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acronym_identification | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- token-classification
task_ids: []
paperswithcode_id: acronym-identification
pretty_name: Acronym Identification Dataset
tags:
- acronym-identification
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: labels
sequence:
class_label:
names:
'0': B-long
'1': B-short
'2': I-long
'3': I-short
'4': O
splits:
- name: train
num_bytes: 7792803
num_examples: 14006
- name: validation
num_bytes: 952705
num_examples: 1717
- name: test
num_bytes: 987728
num_examples: 1750
download_size: 8556464
dataset_size: 9733236
train-eval-index:
- config: default
task: token-classification
task_id: entity_extraction
splits:
eval_split: test
col_mapping:
tokens: tokens
labels: tags
---
# Dataset Card for Acronym Identification 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://sites.google.com/view/sdu-aaai21/shared-task
- **Repository:** https://github.com/amirveyseh/AAAI-21-SDU-shared-task-1-AI
- **Paper:** [What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation](https://arxiv.org/pdf/2010.14678v1.pdf)
- **Leaderboard:** https://competitions.codalab.org/competitions/26609
- **Point of Contact:** [More Information Needed]
### Dataset Summary
This dataset contains the training, validation, and test data for the **Shared Task 1: Acronym Identification** of the AAAI-21 Workshop on Scientific Document Understanding.
### Supported Tasks and Leaderboards
The dataset supports an `acronym-identification` task, where the aim is to predic which tokens in a pre-tokenized sentence correspond to acronyms. The dataset was released for a Shared Task which supported a [leaderboard](https://competitions.codalab.org/competitions/26609).
### Languages
The sentences in the dataset are in English (`en`).
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{'id': 'TR-0',
'labels': [4, 4, 4, 4, 0, 2, 2, 4, 1, 4, 4, 4, 4, 4, 4, 4, 4, 4],
'tokens': ['What',
'is',
'here',
'called',
'controlled',
'natural',
'language',
'(',
'CNL',
')',
'has',
'traditionally',
'been',
'given',
'many',
'different',
'names',
'.']}
```
Please note that in test set sentences only the `id` and `tokens` fields are available. `labels` can be ignored for test set. Labels in the test set are all `O`
### Data Fields
The data instances have the following fields:
- `id`: a `string` variable representing the example id, unique across the full dataset
- `tokens`: a list of `string` variables representing the word-tokenized sentence
- `labels`: a list of `categorical` variables with possible values `["B-long", "B-short", "I-long", "I-short", "O"]` corresponding to a BIO scheme. `-long` corresponds to the expanded acronym, such as *controlled natural language* here, and `-short` to the abbrviation, `CNL` here.
### Data Splits
The training, validation, and test set contain `14,006`, `1,717`, and `1750` sentences respectively.
## Dataset Creation
### Curation Rationale
> First, most of the existing datasets for acronym identification (AI) are either limited in their sizes or created using simple rule-based methods.
> This is unfortunate as rules are in general not able to capture all the diverse forms to express acronyms and their long forms in text.
> Second, most of the existing datasets are in the medical domain, ignoring the challenges in other scientific domains.
> In order to address these limitations this paper introduces two new datasets for Acronym Identification.
> Notably, our datasets are annotated by human to achieve high quality and have substantially larger numbers of examples than the existing AI datasets in the non-medical domain.
### Source Data
#### Initial Data Collection and Normalization
> In order to prepare a corpus for acronym annotation, we collect a corpus of 6,786 English papers from arXiv.
> These papers consist of 2,031,592 sentences that would be used for data annotation for AI in this work.
The dataset paper does not report the exact tokenization method.
#### Who are the source language producers?
The language was comes from papers hosted on the online digital archive [arXiv](https://arxiv.org/). No more information is available on the selection process or identity of the writers.
### Annotations
#### Annotation process
> Each sentence for annotation needs to contain at least one word in which more than half of the characters in are capital letters (i.e., acronym candidates).
> Afterward, we search for a sub-sequence of words in which the concatenation of the first one, two or three characters of the words (in the order of the words in the sub-sequence could form an acronym candidate.
> We call the sub-sequence a long form candidate. If we cannot find any long form candidate, we remove the sentence.
> Using this process, we end up with 17,506 sentences to be annotated manually by the annotators from Amazon Mechanical Turk (MTurk).
> In particular, we create a HIT for each sentence and ask the workers to annotate the short forms and the long forms in the sentence.
> In case of disagreements, if two out of three workers agree on an annotation, we use majority voting to decide the correct annotation.
> Otherwise, a fourth annotator is hired to resolve the conflict
#### Who are the annotators?
Workers were recruited through Amazon MEchanical Turk and paid $0.05 per annotation. No further demographic information is provided.
### Personal and Sensitive Information
Papers published on arXiv are unlikely to contain much personal information, although some do include some poorly chosen examples revealing personal details, so the data should be used with care.
## 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
The dataset provided for this shared task is licensed under CC BY-NC-SA 4.0 international license.
### Citation Information
```
@inproceedings{Veyseh2020,
author = {Amir Pouran Ben Veyseh and
Franck Dernoncourt and
Quan Hung Tran and
Thien Huu Nguyen},
editor = {Donia Scott and
N{\'{u}}ria Bel and
Chengqing Zong},
title = {What Does This Acronym Mean? Introducing a New Dataset for Acronym
Identification and Disambiguation},
booktitle = {Proceedings of the 28th International Conference on Computational
Linguistics, {COLING} 2020, Barcelona, Spain (Online), December 8-13,
2020},
pages = {3285--3301},
publisher = {International Committee on Computational Linguistics},
year = {2020},
url = {https://doi.org/10.18653/v1/2020.coling-main.292},
doi = {10.18653/v1/2020.coling-main.292}
}
```
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
ade_corpus_v2 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
- n<1K
source_datasets:
- original
task_categories:
- text-classification
- token-classification
task_ids:
- coreference-resolution
- fact-checking
pretty_name: Adverse Drug Reaction Data v2
configs:
- Ade_corpus_v2_classification
- Ade_corpus_v2_drug_ade_relation
- Ade_corpus_v2_drug_dosage_relation
dataset_info:
- config_name: Ade_corpus_v2_classification
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Not-Related
'1': Related
splits:
- name: train
num_bytes: 3403711
num_examples: 23516
download_size: 3791162
dataset_size: 3403711
- config_name: Ade_corpus_v2_drug_ade_relation
features:
- name: text
dtype: string
- name: drug
dtype: string
- name: effect
dtype: string
- name: indexes
struct:
- name: drug
sequence:
- name: start_char
dtype: int32
- name: end_char
dtype: int32
- name: effect
sequence:
- name: start_char
dtype: int32
- name: end_char
dtype: int32
splits:
- name: train
num_bytes: 1546021
num_examples: 6821
download_size: 3791162
dataset_size: 1546021
- config_name: Ade_corpus_v2_drug_dosage_relation
features:
- name: text
dtype: string
- name: drug
dtype: string
- name: dosage
dtype: string
- name: indexes
struct:
- name: drug
sequence:
- name: start_char
dtype: int32
- name: end_char
dtype: int32
- name: dosage
sequence:
- name: start_char
dtype: int32
- name: end_char
dtype: int32
splits:
- name: train
num_bytes: 64725
num_examples: 279
download_size: 3791162
dataset_size: 64725
train-eval-index:
- config: Ade_corpus_v2_classification
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for Adverse Drug Reaction Data v2
## 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.sciencedirect.com/science/article/pii/S1532046412000615
- **Repository:** [Needs More Information]
- **Paper:** https://www.sciencedirect.com/science/article/pii/S1532046412000615
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
ADE-Corpus-V2 Dataset: Adverse Drug Reaction Data.
This is a dataset for Classification if a sentence is ADE-related (True) or not (False) and Relation Extraction between Adverse Drug Event and Drug.
DRUG-AE.rel provides relations between drugs and adverse effects.
DRUG-DOSE.rel provides relations between drugs and dosages.
ADE-NEG.txt provides all sentences in the ADE corpus that DO NOT contain any drug-related adverse effects.
### Supported Tasks and Leaderboards
Sentiment classification, Relation Extraction
### Languages
English
## Dataset Structure
### Data Instances
#### Config - `Ade_corpus_v2_classification`
```
{
'label': 1,
'text': 'Intravenous azithromycin-induced ototoxicity.'
}
```
#### Config - `Ade_corpus_v2_drug_ade_relation`
```
{
'drug': 'azithromycin',
'effect': 'ototoxicity',
'indexes': {
'drug': {
'end_char': [24],
'start_char': [12]
},
'effect': {
'end_char': [44],
'start_char': [33]
}
},
'text': 'Intravenous azithromycin-induced ototoxicity.'
}
```
#### Config - `Ade_corpus_v2_drug_dosage_relation`
```
{
'dosage': '4 times per day',
'drug': 'insulin',
'indexes': {
'dosage': {
'end_char': [56],
'start_char': [41]
},
'drug': {
'end_char': [40],
'start_char': [33]}
},
'text': 'She continued to receive regular insulin 4 times per day over the following 3 years with only occasional hives.'
}
```
### Data Fields
#### Config - `Ade_corpus_v2_classification`
- `text` - Input text.
