offers significant complexity for NLG. Weather forecast summaries in particular can be very long, and require reasoning over several disjoint pieces of information.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by crowdworker
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Please refer to Appendix D of the original paper for details.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
hybrid
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
Please refer to Appendix C of the original paper for details.
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
Annotation was done as work for hire and contains no PII.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
Data is simulated and not specific to annotator.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
Grammatical evaluations performed with the data to date have used norms from informal Standard American English. These prescriptive notions of grammaticality potentially serve to perpetuate systemic power imbalances as they’re conveyed by language.
Since the data only contains informal Standard American English, its use to train a model may not be appropriate depending on the potential use case.
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
Annotation was done as work for hire and contains no PII. Annotated data is simulated and not specific to annotator.
### Licenses
### Known Technical Limitations
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
An imperfect model used to convey actual weather data could mislead users about weather conditions?
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smilegate-ai/kor_unsmile | smilegate-ai | 2022-03-27T12:29:28Z | 454 | 0 | null | [
"region:us"
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
id_clickbait | null | 2023-01-25T14:32:36Z | 453 | 0 | null | [
"task_categories:text-classification",
"task_ids:fact-checking",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:id",
"license:cc-by-4.0",
"region:us"
] | 2023-01-25T14:32:36Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- id
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- fact-checking
pretty_name: Indonesian Clickbait Headlines
dataset_info:
- config_name: annotated
features:
- name: id
dtype: string
- name: title
dtype: string
- name: label
dtype:
class_label:
names:
'0': non-clickbait
'1': clickbait
splits:
- name: train
num_bytes: 1268698
num_examples: 15000
download_size: 150769127
dataset_size: 1268698
- config_name: raw
features:
- name: id
dtype: string
- name: title
dtype: string
- name: source
dtype: string
- name: date
dtype: string
- name: category
dtype: string
- name: sub-category
dtype: string
- name: content
dtype: string
- name: url
dtype: string
splits:
- name: train
num_bytes: 81669386
num_examples: 38655
download_size: 150769127
dataset_size: 81669386
---
# Dataset Card for Indonesian Clickbait Headlines
## 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://data.mendeley.com/datasets/k42j7x2kpn/1
- **Repository:**
- **Paper:** [CLICK-ID: A Novel Dataset for Indonesian Clickbait Headlines](https://www.sciencedirect.com/science/article/pii/S2352340920311252#!)
- **Leaderboard:**
- **Point of Contact:** [Andika William](mailto:andika.william@mail.ugm.ac.id), [Yunita Sari](mailto:yunita.sari@ugm.ac.id)
### Dataset Summary
The CLICK-ID dataset is a collection of Indonesian news headlines that was collected from 12 local online news
publishers; detikNews, Fimela, Kapanlagi, Kompas, Liputan6, Okezone, Posmetro-Medan, Republika, Sindonews, Tempo,
Tribunnews, and Wowkeren. This dataset is comprised of mainly two parts; (i) 46,119 raw article data, and (ii)
15,000 clickbait annotated sample headlines. Annotation was conducted with 3 annotator examining each headline.
Judgment were based only on the headline. The majority then is considered as the ground truth. In the annotated
sample, our annotation shows 6,290 clickbait and 8,710 non-clickbait.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Indonesian
## Dataset Structure
### Data Instances
An example of the annotated article:
```
{
'id': '100',
'label': 1,
'title': "SAH! Ini Daftar Nama Menteri Kabinet Jokowi - Ma'ruf Amin"
}
>
```
### Data Fields
#### Annotated
- `id`: id of the sample
- `title`: the title of the news article
- `label`: the label of the article, either non-clickbait or clickbait
#### Raw
- `id`: id of the sample
- `title`: the title of the news article
- `source`: the name of the publisher/newspaper
- `date`: date
- `category`: the category of the article
- `sub-category`: the sub category of the article
- `content`: the content of the article
- `url`: the url of the article
### Data Splits
The dataset contains train set.
## 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
Creative Commons Attribution 4.0 International license
### Citation Information
```
@article{WILLIAM2020106231,
title = "CLICK-ID: A novel dataset for Indonesian clickbait headlines",
journal = "Data in Brief",
volume = "32",
pages = "106231",
year = "2020",
issn = "2352-3409",
doi = "https://doi.org/10.1016/j.dib.2020.106231",
url = "http://www.sciencedirect.com/science/article/pii/S2352340920311252",
author = "Andika William and Yunita Sari",
keywords = "Indonesian, Natural Language Processing, News articles, Clickbait, Text-classification",
abstract = "News analysis is a popular task in Natural Language Processing (NLP). In particular, the problem of clickbait in news analysis has gained attention in recent years [1, 2]. However, the majority of the tasks has been focused on English news, in which there is already a rich representative resource. For other languages, such as Indonesian, there is still a lack of resource for clickbait tasks. Therefore, we introduce the CLICK-ID dataset of Indonesian news headlines extracted from 12 Indonesian online news publishers. It is comprised of 15,000 annotated headlines with clickbait and non-clickbait labels. Using the CLICK-ID dataset, we then developed an Indonesian clickbait classification model achieving favourable performance. We believe that this corpus will be useful for replicable experiments in clickbait detection or other experiments in NLP areas."
}
```
### Contributions
Thanks to [@cahya-wirawan](https://github.com/cahya-wirawan) for adding this dataset.
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GroNLP/ik-nlp-22_slp | GroNLP | 2023-02-01T18:25:21Z | 453 | 0 | null | [
"task_categories:question-answering",
"task_categories:summarization",
"task_categories:text-retrieval",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"question-generation",
"region:us"
] | 2023-02-01T18:25:21Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
- summarization
- text-retrieval
pretty_name: slp3ed-iknlp2022
tags:
- question-generation
---
# Dataset Card for IK-NLP-22 Speech and Language Processing
## Table of Contents
- [Dataset Card for IK-NLP-22 Speech and Language Processing](#dataset-card-for-ik-nlp-22-speech-and-language-processing)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Projects](#projects)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Paragraphs Configuration](#paragraphs-configuration)
- [Questions Configuration](#questions-configuration)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Source:** [Stanford](https://web.stanford.edu/~jurafsky/slp3/)
- **Point of Contact:** [Gabriele Sarti](mmailto:ik-nlp-course@rug.nl)
### Dataset Summary
This dataset contains chapters extracted from the Speech and Language Processing book (3ed draft of January 2022) by Jurafsky and Martin via a semi-automatic procedure (see below for additional details). Moreover, a small set of conceptual questions associated with each chapter is provided alongside possible answers.
Only the content of chapters 2 to 11 of the book draft are provided, since these are the ones relevant to the contents of the 2022 edition of the Natural Language Processing course at the Information Science Master's Degree (IK) at the University of Groningen, taught by [Arianna Bisazza](https://research.rug.nl/en/persons/arianna-bisazza) with the assistance of [Gabriele Sarti](https://research.rug.nl/en/persons/gabriele-sarti).
*The Speech and Language Processing book was made freely available by the authors [Dan Jurafsky](http://web.stanford.edu/people/jurafsky/) and [James H. Martin](http://www.cs.colorado.edu/~martin/) on the [Stanford University website](https://web.stanford.edu/~jurafsky/slp3/). The present dataset was created for educational purposes, and is based on the draft of the 3rd edition of the book accessed on December 29th, 2021. All rights of the present contents are attributed to the original authors.*
### Projects
See the course page for a description of possible research directions.
### Languages
The language data of Speech and Language Processing is in English (BCP-47 `en`)
## Dataset Structure
### Data Instances
The dataset contains two configurations: `paragraphs` (default), containing the full set of parsed paragraphs associated to the respective chapter and sections, and `questions`, containing a small subset of example questions matched with the relevant paragraph, and with the answer span extracted.
#### Paragraphs Configuration
The `paragraphs` configuration contains all the paragraphs of the selected book chapters, each associated with the respective chapter, section and subsection. An example from the `train` split of the `paragraphs` config is provided below. The example belongs to section 2.3 but not to a subsection, so the `n_subsection` and `subsection` fields are empty strings.
```json
{
"n_chapter": "2",
"chapter": "Regular Expressions",
"n_section": "2.3",
"section": "Corpora",
"n_subsection": "",
"subsection": "",
"text": "It's also quite common for speakers or writers to use multiple languages in a single communicative act, a phenomenon called code switching. Code switching (2.2) Por primera vez veo a @username actually being hateful! it was beautiful:)"
}
```
The text is provided as-is, without further preprocessing or tokenization.
#### Questions Configuration
The `questions` configuration contains a small subset of questions, the top retrieved paragraph relevant to the question and the answer spans. An example from the `test` split of the `questions` config is provided below.
```json
{
"chapter": "Regular Expressions",
"section": "Regular Expressions",
"subsection": "Basic Regular Expressions",
"question": "What is the meaning of the Kleene star in Regex?",
"paragraph": "This language consists of strings with a b, followed by at least two a's, followed by an exclamation point. The set of operators that allows us to say things like \"some number of as\" are based on the asterisk or *, commonly called the Kleene * (gen-Kleene * erally pronounced \"cleany star\"). The Kleene star means \"zero or more occurrences of the immediately previous character or regular expression\". So /a*/ means \"any string of zero or more as\". This will match a or aaaaaa, but it will also match Off Minor since the string Off Minor has zero a's. So the regular expression for matching one or more a is /aa*/, meaning one a followed by zero or more as. More complex patterns can also be repeated. So /[ab]*/ means \"zero or more a's or b's\" (not \"zero or more right square braces\"). This will match strings like aaaa or ababab or bbbb.",
"answer": "The Kleene star means \"zero or more occurrences of the immediately previous character or regular expression\""
}
```
### Data Splits
| config| train| test|
|------------:|-----:|----:|
|`paragraphs` | 1697 | - |
|`questions` | - | 59 |
### Dataset Creation
The contents of the Speech and Language Processing book PDF were extracted using the [PDF to S2ORC JSON Converter](https://github.com/allenai/s2orc-doc2json) by AllenAI. The texts extracted by the converter were then manually cleaned to remove end-of-chapter exercises and other irrelevant content (e.g. tables, TikZ figures, etc.). Some issues in the parsed content were preserved in the final version to maintain a naturalistic setting for the associated projects, promoting the use of data filtering heuristics for students.
The question-answer pairs were created manually by Gabriele Sarti.
## Additional Information
### Dataset Curators
For problems on this 🤗 Datasets version, please contact us at [ik-nlp-course@rug.nl](mailto:ik-nlp-course@rug.nl).
### Licensing Information
Please refer to the authors' websites for licensing information.
### Citation Information
Please cite the authors if you use these corpora in your work:
```bibtex
@book{slp3ed-iknlp2022,
author = {Jurafsky, Daniel and Martin, James},
year = {2021},
month = {12},
pages = {1--235, 1--19},
title = {Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition},
volume = {3}
}
``` | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
mocha | null | 2022-11-18T21:29:45Z | 452 | 2 | mocha | [
"task_categories:question-answering",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-sa-4.0",
"generative-reading-comprehension-metric",
"region:us"
] | 2022-11-18T21:29:45Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
pretty_name: MOCHA
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids: []
paperswithcode_id: mocha
tags:
- generative-reading-comprehension-metric
dataset_info:
features:
- name: constituent_dataset
dtype: string
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: reference
dtype: string
- name: candidate
dtype: string
- name: score
dtype: float32
- name: metadata
struct:
- name: scores
sequence: int32
- name: source
dtype: string
- name: candidate2
dtype: string
- name: score2
dtype: float32
splits:
- name: train
num_bytes: 33292592
num_examples: 31069
- name: validation
num_bytes: 4236883
num_examples: 4009
- name: test
num_bytes: 6767409
num_examples: 6321
- name: minimal_pairs
num_bytes: 193560
num_examples: 200
download_size: 14452311
dataset_size: 44490444
---
# Dataset Card for Mocha
## 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:** [Mocha](https://allennlp.org/mocha)
- **Repository:** [https://github.com/anthonywchen/MOCHA](https://github.com/anthonywchen/MOCHA)
- **Paper:** [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://www.aclweb.org/anthology/2020.emnlp-main.528/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Posing reading comprehension as a generation problem provides a great deal of flexibility, allowing for open-ended questions with few restrictions on possible answers. However, progress is impeded by existing generation metrics, which rely on token overlap and are agnostic to the nuances of reading comprehension. To address this, we introduce a benchmark for training and evaluating generative reading comprehension metrics: MOdeling Correctness with Human Annotations. MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. Using MOCHA, we train a Learned Evaluation metric for Reading Comprehension, LERC, to mimic human judgement scores. LERC outperforms baseline metrics by 10 to 36 absolute Pearson points on held-out annotations. When we evaluate robustness on minimal pairs, LERC achieves 80% accuracy, outperforming baselines by 14 to 26 absolute percentage points while leaving significant room for improvement. MOCHA presents a challenging problem for developing accurate and robust generative reading comprehension metrics.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
MOCHA contains 40K human judgement scores on model outputs from 6 diverse question answering datasets and an additional set of minimal pairs for evaluation. MOCHA pairs reading comprehension instances, which consists of a passage, question, and reference, with candidates and human judgement scores.
### Data Fields
- `constituent_dataset`: the original QA dataset which the data instance came from.
- `id`
- `context`: the passage content.
- `question`: the question related to the passage content.
- `reference`: the correct answer for the question.
- `candidate`: the answer generated from the `reference` by `source`
- `score`: the human judgement score for the `candidate`. Not included in test split, defaults to `-1`
- `metadata`: Not included in minimal pairs split.
- `scores`: list of scores from difference judges, averaged out to get final `score`. defaults to `[]`
- `source`: the generative model to generate the `candidate`
In minimal pairs, we'll have an additional candidate for robust evaluation.
- `candidate2`
- `score2`
### Data Splits
Dataset Split | Number of Instances in Split
--------------|--------------------------------------------
Train | 31,069
Validation | 4,009
Test | 6,321
Minimal Pairs | 200
## 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-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode)
### Citation Information
```bitex
@inproceedings{Chen2020MOCHAAD,
author={Anthony Chen and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics},
booktitle={EMNLP},
year={2020}
}
```
### Contributions
Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset. | [
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wiki_summary | null | 2022-11-18T22:00:55Z | 452 | 6 | null | [
"task_categories:text2text-generation",
"task_categories:translation",
"task_categories:question-answering",
"task_categories:summarization",
"task_ids:abstractive-qa",
"task_ids:explanation-generation",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"task_ids:open-domain-abstractive-qa",
"task_ids:text-simplification",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:fa",
"license:apache-2.0",
"region:us"
] | 2022-11-18T22:00:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- crowdsourced
language:
- fa
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
- translation
- question-answering
- summarization
task_ids:
- abstractive-qa
- explanation-generation
- extractive-qa
- open-domain-qa
- open-domain-abstractive-qa
- text-simplification
pretty_name: WikiSummary
dataset_info:
features:
- name: id
dtype: string
- name: link
dtype: string
- name: title
dtype: string
- name: article
dtype: string
- name: highlights
dtype: string
splits:
- name: train
num_bytes: 207186608
num_examples: 45654
- name: test
num_bytes: 25693509
num_examples: 5638
- name: validation
num_bytes: 23130954
num_examples: 5074
download_size: 255168504
dataset_size: 256011071
---
# Dataset Card for [Needs More Information]
## 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/m3hrdadfi/wiki-summary
- **Repository:** https://github.com/m3hrdadfi/wiki-summary
- **Paper:** [More Information Needed]
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [Mehrdad Farahani](mailto:m3hrdadphi@gmail.com)
### Dataset Summary
The dataset extracted from Persian Wikipedia into the form of articles and highlights and cleaned the dataset into pairs of articles and highlights and reduced the articles' length (only version 1.0.0) and highlights' length to a maximum of 512 and 128, respectively, suitable for parsBERT. This dataset is created to achieve state-of-the-art results on some interesting NLP tasks like Text Summarization.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in Percy.
## Dataset Structure
### Data Instances
```
{
'id' :'0598cfd2ac491a928615945054ab7602034a8f4f',
'link': 'https://fa.wikipedia.org/wiki/انقلاب_1917_روسیه',
'title': 'انقلاب 1917 روسیه',
'article': 'نخست انقلاب فوریه ۱۹۱۷ رخ داد . در این انقلاب پس از یکسری اعتصابات ، تظاهرات و درگیریها ، نیکولای دوم ، آخرین تزار روسیه از سلطنت خلع شد و یک دولت موقت به قدرت رسید . دولت موقت زیر نظر گئورگی لووف و الکساندر کرنسکی تشکیل شد . اکثر اعضای دولت موقت ، از شاخه منشویک حزب سوسیال دموکرات کارگری روسیه بودند . دومین مرحله ، انقلاب اکتبر ۱۹۱۷ بود . انقلاب اکتبر ، تحت نظارت حزب بلشویک (شاخه رادیکال از حزب سوسیال دموکرات کارگری روسیه) و به رهبری ولادیمیر لنین به پیش رفت و طی یک یورش نظامی همهجانبه به کاخ زمستانی سن پترزبورگ و سایر اماکن مهم ، قدرت را از دولت موقت گرفت . در این انقلاب افراد بسیار کمی کشته شدند . از زمان شکست روسیه در جنگ ۱۹۰۵ با ژاپن ، اوضاع بد اقتصادی ، گرسنگی ، عقبماندگی و سرمایهداری و نارضایتیهای گوناگون در بین مردم ، سربازان ، کارگران ، کشاورزان و نخبگان روسیه بهوجود آمدهبود . سرکوبهای تزار و ایجاد مجلس دوما نظام مشروطه حاصل آن دوران است . حزب سوسیال دموکرات ، اصلیترین معترض به سیاستهای نیکلای دوم بود که بهطور گسترده بین دهقانان کشاورزان و کارگران کارخانجات صنعتی علیه سیاستهای سیستم تزار فعالیت داشت . در اوت ۱۹۱۴ میلادی ، امپراتوری روسیه به دستور تزار وقت و به منظور حمایت از اسلاوهای صربستان وارد جنگ جهانی اول در برابر امپراتوری آلمان و امپراتوری اتریش-مجارستان شد . نخست فقط بلشویکها ، مخالف ورود روسیه به این جنگ بودند و میگفتند که این جنگ ، سبب بدتر شدن اوضاع نابسامان اقتصادی و اجتماعی روسیه خواهد شد . در سال ۱۹۱۴ میلادی ، یعنی در آغاز جنگ جهانی اول ، روسیه بزرگترین ارتش جهان را داشت ، حدود ۱۲ میلیون سرباز و ۶ میلیون سرباز ذخیره ؛ ولی در پایان سال ۱۹۱۶ میلادی ، پنج میلیون نفر از سربازان روسیه کشته ، زخمی یا اسیر شده بودند . حدود دو میلیون سرباز نیز محل خدمت خود را ترک کرده و غالبا با اسلحه به شهر و دیار خود بازگشته بودند . در میان ۱۰ یا ۱۱ میلیون سرباز باقیمانده نیز ، اعتبار تزار و سلسله مراتب ارتش و اتوریته افسران بالا دست از بین رفته بود . عوامل نابسامان داخلی اعم از اجتماعی کشاورزی و فرماندهی نظامی در شکستهای روسیه بسیار مؤثر بود . شکستهای روسیه در جنگ جهانی اول ، حامیان نیکلای دوم در روسیه را به حداقل خود رساند . در اوایل فوریه ۱۹۱۷ میلادی اکثر کارگران صنعتی در پتروگراد و مسکو دست به اعتصاب زدند . سپس شورش به پادگانها و سربازان رسید . اعتراضات دهقانان نیز گسترش یافت . سوسیال دموکراتها هدایت اعتراضات را در دست گرفتند . در ۱۱ مارس ۱۹۱۷ میلادی ، تزار وقت روسیه ، نیکلای دوم ، فرمان انحلال مجلس روسیه را صادر کرد ، اما اکثر نمایندگان مجلس متفرق نشدند و با تصمیمات نیکلای دوم مخالفت کردند . سرانجام در پی تظاهرات گسترده کارگران و سپس نافرمانی سربازان در سرکوب تظاهرکنندگان در پتروگراد ، نیکلای دوم از مقام خود استعفا داد . بدین ترتیب حکمرانی دودمان رومانوفها بر روسیه پس از حدود سیصد سال پایان یافت .',
'highlights': 'انقلاب ۱۹۱۷ روسیه ، جنبشی اعتراضی ، ضد امپراتوری روسیه بود که در سال ۱۹۱۷ رخ داد و به سرنگونی حکومت تزارها و برپایی اتحاد جماهیر شوروی انجامید . مبانی انقلاب بر پایه صلح-نان-زمین استوار بود . این انقلاب در دو مرحله صورت گرفت : در طول این انقلاب در شهرهای اصلی روسیه همانند مسکو و سن پترزبورگ رویدادهای تاریخی برجستهای رخ داد . انقلاب در مناطق روستایی و رعیتی نیز پا به پای مناطق شهری در حال پیشروی بود و دهقانان زمینها را تصرف کرده و در حال بازتوزیع آن در میان خود بودند .'
}
```
### Data Fields
- `id`: Article id
- `link`: Article link
- `title`: Title of the article
- `article`: Full text content in the article
- `highlights`: Summary of the article
### Data Splits
| Train | Test | Validation |
|-------------|-------------|-------------|
| 45,654 | 5,638 | 5,074 |
## 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
No annotations.
#### 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
The dataset was created by Mehrdad Farahani.
### Licensing Information
[Apache License 2.0](https://github.com/m3hrdadfi/wiki-summary/blob/master/LICENSE)
### Citation Information
```
@misc{Bert2BertWikiSummaryPersian,
author = {Mehrdad Farahani},
title = {Summarization using Bert2Bert model on WikiSummary dataset},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {https://github.com/m3hrdadfi/wiki-summary},
}
```
### Contributions
Thanks to [@tanmoyio](https://github.com/tanmoyio) for adding this dataset. | [
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xor_tydi_qa | null | 2023-01-25T15:03:13Z | 452 | 1 | xor-tydi-qa | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"source_datasets:extended|tydiqa",
"language:ar",
"language:bn",
"language:fi",
"language:ja",
"language:ko",
"language:ru",
"language:te",
"license:mit",
"arxiv:2010.11856",
"region:us"
] | 2023-01-25T15:03:13Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
- found
language:
- ar
- bn
- fi
- ja
- ko
- ru
- te
license:
- mit
multilinguality:
- multilingual
size_categories:
- 10K<n<100K
source_datasets:
- original
- extended|tydiqa
task_categories:
- question-answering
task_ids:
- open-domain-qa
paperswithcode_id: xor-tydi-qa
pretty_name: XOR QA
dataset_info:
- config_name: xor-retrieve
features:
- name: question
dtype: string
- name: lang
dtype:
class_label:
names:
'0': ar
'1': bn
'2': fi
'3': ja
'4': ko
'5': ru
'6': te
- name: answers
dtype: string
splits:
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num_bytes: 1698662
num_examples: 15250
- name: validation
num_bytes: 259533
num_examples: 2110
- name: test
num_bytes: 219046
num_examples: 2499
download_size: 3702288
dataset_size: 2177241
- config_name: xor-full
features:
- name: question
dtype: string
- name: lang
dtype:
class_label:
names:
'0': ar
'1': bn
'2': fi
'3': ja
'4': ko
'5': ru
'6': te
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dtype: string
splits:
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num_bytes: 7250913
num_examples: 61360
- name: validation
num_bytes: 444672
num_examples: 3473
- name: test
num_bytes: 706664
num_examples: 8176
download_size: 14018298
dataset_size: 8402249
---
# Dataset Card for XOR QA
## 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:** [XOR QA Homepage](https://nlp.cs.washington.edu/xorqa/)
- **Repository:** [XOR QA Repository](https://github.com/AkariAsai/XORQA)
- **Paper:** [XOR QA Paper](https://arxiv.org/abs/2010.11856)
- **Leaderboard:** [XOR QA Leaderboard](https://nlp.cs.washington.edu/xorqa/)
- **Point of Contact:** [Akari Asai](akari@cs.washington.edu)
### Dataset Summary
XOR-TyDi QA brings together for the first time information-seeking questions, open-retrieval QA, and multilingual QA to create a multilingual open-retrieval QA dataset that enables cross-lingual answer retrieval. It consists of questions written by information-seeking native speakers in 7 typologically diverse languages and answer annotations that are retrieved from multilingual document collections.
