text-classification bool 2 classes | text stringlengths 0 664k |
|---|---|
false |
# Dataset Card for GigaFren
## 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://opus.nlpl.eu/giga-fren.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are some examples of questions and facts:
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
true |
# Dataset Card for [HasPart]
## 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/haspartkb
- **Repository:**
- **Paper:** https://arxiv.org/abs/2006.07510
- **Leaderboard:**
- **Point of Contact:** Peter Clark <peterc@allenai.org>
### Dataset Summary
This dataset is a new knowledge-base (KB) of hasPart relationships, extracted from a large corpus of generic statements. Complementary to other resources available, it is the first which is all three of: accurate (90% precision), salient (covers relationships a person may mention), and has high coverage of common terms (approximated as within a 10 year old’s vocabulary), as well as having several times more hasPart entries than in the popular ontologies ConceptNet and WordNet. In addition, it contains information about quantifiers, argument modifiers, and links the entities to appropriate concepts in Wikipedia and WordNet.
### Supported Tasks and Leaderboards
Text Classification / Scoring - meronyms (e.g., `plant` has part `stem`)
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
```
{'arg1': 'plant',
'arg2': 'stem',
'score': 0.9991798414303377,
'synset': ['wn.plant.n.02', 'wn.stalk.n.02'],
'wikipedia_primary_page': ['Plant']}
```
### Data Fields
- `arg1`, `arg2`: These are the entities of the meronym, i.e., `arg1` _has\_part_ `arg2`
- `score`: Meronymic score per the procedure described below
- `synset`: Ontological classification from WordNet for the two entities
- `wikipedia_primary_page`: Wikipedia page of the entities
**Note**: some examples contain synset / wikipedia info for only one of the entities.
### Data Splits
Single training file
## Dataset Creation
Our approach to hasPart extraction has five steps:
1. Collect generic sentences from a large corpus
2. Train and apply a RoBERTa model to identify hasPart relations in those sentences
3. Normalize the entity names
4. Aggregate and filter the entries
5. Link the hasPart arguments to Wikipedia pages and WordNet senses
Rather than extract knowledge from arbitrary text, we extract hasPart relations from generic sentences, e.g., “Dogs have tails.”, in order to bias the process towards extractions that are general (apply to most members of a category) and salient (notable enough to write down). As a source of generic sentences, we use **GenericsKB**, a large repository of 3.4M standalone generics previously harvested from a Webcrawl of 1.7B sentences.
### Annotations
#### Annotation process
For each sentence _S_ in GenericsKB, we identify all noun chunks in the sentence using a noun chunker (spaCy's Doc.noun chunks). Each chunk is a candidate whole or part. Then, for each possible pair, we use a RoBERTa model to classify whether a hasPart relationship exists between them. The input sentence is presented to RoBERTa as a sequence of wordpiece tokens, with the start and end of the candidate hasPart arguments identified using special tokens, e.g.:
> `[CLS] [ARG1-B]Some pond snails[ARG1-E] have [ARG2-B]gills[ARG2-E] to
breathe in water.`
where `[ARG1/2-B/E]` are special tokens denoting the argument boundaries. The `[CLS]` token is projected to two class labels (hasPart/notHasPart), and a softmax layer is then applied, resulting in output probabilities for the class labels. We train with cross-entropy loss. We use RoBERTa-large (24 layers), each with a hidden size of 1024, and 16 attention heads, and a total of 355M parameters. We use the pre-trained weights available with the
model and further fine-tune the model parameters by training on our labeled data for 15 epochs. To train the model, we use a hand-annotated set of ∼2k examples.
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
@misc{bhakthavatsalam2020dogs,
title={Do Dogs have Whiskers? A New Knowledge Base of hasPart Relations},
author={Sumithra Bhakthavatsalam and Kyle Richardson and Niket Tandon and Peter Clark},
year={2020},
eprint={2006.07510},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
### Contributions
Thanks to [@jeromeku](https://github.com/jeromeku) for adding this dataset. |
true |
# Dataset Card for KanHope
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://zenodo.org/record/4904729
- **Repository:** [KanHope](https://github.com/adeepH/KanHope)
- **Paper:** [Hope speech detection in Under-resourced Kannada langauge](https://arxiv.org/abs/2108.04616)
- **Leaderboard:** [N/A]
- **Point of Contact:** [Adeep Hande](adeeph18c@iiitt.ac.in)
### Dataset Summary
KanHope dataset is a code-mixed Kannada-English dataset for hope speech detection. All texts are scraped from the comments section of YouTube. The dataset consists of 6,176 user-generated comments in code mixed Kannada scraped from YouTube and manually annotated as bearing hope speech or Not-hope speech.
### Supported Tasks and Leaderboards
This task aims to detect Hope speech content of the code-mixed dataset of comments/posts in Dravidian Languages ( Kannada-English) collected from social media. The comment/post may contain more than one sentence, but the average sentence length of the corpora is 1. Each comment/post is annotated at the comment/post level. This dataset also has class imbalance problems depicting real-world scenarios.
### Languages
Code-mixed text in Dravidian languages (Kannada-English).
## Dataset Structure
### Data Instances
An example from the Kannada dataset looks as follows:
| text | label |
| :------ | :----- |
| ��������� ��ͭ� heartly heltidini... plz avrigella namma nimmellara supprt beku | 0 (Non_hope speech) |
| Next song gu kuda alru andre evaga yar comment madidera alla alrru like madi share madi nam industry na next level ge togond hogaona. | 1 (Hope Speech) |
### Data Fields
Kannada
- `text`: Kannada-English code mixed comment.
- `label`: integer from either of 0 or 1 that corresponds to these values: "Non_hope Speech", "Hope Speech"
### Data Splits
| | train | validation | test |
|---------|------:|-----------:|-----:|
| Kannada | 4941 | 618 | 617 |
## Dataset Creation
### Curation Rationale
Numerous methods have been developed to monitor the spread of negativity in modern years by eliminating vulgar, offensive, and fierce comments from social media platforms. However, there are relatively lesser amounts of study that converges on embracing positivity, reinforcing supportive and reassuring content in online forums.
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
Youtube users
### 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
```
@misc{hande2021hope,
title={Hope Speech detection in under-resourced Kannada language},
author={Adeep Hande and Ruba Priyadharshini and Anbukkarasi Sampath and Kingston Pal Thamburaj and Prabakaran Chandran and Bharathi Raja Chakravarthi},
year={2021},
eprint={2108.04616},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@adeepH](https://github.com/adeepH) for adding this dataset. |
false |
# Dataset Card for taskmaster3
## 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:** [Taskmaster](https://research.google/tools/datasets/taskmaster-1/)
- **Repository:** [GitHub](https://github.com/google-research-datasets/Taskmaster/tree/master/TM-3-2020)
- **Paper:** [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://arxiv.org/abs/1909.05358)
- **Leaderboard:** N/A
- **Point of Contact:** [Taskmaster Googlegroup](taskmaster-datasets@googlegroups.com)
### Dataset Summary
Taskmaster is dataset for goal oriented conversations. The Taskmaster-3 dataset consists of 23,757 movie ticketing dialogs.
By "movie ticketing" we mean conversations where the customer's goal is to purchase tickets after deciding
on theater, time, movie name, number of tickets, and date, or opt out of the transaction. This collection
was created using the "self-dialog" method. This means a single, crowd-sourced worker is
paid to create a conversation writing turns for both speakers, i.e. the customer and the ticketing agent.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is in English language.
## Dataset Structure
### Data Instances
A typical example looks like this
```
{
"conversation_id": "dlg-ddee80da-9ffa-4773-9ce7-f73f727cb79c",
"instructions": "SCENARIO: Pretend you’re *using a digital assistant to purchase tickets for a movie currently showing in theaters*. ...",
"scenario": "4 exchanges with 1 error and predefined variables",
"utterances": [
{
"apis": [],
"index": 0,
"segments": [
{
"annotations": [
{
"name": "num.tickets"
}
],
"end_index": 21,
"start_index": 20,
"text": "2"
},
{
"annotations": [
{
"name": "name.movie"
}
],
"end_index": 42,
"start_index": 37,
"text": "Mulan"
}
],
"speaker": "user",
"text": "I would like to buy 2 tickets to see Mulan."
},
{
"index": 6,
"segments": [],
"speaker": "user",
"text": "Yes.",
"apis": [
{
"args": [
{
"arg_name": "name.movie",
"arg_value": "Mulan"
},
{
"arg_name": "name.theater",
"arg_value": "Mountain AMC 16"
}
],
"index": 6,
"name": "book_tickets",
"response": [
{
"response_name": "status",
"response_value": "success"
}
]
}
]
}
],
"vertical": "Movie Tickets"
}
```
### Data Fields
Each conversation in the data file has the following structure:
- `conversation_id`: A universally unique identifier with the prefix 'dlg-'. The ID has no meaning.
- `utterances`: A list of utterances that make up the conversation.
- `instructions`: Instructions for the crowdsourced worker used in creating the conversation.
- `vertical`: In this dataset the vertical for all dialogs is "Movie Tickets".
- `scenario`: This is the title of the instructions for each dialog.
Each utterance has the following fields:
- `index`: A 0-based index indicating the order of the utterances in the conversation.
- `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance.
- `text`: The raw text of the utterance. In case of self dialogs (one_person_dialogs), this is written by the crowdsourced worker. In case of the WOz dialogs, 'ASSISTANT' turns are written and 'USER' turns are transcribed from the spoken recordings of crowdsourced workers.
- `segments`: A list of various text spans with semantic annotations.
- `apis`: An array of API invocations made during the utterance.
Each API has the following structure:
- `name`: The name of the API invoked (e.g. find_movies).
- `index`: The index of the parent utterance.
- `args`: A `list` of `dict` with keys `arg_name` and `arg_value` which represent the name of the argument and the value for the argument respectively.
- `response`: A `list` of `dict`s with keys `response_name` and `response_value` which represent the name of the response and the value for the response respectively.
Each segment has the following fields:
- `start_index`: The position of the start of the annotation in the utterance text.
- `end_index`: The position of the end of the annotation in the utterance text.
- `text`: The raw text that has been annotated.
- `annotations`: A list of annotation details for this segment.
Each annotation has a single field:
- `name`: The annotation name.
### Data Splits
There are no deafults splits for all the config. The below table lists the number of examples in each config.
| | Train |
|-------------------|--------|
| n_instances | 23757 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is licensed under `Creative Commons Attribution 4.0 License`
### Citation Information
[More Information Needed]
```
@inproceedings{48484,
title = {Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset},
author = {Bill Byrne and Karthik Krishnamoorthi and Chinnadhurai Sankar and Arvind Neelakantan and Daniel Duckworth and Semih Yavuz and Ben Goodrich and Amit Dubey and Kyu-Young Kim and Andy Cedilnik},
year = {2019}
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. |
true |
# ****Dataset Card for French book reviews****
# **I-Dataset Summary**
The majority of review datasets are in English. There are datasets in other languages, but not many. Through this work, I would like to enrich the datasets in the French language(my mother tongue with Arabic).
The data was retrieved from two French websites: [Babelio](https://www.babelio.com/) and [Critiques Libres](http://www.critiqueslibres.com/)
Like Wikipedia, these two French sites are made possible by the contributions of volunteers who use the Internet to share their knowledge and reading experiences.
The French book reviews is a dataset of a huge number of reader reviews on French books that ill be constantly updated over time.
# **II-Supported Tasks and Leaderboards**
- Multi-label text classification : The dataset can be used to train a model for text-classification, which consists of classifying reviews by label value. Success on this task is typically measured by achieving a high or low accuracy.
# **III-Languages**
The texts in the dataset are in French (fr).
# **IV-Dataset Structure**
#### Data Instances
A JSON-formatted example of a typical instance in the dataset:
```python
{
"book_title": "La belle histoire des maths",
"author": "Michel Rousselet",
"reader_review": "C’est un livre impressionnant, qui inspire le respect
par la qualité de sa reliure et son contenu. Je le feuillette et je découvre
à chaque tour de page un thème distinct magnifiquement illustré. Très beau livre !",
"rating": 4.0,
"label": 1
}
```
#### Data Fields
- **book_title**: The title of the book that received the reader's review,
- **author** : The author of the book that received the reader's review,
- **reader_review** : The text of the reader's review,
- **rating**: A five-star rating system is used to rate the book read,
- **label** : A post-processed field indicating if the review is positive (1), neutral(0), or negative(-1) based on the rating field. For more details, see the [Notebook of the Dataset creation](https://github.com/Abirate/Dataset_Creation_Scrapy_Project_French_book_reviews/blob/master/scrapyproject_a_to_z_dataset_book_reviews.ipynb)
#### Data Splits
I kept the dataset as one block (train), so it can be shuffled and split by users later using methods of the hugging face dataset library like the (.train_test_split()) method.
# **V-Dataset Creation**
#### Curation Rationale
The majority of review datasets are in English. There are datasets in other languages, but not many. Through this work, I would like to enrich the datasets in the French language (French is my mother tongue with Arabic) and slightly contribute to advancing data science and AI, not only for English NLP tasks but for other languages around the world.
French is an international language and it is gaining ground. In addition, it is the language of love. The richness of the French language, so appreciated around the world, is largely related to the richness of its culture. The most telling example is French literature, which has many world-famous writers, such as [Gustave Flaubert](https://en.wikipedia.org/wiki/Gustave_Flaubert), [Albert Camus](https://iep.utm.edu/camus/), [Victor Hugo](https://en.wikipedia.org/wiki/Victor_Hugo), [Molière](https://en.wikipedia.org/wiki/Moli%C3%A8re), [Simone de Beauvoir](https://iep.utm.edu/beauvoir/), [Antoine de Saint-Exupéry](https://en.wikipedia.org/wiki/Antoine_de_Saint-Exup%C3%A9ry): the author of "Le Petit Prince" (The Little Prince), which is still among the most translated books in literary history. And one of the world-famous quotes from this book is: "Voici mon secret. Il est très simple: on ne voit bien qu'avec le coeur. L'essentiel est invisible pour les yeux." etc.
#### Source Data
The source of Data is: two French websites: [Babelio](https://www.babelio.com/) and [Critiques Libres](http://www.critiqueslibres.com/)
#### Initial Data Collection and Normalization
The data was collected using web scraping (with Scrapy Framework) and subjected to additional data processing. For more details, see this notebook, which details the dataset creation process. [Notebook of the Dataset creation](https://github.com/Abirate/Dataset_Creation_Scrapy_Project_French_book_reviews/blob/master/scrapyproject_a_to_z_dataset_book_reviews.ipynb)
**Note**: This dataset will be constantly updated to include the most recent reviews on French books by aggregating the old datasets with the updated ones in order to have a huge dataset over time.
#### Who are the source Data producers ?
I created the Dataset using web scraping, by building a spider and a crawler to scrape the two french web sites [Babelio](https://www.babelio.com/) and [Critiques Libres](http://www.critiqueslibres.com/)
#### Annotations
Annotations are part of the initial data collection (see the script above).
# **VI-Additional Informations**
#### Dataset Curators
Abir ELTAIEF
#### Licensing Information
This work is licensed under [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
#### Contributions
Thanks to [@Abirate](https://huggingface.co/Abirate) for creating and adding this dataset.
|
false | # CoNaLa Dataset for Code Generation
## Table of content
- [Dataset Description](#dataset-description)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
## Dataset Descritpion
This dataset has been processed for Code Generation. CMU CoNaLa, the Code/Natural Language Challenge is a joint project of the Carnegie Mellon University NeuLab and STRUDEL Lab. This dataset was designed to test systems for generating program snippets from natural language. It is avilable at https://conala-corpus.github.io/ , and this is about 13k records from the full corpus of about 600k examples.
### Languages
English
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"intent": "convert a list to a dictionary in python",
"snippet": "b = dict(zip(a[0::2], a[1::2]))"
},
{
"intent": "python - sort a list of nested lists",
"snippet": "l.sort(key=sum_nested)"
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"intent": "Value(dtype='string', id=None)",
"snippet": "Value(dtype='string', id=None)"
}
```
### Dataset Splits
This dataset is split into a train, validation and test split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 11125 |
| valid | 1237 |
| test | 500 |
|
false |
# CANTEMIST
## Dataset Description
Manually classified collection of Spanish oncological clinical case reports.
- **Homepage:** [zenodo](https://zenodo.org/record/3978041)
- **Paper:** [Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results](https://www.researchgate.net/profile/Antonio-Miranda-Escalada-2/publication/352786464_Named_Entity_Recognition_Concept_Normalization_and_Clinical_Coding_Overview_of_the_Cantemist_Track_for_Cancer_Text_Mining_in_Spanish_Corpus_Guidelines_Methods_and_Results/links/60d98a3b458515d6fbe382d8/Named-Entity-Recognition-Concept-Normalization-and-Clinical-Coding-Overview-of-the-Cantemist-Track-for-Cancer-Text-Mining-in-Spanish-Corpus-Guidelines-Methods-and-Results.pdf)
- **Point of Contact:** encargo-pln-life@bsc.es
### Dataset Summary
Collection of 1301 oncological clinical case reports written in Spanish, with tumor morphology mentions manually annotated and mapped by clinical experts to a controlled terminology. Every tumor morphology mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O).
The training subset contains 501 documents, the development subsets 500, and the test subset 300. The original dataset is distributed in [Brat](https://brat.nlplab.org/standoff.html) format.
This dataset was designed for the CANcer TExt Mining Shared Task, sponsored by [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
For further information, please visit [the official website](https://temu.bsc.es/cantemist/).
### Supported Tasks
Named Entity Recognition (NER)
### Languages
- Spanish (es)
### Directory Structure
* README.md
* cantemist.py
* train.conll
* dev.conll
* test.conll
## Dataset Structure
### Data Instances
Three four-column files, one for each split.
### Data Fields
Every file has 4 columns:
* 1st column: Word form or punctuation symbol
* 2nd column: Original BRAT file name
* 3rd column: Spans
* 4th column: IOB tag
#### Example
<pre>
El cc_onco101 662_664 O
informe cc_onco101 665_672 O
HP cc_onco101 673_675 O
es cc_onco101 676_678 O
compatible cc_onco101 679_689 O
con cc_onco101 690_693 O
adenocarcinoma cc_onco101 694_708 B-MORFOLOGIA_NEOPLASIA
moderadamente cc_onco101 709_722 I-MORFOLOGIA_NEOPLASIA
diferenciado cc_onco101 723_735 I-MORFOLOGIA_NEOPLASIA
que cc_onco101 736_739 O
afecta cc_onco101 740_746 O
a cc_onco101 747_748 O
grasa cc_onco101 749_754 O
peripancreática cc_onco101 755_770 O
sobrepasando cc_onco101 771_783 O
la cc_onco101 784_786 O
serosa cc_onco101 787_793 O
, cc_onco101 793_794 O
infiltración cc_onco101 795_807 O
perineural cc_onco101 808_818 O
. cc_onco101 818_819 O
</pre>
### Data Splits
| Split | Size |
| ------------- | ------------- |
| `train` | 19,397 |
| `dev` | 18,165 |
| `test` | 11,168 |
## Dataset Creation
### Curation Rationale
For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
### Source Data
#### Initial Data Collection and Normalization
The selected clinical case reports are fairly similar to hospital health records. To increase the usefulness and practical relevance of the CANTEMIST corpus, we selected clinical cases affecting all genders and that comprised most ages (from children to the elderly) and of various complexity levels (solid tumors, hemato-oncological malignancies, neuroendocrine cancer...).
The CANTEMIST cases include clinical signs and symptoms, personal and family history, current illness, physical examination, complementary tests (blood tests, imaging, pathology), diagnosis, treatment (including adverse effects of chemotherapy), evolution and outcome.
#### Who are the source language producers?
Humans, there is no machine generated data.
### Annotations
#### Annotation process
The manual annotation of the Cantemist corpus was performed by clinical experts following the Cantemist guidelines (for more detail refer to this [paper](http://ceur-ws.org/Vol-2664/cantemist_overview.pdf)). These guidelines contain rules for annotating morphology neoplasms in Spanish oncology clinical cases, as well as for mapping these annotations to eCIE-O.
A medical doctor was regularly consulted by annotators (scientists with PhDs on cancer-related subjects) for the most difficult pathology expressions. This same doctor periodically checked a random selection of annotated clinical records and these annotations were compared and discussed with the annotators. To normalize a selection of very complex cases, MD specialists in pathology from one of the largest university hospitals in Spain were consulted.
#### Who are the annotators?
Clinical experts.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
This corpus contributes to the development of medical language models in Spanish.
### Discussion of Biases
Not applicable.
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es).
For further information, send an email to (plantl-gob-es@bsc.es).
This work was funded by the [Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA)](https://avancedigital.mineco.gob.es/en-us/Paginas/index.aspx) within the framework of the [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
### Licensing information
This work is licensed under [CC Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/) License.
Copyright by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) (2022)
### Citation Information
```bibtex
@article{cantemist,
title={Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results.},
author={Miranda-Escalada, Antonio and Farr{\'e}, Eul{\`a}lia and Krallinger, Martin},
journal={IberLEF@ SEPLN},
pages={303--323},
year={2020}
}
```
### Contributions
[N/A]
|
false |
# Dataset Card Creation Guide
## 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:** N/A
- **Repository:** [GitHub](https://github.com/mcobzarenco/mctest/)
- **Paper:** [MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text](https://www.aclweb.org/anthology/D13-1020.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** -
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Microsoft Research License Agreement.
### Citation Information
[More Information Needed]
### Contributions
|
false | # Dataset Card for "code_x_glue_cc_clone_detection_poj_104"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits-sample-size)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104
### Dataset Summary
CodeXGLUE Clone-detection-POJ-104 dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Clone-detection-POJ-104
Given a code and a collection of candidates as the input, the task is to return Top K codes with the same semantic. Models are evaluated by MAP score.
We use POJ-104 dataset on this task.
### Supported Tasks and Leaderboards
- `document-retrieval`: The dataset can be used to train a model for retrieving top-k codes with the same semantics.