- `label` - Whether the adverse drug effect(ADE) related (1) or not (0).
-
#### Config - `Ade_corpus_v2_drug_ade_relation`
- `text` - Input text.
- `drug` - Name of drug.
- `effect` - Effect caused by the drug.
- `indexes.drug.start_char` - Start index of `drug` string in text.
- `indexes.drug.end_char` - End index of `drug` string in text.
- `indexes.effect.start_char` - Start index of `effect` string in text.
- `indexes.effect.end_char` - End index of `effect` string in text.
#### Config - `Ade_corpus_v2_drug_dosage_relation`
- `text` - Input text.
- `drug` - Name of drug.
- `dosage` - Dosage of the drug.
- `indexes.drug.start_char` - Start index of `drug` string in text.
- `indexes.drug.end_char` - End index of `drug` string in text.
- `indexes.dosage.start_char` - Start index of `dosage` string in text.
- `indexes.dosage.end_char` - End index of `dosage` string in text.
### Data Splits
| Train |
| ------ |
| 23516 |
## 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
```
@article{GURULINGAPPA2012885,
title = "Development of a benchmark corpus to support the automatic extraction of drug-related adverse effects from medical case reports",
journal = "Journal of Biomedical Informatics",
volume = "45",
number = "5",
pages = "885 - 892",
year = "2012",
note = "Text Mining and Natural Language Processing in Pharmacogenomics",
issn = "1532-0464",
doi = "https://doi.org/10.1016/j.jbi.2012.04.008",
url = "http://www.sciencedirect.com/science/article/pii/S1532046412000615",
author = "Harsha Gurulingappa and Abdul Mateen Rajput and Angus Roberts and Juliane Fluck and Martin Hofmann-Apitius and Luca Toldo",
keywords = "Adverse drug effect, Benchmark corpus, Annotation, Harmonization, Sentence classification",
abstract = "A significant amount of information about drug-related safety issues such as adverse effects are published in medical case reports that can only be explored by human readers due to their unstructured nature. The work presented here aims at generating a systematically annotated corpus that can support the development and validation of methods for the automatic extraction of drug-related adverse effects from medical case reports. The documents are systematically double annotated in various rounds to ensure consistent annotations. The annotated documents are finally harmonized to generate representative consensus annotations. In order to demonstrate an example use case scenario, the corpus was employed to train and validate models for the classification of informative against the non-informative sentences. A Maximum Entropy classifier trained with simple features and evaluated by 10-fold cross-validation resulted in the F1 score of 0.70 indicating a potential useful application of the corpus."
}
```
### Contributions
Thanks to [@Nilanshrajput](https://github.com/Nilanshrajput), [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
adversarial_qa | ---
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:
- extractive-qa
- open-domain-qa
paperswithcode_id: adversarialqa
pretty_name: adversarialQA
train-eval-index:
- config: adversarialQA
task: question-answering
task_id: extractive_question_answering
splits:
train_split: train
eval_split: validation
col_mapping:
question: question
context: context
answers:
text: text
answer_start: answer_start
metrics:
- type: squad
name: SQuAD
dataset_info:
- config_name: adversarialQA
features:
- name: 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: metadata
struct:
- name: split
dtype: string
- name: model_in_the_loop
dtype: string
splits:
- name: train
num_bytes: 27858794
num_examples: 30000
- name: validation
num_bytes: 2757128
num_examples: 3000
- name: test
num_bytes: 2919643
num_examples: 3000
download_size: 9018914
dataset_size: 33535565
- config_name: dbidaf
features:
- name: 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: metadata
struct:
- name: split
dtype: string
- name: model_in_the_loop
dtype: string
splits:
- name: train
num_bytes: 9282518
num_examples: 10000
- name: validation
num_bytes: 917943
num_examples: 1000
- name: test
num_bytes: 947111
num_examples: 1000
download_size: 9018914
dataset_size: 11147572
- config_name: dbert
features:
- name: 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: metadata
struct:
- name: split
dtype: string
- name: model_in_the_loop
dtype: string
splits:
- name: train
num_bytes: 9345557
num_examples: 10000
- name: validation
num_bytes: 918192
num_examples: 1000
- name: test
num_bytes: 971454
num_examples: 1000
download_size: 9018914
dataset_size: 11235203
- config_name: droberta
features:
- name: 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: metadata
struct:
- name: split
dtype: string
- name: model_in_the_loop
dtype: string
splits:
- name: train
num_bytes: 9270719
num_examples: 10000
- name: validation
num_bytes: 925065
num_examples: 1000
- name: test
num_bytes: 1005406
num_examples: 1000
download_size: 9018914
dataset_size: 11201190
---
# Dataset Card for adversarialQA
## 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:** [adversarialQA homepage](https://adversarialqa.github.io/)
- **Repository:** [adversarialQA repository](https://github.com/maxbartolo/adversarialQA)
- **Paper:** [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://arxiv.org/abs/2002.00293)
- **Leaderboard:** [Dynabench QA Round 1 Leaderboard](https://dynabench.org/tasks/2#overall)
- **Point of Contact:** [Max Bartolo](max.bartolo@ucl.ac.uk)
### Dataset Summary
We have created three new Reading Comprehension datasets constructed using an adversarial model-in-the-loop.
We use three different models; BiDAF (Seo et al., 2016), BERTLarge (Devlin et al., 2018), and RoBERTaLarge (Liu et al., 2019) in the annotation loop and construct three datasets; D(BiDAF), D(BERT), and D(RoBERTa), each with 10,000 training examples, 1,000 validation, and 1,000 test examples.
The adversarial human annotation paradigm ensures that these datasets consist of questions that current state-of-the-art models (at least the ones used as adversaries in the annotation loop) find challenging. The three AdversarialQA round 1 datasets provide a training and evaluation resource for such methods.
### Supported Tasks and Leaderboards
`extractive-qa`: The dataset can be used to train a model for Extractive Question Answering, which consists in selecting the answer to a question from a passage. Success on this task is typically measured by achieving a high word-overlap [F1 score](https://huggingface.co/metrics/f1). The [RoBERTa-Large](https://huggingface.co/roberta-large) model trained on all the data combined with [SQuAD](https://arxiv.org/abs/1606.05250) currently achieves 64.35% F1. This task has an active leaderboard and is available as round 1 of the QA task on [Dynabench](https://dynabench.org/tasks/2#overall) and ranks models based on F1 score.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
Data is provided in the same format as SQuAD 1.1. An example is shown below:
```
{
"data": [
{
"title": "Oxygen",
"paragraphs": [
{
"context": "Among the most important classes of organic compounds that contain oxygen are (where \"R\" is an organic group): alcohols (R-OH); ethers (R-O-R); ketones (R-CO-R); aldehydes (R-CO-H); carboxylic acids (R-COOH); esters (R-COO-R); acid anhydrides (R-CO-O-CO-R); and amides (R-C(O)-NR2). There are many important organic solvents that contain oxygen, including: acetone, methanol, ethanol, isopropanol, furan, THF, diethyl ether, dioxane, ethyl acetate, DMF, DMSO, acetic acid, and formic acid. Acetone ((CH3)2CO) and phenol (C6H5OH) are used as feeder materials in the synthesis of many different substances. Other important organic compounds that contain oxygen are: glycerol, formaldehyde, glutaraldehyde, citric acid, acetic anhydride, and acetamide. Epoxides are ethers in which the oxygen atom is part of a ring of three atoms.",
"qas": [
{
"id": "22bbe104aa72aa9b511dd53237deb11afa14d6e3",
"question": "In addition to having oxygen, what do alcohols, ethers and esters have in common, according to the article?",
"answers": [
{
"answer_start": 36,
"text": "organic compounds"
}
]
},
{
"id": "4240a8e708c703796347a3702cf1463eed05584a",
"question": "What letter does the abbreviation for acid anhydrides both begin and end in?",
"answers": [
{
"answer_start": 244,
"text": "R"
}
]
},
{
"id": "0681a0a5ec852ec6920d6a30f7ef65dced493366",
"question": "Which of the organic compounds, in the article, contains nitrogen?",
"answers": [
{
"answer_start": 262,
"text": "amides"
}
]
},
{
"id": "2990efe1a56ccf81938fa5e18104f7d3803069fb",
"question": "Which of the important classes of organic compounds, in the article, has a number in its abbreviation?",
"answers": [
{
"answer_start": 262,
"text": "amides"
}
]
}
]
}
]
}
]
}
```
### Data Fields
- title: the title of the Wikipedia page from which the context is sourced
- context: the context/passage
- id: a string identifier for each question
- answers: a list of all provided answers (one per question in our case, but multiple may exist in SQuAD) with an `answer_start` field which is the character index of the start of the answer span, and a `text` field which is the answer text.
Note that no answers are provided in the test set. Indeed, this dataset is part of the DynaBench benchmark, for which you can submit your predictions on the [website](https://dynabench.org/tasks/2#1).
### Data Splits
The dataset is composed of three different datasets constructed using different models in the loop: BiDAF, BERT-Large, and RoBERTa-Large. Each of these has 10,000 training examples, 1,000 validation examples, and 1,000 test examples for a total of 30,000/3,000/3,000 train/validation/test examples.