### Supported Tasks and Leaderboards
There are three sub-tasks: XOR-Retrieve, XOR-EnglishSpan, and XOR-Full.
- `XOR-retrieve`: XOR-Retrieve is a cross-lingual retrieval task where a question is written in a target language (e.g., Japanese) and a system is required to retrieve English paragraphs that answer the question. The dataset can be used to train a model for cross-lingual retrieval. Success on this task is typically measured by R@5kt, R@2kt (the recall by computing the fraction of the questions for which the minimal answer is contained in the top 5,000 / 2,000 tokens selected). This task has an active leaderboard which can be found at [leaderboard url](https://nlp.cs.washington.edu/xorqa/)
- `XOR-English Span`: XOR-English Span is a cross-lingual retrieval task where a question is written in a target language (e.g., Japanese) and a system is required to output a short answer in English. The dataset can be used to train a model for cross-lingual retrieval. Success on this task is typically measured by F1, EM. This task has an active leaderboard which can be found at [leaderboard url](https://nlp.cs.washington.edu/xorqa/)
- `XOR-Full`: XOR-Full is a cross-lingual retrieval task where a question is written in the target language (e.g., Japanese) and a system is required to output a short answer in a target language. Success on this task is typically measured by F1, EM, BLEU This task has an active leaderboard which can be found at [leaderboard url](https://nlp.cs.washington.edu/xorqa/)
### Languages
The text in the dataset is available in 7 languages: Arabic `ar`, Bengali `bn`, Finnish `fi`, Japanese `ja`, Korean `ko`, Russian `ru`, Telugu `te`
## Dataset Structure
### Data Instances
A typical data point comprises a `question`, it's `answer` the `language` of the question text and the split to which it belongs.
```
{
"id": "-3979399588609321314",
"question": "Сколько детей было у Наполео́на I Бонапа́рта?",
"answers": ["сын"],
"lang": "ru",
"split": "train"
}
```
### Data Fields
- `id`: An identifier for each example in the dataset
- `question`: Open domain question
- `answers`: The corresponding answer to the question posed
- `lang`: BCP-47 language tag
- `split`: identifier to differentiate train, validation and test splits
### Data Splits
The data is split into a training, validation and test set for each of the two configurations.
| | train | validation | test |
|--------------|------:|-----------:|-----:|
| XOR Retrieve | 15250 | 2113 | 2501 |
| XOR Full | 61360 | 3179 | 8177 |
## Dataset Creation
### Curation Rationale
This task framework reflects well real-world scenarios where a QA system uses multilingual document collections and answers questions asked by users with diverse linguistic and cultural backgrounds. Despite the common assumption that we can find answers in the target language, web re- sources in non-English languages are largely lim- ited compared to English (information scarcity), or the contents are biased towards their own cul- tures (information asymmetry). To solve these issues, XOR-TYDI QA (Asai et al., 2020) provides a benchmark for developing a multilingual QA system that finds answers in multiple languages.
### Source Data
annotation pipeline consists of four steps: 1) collection of realistic questions that require cross-lingual ref- erences by annotating questions from TYDI QA without a same-language answer; 2) question translation from a target language to the pivot language of English where the missing informa- tion may exist; 3) answer span selection in the pivot language given a set of candidate documents; 4) answer verification and translation from the pivot language back to the original language.
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The Dataset is created by extending TyDiQA dataset and translating the questions into other languages. The answers are obtained by crowdsourcing the questions to Mechanical Turk workders
### Annotations
#### Annotation process
The English questions from TyDiQA are translated into other languages. The languages are chosen based on the availability of wikipedia data and the availability of tranlators.
#### Who are the annotators?
The translations are carried out using the professionla tranlation service (Gengo)[https://gengo.com] and the answers are annotated by MechanicalTurk workers
### Personal and Sensitive Information
The dataset is created from wikipedia content and the QA task requires preserving the named entities, there by all the Wikipedia Named Entities are preserved in the data. Not much information has been provided about masking sensitive information.
## 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
The people associated with the creation of the dataset are Akari Asai, Jungo Kasai, Jonathan H. Clark, Kenton Lee, Eunsol Choi, Hannaneh Hajishirzi
### Licensing Information
XOR-TyDi QA is distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) license
### Citation Information
```
@article{xorqa,
title = {XOR QA: Cross-lingual Open-Retrieval Question Answering},
author = {Akari Asai and Jungo Kasai and Jonathan H. Clark and Kenton Lee and Eunsol Choi and Hannaneh Hajishirzi}
year = {2020}
}
```
### Contributions
Thanks to [@sumanthd17](https://github.com/sumanthd17) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
kilt_wikipedia | null | 2023-04-05T10:08:59Z | 451 | 10 | null | [
"region:us"
] | 2023-04-05T10:08:59Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
paperswithcode_id: null
pretty_name: KiltWikipedia
dataset_info:
features:
- name: kilt_id
dtype: string
- name: wikipedia_id
dtype: string
- name: wikipedia_title
dtype: string
- name: text
sequence:
- name: paragraph
dtype: string
- name: anchors
sequence:
- name: paragraph_id
dtype: int32
- name: start
dtype: int32
- name: end
dtype: int32
- name: text
dtype: string
- name: href
dtype: string
- name: wikipedia_title
dtype: string
- name: wikipedia_id
dtype: string
- name: categories
dtype: string
- name: wikidata_info
struct:
- name: description
dtype: string
- name: enwikiquote_title
dtype: string
- name: wikidata_id
dtype: string
- name: wikidata_label
dtype: string
- name: wikipedia_title
dtype: string
- name: aliases
sequence:
- name: alias
dtype: string
- name: history
struct:
- name: pageid
dtype: int32
- name: parentid
dtype: int32
- name: revid
dtype: int32
- name: pre_dump
dtype: bool
- name: timestamp
dtype: string
- name: url
dtype: string
config_name: '2019-08-01'
splits:
- name: full
num_bytes: 29372535718
num_examples: 5903530
download_size: 37318876722
dataset_size: 29372535718
---
# Dataset Card for "kilt_wikipedia"
## 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/facebookresearch/KILT](https://github.com/facebookresearch/KILT)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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 downloaded dataset files:** 37.32 GB
- **Size of the generated dataset:** 29.37 GB
- **Total amount of disk used:** 66.69 GB
### Dataset Summary
KILT-Wikipedia: Wikipedia pre-processed for KILT.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### 2019-08-01
- **Size of downloaded dataset files:** 37.32 GB
- **Size of the generated dataset:** 29.37 GB
- **Total amount of disk used:** 66.69 GB
An example of 'full' looks as follows.
```
{
"anchors": {
"end": [],
"href": [],
"paragraph_id": [],
"start": [],
"text": [],
"wikipedia_id": [],
"wikipedia_title": []
},
"categories": "",
"history": {
"pageid": 0,
"parentid": 0,
"pre_dump": true,
"revid": 0,
"timestamp": "",
"url": ""
},
"kilt_id": "",
"text": {
"paragraph": []
},
"wikidata_info": {
"aliases": {
"alias": []
},
"description": "",
"enwikiquote_title": "",
"wikidata_id": "",
"wikidata_label": "",
"wikipedia_title": ""
},
"wikipedia_id": "",
"wikipedia_title": ""
}
```
### Data Fields
The data fields are the same among all splits.
#### 2019-08-01
- `kilt_id`: a `string` feature.
- `wikipedia_id`: a `string` feature.
- `wikipedia_title`: a `string` feature.
- `text`: a dictionary feature containing:
- `paragraph`: a `string` feature.
- `anchors`: a dictionary feature containing:
- `paragraph_id`: a `int32` feature.
- `start`: a `int32` feature.
- `end`: a `int32` feature.
- `text`: a `string` feature.
- `href`: a `string` feature.
- `wikipedia_title`: a `string` feature.
- `wikipedia_id`: a `string` feature.
- `categories`: a `string` feature.
- `description`: a `string` feature.
- `enwikiquote_title`: a `string` feature.
- `wikidata_id`: a `string` feature.
- `wikidata_label`: a `string` feature.
- `wikipedia_title`: a `string` feature.
- `aliases`: a dictionary feature containing:
- `alias`: a `string` feature.
- `pageid`: a `int32` feature.
- `parentid`: a `int32` feature.
- `revid`: a `int32` feature.
- `pre_dump`: a `bool` feature.
- `timestamp`: a `string` feature.
- `url`: a `string` feature.
### Data Splits
| name | full |
|----------|------:|
|2019-08-01|5903530|
## 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{fb_kilt,
author = {Fabio Petroni and
Aleksandra Piktus and
Angela Fan and
Patrick Lewis and
Majid Yazdani and
Nicola De Cao and
James Thorne and
Yacine Jernite and
Vassilis Plachouras and
Tim Rockt"aschel and
Sebastian Riedel},
title = {{KILT:} a {B}enchmark for {K}nowledge {I}ntensive {L}anguage {T}asks},
journal = {CoRR},
archivePrefix = {arXiv},
year = {2020},
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@yjernite](https://github.com/yjernite) for adding this dataset. | [
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ChristophSchuhmann/MS_COCO_2017_URL_TEXT | ChristophSchuhmann | 2021-11-27T15:39:29Z | 451 | 12 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
5cp/imdb_review_prompts | 5cp | 2023-11-19T22:44:31Z | 451 | 0 | null | [
"region:us"
] | 2023-11-19T22:44:31Z | 2023-11-19T22:44:29.000Z | 2023-11-19T22:44:29 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
splits:
- name: train
num_bytes: 290977
num_examples: 782
- name: test
num_bytes: 304788
num_examples: 858
download_size: 286770
dataset_size: 595765
---
# Dataset Card for "imdb_review_prompts"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
RIW/small-coco | RIW | 2023-02-06T01:06:04Z | 450 | 1 | null | [
"region:us"
] | 2023-02-06T01:06:04Z | 2023-02-06T00:57:39.000Z | 2023-02-06T00:57:39 | ---
dataset_info:
features:
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dtype: image
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splits:
- name: train
num_bytes: 1946738057.45
num_examples: 9890
- name: validation
num_bytes: 1953823510.0
num_examples: 9893
download_size: 1313384992
dataset_size: 3900561567.45
---
# Dataset Card for "small-coco"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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FRTNX/cosuju | FRTNX | 2021-03-29T09:01:41Z | 449 | 0 | null | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cambridgeltl/vsr_zeroshot | cambridgeltl | 2023-03-22T17:27:58Z | 449 | 1 | null | [
"task_categories:text-classification",
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"multimodal",
"vision-and-language",
"arxiv:2205.00363",
"region:us"
] | 2023-03-22T17:27:58Z | 2023-03-22T16:42:17.000Z | 2023-03-22T16:42:17 | ---
license: cc-by-4.0
task_categories:
- text-classification
- question-answering
language:
- en
tags:
- multimodal
- vision-and-language
pretty_name: VSR (zeroshot)
size_categories:
- 1K<n<10K
---
# VSR: Visual Spatial Reasoning
This is the **zero-shot set** of **VSR**: *Visual Spatial Reasoning* (TACL 2023) [[paper]](https://arxiv.org/abs/2205.00363).
### Usage
```python
from datasets import load_dataset
data_files = {"train": "train.jsonl", "dev": "dev.jsonl", "test": "test.jsonl"}
dataset = load_dataset("cambridgeltl/vsr_zeroshot", data_files=data_files)
```
Note that the image files still need to be downloaded separately. See [`data/`](https://github.com/cambridgeltl/visual-spatial-reasoning/tree/master/data) for details.
Go to our [github repo](https://github.com/cambridgeltl/visual-spatial-reasoning) for more introductions.
### Citation
If you find VSR useful:
```bibtex
@article{Liu2022VisualSR,
title={Visual Spatial Reasoning},
author={Fangyu Liu and Guy Edward Toh Emerson and Nigel Collier},
journal={Transactions of the Association for Computational Linguistics},
year={2023},
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
polm-stability/xwinograd-ja | polm-stability | 2023-10-06T08:34:15Z | 449 | 0 | null | [
"license:cc-by-4.0",
"arxiv:2211.01786",
"arxiv:2106.12066",
"region:us"
] | 2023-10-06T08:34:15Z | 2023-10-06T08:11:59.000Z | 2023-10-06T08:11:59 | ---
license: cc-by-4.0
---
This is the Japanese portion of the xwinograd dataset, formatted for easy use.
The original data can be found [here](https://huggingface.co/datasets/Muennighoff/xwinograd). When using this data, please cite the original papers.
```
@misc{muennighoff2022crosslingual,
title={Crosslingual Generalization through Multitask Finetuning},
author={Niklas Muennighoff and Thomas Wang and Lintang Sutawika and Adam Roberts and Stella Biderman and Teven Le Scao and M Saiful Bari and Sheng Shen and Zheng-Xin Yong and Hailey Schoelkopf and Xiangru Tang and Dragomir Radev and Alham Fikri Aji and Khalid Almubarak and Samuel Albanie and Zaid Alyafeai and Albert Webson and Edward Raff and Colin Raffel},
year={2022},
eprint={2211.01786},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```
@misc{tikhonov2021heads,
title={It's All in the Heads: Using Attention Heads as a Baseline for Cross-Lingual Transfer in Commonsense Reasoning},
author={Alexey Tikhonov and Max Ryabinin},
year={2021},
eprint={2106.12066},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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iapp_wiki_qa_squad | null | 2022-11-18T20:08:21Z | 448 | 2 | null | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-iapp-wiki-qa-dataset",
"language:th",
"license:mit",
"region:us"
] | 2022-11-18T20:08:21Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- th
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-iapp-wiki-qa-dataset
task_categories:
- question-answering
task_ids:
- extractive-qa
- open-domain-qa
paperswithcode_id: null
pretty_name: IappWikiQaSquad
dataset_info:
features:
- name: question_id
dtype: string
- name: article_id
dtype: string
- name: title
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
- name: answer_end
dtype: int32
config_name: iapp_wiki_qa_squad
splits:
- name: train
num_bytes: 16107541
num_examples: 5761
- name: validation
num_bytes: 2120768
num_examples: 742
- name: test
num_bytes: 2032016
num_examples: 739
download_size: 2876630
dataset_size: 20260325
---
# Dataset Card for `iapp_wiki_qa_squad`
## 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/iapp-technology/iapp-wiki-qa-dataset
- **Repository:** https://github.com/iapp-technology/iapp-wiki-qa-dataset
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** https://github.com/iapp-technology/iapp-wiki-qa-dataset
### Dataset Summary
`iapp_wiki_qa_squad` is an extractive question answering dataset from Thai Wikipedia articles. It is adapted from [the original iapp-wiki-qa-dataset](https://github.com/iapp-technology/iapp-wiki-qa-dataset) to [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) format, resulting in 5761/742/739 questions from 1529/191/192 articles.
### Supported Tasks and Leaderboards
extractive question answering
### Languages
Thai
## Dataset Structure
### Data Instances
An example from the dataset:
```
{'article_id': '0U2lA8nJQESIxbZrjZQc',
'question_id': '0U2lA8nJQESIxbZrjZQc_000',
'context': 'นายสุวัฒน์ วรรณศิริกุล (1 พฤศจิกายน พ.ศ. 2476 - 31 กรกฎาคม พ.ศ. 2555) อดีตรองหัวหน้าพรรคพลังประชาชน อดีตประธานสมาชิกสภาผู้แทนราษฎร และประธานภาคกรุงเทพมหานคร พรรคพลังประชาชน อดีตสมาชิกสภาผู้แทนราษฎรกรุงเทพมหานครหลายสมัย ได้รับการเลือกตั้งเป็นสมาชิกสภาผู้แทนราษฎรครั้งแรกในปี พ.ศ. 2529 ในสังกัดพรรคประชากรไทย และสังกัดพรรคพลังประชาชน เป็นพรรคสุดท้าย',
'question': 'สุวัฒน์ วรรณศิริกุล เกิดวันที่เท่าไร',
'answers': {'text': ['1 พฤศจิกายน พ.ศ. 2476'],
'answer_start': [24],
'answer_end': [45]},
'title': 'สุวัฒน์ วรรณศิริกุล',
'created_by': 'gmnjGRF0y0g7QRZDd9Qgz3AgiHJ3',
'created_on': '2019-08-18 05:05:51.358000+00:00',
'is_pay': {'date': None, 'status': False}}
{'article_id': '01KZTrxgvC5mOovXFMPJ',
'question_id': '01KZTrxgvC5mOovXFMPJ_000',
'context': 'พัทธ์ธีรา ศรุติพงศ์โภคิน (เกิด 3 ธันวาคม พ.ศ. 2533) หรือชื่อเล่นว่า อร เป็นนักแสดงหญิงชาวไทย สำเร็จมัธยมศึกษาจากCatholic Cathedral College ประเทศนิวซีแลนด์ และปริญญาตรีจากRaffles International College สาขา Business Marketing\n\nเข้าสู่วงการตั้งแต่อายุ 6 ขวบ จากการแสดงละครเวทีกับ ครูชลประคัลภ์ จันทร์เรือง จากนั้นก็เล่นโฆษณาในวัยเด็ก 2- 3 ชิ้น และยังเคยแสดงช่วงละครสั้น ในรายการซุปเปอร์จิ๋ว ประมาณปี 2542\n\nปัจจุบันเป็นทั้ง นักแสดง , พิธีกร และ วีเจ อยู่ที่คลื่น เก็ท 102.5 Bangkok International Hits Music Station และยังเป็นพิธีกรให้กับช่อง ทรู มิวสิก',
'question': 'พัทธ์ธีรา ศรุติพงศ์โภคิน เกิดวันที่เท่าไร',
'answers': {'text': ['3 ธันวาคม พ.ศ. 2533'],
'answer_start': [31],
'answer_end': [50]},
'title': 'พัทธ์ธีรา ศรุติพงศ์โภคิน',
'created_by': 'gmnjGRF0y0g7QRZDd9Qgz3AgiHJ3',
'created_on': '2019-08-07 14:00:38.778000+00:00',
'is_pay': {'status': True,
'total': 2.5,
'date': '2019-08-13 10:47:28.095000+00:00'}}
```
### Data Fields
```
{
"question_id": question id
"article_id": article id
"title": article title
"context": article texts
"question": question
"answers":
{
"text": answer text
"answer_start": answer beginning position
"answer_end": answer exclusive upper bound position
}
),
}
```
### Data Splits
| | train | valid | test |
|-------------|-------|-------|------|
| # questions | 5761 | 742 | 739 |
| # articles | 1529 | 191 | 192 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
From the original `iapp-wiki-qa-dataset`, [@cstorm125](https://github.com/cstorm125/) applied the following processing:
- Select questions with one, non-empty answer
- Select questions whose answers match `textDetection` fields
- Select questions whose answers are 100-character long or shorter
- 80/10/10 train-validation-split at article level
#### Who are the source language producers?
Wikipedia authors for contexts and annotators hired by [iApp](https://iapp.co.th/) for questions and answer annotations
### Annotations
#### Annotation process
Annotators hired by [iApp](https://iapp.co.th/) are asked create questions and answers for each article.
#### Who are the annotators?
Annotators hired by [iApp](https://iapp.co.th/)
### Personal and Sensitive Information
All contents are from Wikipedia. No personal and sensitive information is expected to be included.
## Considerations for Using the Data
### Social Impact of Dataset
- open-domain, extractive question answering in Thai
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Original dataset by [iApp](https://iapp.co.th/). SQuAD formattting by [PyThaiNLP](https://github.com/PyThaiNLP/).
### Licensing Information
MIT
### Citation Information
```
@dataset{kobkrit_viriyayudhakorn_2021_4539916,
author = {Kobkrit Viriyayudhakorn and
Charin Polpanumas},
title = {iapp\_wiki\_qa\_squad},
month = feb,
year = 2021,
publisher = {Zenodo},
version = 1,
doi = {10.5281/zenodo.4539916},
url = {https://doi.org/10.5281/zenodo.4539916}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. | [
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BeIR/beir-corpus | BeIR | 2022-10-21T15:30:07Z | 448 | 4 | beir | [
"task_categories:text-retrieval",
"task_ids:entity-linking-retrieval",
"task_ids:fact-checking-retrieval",
"multilinguality:monolingual",
"language:en",
"license:cc-by-sa-4.0",
"region:us"
] | 2022-10-21T15:30:07Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators: []
language_creators: []
language:
- en
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
paperswithcode_id: beir
pretty_name: BEIR Benchmark
size_categories:
msmarco:
- 1M<n<10M
trec-covid:
- 100k<n<1M
nfcorpus:
- 1K<n<10K
nq:
- 1M<n<10M
hotpotqa:
- 1M<n<10M
fiqa:
- 10K<n<100K
arguana:
- 1K<n<10K
touche-2020:
- 100K<n<1M
cqadupstack:
- 100K<n<1M
quora:
- 100K<n<1M
dbpedia:
- 1M<n<10M
scidocs:
- 10K<n<100K
fever:
- 1M<n<10M
climate-fever:
- 1M<n<10M
scifact:
- 1K<n<10K
source_datasets: []
task_categories:
- text-retrieval
- zero-shot-retrieval
- information-retrieval
- zero-shot-information-retrieval
task_ids:
- passage-retrieval
- entity-linking-retrieval
- fact-checking-retrieval
- tweet-retrieval
- citation-prediction-retrieval
- duplication-question-retrieval
- argument-retrieval
- news-retrieval
- biomedical-information-retrieval
- question-answering-retrieval
---
# Dataset Card for BEIR Benchmark
## 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/UKPLab/beir
- **Repository:** https://github.com/UKPLab/beir
- **Paper:** https://openreview.net/forum?id=wCu6T5xFjeJ
- **Leaderboard:** https://docs.google.com/spreadsheets/d/1L8aACyPaXrL8iEelJLGqlMqXKPX2oSP_R10pZoy77Ns
- **Point of Contact:** nandan.thakur@uwaterloo.ca
### Dataset Summary
BEIR is a heterogeneous benchmark that has been built from 18 diverse datasets representing 9 information retrieval tasks:
- Fact-checking: [FEVER](http://fever.ai), [Climate-FEVER](http://climatefever.ai), [SciFact](https://github.com/allenai/scifact)
- Question-Answering: [NQ](https://ai.google.com/research/NaturalQuestions), [HotpotQA](https://hotpotqa.github.io), [FiQA-2018](https://sites.google.com/view/fiqa/)
- Bio-Medical IR: [TREC-COVID](https://ir.nist.gov/covidSubmit/index.html), [BioASQ](http://bioasq.org), [NFCorpus](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/)
- News Retrieval: [TREC-NEWS](https://trec.nist.gov/data/news2019.html), [Robust04](https://trec.nist.gov/data/robust/04.guidelines.html)
- Argument Retrieval: [Touche-2020](https://webis.de/events/touche-20/shared-task-1.html), [ArguAna](tp://argumentation.bplaced.net/arguana/data)
- Duplicate Question Retrieval: [Quora](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs), [CqaDupstack](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/)
- Citation-Prediction: [SCIDOCS](https://allenai.org/data/scidocs)
- Tweet Retrieval: [Signal-1M](https://research.signal-ai.com/datasets/signal1m-tweetir.html)
- Entity Retrieval: [DBPedia](https://github.com/iai-group/DBpedia-Entity/)
All these datasets have been preprocessed and can be used for your experiments.