### Languages
- C++ **programming** language
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
"code": "\nint f(int shu,int min)\n{ \n int k=1;\n if(shu < min)\n { \n k= 0; \n return k;\n } \n else\n {\n for(int i = min;i<shu;i++)\n { \n if(shu%i == 0)\n { \n k=k+ f(shu/i,i); \n } \n \n \n } \n return k; \n}\n} \n\nmain()\n{\n int n,i,a;\n scanf(\"%d\",&n);\n \n for(i=0;i<n;i++)\n {\n scanf(\"%d\",&a);\n \n if(i!=n-1) \n printf(\"%d\\n\",f(a,2));\n else\n printf(\"%d\",f(a,2)); \n \n \n \n } \n \n \n }",
"id": 0,
"label": "home"
}
```
### Data Fields
In the following each data field in go is explained for each config. The data fields are the same among all splits.
#### default
|field name| type | description |
|----------|------|----------------------------------------------|
|id |int32 | Index of the sample |
|code |string| The full text of the function |
|label |string| The id of problem that the source code solves|
### Data Splits
| name |train|validation|test |
|-------|----:|---------:|----:|
|default|32000| 8000|12000|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
https://github.com/microsoft, https://github.com/madlag
### Licensing Information
Computational Use of Data Agreement (C-UDA) License.
### Citation Information
```
@inproceedings{mou2016convolutional,
title={Convolutional neural networks over tree structures for programming language processing},
author={Mou, Lili and Li, Ge and Zhang, Lu and Wang, Tao and Jin, Zhi},
booktitle={Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence},
pages={1287--1293},
year={2016}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. |
true |
# Dataset Card for Muchocine
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://www.lsi.us.es/~fermin/index.php/Datasets
### Dataset Summary
The Muchocine reviews dataset contains 3,872 longform movie reviews in Spanish language,
each with a shorter summary review, and a rating on a 1-5 scale.
### Supported Tasks and Leaderboards
- `text-classification`: This dataset can be used for Text Classification, more precisely Sentiment Classification where the task is to predict the `star_rating` for a `reveiw_body` or a `review summaray`.
### Languages
Spanish.
## Dataset Structure
### Data Instances
An example from the train split:
```
{
'review_body': 'Zoom nos cuenta la historia de Jack Shepard, anteriormente conocido como el Capitán Zoom, Superhéroe que perdió sus poderes y que actualmente vive en el olvido. La llegada de una amenaza para la Tierra hará que la agencia del gobierno que se ocupa de estos temas acuda a él para que entrene a un grupo de jóvenes con poderes para combatir esta amenaza.Zoom es una comedia familiar, con todo lo que eso implica, es decir, guión flojo y previsible, bromas no salidas de tono, historia amorosa de por medio y un desenlace tópico. La gracia está en que los protagonistas son jóvenes con superpoderes, una producción cargada de efectos especiales y unos cuantos guiños frikis. La película además se pasa volando ya que dura poco mas de ochenta minutos y cabe destacar su prologo en forma de dibujos de comics explicando la historia de la cual partimos en la película.Tim Allen protagoniza la cinta al lado de un envejecido Chevy Chase, que hace de doctor encargado del proyecto, un papel bastante gracioso y ridículo, pero sin duda el mejor papel es el de Courteney Cox, en la piel de una científica amante de los comics y de lo más friki. Del grupito de los cuatro niños sin duda la mas graciosa es la niña pequeña con súper fuerza y la que provocara la mayor parte de los gags debido a su poder.Una comedia entretenida y poca cosa más para ver una tarde de domingo. ',
'review_summary': 'Una comedia entretenida y poca cosa más para ver una tarde de domingo ', 'star_rating': 2
}
```
### Data Fields
- `review_body` - longform review
- `review_summary` - shorter-form review
- `star_rating` - an integer star rating (1-5)
The original source also includes part-of-speech tagging for body and summary fields.
### Data Splits
One split (train) with 3,872 reviews.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Data was collected from www.muchocine.net and uploaded by Dr. Fermín L. Cruz Mata
of La Universidad de Sevilla.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The text reviews and star ratings came directly from users, so no additional annotation was 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
Dr. Fermín L. Cruz Mata.
### Licensing Information
[More Information Needed]
### Citation Information
See http://www.lsi.us.es/~fermin/index.php/Datasets
### Contributions
Thanks to [@mapmeld](https://github.com/mapmeld) for adding this dataset. |
false |
# Dataset Card for OPUS DOGC
## 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://opus.nlpl.eu/DOGC.php
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
OPUS DOGC is a collection of documents from the Official Journal of the Government of Catalonia, in Catalan and Spanish languages, provided by Antoni Oliver Gonzalez from the Universitat Oberta de Catalunya.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is multilingual with parallel text in:
- Catalan
- Spanish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
A data instance contains the following fields:
- `ca`: the Catalan text
- `es`: the aligned Spanish text
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Dataset is in the Public Domain under [CC0 1.0](https://creativecommons.org/publicdomain/zero/1.0/).
### Citation Information
```
@inproceedings{tiedemann-2012-parallel,
title = "Parallel Data, Tools and Interfaces in {OPUS}",
author = {Tiedemann, J{\"o}rg},
booktitle = "Proceedings of the Eighth International Conference on Language Resources and Evaluation ({LREC}'12)",
month = may,
year = "2012",
address = "Istanbul, Turkey",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf",
pages = "2214--2218",
abstract = "This paper presents the current status of OPUS, a growing language resource of parallel corpora and related tools. The focus in OPUS is to provide freely available data sets in various formats together with basic annotation to be useful for applications in computational linguistics, translation studies and cross-linguistic corpus studies. In this paper, we report about new data sets and their features, additional annotation tools and models provided from the website and essential interfaces and on-line services included in the project.",
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. |
true |
## Description
**BAAD16** is an **Authorship Attribution dataset for Bengali Literature**. It was collected and analyzed by the authors of [this paper](https://arxiv.org/abs/2001.05316). It was created by scraping text from an online Bangla e-library using custom web crawler and contains literary works of various famous Bangla writers. It contains novels, stories, series, and other works of 16 authors. Each sample document is created with 750 words. The dataset is imbalanced and resembles real-world scenarios more closely, where not all the authors will have a large number of sample texts. The following table gives more details about the dataset.
| Author Name | Number of Samples | Word Count | Unique Word
| --- | --- | --- | --- |
| zahir rayhan | 185 | 138k | 20k
|nazrul | 223 | 167k | 33k
|manik bandhopaddhay | 469 | 351k | 44k
|nihar ronjon gupta | 476 | 357k | 43k
|bongkim | 562 | 421k | 62k
|tarashonkor | 775 | 581k | 84k
|shottojit roy | 849 | 636k | 67k
|shordindu | 888 | 666k | 84k
|toslima nasrin | 931 | 698k | 76k
|shirshendu | 1048 | 786k | 69k
|zafar iqbal | 1100 | 825k | 53k
|robindronath | 1259 | 944k | 89k
|shorotchandra | 1312 | 984k | 78k
|shomresh | 1408 | 1056k|69k
|shunil gongopaddhay | 1963 | 1472k|109k
|humayun ahmed | 4518 | 3388k |161k
**Total**| 17,966|13,474,500 | 590,660
**Average**|1,122.875|842,156.25| 71,822.25
## Citation
If you use this dataset, please cite the paper [Authorship Attribution in Bangla literature using Character-level CNN](https://ieeexplore.ieee.org/abstract/document/9038560/). [Archive link](https://arxiv.org/abs/2001.05316).
```
@inproceedings{BAAD16Dataset,
title={Authorship Attribution in Bangla literature using Character-level CNN},
author={Khatun, Aisha and Rahman, Anisur and Islam, Md Saiful and others},
booktitle={2019 22nd International Conference on Computer and Information Technology (ICCIT)},
pages={1--5},
year={2019},
organization={IEEE}
doi={10.1109/ICCIT48885.2019.9038560}
}
```
This dataset is also available in Mendeley: [BAAD16 dataset](https://data.mendeley.com/datasets/6d9jrkgtvv/4). Always make sure to use the latest version of the dataset. Cite the dataset directly by:
```
@misc{BAAD6Dataset,
author = {Khatun, Aisha and Rahman, Anisur and Islam, Md. Saiful},
title = {BAAD16: Bangla Authorship Attribution Dataset},
year={2019},
doi = {10.17632/6d9jrkgtvv.4},
howpublished= {\url{https://data.mendeley.com/datasets/6d9jrkgtvv/4}}
}
``` |
true |
## Description
**BAAD6** is an **Authorship Attribution dataset for Bengali Literature**. It was collected and analyzed by Hemayet et al [[1]](https://ieeexplore.ieee.org/document/8631977). The data was obtained from different online posts and blogs. This dataset is balanced among the 6 Authors with 350 sample texts per author. This is a relatively small dataset but is noisy given the sources it was collected from and its cleaning procedure. Nonetheless, it may help evaluate authorship attribution systems as it resembles texts often available on the Internet. Details about the dataset are given in the table below.
| Author | Samples | Word count | Unique word |
| ------ | ------ | ------ | ------ |
|fe|350|357k|53k|
| ij | 350 | 391k | 72k
| mk | 350 | 377k | 47k
| rn | 350 | 231k | 50k
| hm | 350 | 555k | 72k
| rg | 350 | 391k | 58k
**Total** | 2,100 | 2,304,338 | 230,075
**Average** | 350 | 384,056.33 | 59,006.67
## Citation
If you use this dataset, please cite the paper [A Comparative Analysis of Word Embedding Representations in Authorship Attribution of Bengali Literature](https://ieeexplore.ieee.org/document/8631977).
```
@INPROCEEDINGS{BAAD6Dataset,
author={Ahmed Chowdhury, Hemayet and Haque Imon, Md. Azizul and Islam, Md. Saiful},
booktitle={2018 21st International Conference of Computer and Information Technology (ICCIT)},
title={A Comparative Analysis of Word Embedding Representations in Authorship Attribution of Bengali Literature},
year={2018},
volume={},
number={},
pages={1-6},
doi={10.1109/ICCITECHN.2018.8631977}
}
```
This dataset is also available in Mendeley: [BAAD6 dataset](https://data.mendeley.com/datasets/w9wkd7g43f/5). Always make sure to use the latest version of the dataset. Cite the dataset directly by:
```
@misc{BAAD6Dataset,
author = {Ahmed Chowdhury, Hemayet and Haque Imon, Md. Azizul and Khatun, Aisha and Islam, Md. Saiful},
title = {BAAD6: Bangla Authorship Attribution Dataset},
year={2018},
doi = {10.17632/w9wkd7g43f.5},
howpublished= {\url{https://data.mendeley.com/datasets/w9wkd7g43f/5}}
}
``` |
false |
# Dataset Card for GEM/CrossWOZ
## Dataset Description
- **Homepage:** https://github.com/thu-coai/CrossWOZ
- **Repository:** https://github.com/thu-coai/CrossWOZ
- **Paper:** https://aclanthology.org/2020.tacl-1.19
- **Leaderboard:** N/A
- **Point of Contact:** Qi Zhu
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/CrossWOZ).
### Dataset Summary
CrossWOZ is a Chinese multi-domain task-oriented dialogue dataset . It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. About 60{\%} of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/CrossWOZ')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/CrossWOZ).
#### website
[Github](https://github.com/thu-coai/CrossWOZ)
#### paper
[ACL Anthology](https://aclanthology.org/2020.tacl-1.19)
#### authors
Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, and Minlie Huang from CoAI group, Tsinghua University
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/thu-coai/CrossWOZ)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/thu-coai/CrossWOZ)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2020.tacl-1.19)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@article{zhu-etal-2020-crosswoz,
title = "{C}ross{WOZ}: A Large-Scale {C}hinese Cross-Domain Task-Oriented Dialogue Dataset",
author = "Zhu, Qi and
Huang, Kaili and
Zhang, Zheng and
Zhu, Xiaoyan and
Huang, Minlie",
journal = "Transactions of the Association for Computational Linguistics",
volume = "8",
year = "2020",
url = "https://aclanthology.org/2020.tacl-1.19",
doi = "10.1162/tacl_a_00314",
pages = "281--295",
abstract = "To advance multi-domain (cross-domain) dialogue modeling as well as alleviate the shortage of Chinese task-oriented datasets, we propose CrossWOZ, the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts on both user and system sides. About 60{\%} of the dialogues have cross-domain user goals that favor inter-domain dependency and encourage natural transition across domains in conversation. We also provide a user simulator and several benchmark models for pipelined task-oriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus. The large size and rich annotation of CrossWOZ make it suitable to investigate a variety of tasks in cross-domain dialogue modeling, such as dialogue state tracking, policy learning, user simulation, etc.",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Qi Zhu
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
zhuq96@gmail.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`Chinese`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
apache-2.0: Apache License 2.0
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. It contains 6K dialogue sessions and 102K utterances for 5 domains, including hotel, restaurant, attraction, metro, and taxi. Moreover, the corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides. We also provide a user simulator and several benchmark models for pipelined taskoriented dialogue systems, which will facilitate researchers to compare and evaluate their models on this corpus.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Dialog Response Generation
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Generate a response according to the dialog context and database search results.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Tsinghua University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Qi Zhu, Kaili Huang, Zheng Zhang, Xiaoyan Zhu, and Minlie Huang from CoAI group, Tsinghua University
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
National Science Foundation of China, National Key R&D Program of China
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Qi Zhu (Tsinghua University)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `gem_id` (string): GEM-CrossWOZ-{split}-{id}
- `dialog_id` (string): dialog ID
- `sys_id` (string): system annotator ID
- `usr_id` (string): user annotation ID
- `type` (string): dialog type
- `task description` (list of strings): natural language descriptions of the user goal
- `goal` (list of tuples), includes:
- `sub-goal id` (string)
- `domain name` (string)
- `slot name` (string)
- `constraint` if filled, else `requirement` (string)
- `whether be mentioned in previous turns` (string)
- `messages` (list of dict): dialog turns. Each turn contains:
- `content` (string): utterance
- `role` (string): user or system
- `dialog_act` (list of tuples), includes:
- `domain` (string)
- `intent` (string)
- `slot` (string)
- `value` (string)
- `user_state` (list of tuples): same format as "goal", can be viewed as dynamic goal.
- `sys_state_init` (dict): the first db query emitted, records user constraints faithfully. If the system find no result that matches, he/she may relax the constraints manually and search db multiple times.
- `domain` (dict): slot(string)-value(string) pairs
- `selectedResults` (list of string): db search result that would be used in this turn.
- `sys_state` (dict): the final db query emitted, records the db used by the system in this turn. Same format as sys_state_init. Note that this may not satisfy all user constraints.
- `final_goal` (list of tuples): user state/goal at the end of dialog. same format as "goal".
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{'dialog_id': '2303',
'final_goal': [['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '故宫', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
['2', '景点', '电话', '010-85007938', 'True'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
['3', '酒店', '电话', '010-84273030', 'True']],
'gem_id': 'GEM-CrossWOZ-test-0',
'goal': [['1', '餐馆', '人均消费', '50-100元', 'False'],
['1', '餐馆', '推荐菜', "['美食街']", 'False'],
['1', '餐馆', '名称', '', 'False'],
['1', '餐馆', '营业时间', '', 'False'],
['1', '餐馆', '周边景点', '[]', 'False'],
['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
['2', '景点', '评分', '4.5分以上', 'False'],
['2', '景点', '地址', '', 'False'],
['2', '景点', '电话', '', 'False'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
'messages': {'content': ['你好，我想吃美食街，帮我推荐一个人均消费在50-100元的餐馆，谢谢。',
'为您推荐鲜鱼口老字号美食街，人均消费75元，有您想吃的美食街哦。',
'营业时间是什么时间？',
'周一至周日 10:00-22:00。',
'他家周边有什么景点吗？',
'有故宫, 前门大街, 恭王府, 天安门广场。',
'哦，我想在这些附近景点里找一个4.5分以上的，有吗？',
'故宫就是哦，4.7分。',
'好的，电话和地址告诉我一下。',
'010-85007938；北京市东城区景山前街4号。',
'好的，麻烦你帮我查一下桔子水晶酒店(北京安贞店)电话呗。',
'010-84273030。',
'好的，收到，谢谢你！',
'不客气。'],
'dialog_act': [[['General', 'greet', 'none', 'none'],
['General', 'thank', 'none', 'none'],
['Inform', '餐馆', '人均消费', '50-100元'],
['Inform', '餐馆', '推荐菜', '美食街'],
['Request', '餐馆', '名称', '']],
[['Inform', '餐馆', '人均消费', '75元'], ['Inform', '餐馆', '名称', '鲜鱼口老字号美食街']],
[['Request', '餐馆', '营业时间', '']],
[['Inform', '餐馆', '营业时间', '周一至周日 10:00-22:00']],
[['Request', '餐馆', '周边景点', '']],
[['Inform', '餐馆', '周边景点', '前门大街'],
['Inform', '餐馆', '周边景点', '天安门广场'],
['Inform', '餐馆', '周边景点', '恭王府'],
['Inform', '餐馆', '周边景点', '故宫']],
[['Inform', '景点', '评分', '4.5分以上'], ['Select', '景点', '源领域', '餐馆']],
[['Inform', '景点', '名称', '故宫'], ['Inform', '景点', '评分', '4.7分']],
[['Request', '景点', '地址', ''], ['Request', '景点', '电话', '']],
[['Inform', '景点', '地址', '北京市东城区景山前街4号'],
['Inform', '景点', '电话', '010-85007938']],
[['Inform', '酒店', '名称', '桔子水晶酒店(北京安贞店)'], ['Request', '酒店', '电话', '']],
[['Inform', '酒店', '电话', '010-84273030']],
[['General', 'thank', 'none', 'none']],
[['General', 'welcome', 'none', 'none']]],
'role': ['usr',
'sys',
'usr',
'sys',
'usr',
'sys',
'usr',
'sys',
'usr',
'sys',
'usr',
'sys',
'usr',
'sys'],
'sys_state': [{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': ['故宫'],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': ['故宫'],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': ['桔子水晶酒店(北京安贞店)'],
'价格': '',
'名称': '桔子水晶酒店(北京安贞店)',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '桔子水晶酒店(北京安贞店)',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}}],
'sys_state_init': [{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': ['故宫'],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': ['鲜鱼口老字号美食街'],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': ['故宫'],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': ['故宫'],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': ['桔子水晶酒店(北京安贞店)'],
'价格': '',
'名称': '桔子水晶酒店(北京安贞店)',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': [],
'价格': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '',
'评分': ''}},
{'出租': {'selectedResults': [], '出发地': '', '目的地': ''},
'地铁': {'selectedResults': [], '出发地': '', '目的地': ''},
'景点': {'selectedResults': [],
'名称': '故宫',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'游玩时间': '',
'评分': '',
'门票': ''},
'酒店': {'selectedResults': ['桔子水晶酒店(北京安贞店)'],
'价格': '',
'名称': '桔子水晶酒店(北京安贞店)',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'评分': '',
'酒店类型': '',
'酒店设施': ''},
'餐馆': {'selectedResults': [],
'人均消费': '50-100元',
'名称': '',
'周边景点': '',
'周边酒店': '',
'周边餐馆': '',
'推荐菜': '美食街',
'评分': ''}}],
'user_state': [[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '', 'True'],
['1', '餐馆', '营业时间', '', 'False'],
['1', '餐馆', '周边景点', '[]', 'False'],
['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
['2', '景点', '评分', '4.5分以上', 'False'],
['2', '景点', '地址', '', 'False'],
['2', '景点', '电话', '', 'False'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '', 'True'],
['1', '餐馆', '周边景点', '[]', 'False'],
['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
['2', '景点', '评分', '4.5分以上', 'False'],
['2', '景点', '地址', '', 'False'],
['2', '景点', '电话', '', 'False'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', '[]', 'True'],
['2', '景点', '名称', '出现在id=1的周边景点里', 'False'],
['2', '景点', '评分', '4.5分以上', 'False'],
['2', '景点', '地址', '', 'False'],
['2', '景点', '电话', '', 'False'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '出现在id=1的周边景点里', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '', 'False'],
['2', '景点', '电话', '', 'False'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '故宫', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '', 'True'],
['2', '景点', '电话', '', 'True'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'False'],
['3', '酒店', '电话', '', 'False']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '故宫', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
['2', '景点', '电话', '010-85007938', 'True'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
['3', '酒店', '电话', '', 'True']],
[],
[['1', '餐馆', '人均消费', '50-100元', 'True'],
['1', '餐馆', '推荐菜', "['美食街']", 'True'],
['1', '餐馆', '名称', '鲜鱼口老字号美食街', 'True'],
['1', '餐馆', '营业时间', '周一至周日 10:00-22:00', 'True'],
['1', '餐馆', '周边景点', "['天安门广场', '前门大街', '恭王府', '故宫']", 'True'],
['2', '景点', '名称', '故宫', 'True'],
['2', '景点', '评分', '4.5分以上', 'True'],
['2', '景点', '地址', '北京市东城区景山前街4号', 'True'],
['2', '景点', '电话', '010-85007938', 'True'],
['3', '酒店', '名称', '桔子水晶酒店(北京安贞店)', 'True'],
['3', '酒店', '电话', '010-84273030', 'True']],
[]]},
'sys_id': 96,
'task description': ['你要去一个餐馆(id=1)用餐。你希望餐馆的人均消费是50-100元的。你想吃的菜肴是美食街。你想知道这个餐馆的名称、营业时间、周边景点。',
'你要去id=1附近的景点(id=2)游玩。你希望景点的评分是4.5分以上。你想知道这个景点的地址、电话。',
'你要去名叫桔子水晶酒店(北京安贞店)的酒店(id=3)住宿。你想知道这个酒店的电话。'],
'type': '不独立多领域',
'usr_id': 97}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
| Split | Train | Valid | Test |
| --------------------- | ------ | ----- | ----- |
| \# dialogues | 5,012 | 500 | 500 |
| \# Turns (utterances) | 84,692 | 8,458 | 8,476 |
| Vocab | 12,502 | 5,202 | 5,143 |
| Avg. sub-goals | 3.24 | 3.26 | 3.26 |
| Avg. semantic tuples | 14.8 | 14.9 | 15.0 |
| Avg. turns | 16.9 | 16.9 | 17.0 |
| Avg. tokens per turn | 16.3 | 16.3 | 16.2 |
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides, which can be used in a wide range of tasks.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Dialog understanding, dialog policy learning
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`other`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
To adapt to hugging face Datasets, we 1) separate user annotators' ID and system annotations' ID; 2) we convert the data type in goal/user state to string.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
[Code](https://github.com/thu-coai/Convlab-2)
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
According to the type of user goal, we group the dialogues in the training set into five categories:
- S: 417 dialogues have only one sub-goal in HAR domains.