## Dataset Creation
### Curation Rationale
This dataset was collected to provide a more challenging and diverse Reading Comprehension dataset to state-of-the-art models.
### Source Data
#### Initial Data Collection and Normalization
The source passages are from Wikipedia and are the same as those used in [SQuAD v1.1](https://arxiv.org/abs/1606.05250).
#### Who are the source language producers?
The source language produces are Wikipedia editors for the passages, and human annotators on Mechanical Turk for the questions.
### Annotations
#### Annotation process
The dataset is collected through an adversarial human annotation process which pairs a human annotator and a reading comprehension model in an interactive setting. The human is presented with a passage for which they write a question and highlight the correct answer. The model then tries to answer the question, and, if it fails to answer correctly, the human wins. Otherwise, the human modifies or re-writes their question until the successfully fool the model.
#### Who are the annotators?
The annotators are from Amazon Mechanical Turk, geographically restricted the the USA, UK and Canada, having previously successfully completed at least 1,000 HITs, and having a HIT approval rate greater than 98%. Crowdworkers undergo intensive training and qualification prior to annotation.
### Personal and Sensitive Information
No annotator identifying details are provided.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop better question answering systems.
A system that succeeds at the supported task would be able to provide an accurate extractive answer from a short passage. This dataset is to be seen as a test bed for questions which contemporary state-of-the-art models struggle to answer correctly, thus often requiring more complex comprehension abilities than say detecting phrases explicitly mentioned in the passage with high overlap to the question.
It should be noted, however, that the the source passages are both domain-restricted and linguistically specific, and that provided questions and answers do not constitute any particular social application.
### Discussion of Biases
The dataset may exhibit various biases in terms of the source passage selection, annotated questions and answers, as well as algorithmic biases resulting from the adversarial annotation protocol.
### Other Known Limitations
N/a
## Additional Information
### Dataset Curators
This dataset was initially created by Max Bartolo, Alastair Roberts, Johannes Welbl, Sebastian Riedel, and Pontus Stenetorp, during work carried out at University College London (UCL).
### Licensing Information
This dataset is distributed under [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/).
### Citation Information
```
@article{bartolo2020beat,
author = {Bartolo, Max and Roberts, Alastair and Welbl, Johannes and Riedel, Sebastian and Stenetorp, Pontus},
title = {Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension},
journal = {Transactions of the Association for Computational Linguistics},
volume = {8},
number = {},
pages = {662-678},
year = {2020},
doi = {10.1162/tacl\_a\_00338},
URL = { https://doi.org/10.1162/tacl_a_00338 },
eprint = { https://doi.org/10.1162/tacl_a_00338 },
abstract = { Innovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the annotation process: Humans create questions adversarially, such that the model fails to answer them correctly. In this work we investigate this annotation methodology and apply it in three different settings, collecting a total of 36,000 samples with progressively stronger models in the annotation loop. This allows us to explore questions such as the reproducibility of the adversarial effect, transfer from data collected with varying model-in-the-loop strengths, and generalization to data collected without a model. We find that training on adversarially collected samples leads to strong generalization to non-adversarially collected datasets, yet with progressive performance deterioration with increasingly stronger models-in-the-loop. Furthermore, we find that stronger models can still learn from datasets collected with substantially weaker models-in-the-loop. When trained on data collected with a BiDAF model in the loop, RoBERTa achieves 39.9F1 on questions that it cannot answer when trained on SQuAD—only marginally lower than when trained on data collected using RoBERTa itself (41.0F1). }
}
```
### Contributions
Thanks to [@maxbartolo](https://github.com/maxbartolo) for adding this dataset. |
aeslc | ---
annotations_creators:
- crowdsourced
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: 'AESLC: Annotated Enron Subject Line Corpus'
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: aeslc
tags:
- aspect-based-summarization
- conversations-summarization
- multi-document-summarization
- email-headline-generation
dataset_info:
features:
- name: email_body
dtype: string
- name: subject_line
dtype: string
splits:
- name: train
num_bytes: 11902668
num_examples: 14436
- name: validation
num_bytes: 1660730
num_examples: 1960
- name: test
num_bytes: 1384177
num_examples: 1906
download_size: 11643743
dataset_size: 14947575
---
# Dataset Card for "aeslc"
## 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/ryanzhumich/AESLC
- **Paper:** [This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation](https://arxiv.org/abs/1906.03497)
- **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:** 11.64 MB
- **Size of the generated dataset:** 14.95 MB
- **Total amount of disk used:** 26.59 MB
### Dataset Summary
A collection of email messages of employees in the Enron Corporation.
There are two features:
- email_body: email body text.
- subject_line: email subject text.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
Monolingual English (mainly en-US) with some exceptions.
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 11.64 MB
- **Size of the generated dataset:** 14.95 MB
- **Total amount of disk used:** 26.59 MB
An example of 'train' looks as follows.
```
{
"email_body": "B/C\n<<some doc>>\n",
"subject_line": "Service Agreement"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `email_body`: a `string` feature.
- `subject_line`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default|14436| 1960|1906|
## 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{zhang-tetreault-2019-email,
title = "This Email Could Save Your Life: Introducing the Task of Email Subject Line Generation",
author = "Zhang, Rui and
Tetreault, Joel",
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/P19-1043",
doi = "10.18653/v1/P19-1043",
pages = "446--456",
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset. |
afrikaans_ner_corpus | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- af
license:
- other
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- named-entity-recognition
pretty_name: Afrikaans Ner Corpus
license_details: Creative Commons Attribution 2.5 South Africa License
dataset_info:
features:
- name: id
dtype: string
- name: tokens
sequence: string
- name: ner_tags
sequence:
class_label:
names:
'0': OUT
'1': B-PERS
'2': I-PERS
'3': B-ORG
'4': I-ORG
'5': B-LOC
'6': I-LOC
'7': B-MISC
'8': I-MISC
config_name: afrikaans_ner_corpus
splits:
- name: train
num_bytes: 4025667
num_examples: 8962
download_size: 25748344
dataset_size: 4025667
---
# Dataset Card for Afrikaans Ner Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [Afrikaans Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/299)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za)
### Dataset Summary
The Afrikaans Ner Corpus is an Afrikaans dataset developed by [The Centre for Text Technology (CTexT), North-West University, South Africa](http://humanities.nwu.ac.za/ctext). The data is based on documents from the South African goverment domain and crawled from gov.za websites. It was created to support NER task for Afrikaans language. The dataset uses CoNLL shared task annotation standards.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Afrikaans.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
{'id': '0',
'ner_tags': [0, 0, 0, 0, 0],
'tokens': ['Vertaling', 'van', 'die', 'inligting', 'in']
}
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
The NER tags correspond to this list:
```
"OUT", "B-PERS", "I-PERS", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
```
The NER tags have the same format as in the CoNLL shared task: a B denotes the first item of a phrase and an I any non-initial word. There are four types of phrases: person names (PER), organizations (ORG), locations (LOC) and miscellaneous names (MISC). (OUT) is used for tokens not considered part of any named entity.
### Data Splits
The data was not split.
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Afrikaans.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The data is based on South African government domain and was crawled from gov.za websites.
[More Information Needed]
#### Who are the source language producers?
The data was produced by writers of South African government websites - gov.za
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated during the NCHLT text resource development project.
[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 annotated data sets were developed by the Centre for Text Technology (CTexT, North-West University, South Africa).
See: [more information](http://www.nwu.ac.za/ctext)
### Licensing Information
The data is under the [Creative Commons Attribution 2.5 South Africa License](http://creativecommons.org/licenses/by/2.5/za/legalcode)
### Citation Information
```
@inproceedings{afrikaans_ner_corpus,
author = { Gerhard van Huyssteen and
Martin Puttkammer and
E.B. Trollip and
J.C. Liversage and
Roald Eiselen},
title = {NCHLT Afrikaans Named Entity Annotated Corpus},
booktitle = {Eiselen, R. 2016. Government domain named entity recognition for South African languages. Proceedings of the 10th Language Resource and Evaluation Conference, Portorož, Slovenia.},
year = {2016},
url = {https://repo.sadilar.org/handle/20.500.12185/299},
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset. |
ag_news | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- topic-classification
paperswithcode_id: ag-news
pretty_name: AG’s News Corpus
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': World
'1': Sports
'2': Business
'3': Sci/Tech
splits:
- name: train
num_bytes: 29817351
num_examples: 120000
- name: test
num_bytes: 1879478
num_examples: 7600
download_size: 31327765
dataset_size: 31696829
train-eval-index:
- config: default
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
text: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for "ag_news"
## 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://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.html](http://groups.di.unipi.it/~gulli/AG_corpus_of_news_articles.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:** 31.33 MB
- **Size of the generated dataset:** 31.70 MB
- **Total amount of disk used:** 63.02 MB
### Dataset Summary
AG is a collection of more than 1 million news articles. News articles have been
gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of
activity. ComeToMyHead is an academic news search engine which has been running
since July, 2004. The dataset is provided by the academic comunity for research
purposes in data mining (clustering, classification, etc), information retrieval
(ranking, search, etc), xml, data compression, data streaming, and any other
non-commercial activity. For more information, please refer to the link
http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .
The AG's news topic classification dataset is constructed by Xiang Zhang
(xiang.zhang@nyu.edu) from the dataset above. It is used as a text
classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann
LeCun. Character-level Convolutional Networks for Text Classification. Advances
in Neural Information Processing Systems 28 (NIPS 2015).
### 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:** 31.33 MB
- **Size of the generated dataset:** 31.70 MB
- **Total amount of disk used:** 63.02 MB
An example of 'train' looks as follows.
```
{
"label": 3,
"text": "New iPad released Just like every other September, this one is no different. Apple is planning to release a bigger, heavier, fatter iPad that..."
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `World` (0), `Sports` (1), `Business` (2), `Sci/Tech` (3).
### Data Splits
| name |train |test|
|-------|-----:|---:|
|default|120000|7600|
## 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{Zhang2015CharacterlevelCN,
title={Character-level Convolutional Networks for Text Classification},
author={Xiang Zhang and Junbo Jake Zhao and Yann LeCun},
booktitle={NIPS},
year={2015}
}
```
### Contributions
Thanks to [@jxmorris12](https://github.com/jxmorris12), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@lewtun](https://github.com/lewtun) for adding this dataset. |
ai2_arc | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
language_bcp47:
- en-US
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- open-domain-qa
- multiple-choice-qa
paperswithcode_id: null
pretty_name: Ai2Arc
dataset_info:
- config_name: ARC-Challenge
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 351888
num_examples: 1119
- name: test
num_bytes: 377740
num_examples: 1172
- name: validation
num_bytes: 97254
num_examples: 299
download_size: 680841265
dataset_size: 826882
- config_name: ARC-Easy
features:
- name: id
dtype: string
- name: question
dtype: string
- name: choices
sequence:
- name: text
dtype: string
- name: label
dtype: string
- name: answerKey
dtype: string
splits:
- name: train
num_bytes: 623254
num_examples: 2251
- name: test
num_bytes: 661997
num_examples: 2376
- name: validation
num_bytes: 158498
num_examples: 570
download_size: 680841265
dataset_size: 1443749
---
# Dataset Card for "ai2_arc"
## 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://allenai.org/data/arc](https://allenai.org/data/arc)
- **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:** 1361.68 MB
- **Size of the generated dataset:** 2.28 MB
- **Total amount of disk used:** 1363.96 MB
### Dataset Summary
A new dataset of 7,787 genuine grade-school level, multiple-choice science questions, assembled to encourage research in
advanced question-answering. The dataset is partitioned into a Challenge Set and an Easy Set, where the former contains
only questions answered incorrectly by both a retrieval-based algorithm and a word co-occurrence algorithm. We are also
including a corpus of over 14 million science sentences relevant to the task, and an implementation of three neural baseline models for this dataset. We pose ARC as a challenge to the community.
### 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
#### ARC-Challenge
- **Size of downloaded dataset files:** 680.84 MB
- **Size of the generated dataset:** 0.83 MB
- **Total amount of disk used:** 681.67 MB
An example of 'train' looks as follows.
```
{
"answerKey": "B",
"choices": {
"label": ["A", "B", "C", "D"],
"text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."]
},
"id": "Mercury_SC_405487",
"question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?"
}
```
#### ARC-Easy
- **Size of downloaded dataset files:** 680.84 MB
- **Size of the generated dataset:** 1.45 MB
- **Total amount of disk used:** 682.29 MB
An example of 'train' looks as follows.
```
{
"answerKey": "B",
"choices": {
"label": ["A", "B", "C", "D"],
"text": ["Shady areas increased.", "Food sources increased.", "Oxygen levels increased.", "Available water increased."]
},
"id": "Mercury_SC_405487",
"question": "One year, the oak trees in a park began producing more acorns than usual. The next year, the population of chipmunks in the park also increased. Which best explains why there were more chipmunks the next year?"
}
```
### Data Fields
The data fields are the same among all splits.
#### ARC-Challenge
- `id`: a `string` feature.
- `question`: a `string` feature.
- `choices`: a dictionary feature containing:
- `text`: a `string` feature.
- `label`: a `string` feature.
- `answerKey`: a `string` feature.
#### ARC-Easy
- `id`: a `string` feature.
- `question`: a `string` feature.
- `choices`: a dictionary feature containing:
- `text`: a `string` feature.
- `label`: a `string` feature.
- `answerKey`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------------|----:|---------:|---:|
|ARC-Challenge| 1119| 299|1172|
|ARC-Easy | 2251| 570|2376|
## 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{allenai:arc,
author = {Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and
Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
title = {Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
journal = {arXiv:1803.05457v1},
year = {2018},
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
air_dialogue | ---
pretty_name: AirDialogue
annotations_creators:
- crowdsourced
language_creators:
- machine-generated
language:
- en
license:
- cc-by-nc-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- conversational
- text-generation
- fill-mask
task_ids:
- dialogue-generation
- dialogue-modeling
- language-modeling
- masked-language-modeling
paperswithcode_id: null
dataset_info:
- config_name: air_dialogue_data
features:
- name: action
struct:
- name: status
dtype: string
- name: name
dtype: string
- name: flight
sequence: int32
- name: intent
struct:
- name: return_month
dtype: string
- name: return_day
dtype: string
- name: max_price
dtype: int32
- name: departure_airport
dtype: string
- name: max_connections
dtype: int32
- name: departure_day
dtype: string
- name: goal
dtype: string
- name: departure_month
dtype: string
- name: name
dtype: string
- name: return_airport
dtype: string
- name: timestamps
sequence: int64
- name: dialogue
sequence: string
- name: expected_action
struct:
- name: status
dtype: string
- name: name
dtype: string
- name: flight
sequence: int32
- name: search_info
list:
- name: button_name
dtype: string
- name: field_name
dtype: string
- name: field_value
dtype: string
- name: timestmamp
dtype: int64
- name: correct_sample
dtype: bool_
splits:
- name: train
num_bytes: 353721137
num_examples: 321459
- name: validation
num_bytes: 44442238
num_examples: 40363
download_size: 272898923
dataset_size: 398163375
- config_name: air_dialogue_kb
features:
- name: kb
list:
- name: airline
dtype: string
- name: class
dtype: string
- name: departure_airport
dtype: string
- name: departure_day
dtype: string
- name: departure_month
dtype: string
- name: departure_time_num
dtype: int32
- name: flight_number
dtype: int32
- name: num_connections
dtype: int32
- name: price
dtype: int32
- name: return_airport
dtype: string
- name: return_day
dtype: string
- name: return_month
dtype: string
- name: return_time_num
dtype: int32
- name: reservation
dtype: int32
splits:
- name: train
num_bytes: 782592158
num_examples: 321459
- name: validation
num_bytes: 98269789
num_examples: 40363
download_size: 272898923
dataset_size: 880861947
---
# Dataset Card for air_dialogue
## 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://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59
- **Repository:** https://github.com/google/airdialogue
- **Paper:** https://www.aclweb.org/anthology/D18-1419/
- **Leaderboard:** https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59
- **Point of Contact:** [AirDialogue-Google](mailto:airdialogue@gmail.com)
[Aakash Gupta](mailto:aakashg80@gmail.com)
### Dataset Summary
AirDialogue, is a large dataset that contains 402,038 goal-oriented conversations. To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. Then the human annotators are asked to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions.