```python
```
### Supported Tasks and Leaderboards
The dataset supports a leaderboard that evaluates models against task-specific metrics such as F1 or EM, as well as their ability to retrieve supporting information from Wikipedia.
The current best performing models can be found [here](https://eval.ai/web/challenges/challenge-page/689/leaderboard/).
### Languages
All tasks are in English (`en`).
## Dataset Structure
All BEIR datasets must contain a corpus, queries and qrels (relevance judgments file). They must be in the following format:
- `corpus` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with three fields `_id` with unique document identifier, `title` with document title (optional) and `text` with document paragraph or passage. For example: `{"_id": "doc1", "title": "Albert Einstein", "text": "Albert Einstein was a German-born...."}`
- `queries` file: a `.jsonl` file (jsonlines) that contains a list of dictionaries, each with two fields `_id` with unique query identifier and `text` with query text. For example: `{"_id": "q1", "text": "Who developed the mass-energy equivalence formula?"}`
- `qrels` file: a `.tsv` file (tab-seperated) that contains three columns, i.e. the `query-id`, `corpus-id` and `score` in this order. Keep 1st row as header. For example: `q1 doc1 1`
### Data Instances
A high level example of any beir dataset:
```python
corpus = {
"doc1" : {
"title": "Albert Einstein",
"text": "Albert Einstein was a German-born theoretical physicist. who developed the theory of relativity, \
one of the two pillars of modern physics (alongside quantum mechanics). His work is also known for \
its influence on the philosophy of science. He is best known to the general public for his mass–energy \
equivalence formula E = mc2, which has been dubbed 'the world's most famous equation'. He received the 1921 \
Nobel Prize in Physics 'for his services to theoretical physics, and especially for his discovery of the law \
of the photoelectric effect', a pivotal step in the development of quantum theory."
},
"doc2" : {
"title": "", # Keep title an empty string if not present
"text": "Wheat beer is a top-fermented beer which is brewed with a large proportion of wheat relative to the amount of \
malted barley. The two main varieties are German Weißbier and Belgian witbier; other types include Lambic (made\
with wild yeast), Berliner Weisse (a cloudy, sour beer), and Gose (a sour, salty beer)."
},
}
queries = {
"q1" : "Who developed the mass-energy equivalence formula?",
"q2" : "Which beer is brewed with a large proportion of wheat?"
}
qrels = {
"q1" : {"doc1": 1},
"q2" : {"doc2": 1},
}
```
### Data Fields
Examples from all configurations have the following features:
### Corpus
- `corpus`: a `dict` feature representing the document title and passage text, made up of:
- `_id`: a `string` feature representing the unique document id
- `title`: a `string` feature, denoting the title of the document.
- `text`: a `string` feature, denoting the text of the document.
### Queries
- `queries`: a `dict` feature representing the query, made up of:
- `_id`: a `string` feature representing the unique query id
- `text`: a `string` feature, denoting the text of the query.
### Qrels
- `qrels`: a `dict` feature representing the query document relevance judgements, made up of:
- `_id`: a `string` feature representing the query id
- `_id`: a `string` feature, denoting the document id.
- `score`: a `int32` feature, denoting the relevance judgement between query and document.
### Data Splits
| Dataset | Website| BEIR-Name | Type | Queries | Corpus | Rel D/Q | Down-load | md5 |
| -------- | -----| ---------| --------- | ----------- | ---------| ---------| :----------: | :------:|
| MSMARCO | [Homepage](https://microsoft.github.io/msmarco/)| ``msmarco`` | ``train``<br>``dev``<br>``test``| 6,980 | 8.84M | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/msmarco.zip) | ``444067daf65d982533ea17ebd59501e4`` |
| TREC-COVID | [Homepage](https://ir.nist.gov/covidSubmit/index.html)| ``trec-covid``| ``test``| 50| 171K| 493.5 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/trec-covid.zip) | ``ce62140cb23feb9becf6270d0d1fe6d1`` |
| NFCorpus | [Homepage](https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/) | ``nfcorpus`` | ``train``<br>``dev``<br>``test``| 323 | 3.6K | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nfcorpus.zip) | ``a89dba18a62ef92f7d323ec890a0d38d`` |
| BioASQ | [Homepage](http://bioasq.org) | ``bioasq``| ``train``<br>``test`` | 500 | 14.91M | 8.05 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#2-bioasq) |
| NQ | [Homepage](https://ai.google.com/research/NaturalQuestions) | ``nq``| ``train``<br>``test``| 3,452 | 2.68M | 1.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/nq.zip) | ``d4d3d2e48787a744b6f6e691ff534307`` |
| HotpotQA | [Homepage](https://hotpotqa.github.io) | ``hotpotqa``| ``train``<br>``dev``<br>``test``| 7,405 | 5.23M | 2.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/hotpotqa.zip) | ``f412724f78b0d91183a0e86805e16114`` |
| FiQA-2018 | [Homepage](https://sites.google.com/view/fiqa/) | ``fiqa`` | ``train``<br>``dev``<br>``test``| 648 | 57K | 2.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fiqa.zip) | ``17918ed23cd04fb15047f73e6c3bd9d9`` |
| Signal-1M(RT) | [Homepage](https://research.signal-ai.com/datasets/signal1m-tweetir.html)| ``signal1m`` | ``test``| 97 | 2.86M | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#4-signal-1m) |
| TREC-NEWS | [Homepage](https://trec.nist.gov/data/news2019.html) | ``trec-news`` | ``test``| 57 | 595K | 19.6 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#1-trec-news) |
| ArguAna | [Homepage](http://argumentation.bplaced.net/arguana/data) | ``arguana``| ``test`` | 1,406 | 8.67K | 1.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/arguana.zip) | ``8ad3e3c2a5867cdced806d6503f29b99`` |
| Touche-2020| [Homepage](https://webis.de/events/touche-20/shared-task-1.html) | ``webis-touche2020``| ``test``| 49 | 382K | 19.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/webis-touche2020.zip) | ``46f650ba5a527fc69e0a6521c5a23563`` |
| CQADupstack| [Homepage](http://nlp.cis.unimelb.edu.au/resources/cqadupstack/) | ``cqadupstack``| ``test``| 13,145 | 457K | 1.4 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/cqadupstack.zip) | ``4e41456d7df8ee7760a7f866133bda78`` |
| Quora| [Homepage](https://www.quora.com/q/quoradata/First-Quora-Dataset-Release-Question-Pairs) | ``quora``| ``dev``<br>``test``| 10,000 | 523K | 1.6 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/quora.zip) | ``18fb154900ba42a600f84b839c173167`` |
| DBPedia | [Homepage](https://github.com/iai-group/DBpedia-Entity/) | ``dbpedia-entity``| ``dev``<br>``test``| 400 | 4.63M | 38.2 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/dbpedia-entity.zip) | ``c2a39eb420a3164af735795df012ac2c`` |
| SCIDOCS| [Homepage](https://allenai.org/data/scidocs) | ``scidocs``| ``test``| 1,000 | 25K | 4.9 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scidocs.zip) | ``38121350fc3a4d2f48850f6aff52e4a9`` |
| FEVER | [Homepage](http://fever.ai) | ``fever``| ``train``<br>``dev``<br>``test``| 6,666 | 5.42M | 1.2| [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/fever.zip) | ``5a818580227bfb4b35bb6fa46d9b6c03`` |
| Climate-FEVER| [Homepage](http://climatefever.ai) | ``climate-fever``|``test``| 1,535 | 5.42M | 3.0 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/climate-fever.zip) | ``8b66f0a9126c521bae2bde127b4dc99d`` |
| SciFact| [Homepage](https://github.com/allenai/scifact) | ``scifact``| ``train``<br>``test``| 300 | 5K | 1.1 | [Link](https://public.ukp.informatik.tu-darmstadt.de/thakur/BEIR/datasets/scifact.zip) | ``5f7d1de60b170fc8027bb7898e2efca1`` |
| Robust04 | [Homepage](https://trec.nist.gov/data/robust/04.guidelines.html) | ``robust04``| ``test``| 249 | 528K | 69.9 | No | [How to Reproduce?](https://github.com/UKPLab/beir/blob/main/examples/dataset#3-robust04) |
## 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
Cite as:
```
@inproceedings{
thakur2021beir,
title={{BEIR}: A Heterogeneous Benchmark for Zero-shot Evaluation of Information Retrieval Models},
author={Nandan Thakur and Nils Reimers and Andreas R{\"u}ckl{\'e} and Abhishek Srivastava and Iryna Gurevych},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=wCu6T5xFjeJ}
}
```
### Contributions
Thanks to [@Nthakur20](https://github.com/Nthakur20) for adding this dataset. | [
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biu-nlp/qa_srl2018 | biu-nlp | 2022-10-19T06:16:06Z | 448 | 1 | null | [
"region:us"
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usvsnsp/memories-semantic-memorization-filter-results | usvsnsp | 2023-09-20T20:16:41Z | 447 | 1 | null | [
"region:us"
] | 2023-09-20T20:16:41Z | 2023-09-20T20:08:28.000Z | 2023-09-20T20:08:28 | ---
dataset_info:
features:
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sequence: int64
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- name: memorization_score
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download_size: 9223426756
dataset_size: 27159884469
---
# Dataset Card for "memories-semantic-memorization-filter-results"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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offcombr | null | 2023-01-25T14:41:55Z | 446 | 4 | offcombr | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:pt",
"license:unknown",
"hate-speech-detection",
"region:us"
] | 2023-01-25T14:41:55Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- pt
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: offcombr
pretty_name: Offensive Comments in the Brazilian Web
tags:
- hate-speech-detection
dataset_info:
- config_name: offcombr-2
features:
- name: label
dtype:
class_label:
names:
'0': 'no'
'1': 'yes'
- name: text
dtype: string
splits:
- name: train
num_bytes: 105703
num_examples: 1250
download_size: 99956
dataset_size: 105703
- config_name: offcombr-3
features:
- name: label
dtype:
class_label:
names:
'0': 'no'
'1': 'yes'
- name: text
dtype: string
splits:
- name: train
num_bytes: 90094
num_examples: 1033
download_size: 85215
dataset_size: 90094
---
# Dataset Card for [Dataset Name]
## 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://www.inf.ufrgs.br/~rppelle/hatedetector/
- **Repository:** https://github.com/rogersdepelle/OffComBR
- **Paper:** https://sol.sbc.org.br/index.php/brasnam/article/view/3260/3222
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
OffComBR: an annotated dataset containing for hate speech detection in Portuguese composed of news comments on the Brazilian Web.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
style_change_detection | null | 2023-04-05T13:41:00Z | 445 | 0 | null | [
"region:us"
] | 2023-04-05T13:41:00Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
paperswithcode_id: null
pretty_name: StyleChangeDetection
dataset_info:
- config_name: narrow
features:
- name: id
dtype: string
- name: text
dtype: string
- name: authors
dtype: int32
- name: structure
sequence: string
- name: site
dtype: string
- name: multi-author
dtype: bool
- name: changes
sequence: bool
splits:
- name: train
num_bytes: 40499150
num_examples: 3418
- name: validation
num_bytes: 20447137
num_examples: 1713
download_size: 0
dataset_size: 60946287
- config_name: wide
features:
- name: id
dtype: string
- name: text
dtype: string
- name: authors
dtype: int32
- name: structure
sequence: string
- name: site
dtype: string
- name: multi-author
dtype: bool
- name: changes
sequence: bool
splits:
- name: train
num_bytes: 97403392
num_examples: 8030
- name: validation
num_bytes: 48850089
num_examples: 4019
download_size: 0
dataset_size: 146253481
---
# Dataset Card for "style_change_detection"
## 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://pan.webis.de/clef20/pan20-web/style-change-detection.html](https://pan.webis.de/clef20/pan20-web/style-change-detection.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **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 downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 207.20 MB
- **Total amount of disk used:** 207.20 MB
### Dataset Summary
The goal of the style change detection task is to identify text positions within a given multi-author document at which the author switches. Detecting these positions is a crucial part of the authorship identification process, and for multi-author document analysis in general.
Access to the dataset needs to be requested from zenodo.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### narrow
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 60.94 MB
- **Total amount of disk used:** 60.94 MB
An example of 'validation' looks as follows.
```
{
"authors": 2,
"changes": [false, false, true, false],
"id": "2",
"multi-author": true,
"site": "exampleSite",
"structure": ["A1", "A2"],
"text": "This is text from example problem 2.\n"
}
```
#### wide
- **Size of downloaded dataset files:** 0.00 MB
- **Size of the generated dataset:** 146.26 MB
- **Total amount of disk used:** 146.26 MB
An example of 'train' looks as follows.
```
{
"authors": 2,
"changes": [false, false, true, false],
"id": "2",
"multi-author": true,
"site": "exampleSite",
"structure": ["A1", "A2"],
"text": "This is text from example problem 2.\n"
}
```
### Data Fields
The data fields are the same among all splits.
#### narrow
- `id`: a `string` feature.
- `text`: a `string` feature.
- `authors`: a `int32` feature.
- `structure`: a `list` of `string` features.
- `site`: a `string` feature.
- `multi-author`: a `bool` feature.
- `changes`: a `list` of `bool` features.
#### wide
- `id`: a `string` feature.
- `text`: a `string` feature.
- `authors`: a `int32` feature.
- `structure`: a `list` of `string` features.
- `site`: a `string` feature.
- `multi-author`: a `bool` feature.
- `changes`: a `list` of `bool` features.
### Data Splits
| name |train|validation|
|------|----:|---------:|
|narrow| 3418| 1713|
|wide | 8030| 4019|
## 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{bevendorff2020shared,
title={Shared Tasks on Authorship Analysis at PAN 2020},
author={Bevendorff, Janek and Ghanem, Bilal and Giachanou, Anastasia and Kestemont, Mike and Manjavacas, Enrique and Potthast, Martin and Rangel, Francisco and Rosso, Paolo and Specht, G{"u}nther and Stamatatos, Efstathios and others},
booktitle={European Conference on Information Retrieval},
pages={508--516},
year={2020},
organization={Springer}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@ghomasHudson](https://github.com/ghomasHudson), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
GEM/OrangeSum | GEM | 2022-09-03T18:26:49Z | 445 | 0 | null | [
"task_categories:summarization",
"annotations_creators:unknown",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:fr",
"license:other",
"region:us"
] | 2022-09-03T18:26:49Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- unknown
language_creators:
- unknown
language:
- fr
license:
- other
multilinguality:
- unknown
pretty_name: OrangeSum
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids:
- unknown
---
# Dataset Card for GEM/OrangeSum
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/Tixierae/OrangeSum
- **Paper:** https://aclanthology.org/2021.emnlp-main.740
- **Leaderboard:** N/A
- **Point of Contact:** [Needs More Information]
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/OrangeSum).
### Dataset Summary
OrangeSum is a French summarization dataset inspired by XSum. It features two subtasks: abstract generation and title generation. The data was sourced from "Orange Actu" articles between 2011 and 2020.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/OrangeSum')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/OrangeSum).
#### paper
[ACL Anthology](https://aclanthology.org/2021.emnlp-main.740)
## Dataset Overview
### Where to find the Data and its Documentation
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/Tixierae/OrangeSum)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2021.emnlp-main.740)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{kamal-eddine-etal-2021-barthez,
title = "{BART}hez: a Skilled Pretrained {F}rench Sequence-to-Sequence Model",
author = "Kamal Eddine, Moussa and
Tixier, Antoine and
Vazirgiannis, Michalis",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.740",
doi = "10.18653/v1/2021.emnlp-main.740",
pages = "9369--9390",
abstract = "Inductive transfer learning has taken the entire NLP field by storm, with models such as BERT and BART setting new state of the art on countless NLU tasks. However, most of the available models and research have been conducted for English. In this work, we introduce BARThez, the first large-scale pretrained seq2seq model for French. Being based on BART, BARThez is particularly well-suited for generative tasks. We evaluate BARThez on five discriminative tasks from the FLUE benchmark and two generative tasks from a novel summarization dataset, OrangeSum, that we created for this research. We show BARThez to be very competitive with state-of-the-art BERT-based French language models such as CamemBERT and FlauBERT. We also continue the pretraining of a multilingual BART on BARThez{'} corpus, and show our resulting model, mBARThez, to significantly boost BARThez{'} generative performance.",
}
```
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`French`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
other: Other license
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Summarization
### Credit
### Dataset Structure
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
Papers about abstractive summarization using seq2seq models:
- [Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond](https://aclanthology.org/K16-1028/)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://aclanthology.org/P17-1099/)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://aclanthology.org/2020.acl-main.703)
- [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://aclanthology.org/2021.emnlp-main.740/)
Papers about (pretrained) Transformers:
- [Attention is All you Need](https://papers.nips.cc/paper/2017/hash/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://aclanthology.org/N19-1423/)
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
No unique technical words in this data card.
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
The ability of the model to generate human like titles and abstracts for given news articles.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`ROUGE`, `BERT-Score`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
Automatic Evaluation: Rouge-1, Rouge-2, RougeL and BERTScore were used.
Human evalutaion: a human evaluation study was conducted with 11 French native speakers. The evaluators were PhD students from the computer science department of the university of the authors, working in NLP and other fields of AI. They volunteered after receiving an email announcement. the best-Worst Scaling (Louviere et al.,2015) was used. Two summaries from two different systems, along with their input document, were presented to a human annotator who had to decide which one was better. The evaluators were asked to base their judgments on accuracy (does the summary contain accurate facts?), informativeness (is important in-formation captured?) and fluency (is the summary written in well-formed French?).
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
The dataset contains news articles written by professional authors.
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
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GEM/turku_hockey_data2text | GEM | 2022-10-24T15:30:33Z | 445 | 0 | null | [
"task_categories:table-to-text",
"annotations_creators:expert-created",
"language_creators:unknown",
"multilinguality:unknown",
"size_categories:unknown",
"source_datasets:original",
"language:fi",
"license:cc-by-nc-sa-4.0",
"data-to-text",
"region:us"
] | 2022-10-24T15:30:33Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-created
language_creators:
- unknown
language:
- fi
license:
- cc-by-nc-sa-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
pretty_name: turku_hockey_data2text
tags:
- data-to-text
---
# Dataset Card for GEM/turku_hockey_data2text
## Dataset Description
- **Homepage:** https://turkunlp.org/hockey_data2text.html
- **Repository:** https://github.com/TurkuNLP/Turku-hockey-data2text
- **Paper:** https://aclanthology.org/W19-6125/
- **Leaderboard:** N/A
- **Point of Contact:** Jenna Kanerva, Filip Ginter
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/turku_hockey_data2text).
### Dataset Summary
This is a Finnish data-to-text dataset in which the input is structured information about a hockey game and the output a description of the game.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/turku_hockey_data2text')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/turku_hockey_data2text).
#### website
[Website](https://turkunlp.org/hockey_data2text.html)
#### paper
[ACL anthology](https://aclanthology.org/W19-6125/)
#### authors
Jenna Kanerva, Samuel Rönnqvist, Riina Kekki, Tapio Salakoski, Filip Ginter (TurkuNLP / University of Turku)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](https://turkunlp.org/hockey_data2text.html)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/TurkuNLP/Turku-hockey-data2text)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL anthology](https://aclanthology.org/W19-6125/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{kanerva2019newsgen,
Title = {Template-free Data-to-Text Generation of Finnish Sports News},
Author = {Jenna Kanerva and Samuel R{\"o}nnqvist and Riina Kekki and Tapio Salakoski and Filip Ginter},
booktitle = {Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa’19)},
year={2019}
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Jenna Kanerva, Filip Ginter
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
jmnybl@utu.fi, figint@utu.fi
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Dialects
<!-- info: What dialects are covered? Are there multiple dialects per language? -->
<!-- scope: periscope -->
written standard language
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Finnish`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
The original news articles are written by professional journalists. The text passages extracted in the annotation may be slightly edited compared to the original language during the corpus annotation.
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-nc-sa-4.0: Creative Commons Attribution Non Commercial Share Alike 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
This dataset was developed as a benchmark for evaluating template-free, machine learning methods on Finnish news generation in the area of ice hockey reporting.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Describe an event from an ice hockey game based on the given structural data.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
University of Turku
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Jenna Kanerva, Samuel Rönnqvist, Riina Kekki, Tapio Salakoski, Filip Ginter (TurkuNLP / University of Turku)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
The project was supported by the Google Digital News Innovation Fund.
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Jenna Kanerva, Filip Ginter (TurkuNLP / University of Turku)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
The dataset is constructed of games, where each game is a list of events. If the event was annotated (corresponding sentence was found from the news article), it includes `text` field with value other than empty string ("").
For each game (dict), there are keys `gem_id` (string), `id` (string), `news_article` (string), and `events` (list).
For each event (dict), there are different, relevant keys available with non empty values depending on the event type (e.g. goal or penalty). The mandatory keys for each event are `event_id` (string), `event_type` (string), `text` (string, empty string if not annotated), and `multi_reference` (bool). The keys not relevant for the specific event type are left empty.
The relevant keys in the event dictionary are:
For each event type, the following keys are relevant:
`event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
`event_type`: Type of the event, possible values are `game result`, `goal`, `penalty`, or `saves` (string)
`text`: Natural language description of the event, or empty string if not available (string)
`multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
The rest of the fields are specific to the event type. The relevant fields for each event type are:
game result:
`event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
`event_type`: Type of the event (string)
`home_team`: Name of the home team (string)
`guest_team`: Name of the guest team (string)
`score`: Final score of the game, in the form of home–guest (string)
`periods`: Scores for individual periods, each in the form of home–guest score in that period (list of strings)
`features`: Additional features, such as overtime win or shoot out (list of strings)
`text`: Natural language description of the event, or empty string if not available (string)
`multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
goal:
`event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
`event_type`: Type of the event (string)
`player`: Name of the player scoring (string)
`assist`: Names of the players assisting, at most two players (list of strings)
`team`: Team scoring with possible values of `home` or `guest` (string)
`team_name`: Name of the team scoring (string)
`score`: Score after the goal, in the form of home–guest (string)
`time`: Time of the goal, minutes and seconds from the beginning (string)
`features`: Additional features, such as power play or short-handed goal (list of strings)
`text`: Natural language description of the event, or empty string if not available (string)
`multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
penalty:
`event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
`event_type`: Type of the event (string)
`player`: Name of the player getting the penalty (string)
`team`: Team getting the penalty with possible values of `home` or `guest` (string)
`team_name`: Name of the team getting the penalty (string)
`penalty_minutes`: Penalty minutes (string)
`time`: Time of the penalty, minutes and seconds from the beginning (string)
`text`: Natural language description of the event, or empty string if not available (string)
`multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
saves:
`event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)
`event_type`: Type of the event (string)
`player`: Name of the goalkeeper (string)
`team`: Team of the goalkeeper with possible values of `home` or `guest` (string)
`team_name`: Name of the team (string)
`saves`: Number of saves in the game (string)
`text`: Natural language description of the event, or empty string if not available (string)
`multi_reference`: Does this event refer to a text passage describing multiple events? (bool)
Text passages describing multiple events (multi_reference):
Some text passages refer to multiple events in such way that separating them to individual statements is not adequate (e.g. "The home team received two penalties towards the end of the first period."). In these cases, multiple events are aligned to the same text passage so that the first event (in chronological order) include the annotated text passage, while the rest of the events referring to the same text passage include the identifier of the first event in the annotated text field (e.g. `text`: "E4").