- M: 1573 dialogues have multiple sub-goals (2-3) in HAR domains. However, these sub-goals do not have cross-domain informable slots.
- M+T: 691 dialogues have multiple sub-goals in HAR domains and at least one sub-goal in the metro or taxi domain (3-5 sub-goals). The sub-goals in HAR domains do not have cross-domain informable slots.
- CM: 1,759 dialogues have multiple sub-goals (2-5) in HAR domains with cross-domain informable slots.
- CM+T: 572 dialogues have multiple sub-goals in HAR domains with cross-domain informable slots and at least one sub-goal in the metro or taxi domain (3-5 sub-goals).
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Dialog understanding, dialog policy learning
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
BLEU evaluates the generation quality.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
Inform rate: how many entities in the gold response appear in the generated response.
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
BLEU on MultiWOZ dataset.
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Gather human-to-human dialog in Chinese.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Generate a response according to the dialog context and database search results.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Participatory experiment`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
An usr/sys ID indicates the creator of different data points.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
domains: attraction, hotel, restaurant, metro, taxi
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
Annotators agree using the dataset for research purpose.
#### Other Consented Downstream Use
<!-- info: What other downstream uses of the data did the original data creators and the data curators consent to? -->
<!-- scope: microscope -->
Any
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
unlikely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
yes
#### Details on how Dataset Addresses the Needs
<!-- info: Describe how this dataset addresses the needs of underserved communities. -->
<!-- scope: microscope -->
CrossWOZ is the first large-scale Chinese Cross-Domain Wizard-of-Oz task-oriented dataset. The corpus contains rich annotation of dialogue states and dialogue acts at both user and system sides, which can be used in a wide range of tasks.
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
Yes
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
No
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
No
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
Model may not handle unknown values in the dialog
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
Responses can be diverse, which is not captured by BLEU
|
false |
# Dataset Card for ClarinPL Sejm/Senat Speech Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [CLARIN-PL mowa](https://mowa.clarin-pl.eu/)
- **Repository:** [Needs More Information]
- **Paper:** [System for Automatic Transcription of Sessions of the Polish Senate](https://acoustics.ippt.pan.pl/index.php/aa/article/view/327/pdf_32)
- **Leaderboard:** [Paperswithcode Leaderboard][Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
A collection of 97 hours of parliamentary speeches published on the ClarinPL website.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The audio is in Polish.
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`.
An example from the dataset is:
```
{'file': '/root/.cache/huggingface/datasets/downloads/extracted/4143b1d75559b10028c1c7e8800c9ccc05934ca5a8ea15f8f9a92770576a1ee3/SejmSenat/audio/AdamAbramowicz-20130410/file000.wav',
'id': 'AdamAbramowicz-20130410-file000',
'speaker_id': 'AdamAbramowicz',
'text': 'panie marszałku wysoka izbo panie ministrze próbuje się przedstawiać polskę jako zieloną wyspę kraj który się szybko rozwija tymczasem rzeczywistość jest zupełnie inna a widać ją także dzisiaj przed polskim parlamentem próbuje się rząd próbuje zagonić polaków do pracy aż do śmierci przedłużać wiek emerytalny czyliczyli sytuacja gospodarcza polski w tym wypadku jest przedstawiana już zupełnie inaczej pakiet klimatyczny i protokół z kioto jak się zgadzają fachowcy od gospodarki jest szkodliwy dla krajów które są na dorobku a polska właśnie jest takim krajem'}
```
### Data Fields
- file: A path to the downloaded audio file in .wav format.
- text: the transcription of the audio file.
- speaker_id: The ID of the speaker of the audio.
### Data Splits
| | Train | Test |
| ----- | ----- | ---- |
| dataset | 6622 | 130 |
## 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
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
[Needs More Information]
### Contributions
[Needs More Information] |
false |
# Dataset Card for `best2009`
## 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://aiforthai.in.th/
- **Repository:** https://aiforthai.in.th/corpus.php
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** https://aiforthai.in.th/
### Dataset Summary
`best2009` is a Thai word-tokenization dataset from encyclopedia, novels, news and articles by [NECTEC](https://www.nectec.or.th/) (148,995/2,252 lines of train/test). It was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10). The test set answers are not provided publicly.
### Supported Tasks and Leaderboards
word tokenization
### Languages
Thai
## Dataset Structure
### Data Instances
```
{'char': ['?', 'ภ', 'ู', 'ม', 'ิ', 'ป', 'ั', 'ญ', 'ญ', 'า', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', '\n'], 'char_type': [4, 1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1]}
{'char': ['ภ', 'ู', 'ม', 'ิ', 'ป', 'ั', 'ญ', 'ญ', 'า', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', ' ', 'ห', 'ม', 'า', 'ย', 'ถ', 'ึ', 'ง', ' ', 'ค', 'ว', 'า', 'ม', 'ร', 'ู', '้', 'ข', 'อ', 'ง', 'ช', 'า', 'ว', 'บ', '้', 'า', 'น', ' ', 'ซ', 'ึ', '่', 'ง', 'เ', 'ร', 'ี', 'ย', 'น', 'ร', 'ู', '้', 'ม', 'า', 'จ', 'า', 'ก', 'พ', '่', 'อ', 'แ', 'ม', '่', ' ', 'ป', 'ู', '่', 'ย', '่', 'า', 'ต', 'า', 'ย', 'า', 'ย', ' ', 'ญ', 'า', 'ต', 'ิ', 'พ', 'ี', '่', 'น', '้', 'อ', 'ง', ' ', 'ห', 'ร', 'ื', 'อ', 'ผ', 'ู', '้', 'ม', 'ี', 'ค', 'ว', 'า', 'ม', 'ร', 'ู', '้', 'ใ', 'น', 'ห', 'ม', 'ู', '่', 'บ', '้', 'า', 'น', 'ใ', 'น', 'ท', '้', 'อ', 'ง', 'ถ', 'ิ', '่', 'น', 'ต', '่', 'า', 'ง', 'ๆ', '\n'], 'char_type': [1, 10, 1, 10, 1, 4, 1, 1, 10, 1, 10, 1, 1, 9, 10, 1, 5, 3, 1, 10, 1, 1, 10, 1, 5, 1, 1, 10, 1, 1, 10, 9, 1, 1, 1, 1, 10, 1, 1, 9, 10, 1, 5, 1, 10, 9, 1, 11, 1, 10, 1, 1, 1, 10, 9, 1, 10, 1, 10, 1, 1, 9, 1, 11, 1, 9, 5, 1, 10, 9, 1, 9, 10, 1, 10, 1, 10, 1, 5, 1, 10, 1, 10, 1, 10, 9, 1, 9, 1, 1, 5, 3, 1, 10, 1, 3, 10, 9, 1, 10, 1, 1, 10, 1, 1, 10, 9, 11, 1, 3, 1, 10, 9, 1, 9, 10, 1, 11, 1, 1, 9, 1, 1, 1, 10, 9, 1, 1, 9, 10, 1, 7, 4], 'fname': 'encyclopedia_00031.txt', 'is_beginning': [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1]}
```
### Data Fields
- `fname`: file name; also marks if article is articles, news, encyclopedia or novels
- `char`: characters
- `char_type`: character types as adopted from []() by [deepcut](https://github.com/rkcosmos/deepcut)
- `is_beginning`: is beginning of word
### Data Splits
| | train | test |
|-------------------------|------------|---------|
| # lines | 148,995 | 2,252 |
| avg words per line | 39.05 | NA |
| total words | 5,818,521 | NA |
| avg characters per line | 140.39 | 202.79 |
| total characters | 20,918,132 | 456,684 |
| # lines articles | 16,990 | NA |
| # lines encyclopedia | 50,631 | NA |
| # lines novels | 50,140 | NA |
| # lines news | 31,234 | NA |
## Dataset Creation
### Curation Rationale
The dataset was created for [BEST 2010: Word Tokenization Competition](https://thailang.nectec.or.th/archive/indexa290.html?q=node/10) by [NECTEC](https://www.nectec.or.th/).
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Respective authors of the articles, news, encyclopedia and novels
### Annotations
#### Annotation process
Detailed annotation guidelines can be found in `BEST_Guideline_Release1.pdf` as part of the uncompressed files. Word tokenization standard used was [InterBEST2009](http://hltshare.fbk.eu/IWSLT2015/InterBEST2009Guidelines-2.pdf)
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
All data are curated from public sources. No personal and sensitive information is expected to be included.
## Considerations for Using the Data
### Social Impact of Dataset
- word tokenization dataset from articles, news, encyclopedia and novels
### Discussion of Biases
- texts are relatively formal ones from articles, news, encyclopedia and novels.
- word tokenization standard used was [InterBEST2009](http://hltshare.fbk.eu/IWSLT2015/InterBEST2009Guidelines-2.pdf).
### Other Known Limitations
- some tags unrelated to word tokenization (`<NE>` and `<AB>`) are cleaned out.
- no word boundary provdied for the test set
## Additional Information
### Dataset Curators
[NECTEC](https://www.nectec.or.th/)
### Licensing Information
CC-BY-NC-SA 3.0
### Citation Information
Dataset:
```
@inproceedings{kosawat2009best,
title={BEST 2009: Thai word segmentation software contest},
author={Kosawat, Krit and Boriboon, Monthika and Chootrakool, Patcharika and Chotimongkol, Ananlada and Klaithin, Supon and Kongyoung, Sarawoot and Kriengket, Kanyanut and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and others},
booktitle={2009 Eighth International Symposium on Natural Language Processing},
pages={83--88},
year={2009},
organization={IEEE}
}
@inproceedings{boriboon2009best,
title={Best corpus development and analysis},
author={Boriboon, Monthika and Kriengket, Kanyanut and Chootrakool, Patcharika and Phaholphinyo, Sitthaa and Purodakananda, Sumonmas and Thanakulwarapas, Tipraporn and Kosawat, Krit},
booktitle={2009 International Conference on Asian Language Processing},
pages={322--327},
year={2009},
organization={IEEE}
}
```
Character type features:
```
@inproceedings{haruechaiyasak2009tlex,
title={TLex: Thai lexeme analyser based on the conditional random fields},
author={Haruechaiyasak, Choochart and Kongyoung, Sarawoot},
booktitle={Proceedings of 8th International Symposium on Natural Language Processing},
year={2009}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. |
false |
# Dataset Card for CANER
## 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:** [Classical-Arabic-Named-Entity-Recognition-Corpus](https://github.com/RamziSalah)
- **Paper:** [Researchgate](https://www.researchgate.net/publication/330075080_BUILDING_THE_CLASSICAL_ARABIC_NAMED_ENTITY_RECOGNITION_CORPUS_CANERCORPUS)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Classical Arabic Named Entity Recognition corpus is a new corpus of tagged data that can be useful for handling the issues in recognition of Arabic named entities.
### Supported Tasks and Leaderboards
- Named Entity Recognition
### Languages
Classical Arabic
## Dataset Structure
### Data Instances
An example from the dataset:
```
{'ner_tag': 1, 'token': 'الجامع'}
```
Where 1 stands for "Book"
### Data Fields
- `id`: id of the sample
- `token`: the tokens of the example text
- `ner_tag`: the NER tags of each token
The NER tags correspond to this list:
```
"Allah",
"Book",
"Clan",
"Crime",
"Date",
"Day",
"Hell",
"Loc",
"Meas",
"Mon",
"Month",
"NatOb",
"Number",
"O",
"Org",
"Para",
"Pers",
"Prophet",
"Rlig",
"Sect",
"Time"
```
### Data Splits
Training splits only
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
Ramzi Salah and Lailatul Qadri Zakaria
### 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
[More Information Needed]
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
@article{article,
author = {Salah, Ramzi and Zakaria, Lailatul},
year = {2018},
month = {12},
pages = {},
title = {BUILDING THE CLASSICAL ARABIC NAMED ENTITY RECOGNITION CORPUS (CANERCORPUS)},
volume = {96},
journal = {Journal of Theoretical and Applied Information Technology}
}
### Contributions
Thanks to [@KMFODA](https://github.com/KMFODA) for adding this dataset. |
true |
# Dataset Card for "coarse_discourse"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/google-research-datasets/coarse-discourse
- **Paper:** [Characterizing Online Discussion Using Coarse Discourse Sequences](https://research.google/pubs/pub46055/)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 4.63 MB
- **Size of the generated dataset:** 45.45 MB
- **Total amount of disk used:** 50.08 MB
### Dataset Summary
A large corpus of discourse annotations and relations on ~10K forum threads.
We collect and release a corpus of over 9,000 threads comprising over 100,000 comments manually annotated via paid crowdsourcing with discourse acts and randomly sampled from the site Reddit.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 4.63 MB
- **Size of the generated dataset:** 45.45 MB
- **Total amount of disk used:** 50.08 MB
An example of 'train' looks as follows.
```
{
"annotations": {
"annotator": ["fc96a15ab87f02dd1998ff55a64f6478", "e9e4b3ab355135fa954badcc06bfccc6", "31ac59c1734c1547d4d0723ff254c247"],
"link_to_post": ["", "", ""],
"main_type": ["elaboration", "elaboration", "elaboration"]
},
"id_post": "t1_c9b30i1",
"in_reply_to": "t1_c9b2nyd",
"is_first_post": false,
"is_self_post": true,
"majority_link": "t1_c9b2nyd",
"majority_type": "elaboration",
"post_depth": 2,
"subreddit": "100movies365days",
"title": "DTX120: #87 - Nashville",
"url": "https://www.reddit.com/r/100movies365days/comments/1bx6qw/dtx120_87_nashville/"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `title`: a `string` feature.
- `is_self_post`: a `bool` feature.
- `subreddit`: a `string` feature.
- `url`: a `string` feature.
- `majority_link`: a `string` feature.
- `is_first_post`: a `bool` feature.
- `majority_type`: a `string` feature.
- `id_post`: a `string` feature.
- `post_depth`: a `int32` feature.
- `in_reply_to`: a `string` feature.
- `annotations`: a dictionary feature containing:
- `annotator`: a `string` feature.
- `link_to_post`: a `string` feature.
- `main_type`: a `string` feature.
### Data Splits
| name |train |
|-------|-----:|
|default|116357|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{coarsediscourse, title={Characterizing Online Discussion Using Coarse Discourse Sequences}, author={Zhang, Amy X. and Culbertson, Bryan and Paritosh, Praveen}, booktitle={Proceedings of the 11th International AAAI Conference on Weblogs and Social Media}, series={ICWSM '17}, year={2017}, location = {Montreal, Canada} }
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@jplu](https://github.com/jplu) for adding this dataset. |
false |
# Dataset Card for OpusTedtalks
## 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://opus.nlpl.eu/TedTalks.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
This is a Croatian-English parallel corpus of transcribed and translated TED talks, originally extracted from https://wit3.fbk.eu. The corpus is compiled by Željko Agić and is taken from http://lt.ffzg.hr/zagic provided under the CC-BY-NC-SA license. This corpus is sentence aligned for both language pairs. The documents were collected and aligned using the Hunalign algorithm.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[CC-BY-NC-SA license]<http://creativecommons.org/licenses/by-sa/3.0/>
### Citation Information
@InProceedings{TIEDEMANN12.463,
author = {J{\"o}rg Tiedemann},
title = {Parallel Data, Tools and Interfaces in OPUS},
booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
year = {2012},
month = {may},
date = {23-25},
address = {Istanbul, Turkey},
editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Ugur Dogan and Bente Maegaard and Joseph Mariani and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-7-7},
language = {english}
}
### Contributions
Thanks to [@rkc007](https://github.com/rkc007) for adding this dataset. |
true |
# Dataset Card for `thai_toxicity_tweet`
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/tmu-nlp/ThaiToxicityTweetCorpus/
- **Repository:** https://github.com/tmu-nlp/ThaiToxicityTweetCorpus/
- **Paper:** https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf
- **Leaderboard:**
- **Point of Contact:** https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf
### Dataset Summary
Thai Toxicity Tweet Corpus contains 3,300 tweets (506 tweets with texts missing) annotated by humans with guidelines including a 44-word dictionary.
The author obtained 2,027 and 1,273 toxic and non-toxic tweets, respectively; these were labeled by three annotators. The result of corpus
analysis indicates that tweets that include toxic words are not always toxic. Further, it is more likely that a tweet is toxic, if it contains
toxic words indicating their original meaning. Moreover, disagreements in annotation are primarily because of sarcasm, unclear existing
target, and word sense ambiguity.
Notes from data cleaner: The data is included into [huggingface/datasets](https://www.github.com/huggingface/datasets) in Dec 2020. By this time, 506 of the tweets are not available publicly anymore. We denote these by `TWEET_NOT_FOUND` in `tweet_text`.
Processing can be found at [this PR](https://github.com/tmu-nlp/ThaiToxicityTweetCorpus/pull/1).
### Supported Tasks and Leaderboards
text classification
### Languages
Thai (`th`)
## Dataset Structure
### Data Instances
```
{'is_toxic': 0, 'nontoxic_votes': 3, 'toxic_votes': 0, 'tweet_id': '898576382384418817', 'tweet_text': 'วันๆ นี่คุยกะหมา แมว หมู ไก่ ม้า ควาย มากกว่าคุยกับคนไปละ'}
{'is_toxic': 1, 'nontoxic_votes': 0, 'toxic_votes': 3, 'tweet_id': '898573084981985280', 'tweet_text': 'ควายแดงเมิงด่ารัฐบาลจนรองนายกป่วย พวกมึงกำลังทำลายชาติรู้มั้ย มั้ย มั้ย มั้ยยยยยยยยย news.voicetv.co.th/thailand/51672…'}
```
### Data Fields
"tweet_id": Id of tweet on Twitter
"tweet_text": text of the tweet
"toxic_votes": how many annotators say it is toxic, out of 3 annotators
"nontoxic_votes": how many annotators say it is NOT toxic, out of 3 annotators
"is_toxic": 1 if tweet is toxic else 0 (majority rules)
### Data Splits
No explicit split is given.
## Dataset Creation
### Curation Rationale
The dataset is created as part of [Sirihattasak et al (2019)](https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf).
### Source Data
#### Initial Data Collection and Normalization
The authors used the public Twitter Search API to collect 9,819 tweets from January–December 2017 based on our keyword dictionary. Then, they selected 75 tweets for each keyword. In total, they collected 3,300 tweets for annotation. To ensure quality of data, they set the following selection criteria.
1. All tweets are selected by humans to prevent word ambiguity. (The Twitter API selected the tweets based on characters in the keyword. For example, in the case of “บ้า(crazy),” the API will also select “บ้านนอก” (countryside)” which is not our target.)
2. The length of the tweet should be sufficiently long to discern the context of the tweet. Hence, they set five words as the minimum limit.
3. The tweets that contain only extremely toxic words, (for example: “damn, retard, bitch, f*ck, slut!!!”) are not considered.
4. In addition, they allowed tweets with English words if they were not critical elements in the labeling decision, for example, the word “f*ck.” As a result, our corpus contains English words, but they are less than 2% of the total.
All hashtags, re-tweets, and links were removed from these tweets. However, they did not delete emoticons because these emotional icons can imply the real intent of the post owners. Furthermore, only in the case of annotation, some entries such as the names of famous people were replaced with a tag <ไม่ขอเปิดเผยชื่อ>, for anonymity to prevent individual bias.
#### Who are the source language producers?
Twitter users in Thailand
### Annotations
#### Annotation process
We manually annotated our dataset with two labels: Toxic and Non-Toxic. We define a message as toxic if it indicates any harmful, damage, or negative intent based on our definition of toxicity. Furthermore, all the tweets were annotated by three annotators to identify toxicity; the conditions used for this identification are presented in the following list.
- A toxic message is a message that should be deleted or not be allowed in public.
- A message’s target or consequence must exist. It can either be an individual or a generalized group based on a commonality such as religion or ethnicity, or an entire community.
- Self-complain is not considered toxic, because it is not harmful to anyone. However, if self-complain is intended to indicate something bad, it will be considered as toxic.
- Both direct and indirect messages including those with sarcasm are taken into consideration.
We strictly instructed all the annotators about these concepts and asked them to perform a small test to ensure they understood these conditions. The annotation process was divided into two rounds. We asked the candidates to annotate their answers in the first round to learn our annotation standard. Then, we asked them to annotate a different dataset and selected the ones who obtained a full-score for the second round as an annotator. From among these annotators, 20% of the annotators failed the first round and were not involved in the final annotation.
#### Who are the annotators?
Three annotators hired by [Sirihattasak et al (2019)](https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf)
### Personal and Sensitive Information
Despite all tweets being public, due to the nature of toxic tweets, there might be personal attacks and toxic language used.
## Considerations for Using the Data
### Social Impact of Dataset
- toxic social media message classification dataset
### Discussion of Biases
- Users are masked before annotation by the annotators to prevent biases based on tweet authors
### Other Known Limitations
- The data is included into [huggingface/datasets](https://www.github.com/huggingface/datasets) in Dec 2020. By this time, 506 of the tweets are not available publicly anymore. We denote these by `TWEET_NOT_FOUND` in `tweet_text`.