### Supported Tasks and Leaderboards
We use perplexity and BLEU score to evaluate the quality of the language generated by the model. We also compare the dialogue state generated by the model s and the ground truth state s0. Two categories of the metrics are used: exact match scores and scaled scores
The inference competition & leaderboard can be found here:
https://worksheets.codalab.org/worksheets/0xa79833f4b3c24f4188cee7131b120a59
### Languages
The text in the dataset is in English. The BCP 47 code is `en`
## Dataset Structure
### Data Instances
The data is provided in two set of files. The first one has the dialogues (`air_dialogue_data`) and the knowledge-base (`air_dialogue_kb`)
BuilderConfig: `air_dialogue_data`
```
{"action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "intent": {"return_month": "June", "return_day": "14", "max_price": 200, "departure_airport": "DFW", "return_time": "afternoon", "max_connections": 1, "departure_day": "12", "goal": "book", "departure_month": "June", "name": "Emily Edwards", "return_airport": "IAD"}, "timestamps": [1519233239, 1519233244, 1519233249, 1519233252, 1519233333, 1519233374, 1519233392, 1519233416, 1519233443, 1519233448, 1519233464, 1519233513, 1519233525, 1519233540, 1519233626, 1519233628, 1519233638], "dialogue": ["customer: Hello.", "agent: Hello.", "customer: My name is Emily Edwards.", "agent: How may I help you out?", "customer: I need some help in my flight ticket reservation to attend a convocation meeting, can you please help me?", "agent: Sure, I will help you out. May I know your travelling dates please?", "customer: Thank you and my dates are 06/12 and back on 06/14.", "agent: Can I know your airport codes?", "customer: The airport codes are from DFW to IAD.", "agent: Ok, please wait a moment.", "customer: Sure.", "agent: There is a flight with connection 1 and price 200, can I proceed with this flight?", "customer: Yes, do proceed with booking.", "agent: Ok, your ticket has been booked.", "customer: Thank you for your assistance in my flight ticket reservation.", "agent: Thank you for choosing us.", "customer: You are welcome."], "expected_action": {"status": "book", "name": "Emily Edwards", "flight": [1027]}, "correct_sample": true}
```
BuilderConfig: `air_dialogue_kb`
```
{"kb": [{"return_airport": "DTW", "airline": "Spirit", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1000, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1001, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 15, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 500}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1002, "departure_month": "June", "departure_time_num": 0, "class": "business", "return_time_num": 13, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 600}, {"return_airport": "IAD", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1003, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 5, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1004, "departure_month": "June", "departure_time_num": 9, "class": "economy", "return_time_num": 11, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "AA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1005, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 17, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Frontier", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1006, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "IAD", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1007, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 20, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "AA", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1008, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1009, "departure_month": "June", "departure_time_num": 18, "class": "economy", "return_time_num": 6, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Frontier", "departure_day": "13", "departure_airport": "DTW", "flight_number": 1010, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 2, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1011, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 100}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1012, "departure_month": "June", "departure_time_num": 13, "class": "economy", "return_time_num": 22, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1013, "departure_month": "June", "departure_time_num": 16, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1014, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 8, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "Southwest", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1015, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 300}, {"return_airport": "DTW", "airline": "UA", "departure_day": "11", "departure_airport": "DFW", "flight_number": 1016, "departure_month": "June", "departure_time_num": 10, "class": "economy", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 200}, {"return_airport": "DFW", "airline": "AA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1017, "departure_month": "June", "departure_time_num": 14, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 400}, {"return_airport": "DTW", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1018, "departure_month": "June", "departure_time_num": 3, "class": "economy", "return_time_num": 1, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Hawaiian", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1019, "departure_month": "June", "departure_time_num": 7, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "Delta", "departure_day": "12", "departure_airport": "IAD", "flight_number": 1020, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 2, "price": 200}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1021, "departure_month": "June", "departure_time_num": 11, "class": "business", "return_time_num": 8, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 1000}, {"return_airport": "IAD", "airline": "JetBlue", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1022, "departure_month": "June", "departure_time_num": 4, "class": "economy", "return_time_num": 14, "return_month": "June", "return_day": "13", "num_connections": 0, "price": 200}, {"return_airport": "IAD", "airline": "Frontier", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1023, "departure_month": "June", "departure_time_num": 19, "class": "economy", "return_time_num": 23, "return_month": "June", "return_day": "13", "num_connections": 1, "price": 200}, {"return_airport": "DFW", "airline": "UA", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1024, "departure_month": "June", "departure_time_num": 11, "class": "economy", "return_time_num": 19, "return_month": "June", "return_day": "15", "num_connections": 1, "price": 200}, {"return_airport": "DTW", "airline": "Hawaiian", "departure_day": "11", "departure_airport": "IAD", "flight_number": 1025, "departure_month": "June", "departure_time_num": 6, "class": "economy", "return_time_num": 10, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DTW", "airline": "UA", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1026, "departure_month": "June", "departure_time_num": 0, "class": "economy", "return_time_num": 18, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 300}, {"return_airport": "IAD", "airline": "Delta", "departure_day": "12", "departure_airport": "DFW", "flight_number": 1027, "departure_month": "June", "departure_time_num": 17, "class": "economy", "return_time_num": 15, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 200}, {"return_airport": "IAD", "airline": "Southwest", "departure_day": "12", "departure_airport": "DTW", "flight_number": 1028, "departure_month": "June", "departure_time_num": 23, "class": "economy", "return_time_num": 13, "return_month": "June", "return_day": "14", "num_connections": 1, "price": 100}, {"return_airport": "DFW", "airline": "Spirit", "departure_day": "11", "departure_airport": "DTW", "flight_number": 1029, "departure_month": "June", "departure_time_num": 22, "class": "business", "return_time_num": 4, "return_month": "June", "return_day": "14", "num_connections": 0, "price": 800}], "reservation": 0}
```
### Data Fields
BuilderConfig: `air_dialogue_data`:
Provides for customer context, dialogue states and environment
key name | Description |
|---|---|
|'search_action' | search action performed by customer |
|'action' | Action taken by the agent |
|'intent' | Intents from the conversation |
|'timestamps' | Timestamp for each of the dialogues |
|'dialogue' | Dialogue recorded between agent & customer |
|'expected_action' | Expected action from agent (human-annotated)|
|'correct_sample' | whether action performed by agent was same as expected_action |
BuilderConfig: `air_dialogue_kb`:
Provides for the Agent Context _ca_ = (_db_, _r_ )
key name | Description |
|---|---|
|'kb' | Available flights in the database |
|'reservation' | whether customer has an existing reservation|
### Data Splits
Data is split into Train/Dev & Test in the ration of 80%, 10% and 10%
## 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
To collect this dataset, we create a contextgenerator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail.
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
No personal and sensitive information is stored
## 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
[AirDialogue team](mailto:airdialogue@gmail.com)
For issues regarding HuggingFace Dataset Hub implementation [Aakash Gupta](mailto:aakashg80@gmail.com)
### Licensing Information
cc-by-nc-4.0
### Citation Information
@inproceedings{wei-etal-2018-airdialogue,
title = "{A}ir{D}ialogue: An Environment for Goal-Oriented Dialogue Research",
author = "Wei, Wei and
Le, Quoc and
Dai, Andrew and
Li, Jia",
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://www.aclweb.org/anthology/D18-1419",
doi = "10.18653/v1/D18-1419",
pages = "3844--3854",
abstract = "Recent progress in dialogue generation has inspired a number of studies on dialogue systems that are capable of accomplishing tasks through natural language interactions. A promising direction among these studies is the use of reinforcement learning techniques, such as self-play, for training dialogue agents. However, current datasets are limited in size, and the environment for training agents and evaluating progress is relatively unsophisticated. We present AirDialogue, a large dataset that contains 301,427 goal-oriented conversations. To collect this dataset, we create a context-generator which provides travel and flight restrictions. We then ask human annotators to play the role of a customer or an agent and interact with the goal of successfully booking a trip given the restrictions. Key to our environment is the ease of evaluating the success of the dialogue, which is achieved by using ground-truth states (e.g., the flight being booked) generated by the restrictions. Any dialogue agent that does not generate the correct states is considered to fail. Our experimental results indicate that state-of-the-art dialogue models can only achieve a score of 0.17 while humans can reach a score of 0.91, which suggests significant opportunities for future improvement.",
}
### Contributions
Thanks to [@skyprince999](https://github.com/skyprince999) for adding this dataset. |
ajgt_twitter_ar | ---
annotations_creators:
- found
language_creators:
- found
language:
- ar
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Arabic Jordanian General Tweets
dataset_info:
features:
- name: text
dtype: string
- name: label
dtype:
class_label:
names:
'0': Negative
'1': Positive
config_name: plain_text
splits:
- name: train
num_bytes: 175424
num_examples: 1800
download_size: 107395
dataset_size: 175424
---
# Dataset Card for Arabic Jordanian General Tweets
## Table of Contents
- [Dataset Card for Arabic Jordanian General Tweets](#dataset-card-for-arabic-jordanian-general-tweets)
- [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)
- [|split|num examples|](#splitnum-examples)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [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:** [Arabic Jordanian General Tweets](https://github.com/komari6/Arabic-twitter-corpus-AJGT)
- **Paper:** [Arabic Tweets Sentimental Analysis Using Machine Learning](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66)
- **Point of Contact:** [Khaled Alomari](khaled.alomari@adu.ac.ae)
### Dataset Summary
Arabic Jordanian General Tweets (AJGT) Corpus consisted of 1,800 tweets annotated as positive and negative. Modern Standard Arabic (MSA) or Jordanian dialect.
### Supported Tasks and Leaderboards
The dataset was published on this [paper](https://link.springer.com/chapter/10.1007/978-3-319-60042-0_66).
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
A binary datset with with negative and positive sentiments.
### Data Fields
- `text` (str): Tweet text.
- `label` (int): Sentiment.
### Data Splits
The dataset is not split.
| | train |
|----------|------:|
| no split | 1,800 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
Contains 1,800 tweets collected from twitter.
#### Who are the source language producers?
From tweeter.
### Annotations
The dataset does not contain any additional 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
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{alomari2017arabic,
title={Arabic tweets sentimental analysis using machine learning},
author={Alomari, Khaled Mohammad and ElSherif, Hatem M and Shaalan, Khaled},
booktitle={International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems},
pages={602--610},
year={2017},
organization={Springer}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai), [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
allegro_reviews | ---
annotations_creators:
- found
language_creators:
- found
language:
- pl
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-scoring
- text-scoring
paperswithcode_id: allegro-reviews
pretty_name: Allegro Reviews
dataset_info:
features:
- name: text
dtype: string
- name: rating
dtype: float32
splits:
- name: train
num_bytes: 4899539
num_examples: 9577
- name: test
num_bytes: 514527
num_examples: 1006
- name: validation
num_bytes: 515785
num_examples: 1002
download_size: 2314847
dataset_size: 5929851
---
# 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://klejbenchmark.com/
- **Repository:**
https://github.com/allegro/klejbenchmark-allegroreviews
- **Paper:**
KLEJ: Comprehensive Benchmark for Polish Language Understanding (Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz)
- **Leaderboard:**
https://klejbenchmark.com/leaderboard/
- **Point of Contact:**
klejbenchmark@allegro.pl
### Dataset Summary
Allegro Reviews is a sentiment analysis dataset, consisting of 11,588 product reviews written in Polish and extracted from Allegro.pl - a popular e-commerce marketplace. Each review contains at least 50 words and has a rating on a scale from one (negative review) to five (positive review).