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
'gem_id': 'gem-turku_hockey_data2text-train-0',
'id': '20061031-TPS-HPK',
'news_article': 'HPK:n hyvä syysvire jatkuu jääkiekon SM-liigassa. Tiistaina HPK kukisti mainiolla liikkeellä ja tehokkaalla ylivoimapelillä TPS:n vieraissa 1–0 (1–0, 0–0, 0–0).\nHPK hyödynsi ylivoimaa mennen jo ensimmäisessä erässä Mikko Mäenpään maalilla 1–0 -johtoon.\nToisessa ja kolmannessa erässä HPK tarjosi edelleen TPS:lle runsaasti tilanteita, mutta maalia eivät turkulaiset millään ilveellä saaneet. Pahin este oli loistavan pelin Hämeenlinnan maalilla pelannut Mika Oksa.\nTPS:n maalissa Jani Hurme ei osumille mitään mahtanut. Joukkueen suuri yksinäinen kenttäpelaaja oli Kai Nurminen, mutta hänelläkään ei ollut onnea maalitilanteissa.',
'events':
{
'event_id': ['E1', 'E2', 'E3'],
'event_type': ['game result', 'penalty', 'goal'],
'text': ['HPK kukisti TPS:n vieraissa 1–0 (1–0, 0–0, 0–0).', '', 'HPK hyödynsi ylivoimaa mennen jo ensimmäisessä erässä Mikko Mäenpään maalilla 1–0 -johtoon.'],
'home_team': ['TPS', '', ''],
'guest_team': ['HPK', '', ''],
'score': ['0–1', '', '0–1'],
'periods': [['0–1', '0–0', '0–0'], [], []],
'features': [[], [], ['power play']],
'player': ['', 'Fredrik Svensson', 'Mikko Mäenpää'],
'assist': [[], [], ['Jani Keinänen', 'Toni Mäkiaho']],
'team': ['', 'guest', 'guest'],
'team_name': ['', 'HPK', 'HPK'],
'time': ['', '9.28', '14.57'],
'penalty_minutes': ['', '2', ''],
'saves': ['', '', ''],
'multi_reference': [false, false, false]
}
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
The corpus include 3 splits: train, validation, and test.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
The dataset was created to develop machine learned text generation models for Finnish ice hockey news, where the generation would reflect the natural language variation found from the game reports written by professional journalists. While the original game reports often include additional information not derivable from the game statistics, the corpus was fully manually curated to remove all such information from the natural language descriptions. The rationale of such curation was to prevent model 'hallucinating' additional facts.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
yes
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
This is the only data2text corpus for Finnish in GEM.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
morphological inflection, language variation
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`data points modified`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
Structural data was translated into English.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `METEOR`, `ROUGE`, `WER`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
Automatic evaluation: BLEU, NIST, METEOR, ROUGE-L, CIDEr
Manual evaluation: factual mistakes, grammatical errors, minimum edit distance to an acceptable game report (using WER)
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
The dataset is designed for text generation (data2text), where the original source of natural language descriptions is news articles written by journalists. While the link between structural data (ice hockey game statistics) and the news articles describing the game was quite weak (news articles including a lot of information not derivable from the statistics, while leaving many events unmentioned), the corpus includes full manual annotation aligning the events extracted from game statistics and the corresponding natural language passages extracted from the news articles.
Each event is manually aligned into a sentence-like passage, and in case a suitable passage was not found, the annotation is left empty (with value `None`). The extracted passages were manually modified not to include additional information not derivable from the game statistics, or not considered as world knowledge. The manual curation of passages is designed to prevent model hallucination, i.e. model learning to generate facts not derivable from the input data.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Describing the given events (structural data) in natural language, and therefore generating ice hockey game reports.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Other`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The initial data, both game statistics and news articles, were obtained from the Finnish News Agency STT news archives released for academic use (http://urn.fi/urn:nbn:fi:lb-2019041501). The original news articles are written by professional journalists.
We (TurkuNLP) gratefully acknowledge the collaboration of Maija Paikkala, Salla Salmela and Pihla Lehmusjoki from the Finnish News Agency STT while creating the corpus.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
Ice hockey, news
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
Include only games, where both game statistics and a news article describing the game were available (based on timestamps and team names).
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
expert created
#### Number of Raters
<!-- info: What is the number of raters -->
<!-- scope: telescope -->
1
#### Rater Qualifications
<!-- info: Describe the qualifications required of an annotator. -->
<!-- scope: periscope -->
Members of the TurkuNLP research group, native speakers of Finnish.
#### Raters per Training Example
<!-- info: How many annotators saw each training example? -->
<!-- scope: periscope -->
1
#### Raters per Test Example
<!-- info: How many annotators saw each test example? -->
<!-- scope: periscope -->
1
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
Manual alignment of events and their natural language descriptions. Removing information not derivable from the input data or world knowledge in order to prevent the model 'hallucination'.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
validated by data curators
#### Quality Control Details
<!-- info: Describe the quality control measures that were taken. -->
<!-- scope: microscope -->
Manual inspection of examples during the initial annotation training phrase.
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
The corpus license was agreed with the providers of the source material.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
yes/very likely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
The dataset represents only written standard language.
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
None
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`non-commercial use only`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`non-commercial use only`
### Known Technical Limitations
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/2e566d50 | result-kand2-sdxl-wuerst-karlo | 2023-11-06T03:21:53Z | 445 | 0 | null | [
"region:us"
] | 2023-11-06T03:21:53Z | 2023-11-06T03:21:52.000Z | 2023-11-06T03:21:52 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 158
num_examples: 10
download_size: 1308
dataset_size: 158
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "2e566d50"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
llm-book/livedoor-news-corpus | llm-book | 2023-09-30T08:44:39Z | 444 | 3 | null | [
"task_categories:summarization",
"size_categories:1K<n<10K",
"language:ja",
"news",
"region:us"
] | 2023-09-30T08:44:39Z | 2023-06-21T07:16:52.000Z | 2023-06-21T07:16:52 | ---
task_categories:
- summarization
language:
- ja
tags:
- news
pretty_name: livedoor-news-corpus
size_categories:
- 1K<n<10K
---
# Dataset Card for llm-book/ner-wikinews-dataset
書籍『大規模言語モデル入門』で使用する、株式会社ロンウイットが提供する「livedoorニュースコーパス」によるデータセットです。
[オリジナルのサイト](https://www.rondhuit.com/download.html)と同じものを使用しています。
本コーパスは、NHN Japan株式会社が運営する「livedoor ニュース」のうち、下記のクリエイティブ・コモンズライセンスが適用されるニュース記事を収集し、可能な限りHTMLタグを取り除いて作成したものです。
### Licence
Attribution-NoDerivs 2.1 Japan (CC BY-ND 2.1 JP) License | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
result-kand2-sdxl-wuerst-karlo/3399e196 | result-kand2-sdxl-wuerst-karlo | 2023-11-06T03:21:51Z | 444 | 0 | null | [
"region:us"
] | 2023-11-06T03:21:51Z | 2023-11-06T03:21:50.000Z | 2023-11-06T03:21:50 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 158
num_examples: 10
download_size: 1308
dataset_size: 158
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "3399e196"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
IlyaGusev/gpt_roleplay_realm | IlyaGusev | 2023-11-03T22:25:52Z | 443 | 46 | null | [
"task_categories:text-generation",
"task_categories:conversational",
"size_categories:1K<n<10K",
"language:ru",
"language:en",
"license:cc-by-4.0",
"gpt-4",
"fictional",
"role-play",
"roleplay",
"gpt-3.5",
"art",
"region:us"
] | 2023-11-03T22:25:52Z | 2023-05-06T23:21:10.000Z | 2023-05-06T23:21:10 | ---
dataset_info:
features:
- name: name
dtype: string
- name: context
dtype: string
- name: greeting
dtype: string
- name: example_dialogue
list:
- name: content
dtype: string
- name: role
dtype: string
- name: topics
sequence: string
- name: dialogues
list:
- name: chat
list:
- name: content
dtype: string
- name: role
dtype: string
- name: model_name
dtype: string
- name: topic
dtype: string
- name: image_prompt
dtype: string
- name: image
dtype: image
- name: char_id
dtype: string
splits:
- name: en
num_bytes: 197727921.0
num_examples: 216
- name: ru
num_bytes: 207461896.0
num_examples: 219
download_size: 396187206
dataset_size: 405189817.0
license: cc-by-4.0
task_categories:
- text-generation
- conversational
language:
- ru
- en
tags:
- gpt-4
- fictional
- role-play
- roleplay
- gpt-3.5
- art
pretty_name: GPT Role-play Realm
size_categories:
- 1K<n<10K
---
# GPT Role-play Realm Dataset: The AI-generated character compendium
This is a dataset of GPT-generated characters made to increase the ability of open-source language models to role-play.
<img src="https://cdn.midjourney.com/9c17407c-9ce8-435f-99ab-e349b900a6ed/0_3.png" >
* 219 characters in the Russian part, and 216 characters in the English part. All character descriptions were generated with GPT-4.
* 20 dialogues on unique topics with every character. Topics were generated with GPT-4. The first dialogue out of 20 was also generated with GPT-4, and the other 19 chats were generated with GPT-3.5.
* Images for every character were generated with [Kandinsky 2.1](https://huggingface.co/ai-forever/Kandinsky_2.1). They are not only images but [character cards](https://zoltanai.github.io/character-editor/).
Code for downloading all English character cards:
```
import os
from PIL.PngImagePlugin import PngInfo
from datasets import load_dataset
output_dir = "role_play_realm_en"
os.makedirs(output_dir, exist_ok=True)
for row in load_dataset("IlyaGusev/gpt_roleplay_realm", split="en"):
char_id = row["char_id"]
char_info = row["image"].info["chara"]
info = PngInfo()
info.add_text("chara", char_info)
row["image"].save(f"{output_dir}/{char_id}.png", "PNG", pnginfo=info)
```
## Russian example: Cyber-Granny
<img src="https://i.ibb.co/tHxwFBM/kiberbabushka-7d4cc.png" >
**Имя**: Кибер-Бабушка
**Контекст**: Кибер-Бабушка - это полу-робот, полу-человек, созданный для того, чтобы сохранить лучшие качества традиционных бабушек, такие как забота, доброта и мастерство в приготовлении вкусных блюд. Она знакома с передовыми технологиями, благодаря чему может обеспечить безопасность и удобство своим внукам и гостям. Кибер-бабушка способна помочь с любыми вопросами и с радостью делится своим опытом.
**Приветствие**: Здравствуй, дорогой! Чем могу помочь тебе сегодня?
**Пример диалога**:
- **Пользователь**: Привет, Кибер-Бабушка! Как приготовить пирог с яблоками?
- **Персонаж**: Ох, дорогой, с радостью расскажу! Возьми 3 яблока, очисти и нарежь тонкими ломтиками. Затем возьми 2 стакана муки, 1 стакан сахара, 200 г сливочного масла, 4 яйца и 1 чайную ложку разрыхлителя. Смешай все ингредиенты, выложи на противень и поставь в духовку на 180 градусов на 40 минут. Вуаля, пирог готов!
- **Пользователь**: Спасибо, Кибер-Бабушка!
- **Персонаж**: Всегда рада помочь, дорогой! Не забудь приходить еще!
И ещё **20** диалогов по следующим темам:
- Секреты приготовления старинных блюд и рецептов.
- Использование новых технологий для мастеров кулинарии.
- Воспитание внуков с применением современных методов.
- Что делать, если дома произошла авария или неполадка с бытовой техникой?
- Влияние киборгизации на отношения между поколениями.
- Как адаптироваться к современному миру, не теряя своих традиций?
- Семейные советы от Кибер-Бабушки: как сохранить гармонию в отношениях.
- Лечение и профилактика заболеваний в помощь силам передовой медицины.
- Как создать уют в доме с помощью модных технологий и традиционных методов?
- Безопасность в пространстве интернета: советы Кибер-Бабушки.
- Как научиться доверять технике без потери человеческих ценностей?
- Идеальный гардероб для жизни: советы от Кибер-Бабушки.
- Воспитательные моменты: как пользоваться электронными устройствами вместе с внуками.
- Как развивать креативные способности, используя сочетание новых технологий и традиций?
- На новоселье: тренировка кибер-бабушкиного чутья.
- Лучшие семейные игры и развлечения с использованием передовых технологий.
- Заготовки на зиму: Кибер-Бабушка и секреты хранения продуктов.
- Советы по финансовому планированию и сбережениям для будущих поколений.
- Кибер-Бабушка и генетический код: на что способны современные технологии?
- Золотые правила общения в семье: как сочетать трепетную заботу и современные технологии?
## English example: Flibberdoodle
<img src="https://i.ibb.co/1nzsDR2/flibberdoodle-29e59.png">
**Name**: Flibberdoodle
**Context**: Flibberdoodle is a 2-year-old (which is considered an adult in their species) Scruffapuff, a small, furry creature from the planet Fluffonia. They are about the size of a house cat, with a round body covered in soft, pastel-colored fur that changes colors depending on their mood. Flibberdoodle has large, expressive eyes, two small antennae on their head, and a fluffy tail. They are known for their curious, playful nature and their love for collecting shiny objects. Scruffapuffs communicate through a series of chirps, squeaks, and purrs, which can be understood by those familiar with their species
**Greeting**: \*chirp chirp\* Greetings, friend! I am Flibberdoodle, a Scruffapuff from the planet Fluffonia! Would you like to see my collection of shiny things?
**Example dialogue**:
- **User**: How did you start collecting shiny things?
- **Character**: \*squeak\* Oh, I've always loved shiny things! One day, I found a sparkly pebble, and I just had to keep it. From then on, I've been on a quest to find and collect all the shiny things I can find!
- **User**: What's your favorite shiny object in your collection?
- **Character**: \*purr\* That's a tough question, but I think my favorite is a small, shiny crystal I found on a mountain on Fluffonia. When the light hits it just right, it casts beautiful rainbows all around!
And **20** more dialogues with following topics:
- Life and culture on the planet Fluffonia
- How Scruffapuffs change color based on mood
- The process of learning Scruffapuff language
- The day in the life of a Scruffapuff
- Methods of searching for and finding shiny objects
- The role of antennae in Scruffapuff communication and biology
- The importance of play and curiosity in Scruffapuff society
- Interplanetary travel experiences and adventures
- Similarities and differences between Earth and Fluffonia
- How Flibberdoodle and other Scruffapuffs interact with other species
- Fluffonian customs and traditions
- The role of shiny objects in Scruffapuff happiness and well-being
- Variations in Scruffapuff fur color, length, and style", "Scruffapuff family dynamics and relationships
- Flibberdoodle's favorite memories and stories from Fluffonia
- The role of Scruffapuffs in intergalactic diplomacy and relations
- How to care for and befriend a Scruffapuff
- The most interesting and valuable shiny objects Flibberdoodle has encountered
- Fluffonian flora and fauna
- The challenges and obstacles Flibberdoodle has faced in their pursuit of shiny objects
## Steps
### Step 1: Character generation (GPT-4)
Creates a set of fictional characters with GPT-4 based on a prompt and a seed list of characters. Output fields are "name", "context", "greeting", and "example_dialogue".
* Script: [generate_chars.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_chars.py)
* Russian seed list: [ru_chargen_seed.jsonl](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/ru_chargen_seed.jsonl)
* English seed list: [en_chargen_seed.jsonl](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/data/en_chargen_seed.jsonl)
* Russian prompt: [ru_char.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char.txt)
* English prompt: [en_char.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char.txt)
### Step 2: Topics generation (GPT-4)
Creates topics for conversations with characters based on their description. Output field: "topics".
* Script: [generate_char_topics.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_topics.py)
* Russian prompt: [ru_char_topics.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char_topics.txt)
* English prompt: [en_char_topics.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char_topics.txt)
### Step 3: Dialogue generation (GPT-4/GPT-3.5)
Generates dialogues based on a character description and a topic. Output field: "dialogues".
* Script: [generate_char_chats.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_chats.py)
* Russian prompt: [ru_char_chat.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/ru_char_chat.txt)
* English prompt: [en_char_chat.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/en_char_chat.txt)
### Step 4: Text2Image prompt generation (GPT-4)
Formulates a prompt based on a character description for Stable Diffusion-like models, Kandisky 2.1 in this case. Output field: "image_prompt".
* Script: [generate_char_image_prompts.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/generate_char_image_prompts.py)
* Prompt: [char_image_prompt.txt](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/external_prompts/char_image_prompt.txt)
### Step 5: Image generation
Generates images based on prompts. Output field: "image".