## Additional Information
### Dataset Curators
[Sirihattasak et al (2019)](https://www.ta-cos.org/sites/ta-cos.org/files/1_W32.pdf)
### Licensing Information
CC-BY-NC 3.0
### Citation Information
Please cite the following if you make use of the dataset:
```
@article{sirihattasak2019annotation,
title={Annotation and Classification of Toxicity for Thai Twitter},
author={Sirihattasak, Sugan and Komachi, Mamoru and Ishikawa, Hiroshi},
year={2019}
}
```
### Contributions
Thanks to [@cstorm125](https://github.com/cstorm125) for adding this dataset. |
false |
# Dataset Card for naamapadam
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** [Needs More Information]
- **Repository:** https://github.com/AI4Bharat/indicner
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** Anoop Kunchukuttan
### Dataset Summary
Naamapadam is the largest publicly available Named Entity Annotated dataset for 11 Indic languages. This corpora was created by projecting named entities from English side to the Indic language side of the English-Indic languages parallel corpus. The dataset additionally contains manually labelled test set for 8 Indic languages containing 500-1000 sentences.
### Supported Tasks and Leaderboards
**Tasks:** NER on Indian languages.
**Leaderboards:** Currently there is no Leaderboard for this dataset.
### Languages
- `Assamese (as)`
- `Bengali (bn)`
- `Gujarati (gu)`
- `Kannada (kn)`
- `Hindi (hi)`
- `Malayalam (ml)`
- `Marathi (mr)`
- `Oriya (or)`
- `Punjabi (pa)`
- `Tamil (ta)`
- `Telugu (te)`
## Dataset Structure
### Data Instances
{'words': ['उन्हेनें', 'शिकांगों','में','बोरोडिन','की','पत्नी','को','तथा','वाशिंगटन','में','रूसी','व्यापार','संघ','को','पैसे','भेजे','।'],
'ner': [0, 3, 0, 1, 0, 0, 0, 0, 3, 0, 5, 6, 6, 0, 0, 0, 0],
}
### Data Fields
- `words`: Raw tokens in the dataset.
- `ner`: the NER tags for this dataset.
### Data Splits
(to be updated, see paper for correct numbers)
| Language | Train | Validation | Test |
|---:|---:|---:|---:|
| as | 10266 | 52 | 51 |
| bn | 961679 | 4859 | 607 |
| gu | 472845 | 2389 | 50 |
| hi | 985787 | 13460 | 437 |
| kn | 471763 | 2381 | 1019 |
| ml | 716652 | 3618 | 974 |
| mr | 455248 | 2300 | 1080 |
| or | 196793 | 993 | 994 |
| pa | 463534 | 2340 | 2342 |
| ta | 497882 | 2795 | 49 |
| te | 507741 | 2700 | 53 |
## Usage
You should have the 'datasets' packages installed to be able to use the :rocket: HuggingFace datasets repository. Please use the following command and install via pip:
```code
pip install datasets
```
To use the dataset, please use:<br/>
```python
from datasets import load_dataset
hiner = load_dataset('ai4bharat/naamapadam')
```
## Dataset Creation
We use the parallel corpus from the Samanantar Dataset between English and the 11 major Indian languages to create the NER dataset. We annotate the English portion of the parallel corpus with existing state-of-the-art NER model. We use word-level alignments learned from the parallel corpus to project the entity labels from English to the Indian language.
### Curation Rationale
naamapadam was built from [Samanantar dataset](https://indicnlp.ai4bharat.org/samanantar/). This dataset was built for the task of Named Entity Recognition in Indic languages. The dataset was introduced to introduce new resources to the Indic languages language that was under-served for Natural Language Processing.
### Source Data
[Samanantar dataset](https://indicnlp.ai4bharat.org/samanantar/)
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
NER annotations were done following the CoNLL-2003 guidelines.
#### Who are the annotators?
The annotations for the testset have been done by volunteers who are proficient in the respective languages. We would like to thank all the volunteers:
- Anil Mhaske
- Anoop Kunchukuttan
- Archana Mhaske
- Arnav Mhaske
- Gowtham Ramesh
- Harshit Kedia
- Nitin Kedia
- Rudramurthy V
- Sangeeta Rajagopal
- Sumanth Doddapaneni
- Vindhya DS
- Yash Madhani
- Kabir Ahuja
- Shallu Rani
- Armin Virk
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to provide a large-scale Named Entity Recognition dataset for Indic languages. Since the information (data points) has been obtained from public resources, we do not think there is a negative social impact in releasing this data.
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
<!-- <a rel="license" float="left" href="http://creativecommons.org/publicdomain/zero/1.0/">
<img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100" />
<img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by.png" style="border-style: none;" alt="CC-BY" width="100" href="http://creativecommons.org/publicdomain/zero/1.0/"/>
</a>
<br/> -->
**CC0 License Statement**
<a rel="license" float="left" href="https://creativecommons.org/about/cclicenses/">
<img src="https://licensebuttons.net/p/zero/1.0/88x31.png" style="border-style: none;" alt="CC0" width="100"/>
</a>
<br>
<br>
- We do not own any of the text from which this data has been extracted.
- We license the actual packaging of the mined data under the [Creative Commons CC0 license (“no rights reserved”)](http://creativecommons.org/publicdomain/zero/1.0).
- To the extent possible under law, <a rel="dct:publisher" href="https://ai4bharat.iitm.ac.in/"> <span property="dct:title">AI4Bharat</span></a> has waived all copyright and related or neighboring rights to <span property="dct:title">Naamapadam</span> manually collected data and existing sources.
- This work is published from: India.
### Citation Information
If you are using the Naampadam corpus, please cite the following article:
```
@misc{mhaske2022naamapadam,
doi = {10.48550/ARXIV.2212.10168},
url = {https://arxiv.org/abs/2212.10168},
author = {Mhaske, Arnav and Kedia, Harshit and Doddapaneni, Sumanth and Khapra, Mitesh M. and Kumar, Pratyush and Murthy, Rudra and Kunchukuttan, Anoop},
title = {Naamapadam: A Large-Scale Named Entity Annotated Data for Indic Languages}
publisher = {arXiv},
year = {2022},
}
```
<!-- Contributors -->
### Contributors
- Arnav Mhaske <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
- Harshit Kedia <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
- Sumanth Doddapaneni <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
- Mitesh M. Khapra <sub> ([AI4Bharat](https://ai4bharat.org), [IITM](https://www.iitm.ac.in)) </sub>
- Pratyush Kumar <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/), [IITM](https://www.iitm.ac.in)) </sub>
- Rudra Murthy <sub> ([AI4Bharat](https://ai4bharat.org), [IBM](https://www.ibm.com))</sub>
- Anoop Kunchukuttan <sub> ([AI4Bharat](https://ai4bharat.org), [Microsoft](https://www.microsoft.com/en-in/), [IITM](https://www.iitm.ac.in)) </sub>
This work is the outcome of a volunteer effort as part of the [AI4Bharat initiative](https://ai4bharat.iitm.ac.in).
<!-- Contact -->
### Contact
- Anoop Kunchukuttan ([anoop.kunchukuttan@gmail.com](mailto:anoop.kunchukuttan@gmail.com))
- Rudra Murthy V ([rmurthyv@in.ibm.com](mailto:rmurthyv@in.ibm.com)) |
false | # Adult
The [Adult dataset](https://archive.ics.uci.edu/ml/datasets/Adult) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Census dataset including personal characteristic of a person, and their income threshold.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|-----------------------------------------------------------------|
| encoding | | Encoding dictionary showing original values of encoded features.|
| income | Binary classification | Classify the person's income as over or under the threshold. |
| income-no race | Binary classification | As `income`, but the `race` feature is removed. |
| race | Multiclass classification | Predict the race of the individual. |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/adult", "income")["train"]
```
# Features
Target feature changes according to the selected configuration and is always in last position in the dataset.
|**Feature** |**Type** | **Description** |
|-------------------------------|-----------|------------------------------------------------------------|
|`age` |`[int64]` | Age of the person. |
|`capital_gain` |`[float64]`| Capital gained by the person. |
|`capital_loss` |`[float64]`| Capital lost by the person. |
|`education` |`[int8]` | Education level: the higher, the more educated the person. |
|`final_weight` |`[int64]` | |
|`hours_worked_per_week` |`[int64]` | Hours worked per week. |
|`marital_status` |`[string]` | Marital status of the person. |
|`native_country` |`[string]` | Native country of the person. |
|`occupation` |`[string]` | Job of the person. |
|`race` |`[string]` | Race of the person. |
|`relationship` |`[string]` | |
|`is_male` |`[bool]` | Man/Woman. |
|`workclass` |`[string]` | Type of job of the person. |
|**over_threshold** |`int8` | `1` for income `>= 50k$`, `0` otherwise. | |
false |
# Dataset Card for BnL Historical Newspapers
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://data.bnl.lu/data/historical-newspapers/
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** opendata@bnl.etat.lu
### Dataset Summary
The BnL has digitised over 800.000 pages of Luxembourg newspapers. This dataset currently has one configuration covering a subset of these newspapers, which sit under the "Processed Datasets" collection. The BNL:
> processed all newspapers and monographs that are in the public domain and extracted the full text and associated meta data of every single article, section, advertisement… The result is a large number of small, easy to use XML files formatted using Dublin Core.
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
The dataset currently contains a single configuration.
### Data Instances
An example instance from the datasets:
``` python
{'id': 'https://persist.lu/ark:/70795/wx8r4c/articles/DTL47',
'article_type': 8,
'extent': 49,
'ispartof': 'Luxemburger Wort',
'pub_date': datetime.datetime(1853, 3, 23, 0, 0),
'publisher': 'Verl. der St-Paulus-Druckerei',
'source': 'newspaper/luxwort/1853-03-23',
'text': 'Asien. Eine neue Nedcrland-Post ist angekommen mil Nachrichten aus Calcutta bis zum 5. Febr.; Vom» vay, 12. Febr. ; Nangun und HongKong, 13. Jan. Die durch die letzte Post gebrachle Nachricht, der König von Ava sei durch seinen Bruder enlhronl worden, wird bestätigt. (K. Z.) Verantwortl. Herausgeber, F. Schümann.',
'title': 'Asien.',
'url': 'http://www.eluxemburgensia.lu/webclient/DeliveryManager?pid=209701#panel:pp|issue:209701|article:DTL47',
'language': 'de'
}
```
### Data Fields
- 'id': This is a unique and persistent identifier using ARK.
- 'article_type': The type of the exported data, possible values ('ADVERTISEMENT_SECTION',
'BIBLIOGRAPHY',
'CHAPTER',
'INDEX',
'CONTRIBUTION',
'TABLE_OF_CONTENTS',
'WEATHER',
'SHIPPING',
'SECTION',
'ARTICLE',
'TITLE_SECTION',
'DEATH_NOTICE',
'SUPPLEMENT',
'TABLE',
'ADVERTISEMENT',
'CHART_DIAGRAM',
'ILLUSTRATION',
'ISSUE')
- 'extent': The number of words in the text field
- 'ispartof: The complete title of the source document e.g. “Luxemburger Wort”.
- 'pub_date': The publishing date of the document e.g “1848-12-15”
- 'publisher':The publisher of the document e.g. “Verl. der St-Paulus-Druckerei”.
- 'source': Describes the source of the document. For example
<dc:source>newspaper/luxwort/1848-12-15</dc:source> means that this article comes from the newspaper “luxwort” (ID for Luxemburger Wort) issued on 15.12.1848.
- 'text': The full text of the entire article, section, advertisement etc. It includes any titles and subtitles as well. The content does not contain layout information, such as headings, paragraphs or lines.
- 'title': The main title of the article, section, advertisement, etc.
- 'url': The link to the BnLViewer on eluxemburgensia.lu to view the resource online.
- 'language': The language of the text, possible values ('ar', 'da', 'de', 'fi', 'fr', 'lb', 'nl', 'pt')
### Data Splits
This dataset contains a single split `train`.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{bnl_newspapers,
title={Historical Newspapers},
url={https://data.bnl.lu/data/historical-newspapers/},
author={ Bibliothèque nationale du Luxembourg},
```
### Contributions
Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset. |
true |
# Dataset Card for Dengue Dataset in Filipino
## 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:** [Dengue Dataset in Filipino homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Repository:** [Dengue Dataset in Filipino repository](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Paper:** [IEEE paper](https://ieeexplore.ieee.org/document/8459963)
- **Leaderboard:**
- **Point of Contact:** [Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph)
### Dataset Summary
Benchmark dataset for low-resource multiclass classification, with 4,015 training, 500 testing, and 500 validation examples, each labeled as part of five classes. Each sample can be a part of multiple classes. Collected as tweets.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is primarily in Filipino, with the addition of some English words commonly used in Filipino vernacular.
## Dataset Structure
### Data Instances
Sample data:
```
{
"text": "Tapos ang dami pang lamok.",
"absent": "0",
"dengue": "0",
"health": "0",
"mosquito": "1",
"sick": "0"
}
```
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph)
### Licensing Information
[More Information Needed]
### Citation Information
@INPROCEEDINGS{8459963,
author={E. D. {Livelo} and C. {Cheng}},
booktitle={2018 IEEE International Conference on Agents (ICA)},
title={Intelligent Dengue Infoveillance Using Gated Recurrent Neural Learning and Cross-Label Frequencies},
year={2018},
volume={},
number={},
pages={2-7},
doi={10.1109/AGENTS.2018.8459963}}
}
### Contributions
Thanks to [@anaerobeth](https://github.com/anaerobeth) for adding this dataset. |
true |
# Dataset Card for Discourse Analysis 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:**
- **Repository:** https://github.com/midas-research/hindi-discourse
- **Paper:** [An Annotated Dataset of Discourse Modes in Hindi Stories](https://aclanthology.org/2020.lrec-1.149/)
- **Point of Contact:** https://github.com/midas-research/MeTooMA
### Dataset Summary
- The Hindi Discourse Analysis dataset is a corpus for analyzing discourse modes present in its sentences.
- It contains sentences from stories written by 11 famous authors from the 20th Century.
- 4-5 stories by each author have been selected which were available in the public domain resulting in a collection of 53 stories.
- Most of these short stories were originally written in Hindi but some of them were written in other Indian languages and later translated to Hindi.
The corpus contains a total of 10472 sentences belonging to the following categories:
- Argumentative
- Descriptive
- Dialogic
- Informative
- Narrative
### Supported Tasks and Leaderboards
- Discourse Analysis of Hindi.
### Languages
Hindi
## Dataset Structure
- The dataset is structured into JSON format.
### Data Instances
{'Story_no': 15, 'Sentence': ' गाँठ से साढ़े तीन रुपये लग गये, जो अब पेट में जाकर खनकते भी नहीं! जो तेरी करनी मालिक! ” “इसमें मालिक की क्या करनी है? ”', 'Discourse Mode': 'Dialogue'}
### Data Fields
Sentence number, story number, sentence and discourse mode
### Data Splits
- Train: 9983
## Dataset Creation
### Curation Rationale
- Present a new publicly available corpus
consisting of sentences from short stories written in a
low-resource language of Hindi having high quality annotation for five different discourse modes -
argumentative, narrative, descriptive, dialogic and informative.
- Perform a detailed analysis of the proposed annotated corpus and characterize the performance of
different classification algorithms.
### Source Data
- Source of all the data points in this dataset is Hindi stories written by famous authors of Hindi literature.
#### Initial Data Collection and Normalization
- All the data was collected from various Hindi websites.
- We chose against crowd-sourcing the annotation pro- cess because we wanted to directly work with the an- notators for qualitative feedback and to also ensure high quality annotations.
- We employed three native Hindi speakers with college level education for the an- notation task.
- We first selected two random stories from our corpus and had the three annotators work on them independently and classify each sentence based on the discourse mode.
- Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
#### Who are the source language producers?
Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
### Annotations
#### Annotation process
- The authors chose against crowd sourcing for labeling this dataset due to its highly sensitive nature.
- The annotators are domain experts having degress in advanced clinical psychology and gender studies.
- They were provided a guidelines document with instructions about each task and its definitions, labels and examples.
- They studied the document, worked a few examples to get used to this annotation task.
- They also provided feedback for improving the class definitions.
- The annotation process is not mutually exclusive, implying that presence of one label does not mean the
absence of the other one.
#### Who are the annotators?
- The annotators were three native Hindi speakers with college level education.
- Please refer to the accompnaying paper for a detailed annotation process.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
- As a future work we would also like to use the presented corpus to see how it could be further used
in certain downstream tasks such as emotion analysis, machine translation,
textual entailment, and speech sythesis for improving storytelling experience in Hindi language.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
- We could not get the best performance using the deep learning model trained on the data, due to
insufficient data for DL models.
## Additional Information
Please refer to this link: https://github.com/midas-research/hindi-discourse
### Dataset Curators
- If you use the corpus in a product or application, then please credit the authors
and [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi]
(http://midas.iiitd.edu.in) appropriately.
Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India
disclaims any responsibility for the use of the corpus and does not provide technical support.
However, the contact listed above will be happy to respond to queries and clarifications
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your social media data.
- if interested in a collaborative research project.
### Licensing Information
- If you use the corpus in a product or application, then please credit the authors
and [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi]
(http://midas.iiitd.edu.in) appropriately.
### Citation Information
Please cite the following publication if you make use of the dataset: https://aclanthology.org/2020.lrec-1.149/
```
@inproceedings{dhanwal-etal-2020-annotated,
title = "An Annotated Dataset of Discourse Modes in {H}indi Stories",
author = "Dhanwal, Swapnil and
Dutta, Hritwik and
Nankani, Hitesh and
Shrivastava, Nilay and
Kumar, Yaman and
Li, Junyi Jessy and
Mahata, Debanjan and
Gosangi, Rakesh and
Zhang, Haimin and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.149",
pages = "1191--1196",
abstract = "In this paper, we present a new corpus consisting of sentences from Hindi short stories annotated for five different discourse modes argumentative, narrative, descriptive, dialogic and informative. We present a detailed account of the entire data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.87 k-alpha). We analyze the data in terms of label distributions, part of speech tags, and sentence lengths. We characterize the performance of various classification algorithms on this dataset and perform ablation studies to understand the nature of the linguistic models suitable for capturing the nuances of the embedded discourse structures in the presented corpus.",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
### Contributions
Thanks to [@duttahritwik](https://github.com/duttahritwik) for adding this dataset. |
false |
# Dataset Card for IgboNLP Datasets
## 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:** None
- **Repository:** https://github.com/IgnatiusEzeani/IGBONLP/tree/master/ig_en_mt
- **Paper:** https://arxiv.org/abs/2004.00648
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
true |
# Dataset Card for ImDB Urdu Reviews
## 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:** [Github](https://github.com/mirfan899/Urdu)
- **Repository:** [Github](https://github.com/mirfan899/Urdu)
- **Paper:** [Aclweb](http://www.aclweb.org/anthology/P11-1015)
- **Leaderboard:**
- **Point of Contact:** [Ikram Ali](https://github.com/akkefa)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- sentence: The movie review which was translated into Urdu.
- sentiment: The sentiment exhibited in the review, either positive or negative.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@chaitnayabasava](https://github.com/chaitnayabasava) for adding this dataset. |
true |
# Dataset Card for LABR
## Table of Contents
- [Dataset Card for LABR](#dataset-card-for-labr)
- [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:** [LABR](https://github.com/mohamedadaly/LABR)
- **Paper:** [LABR: Large-scale Arabic Book Reviews Dataset](https://aclanthology.org/P13-2088/)
- **Point of Contact:** [Mohammed Aly](mailto:mohamed@mohamedaly.info)
### Dataset Summary
This dataset contains over 63,000 book reviews in Arabic. It is the largest sentiment analysis dataset for Arabic to-date. The book reviews were harvested from the website Goodreads during the month or March 2013. Each book review comes with the goodreads review id, the user id, the book id, the rating (1 to 5) and the text of the review.
### Supported Tasks and Leaderboards
The dataset was published on this [paper](https://www.aclweb.org/anthology/P13-2088.pdf).
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
A typical data point comprises a rating from 1 to 5 where the higher the rating the better the review.
### Data Fields
- `text` (str): Review text.
- `label` (int): Review rating.
### Data Splits
The data is split into a training and testing. The split is organized as the following
| | train | test |
|---------- |-------:|------:|
|data split | 11,760 | 2,935 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
downloaded over 220,000 reviews from the
book readers social network www.goodreads.com
during the month of March 2013
#### Who are the source language producers?
Reviews.
### 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{aly2013labr,
title={Labr: A large scale arabic book reviews dataset},
author={Aly, Mohamed and Atiya, Amir},
booktitle={Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
pages={494--498},
year={2013}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. |
false |
# Dataset Card for Yoruba GV 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:**
- **Repository:** [Yoruba GV NER](https://github.com/ajesujoba/YorubaTwi-Embedding/tree/master/Yoruba/Yoruba-NER)
- **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335/
- **Leaderboard:**
- **Point of Contact:** [David Adelani](mailto:didelani@lsv.uni-saarland.de)
### Dataset Summary
The Yoruba GV NER is a named entity recognition (NER) dataset for Yorùbá language based on the [Global Voices news](https://yo.globalvoices.org/) corpus. Global Voices (GV) is a multilingual news platform with articles contributed by journalists, translators, bloggers, and human rights activists from around the world with a coverage of over 50 languages. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Yorùbá.
## Dataset Structure
### Data Instances
A data point consists of sentences seperated by empty line and tab-seperated tokens and tags.
{'id': '0',
'ner_tags': [B-LOC, 0, 0, 0, 0],
'tokens': ['Tanzania', 'fi', 'Ajìjàgbara', 'Ọmọ', 'Orílẹ̀-èdèe']
}
### 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:
```
"O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-DATE", "I-DATE",
```
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 dates & times (DATE). (O) is used for tokens not considered part of any named entity.
### Data Splits
Training (19,421 tokens), validation (2,695 tokens) and test split (5,235 tokens)
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Yorùbá.
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The dataset is based on the news domain and was crawled from [Global Voices Yorùbá news](https://yo.globalvoices.org/).