We recommend using the provided train/dev/test split. The ratings for the test set reviews are kept hidden. You can evaluate your model using the online evaluation tool available on klejbenchmark.com.
### Supported Tasks and Leaderboards
Product reviews sentiment analysis.
https://klejbenchmark.com/leaderboard/
### Languages
Polish
## Dataset Structure
### Data Instances
Two tsv files (train, dev) with two columns (text, rating) and one (test) with just one (text).
### Data Fields
- text: a product review of at least 50 words
- rating: product rating of a scale of one (negative review) to five (positive review)
### Data Splits
Data is splitted in train/dev/test split.
## Dataset Creation
### Curation Rationale
This dataset is one of nine evaluation tasks to improve polish language processing.
### Source Data
#### Initial Data Collection and Normalization
The Allegro Reviews is a set of product reviews from a popular e-commerce marketplace (Allegro.pl).
#### Who are the source language producers?
Customers of an e-commerce marketplace.
### 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
Allegro Machine Learning Research team klejbenchmark@allegro.pl
### Licensing Information
Dataset licensed under CC BY-SA 4.0
### Citation Information
@inproceedings{rybak-etal-2020-klej,
title = "{KLEJ}: Comprehensive Benchmark for Polish Language Understanding",
author = "Rybak, Piotr and Mroczkowski, Robert and Tracz, Janusz and Gawlik, Ireneusz",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.111",
pages = "1191--1201",
}
### Contributions
Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset. |
allocine | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- fr
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: allocine
pretty_name: Allociné
dataset_info:
features:
- name: review
dtype: string
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
config_name: allocine
splits:
- name: train
num_bytes: 91330696
num_examples: 160000
- name: validation
num_bytes: 11546250
num_examples: 20000
- name: test
num_bytes: 11547697
num_examples: 20000
download_size: 66625305
dataset_size: 114424643
train-eval-index:
- config: allocine
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
review: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for Allociné
## 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:** [Allociné dataset repository](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/tree/master/allocine_dataset)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Théophile Blard](mailto:theophile.blard@gmail.com)
### Dataset Summary
The Allociné dataset is a French-language dataset for sentiment analysis. The texts are movie reviews written between 2006 and 2020 by members of the [Allociné.fr](https://www.allocine.fr/) community for various films. It contains 100k positive and 100k negative reviews divided into train (160k), validation (20k), and test (20k).
### Supported Tasks and Leaderboards
- `text-classification`, `sentiment-classification`: The dataset can be used to train a model for sentiment classification. The model performance is evaluated based on the accuracy of the predicted labels as compared to the given labels in the dataset. A BERT-based model, [tf-allociné](https://huggingface.co/tblard/tf-allocine), achieves 97.44% accuracy on the test set.
### Languages
The text is in French, as spoken by users of the [Allociné.fr](https://www.allocine.fr/) website. The BCP-47 code for French is fr.
## Dataset Structure
### Data Instances
Each data instance contains the following features: _review_ and _label_. In the Hugging Face distribution of the dataset, the _label_ has 2 possible values, _0_ and _1_, which correspond to _negative_ and _positive_ respectively. See the [Allociné corpus viewer](https://huggingface.co/datasets/viewer/?dataset=allocine) to explore more examples.
An example from the Allociné train set looks like the following:
```
{'review': 'Premier film de la saga Kozure Okami, "Le Sabre de la vengeance" est un très bon film qui mêle drame et action, et qui, en 40 ans, n'a pas pris une ride.',
'label': 1}
```
### Data Fields
- 'review': a string containing the review text
- 'label': an integer, either _0_ or _1_, indicating a _negative_ or _positive_ review, respectively
### Data Splits
The Allociné dataset has 3 splits: _train_, _validation_, and _test_. The splits contain disjoint sets of movies. The following table contains the number of reviews in each split and the percentage of positive and negative reviews.
| Dataset Split | Number of Instances in Split | Percent Negative Reviews | Percent Positive Reviews |
| ------------- | ---------------------------- | ------------------------ | ------------------------ |
| Train | 160,000 | 49.6% | 50.4% |
| Validation | 20,000 | 51.0% | 49.0% |
| Test | 20,000 | 52.0% | 48.0% |
## Dataset Creation
### Curation Rationale
The Allociné dataset was developed to support large-scale sentiment analysis in French. It was released alongside the [tf-allociné](https://huggingface.co/tblard/tf-allocine) model and used to compare the performance of several language models on this task.
### Source Data
#### Initial Data Collection and Normalization
The reviews and ratings were collected using a list of [film page urls](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_films_urls.txt) and the [allocine_scraper.py](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_scraper.py) tool. Up to 30 reviews were collected for each film.
The reviews were originally labeled with a rating from 0.5 to 5.0 with a step of 0.5 between each rating. Ratings less than or equal to 2 are labeled as negative and ratings greater than or equal to 4 are labeled as positive. Only reviews with less than 2000 characters are included in the dataset.
#### Who are the source language producers?
The dataset contains movie reviews produced by the online community of the [Allociné.fr](https://www.allocine.fr/) website.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Reviewer usernames or personal information were not collected with the reviews, but could potentially be recovered. The content of each review may include information and opinions about the film's actors, film crew, and plot.
## Considerations for Using the Data
### Social Impact of Dataset
Sentiment classification is a complex task which requires sophisticated language understanding skills. Successful models can support decision-making based on the outcome of the sentiment analysis, though such models currently require a high degree of domain specificity.
It should be noted that the community represented in the dataset may not represent any downstream application's potential users, and the observed behavior of a model trained on this dataset may vary based on the domain and use case.
### Discussion of Biases
The Allociné website lists a number of topics which violate their [terms of service](https://www.allocine.fr/service/conditions.html#charte). Further analysis is needed to determine the extent to which moderators have successfully removed such content.
### Other Known Limitations
The limitations of the Allociné dataset have not yet been investigated, however [Staliūnaitė and Bonfil (2017)](https://www.aclweb.org/anthology/W17-5410.pdf) detail linguistic phenomena that are generally present in sentiment analysis but difficult for models to accurately label, such as negation, adverbial modifiers, and reviewer pragmatics.
## Additional Information
### Dataset Curators
The Allociné dataset was collected by Théophile Blard.
### Licensing Information
The Allociné dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT).
### Citation Information
> Théophile Blard, French sentiment analysis with BERT, (2020), GitHub repository, <https://github.com/TheophileBlard/french-sentiment-analysis-with-bert>
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@TheophileBlard](https://github.com/TheophileBlard), [@lewtun](https://github.com/lewtun) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset. |
alt | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- bn
- en
- fil
- hi
- id
- ja
- km
- lo
- ms
- my
- th
- vi
- zh
license:
- cc-by-4.0
multilinguality:
- multilingual
- translation
size_categories:
- 100K<n<1M
- 10K<n<100K
source_datasets:
- original
task_categories:
- translation
- token-classification
task_ids:
- parsing
paperswithcode_id: alt
pretty_name: Asian Language Treebank
configs:
- alt-en
- alt-jp
- alt-km
- alt-my
- alt-my-transliteration
- alt-my-west-transliteration
- alt-parallel
dataset_info:
- config_name: alt-parallel
features:
- name: SNT.URLID
dtype: string
- name: SNT.URLID.SNTID
dtype: string
- name: url
dtype: string
- name: translation
dtype:
translation:
languages:
- bg
- en
- en_tok
- fil
- hi
- id
- ja
- khm
- lo
- ms
- my
- th
- vi
- zh
splits:
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num_examples: 18088
- name: validation
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num_examples: 1019
download_size: 21285784
dataset_size: 75971326
- config_name: alt-en
features:
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dtype: string
- name: SNT.URLID.SNTID
dtype: string
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- config_name: alt-km
features:
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dtype: string
- name: SNT.URLID.SNTID
dtype: string
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- name: km_pos_tag
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features:
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sequence: string
splits:
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features:
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dtype: string
- name: my
sequence: string
splits:
- name: train
num_bytes: 7412043
num_examples: 107121
download_size: 2830071
dataset_size: 7412043
---
# Dataset Card for Asian Language Treebank (ALT)
## 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://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/
- **Leaderboard:**
- **Paper:** [Introduction of the Asian Language Treebank](https://ieeexplore.ieee.org/abstract/document/7918974)
- **Point of Contact:** [ALT info](alt-info@khn.nict.go.jp)
### Dataset Summary
The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under [ASEAN IVO](https://www.nict.go.jp/en/asean_ivo/index.html) as described in this Web page.
The process of building ALT began with sampling about 20,000 sentences from English Wikinews, and then these sentences were translated into the other languages.