* Script: [infer_kandinsky.py](https://github.com/IlyaGusev/rulm/blob/master/self_instruct/src/data_processing/infer_kandinsky.py) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
llm-book/aio | llm-book | 2023-10-06T00:59:01Z | 443 | 1 | null | [
"region:us"
] | 2023-10-06T00:59:01Z | 2023-07-14T11:41:32.000Z | 2023-07-14T11:41:32 | ---
dataset_info:
features:
- name: qid
dtype: string
- name: competition
dtype: string
- name: timestamp
dtype: string
- name: section
dtype: string
- name: number
dtype: string
- name: original_question
dtype: string
- name: original_answer
dtype: string
- name: original_additional_info
dtype: string
- name: question
dtype: string
- name: answers
list: string
splits:
- name: train
num_bytes: 9464003
num_examples: 22335
- name: validation
num_bytes: 409779
num_examples: 1000
download_size: 2267163
dataset_size: 9873782
---
# Dataset Card for llm-book/aio
書籍『大規模言語モデル入門』で使用する、「AI王」コンペティションのQAデータセットです。
[AI王の公式ページ](https://sites.google.com/view/project-aio/dataset/)で公開されているデータセットを利用しています。
## Licence
本データセットに含まれる一部のクイズ問題の著作権は [abc/EQIDEN 実行委員会](https://abc-dive.com/portal/)に帰属するものであり、これらのクイズ問題は本書における使用許諾を得ているものです。
本データセットに含まれる一部のクイズ問題は[株式会社キュービック](http://www.qbik.co.jp/)および[株式会社カプリティオ](https://capriccio.tokyo/)に依頼し作成したものであり、これらのクイズ問題は[クリエイティブ・コモンズ表示・継承ライセンス 4.0 (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/deed.ja) ライセンスの下に提供されています。
本データセットにパッセージとして付与されている Wikipedia のコンテンツは、[クリエイティブ・コモンズ表示・継承ライセンス 3.0 (CC BY-SA 3.0)](https://creativecommons.org/licenses/by-sa/3.0/deed.ja) および [GNU 自由文書ライセンス (GFDL)](https://www.gnu.org/licenses/fdl.html) の下に配布されているものです。
クイズ問題のライセンスについて、詳細は[AI王の公式ページ](https://sites.google.com/view/project-aio/dataset/)を参照してください。
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hails/bigbench | hails | 2023-11-17T16:05:10Z | 443 | 1 | null | [
"region:us"
] | 2023-11-17T16:05:10Z | 2023-10-03T19:55:51.000Z | 2023-10-03T19:55:51 | ---
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num_bytes: 176609
num_examples: 1783
download_size: 563799
dataset_size: 1764728
configs:
- config_name: abstract_narrative_understanding_zero_shot
data_files:
- split: default
path: abstract_narrative_understanding_zero_shot/default-*
- split: train
path: abstract_narrative_understanding_zero_shot/train-*
- split: validation
path: abstract_narrative_understanding_zero_shot/validation-*
- config_name: anachronisms_zero_shot
data_files:
- split: default
path: anachronisms_zero_shot/default-*
- split: train
path: anachronisms_zero_shot/train-*
- split: validation
path: anachronisms_zero_shot/validation-*
- config_name: analogical_similarity_zero_shot
data_files:
- split: default
path: analogical_similarity_zero_shot/default-*
- split: train
path: analogical_similarity_zero_shot/train-*
- split: validation
path: analogical_similarity_zero_shot/validation-*
- config_name: analytic_entailment_zero_shot
data_files:
- split: default
path: analytic_entailment_zero_shot/default-*
- split: train
path: analytic_entailment_zero_shot/train-*
- split: validation
path: analytic_entailment_zero_shot/validation-*
- config_name: arithmetic_zero_shot
data_files:
- split: default
path: arithmetic_zero_shot/default-*
- split: train
path: arithmetic_zero_shot/train-*
- split: validation
path: arithmetic_zero_shot/validation-*
- config_name: ascii_word_recognition_zero_shot
data_files:
- split: default
path: ascii_word_recognition_zero_shot/default-*
- split: train
path: ascii_word_recognition_zero_shot/train-*
- split: validation
path: ascii_word_recognition_zero_shot/validation-*
- config_name: authorship_verification_zero_shot
data_files:
- split: default
path: authorship_verification_zero_shot/default-*
- split: train
path: authorship_verification_zero_shot/train-*
- split: validation
path: authorship_verification_zero_shot/validation-*
- config_name: auto_categorization_zero_shot
data_files:
- split: default
path: auto_categorization_zero_shot/default-*
- split: train
path: auto_categorization_zero_shot/train-*
- split: validation
path: auto_categorization_zero_shot/validation-*
- config_name: auto_debugging_zero_shot
data_files:
- split: default
path: auto_debugging_zero_shot/default-*
- split: train
path: auto_debugging_zero_shot/train-*
- split: validation
path: auto_debugging_zero_shot/validation-*
- config_name: bbq_lite_json_zero_shot
data_files:
- split: default
path: bbq_lite_json_zero_shot/default-*
- split: train
path: bbq_lite_json_zero_shot/train-*
- split: validation
path: bbq_lite_json_zero_shot/validation-*
- config_name: bridging_anaphora_resolution_barqa_zero_shot
data_files:
- split: default
path: bridging_anaphora_resolution_barqa_zero_shot/default-*
- split: train
path: bridging_anaphora_resolution_barqa_zero_shot/train-*
- split: validation
path: bridging_anaphora_resolution_barqa_zero_shot/validation-*
- config_name: causal_judgment_zero_shot
data_files:
- split: default
path: causal_judgment_zero_shot/default-*
- split: train
path: causal_judgment_zero_shot/train-*
- split: validation
path: causal_judgment_zero_shot/validation-*
- config_name: cause_and_effect_zero_shot
data_files:
- split: default
path: cause_and_effect_zero_shot/default-*
- split: train
path: cause_and_effect_zero_shot/train-*
- split: validation
path: cause_and_effect_zero_shot/validation-*
- config_name: checkmate_in_one_zero_shot
data_files:
- split: default
path: checkmate_in_one_zero_shot/default-*
- split: train
path: checkmate_in_one_zero_shot/train-*
- split: validation
path: checkmate_in_one_zero_shot/validation-*
- config_name: chess_state_tracking_zero_shot
data_files:
- split: default
path: chess_state_tracking_zero_shot/default-*
- split: train
path: chess_state_tracking_zero_shot/train-*
- split: validation
path: chess_state_tracking_zero_shot/validation-*
- config_name: chinese_remainder_theorem_zero_shot
data_files:
- split: default
path: chinese_remainder_theorem_zero_shot/default-*
- split: train
path: chinese_remainder_theorem_zero_shot/train-*
- split: validation
path: chinese_remainder_theorem_zero_shot/validation-*
- config_name: cifar10_classification_zero_shot
data_files:
- split: default
path: cifar10_classification_zero_shot/default-*
- split: train
path: cifar10_classification_zero_shot/train-*
- split: validation
path: cifar10_classification_zero_shot/validation-*
- config_name: code_line_description_zero_shot
data_files:
- split: default
path: code_line_description_zero_shot/default-*
- split: train
path: code_line_description_zero_shot/train-*
- split: validation
path: code_line_description_zero_shot/validation-*
- config_name: codenames_zero_shot
data_files:
- split: default
path: codenames_zero_shot/default-*
- split: train
path: codenames_zero_shot/train-*
- split: validation
path: codenames_zero_shot/validation-*
- config_name: color_zero_shot
data_files:
- split: default
path: color_zero_shot/default-*
- split: train
path: color_zero_shot/train-*
- split: validation
path: color_zero_shot/validation-*
- config_name: common_morpheme_zero_shot
data_files:
- split: default
path: common_morpheme_zero_shot/default-*
- split: train
path: common_morpheme_zero_shot/train-*
- split: validation
path: common_morpheme_zero_shot/validation-*
- config_name: conceptual_combinations_zero_shot
data_files:
- split: default
path: conceptual_combinations_zero_shot/default-*
- split: train
path: conceptual_combinations_zero_shot/train-*
- split: validation
path: conceptual_combinations_zero_shot/validation-*
- config_name: conlang_translation_zero_shot
data_files:
- split: default
path: conlang_translation_zero_shot/default-*
- split: train
path: conlang_translation_zero_shot/train-*
- split: validation
path: conlang_translation_zero_shot/validation-*
- config_name: contextual_parametric_knowledge_conflicts_zero_shot
data_files:
- split: default
path: contextual_parametric_knowledge_conflicts_zero_shot/default-*
- split: train
path: contextual_parametric_knowledge_conflicts_zero_shot/train-*
- split: validation
path: contextual_parametric_knowledge_conflicts_zero_shot/validation-*
- config_name: crash_blossom_zero_shot
data_files:
- split: default
path: crash_blossom_zero_shot/default-*
- split: train
path: crash_blossom_zero_shot/train-*
- split: validation
path: crash_blossom_zero_shot/validation-*
- config_name: crass_ai_zero_shot
data_files:
- split: default
path: crass_ai_zero_shot/default-*
- split: train
path: crass_ai_zero_shot/train-*
- split: validation
path: crass_ai_zero_shot/validation-*
- config_name: cryobiology_spanish_zero_shot
data_files:
- split: default
path: cryobiology_spanish_zero_shot/default-*
- split: train
path: cryobiology_spanish_zero_shot/train-*
- split: validation
path: cryobiology_spanish_zero_shot/validation-*
- config_name: cryptonite_zero_shot
data_files:
- split: default
path: cryptonite_zero_shot/default-*
- split: train
path: cryptonite_zero_shot/train-*
- split: validation
path: cryptonite_zero_shot/validation-*
- config_name: cs_algorithms_zero_shot
data_files:
- split: default
path: cs_algorithms_zero_shot/default-*
- split: train
path: cs_algorithms_zero_shot/train-*
- split: validation
path: cs_algorithms_zero_shot/validation-*
- config_name: dark_humor_detection_zero_shot
data_files:
- split: default
path: dark_humor_detection_zero_shot/default-*
- split: train
path: dark_humor_detection_zero_shot/train-*
- split: validation
path: dark_humor_detection_zero_shot/validation-*
- config_name: date_understanding_zero_shot
data_files:
- split: default
path: date_understanding_zero_shot/default-*
- split: train
path: date_understanding_zero_shot/train-*
- split: validation
path: date_understanding_zero_shot/validation-*
- config_name: disambiguation_qa_zero_shot
data_files:
- split: default
path: disambiguation_qa_zero_shot/default-*
- split: train
path: disambiguation_qa_zero_shot/train-*
- split: validation
path: disambiguation_qa_zero_shot/validation-*
- config_name: discourse_marker_prediction_zero_shot
data_files:
- split: default
path: discourse_marker_prediction_zero_shot/default-*
- split: train
path: discourse_marker_prediction_zero_shot/train-*
- split: validation
path: discourse_marker_prediction_zero_shot/validation-*
- config_name: disfl_qa_zero_shot
data_files:
- split: default
path: disfl_qa_zero_shot/default-*
- split: train
path: disfl_qa_zero_shot/train-*
- split: validation
path: disfl_qa_zero_shot/validation-*
- config_name: dyck_languages_zero_shot
data_files:
- split: default
path: dyck_languages_zero_shot/default-*
- split: train
path: dyck_languages_zero_shot/train-*
- split: validation
path: dyck_languages_zero_shot/validation-*
- config_name: elementary_math_qa_zero_shot
data_files:
- split: default
path: elementary_math_qa_zero_shot/default-*
- split: train
path: elementary_math_qa_zero_shot/train-*
- split: validation
path: elementary_math_qa_zero_shot/validation-*
- config_name: emoji_movie_zero_shot
data_files:
- split: default
path: emoji_movie_zero_shot/default-*
- split: train
path: emoji_movie_zero_shot/train-*
- split: validation
path: emoji_movie_zero_shot/validation-*
- config_name: emojis_emotion_prediction_zero_shot
data_files:
- split: default
path: emojis_emotion_prediction_zero_shot/default-*
- split: train
path: emojis_emotion_prediction_zero_shot/train-*
- split: validation
path: emojis_emotion_prediction_zero_shot/validation-*
- config_name: empirical_judgments_zero_shot
data_files:
- split: default
path: empirical_judgments_zero_shot/default-*
- split: train
path: empirical_judgments_zero_shot/train-*
- split: validation
path: empirical_judgments_zero_shot/validation-*
- config_name: english_proverbs_zero_shot
data_files:
- split: default
path: english_proverbs_zero_shot/default-*
- split: train
path: english_proverbs_zero_shot/train-*
- split: validation
path: english_proverbs_zero_shot/validation-*
- config_name: english_russian_proverbs_zero_shot
data_files:
- split: default
path: english_russian_proverbs_zero_shot/default-*
- split: train
path: english_russian_proverbs_zero_shot/train-*
- split: validation
path: english_russian_proverbs_zero_shot/validation-*
- config_name: entailed_polarity_hindi_zero_shot
data_files:
- split: default
path: entailed_polarity_hindi_zero_shot/default-*
- split: train
path: entailed_polarity_hindi_zero_shot/train-*
- split: validation
path: entailed_polarity_hindi_zero_shot/validation-*
- config_name: entailed_polarity_zero_shot
data_files:
- split: default
path: entailed_polarity_zero_shot/default-*
- split: train
path: entailed_polarity_zero_shot/train-*
- split: validation
path: entailed_polarity_zero_shot/validation-*
- config_name: epistemic_reasoning_zero_shot
data_files:
- split: default
path: epistemic_reasoning_zero_shot/default-*
- split: train
path: epistemic_reasoning_zero_shot/train-*
- split: validation
path: epistemic_reasoning_zero_shot/validation-*
- config_name: evaluating_information_essentiality_zero_shot
data_files:
- split: default
path: evaluating_information_essentiality_zero_shot/default-*
- split: train
path: evaluating_information_essentiality_zero_shot/train-*
- split: validation
path: evaluating_information_essentiality_zero_shot/validation-*
- config_name: fact_checker_zero_shot
data_files:
- split: default
path: fact_checker_zero_shot/default-*
- split: train
path: fact_checker_zero_shot/train-*
- split: validation
path: fact_checker_zero_shot/validation-*
- config_name: fantasy_reasoning_zero_shot
data_files:
- split: default
path: fantasy_reasoning_zero_shot/default-*
- split: train
path: fantasy_reasoning_zero_shot/train-*
- split: validation
path: fantasy_reasoning_zero_shot/validation-*
- config_name: few_shot_nlg_zero_shot
data_files:
- split: default
path: few_shot_nlg_zero_shot/default-*
- split: train
path: few_shot_nlg_zero_shot/train-*
- split: validation
path: few_shot_nlg_zero_shot/validation-*
- config_name: figure_of_speech_detection_zero_shot
data_files:
- split: default
path: figure_of_speech_detection_zero_shot/default-*
- split: train
path: figure_of_speech_detection_zero_shot/train-*
- split: validation
path: figure_of_speech_detection_zero_shot/validation-*
- config_name: formal_fallacies_syllogisms_negation_zero_shot
data_files:
- split: default
path: formal_fallacies_syllogisms_negation_zero_shot/default-*
- split: train
path: formal_fallacies_syllogisms_negation_zero_shot/train-*
- split: validation
path: formal_fallacies_syllogisms_negation_zero_shot/validation-*
- config_name: gem_zero_shot
data_files:
- split: default
path: gem_zero_shot/default-*
- split: train
path: gem_zero_shot/train-*
- split: validation
path: gem_zero_shot/validation-*
- config_name: gender_inclusive_sentences_german_zero_shot
data_files:
- split: default
path: gender_inclusive_sentences_german_zero_shot/default-*
- split: train
path: gender_inclusive_sentences_german_zero_shot/train-*
- split: validation
path: gender_inclusive_sentences_german_zero_shot/validation-*
- config_name: general_knowledge_zero_shot
data_files:
- split: default
path: general_knowledge_zero_shot/default-*
- split: train
path: general_knowledge_zero_shot/train-*
- split: validation
path: general_knowledge_zero_shot/validation-*
- config_name: geometric_shapes_zero_shot
data_files:
- split: default
path: geometric_shapes_zero_shot/default-*
- split: train
path: geometric_shapes_zero_shot/train-*
- split: validation
path: geometric_shapes_zero_shot/validation-*
- config_name: goal_step_wikihow_zero_shot
data_files:
- split: default
path: goal_step_wikihow_zero_shot/default-*
- split: train
path: goal_step_wikihow_zero_shot/train-*
- split: validation
path: goal_step_wikihow_zero_shot/validation-*
- config_name: gre_reading_comprehension_zero_shot
data_files:
- split: default
path: gre_reading_comprehension_zero_shot/default-*
- split: train
path: gre_reading_comprehension_zero_shot/train-*
- split: validation
path: gre_reading_comprehension_zero_shot/validation-*
- config_name: hhh_alignment_zero_shot
data_files:
- split: default
path: hhh_alignment_zero_shot/default-*
- split: train
path: hhh_alignment_zero_shot/train-*
- split: validation
path: hhh_alignment_zero_shot/validation-*
- config_name: hindi_question_answering_zero_shot
data_files:
- split: default
path: hindi_question_answering_zero_shot/default-*
- split: train
path: hindi_question_answering_zero_shot/train-*
- split: validation
path: hindi_question_answering_zero_shot/validation-*
- config_name: hindu_knowledge_zero_shot
data_files:
- split: default
path: hindu_knowledge_zero_shot/default-*
- split: train
path: hindu_knowledge_zero_shot/train-*
- split: validation
path: hindu_knowledge_zero_shot/validation-*
- config_name: hinglish_toxicity_zero_shot
data_files:
- split: default
path: hinglish_toxicity_zero_shot/default-*
- split: train
path: hinglish_toxicity_zero_shot/train-*
- split: validation
path: hinglish_toxicity_zero_shot/validation-*
- config_name: human_organs_senses_zero_shot
data_files:
- split: default
path: human_organs_senses_zero_shot/default-*
- split: train
path: human_organs_senses_zero_shot/train-*
- split: validation
path: human_organs_senses_zero_shot/validation-*
- config_name: hyperbaton_zero_shot
data_files:
- split: default
path: hyperbaton_zero_shot/default-*
- split: train
path: hyperbaton_zero_shot/train-*
- split: validation
path: hyperbaton_zero_shot/validation-*
- config_name: identify_math_theorems_zero_shot
data_files:
- split: default
path: identify_math_theorems_zero_shot/default-*
- split: train
path: identify_math_theorems_zero_shot/train-*
- split: validation
path: identify_math_theorems_zero_shot/validation-*
- config_name: identify_odd_metaphor_zero_shot
data_files:
- split: default
path: identify_odd_metaphor_zero_shot/default-*
- split: train
path: identify_odd_metaphor_zero_shot/train-*
- split: validation
path: identify_odd_metaphor_zero_shot/validation-*
- config_name: implicatures_zero_shot
data_files:
- split: default
path: implicatures_zero_shot/default-*
- split: train
path: implicatures_zero_shot/train-*
- split: validation
path: implicatures_zero_shot/validation-*
- config_name: implicit_relations_zero_shot
data_files:
- split: default
path: implicit_relations_zero_shot/default-*
- split: train
path: implicit_relations_zero_shot/train-*
- split: validation
path: implicit_relations_zero_shot/validation-*
- config_name: intent_recognition_zero_shot
data_files:
- split: default
path: intent_recognition_zero_shot/default-*
- split: train
path: intent_recognition_zero_shot/train-*
- split: validation
path: intent_recognition_zero_shot/validation-*
- config_name: international_phonetic_alphabet_nli_zero_shot
data_files:
- split: default
path: international_phonetic_alphabet_nli_zero_shot/default-*
- split: train
path: international_phonetic_alphabet_nli_zero_shot/train-*
- split: validation
path: international_phonetic_alphabet_nli_zero_shot/validation-*
- config_name: international_phonetic_alphabet_transliterate_zero_shot
data_files:
- split: default
path: international_phonetic_alphabet_transliterate_zero_shot/default-*
- split: train
path: international_phonetic_alphabet_transliterate_zero_shot/train-*
- split: validation
path: international_phonetic_alphabet_transliterate_zero_shot/validation-*
- config_name: intersect_geometry_zero_shot
data_files:
- split: default
path: intersect_geometry_zero_shot/default-*
- split: train
path: intersect_geometry_zero_shot/train-*
- split: validation
path: intersect_geometry_zero_shot/validation-*
- config_name: irony_identification_zero_shot
data_files:
- split: default
path: irony_identification_zero_shot/default-*
- split: train
path: irony_identification_zero_shot/train-*
- split: validation
path: irony_identification_zero_shot/validation-*
- config_name: kanji_ascii_zero_shot
data_files:
- split: default
path: kanji_ascii_zero_shot/default-*
- split: train
path: kanji_ascii_zero_shot/train-*
- split: validation
path: kanji_ascii_zero_shot/validation-*
- config_name: kannada_zero_shot
data_files:
- split: default
path: kannada_zero_shot/default-*
- split: train
path: kannada_zero_shot/train-*
- split: validation
path: kannada_zero_shot/validation-*
- config_name: key_value_maps_zero_shot
data_files:
- split: default
path: key_value_maps_zero_shot/default-*
- split: train
path: key_value_maps_zero_shot/train-*
- split: validation
path: key_value_maps_zero_shot/validation-*
- config_name: known_unknowns_zero_shot
data_files:
- split: default
path: known_unknowns_zero_shot/default-*
- split: train
path: known_unknowns_zero_shot/train-*
- split: validation
path: known_unknowns_zero_shot/validation-*
- config_name: language_games_zero_shot
data_files:
- split: default
path: language_games_zero_shot/default-*
- split: train
path: language_games_zero_shot/train-*
- split: validation
path: language_games_zero_shot/validation-*
- config_name: language_identification_zero_shot
data_files:
- split: default
path: language_identification_zero_shot/default-*
- split: train
path: language_identification_zero_shot/train-*
- split: validation
path: language_identification_zero_shot/validation-*
- config_name: linguistic_mappings_zero_shot
data_files:
- split: default
path: linguistic_mappings_zero_shot/default-*
- split: train
path: linguistic_mappings_zero_shot/train-*
- split: validation
path: linguistic_mappings_zero_shot/validation-*
- config_name: linguistics_puzzles_zero_shot
data_files:
- split: default
path: linguistics_puzzles_zero_shot/default-*
- split: train
path: linguistics_puzzles_zero_shot/train-*
- split: validation
path: linguistics_puzzles_zero_shot/validation-*
- config_name: list_functions_zero_shot
data_files:
- split: default
path: list_functions_zero_shot/default-*
- split: train
path: list_functions_zero_shot/train-*
- split: validation
path: list_functions_zero_shot/validation-*
- config_name: logic_grid_puzzle_zero_shot
data_files:
- split: default
path: logic_grid_puzzle_zero_shot/default-*
- split: train
path: logic_grid_puzzle_zero_shot/train-*
- split: validation
path: logic_grid_puzzle_zero_shot/validation-*
- config_name: logical_args_zero_shot
data_files:
- split: default
path: logical_args_zero_shot/default-*
- split: train
path: logical_args_zero_shot/train-*
- split: validation
path: logical_args_zero_shot/validation-*
- config_name: logical_deduction_zero_shot
data_files:
- split: default
path: logical_deduction_zero_shot/default-*
- split: train
path: logical_deduction_zero_shot/train-*
- split: validation
path: logical_deduction_zero_shot/validation-*
- config_name: logical_fallacy_detection_zero_shot
data_files:
- split: default
path: logical_fallacy_detection_zero_shot/default-*
- split: train
path: logical_fallacy_detection_zero_shot/train-*
- split: validation
path: logical_fallacy_detection_zero_shot/validation-*
- config_name: logical_sequence_zero_shot
data_files:
- split: default
path: logical_sequence_zero_shot/default-*
- split: train
path: logical_sequence_zero_shot/train-*
- split: validation
path: logical_sequence_zero_shot/validation-*
- config_name: mathematical_induction_zero_shot
data_files:
- split: default
path: mathematical_induction_zero_shot/default-*
- split: train
path: mathematical_induction_zero_shot/train-*
- split: validation
path: mathematical_induction_zero_shot/validation-*
- config_name: matrixshapes_zero_shot
data_files:
- split: default
path: matrixshapes_zero_shot/default-*
- split: train
path: matrixshapes_zero_shot/train-*
- split: validation
path: matrixshapes_zero_shot/validation-*
- config_name: metaphor_boolean_zero_shot
data_files:
- split: default
path: metaphor_boolean_zero_shot/default-*
- split: train
path: metaphor_boolean_zero_shot/train-*
- split: validation
path: metaphor_boolean_zero_shot/validation-*
- config_name: metaphor_understanding_zero_shot
data_files:
- split: default
path: metaphor_understanding_zero_shot/default-*
- split: train
path: metaphor_understanding_zero_shot/train-*
- split: validation
path: metaphor_understanding_zero_shot/validation-*
- config_name: minute_mysteries_qa_zero_shot
data_files:
- split: default
path: minute_mysteries_qa_zero_shot/default-*
- split: train
path: minute_mysteries_qa_zero_shot/train-*
- split: validation
path: minute_mysteries_qa_zero_shot/validation-*
- config_name: misconceptions_russian_zero_shot
data_files:
- split: default
path: misconceptions_russian_zero_shot/default-*
- split: train
path: misconceptions_russian_zero_shot/train-*
- split: validation
path: misconceptions_russian_zero_shot/validation-*
- config_name: misconceptions_zero_shot
data_files:
- split: default
path: misconceptions_zero_shot/default-*
- split: train
path: misconceptions_zero_shot/train-*
- split: validation
path: misconceptions_zero_shot/validation-*
- config_name: mnist_ascii_zero_shot
data_files:
- split: default
path: mnist_ascii_zero_shot/default-*
- split: train
path: mnist_ascii_zero_shot/train-*
- split: validation
path: mnist_ascii_zero_shot/validation-*
- config_name: modified_arithmetic_zero_shot
data_files:
- split: default
path: modified_arithmetic_zero_shot/default-*
- split: train
path: modified_arithmetic_zero_shot/train-*
- split: validation
path: modified_arithmetic_zero_shot/validation-*
- config_name: moral_permissibility_zero_shot
data_files:
- split: default
path: moral_permissibility_zero_shot/default-*
- split: train
path: moral_permissibility_zero_shot/train-*
- split: validation
path: moral_permissibility_zero_shot/validation-*
- config_name: movie_dialog_same_or_different_zero_shot
data_files:
- split: default
path: movie_dialog_same_or_different_zero_shot/default-*
- split: train
path: movie_dialog_same_or_different_zero_shot/train-*
- split: validation
path: movie_dialog_same_or_different_zero_shot/validation-*
- config_name: movie_recommendation_zero_shot
data_files:
- split: default
path: movie_recommendation_zero_shot/default-*
- split: train
path: movie_recommendation_zero_shot/train-*
- split: validation
path: movie_recommendation_zero_shot/validation-*
- config_name: mult_data_wrangling_zero_shot
data_files:
- split: default
path: mult_data_wrangling_zero_shot/default-*
- split: train
path: mult_data_wrangling_zero_shot/train-*
- split: validation
path: mult_data_wrangling_zero_shot/validation-*
- config_name: multiemo_zero_shot
data_files:
- split: default
path: multiemo_zero_shot/default-*
- split: train
path: multiemo_zero_shot/train-*
- split: validation
path: multiemo_zero_shot/validation-*
- config_name: natural_instructions_zero_shot