[More Information Needed]
#### Who are the source language producers?
The dataset contributed by journalists, translators, bloggers, and human rights activists from around the world. Most of the texts used in creating the Yoruba GV NER are translations from other languages to Yorùbá
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The data was annotated by Jesujoba Alabi and David Adelani for the paper:
[Massive vs. Curated Embeddings for Low-Resourced Languages: the case of Yorùbá and Twi](https://www.aclweb.org/anthology/2020.lrec-1.335/).
[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 students of Saarland University, Saarbrücken, Germany .
### Licensing Information
The data is under the [Creative Commons Attribution 3.0 ](https://creativecommons.org/licenses/by/3.0/)
### Citation Information
```
@inproceedings{alabi-etal-2020-massive,
title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi",
author = "Alabi, Jesujoba and
Amponsah-Kaakyire, Kwabena and
Adelani, David and
Espa{\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
pages = "2754--2762",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
### Contributions
Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset. |
false |
# Dataset Card for caWaC
## 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://nlp.ffzg.hr/resources/corpora/cawac/
- **Repository:** http://nlp.ffzg.hr/data/corpora/cawac.uniq.sortr.gz
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2014/pdf/841_Paper.pdf
- **Leaderboard:**
- **Point of Contact:** [Nikola Ljubešič](mailto:nikola.ljubesic@ffzg.hr)
### Dataset Summary
caWaC is a 780-million-token web corpus of Catalan built from the .cat top-level-domain in late 2013.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is monolingual in Catalan language.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@inproceedings{DBLP:conf/lrec/LjubesicT14,
author = {Nikola Ljubesic and
Antonio Toral},
editor = {Nicoletta Calzolari and
Khalid Choukri and
Thierry Declerck and
Hrafn Loftsson and
Bente Maegaard and
Joseph Mariani and
Asunci{\'{o}}n Moreno and
Jan Odijk and
Stelios Piperidis},
title = {caWaC - {A} web corpus of Catalan and its application to language
modeling and machine translation},
booktitle = {Proceedings of the Ninth International Conference on Language Resources
and Evaluation, {LREC} 2014, Reykjavik, Iceland, May 26-31, 2014},
pages = {1728--1732},
publisher = {European Language Resources Association {(ELRA)}},
year = {2014},
url = {http://www.lrec-conf.org/proceedings/lrec2014/summaries/841.html},
timestamp = {Mon, 19 Aug 2019 15:23:35 +0200},
biburl = {https://dblp.org/rec/conf/lrec/LjubesicT14.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. |
true |
# Dataset Card for [Dataset Name]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
http://2019.poleval.pl/index.php/tasks/
- **Repository:**
https://github.com/ptaszynski/cyberbullying-Polish
- **Paper:**
- **Leaderboard:**
https://klejbenchmark.com/leaderboard/
- **Point of Contact:**
### Dataset Summary
The Cyberbullying Detection task was part of 2019 edition of PolEval competition. The goal is to predict if a given Twitter message contains a cyberbullying (harmful) content.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Polish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- sentence: an anonymized tweet in polish
- target: 1 if tweet is described as bullying, 0 otherwise. The test set doesn't have labels so -1 is used instead.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
BSD 3-Clause
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abecadel](https://github.com/abecadel) for adding this dataset. |
false |
# Dataset Card for Coached Conversational Preference Elicitation
## 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:** [Coached Conversational Preference Elicitation Homepage](https://research.google/tools/datasets/coached-conversational-preference-elicitation/)
- **Repository:** [Coached Conversational Preference Elicitation Repository](https://github.com/google-research-datasets/ccpe)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/W19-5941/)
### Dataset Summary
A dataset consisting of 502 English dialogs with 12,000 annotated utterances between a user and an assistant discussing movie preferences in natural language. It was collected using a Wizard-of-Oz methodology between two paid crowd-workers, where one worker plays the role of an 'assistant', while the other plays the role of a 'user'. The 'assistant' elicits the 'user’s' preferences about movies following a Coached Conversational Preference Elicitation (CCPE) method. The assistant asks questions designed to minimize the bias in the terminology the 'user' employs to convey his or her preferences as much as possible, and to obtain these preferences in natural language. Each dialog is annotated with entity mentions, preferences expressed about entities, descriptions of entities provided, and other statements of entities.
### Supported Tasks and Leaderboards
* `other-other-Conversational Recommendation`: The dataset can be used to train a model for Conversational recommendation, which consists in Coached Conversation Preference Elicitation.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
A typical data point comprises of a series of utterances between the 'assistant' and the 'user'. Each such utterance is annotated into categories mentioned in data fields.
An example from the Coached Conversational Preference Elicitation dataset looks as follows:
```
{'conversationId': 'CCPE-6faee',
'utterances': {'index': [0,
1,
2,
3,
4,
5,
6,
7,
8,
9,
10,
11,
12,
13,
14,
15],
'segments': [{'annotations': [{'annotationType': [], 'entityType': []}],
'endIndex': [0],
'startIndex': [0],
'text': ['']},
{'annotations': [{'annotationType': [0], 'entityType': [0]},
{'annotationType': [1], 'entityType': [0]}],
'endIndex': [20, 27],
'startIndex': [14, 0],
'text': ['comedy', 'I really like comedy movies']},
{'annotations': [{'annotationType': [0], 'entityType': [0]}],
'endIndex': [24],
'startIndex': [16],
'text': ['comedies']},
{'annotations': [{'annotationType': [1], 'entityType': [0]}],
'endIndex': [15],
'startIndex': [0],
'text': ['I love to laugh']},
{'annotations': [{'annotationType': [], 'entityType': []}],
'endIndex': [0],
'startIndex': [0],
'text': ['']},
{'annotations': [{'annotationType': [0], 'entityType': [1]},
{'annotationType': [1], 'entityType': [1]}],
'endIndex': [21, 21],
'startIndex': [8, 0],
'text': ['Step Brothers', 'I liked Step Brothers']},
{'annotations': [{'annotationType': [], 'entityType': []}],
'endIndex': [0],
'startIndex': [0],
'text': ['']},
{'annotations': [{'annotationType': [1], 'entityType': [1]}],
'endIndex': [32],
'startIndex': [0],
'text': ['Had some amazing one-liners that']},
{'annotations': [{'annotationType': [], 'entityType': []}],
'endIndex': [0],
'startIndex': [0],
'text': ['']},
{'annotations': [{'annotationType': [0], 'entityType': [1]},
{'annotationType': [1], 'entityType': [1]}],
'endIndex': [15, 15],
'startIndex': [13, 0],
'text': ['RV', "I don't like RV"]},
{'annotations': [{'annotationType': [], 'entityType': []}],
'endIndex': [0],
'startIndex': [0],
'text': ['']},
{'annotations': [{'annotationType': [1], 'entityType': [1]},
{'annotationType': [1], 'entityType': [1]}],
'endIndex': [48, 66],
'startIndex': [18, 50],
'text': ['It was just so slow and boring', "I didn't like it"]},
{'annotations': [{'annotationType': [0], 'entityType': [1]}],
'endIndex': [63],
'startIndex': [33],
'text': ['Jurassic World: Fallen Kingdom']},
{'annotations': [{'annotationType': [0], 'entityType': [1]},
{'annotationType': [3], 'entityType': [1]}],
'endIndex': [52, 52],
'startIndex': [22, 0],
'text': ['Jurassic World: Fallen Kingdom',
'I have seen the movie Jurassic World: Fallen Kingdom']},
{'annotations': [{'annotationType': [], 'entityType': []}],
'endIndex': [0],
'startIndex': [0],
'text': ['']},
{'annotations': [{'annotationType': [1], 'entityType': [1]},
{'annotationType': [1], 'entityType': [1]},
{'annotationType': [1], 'entityType': [1]}],
'endIndex': [24, 125, 161],
'startIndex': [0, 95, 135],
'text': ['I really like the actors',
'I just really like the scenery',
'the dinosaurs were awesome']}],
'speaker': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
'text': ['What kinds of movies do you like?',
'I really like comedy movies.',
'Why do you like comedies?',
"I love to laugh and comedy movies, that's their whole purpose. Make you laugh.",
'Alright, how about a movie you liked?',
'I liked Step Brothers.',
'Why did you like that movie?',
'Had some amazing one-liners that still get used today even though the movie was made awhile ago.',
'Well, is there a movie you did not like?',
"I don't like RV.",
'Why not?',
"And I just didn't It was just so slow and boring. I didn't like it.",
'Ok, then have you seen the movie Jurassic World: Fallen Kingdom',
'I have seen the movie Jurassic World: Fallen Kingdom.',
'What is it about these kinds of movies that you like or dislike?',
'I really like the actors. I feel like they were doing their best to make the movie better. And I just really like the scenery, and the the dinosaurs were awesome.']}}
```
### Data Fields
Each conversation has the following fields:
* `conversationId`: A unique random ID for the conversation. The ID has no meaning.
* `utterances`: An array of utterances by the workers.
Each utterance has the following fields:
* `index`: A 0-based index indicating the order of the utterances in the conversation.
* `speaker`: Either USER or ASSISTANT, indicating which role generated this utterance.
* `text`: The raw text as written by the ASSISTANT, or transcribed from the spoken recording of USER.
* `segments`: An array of semantic annotations of spans in the text.
Each semantic annotation segment has the following fields:
* `startIndex`: The position of the start of the annotation in the utterance text.
* `endIndex`: The position of the end of the annotation in the utterance text.
* `text`: The raw text that has been annotated.
* `annotations`: An array of annotation details for this segment.
Each annotation has two fields:
* `annotationType`: The class of annotation (see ontology below).
* `entityType`: The class of the entity to which the text refers (see ontology below).
**EXPLANATION OF ONTOLOGY**
In the corpus, preferences and the entities that these preferences refer to are annotated with an annotation type as well as an entity type.
Annotation types fall into four categories:
* `ENTITY_NAME` (0): These mark the names of relevant entities mentioned.
* `ENTITY_PREFERENCE` (1): These are defined as statements indicating that the dialog participant does or does not like the relevant entity in general, or that they do or do not like some aspect of the entity. This may also be thought of the participant having some sentiment about what is being discussed.
* `ENTITY_DESCRIPTION` (2): Neutral descriptions that describe an entity but do not convey an explicit liking or disliking.
* `ENTITY_OTHER` (3): Other relevant statements about an entity that convey relevant information of how the participant relates to the entity but do not provide a sentiment. Most often, these relate to whether a participant has seen a particular movie, or knows a lot about a given entity.
Entity types are marked as belonging to one of four categories:
* `MOVIE_GENRE_OR_CATEGORY` (0): For genres or general descriptions that capture a particular type or style of movie.
* `MOVIE_OR_SERIES` (1): For the full or partial name of a movie or series of movies.
* `PERSON` (2): For the full or partial name of an actual person.
* `SOMETHING_ELSE ` (3): For other important proper nouns, such as the names of characters or locations.
### Data Splits
There is a single split of the dataset named 'train' which contains the whole datset.
| | Train |
| ------------------- | ----- |
| Input Conversations | 502 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[Creative Commons Attribution 4.0 License](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
```
@inproceedings{radlinski-etal-2019-ccpe,
title = {Coached Conversational Preference Elicitation: A Case Study in Understanding Movie Preferences},
author = {Filip Radlinski and Krisztian Balog and Bill Byrne and Karthik Krishnamoorthi},
booktitle = {Proceedings of the Annual Meeting of the Special Interest Group on Discourse and Dialogue ({SIGDIAL})},
year = 2019
}
```
### Contributions
Thanks to [@vineeths96](https://github.com/vineeths96) for adding this dataset. |
true |
# Dataset Card for COVID-19 日本語Twitterデータセット (COVID-19 Japanese Twitter 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:** [COVID-19 日本語Twitterデータセット homepage](http://www.db.info.gifu-u.ac.jp/data/Data_5f02db873363f976fce930d1)
- **Repository:** [N/A]
- **Paper:** [N/A]
- **Leaderboard:** [N/A]
- **Point of Contact:** Check the homepage.
### Dataset Summary
53,640 Japanese tweets with annotation if a tweet is related to COVID-19 or not. The annotation is by majority decision by 5 - 10 crowd workers. Target tweets include "COVID" or "コロナ". The period of the tweets is from around January 2020 to around June 2020. The original tweets are not contained. Please use Twitter API to get them, for example.
### Supported Tasks and Leaderboards
Text-classification, Whether the tweet is related to COVID-19, and whether it is fact or opinion.
### Languages
The text can be gotten using the IDs in this dataset is Japanese, posted on Twitter.
## Dataset Structure
### Data Instances
CSV file with the 1st column is Twitter ID and the 2nd column is assessment option ID.
### Data Fields
- `tweet_id`: Twitter ID.
- `assessment_option_id`: The selection result. It has the following meanings:
- 63: a general fact: generally published information, such as news.
- 64: a personal fact: personal news. For example, a person heard that the next-door neighbor, XX, has infected COVID-19, which has not been in a news.
- 65: an opinion/feeling
- 66: difficult to determine if they are related to COVID-19 (it is definitely the tweet is not "67: unrelated", but 63, 64, 65 cannot be determined)
- 67: unrelated
- 68: it is a fact, but difficult to determine whether general facts, personal facts, or impressions (it may be irrelevant to COVID-19 since it is indistinguishable between 63 - 65 and 67).
### Data Splits
No articles have been published for this dataset, and it appears that the author of the dataset is willing to publish an article (it is not certain that the splitting information will be included). Therefore, at this time, information on data splits is not provided.
## Dataset Creation
### Curation Rationale
[More Information Needed] because the paper is not yet published.
### Source Data
#### Initial Data Collection and Normalization
53,640 Japanese tweets with annotation if a tweet is related to COVID-19 or not. Target tweets include "COVID" or "コロナ". The period of the tweets is from around January 2020 to around June 2020.
#### Who are the source language producers?
The language producers are users of Twitter.
### Annotations
#### Annotation process
The annotation is by majority decision by 5 - 10 crowd workers.
#### Who are the annotators?
Crowd workers.
### Personal and Sensitive Information
The author does not contain original tweets.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset is hosted by Suzuki Laboratory, Gifu University, Japan.
### Licensing Information
CC-BY-ND 4.0
### Citation Information
A related paper has not yet published.
The author shows how to cite as「鈴木 優: COVID-19 日本語 Twitter データセット （ http://www.db.info.gifu-u.ac.jp/data/Data_5f02db873363f976fce930d1 ） 」.
### Contributions
Thanks to [@forest1988](https://github.com/forest1988) for adding this dataset. |
false |
# Dataset Card for MENYO-20k
## 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/uds-lsv/menyo-20k_MT/
- **Paper:** [The Effect of Domain and Diacritics in Yorùbá-English Neural Machine Translation](https://arxiv.org/abs/2103.08647)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, 3,397 development sentences, and 6,633 test sentences (3,419 multi-domain, 1,714 news domain, and 1,500 ted talks speech transcript domain).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Languages are English and Yoruba.
## Dataset Structure
### Data Instances
An instance example:
```
{'translation':
{'en': 'Unit 1: What is Creative Commons?',
'yo': 'Ìdá 1: Kín ni Creative Commons?'
}
}
```
### Data Fields
- `translation`:
- `en`: English sentence.
- `yo`: Yoruba sentence.
### Data Splits
Training, validation and test splits are available.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is open but for non-commercial use because some data sources like Ted talks and JW news require permission for commercial use.
The dataset is licensed under Creative Commons [Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) License: https://github.com/uds-lsv/menyo-20k_MT/blob/master/LICENSE
### Citation Information
If you use this dataset, please cite this paper:
```
@inproceedings{adelani-etal-2021-effect,
title = "The Effect of Domain and Diacritics in {Y}oruba{--}{E}nglish Neural Machine Translation",
author = "Adelani, David and
Ruiter, Dana and
Alabi, Jesujoba and
Adebonojo, Damilola and
Ayeni, Adesina and
Adeyemi, Mofe and
Awokoya, Ayodele Esther and
Espa{\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 18th Biennial Machine Translation Summit (Volume 1: Research Track)",
month = aug,
year = "2021",
address = "Virtual",
publisher = "Association for Machine Translation in the Americas",
url = "https://aclanthology.org/2021.mtsummit-research.6",
pages = "61--75",
abstract = "Massively multilingual machine translation (MT) has shown impressive capabilities and including zero and few-shot translation between low-resource language pairs. However and these models are often evaluated on high-resource languages with the assumption that they generalize to low-resource ones. The difficulty of evaluating MT models on low-resource pairs is often due to lack of standardized evaluation datasets. In this paper and we present MENYO-20k and the first multi-domain parallel corpus with a especially curated orthography for Yoruba{--}English with standardized train-test splits for benchmarking. We provide several neural MT benchmarks and compare them to the performance of popular pre-trained (massively multilingual) MT models both for the heterogeneous test set and its subdomains. Since these pre-trained models use huge amounts of data with uncertain quality and we also analyze the effect of diacritics and a major characteristic of Yoruba and in the training data. We investigate how and when this training condition affects the final quality of a translation and its understandability.Our models outperform massively multilingual models such as Google ($+8.7$ BLEU) and Facebook M2M ($+9.1$) when translating to Yoruba and setting a high quality benchmark for future research.",
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset.
|
false |
# Dataset Card for Shaping Answers with Rules through Conversation
## 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:** [ShARC](https://sharc-data.github.io/index.html)
- **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]()
- **Paper:** [Interpretation of Natural Language Rules in Conversational Machine Reading](https://arxiv.org/abs/1809.01494)
- **Leaderboard:** [leaderboard](https://sharc-data.github.io/leaderboard.html)
- **Point of Contact:** [Marzieh Saeidi](marzieh.saeidi@gmail.com), [Max Bartolo](maxbartolo@gmail.com), [Patrick Lewis](patrick.s.h.lewis@gmail.com), [Sebastian Riedel](s.riedel@cs.ucl.ac.uk)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. |
false |
# Dataset Card for Times of India News Headlines
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/J7BYRX
- **Repository:** [More Information Needed]
- **Paper:** [More Information Needed]
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
This news dataset is a persistent historical archive of noteable events in the Indian subcontinent from start-2001 to mid-2020, recorded in realtime by the journalists of India. It contains approximately 3.3 million events published by Times of India. Times Group as a news agency, reaches out a very wide audience across Asia and drawfs every other agency in the quantity of english articles published per day. Due to the heavy daily volume over multiple years, this data offers a deep insight into Indian society, its priorities, events, issues and talking points and how they have unfolded over time. It is possible to chop this dataset into a smaller piece for a more focused analysis, based on one or more facets.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
```
{
'publish_date': '20010530',
'headline_category': city.kolkata,
'headline_text': "Malda fake notes"
}
```
### Data Fields
- `publish_date`: Date of publishing in yyyyMMdd format
- `headline_category`: Category of event in ascii, dot-delimited values
- `headline_text`: Headline of article en la Engrezi (2020-07-10)
### Data Splits
This dataset has no splits.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was created by Rohit Kulkarni.
### Licensing Information
The data is under the [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
```
@data{DVN/DPQMQH_2020,
author = {Kulkarni, Rohit},
publisher = {Harvard Dataverse},
title = {{Times of India News Headlines}},
year = {2020},
version = {V1},
doi = {10.7910/DVN/DPQMQH},
url = {https://doi.org/10.7910/DVN/DPQMQH}
}
```
### Contributions
Thanks to [@tanmoyio](https://github.com/tanmoyio) for adding this dataset. |
true | # Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset is comprised of `emoji` and `emotion` subsets of [tweet_eval](https://huggingface.co/datasets/tweet_eval). The motivation
is that the original `emoji` subset essentially contains only positive/neutral emojis, while `emotion` subset contains a varied array
of emotions. So, the idea was to replace emotion labels with corresponding emojis (sad, angry) in the `emotion` subset and mix it together
with the `emoji` subset.
### Supported Tasks and Leaderboards
Similar to tweet eval the expected usage is text classification.
### Languages
Only English is present in the dataset.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
Refer to [tweet_eval](https://huggingface.co/datasets/tweet_eval). No additional data was added.
#### Annotation process
Same as tweet eval.
#### Who are the annotators?
Same as tweet eval.
### Personal and Sensitive Information
Same as tweet eval.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] |
false | # Dataset Card
## 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://zenodo.org/record/2707356](https://zenodo.org/record/2707356)
- **Repository:** [https://github.com/NLPH/knesset-2004-2005](https://github.com/NLPH/knesset-2004-2005)
- **Paper:**
- **Point of Contact:**
- **Size of downloaded dataset files:**
- **Size of the generated dataset:**
- **Total amount of disk used:**
### Dataset Summary
An example of a sample:
```
{
"text": <text content of given document>,
"path": <file path to docx>
}
```
Dataset usage
Available "kneset16","kneset17","knesset_tagged" configurations
And only train set.
```python
train_ds = load_dataset("imvladikon/knesset_meetings_corpus", "kneset16", split="train")
```
The Knesset Meetings Corpus 2004-2005 is made up of two components:
* Raw texts - 282 files made up of 867,725 lines together. These can be downloaded in two formats:
* As ``doc`` files, encoded using ``windows-1255`` encoding:
* ``kneset16.zip`` - Contains 164 text files made up of 543,228 lines together. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/txt/docs/kneset16.zip>`_ `[Github Mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/kneset16.zip?raw=true>`_
* ``kneset17.zip`` - Contains 118 text files made up of 324,497 lines together. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/txt/docs/kneset17.zip>`_ `[Github Mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/kneset17.zip?raw=true>`_
* As ``txt`` files, encoded using ``utf8`` encoding:
* ``kneset.tar.gz`` - An archive of all the raw text files, divided into two folders: `[Github mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/kneset.tar.gz>`_
* ``16`` - Contains 164 text files made up of 543,228 lines together.
* ``17`` - Contains 118 text files made up of 324,497 lines together.