### Supported Tasks and Leaderboards
Machine Translation, Dependency Parsing
### Languages
It supports 13 language:
* Bengali
* English
* Filipino
* Hindi
* Bahasa Indonesia
* Japanese
* Khmer
* Lao
* Malay
* Myanmar (Burmese)
* Thai
* Vietnamese
* Chinese (Simplified Chinese).
## Dataset Structure
### Data Instances
#### ALT Parallel Corpus
```
{
"SNT.URLID": "80188",
"SNT.URLID.SNTID": "1",
"url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal",
"bg": "[translated sentence]",
"en": "[translated sentence]",
"en_tok": "[translated sentence]",
"fil": "[translated sentence]",
"hi": "[translated sentence]",
"id": "[translated sentence]",
"ja": "[translated sentence]",
"khm": "[translated sentence]",
"lo": "[translated sentence]",
"ms": "[translated sentence]",
"my": "[translated sentence]",
"th": "[translated sentence]",
"vi": "[translated sentence]",
"zh": "[translated sentence]"
}
```
#### ALT Treebank
```
{
"SNT.URLID": "80188",
"SNT.URLID.SNTID": "1",
"url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal",
"status": "draft/reviewed",
"value": "(S (S (BASENP (NNP Italy)) (VP (VBP have) (VP (VP (VP (VBN defeated) (BASENP (NNP Portugal))) (ADVP (RB 31-5))) (PP (IN in) (NP (BASENP (NNP Pool) (NNP C)) (PP (IN of) (NP (BASENP (DT the) (NN 2007) (NNP Rugby) (NNP World) (NNP Cup)) (PP (IN at) (NP (BASENP (NNP Parc) (FW des) (NNP Princes)) (COMMA ,) (BASENP (NNP Paris) (COMMA ,) (NNP France))))))))))) (PERIOD .))"
}
```
#### ALT Myanmar transliteration
```
{
"en": "CASINO",
"my": [
"ကက်စီနို",
"ကစီနို",
"ကာစီနို",
"ကာဆီနို"
]
}
```
### Data Fields
#### ALT Parallel Corpus
- SNT.URLID: URL link to the source article listed in [URL.txt](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/ALT-Parallel-Corpus-20191206/URL.txt)
- SNT.URLID.SNTID: index number from 1 to 20000. It is a seletected sentence from `SNT.URLID`
and bg, en, fil, hi, id, ja, khm, lo, ms, my, th, vi, zh correspond to the target language
#### ALT Treebank
- status: it indicates how a sentence is annotated; `draft` sentences are annotated by one annotater and `reviewed` sentences are annotated by two annotater
The annotatation is different from language to language, please see [their guildlines](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/) for more detail.
### Data Splits
| | train | valid | test |
|-----------|-------|-------|-------|
| # articles | 1698 | 98 | 97 |
| # sentences | 18088 | 1000 | 1018 |
## Dataset Creation
### Curation Rationale
The ALT project was initiated by the [National Institute of Information and Communications Technology, Japan](https://www.nict.go.jp/en/) (NICT) in 2014. NICT started to build Japanese and English ALT and worked with the University of Computer Studies, Yangon, Myanmar (UCSY) to build Myanmar ALT in 2014. Then, the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT), the Institute for Infocomm Research, Singapore (I2R), the Institute of Information Technology, Vietnam (IOIT), and the National Institute of Posts, Telecoms and ICT, Cambodia (NIPTICT) joined to make ALT for Indonesian, Malay, Vietnamese, and Khmer in 2015.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The dataset is sampled from the English Wikinews in 2014. These will be annotated with word segmentation, POS tags, and syntax information, in addition to the word alignment information by linguistic experts from
* National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English
* University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar
* the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian
* the Institute for Infocomm Research, Singapore (I2R) for Malay
* the Institute of Information Technology, Vietnam (IOIT) for Vietnamese
* the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer
### 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
* National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English
* University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar
* the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian
* the Institute for Infocomm Research, Singapore (I2R) for Malay
* the Institute of Information Technology, Vietnam (IOIT) for Vietnamese
* the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer
### Licensing Information
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
Please cite the following if you make use of the dataset:
Hammam Riza, Michael Purwoadi, Gunarso, Teduh Uliniansyah, Aw Ai Ti, Sharifah Mahani Aljunied, Luong Chi Mai, Vu Tat Thang, Nguyen Phuong Thai, Vichet Chea, Rapid Sun, Sethserey Sam, Sopheap Seng, Khin Mar Soe, Khin Thandar Nwet, Masao Utiyama, Chenchen Ding. (2016) "Introduction of the Asian Language Treebank" Oriental COCOSDA.
BibTeX:
```
@inproceedings{riza2016introduction,
title={Introduction of the asian language treebank},
author={Riza, Hammam and Purwoadi, Michael and Uliniansyah, Teduh and Ti, Aw Ai and Aljunied, Sharifah Mahani and Mai, Luong Chi and Thang, Vu Tat and Thai, Nguyen Phuong and Chea, Vichet and Sam, Sethserey and others},
booktitle={2016 Conference of The Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques (O-COCOSDA)},
pages={1--6},
year={2016},
organization={IEEE}
}
```
### Contributions
Thanks to [@chameleonTK](https://github.com/chameleonTK) for adding this dataset. |
amazon_polarity | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- apache-2.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
pretty_name: Amazon Review Polarity
dataset_info:
features:
- name: label
dtype:
class_label:
names:
'0': negative
'1': positive
- name: title
dtype: string
- name: content
dtype: string
config_name: amazon_polarity
splits:
- name: train
num_bytes: 1604364432
num_examples: 3600000
- name: test
num_bytes: 178176193
num_examples: 400000
download_size: 688339454
dataset_size: 1782540625
train-eval-index:
- config: amazon_polarity
task: text-classification
task_id: binary_classification
splits:
train_split: train
eval_split: test
col_mapping:
content: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for Amazon Review Polarity
## 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://registry.opendata.aws/
- **Repository:** https://github.com/zhangxiangxiao/Crepe
- **Paper:** https://arxiv.org/abs/1509.01626
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Xiang Zhang](mailto:xiang.zhang@nyu.edu)
### Dataset Summary
The Amazon reviews dataset consists of reviews from amazon.
The data span a period of 18 years, including ~35 million reviews up to March 2013.
Reviews include product and user information, ratings, and a plaintext review.
### Supported Tasks and Leaderboards
- `text-classification`, `sentiment-classification`: The dataset is mainly used for text classification: given the content and the title, predict the correct star rating.
### Languages
Mainly English.
## Dataset Structure
### Data Instances
A typical data point, comprises of a title, a content and the corresponding label.
An example from the AmazonPolarity test set looks as follows:
```
{
'title':'Great CD',
'content':"My lovely Pat has one of the GREAT voices of her generation. I have listened to this CD for YEARS and I still LOVE IT. When I'm in a good mood it makes me feel better. A bad mood just evaporates like sugar in the rain. This CD just oozes LIFE. Vocals are jusat STUUNNING and lyrics just kill. One of life's hidden gems. This is a desert isle CD in my book. Why she never made it big is just beyond me. Everytime I play this, no matter black, white, young, old, male, female EVERYBODY says one thing ""Who was that singing ?""",
'label':1
}
```
### Data Fields
- 'title': a string containing the title of the review - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
- 'content': a string containing the body of the document - escaped using double quotes (") and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".
- 'label': either 1 (positive) or 0 (negative) rating.
### Data Splits
The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. Each class has 1,800,000 training samples and 200,000 testing samples.
## Dataset Creation
### Curation Rationale
The Amazon reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu). It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
### 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
Apache License 2.0
### Citation Information
McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013.
Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015)
### Contributions
Thanks to [@hfawaz](https://github.com/hfawaz) for adding this dataset. |
amazon_reviews_multi | ---
annotations_creators:
- found
language_creators:
- found
language:
- de
- en
- es
- fr
- ja
- zh
license:
- other
multilinguality:
- monolingual
- multilingual
size_categories:
- 100K<n<1M
- 1M<n<10M
source_datasets:
- original
task_categories:
- summarization
- text-generation
- fill-mask
- text-classification
task_ids:
- text-scoring
- language-modeling
- masked-language-modeling
- sentiment-classification
- sentiment-scoring
- topic-classification
paperswithcode_id: null
pretty_name: The Multilingual Amazon Reviews Corpus
configs:
- all_languages
- de
- en
- es
- fr
- ja
- zh
dataset_info:
- config_name: all_languages
features:
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: reviewer_id
dtype: string
- name: stars
dtype: int32
- name: review_body
dtype: string
- name: review_title
dtype: string
- name: language
dtype: string
- name: product_category
dtype: string
splits:
- name: train
num_bytes: 364405048
num_examples: 1200000
- name: validation
num_bytes: 9047533
num_examples: 30000
- name: test
num_bytes: 9099141
num_examples: 30000
download_size: 640320386
dataset_size: 382551722
- config_name: de
features:
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: reviewer_id
dtype: string
- name: stars
dtype: int32
- name: review_body
dtype: string
- name: review_title
dtype: string
- name: language
dtype: string
- name: product_category
dtype: string
splits:
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num_bytes: 64485678
num_examples: 200000
- name: validation
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num_examples: 5000
- name: test
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num_examples: 5000
download_size: 94802490
dataset_size: 67702449
- config_name: en
features:
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: reviewer_id
dtype: string
- name: stars
dtype: int32
- name: review_body
dtype: string
- name: review_title
dtype: string
- name: language
dtype: string
- name: product_category
dtype: string
splits:
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num_bytes: 58601089
num_examples: 200000
- name: validation
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num_examples: 5000
- name: test
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num_examples: 5000
download_size: 86094112
dataset_size: 61536326
- config_name: es
features:
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: reviewer_id
dtype: string
- name: stars
dtype: int32
- name: review_body
dtype: string
- name: review_title
dtype: string
- name: language
dtype: string
- name: product_category
dtype: string
splits:
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num_bytes: 52375658
num_examples: 200000
- name: validation
num_bytes: 1303958
num_examples: 5000
- name: test
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num_examples: 5000
download_size: 81345461
dataset_size: 54991963
- config_name: fr
features:
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: reviewer_id
dtype: string
- name: stars
dtype: int32
- name: review_body
dtype: string
- name: review_title
dtype: string
- name: language
dtype: string
- name: product_category
dtype: string
splits:
- name: train
num_bytes: 54593565
num_examples: 200000
- name: validation
num_bytes: 1340763
num_examples: 5000
- name: test
num_bytes: 1364510
num_examples: 5000
download_size: 85917293
dataset_size: 57298838
- config_name: ja
features:
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: reviewer_id
dtype: string
- name: stars
dtype: int32
- name: review_body
dtype: string
- name: review_title
dtype: string
- name: language
dtype: string
- name: product_category
dtype: string
splits:
- name: train
num_bytes: 82401390
num_examples: 200000
- name: validation
num_bytes: 2035391
num_examples: 5000
- name: test
num_bytes: 2048048
num_examples: 5000
download_size: 177773783
dataset_size: 86484829
- config_name: zh
features:
- name: review_id
dtype: string
- name: product_id
dtype: string
- name: reviewer_id
dtype: string
- name: stars
dtype: int32
- name: review_body
dtype: string
- name: review_title
dtype: string
- name: language
dtype: string
- name: product_category
dtype: string
splits:
- name: train
num_bytes: 51947668
num_examples: 200000
- name: validation
num_bytes: 1287106
num_examples: 5000
- name: test
num_bytes: 1302711
num_examples: 5000
download_size: 114387247
dataset_size: 54537485
---
# Dataset Card for The Multilingual Amazon Reviews Corpus
## Table of Contents
- [Dataset Card for amazon_reviews_multi](#dataset-card-for-amazon_reviews_multi)
- [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)
- [plain_text](#plain_text)
- [Data Fields](#data-fields)
- [plain_text](#plain_text-1)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [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
- **Webpage:** https://registry.opendata.aws/amazon-reviews-ml/
- **Paper:** https://arxiv.org/abs/2010.02573
- **Point of Contact:** [multilingual-reviews-dataset@amazon.com](mailto:multilingual-reviews-dataset@amazon.com)
### Dataset Summary
We provide an Amazon product reviews dataset for multilingual text classification. The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish, collected between November 1, 2015 and November 1, 2019. Each record in the dataset contains the review text, the review title, the star rating, an anonymized reviewer ID, an anonymized product ID and the coarse-grained product category (e.g. ‘books’, ‘appliances’, etc.) The corpus is balanced across stars, so each star rating constitutes 20% of the reviews in each language.
For each language, there are 200,000, 5,000 and 5,000 reviews in the training, development and test sets respectively. The maximum number of reviews per reviewer is 20 and the maximum number of reviews per product is 20. All reviews are truncated after 2,000 characters, and all reviews are at least 20 characters long.
Note that the language of a review does not necessarily match the language of its marketplace (e.g. reviews from amazon.de are primarily written in German, but could also be written in English, etc.). For this reason, we applied a language detection algorithm based on the work in Bojanowski et al. (2017) to determine the language of the review text and we removed reviews that were not written in the expected language.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset contains reviews in English, Japanese, German, French, Chinese and Spanish.
## Dataset Structure
### Data Instances
Each data instance corresponds to a review. The original JSON for an instance looks like so (German example):
```json
{
"review_id": "de_0784695",
"product_id": "product_de_0572654",
"reviewer_id": "reviewer_de_0645436",
"stars": "1",
"review_body": "Leider, leider nach einmal waschen ausgeblichen . Es sieht super h\u00fcbsch aus , nur leider stinkt es ganz schrecklich und ein Waschgang in der Maschine ist notwendig ! Nach einem mal waschen sah es aus als w\u00e4re es 10 Jahre alt und hatte 1000 e von Waschg\u00e4ngen hinter sich :( echt schade !",
"review_title": "Leider nicht zu empfehlen",
"language": "de",
"product_category": "home"
}
```
### Data Fields
- `review_id`: A string identifier of the review.
- `product_id`: A string identifier of the product being reviewed.
- `reviewer_id`: A string identifier of the reviewer.
- `stars`: An int between 1-5 indicating the number of stars.
- `review_body`: The text body of the review.
- `review_title`: The text title of the review.
- `language`: The string identifier of the review language.
- `product_category`: String representation of the product's category.
### Data Splits
Each language configuration comes with its own `train`, `validation`, and `test` splits. The `all_languages` split
is simply a concatenation of the corresponding split across all languages. That is, the `train` split for
`all_languages` is a concatenation of the `train` splits for each of the languages and likewise for `validation` and
`test`.
## Dataset Creation
### Curation Rationale
The dataset is motivated by the desire to advance sentiment analysis and text classification in other (non-English)
languages.
### Source Data
#### Initial Data Collection and Normalization
The authors gathered the reviews from the marketplaces in the US, Japan, Germany, France, Spain, and China for the
English, Japanese, German, French, Spanish, and Chinese languages, respectively. They then ensured the correct
language by applying a language detection algorithm, only retaining those of the target language. In a random sample
of the resulting reviews, the authors observed a small percentage of target languages that were incorrectly filtered
out and a very few mismatched languages that were incorrectly retained.
#### Who are the source language producers?
The original text comes from Amazon customers reviewing products on the marketplace across a variety of product
categories.
### Annotations
#### Annotation process
Each of the fields included are submitted by the user with the review or otherwise associated with the review. No
manual or machine-driven annotation was necessary.
#### Who are the annotators?
N/A
### Personal and Sensitive Information
According to the original dataset [license terms](https://docs.opendata.aws/amazon-reviews-ml/license.txt), you may not:
- link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or
- attempt to determine the identity of the author of any content in the Reviews Corpus.
If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically
terminate without prejudice to any of the other rights or remedies Amazon may have.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset is part of an effort to encourage text classification research in languages other than English. Such
work increases the accessibility of natural language technology to more regions and cultures. Unfortunately, each of
the languages included here is relatively high resource and well studied.
### Discussion of Biases
The dataset contains only reviews from verified purchases (as described in the paper, section 2.1), and the reviews
should conform the [Amazon Community Guidelines](https://www.amazon.com/gp/help/customer/display.html?nodeId=GLHXEX85MENUE4XF).
### Other Known Limitations
The dataset is constructed so that the distribution of star ratings is balanced. This feature has some advantages for
purposes of classification, but some types of language may be over or underrepresented relative to the original
distribution of reviews to achieve this balance.
## Additional Information
### Dataset Curators
Published by Phillip Keung, Yichao Lu, György Szarvas, and Noah A. Smith. Managed by Amazon.
### Licensing Information
Amazon has licensed this dataset under its own agreement for non-commercial research usage only. This licence is quite restrictive preventing use anywhere a fee is received including paid for internships etc. A copy of the agreement can be found at the dataset webpage here:
https://docs.opendata.aws/amazon-reviews-ml/license.txt
By accessing the Multilingual Amazon Reviews Corpus ("Reviews Corpus"), you agree that the Reviews Corpus is an Amazon Service subject to the [Amazon.com Conditions of Use](https://www.amazon.com/gp/help/customer/display.html/ref=footer_cou?ie=UTF8&nodeId=508088) and you agree to be bound by them, with the following additional conditions:
In addition to the license rights granted under the Conditions of Use, Amazon or its content providers grant you a limited, non-exclusive, non-transferable, non-sublicensable, revocable license to access and use the Reviews Corpus for purposes of academic research. You may not resell, republish, or make any commercial use of the Reviews Corpus or its contents, including use of the Reviews Corpus for commercial research, such as research related to a funding or consultancy contract, internship, or other relationship in which the results are provided for a fee or delivered to a for-profit organization. You may not (a) link or associate content in the Reviews Corpus with any personal information (including Amazon customer accounts), or (b) attempt to determine the identity of the author of any content in the Reviews Corpus. If you violate any of the foregoing conditions, your license to access and use the Reviews Corpus will automatically terminate without prejudice to any of the other rights or remedies Amazon may have.
### Citation Information
Please cite the following paper (arXiv) if you found this dataset useful:
Phillip Keung, Yichao Lu, György Szarvas and Noah A. Smith. “The Multilingual Amazon Reviews Corpus.” In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020.
```
@inproceedings{marc_reviews,
title={The Multilingual Amazon Reviews Corpus},
author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
year={2020}
}
```
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset.
|
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