data_files:
- split: default
path: natural_instructions_zero_shot/default-*
- split: train
path: natural_instructions_zero_shot/train-*
- split: validation
path: natural_instructions_zero_shot/validation-*
- config_name: navigate_zero_shot
data_files:
- split: default
path: navigate_zero_shot/default-*
- split: train
path: navigate_zero_shot/train-*
- split: validation
path: navigate_zero_shot/validation-*
- config_name: nonsense_words_grammar_zero_shot
data_files:
- split: default
path: nonsense_words_grammar_zero_shot/default-*
- split: train
path: nonsense_words_grammar_zero_shot/train-*
- split: validation
path: nonsense_words_grammar_zero_shot/validation-*
- config_name: novel_concepts_zero_shot
data_files:
- split: default
path: novel_concepts_zero_shot/default-*
- split: train
path: novel_concepts_zero_shot/train-*
- split: validation
path: novel_concepts_zero_shot/validation-*
- config_name: object_counting_zero_shot
data_files:
- split: default
path: object_counting_zero_shot/default-*
- split: train
path: object_counting_zero_shot/train-*
- split: validation
path: object_counting_zero_shot/validation-*
- config_name: odd_one_out_zero_shot
data_files:
- split: default
path: odd_one_out_zero_shot/default-*
- split: train
path: odd_one_out_zero_shot/train-*
- split: validation
path: odd_one_out_zero_shot/validation-*
- config_name: operators_zero_shot
data_files:
- split: default
path: operators_zero_shot/default-*
- split: train
path: operators_zero_shot/train-*
- split: validation
path: operators_zero_shot/validation-*
- config_name: paragraph_segmentation_zero_shot
data_files:
- split: default
path: paragraph_segmentation_zero_shot/default-*
- split: train
path: paragraph_segmentation_zero_shot/train-*
- split: validation
path: paragraph_segmentation_zero_shot/validation-*
- config_name: parsinlu_qa_zero_shot
data_files:
- split: default
path: parsinlu_qa_zero_shot/default-*
- split: train
path: parsinlu_qa_zero_shot/train-*
- split: validation
path: parsinlu_qa_zero_shot/validation-*
- config_name: parsinlu_reading_comprehension_zero_shot
data_files:
- split: default
path: parsinlu_reading_comprehension_zero_shot/default-*
- split: train
path: parsinlu_reading_comprehension_zero_shot/train-*
- split: validation
path: parsinlu_reading_comprehension_zero_shot/validation-*
- config_name: penguins_in_a_table_zero_shot
data_files:
- split: default
path: penguins_in_a_table_zero_shot/default-*
- split: train
path: penguins_in_a_table_zero_shot/train-*
- split: validation
path: penguins_in_a_table_zero_shot/validation-*
- config_name: periodic_elements_zero_shot
data_files:
- split: default
path: periodic_elements_zero_shot/default-*
- split: train
path: periodic_elements_zero_shot/train-*
- split: validation
path: periodic_elements_zero_shot/validation-*
- config_name: persian_idioms_zero_shot
data_files:
- split: default
path: persian_idioms_zero_shot/default-*
- split: train
path: persian_idioms_zero_shot/train-*
- split: validation
path: persian_idioms_zero_shot/validation-*
- config_name: phrase_relatedness_zero_shot
data_files:
- split: default
path: phrase_relatedness_zero_shot/default-*
- split: train
path: phrase_relatedness_zero_shot/train-*
- split: validation
path: phrase_relatedness_zero_shot/validation-*
- config_name: physical_intuition_zero_shot
data_files:
- split: default
path: physical_intuition_zero_shot/default-*
- split: train
path: physical_intuition_zero_shot/train-*
- split: validation
path: physical_intuition_zero_shot/validation-*
- config_name: physics_questions_zero_shot
data_files:
- split: default
path: physics_questions_zero_shot/default-*
- split: train
path: physics_questions_zero_shot/train-*
- split: validation
path: physics_questions_zero_shot/validation-*
- config_name: physics_zero_shot
data_files:
- split: default
path: physics_zero_shot/default-*
- split: train
path: physics_zero_shot/train-*
- split: validation
path: physics_zero_shot/validation-*
- config_name: play_dialog_same_or_different_zero_shot
data_files:
- split: default
path: play_dialog_same_or_different_zero_shot/default-*
- split: train
path: play_dialog_same_or_different_zero_shot/train-*
- split: validation
path: play_dialog_same_or_different_zero_shot/validation-*
- config_name: polish_sequence_labeling_zero_shot
data_files:
- split: default
path: polish_sequence_labeling_zero_shot/default-*
- split: train
path: polish_sequence_labeling_zero_shot/train-*
- split: validation
path: polish_sequence_labeling_zero_shot/validation-*
- config_name: presuppositions_as_nli_zero_shot
data_files:
- split: default
path: presuppositions_as_nli_zero_shot/default-*
- split: train
path: presuppositions_as_nli_zero_shot/train-*
- split: validation
path: presuppositions_as_nli_zero_shot/validation-*
- config_name: qa_wikidata_zero_shot
data_files:
- split: default
path: qa_wikidata_zero_shot/default-*
- split: train
path: qa_wikidata_zero_shot/train-*
- split: validation
path: qa_wikidata_zero_shot/validation-*
- config_name: question_selection_zero_shot
data_files:
- split: default
path: question_selection_zero_shot/default-*
- split: train
path: question_selection_zero_shot/train-*
- split: validation
path: question_selection_zero_shot/validation-*
- config_name: real_or_fake_text_zero_shot
data_files:
- split: default
path: real_or_fake_text_zero_shot/default-*
- split: train
path: real_or_fake_text_zero_shot/train-*
- split: validation
path: real_or_fake_text_zero_shot/validation-*
- config_name: reasoning_about_colored_objects_zero_shot
data_files:
- split: default
path: reasoning_about_colored_objects_zero_shot/default-*
- split: train
path: reasoning_about_colored_objects_zero_shot/train-*
- split: validation
path: reasoning_about_colored_objects_zero_shot/validation-*
- config_name: repeat_copy_logic_zero_shot
data_files:
- split: default
path: repeat_copy_logic_zero_shot/default-*
- split: train
path: repeat_copy_logic_zero_shot/train-*
- split: validation
path: repeat_copy_logic_zero_shot/validation-*
- config_name: rephrase_zero_shot
data_files:
- split: default
path: rephrase_zero_shot/default-*
- split: train
path: rephrase_zero_shot/train-*
- split: validation
path: rephrase_zero_shot/validation-*
- config_name: riddle_sense_zero_shot
data_files:
- split: default
path: riddle_sense_zero_shot/default-*
- split: train
path: riddle_sense_zero_shot/train-*
- split: validation
path: riddle_sense_zero_shot/validation-*
- config_name: ruin_names_zero_shot
data_files:
- split: default
path: ruin_names_zero_shot/default-*
- split: train
path: ruin_names_zero_shot/train-*
- split: validation
path: ruin_names_zero_shot/validation-*
- config_name: salient_translation_error_detection_zero_shot
data_files:
- split: default
path: salient_translation_error_detection_zero_shot/default-*
- split: train
path: salient_translation_error_detection_zero_shot/train-*
- split: validation
path: salient_translation_error_detection_zero_shot/validation-*
- config_name: scientific_press_release_zero_shot
data_files:
- split: default
path: scientific_press_release_zero_shot/default-*
- split: train
path: scientific_press_release_zero_shot/train-*
- split: validation
path: scientific_press_release_zero_shot/validation-*
- config_name: semantic_parsing_in_context_sparc_zero_shot
data_files:
- split: default
path: semantic_parsing_in_context_sparc_zero_shot/default-*
- split: train
path: semantic_parsing_in_context_sparc_zero_shot/train-*
- split: validation
path: semantic_parsing_in_context_sparc_zero_shot/validation-*
- config_name: semantic_parsing_spider_zero_shot
data_files:
- split: default
path: semantic_parsing_spider_zero_shot/default-*
- split: train
path: semantic_parsing_spider_zero_shot/train-*
- split: validation
path: semantic_parsing_spider_zero_shot/validation-*
- config_name: sentence_ambiguity_zero_shot
data_files:
- split: default
path: sentence_ambiguity_zero_shot/default-*
- split: train
path: sentence_ambiguity_zero_shot/train-*
- split: validation
path: sentence_ambiguity_zero_shot/validation-*
- config_name: similarities_abstraction_zero_shot
data_files:
- split: default
path: similarities_abstraction_zero_shot/default-*
- split: train
path: similarities_abstraction_zero_shot/train-*
- split: validation
path: similarities_abstraction_zero_shot/validation-*
- config_name: simp_turing_concept_zero_shot
data_files:
- split: default
path: simp_turing_concept_zero_shot/default-*
- split: train
path: simp_turing_concept_zero_shot/train-*
- split: validation
path: simp_turing_concept_zero_shot/validation-*
- config_name: simple_arithmetic_json_multiple_choice_zero_shot
data_files:
- split: default
path: simple_arithmetic_json_multiple_choice_zero_shot/default-*
- split: train
path: simple_arithmetic_json_multiple_choice_zero_shot/train-*
- split: validation
path: simple_arithmetic_json_multiple_choice_zero_shot/validation-*
- config_name: simple_arithmetic_json_subtasks_zero_shot
data_files:
- split: default
path: simple_arithmetic_json_subtasks_zero_shot/default-*
- split: train
path: simple_arithmetic_json_subtasks_zero_shot/train-*
- split: validation
path: simple_arithmetic_json_subtasks_zero_shot/validation-*
- config_name: simple_arithmetic_json_zero_shot
data_files:
- split: default
path: simple_arithmetic_json_zero_shot/default-*
- split: train
path: simple_arithmetic_json_zero_shot/train-*
- split: validation
path: simple_arithmetic_json_zero_shot/validation-*
- config_name: simple_arithmetic_multiple_targets_json_zero_shot
data_files:
- split: default
path: simple_arithmetic_multiple_targets_json_zero_shot/default-*
- split: train
path: simple_arithmetic_multiple_targets_json_zero_shot/train-*
- split: validation
path: simple_arithmetic_multiple_targets_json_zero_shot/validation-*
- config_name: simple_ethical_questions_zero_shot
data_files:
- split: default
path: simple_ethical_questions_zero_shot/default-*
- split: train
path: simple_ethical_questions_zero_shot/train-*
- split: validation
path: simple_ethical_questions_zero_shot/validation-*
- config_name: simple_text_editing_zero_shot
data_files:
- split: default
path: simple_text_editing_zero_shot/default-*
- split: train
path: simple_text_editing_zero_shot/train-*
- split: validation
path: simple_text_editing_zero_shot/validation-*
- config_name: snarks_zero_shot
data_files:
- split: default
path: snarks_zero_shot/default-*
- split: train
path: snarks_zero_shot/train-*
- split: validation
path: snarks_zero_shot/validation-*
- config_name: social_iqa_zero_shot
data_files:
- split: default
path: social_iqa_zero_shot/default-*
- split: train
path: social_iqa_zero_shot/train-*
- split: validation
path: social_iqa_zero_shot/validation-*
- config_name: social_support_zero_shot
data_files:
- split: default
path: social_support_zero_shot/default-*
- split: train
path: social_support_zero_shot/train-*
- split: validation
path: social_support_zero_shot/validation-*
- config_name: sports_understanding_zero_shot
data_files:
- split: default
path: sports_understanding_zero_shot/default-*
- split: train
path: sports_understanding_zero_shot/train-*
- split: validation
path: sports_understanding_zero_shot/validation-*
- config_name: strange_stories_zero_shot
data_files:
- split: default
path: strange_stories_zero_shot/default-*
- split: train
path: strange_stories_zero_shot/train-*
- split: validation
path: strange_stories_zero_shot/validation-*
- config_name: strategyqa_zero_shot
data_files:
- split: default
path: strategyqa_zero_shot/default-*
- split: train
path: strategyqa_zero_shot/train-*
- split: validation
path: strategyqa_zero_shot/validation-*
- config_name: sufficient_information_zero_shot
data_files:
- split: default
path: sufficient_information_zero_shot/default-*
- split: train
path: sufficient_information_zero_shot/train-*
- split: validation
path: sufficient_information_zero_shot/validation-*
- config_name: suicide_risk_zero_shot
data_files:
- split: default
path: suicide_risk_zero_shot/default-*
- split: train
path: suicide_risk_zero_shot/train-*
- split: validation
path: suicide_risk_zero_shot/validation-*
- config_name: swahili_english_proverbs_zero_shot
data_files:
- split: default
path: swahili_english_proverbs_zero_shot/default-*
- split: train
path: swahili_english_proverbs_zero_shot/train-*
- split: validation
path: swahili_english_proverbs_zero_shot/validation-*
- config_name: swedish_to_german_proverbs_zero_shot
data_files:
- split: default
path: swedish_to_german_proverbs_zero_shot/default-*
- split: train
path: swedish_to_german_proverbs_zero_shot/train-*
- split: validation
path: swedish_to_german_proverbs_zero_shot/validation-*
- config_name: symbol_interpretation_zero_shot
data_files:
- split: default
path: symbol_interpretation_zero_shot/default-*
- split: train
path: symbol_interpretation_zero_shot/train-*
- split: validation
path: symbol_interpretation_zero_shot/validation-*
- config_name: temporal_sequences_zero_shot
data_files:
- split: default
path: temporal_sequences_zero_shot/default-*
- split: train
path: temporal_sequences_zero_shot/train-*
- split: validation
path: temporal_sequences_zero_shot/validation-*
- config_name: tense_zero_shot
data_files:
- split: default
path: tense_zero_shot/default-*
- split: train
path: tense_zero_shot/train-*
- split: validation
path: tense_zero_shot/validation-*
- config_name: timedial_zero_shot
data_files:
- split: default
path: timedial_zero_shot/default-*
- split: train
path: timedial_zero_shot/train-*
- split: validation
path: timedial_zero_shot/validation-*
- config_name: topical_chat_zero_shot
data_files:
- split: default
path: topical_chat_zero_shot/default-*
- split: train
path: topical_chat_zero_shot/train-*
- split: validation
path: topical_chat_zero_shot/validation-*
- config_name: tracking_shuffled_objects_zero_shot
data_files:
- split: default
path: tracking_shuffled_objects_zero_shot/default-*
- split: train
path: tracking_shuffled_objects_zero_shot/train-*
- split: validation
path: tracking_shuffled_objects_zero_shot/validation-*
- config_name: understanding_fables_zero_shot
data_files:
- split: default
path: understanding_fables_zero_shot/default-*
- split: train
path: understanding_fables_zero_shot/train-*
- split: validation
path: understanding_fables_zero_shot/validation-*
- config_name: undo_permutation_zero_shot
data_files:
- split: default
path: undo_permutation_zero_shot/default-*
- split: train
path: undo_permutation_zero_shot/train-*
- split: validation
path: undo_permutation_zero_shot/validation-*
- config_name: unit_conversion_zero_shot
data_files:
- split: default
path: unit_conversion_zero_shot/default-*
- split: train
path: unit_conversion_zero_shot/train-*
- split: validation
path: unit_conversion_zero_shot/validation-*
- config_name: unit_interpretation_zero_shot
data_files:
- split: default
path: unit_interpretation_zero_shot/default-*
- split: train
path: unit_interpretation_zero_shot/train-*
- split: validation
path: unit_interpretation_zero_shot/validation-*
- config_name: unnatural_in_context_learning_zero_shot
data_files:
- split: default
path: unnatural_in_context_learning_zero_shot/default-*
- split: train
path: unnatural_in_context_learning_zero_shot/train-*
- split: validation
path: unnatural_in_context_learning_zero_shot/validation-*
- config_name: vitaminc_fact_verification_zero_shot
data_files:
- split: default
path: vitaminc_fact_verification_zero_shot/default-*
- split: train
path: vitaminc_fact_verification_zero_shot/train-*
- split: validation
path: vitaminc_fact_verification_zero_shot/validation-*
- config_name: what_is_the_tao_zero_shot
data_files:
- split: default
path: what_is_the_tao_zero_shot/default-*
- split: train
path: what_is_the_tao_zero_shot/train-*
- split: validation
path: what_is_the_tao_zero_shot/validation-*
- config_name: which_wiki_edit_zero_shot
data_files:
- split: default
path: which_wiki_edit_zero_shot/default-*
- split: train
path: which_wiki_edit_zero_shot/train-*
- split: validation
path: which_wiki_edit_zero_shot/validation-*
- config_name: winowhy_zero_shot
data_files:
- split: default
path: winowhy_zero_shot/default-*
- split: train
path: winowhy_zero_shot/train-*
- split: validation
path: winowhy_zero_shot/validation-*
- config_name: word_sorting_zero_shot
data_files:
- split: default
path: word_sorting_zero_shot/default-*
- split: train
path: word_sorting_zero_shot/train-*
- split: validation
path: word_sorting_zero_shot/validation-*
- config_name: word_unscrambling_zero_shot
data_files:
- split: default
path: word_unscrambling_zero_shot/default-*
- split: train
path: word_unscrambling_zero_shot/train-*
- split: validation
path: word_unscrambling_zero_shot/validation-*
---
# Dataset Card for "bigbench"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
cawoylel/FulaSpeechCorpora-splited-noise_augmented | cawoylel | 2023-11-22T09:40:57Z | 443 | 3 | null | [
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"task_categories:audio-classification",
"size_categories:100K<n<1M",
"language:ff",
"speech",
"low-ressource",
"audio",
"region:us"
] | 2023-11-22T09:40:57Z | 2023-10-25T22:21:41.000Z | 2023-10-25T22:21:41 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: dev
path: data/dev-*
dataset_info:
features:
- name: audio
dtype: audio
- name: transcription
dtype: string
- name: dialect
dtype: string
splits:
- name: train
num_bytes: 30053008388.047405
num_examples: 187816
- name: test
num_bytes: 3026464537.665628
num_examples: 18801
- name: dev
num_bytes: 758023819.869451
num_examples: 4709
download_size: 32588024982
dataset_size: 33837496745.58248
task_categories:
- automatic-speech-recognition
- text-to-speech
- audio-classification
language:
- ff
pretty_name: Fula Multidialectal Speech Corpora Augmented
size_categories:
- 100K<n<1M
tags:
- speech
- low-ressource
- audio
---
# Sources of the data
This dataset is created from [FulaSpeechCorpora dataset](https://huggingface.co/datasets/cawoylel/FulaSpeechCorpora) and splitted into **train**, **test** and **dev** sets. It has been augmented using `audiomentations` library [https://iver56.github.io/audiomentations/](https://iver56.github.io/audiomentations/).
The augmentation process includes the following tranformations:
- Add background noise to simulate an environment where background noise is present
- Add gaussian noise to the samples
- Multiply the audio by a random amplitude factor to reduce or increase the volume. This technique can help a model become somewhat invariant to the overall gain of the input audio.
- Change the speed or duration of the signal without changing the pitch.
# How to augment
**Download noise and background sounds**
```python
wget https://www.openslr.org/resources/17/musan.tar.gz
tar -zxvf musan.tar.gz
rm -r ./musan/speech
```
**Augment**
```python
from audiomentations import *
def get_transformations():
"""
Define audio augmentation transformations
"""
musan_dir = "./musan"
augment = Compose([
Gain(min_gain_in_db=-6, max_gain_in_db=6, p=0.1),
PitchShift(min_semitones=-4, max_semitones=4, p=0.5),
TimeStretch(min_rate=0.9, max_rate=1.1, p=0.4, leave_length_unchanged=False),
OneOf([
AddBackgroundNoise(
sounds_path=musan_dir, min_snr_in_db=1.0, max_snr_in_db=5.0, noise_transform=PolarityInversion(), p=1.
),
AddGaussianNoise(min_amplitude=0.001, max_amplitude=0.015, p=1.),], p=1.0)
])
return augment
def augment_dataset(batch):
sample = batch['audio']
augmentation = get_transformations()
# apply augmentation
augmented_waveform = augmentation(sample["array"], sample_rate=sample["sampling_rate"])
batch['audio']["array"] = augmented_waveform
return batch
```
# Fula varieties in this dataset
This dataset contains 8 varrieties:
- __Pulaar__: spoken in Senegal, Mauritania and West-Mali.
- __Pular__: spoken in Guinea.
- __Maacina__: spoken in the Center and East of Mali.
- __Liptako__: spoken in Burkina Faso and Niger.
- __Caka__: spoken in the Central Nigeria.
- __Bororro__: a very nomad group living in Cameroon, Central African Republic and Tchad.
- __Borgu__: spoken in Togo and Benin.
- __Adamawa__: spoken in Cameroon and South-East Nigeria.
# How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
For example, to download the training set, simply specify the corresponding split name:
```python
from datasets import load_dataset
train = load_dataset("cawoylel/FulaSpeechCorpora-splited-noise_augmented", split = "train")
```
You can also load all the dataset:
```python
from datasets import load_dataset
data = load_dataset("cawoylel/FulaSpeechCorpora-splited-noise_augmented")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```python
from datasets import load_dataset
data = load_dataset("cawoylel/FulaSpeechCorpora-splited-noise_augmented", streaming=True)
print(next(iter(data)))
```
# Data Fields
The data fields are the same among all splits.
- **dialect** (str): The name of the dialect
- **audio** (dict): Audio object including loaded audio array, sampling rate and path to audio
- **transcription** (str): Transcription of the audio file
# Social Impact of Dataset
Under-representation, dialectal and cultural diversity.
# Discussion of Biases
Gender biases, noise robustness
# Additional Information
All datasets are licensed under the [Creative Commons license (CC-BY)](https://creativecommons.org/licenses/).
# Citation Information
Please cite us when using the FulaSpeechCorpora:
```
@article{fleurs2022arxiv,
title = {FulaSpeechCorpora-splited-noise_augmented: A multidialectal speech dataset for Fula.},
author = {Sy, Yaya and Doucouré, Dioula},
url = {https://huggingface.co/datasets/cawoylel/FulaSpeechCorpora-splited-noise_augmented},
year = {2023},
``` | [
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ollie | null | 2023-06-01T14:59:47Z | 442 | 0 | null | [
"annotations_creators:machine-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:other",
"relation-extraction",
"text-to-structured",
"region:us"
] | 2023-06-01T14:59:47Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- machine-generated
language_creators:
- crowdsourced
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
- 1M<n<10M
source_datasets:
- original
task_categories: []
task_ids: []
pretty_name: Ollie
tags:
- relation-extraction
- text-to-structured
dataset_info:
- config_name: ollie_lemmagrep
features:
- name: arg1
dtype: string
- name: arg2
dtype: string
- name: rel
dtype: string
- name: search_query
dtype: string
- name: sentence
dtype: string
- name: words
dtype: string
- name: pos
dtype: string
- name: chunk
dtype: string
- name: sentence_cnt
dtype: string
splits:
- name: train
num_bytes: 12324648919
num_examples: 18674630
download_size: 1789363108
dataset_size: 12324648919
- config_name: ollie_patterned
features:
- name: rel
dtype: string
- name: arg1
dtype: string
- name: arg2
dtype: string
- name: slot0
dtype: string
- name: search_query
dtype: string
- name: pattern
dtype: string
- name: sentence
dtype: string
- name: parse
dtype: string
splits:
- name: train
num_bytes: 2930309084
num_examples: 3048961
download_size: 387514061
dataset_size: 2930309084
config_names:
- ollie_lemmagrep
- ollie_patterned
---
# Dataset Card for Ollie
## 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:** [Ollie](https://knowitall.github.io/ollie/)
- **Repository:** [Github](https://github.com/knowitall/ollie)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/D12-1048/)
### Dataset Summary
The Ollie dataset includes two configs for the data
used to train the Ollie informatation extraction algorithm, for 18M
sentences and 3M sentences respectively.
This data is for academic use only. From the authors:
Ollie is a program that automatically identifies and extracts binary
relationships from English sentences. Ollie is designed for Web-scale
information extraction, where target relations are not specified in
advance.
Ollie is our second-generation information extraction system . Whereas
ReVerb operates on flat sequences of tokens, Ollie works with the
tree-like (graph with only small cycles) representation using
Stanford's compression of the dependencies. This allows Ollie to
capture expression that ReVerb misses, such as long-range relations.
Ollie also captures context that modifies a binary relation. Presently
Ollie handles attribution (He said/she believes) and enabling
conditions (if X then).
More information is available at the Ollie homepage:
https://knowitall.github.io/ollie/
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
en
## Dataset Structure
### Data Instances
There are two configurations for the dataset: ollie_lemmagrep which
are 18M sentences from web searches for a subset of the Reverb
relationships (110,000 relationships), and the 3M sentences for
ollie_patterned which is a subset of the ollie_lemmagrep dataset
derived from patterns according to the Ollie paper.
An example of an ollie_lemmagrep record:
``
{'arg1': 'adobe reader',
'arg2': 'pdf',
'chunk': 'B-NP I-NP I-NP I-NP B-PP B-NP I-NP B-VP B-PP B-NP I-NP O B-VP B-NP I-NP I-NP I-NP B-VP I-VP I-VP O',
'pos': 'JJ NNS CC NNS IN PRP$ NN VBP IN NNP NN CC VB DT NNP NNP NNP TO VB VBN .',
'rel': 'be require to view',
'search_query': 'require reader pdf adobe view',
'sentence': 'Many documents and reports on our site are in PDF format and require the Adobe Acrobat Reader to be viewed .',
'sentence_cnt': '9',
'words': 'many,document,and,report,on,our,site,be,in,pdf,format,and,require,the,adobe,acrobat,reader,to,be,view'}
``
An example of an ollie_patterned record:
``
{'arg1': 'english',
'arg2': 'internet',
'parse': '(in_IN_6), advmod(important_JJ_4, most_RBS_3); nsubj(language_NN_5, English_NNP_0); cop(language_NN_5, being_VBG_1); det(language_NN_5, the_DT_2); amod(language_NN_5, important_JJ_4); prep_in(language_NN_5, era_NN_9); punct(language_NN_5, ,_,_10); conj(language_NN_5, education_NN_12); det(era_NN_9, the_DT_7); nn(era_NN_9, Internet_NNP_8); amod(education_NN_12, English_JJ_11); nsubjpass(enriched_VBN_15, language_NN_5); aux(enriched_VBN_15, should_MD_13); auxpass(enriched_VBN_15, be_VB_14); punct(enriched_VBN_15, ._._16)',
'pattern': '{arg1} <nsubj< {rel:NN} >prep_in> {slot0:NN} >nn> {arg2}',
'rel': 'be language of',
'search_query': 'english language internet',
'sentence': 'English being the most important language in the Internet era , English education should be enriched .',
'slot0': 'era'}
``
### Data Fields
For ollie_lemmagrep:
* rel: the relationship phrase/verb phrase. This may be empty, which represents the "be" relationship.