* ``knesset_txt_16.tar.gz``- Contains 164 text files made up of 543,228 lines together. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/txt/utf8/knesset_txt_16.tar.gz>`_ `[Github Mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/knesset_txt_16.tar.gz?raw=true>`_
* ``knesset_txt_17.zip`` - Contains 118 text files made up of 324,497 lines together. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/txt/utf8/knesset_txt_17.zip>`_ `[Github Mirror] <https://github.com/NLPH/knesset-2004-2005/blob/master/knesset_txt_17.zip?raw=true>`_
* Tokenized and morphologically tagged texts - Tagged versions exist only for the files in the ``16`` folder. The texts are encoded using `MILA's XML schema for corpora <http://www.mila.cs.technion.ac.il/eng/resources_standards.html>`_. These can be downloaded in two ways:
* ``knesset_tagged_16.tar.gz`` - An archive of all tokenized and tagged files. `[MILA host] <http://yeda.cs.technion.ac.il:8088/corpus/software/corpora/knesset/tagged/knesset_tagged_16.tar.gz>`_ `[Archive.org mirror] <https://archive.org/details/knesset_transcripts_2004_2005>`_
Mirrors
-------
This repository is a mirror of this dataset `found on MILA's website <http://www.mila.cs.technion.ac.il/eng/resources_corpora_haknesset.html>`_.
Zenodo mirror: `https://zenodo.org/record/2707356 <https://zenodo.org/record/2707356>`_
License
-------
All Knesset meeting protocols are in the `public domain <https://en.wikipedia.org/wiki/Public_domain>`_ (`רשות הציבור <https://he.wikipedia.org/wiki/%D7%A8%D7%A9%D7%95%D7%AA_%D7%94%D7%A6%D7%99%D7%91%D7%95%D7%A8>`_) by law. These files are thus in the public doamin and do not require any license or public domain dedication to set their status.
.. |DOI| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2707356.svg
:target: https://doi.org/10.5281/zenodo.2707356
.. |LICENCE| image:: https://github.com/NLPH/knesset-2004-2005/blob/master/public_domain_shield.svg
:target: https://en.wikipedia.org/wiki/Public_domain
.. |PUBDOM| image:: https://github.com/NLPH/knesset-2004-2005/blob/master/public_domain.png
:target: https://en.wikipedia.org/wiki/Public_domain
### 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
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
The dataset is available under the [ Open Data Commons Public Domain Dedication & License 1.0](https://opendatacommons.org/licenses/pddl/).
### Citation Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Contributions
|
false |
# Dataset Card for eHealth-KD
## 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:** [eHealth-KD homepage](https://knowledge-learning.github.io/ehealthkd-2020/)
- **Repository:** [eHealth-KD repository](https://github.com/knowledge-learning/ehealthkd-2020)
- **Paper:** [eHealth-KD overview paper](http://ceur-ws.org/Vol-2664/eHealth-KD_overview.pdf)
- **Leaderboard:** [eHealth-KD Challenge 2020 official results](https://knowledge-learning.github.io/ehealthkd-2020/results)
- **Point of Contact:** [Yoan Gutiérrez Vázquez](mailto:ygutierrez@dlsi.ua.es) (Organization Committee), [María Grandury](mailto:yacine@huggingface.co) (Dataset Submitter)
### Dataset Summary
Dataset of the eHealth-KD Challenge at IberLEF 2020. It is designed for the identification of semantic
entities and relations in Spanish health documents.
### Supported Tasks and Leaderboards
The eHealth-KD challenge proposes two computational subtasks:
- `named-entity-recognition`: Given a sentence of an eHealth document written in Spanish, the goal of this subtask is to
identify all the entities and their types.
- `relation-prediction`: The purpose of this subtask is to recognise all relevant semantic relationships between the entities recognised.
For an analysis of the most successful approaches of this challenge, read the [eHealth-KD overview paper](http://ceur-ws.org/Vol-2664/eHealth-KD_overview.pdf).
### Languages
The text in the dataset is in Spanish (BCP-47 code: `es`).
## Dataset Structure
### Data Instances
The first example of the eHeatlh-KD Corpus train set looks as follows:
```
{
'sentence': 'En la leucemia linfocítica crónica, hay demasiados linfocitos, un tipo de glóbulos blancos.',
'entities': {
[
'ent_id: 'T1',
'ent_text': 'leucemia linfocítica crónica',
'ent_label': 0,
'start_character': 6,
'end_character': 34
],
[
'ent_id: 'T2',
'ent_text': 'linfocitos',
'ent_label': 0,
'start_character': 51,
'end_character': 61
],
[
'ent_id: 'T3',
'ent_text': 'glóbulos blancos',
'ent_label': 0,
'start_character': 74,
'end_character': 90
]
},
relations: {
[
'rel_id: 'R0'
'rel_label': 0,
'arg1': T2
'arg2': T3
],
[
'rel_id': 'R1'
'rel_label': 5,
'arg1': T1,
'arg2': T2
]
}
}
```
### Data Fields
- `sentence`: sentence of an eHealth document written in Spanish
- `entities`: list of entities identified in the sentence
- `ent_id`: entity identifier (`T`+ a number)
- `ent_text`: entity, can consist of one or more complete words (i.e., not a prefix or a suffix of a word), and will
never include any surrounding punctuation symbols, parenthesis, etc.
- `ent_label`: type of entity (`Concept`, `Action`, `Predicate` or `Reference`)
- `start_character`: position of the first character of the entity
- `end_character`: position of the last character of the entity
- `relations`: list of semantic relationships between the entities recognised
- `rel_id`: relation identifier (`R` + a number)
- `rel_label`: type of relation, can be a general relation (`is-a`, `same-as`, `has-property`, `part-of`, `causes`, `entails`),
a contextual relation (`in-time`, `in-place`, `in-context`) an action role (`subject`, `target`) or a predicate role (`domain`, `arg`).
- `arg1`: ID of the first entity of the relation
- `arg2`: ID of the second entity of the relation
For more information about the types of entities and relations, click [here](https://knowledge-learning.github.io/ehealthkd-2020/tasks).
### Data Splits
The data is split into a training, validation and test set. The split sizes are as follow:
| | Train | Val | Test |
| ----- | ------ | ----- | ---- |
| eHealth-KD 2020 | 800 | 199 | 100 |
In the challenge there are 4 different scenarios for testing. The test data of this dataset corresponds to the third scenario.
More information about the testing data [here](https://github.com/knowledge-learning/ehealthkd-2020/tree/master/data/testing).
## Dataset Creation
### Curation Rationale
The vast amount of clinical text available online has motivated the development of automatic
knowledge discovery systems that can analyse this data and discover relevant facts.
The eHealth Knowledge Discovery (eHealth-KD) challenge, in its third edition, leverages
a semantic model of human language that encodes the most common expressions of factual
knowledge, via a set of four general-purpose entity types and thirteen semantic relations among
them. The challenge proposes the design of systems that can automatically annotate entities and
relations in clinical text in the Spanish language.
### Source Data
#### Initial Data Collection and Normalization
As in the previous edition, the corpus for eHealth-KD 2020 has been extracted from MedlinePlus sources. This platform
freely provides large health textual data from which we have made a selection for constituting the eHealth-KD corpus.
The selection has been made by sampling specific XML files from the collection available in the [Medline website](https://medlineplus.gov/xml.html).
```
“MedlinePlus is the National Institutes of Health’s Website for patients and their families and
friends. Produced by the National Library of Medicine, the world’s largest medical library, it
brings you information about diseases, conditions, and wellness issues in language you can
understand. MedlinePlus offers reliable, up-to-date health information, anytime, anywhere, for free.”
```
These files contain several entries related to health and medicine topics and have been processed to remove all
XML markup to extract the textual content. Only Spanish language items were considered. Once cleaned, each individual
item was converted to a plain text document, and some further post-processing is applied to remove unwanted sentences,
such as headers, footers and similar elements, and to flatten HTML lists into plain sentences.
#### Who are the source language producers?
As in the previous edition, the corpus for eHealth-KD 2020 was extracted from [MedlinePlus](https://medlineplus.gov/xml.html) sources.
### Annotations
#### Annotation process
Once the MedlinePlus files were cleaned, they were manually tagged using [BRAT](http://brat.nlplab.org/) by a group of
annotators. After tagging, a post-processing was applied to BRAT’s output files (ANN format) to obtain the output files
in the formats needed for the challenge.
#### Who are the annotators?
The data was manually tagged.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
"The eHealth-KD 2020 proposes –as the previous editions– modeling the human language in a scenario in which Spanish
electronic health documents could be machine-readable from a semantic point of view.
With this task, we expect to encourage the development of software technologies to automatically extract a large variety
of knowledge from eHealth documents written in the Spanish Language."
### 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
#### Organization Committee
| Name | Email | Institution |
|:---------------------------------------:|:---------------------:|:-----------------------------:|
| Yoan Gutiérrez Vázquez (contact person) | ygutierrez@dlsi.ua.es | University of Alicante, Spain |
| Suilan Estévez Velarde | sestevez@matcom.uh.cu | University of Havana, Cuba |
| Alejandro Piad Morffis | apiad@matcom.uh.cu | University of Havana, Cuba |
| Yudivián Almeida Cruz | yudy@matcom.uh.cu | University of Havana, Cuba |
| Andrés Montoyo Guijarro | montoyo@dlsi.ua.es | University of Alicante, Spain |
| Rafael Muñoz Guillena | rafael@dlsi.ua.es | University of Alicante, Spain |
#### Funding
This research has been supported by a Carolina Foundation grant in agreement with University of Alicante and University
of Havana. Moreover, it has also been partially funded by both aforementioned universities, IUII, Generalitat Valenciana,
Spanish Government, Ministerio de Educación, Cultura y Deporte through the projects SIIA (PROMETEU/2018/089) and
LIVINGLANG (RTI2018-094653-B-C22).
### Licensing Information
This dataset is under the Attribution-NonCommercial-ShareAlike 4.0 International
[(CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/).
To accept the distribution terms, please fill in the following [form](https://forms.gle/pUJutSDq2FYLwNWQA).
### Citation Information
In the following link you can find the
[preliminar bibtexts of the systems’ working-notes](https://knowledge-learning.github.io/ehealthkd-2020/shared/eHealth-KD_2020_bibtexts.zip).
In addition, to cite the eHealth-KD challenge you can use the following preliminar bibtext:
```
@inproceedings{overview_ehealthkd2020,
author = {Piad{-}Morffis, Alejandro and
Guti{\'{e}}rrez, Yoan and
Ca{\~{n}}izares-Diaz, Hian and
Estevez{-}Velarde, Suilan and
Almeida{-}Cruz, Yudivi{\'{a}}n and
Mu{\~{n}}oz, Rafael and
Montoyo, Andr{\'{e}}s},
title = {Overview of the eHealth Knowledge Discovery Challenge at IberLEF 2020},
booktitle = ,
year = {2020},
}
```
### Contributions
Thanks to [@mariagrandury](https://github.com/mariagrandury) for adding this dataset. |
false |
# 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:** [Github](https://github.com/mpsilfve/finer-data)
- **Repository:** [Github](https://github.com/mpsilfve/finer-data)
- **Paper:** [Arxiv](https://arxiv.org/abs/1908.04212)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
Each row consists of the following fields:
* `id`: The sentence id
* `tokens`: An ordered list of tokens from the full text
* `ner_tags`: Named entity recognition tags for each token
* `nested_ner_tags`: Nested named entity recognition tags for each token
Note that by design, the length of `tokens`, `ner_tags`, and `nested_ner_tags` will always be identical.
`ner_tags` and `nested_ner_tags` correspond to the list below:
```
[ "O", "B-DATE", "B-EVENT", "B-LOC", "B-ORG", "B-PER", "B-PRO", "I-DATE", "I-EVENT", "I-LOC", "I-ORG", "I-PER", "I-PRO" ]
```
IOB2 labeling scheme is used.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@stefan-it](https://github.com/stefan-it) for adding this dataset. |
false |
# Dataset Card for RO-STS-Parallel
## 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:** [GitHub](https://github.com/dumitrescustefan/RO-STS)
- **Repository:** [GitHub](https://github.com/dumitrescustefan/RO-STS)
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [email](dumitrescu.stefan@gmail.com)
### Dataset Summary
We present RO-STS-Parallel - a Parallel Romanian-English dataset obtained by translating the [STS English dataset](https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark) dataset into Romanian. It contains 17256 sentences in Romanian and English.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The text dataset is in Romanian and English (`ro`, `en`)
## Dataset Structure
### Data Instances
An example looks like this:
```
{
'translation': {
'ro': 'Problema e si mai simpla.',
'en': 'The problem is simpler than that.'
}
}
```
### Data Fields
- translation:
- ro: text in Romanian
- en: text in English
### Data Splits
The train/validation/test split contain 11,498/3,000/2,758 sentence pairs.
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
*To construct the dataset, we first obtained automatic translations using Google's translation engine. These were then manually checked, corrected, and cross-validated by human volunteers. *
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
CC BY-SA 4.0 License
### Citation Information
```
@inproceedings{dumitrescu2021liro,
title={Liro: Benchmark and leaderboard for romanian language tasks},
author={Dumitrescu, Stefan Daniel and Rebeja, Petru and Lorincz, Beata and Gaman, Mihaela and Avram, Andrei and Ilie, Mihai and Pruteanu, Andrei and Stan, Adriana and Rosia, Lorena and Iacobescu, Cristina and others},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},
year={2021}
}
```
### Contributions
Thanks to [@lorinczb](https://github.com/lorinczb) for adding this dataset. |
true | |
false | # C4 200M
# Dataset Summary
C4 200M Sample Dataset adopted from https://huggingface.co/datasets/liweili/c4_200m
C4_200m is a collection of 185 million sentence pairs generated from the cleaned English dataset from C4. This dataset can be used in grammatical error correction (GEC) tasks.
The corruption edits and scripts used to synthesize this dataset is referenced from: [C4_200M Synthetic Dataset](https://github.com/google-research-datasets/C4_200M-synthetic-dataset-for-grammatical-error-correction)
# Description
As discussed before, this dataset contains 185 million sentence pairs. Each article has these two attributes: `input` and `output`. Here is a sample of dataset:
```
{
"input": "Bitcoin is for $7,094 this morning, which CoinDesk says."
"output": "Bitcoin goes for $7,094 this morning, according to CoinDesk."
}
``` |
true |
# Dataset Card for BBC Hindi NLI 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
- **Repository:** [GitHub](https://github.com/midas-research/hindi-nli-data)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.aacl-main.71)
- **Point of Contact:** [GitHub](https://github.com/midas-research/hindi-nli-data)
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. BBC Hindi Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Context and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
[More Information Needed]
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- Train and Test files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह खबर की सूचना है|', 'label': 'entailed', 'premise': 'गोपनीयता की नीति', 'topic': '1'}
```
### Data Fields
- Each row contatins 4 columns - Premise, Hypothesis, Label and Topic.
### Data Splits
- Train : 15553
- Valid : 2581
- Test : 2593
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available BBC Hindi news text classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- BBC Hindi News Classification Dataset contains 4, 335 Hindi news headlines tagged across 14 categories: India, Pakistan,news, International, entertainment, sport, science, China, learning english, social, southasia, business, institutional, multimedia
- We processed this dataset to combine two sets of relevant but low prevalence classes.
- Namely, we merged the samples from Pakistan, China, international, and southasia as one class called international.
- Likewise, we also merged samples from news, business, social, learning english, and institutional as news.
- Lastly, we also removed the class multimedia because there were very few samples.
#### Who are the source language producers?
Pls refer to this paper: "https://www.aclweb.org/anthology/2020.aacl-main.71"
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/avinsit123/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
### Contributions
Thanks to [@avinsit123](https://github.com/avinsit123) for adding this dataset. |
false |
# Dataset Card for "eraser_multi_rc"
## 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://cogcomp.org/multirc/
- **Repository:** https://github.com/CogComp/multirc
- **Paper:** [Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences](https://cogcomp.seas.upenn.edu/page/publication_view/833)
- **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:** 1.67 MB
- **Size of the generated dataset:** 63.65 MB
- **Total amount of disk used:** 65.32 MB
### Dataset Summary
MultiRC (Multi-Sentence Reading Comprehension) is a dataset of short paragraphs and multi-sentence questions that can be answered from the content of the paragraph.
We have designed the dataset with three key challenges in mind:
- The number of correct answer-options for each question is not pre-specified. This removes the over-reliance of current approaches on answer-options and forces them to decide on the correctness of each candidate answer independently of others. In other words, unlike previous work, the task here is not to simply identify the best answer-option, but to evaluate the correctness of each answer-option individually.
- The correct answer(s) is not required to be a span in the text.
- The paragraphs in our dataset have diverse provenance by being extracted from 7 different domains such as news, fiction, historical text etc., and hence are expected to be more diverse in their contents as compared to single-domain datasets.
The goal of this dataset is to encourage the research community to explore approaches that can do more than sophisticated lexical-level matching.
### 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:** 1.67 MB
- **Size of the generated dataset:** 63.65 MB
- **Total amount of disk used:** 65.32 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"evidences": "[\"Allan sat down at his desk and pulled the chair in close .\", \"Opening a side drawer , he took out a piece of paper and his ink...",
"label": 0,
"passage": "\"Allan sat down at his desk and pulled the chair in close .\\nOpening a side drawer , he took out a piece of paper and his inkpot...",
"query_and_answer": "Name few objects said to be in or on Allan 's desk || Eraser"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `passage`: a `string` feature.
- `query_and_answer`: a `string` feature.
- `label`: a classification label, with possible values including `False` (0), `True` (1).
- `evidences`: a `list` of `string` features.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default|24029| 3214|4848|
## 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
https://github.com/CogComp/multirc/blob/master/LICENSE
Research and Academic Use License
Cognitive Computation Group
University of Illinois at Urbana-Champaign
Downloading software implies that you accept the following license terms:
Under this Agreement, The Board of Trustees of the University of Illinois ("University"), a body corporate and politic of the State of Illinois with its principal offices at 506 South Wright Street, Urbana, Illinois 61801, U.S.A., on behalf of its Department of Computer Science on the Urbana-Champaign Campus, provides the software ("Software") described in Appendix A, attached hereto and incorporated herein, to the Licensee identified below ("Licensee") subject to the following conditions:
1. Upon execution of this Agreement by Licensee below, the University grants, and Licensee accepts, a roylaty-free, non-exclusive license:
A. To use unlimited copies of the Software for its own academic and research purposes.
B. To make derivative works. However, if Licensee distributes any derivative work based on or derived from the Software (with such distribution limited to binary form only), then Licensee will (1) notify the University (c/o Professor Dan Roth, e-mail: danr@cs.uiuc.edu) regarding its distribution of the derivative work and provide a copy if requested, and (2) clearly notify users that such derivative work is a modified version and not the original Software distributed by the University.
C. To redistribute (sublicense) derivative works based on the Software in binary form only to third parties provided that (1) the copyright notice and any accompanying legends or proprietary notices are reproduced on all copies, (2) no royalty is charged for such copies, and (3) third parties are restricted to using the derivative work for academic and research purposes only, without further sublicensing rights.
No license is granted herein that would permit Licensee to incorporate the Software into a commercial product, or to otherwise commercially exploit the Software. Should Licensee wish to make commercial use of the Software, Licensee should contact the University, c/o the Office of Technology Management ("OTM") to negotiate an appropriate license for such commercial use. To contact the OTM: otmmailaccount@ad.uiuc.edu; telephone: (217)333-3781; fax: (217) 265-5530.
2. THE UNIVERSITY GIVES NO WARRANTIES, EITHER EXPRESSED OR IMPLIED, FOR THE SOFTWARE AND/OR ASSOCIATED MATERIALS PROVIDED UNDER THIS AGREEMENT, INCLUDING, WITHOUT LIMITATION, WARRANTY OF MERCHANTABILITY AND WARRANTY OF FITNESS FOR A PARTICULAR PURPOSE, AND ANY WARRANTY AGAINST INFRINGEMENT OF ANY INTELLECTUAL PROPERTY RIGHTS.
3. Licensee understands the Software is a research tool for which no warranties as to capabilities or accuracy are made, and Licensee accepts the Software on an "as is, with all defects" basis, without maintenance, debugging , support or improvement. Licensee assumes the entire risk as to the results and performance of the Software and/or associated materials. Licensee agrees that University shall not be held liable for any direct, indirect, consequential, or incidental damages with respect to any claim by Licensee or any third party on account of or arising from this Agreement or use of the Software and/or associated materials.
4. Licensee understands the Software is proprietary to the University. Licensee will take all reasonable steps to insure that the source code is protected and secured from unauthorized disclosure, use, or release and will treat it with at least the same level of care as Licensee would use to protect and secure its own proprietary computer programs and/or information, but using no less than reasonable care.
5. In the event that Licensee shall be in default in the performance of any material obligations under this Agreement, and if the default has not been remedied within sixty (60) days after the date of notice in writing of such default, University may terminate this Agreement by written notice. In the event of termination, Licensee shall promptly return to University the original and any copies of licensed Software in Licensee's possession. In the event of any termination of this Agreement, any and all sublicenses granted by Licensee to third parties pursuant to this Agreement (as permitted by this Agreement) prior to the date of such termination shall nevertheless remain in full force and effect.
6. The Software was developed, in part, with support from the National Science Foundation, and the Federal Government has certain license rights in the Software.
7. This Agreement shall be construed and interpreted in accordance with the laws of the State of Illinois, U.S.A..
8. This Agreement shall be subject to all United States Government laws and regulations now and hereafter applicable to the subject matter of this Agreement, including specifically the Export Law provisions of the Departments of Commerce and State. Licensee will not export or re-export the Software without the appropriate United States or foreign government license.
By its registration below, Licensee confirms that it understands the terms and conditions of this Agreement, and agrees to be bound by them. This Agreement shall become effective as of the date of execution by Licensee.
### Citation Information
```
@unpublished{eraser2019,
title = {ERASER: A Benchmark to Evaluate Rationalized NLP Models},
author = {Jay DeYoung and Sarthak Jain and Nazneen Fatema Rajani and Eric Lehman and Caiming Xiong and Richard Socher and Byron C. Wallace}
}
@inproceedings{MultiRC2018,
author = {Daniel Khashabi and Snigdha Chaturvedi and Michael Roth and Shyam Upadhyay and Dan Roth},
title = {Looking Beyond the Surface:A Challenge Set for Reading Comprehension over Multiple Sentences},
booktitle = {Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL)},
year = {2018}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
true |
# Dataset Card for NewsPH NLI
## 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:** [NewsPH NLI homepage](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Repository:** [NewsPH NLI repository](https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks)
- **Paper:** [Arxiv paper](https://arxiv.org/pdf/2010.11574.pdf)
- **Leaderboard:**
- **Point of Contact:** [Jan Christian Cruz](mailto:jan_christian_cruz@dlsu.edu.ph)
### Dataset Summary
First benchmark dataset for sentence entailment in the low-resource Filipino language. Constructed through exploting the structure of news articles. Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset contains news articles in Filipino (Tagalog) scraped rom all major Philippine news sites online.
## Dataset Structure
### Data Instances
Sample data:
{
"premise": "Alam ba ninyo ang ginawa ni Erap na noon ay lasing na lasing na rin?",
"hypothesis": "Ininom niya ang alak na pinagpulbusan!",
"label": "0"
}
### Data Fields
[More Information Needed]
### Data Splits
Contains 600,000 premise-hypothesis pairs, in 70-15-15 split for training, validation, and testing.
## Dataset Creation
### Curation Rationale
We propose the use of news articles for automatically creating benchmark datasets for NLI because of two reasons. First, news articles commonly use single-sentence paragraphing, meaning every paragraph in a news article is limited to a single sentence. Second, straight news articles follow the “inverted pyramid” structure, where every succeeding paragraph builds upon the premise of those that came before it, with the most important information on top and the least important towards the end.
### Source Data
#### Initial Data Collection and Normalization
To create the dataset, we scrape news articles from all major Philippine news sites online. We collect a total of 229,571 straight news articles, which we then lightly preprocess to remove extraneous unicode characters and correct minimal misspellings. No further preprocessing is done to preserve information in the data.
#### Who are the source language producers?
The dataset was created by Jan Christian, Blaise Cruz, Jose Kristian Resabal, James Lin, Dan John Velasco, and Charibeth Cheng from De La Salle University and the University of the Philippines
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
Jan Christian Blaise Cruz, Jose Kristian Resabal, James Lin, Dan John Velasco and Charibeth Cheng
### 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
[Jan Christian Blaise Cruz] (mailto:jan_christian_cruz@dlsu.edu.ph)
### Licensing Information
[More Information Needed]
### Citation Information
@article{cruz2020investigating,
title={Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation},
author={Jan Christian Blaise Cruz and Jose Kristian Resabal and James Lin and Dan John Velasco and Charibeth Cheng},
journal={arXiv preprint arXiv:2010.11574},
year={2020}
}
### Contributions
Thanks to [@anaerobeth](https://github.com/anaerobeth) for adding this dataset. |
false |
# Dataset Card for [opus_finlex]
## 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:**[Finlex](http://opus.nlpl.eu/Finlex.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Finlex Data Base is a comprehensive collection of legislative and other judicial information of Finland, which is available in Finnish, Swedish and partially in English. This corpus is taken from the Semantic Finlex serice that provides the Finnish and Swedish data as linked open data and also raw XML files.
### Supported Tasks and Leaderboards
The underlying task is machine translation for language pair Swedish and Finnish.
### Languages
Swedish and Finnish
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. |
false |
# Dataset Card for [opus_fiskmo]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[fiskmo](http://opus.nlpl.eu/fiskmo.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
fiskmo, a massive parallel corpus for Finnish and Swedish.
### Supported Tasks and Leaderboards
The underlying task is machine translation for language pair Finnish and Swedish.
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. |
false |
# Dataset Card for [opus_memat]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[memat](http://opus.nlpl.eu/memat.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Xhosa-English parallel corpora, funded by EPSRC, the Medical Machine Translation project worked on machine translation between ixiXhosa and English, with a focus on the medical domain.
### Supported Tasks and Leaderboards
The underlying task is machine translation from Xhosa to English
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. |
false |
# Dataset Card for WMT20 - MultiLingual Quality Estimation (MLQE) Task3
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [WMT20 Quality Estimation Shared Task](http://www.statmt.org/wmt20/quality-estimation-task.html)
- **Repository**: [Github repository](https://github.com/deep-spin/deep-spin.github.io/tree/master/docs/data/wmt2020_qe)
- **Paper:** *Not available*
### Dataset Summary
From the homepage:
*This shared task (part of WMT20) will build on its previous editions to further examine automatic methods for estimating the quality of neural machine translation output at run-time, without relying on reference translations. As in previous years, we cover estimation at various levels. Important elements introduced this year include: a new task where sentences are annotated with Direct Assessment (DA) scores instead of labels based on post-editing; a new multilingual sentence-level dataset mainly from Wikipedia articles, where the source articles can be retrieved for document-wide context; the availability of NMT models to explore system-internal information for the task.*
*The goal of this task 3 is to predict document-level quality scores as well as fine-grained annotations.*
*Each document has a product title and its description, and is annotated for translation errors according to the MQM framework. Each error annotation has:*
- ***Word span(s).*** *Errors may consist of one or more words, not necessarily contiguous.*
- ***Severity.*** *An error can be minor (if it doesn't lead to a loss of meaning and it doesn't confuse or mislead the user), major (if it changes the meaning) or critical (if it changes the meaning and carry any type of implication, or could be seen as offensive).*
- ***Type.*** *A label specifying the error type, such as wrong word order, missing words, agreement, etc. They may provide additional information, but systems don't need to predict them.*
### Supported Tasks and Leaderboards
From the homepage:
*Submissions will be evaluated as in Task 1, in terms of Pearson's correlation between the true and predicted MQM document-level scores. Additionally, the predicted annotations will be evaluated in terms of their F1 scores with respect to the gold annotations. The [official evaluation scripts](https://github.com/sheffieldnlp/qe-eval-scripts) are available.*
### Languages
There is a single language pair in the dataset: English (`en`) - French (`fr`).
## Dataset Structure
### Data Instances
An example looks like this:
```
{
'document_id': 'B0000568SY',
'source_segments': ['Razor Scooter Replacement Wheels Set with Bearings', 'Scooter Wheels w/Bearings-Blue'],
'source_tokenized': ['Razor Scooter Replacement Wheels Set with Bearings', 'Scooter Wheels w / Bearings-Blue'],
'mt_segments': ['Roues de rechange Razor Scooter sertie de roulements', 'Roues de scooter w/roulements-bleu'],
'mt_tokenized': ['Roues de rechange Razor Scooter sertie de roulements', 'Roues de scooter w / roulements-bleu'],
'annotations': {
'segment_id': [[0], [1], [1], [0, 0], [0], [1], [1]],
'annotation_start': [[42], [19], [9], [0, 32], [9], [17], [30]],
'annotation_length': [[10], [10], [7], [5, 6], [8], [1], [4]],
'severity': [0, 0, 0, 0, 0, 1, 0],
'severity_weight': [1.0, 1.0, 1.0, 1.0, 1.0, 5.0, 1.0]
'category': [3, 3, 3, 1, 3, 36, 3],
},
'token_annotations': {
'category': [3, 3, 3, 1, 3, 36, 3],
'first_token': [[7], [5], [2], [0, 5], [2], [3], [5]],
'last_token': [[7], [5], [2], [0, 5], [2], [3], [5]],
'segment_id': [[0], [1], [1], [0, 0], [0], [1], [1]],
'severity': [0, 0, 0, 0, 0, 1, 0],
'token_after_gap': [[-1], [-1], [-1], [-1, -1], [-1], [-1], [-1]]
},
'token_index': [[[0, 5], [6, 2], [9, 8], [18, 5], [24, 7], [32, 6], [39, 2], [42, 10]], [[0, 5], [6, 2], [9, 7], [17, 1], [18, 1], [19, 15]]],
'total_words': 16
}
```
### Data Fields
- `document_id`: the document id (name of the folder).
- `source_segments`: the original source text, one sentence per line (i.e. per element of the list).
- `source_tokenized`: a tokenized version of `source_segments`.
- `mt_segments`: the original machine-translated text, one sentence per line (i.e. per element of the list).
- `mt_tokenized`: a tokenized version of `mt_segments`. Default value is `[]` when this information is not available (it happens 3 times in the train set: `B0001BW0PQ`, `B0001GS19U` and `B000A6SMJ0`).
- `annotations`: error annotations for the document. Each item of the list corresponds to an error annotation, which in turn may contain one or more error spans. Error fields are encoded in a dictionary. In the case of a multi-span error, multiple starting positions and lengths are encoded in the list. Note that these positions points to `mt.segments`, not `mt_tokenized`.
- `segment_id`: List of list of integers. Id of each error.
- `annotation_start`: List of list of integers. Start of each error.
- `annotation_length`: List of list of intergers. Length of each error.
- `severity`: List of one hot. Severity category of each error.
- `severity_weight`: List of floats. Severity weight of each error.
- `category`: List of one hot. Category of each error. See the 45 categories in `_ANNOTATION_CATEGORIES_MAPPING`.
- `token_annotations`: tokenized version of `annotations`. Each error span that contains one or more tokens has a "first token" and "last token". Again, multi-span errors have their first and last tokens encoded in a list. When a span is over a gap between two tokens, the "first" and "last" positions are `-1` (encoded as `-` in the original data), and instead the `token_after_gap` column points to the token immediately after the gap. In case of a gap occurring at the end of the sentence, this value will be equal to the number of tokens.
- `segment_id`: List of list of integers. Id of each error.
- `first_token`: List of list of integers. Start of each error.
- `last_token`: List of list of intergers. End of each error.
- `token_after_gap`: List of list of integers. Token after gap of each error.
- `severity`: List of one hot. Severity category of each error.
- `category`: List of one hot. Category of each error. See the 45 categories in `_ANNOTATION_CATEGORIES_MAPPING`.
- `token_index`: a mapping of tokens to their start and ending positions in `mt_segments`. For each token, a start and end value are encoded in a list of length 2, and all tokens represent one item in the list.
- `total_words`: total number of words in the document
```
_ANNOTATION_CATEGORIES_MAPPING = {
0: 'Addition',
1: 'Agreement',
2: 'Ambiguous Translation',
3: 'Capitalization',
4: 'Character Encoding',
5: 'Company Terminology',
6: 'Date/Time',
7: 'Diacritics',
8: 'Duplication',
9: 'False Friend',
10: 'Grammatical Register',
11: 'Hyphenation',
12: 'Inconsistency',
13: 'Lexical Register',
14: 'Lexical Selection',
15: 'Named Entity',
16: 'Number',
17: 'Omitted Auxiliary Verb',
18: 'Omitted Conjunction',
19: 'Omitted Determiner',
20: 'Omitted Preposition',
21: 'Omitted Pronoun',
22: 'Orthography',
23: 'Other POS Omitted',
24: 'Over-translation',
25: 'Overly Literal',
26: 'POS',
27: 'Punctuation',
28: "Shouldn't Have Been Translated",
29: "Shouldn't have been translated",
30: 'Spelling',
31: 'Tense/Mood/Aspect',
32: 'Under-translation',
33: 'Unidiomatic',
34: 'Unintelligible',
35: 'Unit Conversion',
36: 'Untranslated',
37: 'Whitespace',
38: 'Word Order',
39: 'Wrong Auxiliary Verb',
40: 'Wrong Conjunction',
41: 'Wrong Determiner',
42: 'Wrong Language Variety',
43: 'Wrong Preposition',
44: 'Wrong Pronoun'
}
```
### Data Splits
The dataset contains 1,448 documents for training, 200 documents for validation and 180 for (blind) test (all English-French).
## Dataset Creation
### Curation Rationale
The data is dervied from the [Amazon Product Reviews dataset](http://jmcauley.ucsd.edu/data/amazon/).
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Unknown
### Citation Information
```
Not available.
```
### Contributions
Thanks to [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. |
true |
# 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. |
false | # NST Swedish ASR Database (16 kHz) – reorganized
This database was created by Nordic Language Technology for the development of automatic speech recognition and dictation in Swedish. In this updated version, the organization of the data have been altered to improve the usefulness of the database.
In the original version of the material, the files were organized in a specific folder structure where the folder names were meaningful. However, the file names were not meaningful, and there were also cases of files with identical names in different folders. This proved to be impractical, since users had to keep the original folder structure in order to use the data. The files have been renamed, such that the file names are unique and meaningful regardless of the folder structure. The original metadata files were in spl format. These have been converted to JSON format. The converted metadata files are also anonymized and the text encoding has been converted from ANSI to UTF-8.
See the documentation file for a full description of the data and the changes made to the database.
The data is originally hosted on the National Library of Norway website.
https://www.nb.no/sprakbanken/en/resource-catalogue/oai-nb-no-sbr-56/
Hosting on Hugging Face datasets for convenience.
Licence
CC0 1.0 Universal (CC0 1.0)
Public Domain Dedication |
true |
# Dataset Card Creation Guide
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** N/A
- **Repository:** [GitHub](https://github.com/ltgoslo/NorBERT/)
- **Paper:** [A Fine-grained Sentiment Dataset for Norwegian](https://www.aclweb.org/anthology/2020.lrec-1.618/)
- **Leaderboard:** N/A
- **Point of Contact:** -
### Dataset Summary
Aggregated NoRec_fine: A Fine-grained Sentiment Dataset for Norwegian.
This dataset was created by the Nordic Language Processing Laboratory by aggregating the fine-grained annotations in NoReC_fine and removing sentences with conflicting or no sentiment.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in Norwegian.
## Dataset Structure
### Data Instances
Example of one instance in the dataset.
```{'label': 0, 'text': 'Verre er det med slagsmålene .'}```
### Data Fields
- `id`: index of the example
- `text`: Text of a sentence
- `label`: The sentiment label. Here
- 0 = negative
- 1 = positive
### Data Splits
The dataset is split into a `train`, `validation`, and `test` split with the following sizes:
| | Tain | Valid | Test |
| ----- | ------ | ----- | ----- |
| Number of examples | 2675 | 516 | 417 |
## Dataset Creation
This dataset is based largely on the original data described in the paper _A Fine-Grained Sentiment Dataset for Norwegian_ by L. Øvrelid, P. Mæhlum, J. Barnes, and E. Velldal, accepted at LREC 2020, [paper available](https://www.aclweb.org/anthology/2020.lrec-1.618). However, we have since added annotations for another 3476 sentences, increasing the overall size and scope of the dataset.
## Additional Information
### Licensing Information
This work is licensed under a Creative Commons Attribution 4.0 International License
### Citation Information
```latex
@misc{sheng2020investigating,
title={Investigating Societal Biases in a Poetry Composition System},
author={Emily Sheng and David Uthus},
year={2020},
eprint={2011.02686},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
true |
# Dataset Card for STS-ca
## Dataset Description
- **Website:** https://zenodo.org/record/4761434
- **Paper:** [Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? A Comprehensive Assessment for Catalan](https://arxiv.org/abs/2107.07903)
- **Point of Contact:** [Carlos Rodríguez-Penagos](carlos.rodriguez1@bsc.es) and [Carme Armentano-Oller](carme.armentano@bsc.es)
### Dataset Summary
STS-ca corpus is a benchmark for evaluating Semantic Text Similarity in Catalan. This dataset was developed by [BSC TeMU](https://temu.bsc.es/) as part of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/), to enrich the [Catalan Language Understanding Benchmark (CLUB)](https://club.aina.bsc.es/).
### Supported Tasks and Leaderboards
This dataset can be used to build and score semantic similarity models in Catalan.
### Languages
The dataset is in Catalan (`ca-CA`).
## Dataset Structure
### Data Instances
Follows [SemEval challenges](https://www.aclweb.org/anthology/S13-1004.pdf):
* index (int)
* id (str): Unique ID assigned to the sentence pair.
* sentence 1 (str): First sentence of the pair.
* sentence 2 (str): Second sentence of the pair.
* avg (float): Gold truth
#### Example
| index | id | sentence 1 | sentence 2 | avg |
| ------- | ---- | ------------ | ------------ | ----- |
| 19 | ACN2_131 | Els manifestants ocupen l'Imperial Tarraco durant una hora fent jocs de taula | Els manifestants ocupen l'Imperial Tarraco i fan jocs de taula | 4 |
| 21 | TE2_80 | El festival comptarà amb cinc escenaris i se celebrarà entre el 7 i el 9 de juliol al Parc del Fòrum. | El festival se celebrarà el 7 i 8 de juliol al Parc del Fòrum de Barcelona | 3 |
| 23 | Oscar2_609 | Aleshores hi posarem un got de vi i continuarem amb la cocció fins que s'hagi evaporat el vi i ho salpebrarem. | Mentre, hi posarem el vi al sofregit i deixarem coure uns 7/8′, fins que el vi s'evapori. | 3 |
| 25 | Viqui2_48 | L'arboç grec (Arbutus andrachne) és un arbust o un petit arbre dins la família ericàcia. | El ginjoler ("Ziziphus jujuba") és un arbust o arbre petit de la família de les "Rhamnaceae". | 2.75 |
| 27 | ACN2_1072 | Mentre han estat davant la comandància, els manifestants han cridat consignes a favor de la independència i han cantat cançons com 'L'estaca'. | Entre les consignes que han cridat s'ha pogut escoltar càntics com 'els carrers seran sempre nostres' i contínues consignes en favor de la independència. | 3 |
| 28 | Viqui2_587 | Els cinc municipis ocupen una superfície de poc més de 100 km2 i conjuntament sumen una població total aproximada de 3.691 habitants (any 2019). | Té una població d'1.811.177 habitants (2005) repartits en 104 municipis d'una superfície total de 14.001 km2. | 2.67 |
### Data Fields
This dataset follows [SemEval](https://www.aclweb.org/anthology/S13-1004.pdf) challenges formats and conventions.
### Data Splits
- sts_cat_dev_v1.tsv (500 annotated pairs)
- sts_cat_train_v1.tsv (2073 annotated pairs)
- sts_cat_test_v1.tsv (500 annotated pairs)
## Dataset Creation
### Curation Rationale
We created this dataset to contribute to the development of language models in Catalan, a low-resource language.
### Source Data
#### Initial Data Collection and Normalization
Random sentences were extracted from 3 Catalan subcorpus from the [Catalan Textual Corpus](https://zenodo.org/record/4519349#.Ys_0PexBzOs): [ACN](https://www.acn.cat/), [Oscar](https://oscar-corpus.com/) and [Wikipedia](https://ca.wikipedia.org/wiki/Portada).
We generated candidate pairs using a combination of metrics from Doc2Vec, Jaccard and a BERT-like model (“[distiluse-base-multilingual-cased-v2](https://huggingface.co/distilbert-base-multilingual-cased)”). Finally, we manually reviewed the generated pairs to reject non-relevant pairs (identical or ungrammatical sentences, etc.) before providing them to the annotation team.
The average of the four annotations was selected as a “ground truth” for each sentence pair, except when an annotator diverged in more than one unit from the average. In these cases, we discarded the divergent annotation and recalculated the average without it. We also discarded 45 sentence pairs because the annotators disagreed too much.
For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
#### Who are the source language producers?
The [Catalan Textual Corpus](https://zenodo.org/record/4519349#.Ys_0PexBzOs) is a 1760-million-token web corpus of Catalan built from several sources: existing corpus such as DOGC, CaWac (non-deduplicated version), Oscar (unshuffled version), Open Subtitles, Catalan Wikipedia; and three brand new crawlings: the Catalan General Crawling, obtained by crawling the 500 most popular .cat and .ad domains; the Catalan Government Crawling, obtained by crawling the .gencat domain and subdomains, belonging to the Catalan Government; and the ACN corpus with 220k news items from March 2015 until October 2020, crawled from the Catalan News Agency.
### Annotations
#### Annotation process
We comissioned the manual annotation of the similarity between the sentences of each pair, following the provided guidelines.
#### Who are the annotators?
A team of native language speakers from 2 different companies, working independently.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
We hope this dataset contributes to the development of language models in Catalan, a low-resource language.
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
Text Mining Unit (TeMU) at the Barcelona Supercomputing Center (bsc-temu@bsc.es)
This work was funded by the [Departament de la Vicepresidència i de Polítiques Digitals i Territori de la Generalitat de Catalunya](https://politiquesdigitals.gencat.cat/en/inici/index.html) within the framework of [Projecte AINA](https://politiquesdigitals.gencat.cat/ca/economia/catalonia-ai/aina/).
### Licensing Information
This work is licensed under a <a rel="license" href="https://creativecommons.org/licenses/by-sa/4.0/">Attribution-ShareAlike 4.0 International License</a>.
### Citation Information
```
@inproceedings{armengol-estape-etal-2021-multilingual,
title = "Are Multilingual Models the Best Choice for Moderately Under-resourced Languages? {A} Comprehensive Assessment for {C}atalan",
author = "Armengol-Estap{\'e}, Jordi and
Carrino, Casimiro Pio and
Rodriguez-Penagos, Carlos and
de Gibert Bonet, Ona and
Armentano-Oller, Carme and
Gonzalez-Agirre, Aitor and
Melero, Maite and
Villegas, Marta",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.437",
doi = "10.18653/v1/2021.findings-acl.437",
pages = "4933--4946",
}
```
[DOI](https://doi.org/10.5281/zenodo.4529183)
### Contributions
[N/A]
|
false |
# Dataset Card for NYU Depth V2
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Visualization](#visualization)
- [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:** [NYU Depth Dataset V2 homepage](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html)
- **Repository:** Fast Depth [repository](https://github.com/dwofk/fast-depth) which was used to source the dataset in this repository. It is a preprocessed version of the original NYU Depth V2 dataset linked above. It is also used in [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/nyu_depth_v2).