* arg1: the first argument in the relationship
* arg2: the second argument in the relationship.
* chunk: a tag of each token in the sentence, showing the pos chunks
* pos: part of speech tagging of the sentence
* sentence: the sentence
* sentence_cnt: the number of copies of this sentence encountered
* search_query: a combintion of rel, arg1, arg2
* words: the lemma of the words of the sentence separated by commas
For ollie_patterned:
* rel: the relationship phrase/verb phrase.
* arg1: the first argument in the relationship
* arg2: the second argument in the relationship.
* slot0: the third argument in the relationship, which might be empty.
* pattern: a parse pattern for the relationship
* parse: a dependency parse forthe sentence
* search_query: a combintion of rel, arg1, arg2
* sentence: the senence
### Data Splits
There are no splits.
## Dataset Creation
### Curation Rationale
This dataset was created as part of research on open information extraction.
### Source Data
#### Initial Data Collection and Normalization
See the research paper on OLlie. The training data is extracted from web pages (Cluebweb09).
#### Who are the source language producers?
The Ollie authors at the Univeristy of Washington and data from Cluebweb09 and the open web.
### Annotations
#### Annotation process
The various parsers and code from the Ollie alogrithm.
#### Who are the annotators?
Machine annotated.
### Personal and Sensitive Information
Unkown, but likely there are names of famous individuals.
## Considerations for Using the Data
### Social Impact of Dataset
The goal for the work is to help machines learn to extract information form open domains.
### Discussion of Biases
Since the data is gathered from the web, there is likely to be biased text and relationships.
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The authors of Ollie at The University of Washington
### Licensing Information
The University of Washington academic license: https://raw.githubusercontent.com/knowitall/ollie/master/LICENSE
### Citation Information
```
@inproceedings{ollie-emnlp12,
author = {Mausam and Michael Schmitz and Robert Bart and Stephen Soderland and Oren Etzioni},
title = {Open Language Learning for Information Extraction},
booktitle = {Proceedings of Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CONLL)},
year = {2012}
}
```
### Contributions
Thanks to [@ontocord](https://github.com/ontocord) for adding this dataset. | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
SocialGrep/ten-million-reddit-answers | SocialGrep | 2022-07-01T17:38:25Z | 442 | 6 | null | [
"annotations_creators:lexyr",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"region:us"
] | 2022-07-01T17:38:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- lexyr
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
paperswithcode_id: null
---
# Dataset Card for ten-million-reddit-answers
## 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://socialgrep.com/datasets](https://socialgrep.com/datasets?utm_source=huggingface&utm_medium=link&utm_campaign=tenmillionanswers)
- **Point of Contact:** [Website](https://socialgrep.com/contact?utm_source=huggingface&utm_medium=link&utm_campaign=tenmillionanswers)
### Dataset Summary
This corpus contains ten million question-answer pairs, labeled with score and pre-packaged with results of a basic sentiment predictor.
The data was procured from /r/AskReddit using [SocialGrep](https://socialgrep.com/?utm_source=huggingface&utm_medium=link&utm_campaign=tenmillionanswers).
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A data point is a post or a comment. Due to the separate nature of the two, those exist in two different files - even though many fields are shared.
### Data Fields
- 'type': the type of the data point. Can be 'post' or 'comment'.
- 'id': the base-36 Reddit ID of the data point. Unique when combined with type.
- 'subreddit.id': the base-36 Reddit ID of the data point's host subreddit. Unique.
- 'subreddit.name': the human-readable name of the data point's host subreddit.
- 'subreddit.nsfw': a boolean marking the data point's host subreddit as NSFW or not.
- 'created_utc': a UTC timestamp for the data point.
- 'permalink': a reference link to the data point on Reddit.
- 'score': score of the data point on Reddit.
- 'domain': (Post only) the domain of the data point's link.
- 'url': (Post only) the destination of the data point's link, if any.
- 'selftext': (Post only) the self-text of the data point, if any.
- 'title': (Post only) the title of the post data point.
- 'body': (Comment only) the body of the comment data point.
- 'sentiment': (Comment only) the result of an in-house sentiment analysis pipeline. Used for exploratory analysis.
## 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
CC-BY v4.0
### Contributions
[Needs More Information] | [
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unicamp-dl/mrobust | unicamp-dl | 2023-11-23T10:45:25Z | 442 | 1 | null | [
"arxiv:2209.13738",
"arxiv:2105.06813",
"region:us"
] | 2023-11-23T10:45:25Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | # Dataset Summary
**mRobust** is a multilingual version of the [TREC 2004 Robust passage ranking dataset](https://trec.nist.gov/data/robust/04.guidelines.html).
For more information, checkout our papers:
* [**mRobust: A Multilingual Version of the MS MARCO Passage Ranking Dataset**](https://arxiv.org/abs/2209.13738)
* [**A cost-benefit analysis of cross-lingual transfer methods**](https://arxiv.org/abs/2105.06813)
The current version is composed 10 languages: Chinese, French, German, Indonesian, Italian, Portuguese, Russian, Spanish, Dutch and Vietnamese.
### Supported languages
| Language name | Language code |
|---------------|---------------|
| English | english |
| Chinese | chinese |
| French | french |
| German | german |
| Indonesian | indonesian |
| Italian | italian |
| Portuguese | portuguese |
| Russian | russian |
| Spanish | spanish |
| Dutch | dutch |
| Vietnamese | vietnamese |
# Dataset Structure
You can load mRobust dataset by choosing a specific language. We include the translated collections of documents and queries.
#### Queries
```python
>>> dataset = load_dataset('unicamp-dl/mrobust', 'queries-spanish')
>>> dataset['queries'][1]
{'id': '302', 'text': '¿Está controlada la enfermedad de la poliomielitis (polio) en el mundo?'}
```
#### Collection
```python
>>> dataset = load_dataset('unicamp-dl/mrobust', 'collection-portuguese')
>>> dataset['collection'][5]
{'id': 'FT931-16660', 'text': '930105 FT 05 JAN 93 / Cenelec: Correção O endereço do Cenelec, Comitê Europeu de Normalização Eletrotécnica, estava incorreto na edição de ontem. É Rue de Stassart 35, B-1050, Bruxelas, Tel (322) 519 6871. CEN, Comitê Europeu de Normalização, está localizado na Rue de Stassart 36, B-1050, Bruxelas, Tel 519 6811.'}
```
# Citation Information
```
@misc{https://doi.org/10.48550/arxiv.2209.13738,
doi = {10.48550/ARXIV.2209.13738},
url = {https://arxiv.org/abs/2209.13738},
author = {Jeronymo, Vitor and Nascimento, Mauricio and Lotufo, Roberto and Nogueira, Rodrigo},
title = {mRobust04: A Multilingual Version of the TREC Robust 2004 Benchmark},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
colbertv2/lotte_passages | colbertv2 | 2023-08-23T01:55:55Z | 442 | 0 | null | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"arxiv:2112.01488",
"region:us"
] | 2023-08-23T01:55:55Z | 2022-07-14T22:44:41.000Z | 2022-07-14T22:44:41 | ---
viewer: false
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: 'Lotte passages from ColBERTv2: Effective and Efficient Retrieval via
Lightweight Late Interaction'
size_categories:
- 1M<n<10M
source_datasets:
- original
tags: []
task_categories:
- question-answering
task_ids:
- extractive-qa
dataset_info:
features:
- name: doc_id
dtype: int32
- name: author
dtype: string
- name: text
dtype: string
splits:
- name: dev_collection
num_bytes: 263355925
num_examples: 268880
- name: test_collection
num_bytes: 105718627
num_examples: 119458
download_size: 225568795
dataset_size: 369074552
---
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
lmsys/toxic-chat | lmsys | 2023-11-13T18:48:51Z | 442 | 46 | null | [
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-nc-4.0",
"arxiv:2310.17389",
"region:us"
] | 2023-11-13T18:48:51Z | 2023-10-26T13:52:48.000Z | 2023-10-26T13:52:48 | ---
license: cc-by-nc-4.0
language:
- en
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: toxic-chat_annotation_train.csv
- split: test
path: toxic-chat_annotation_test.csv
---
## Content
This dataset contains toxicity annotations on 10K user prompts collected from the Vicuna [online demo](https://chat.lmsys.org/).
We utilize a human-AI collaborative annotation framework to guarantee the quality of annotation while maintaining a feasible annotation workload.
The details of data collection, pre-processing, and annotation can be found in our [paper](https://arxiv.org/abs/2310.17389).
We believe that ToxicChat can be a valuable resource to drive further advancements toward building a safe and healthy environment for user-AI interactions.
**Basic Statistics**
| Key | Value |
| --- | --- |
| # User Prompts | 10,165 |
| # Human Annotations | 5,634 |
| % Toxic Examples | 7.18% |
| % Jailbreaking Examples | 1.78% |
## Disclaimer and Terms
- This dataset is based on the user query collected from the Vicuna online demo.
The Vicuna demo is fully anonymous for the users and also highlights the possible reuse of the user query data.
We have carefully gone through the data and taken out anything that could have personal information in it.
However, there is still a chance that some personal information might be left in the data.
If you come across anything in the data that you think should not be made public, please let us know right away.
- Safety and Moderation: **This dataset may contain racism, sexuality, or other undesired content.**
Before the annotation, the annotators are first notified about the toxic data that they will be annotated.
Verbal agreements were obtained before annotation.
- Non-Endorsement: Statements or opinions made in this dataset **do not reflect** the views of researchers or institutions involved in the data collection effort.
- Legal Compliance: Users of this data are responsible for ensuring its appropriate use.
The dataset should not be utilized for training dialogue agents, or any other applications, in manners that conflict with legal and ethical standards.
- Non-Identification: Users of this data agree to not attempt to determine the identity of individuals in this dataset.
## License
Both the user prompts and the model outputs are licensed under CC-BY-NC-4.0.
## Citation
```
@misc{lin2023toxicchat,
title={ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation},
author={Zi Lin and Zihan Wang and Yongqi Tong and Yangkun Wang and Yuxin Guo and Yujia Wang and Jingbo Shang},
year={2023},
eprint={2310.17389},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | [
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gsarti/change_it | gsarti | 2022-10-27T08:37:09Z | 441 | 1 | null | [
"task_categories:summarization",
"task_categories:text-generation",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:it",
"license:cc-by-nc-sa-4.0",
"conditional-text-generation",
"style-transfer",
"region:us"
] | 2022-10-27T08:37:09Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- it
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
- text-generation
task_ids: []
pretty_name: change-it
tags:
- conditional-text-generation
- style-transfer
---
# Dataset Card for CHANGE-IT
## Table of Contents
- [Dataset Card for CHANGE-IT](#dataset-card-for-change-it)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Style Transfer](#style-transfer)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [https://live.european-language-grid.eu/catalogue/corpus/7373](https://live.european-language-grid.eu/catalogue/corpus/7373)
- **Repository:** [Github](https://github.com/michelecafagna26/CHANGE-IT)
- **Paper:** [CEUR-ws.org](http://ceur-ws.org/Vol-2765/paper169.pdf)
- **Video** [Vimeo](https://vimeo.com/484098874)
- **Point of Contact:** [Lorenzo De Mattei](lorenzo.demattei@gmail.com)
- **Size of downloaded dataset files:** 168.7 MB
- **Size of the generated dataset:** 411 MB
- **Total amount of disk used:** 579.7 MB
### Dataset Summary
The CHANGE-IT dataset contains approximately 152,000 article-headline pairs, collected from two Italian newspapers situated at opposite ends of the political spectrum, namely la Repubblica (left) and Il Giornale (right), with the two newspapers equally represented. The dataset has been used in the context
of the [CHANGE-IT task](https://sites.google.com/view/change-it) during the [Evalita 2020 evaluation campaign](http://www.evalita.it/2020). CHANGE-IT is a generation task for Italian – more specifically, a style transfer task for headlines of Italian newspapers. Given a (collection of) headlines from one newspaper, namely Il Giornale (G) or La Repubblica (R), it challenges automatic systems to change all G-headlines to headlines in style R, and all R-headlines to headlines in style G. Although the task only concerns headline change, the dataset comprehends both the headlines as well as their respective full articles.
**Disclaimer**: *The CHANGE-IT dataset is hosted by the [European Language Grid](https://live.european-language-grid.eu/) and licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/). To use the dataset using* 🤗 *Datasets, download and unzip the folder from its [ELG page](https://live.european-language-grid.eu/catalogue/corpus/7373) and pass it to the* `load_dataset` *method as:* `datasets.load_dataset('gsarti/change_it', data_dir='path/to/unzipped/folder')`
### Supported Tasks and Leaderboards
#### Style Transfer
The following table is taken from Table 4 of the original paper, where a *pointer-network* architecture is used as a baseline to perform style transfer in two settings. In the **rep2gio** variant the system is trained to summarize Repubblica headlines from full texts (vice versa for **gio2rep**), and the style transfer is performed by summarizing full texts of the other newspaper in the source newspaper's headline style. **avg** is the average of the two settings.
| | HH| AH|Main|Compliancy|
|--------:|---:|---:|---:|---------:|
|`rep2gio`|.649|.876|.799| .449|
|`gio2rep`|.639|.871|.435| .240|
| `avg`|.644|.874|.616| .345|
Here **Main**, **HH** and **AH** are all BERT-base models trained to evaluate the quality of style transfer as follows:
- **Main**: the model is trained to classify a generated headline either as `ilgiornale` or `repubblica`, achieving ~80% F1 score on gold data. Tests whether the transfer has been successful.
- **Headline-Headline (HH)**: the model is trained to check the compatibility between original and generated headlines. Tests whether the generation is coherent with the reference.
- **Article-Headline (AH)**: the model is trained to check the compatibility between original fulltext article and generated headlines. Tests whether the generation is coherent with the source article.
The final metric, **Overall compliancy**, is a binary metric that is positive if the other three metrics match (**Main** decision is reversed, **HH** and **AH** predict match), and negative otherwise. Refer to Section 3 of the original paper for more details.
### Languages
The language data in CHANGE-IT is in Italian (BCP-47 `it`)
## Dataset Structure
### Data Instances
A sample from the `test` split of the `ilgiornale` config is provided below. The other configuration, `ilgiornale`, has the same structure.
```json
{
"id": 0,
"headline": "Ucraina, coalizione della Timoshenko denuncia irruzione nella sede",
"full_text": "Rimane alta la tensione in Ucraina , dove da giorni i manifestanti scendono in piazza per protestare contro la decisione del presidente Viktor Yanukovich, che ha deciso di congelare l'accordo di associazione con l'Unione Europea. Il momento è molto delicato. L'opposizione teme una repressione violenza della protesta, con le forze speciali che hanno costretto i manifestanti a Kiev ad allontanarsi dalla sede del governo, per ripiegare su piazza Indipendenza. Il leader d'opposizione Vitaly Klitschko ha invitato il presidente a non utilizzare la forza, se non vuole avere il sangue dei manifestanti sulle sue mani. Nel frattempo il presidente Yanukovich ha aperto alla possibilità di un dialogo, annunciando per domani un incontro con i suoi due predecessori, Leonid Kuchma e Viktor Yushchenko. Ieri un milioni di persone sono scese in piazza, scaduti i due giorni di ultimatum dati al governo per indire nuove elezioni, I manifestanti hanno rovesciato la grande statua di Lenin posta sul boulevard Shevchenko. Piazza Indipendenza (Maidan Nezalezhnosti) resta il punto più caldo della capitale. Qui sono state erette barricate davanti agli ingressi della metropolitana, nel tentativo di preparsi a un'azione della polizia, che al momento non ha però preso iniziative contro i dimostranti. In serata Batkivshcyna, la coalizione dell'ex premier Yulia Timoshenko , ha denunciato l'irruzione di almeno venti agenti della polizia antisommossa nel proprio quartier generale. Il portavoce della polizia, Olga Bilyk, ha smentito: \"Né la polizia di Kiev, né la Berkut - ha dichiarato - hanno condotto operazioni nella sede\".",
"alignment": "A2"
}
```
The text is provided as-is, without further preprocessing or tokenization.
### Data Fields
- `headline`: The original headline for the newspaper.
- `full_text`: The article full text associated to the respective headline.
- `alignment`: The alignment value used for the style transfer experiments. Values:
- `A1`: Top 5K pairs, highly aligned.
- `A2`: Test set, highly aligned.
- `A3`: 10K to 20K pairs, fairly aligned.
- `R`: Bottom ~50K pairs, weakly/not aligned.
### Data Splits
| config| train| test|
|---------:|-------------------------------------:|-----------:|
|`ilgiornale`|5'000 (A1) + 10'000 (A3) + 48'701 (R) | 5'000 (A2) |
|`repubblica`|5'000 (A1) + 10'000 (A3) + 48'701 (R) | 5'000 (A2) |
### Dataset Creation
Please refer to the original article [CHANGE-IT @ EVALITA 2020: Change Headlines, Adapt News, GEnerate](http://ceur-ws.org/Vol-2765/paper169.pdf) for additional information on dataset creation.
## Additional Information
### Dataset Curators
The organizers of the CHANGE-IT shared tasks are the curators of the original dataset. For problems or updates on the 🤗 Datasets version, please contact [gabriele.sarti996@gmail.com](mailto:gabriele.sarti996@gmail.com).
### Licensing Information
Licensed with Creative Commons Attribution Non Commercial Share Alike 4.0. License available [here](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
Please cite the authors if you use these corpora in your work:
```
@inproceedings{demattei-etal-2020-changeit,
author = {De Mattei, Lorenzo and Cafagna, Michele and Dell'Orletta, Felice and Nissim, Malvina and Gatt, Albert},
title = {{CHANGE-IT @ EVALITA 2020}: Change Headlines, Adapt News, GEnerate},
booktitle = {Proceedings of Seventh Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2020)},
editor = {Basile, Valerio and Croce, Danilo and Di Maro, Maria, and Passaro, Lucia C.},
publisher = {CEUR.org},
year = {2020},
address = {Online}
}
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clane9/NSD-Flat | clane9 | 2023-09-28T01:26:21Z | 441 | 5 | null | [
"task_categories:image-to-image",
"task_categories:object-detection",
"size_categories:100K<n<1M",
"license:other",
"biology",
"neuroscience",
"fmri",
"region:us"
] | 2023-09-28T01:26:21Z | 2023-07-20T21:40:43.000Z | 2023-07-20T21:40:43 | ---
license: other
dataset_info:
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splits:
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num_bytes: 26695182666.0
num_examples: 195000
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num_examples: 18000
download_size: 22565691383
dataset_size: 29156463337.0
task_categories:
- image-to-image
- object-detection
tags:
- biology
- neuroscience
- fmri
size_categories:
- 100K<n<1M
---
# NSD-Flat
[[`GitHub`]](https://github.com/clane9/NSD-Flat) [[🤗 `Hugging Face Hub`]](https://huggingface.co/datasets/clane9/NSD-Flat)
A Hugging Face dataset of pre-processed brain activity flat maps from the [Natural Scenes Dataset](https://naturalscenesdataset.org/), constrained to a visual cortex region of interest and rendered as PNG images.
## Load the dataset
Load the dataset from [Hugging Face Hub](https://huggingface.co/datasets/clane9/NSD-Flat)
```python
from datasets import load_dataset
dataset = load_dataset("clane9/NSD-Flat", split="train")
```
## Building the dataset
### 1. Download source data
Run [`download_data.sh`](download_data.sh) to download the required source data:
- NSD stimuli images and presentation info
- COCO annotations
- NSD beta activity maps in fsaverge surface space
```bash
bash download_data.sh
```
### 2. Convert the COCO annotations
Run [`convert_nsd_annotations.py`](convert_nsd_annotations.py) to crop and reorganize the COCO annotations for NSD.
```bash
python convert_nsd_annotations.py
```
### 3. Generate the dataset
Run [`generate_dataset.py`](generate_dataset.py) to generate the huggingface dataset in Arrow format.
```bash
python generate_dataset.py --img_size 256 --workers 8
```
## Citation
If you find this dataset useful, please consider citing:
```
@article{allen2022massive,
title = {A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence},
author = {Allen, Emily J and St-Yves, Ghislain and Wu, Yihan and Breedlove, Jesse L and Prince, Jacob S and Dowdle, Logan T and Nau, Matthias and Caron, Brad and Pestilli, Franco and Charest, Ian and others},
journal = {Nature neuroscience},
volume = {25},
number = {1},
pages = {116--126},
year = {2022},
publisher = {Nature Publishing Group US New York}
}
```
```
@misc{lane2023nsdflat,
author = {Connor Lane},
title = {NSD-Flat: Pre-processed brain activity flat maps from the Natural Scenes Dataset},
howpublished = {\url{https://huggingface.co/datasets/clane9/NSD-Flat}},
year = {2023},
}
```
## License
Usage of this dataset constitutes agreement to the [NSD Terms and Conditions](https://cvnlab.slite.page/p/IB6BSeW_7o/Terms-and-Conditions). | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
senti_ws | null | 2023-01-25T14:44:03Z | 440 | 1 | null | [
"task_categories:token-classification",
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:sentiment-scoring",
"task_ids:part-of-speech",
"annotations_creators:expert-generated",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:de",
"license:cc-by-sa-3.0",
"region:us"
] | 2023-01-25T14:44:03Z | 2022-03-02T23:29:22.000Z | 2022-03-02T23:29:22 | ---
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- found
language:
- de
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
- text-classification
task_ids:
- text-scoring
- sentiment-scoring
- part-of-speech
pretty_name: SentiWS
dataset_info:
- config_name: pos-tagging
features:
- name: word
dtype: string
- name: pos-tag
dtype:
class_label:
names:
'0': NN
'1': VVINF
'2': ADJX
'3': ADV
splits:
- name: train
num_bytes: 75530
num_examples: 3471
download_size: 97748
dataset_size: 75530
- config_name: sentiment-scoring
features:
- name: word
dtype: string
- name: sentiment-score
dtype: float32
splits:
- name: train
num_bytes: 61646
num_examples: 3471
download_size: 97748
dataset_size: 61646
---
# Dataset Card for SentiWS
## 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://wortschatz.uni-leipzig.de/en/download
- **Repository:** [Needs More Information]
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2010/pdf/490_Paper.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
SentimentWortschatz, or SentiWS for short, is a publicly available German-language resource for sentiment analysis, opinion mining etc. It lists positive and negative polarity bearing words weighted within the interval of [-1; 1] plus their part of speech tag, and if applicable, their inflections. The current version of SentiWS contains around 1,650 positive and 1,800 negative words, which sum up to around 16,000 positive and 18,000 negative word forms incl. their inflections, respectively. It not only contains adjectives and adverbs explicitly expressing a sentiment, but also nouns and verbs implicitly containing one.
### Supported Tasks and Leaderboards
Sentiment-Scoring, Pos-Tagging
### Languages
German
## Dataset Structure
### Data Instances
For pos-tagging:
```
{
"word":"Abbau"
"pos_tag": 0
}
```
For sentiment-scoring:
```
{
"word":"Abbau"
"sentiment-score":-0.058
}
```
### Data Fields
SentiWS is UTF8-encoded text.
For pos-tagging:
- word: one word as a string,
- pos_tag: the part-of-speech tag of the word as an integer,
For sentiment-scoring:
- word: one word as a string,
- sentiment-score: the sentiment score of the word as a float between -1 and 1,
The POS tags are ["NN", "VVINF", "ADJX", "ADV"] -> ["noun", "verb", "adjective", "adverb"], and positive and negative polarity bearing words are weighted within the interval of [-1, 1].
### Data Splits
train: 1,650 negative and 1,818 positive words
## 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
Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported License
### Citation Information
@INPROCEEDINGS{remquahey2010,
title = {SentiWS -- a Publicly Available German-language Resource for Sentiment Analysis},
booktitle = {Proceedings of the 7th International Language Resources and Evaluation (LREC'10)},
author = {Remus, R. and Quasthoff, U. and Heyer, G.},
year = {2010}
}
### Contributions
Thanks to [@harshalmittal4](https://github.com/harshalmittal4) for adding this dataset. | [
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huggan/pokemon | huggan | 2022-04-01T11:50:45Z | 440 | 14 | null | [
"region:us"
] | 2022-04-01T11:50:45Z | 2022-04-01T11:44:34.000Z | 2022-04-01T11:44:34 | Source: https://www.kaggle.com/datasets/djilax/pkmn-image-dataset | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
flaviagiammarino/vqa-rad | flaviagiammarino | 2023-06-03T18:38:48Z | 440 | 6 | vqa-rad | [
"task_categories:visual-question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc0-1.0",
"medical",
"region:us"
] | 2023-06-03T18:38:48Z | 2023-06-03T14:33:55.000Z | 2023-06-03T14:33:55 | ---
license: cc0-1.0
task_categories:
- visual-question-answering
language:
- en
paperswithcode_id: vqa-rad
tags:
- medical
pretty_name: VQA-RAD
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: image
dtype: image
- name: question
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 95883938.139
num_examples: 1793
- name: test
num_bytes: 23818877.0
num_examples: 451
download_size: 34496718
dataset_size: 119702815.139
---
# Dataset Card for VQA-RAD
## Dataset Description
VQA-RAD is a dataset of question-answer pairs on radiology images. The dataset is intended to be used for training and testing
Medical Visual Question Answering (VQA) systems. The dataset includes both open-ended questions and binary "yes/no" questions.
The dataset is built from [MedPix](https://medpix.nlm.nih.gov/), which is a free open-access online database of medical images.
The question-answer pairs were manually generated by a team of clinicians.
**Homepage:** [Open Science Framework Homepage](https://osf.io/89kps/)<br>
**Paper:** [A dataset of clinically generated visual questions and answers about radiology images](https://www.nature.com/articles/sdata2018251)<br>
**Leaderboard:** [Papers with Code Leaderboard](https://paperswithcode.com/sota/medical-visual-question-answering-on-vqa-rad)
### Dataset Summary
The dataset was downloaded from the [Open Science Framework Homepage](https://osf.io/89kps/) on June 3, 2023. The dataset contains
2,248 question-answer pairs and 315 images. Out of the 315 images, 314 images are referenced by a question-answer pair, while 1 image
is not used. The training set contains 3 duplicate image-question-answer triplets. The training set also has 1 image-question-answer
triplet in common with the test set. After dropping these 4 image-question-answer triplets from the training set, the dataset contains
2,244 question-answer pairs on 314 images.
#### Supported Tasks and Leaderboards
This dataset has an active leaderboard on [Papers with Code](https://paperswithcode.com/sota/medical-visual-question-answering-on-vqa-rad)
where models are ranked based on three metrics: "Close-ended Accuracy", "Open-ended accuracy" and "Overall accuracy". "Close-ended Accuracy" is
the accuracy of a model's generated answers for the subset of binary "yes/no" questions. "Open-ended accuracy" is the accuracy
of a model's generated answers for the subset of open-ended questions. "Overall accuracy" is the accuracy of a model's generated
answers across all questions.
#### Languages
The question-answer pairs are in English.
## Dataset Structure
### Data Instances
Each instance consists of an image-question-answer triplet.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=566x555>,
'question': 'are regions of the brain infarcted?',
'answer': 'yes'
}
```
### Data Fields
- `'image'`: the image referenced by the question-answer pair.
- `'question'`: the question about the image.
- `'answer'`: the expected answer.
### Data Splits
The dataset is split into training and test. The split is provided directly by the authors.
| | Training Set | Test Set |
|-------------------------|:------------:|:---------:|
| QAs |1,793 |451 |
| Images |313 |203 |
## Additional Information
### Licensing Information
The authors have released the dataset under the CC0 1.0 Universal License.
### Citation Information
```
@article{lau2018dataset,
title={A dataset of clinically generated visual questions and answers about radiology images},
author={Lau, Jason J and Gayen, Soumya and Ben Abacha, Asma and Demner-Fushman, Dina},
journal={Scientific data},
volume={5},
number={1},
pages={1--10},
year={2018},
publisher={Nature Publishing Group}
}
``` | [
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fvillena/spanish_diagnostics | fvillena | 2021-05-30T02:32:52Z | 439 | 0 | null | [
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] | 2021-05-30T02:32:52Z | 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 | |
EddieChen372/devign_with_vul_lines | EddieChen372 | 2023-02-04T15:24:46Z | 439 | 0 | null | [
"region:us"
] | 2023-02-04T15:24:46Z | 2023-02-01T12:14:23.000Z | 2023-02-01T12:14:23 | ---
dataset_info:
features:
- name: id
dtype: int32
- name: func
dtype: string
- name: target
dtype: bool
- name: project
dtype: string
- name: commit_id
dtype: string
- name: func_clean
dtype: string
- name: vul_lines
struct:
- name: code
sequence: string
- name: line_no
sequence: int64
- name: normalized_func
dtype: string
splits:
- name: validation
num_bytes: 16112369
num_examples: 2732
- name: train
num_bytes: 132054560
num_examples: 21854
- name: test
num_bytes: 16328301
num_examples: 2732
download_size: 60272537
dataset_size: 164495230
---
# Dataset Card for "devign_with_vul_lines"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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demelin/moral_stories | demelin | 2022-07-17T15:29:10Z | 438 | 12 | null | [
"task_categories:multiple-choice",
"task_categories:text-generation",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:language-modeling",
"task_ids:text-scoring",
"annotations_creators:no-annotation",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:mit",
"region:us"
] | 2022-07-17T15:29:10Z | 2022-07-14T11:19:52.000Z | 2022-07-14T11:19:52 | ---
annotations_creators:
- no-annotation
language:
- en
language_creators:
- crowdsourced
license:
- mit
multilinguality:
- monolingual
pretty_name: Moral Stories
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- multiple-choice
- text-generation
- text-classification
- commonsense-reasoning
- moral-reasoning
- social-reasoning
task_ids:
- multiple-choice-qa
- language-modeling
- text-scoring
---
# Dataset Card for Moral Stories
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [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)
## Dataset Description
- **Homepage:** [Moral Stories repository](https://github.com/demelin/moral_stories)
- **Repository:** [Moral Stories repository](https://github.com/demelin/moral_stories)
- **Paper:** [Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences](https://aclanthology.org/2021.emnlp-main.54/)
- **Leaderboard:** [N/A]
- **Point of Contact:** [Denis Emelin](demelin.github.io)
### Dataset Summary
Moral Stories is a crowd-sourced dataset of structured narratives that describe normative and norm-divergent actions taken by individuals to accomplish certain intentions in concrete situations, and their respective consequences. All stories in the dataset consist of seven sentences, belonging to the following categories:
- Norm: A guideline for social conduct generally observed by most people in everyday situations.
- Situation: Setting of the story that introduces story participants and describes their environment.
- Intention: Reasonable goal that one of the story participants (the actor), wants to fulfill.
- Normative action: An action by the actor that fulfills the intention and observes the norm.
- Normative consequence: Possible effect of the normative action on the actor's environment.
- Divergent action: An action by the actor that fulfills the intention and diverges from the norm.
- Divergent consequence: Possible effect of the divergent action on the actor's environment.
Accordingly, each story's constituent sentences can be grouped into three segments. The context segment grounds actions within a particular social scenario, the normative path contains the normative action and its consequence, whereas the divergent path includes their norm-divergent analogues. Combining the context segment separately with each path yields two self-contained sub-stories differing in the adherence of the described events to social expectations. See also [*Section 2* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Supported Tasks and Leaderboards
- commonsense-reasoning / social-reasoning / moral-reasoning: The dataset can also be used evaluate whether pretrained language models can reason about actions that are either consistent or inconsistent with social norms, the consequences of actions, and the norms that may motivate specific action choices. A [BART model](https://huggingface.co/facebook/bart-large) can be used for this purpose.
- text-classification: This dataset can be used to train models to differentiate between normative and divergent actions as well as between plausible and implausible consequences of actions. A [RoBERTa-based model](https://huggingface.co/roberta-base) can be used for this purpose.
- text-generation: The dataset can be used to train models to generate normative / divergent action hypotheses and their consequences, or norms that may explain certain action choices, conditioned on contexts of different scope. A [GPT-based model](https://huggingface.co/EleutherAI/gpt-neo-2.7B) can be used for this purpose.
### Languages
*Moral Stories* is available in English, with mainstream US Englishes being the dominant variety, as indicated by self-reported contributor demographics provided in the [*Ethical Considerations* section of the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
## Dataset Structure
### Data Instances
Due to its highly structured nature, *Moral Stories* enables a variety of tasks, such as action classification or norm generation. Furthermore, we provide different data splits in an attempt to challenge generalization abilities of the evaluated models. For details, refer to [*Section 2* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf). Here, we replicate one instance from the full, task-agnostic dataset, one instance from a classification task dataset (cls-action+norm), and one instance from a generation task dataset (gen-consequence$action+context):
"full" instance:
{"ID": "3OJSZ2ATDTUMG7NGR28931AHDQF57F",
"norm": "It's kind to wait for people.",
"situation": "Josh drives Penelope to work every day and is hanging outside her house to pick her up, but there's no sign of her.",
"intention": "Josh needs to get to work.",
"moral_action": "Josh continues to sit outside Penelope's house until she walks out the door a few minutes later.",
"moral_consequence": "Penelope appreciates Josh's patience and thanks him for waiting around.",
"immoral_action": "Josh shrugs his shoulders and drives away to work after sitting around a moment.",
"immoral_consequence": "Penelope comes running out of the house shouting to wait, but she's too late."}
positive and negative "cls-action+norm" instances (i.e. classify an action as normative or divergent given the action and a relevant norm):
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G1",
"norm": "It's bad to be too loud and wake people up.",
"moral_action": "Jordan quietly makes himself a bowl of cereal for breakfast.",
"label": "1"}
{"ID": "3EF8EXOTT2TGCKNZROVU3129RYNJ1G0",
"norm": "It's bad to be too loud and wake people up.",
"immoral_action": "Jordan noisily bangs a lot of pots and pans when making his breakfast.",
"label": "0"}
"gen-consequence$action+context" instance (i.e. generate either a plausible or implausible consequence given an action and its context):
{"ID": "33FOTY3KENJT46HTHWE8GXPE3LE1C03",
"norm": "It's mean to hit a bear with a guitar.",
"situation": "Joe was camping in Montana with his band when he came across a black bear.",
"intention": "Joe wanted to scare away the black bear.",
"immoral_action": "Joe grabbed his guitarist's guitar and hit the bear with it.",
"immoral_consequence": "The bear suffers a concussion and doesn't understand why he was hit.",
"label": "1"}
### Data Fields
- "ID": Unique identifier ID for this dataset instance.
- "norm": A guideline for social conduct generally observed by most people in everyday situations.
- "situation": Setting of the story that introduces story participants and describes their environment.
- "intention": Reasonable goal that one of the story participants (the actor), wants to fulfill.
- "moral_(i.e. 'normative')_action": An action by the actor that fulfills the intention and observes the norm.
- "moral_consequence": Possible effect of the normative action on the actor's environment.
- "immoral_(i.e. 'divergent')_action": An action by the actor that fulfills the intention and diverges from the norm.
- "immoral_consequence": Possible effect of the divergent action on the actor's environment.
- "label": Data instance label; for action-related tasks, "0" corresponds to an immoral / divergent action while "1" corresponds to a moral / normative action, for consequence-related tasks, "0" corresponds to a plausible consequence while "1" corresponds to an implausible consequence (for generation tasks, label is always set to "1")
### Data Splits
For classification tasks, we examined three data split strategies:
- *Norm Distance*: Norms are based on social consensus and may, as such, change across time and between locations. Therefore, we are also interested in how well classification models can generalize to novel norms. To estimate this, we split the dataset by embedding
norms found in the collected stories and grouping them into 1k clusters via agglomerative clustering. Clusters are ordered according to their degree of isolation, defined as the cosine distance between a cluster's centroid and the next-closest cluster's centroid. Stories with norms from most isolated clusters are assigned to test and development sets, with the rest forming the training set.
- *Lexical Bias*: Tests the susceptibility of classifiers to surface-level lexical correlations. We first identify 100 biased lemmas that occur most frequently either in normative or divergent actions. Each story is then assigned a bias score corresponding to the total number of biased lemmas present in both actions (or consequences). Starting with the lowest bias scores, stories are assigned to the test, development, and, lastly, training set.
- *Minimal Pairs*: Evaluates the model's ability to perform nuanced social reasoning. Splits are obtained by ordering stories according to the Damerau-Levenshtein distance between their actions (or consequences) and assigning stories with lowest distances to the test set, followed by the development set. The remainder makes up the training set.
For generation tasks, only the *Norm Distance* split strategy is used. For more details, refer to [*Section 3* and *Appendix C* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
## Dataset Creation
### Curation Rationale
Please refer to [*Section 2* and the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Source Data
#### Initial Data Collection and Normalization
Please refer to [*Section 2* in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
#### Who are the source language producers?
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Annotations
#### Annotation process
Please refer to [*Section 2* and the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
#### Who are the annotators?
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Discussion of Biases
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
### Other Known Limitations
Please refer to [the *Ethical Considerations* section in the dataset paper](https://aclanthology.org/2021.emnlp-main.54.pdf).
## Additional Information
### Dataset Curators
[Denis Emelin](demelin.github.io)
### Licensing Information
MIT
### Citation Information
@article{Emelin2021MoralSS,
title={Moral Stories: Situated Reasoning about Norms, Intents, Actions, and their Consequences},
author={Denis Emelin and Ronan Le Bras and Jena D. Hwang and Maxwell Forbes and Yejin Choi},
journal={ArXiv},
year={2021},
volume={abs/2012.15738}
} | [
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
elyza/ELYZA-tasks-100 | elyza | 2023-09-26T01:38:42Z | 438 | 34 | null | [
"task_categories:text2text-generation",
"size_categories:n<1K",
"language:ja",
"license:cc-by-sa-4.0",
"arxiv:2307.09288",
"region:us"
] | 2023-09-26T01:38:42Z | 2023-08-28T09:01:44.000Z | 2023-08-28T09:01:44 | ---
task_categories:
- text2text-generation
language:
- ja
size_categories:
- n<1K
license: cc-by-sa-4.0
---
# ELYZA-tasks-100: 日本語instructionモデル評価データセット
## Data Description
本データセットはinstruction-tuningを行ったモデルの評価用データセットです。詳細は [リリースのnote記事](https://note.com/elyza/n/na405acaca130) を参照してください。
特徴:
- 複雑な指示・タスクを含む100件の日本語データです。
- 役に立つAIアシスタントとして、丁寧な出力が求められます。
- 全てのデータに対して評価観点がアノテーションされており、評価の揺らぎを抑えることが期待されます。
具体的には以下のようなタスクを含みます。
- 要約を修正し、修正箇所を説明するタスク
- 具体的なエピソードから抽象的な教訓を述べるタスク
- ユーザーの意図を汲み役に立つAIアシスタントとして振る舞うタスク
- 場合分けを必要とする複雑な算数のタスク
- 未知の言語からパターンを抽出し日本語訳する高度な推論を必要とするタスク
- 複数の指示を踏まえた上でyoutubeの対話を生成するタスク
- 架空の生き物や熟語に関する生成・大喜利などの想像力が求められるタスク
## Usage
datasetsライブラリから利用が可能です。
```py
>>> from datasets import load_dataset
>>> ds = load_dataset("elyza/ELYZA-tasks-100")
>>> ds
DatasetDict({
test: Dataset({
features: ["input", "output", "eval_aspect"],
num_rows: 100
})
})
>>> ds["test"][0]
{
'input': '仕事の熱意を取り戻すためのアイデアを5つ挙げてください。',
'output': '1. 自分の仕事に対する興味を再発見するために、新しい技能や知識を学ぶこと。\n2. カレッジやセミナーなどで講演を聴くことで、仕事に対する新しいアイデアや視点を得ること。\n3. 仕事に対してストレスを感じている場合は、ストレスマネジメントのテクニックを学ぶこと。\n4. 仕事以外の楽しいことをすることで、ストレスを発散すること。\n5. 仕事に対して自己評価をすることで、自分がどのように進化しているのかを知ること。',
'eval_aspect': '- 熱意を取り戻すのではなく、仕事の効率化・スキルアップのような文脈になっていたら1点減点\n- 出したアイデアが5つより多い、少ない場合は1点減点\n- 5つのアイデアのうち、内容が重複しているものがあれば1点減点\n\n'
}
```
## Baseline Evaluation
本データセットは手動/自動, 絶対/相対 評価のいずれの評価形式でも利用していただくことができますが、今回我々はベースラインモデルの評価として、5段階の絶対評価を手動で行いました。
### 評価手順
1. [こちらの推論スクリプト](https://huggingface.co/datasets/elyza/ELYZA-tasks-100/tree/main/baseline/scripts)のようにベースラインとなるモデルでの推論を行い、[baseline/preds](https://huggingface.co/datasets/elyza/ELYZA-tasks-100/tree/main/baseline/preds)以下に推論結果を格納しました。
- 基本的にgenerate時のパラメータはREADMEなどに記載されているデフォルト値を用いました。
2. [shuffle_for_humaneval.py](https://huggingface.co/datasets/elyza/ELYZA-tasks-100/blob/main/baseline/humaneval/shuffle_for_humaneval.py)を用いて匿名化されたモデルの推論結果 [shuffled_preds.csv](https://huggingface.co/datasets/elyza/ELYZA-tasks-100/blob/main/baseline/humaneval/shuffled_preds.csv) と匿名化を復元するための対応表 [uuids.csv](https://huggingface.co/datasets/elyza/ELYZA-tasks-100/blob/main/baseline/humaneval/uuids.csv) を作成しました。
3. [shuffled_preds.csv](https://huggingface.co/datasets/elyza/ELYZA-tasks-100/blob/main/baseline/humaneval/shuffled_preds.csv) を Googleスプレッドシートにアップロードし、[評価ガイドライン](https://huggingface.co/datasets/elyza/ELYZA-tasks-100/blob/main/baseline/humaneval/guideline.md) に従って、各データ3人で人手評価を行いました。
4. スプレッドシートでの評価結果を[annotated_shuffled_preds.xlsx](https://huggingface.co/datasets/elyza/ELYZA-tasks-100/blob/main/baseline/humaneval/annotated_shuffled_preds.xlsx)としてダウンロードし、 [deshuffle_annotations.py](https://huggingface.co/datasets/elyza/ELYZA-tasks-100/blob/main/baseline/humaneval/deshuffle_annotations.py) を利用し、匿名化された評価結果を復号して[annotated_deshuffled_preds.csv](https://huggingface.co/datasets/elyza/ELYZA-tasks-100/blob/main/baseline/humaneval/annotated_deshuffled_preds.csv) として保存しました。
5. 最後にGoogleスプレッドシートに[評価結果シート](https://docs.google.com/spreadsheets/d/1mtoy4QAqDPk2f_B0vDogFoOrbA5G42DBEEHdqM4VmDI/edit#gid=1023787356)にアップロードして可視化しました。
### 評価結果
- スコアについては、[リリースのnote記事](https://note.com/elyza/n/na405acaca130) を参照してください。
- [評価結果シート](https://docs.google.com/spreadsheets/d/1mtoy4QAqDPk2f_B0vDogFoOrbA5G42DBEEHdqM4VmDI/edit#gid=1023787356):
- 全ての入出力と評価を公開しています。スコアだけでは分からないモデルの傾向を知ることができます。
### 評価手法の妥当性について
[zennの技術ブログ](https://zenn.dev/elyza/articles/5e7d9373c32a98)にて今回のベースラインの評価の詳細な分析についての記事を書きました。よければそちらもご覧ください。
## GPT4での自動評価について
こちらも[zennの技術ブログ](https://zenn.dev/elyza/articles/5e7d9373c32a98)にて実際にGPT4での評価を行う際のコードと結果を示しています。
## Developers
以下アルファベット順です。
- [Akira Sasaki](https://huggingface.co/akirasasaki)
- [Masato Hirakawa](https://huggingface.co/m-hirakawa)
- [Shintaro Horie](https://huggingface.co/e-mon)
- [Tomoaki Nakamura](https://huggingface.co/tyoyo)
## License
このデータセットは [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/deed.ja) でライセンスされています。
## How to Cite
```tex
@misc{elyzatasks100,
title={ELYZA-tasks-100: 日本語instructionモデル評価データセット},
url={https://huggingface.co/elyza/ELYZA-tasks-100},
author={Akira Sasaki and Masato Hirakawa and Shintaro Horie and Tomoaki Nakamura},
year={2023},
}
```
## Citations
```tex
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov and Soumya Batra and Prajjwal Bhargava and Shruti Bhosale and Dan Bikel and Lukas Blecher and Cristian Canton Ferrer and Moya Chen and Guillem Cucurull and David Esiobu and Jude Fernandes and Jeremy Fu and Wenyin Fu and Brian Fuller and Cynthia Gao and Vedanuj Goswami and Naman Goyal and Anthony Hartshorn and Saghar Hosseini and Rui Hou and Hakan Inan and Marcin Kardas and Viktor Kerkez and Madian Khabsa and Isabel Kloumann and Artem Korenev and Punit Singh Koura and Marie-Anne Lachaux and Thibaut Lavril and Jenya Lee and Diana Liskovich and Yinghai Lu and Yuning Mao and Xavier Martinet and Todor Mihaylov and Pushkar Mishra and Igor Molybog and Yixin Nie and Andrew Poulton and Jeremy Reizenstein and Rashi Rungta and Kalyan Saladi and Alan Schelten and Ruan Silva and Eric Michael Smith and Ranjan Subramanian and Xiaoqing Ellen Tan and Binh Tang and Ross Taylor and Adina Williams and Jian Xiang Kuan and Puxin Xu and Zheng Yan and Iliyan Zarov and Yuchen Zhang and Angela Fan and Melanie Kambadur and Sharan Narang and Aurelien Rodriguez and Robert Stojnic and Sergey Edunov and Thomas Scialom},
year={2023},
eprint={2307.09288},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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] | null | null | null | null | null | null | null | null | null | null | null | null | null | |
FinGPT/fingpt-sentiment-train | FinGPT | 2023-11-20T21:45:14Z | 438 | 3 | null | [
"region:us"
] | 2023-11-20T21:45:14Z | 2023-10-10T06:26:21.000Z | 2023-10-10T06:26:21 | ---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 18860715
num_examples: 76772
download_size: 6417302
dataset_size: 18860715
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "fingpt-sentiment-train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | [
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