- **Papers:** [Indoor Segmentation and Support Inference from RGBD Images](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf) and [FastDepth: Fast Monocular Depth Estimation on Embedded Systems](https://arxiv.org/abs/1903.03273)
- **Point of Contact:** [Nathan Silberman](mailto:silberman@@cs.nyu.edu) and [Diana Wofk](mailto:dwofk@alum.mit.edu)
### Dataset Summary
As per the [dataset homepage](https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html):
The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft [Kinect](http://www.xbox.com/kinect). It features:
* 1449 densely labeled pairs of aligned RGB and depth images
* 464 new scenes taken from 3 cities
* 407,024 new unlabeled frames
* Each object is labeled with a class and an instance number (cup1, cup2, cup3, etc)
The dataset has several components:
* Labeled: A subset of the video data accompanied by dense multi-class labels. This data has also been preprocessed to fill in missing depth labels.
* Raw: The raw rgb, depth and accelerometer data as provided by the Kinect.
* Toolbox: Useful functions for manipulating the data and labels.
### Supported Tasks
- `depth-estimation`: Depth estimation is the task of approximating the perceived depth of a given image. In other words, it's about measuring the distance of each image pixel from the camera.
- `semantic-segmentation`: Semantic segmentation is the task of associating every pixel of an image to a class label.
There are other tasks supported by this dataset as well. You can find more about them by referring to [this resource](https://paperswithcode.com/dataset/nyuv2).
### Languages
English.
## Dataset Structure
### Data Instances
A data point comprises an image and its annotation depth map for both the `train` and `validation` splits.
```
{
'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB at 0x1FF32A3EDA0>,
'depth_map': <PIL.PngImagePlugin.PngImageFile image mode=L at 0x1FF32E5B978>,
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `depth_map`: A `PIL.Image.Image` object containing the annotation depth map.
### Data Splits
The data is split into training, and validation splits. The training data contains 47584 images, and the validation data contains 654 images.
## Visualization
You can use the following code snippet to visualize samples from the dataset:
```py
from datasets import load_dataset
import numpy as np
import matplotlib.pyplot as plt
cmap = plt.cm.viridis
ds = load_dataset("sayakpaul/nyu_depth_v2")
def colored_depthmap(depth, d_min=None, d_max=None):
if d_min is None:
d_min = np.min(depth)
if d_max is None:
d_max = np.max(depth)
depth_relative = (depth - d_min) / (d_max - d_min)
return 255 * cmap(depth_relative)[:,:,:3] # H, W, C
def merge_into_row(input, depth_target):
input = np.array(input)
depth_target = np.squeeze(np.array(depth_target))
d_min = np.min(depth_target)
d_max = np.max(depth_target)
depth_target_col = colored_depthmap(depth_target, d_min, d_max)
img_merge = np.hstack([input, depth_target_col])
return img_merge
random_indices = np.random.choice(len(ds["train"]), 9).tolist()
train_set = ds["train"]
plt.figure(figsize=(15, 6))
for i, idx in enumerate(random_indices):
ax = plt.subplot(3, 3, i + 1)
image_viz = merge_into_row(
train_set[idx]["image"], train_set[idx]["depth_map"]
)
plt.imshow(image_viz.astype("uint8"))
plt.axis("off")
```
## Dataset Creation
### Curation Rationale
The rationale from [the paper](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf) that introduced the NYU Depth V2 dataset:
> We present an approach to interpret the major surfaces, objects, and support relations of an indoor scene from an RGBD image. Most existing work ignores physical interactions or is applied only to tidy rooms and hallways. Our goal is to parse typical, often messy, indoor scenes into floor, walls, supporting surfaces, and object regions, and to recover support relationships. One of our main interests is to better understand how 3D cues can best inform a structured 3D interpretation.
### Source Data
#### Initial Data Collection
> The dataset consists of 1449 RGBD images, gathered from a wide range
of commercial and residential buildings in three different US cities, comprising
464 different indoor scenes across 26 scene classes.A dense per-pixel labeling was
obtained for each image using Amazon Mechanical Turk.
### Annotations
#### Annotation process
This is an involved process. Interested readers are referred to Sections 2, 3, and 4 of the [original paper](http://cs.nyu.edu/~silberman/papers/indoor_seg_support.pdf).
#### Who are the annotators?
AMT annotators.
### 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
* Original NYU Depth V2 dataset: Nathan Silberman, Derek Hoiem, Pushmeet Kohli, Rob Fergus
* Preprocessed version: Diana Wofk, Fangchang Ma, Tien-Ju Yang, Sertac Karaman, Vivienne Sze
### Licensing Information
The preprocessed NYU Depth V2 dataset is licensed under a [MIT License](https://github.com/dwofk/fast-depth/blob/master/LICENSE).
### Citation Information
```bibtex
@inproceedings{Silberman:ECCV12,
author = {Nathan Silberman, Derek Hoiem, Pushmeet Kohli and Rob Fergus},
title = {Indoor Segmentation and Support Inference from RGBD Images},
booktitle = {ECCV},
year = {2012}
}
@inproceedings{icra_2019_fastdepth,
author = {{Wofk, Diana and Ma, Fangchang and Yang, Tien-Ju and Karaman, Sertac and Sze, Vivienne}},
title = {{FastDepth: Fast Monocular Depth Estimation on Embedded Systems}},
booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
year = {{2019}}
}
```
### Contributions
Thanks to [@sayakpaul](https://huggingface.co/sayakpaul) for adding this dataset. |
true |
# Dataset Card for DanishPoliticalComments
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/steffan267/Sentiment-Analysis-on-Danish-Social-Media
- **Repository:** https://github.com/steffan267/Sentiment-Analysis-on-Danish-Social-Media
- **Paper:** https://github.com/lucaspuvis/SAM/blob/master/Thesis.pdf
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
true |
# Dataset Card for FACTCK BR
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/jghm-f/FACTCK.BR
- **Repository:** https://github.com/jghm-f/FACTCK.BR
- **Paper:** https://dl.acm.org/doi/10.1145/3323503.3361698
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
A dataset to study Fake News in Portuguese, presenting a supposedly false News along with their respective fact check and classification.
The data is collected from the ClaimReview, a structured data schema used by fact check agencies to share their results in search engines, enabling data collect in real time.
The FACTCK.BR dataset contains 1309 claims with its corresponding label.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset. |
true |
# Dataset Card for Hindi Discourse Analysis 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:** [GitHub](https://github.com/midas-research/hindi-nli-data)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.aacl-main.71)
- **Point of Contact:** [GitHub](https://github.com/midas-research/hindi-nli-data)
### Dataset Summary
- Dataset for Natural Language Inference in Hindi Language. Hindi Discourse Analysis (HDA) Dataset consists of textual-entailment pairs.
- Each row of the Datasets if made up of 4 columns - Premise, Hypothesis, Label and Topic.
- Premise and Hypothesis is written in Hindi while Entailment_Label is in English.
- Entailment_label is of 2 types - entailed and not-entailed.
- Entailed means that hypotheis can be inferred from premise and not-entailed means vice versa
- Dataset can be used to train models for Natural Language Inference tasks in Hindi Language.
### Supported Tasks and Leaderboards
- Natural Language Inference for Hindi
### Languages
- Dataset is in Hindi
## Dataset Structure
- Data is structured in TSV format.
- train, test and dev files are in seperate files
### Dataset Instances
An example of 'train' looks as follows.
```
{'hypothesis': 'यह एक वर्णनात्मक कथन है।', 'label': 1, 'premise': 'जैसे उस का सारा चेहरा अपना हो और आँखें किसी दूसरे की जो चेहरे पर पपोटों के पीछे महसूर कर दी गईं।', 'topic': 1}
```
### Data Fields
Each row contatins 4 columns:
- premise: string
- hypothesis: string
- label: class label with values that correspond to "not-entailment" (0) or "entailment" (1)
- topic: class label with values that correspond to "Argumentative" (0), "Descriptive" (1), "Dialogic" (2), "Informative" (3) or "Narrative" (4).
### Data Splits
- Train : 31892
- Valid : 9460
- Test : 9970
## Dataset Creation
- We employ a recasting technique from Poliak et al. (2018a,b) to convert publicly available Hindi Discourse Analysis classification datasets in Hindi and pose them as TE problems
- In this recasting process, we build template hypotheses for each class in the label taxonomy
- Then, we pair the original annotated sentence with each of the template hypotheses to create TE samples.
- For more information on the recasting process, refer to paper https://www.aclweb.org/anthology/2020.aacl-main.71
### Source Data
Source Dataset for the recasting process is the BBC Hindi Headlines Dataset(https://github.com/NirantK/hindi2vec/releases/tag/bbc-hindi-v0.1)
#### Initial Data Collection and Normalization
- Initial Data was collected by members of MIDAS Lab from Hindi Websites. They crowd sourced the data annotation process and selected two random stories from our corpus and had the three annotators work on them independently and classify each sentence based on the discourse mode.
- Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
- The Discourse is further classified into "Argumentative" , "Descriptive" , "Dialogic" , "Informative" and "Narrative" - 5 Clases.
#### Who are the source language producers?
Please refer to this paper for detailed information: https://www.aclweb.org/anthology/2020.lrec-1.149/
### Annotations
#### Annotation process
Annotation process has been described in Dataset Creation Section.
#### Who are the annotators?
Annotation is done automatically by machine and corresponding recasting process.
### Personal and Sensitive Information
No Personal and Sensitive Information is mentioned in the Datasets.
## Considerations for Using the Data
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Discussion of Biases
No known bias exist in the dataset.
Pls refer to this paper: https://www.aclweb.org/anthology/2020.aacl-main.71
### Other Known Limitations
No other known limitations . Size of data may not be enough to train large models
## Additional Information
Pls refer to this link: https://github.com/midas-research/hindi-nli-data
### Dataset Curators
It is written in the repo : https://github.com/midas-research/hindi-nli-data that
- This corpus can be used freely for research purposes.
- The paper listed below provide details of the creation and use of the corpus. If you use the corpus, then please cite the paper.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- If you use the corpus in a product or application, then please credit the authors and Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi appropriately. Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India disclaims any responsibility for the use of the corpus and does not provide technical support. However, the contact listed above will be happy to respond to queries and clarifications.
- Rather than redistributing the corpus, please direct interested parties to this page
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your data for natural language inference.
- if interested in a collaborative research project.
### Licensing Information
Copyright (C) 2019 Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi (MIDAS, IIIT-Delhi).
Pls contact authors for any information on the dataset.
### Citation Information
```
@inproceedings{uppal-etal-2020-two,
title = "Two-Step Classification using Recasted Data for Low Resource Settings",
author = "Uppal, Shagun and
Gupta, Vivek and
Swaminathan, Avinash and
Zhang, Haimin and
Mahata, Debanjan and
Gosangi, Rakesh and
Shah, Rajiv Ratn and
Stent, Amanda",
booktitle = "Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing",
month = dec,
year = "2020",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.aacl-main.71",
pages = "706--719",
abstract = "An NLP model{'}s ability to reason should be independent of language. Previous works utilize Natural Language Inference (NLI) to understand the reasoning ability of models, mostly focusing on high resource languages like English. To address scarcity of data in low-resource languages such as Hindi, we use data recasting to create NLI datasets for four existing text classification datasets. Through experiments, we show that our recasted dataset is devoid of statistical irregularities and spurious patterns. We further study the consistency in predictions of the textual entailment models and propose a consistency regulariser to remove pairwise-inconsistencies in predictions. We propose a novel two-step classification method which uses textual-entailment predictions for classification task. We further improve the performance by using a joint-objective for classification and textual entailment. We therefore highlight the benefits of data recasting and improvements on classification performance using our approach with supporting experimental results.",
}
```
### Contributions
Thanks to [@avinsit123](https://github.com/avinsit123) for adding this dataset. |
false |
# Dataset Card for HoVer
## 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://hover-nlp.github.io/
- **Repository:** https://github.com/hover-nlp/hover
- **Paper:** https://arxiv.org/abs/2011.03088
- **Leaderboard:** https://hover-nlp.github.io/
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
A sample training set is provided below
```
{'id': 14856, 'uid': 'a0cf45ea-b5cd-4c4e-9ffa-73b39ebd78ce', 'claim': 'The park at which Tivolis Koncertsal is located opened on 15 August 1843.', 'supporting_facts': [{'key': 'Tivolis Koncertsal', 'value': 0}, {'key': 'Tivoli Gardens', 'value': 1}], 'label': 'SUPPORTED', 'num_hops': 2, 'hpqa_id': '5abca1a55542993a06baf937'}
```
Please note that in test set sentence only id, uid and claim are available. Labels are not available in test set and are represented by -1.
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
false |
# Dataset Card for id_puisi
## 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:** [puisi-pantun-generator](https://github.com/ilhamfp/puisi-pantun-generator)
- **Repository:** [puisi-pantun-generator](https://github.com/ilhamfp/puisi-pantun-generator)
- **Paper:** [N/A]
- **Leaderboard:** [N/A]
- **Point of Contact:** [Ilham Firdausi Putra](ilhamfputra31@gmail.com)
### Dataset Summary
Puisi (poem) is an Indonesian poetic form. The dataset contains 7223 Indonesian puisi with its title and author.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Indonesian
## Dataset Structure
### Data Instances
```
{
'puisi_with_header': 'TEPERANGKAP
Oleh Mangku Langit Jingga
Mungkin kau membiarkan aku
Membiarkan perasaan ini larut
Memberi ruang jiwaku hampa
Agar tetap terbiasa nikmati
Perangkap yang kau buat
Perisai yang kau banggakan
Takkan jadi tameng bagimu
Aku mengerti betapa hebatnya
Perangkap mu hei sang dewi
Ku akan terus merasa terbiasa
Dengan pesona indahmu
Ku masih akan nikmati hadirmu
Berjalanlah pada hati yang sama
Satu hati denganku
Walau ku terperangkap
Namunku nikmati dan jalani',
'title': 'TEPERANGKAP',
'author': 'Oleh Mangku Langit Jingga',
'puisi': 'Mungkin kau membiarkan aku
Membiarkan perasaan ini larut
Memberi ruang jiwaku hampa
Agar tetap terbiasa nikmati
Perangkap yang kau buat
Perisai yang kau banggakan
Takkan jadi tameng bagimu
Aku mengerti betapa hebatnya
Perangkap mu hei sang dewi
Ku akan terus merasa terbiasa
Dengan pesona indahmu
Ku masih akan nikmati hadirmu
Berjalanlah pada hati yang sama
Satu hati denganku
Walau ku terperangkap
Namunku nikmati dan jalani',
}
```
### Data Fields
- `puisi_with_header`: the raw text from scraping
- `title`: the title extracted from the raw text using regex
- `author`: the author extracted from the raw text using regex
- `puisi`: the poem with title and author extracted out using regex
### Data Splits
The dataset contains only a train set.
## Dataset Creation
### Curation Rationale
The dataset was initially collected as an experiment to generate an Indonesian poem using GPT-2.
### Source Data
#### Initial Data Collection and Normalization
The dataset was scraped using BeautifulSoup from lokerpuisi.web.id (the data no longer exist on the original blog). The title and author column was produced using regex match from puisi_with_header column.
#### Who are the source language producers?
The poems were generated by humans. The users of the original blog voluntarily submit their original poems to get published on the blog.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### 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
The regex match used to extract the title & author from the raw text is not perfect. Some title & text is still failed to get extracted.
## Additional Information
### Dataset Curators
Ilham Firdausi Putra
### Licensing Information
MIT License
### Citation Information
[N/A]
### Contributions
Thanks to [@ilhamfp](https://github.com/ilhamfp) for adding this dataset. |
false |
# Dataset Card for InquisitiveQg
## 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:** [Add homepage URL here if available (unless it's a GitHub repository)]()
- **Repository:** [If the dataset is hosted on github or has a github homepage, add URL here]()
- **Paper:** [If the dataset was introduced by a paper or there was a paper written describing the dataset, add URL here (landing page for Arxiv paper preferred)]()
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** [If known, name and email of at least one person the reader can contact for questions about the dataset.]()
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. |
true |
# Dataset Card for kannada_news 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:** [Kaggle link](https://www.kaggle.com/disisbig/kannada-news-dataset) for kannada news headlines dataset
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** More information about the dataset and the models can be found [here](https://github.com/goru001/nlp-for-kannada)
### Dataset Summary
The Kannada news dataset contains only the headlines of news article in three categories:
Entertainment, Tech, and Sports.
The data set contains around 6300 news article headlines which are collected from Kannada news websites.
The data set has been cleaned and contains train and test set using which can be used to benchmark topic classification models in Kannada.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Kannada (kn)
## Dataset Structure
### Data Instances
The data has two files. A train.csv and valid.csv. An example row of the dataset is as below:
```
{
'headline': 'ಫಿಫಾ ವಿಶ್ವಕಪ್ ಫೈನಲ್: ಅತಿರೇಕಕ್ಕೇರಿದ ಸಂಭ್ರಮಾಚರಣೆ; ಅಭಿಮಾನಿಗಳ ಹುಚ್ಚು ವರ್ತನೆಗೆ ವ್ಯಾಪಕ ಖಂಡನೆ',
'label':'sports'
}
```
NOTE: The data has very few examples on the technology (class label: 'tech') topic. [More Information Needed]
### Data Fields
Data has two fields:
- headline: text headline in kannada (string)
- label : corresponding class label which the headlines pertains to in english (string)
### Data Splits
The dataset is divided into two splits. All the headlines are scraped from news websites on the internet.
| | train | validation |
|-----------------|--------:|-----------:|
| Input Sentences | 5167 | 1293 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
There are starkingly less amount of data for South Indian languages, especially Kannada, available in digital format which can be used for NLP purposes.
Though having roughly 38 million native speakers, it is a little under-represented language and will benefit from active contribution from the community.
This dataset, however, can just help people get exposed to Kannada and help proceed further active participation for enabling continuous progress and development.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[Gaurav Arora] (https://github.com/goru001/nlp-for-kannada). Has also got some starter models an embeddings to help get started.
### Licensing Information
cc-by-sa-4.0
### Citation Information
https://www.kaggle.com/disisbig/kannada-news-dataset
### Contributions
Thanks to [@vrindaprabhu](https://github.com/vrindaprabhu) for adding this dataset. |
false |
# 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:** [linnaeus](http://linnaeus.sourceforge.net/)
- **Repository:** https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/linnaeus-IOB
- **Paper:** [BMC Bioinformatics](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-85)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The LINNAEUS corpus consists of 100 full-text documents from the PMCOA
document set which were randomly selected. All mentions of species terms were manually
annotated and normalized to the NCBI taxonomy IDs of the intended species.
The original LINNAEUS corpus is available in a TAB-separated standoff format. The resource does not define training,
development or test subsets.
We converted the corpus into BioNLP shared task standoff format using a custom script, split it into 50-, 17-, and 33-
document training, development and test sets, and then converted these into the CoNLL format using standoff2conll.
As a full-text corpus, LINNAEUS contains comparatively frequent
non-ASCII characters, which were mapped to ASCII using the
standoff2conll -a option.
The conversion was highly accurate, but due to sentence-splitting errors within entity mentions,
the number of annotations in the converted data was larger by four (100.09%) than that
in the source data. 99.77% of names in the original annotation matched names in the converted
data.
### Supported Tasks and Leaderboards
This dataset is used for species Named Entity Recognition.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
An example from the dataset is:
```
{'id': '2',
'tokens': ['Scp160p', 'is', 'a', '160', 'kDa', 'protein', 'in', 'the', 'yeast', 'Saccharomyces', 'cerevisiae', 'that', 'contains', '14', 'repeats', 'of', 'the', 'hnRNP', 'K', '-', 'homology', '(', 'KH', ')', 'domain', ',', 'and', 'demonstrates', 'significant', 'sequence', 'homology', 'to', 'a', 'family', 'of', 'proteins', 'collectively', 'known', 'as', 'vigilins', '.'],
'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}
```
### Data Fields
- `id`: Sentence identifier.
- `tokens`: Array of tokens composing a sentence.
- `ner_tags`: Array of tags, where `0` indicates no species mentioned, `1` signals the first token of a species and `2` the subsequent tokens of the species.
### Data Splits
| name |train|validation|test|
|----------|----:|---------:|---:|
| linnaeus |11936| 4079|7143|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This version of the dataset is licensed under [Creative Commons Attribution 4.0 International](https://github.com/cambridgeltl/MTL-Bioinformatics-2016/blob/master/LICENSE.md).
### Citation Information
```bibtex
@article{crichton2017neural,
title={A neural network multi-task learning approach to biomedical named entity recognition},
author={Crichton, Gamal and Pyysalo, Sampo and Chiu, Billy and Korhonen, Anna},
journal={BMC Bioinformatics},
volume={18},
number={1},
pages={368},
year={2017},
publisher={BioMed Central}
doi = {10.1186/s12859-017-1776-8},
issn = {1471-2105},
url = {https://doi.org/10.1186/s12859-017-1776-8},
}
@article{Gerner2010,
author = {Gerner, Martin and Nenadic, Goran and Bergman, Casey M},
doi = {10.1186/1471-2105-11-85},
issn = {1471-2105},
journal = {BMC Bioinformatics},
number = {1},
pages = {85},
title = {{LINNAEUS: A species name identification system for biomedical literature}},
url = {https://doi.org/10.1186/1471-2105-11-85},
volume = {11},
year = {2010}
}
```
### Contributions
Thanks to [@edugp](https://github.com/edugp) for adding this dataset. |
true |
# Dataset Card for MetRec
## 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:** [Metrec](https://github.com/zaidalyafeai/MetRec)
- **Repository:** [Metrec repository](https://github.com/zaidalyafeai/MetRec)
- **Paper:** [MetRec: A dataset for meter classification of arabic poetry](https://www.sciencedirect.com/science/article/pii/S2352340920313792)
- **Point of Contact:** [Zaid Alyafeai](mailto:alyafey22@gmail.com)
### Dataset Summary
The dataset contains the verses and their corresponding meter classes.
Meter classes are represented as numbers from 0 to 13.
The dataset can be highly useful for further research in order to improve the field of Arabic poems’ meter classification.
The train dataset contains 47,124 records and the test dataset contains 8,316 records.
### Supported Tasks and Leaderboards
The dataset was published on this [paper](https://www.sciencedirect.com/science/article/pii/S2352340920313792). A benchmark is acheived on this [paper](https://www.sciencedirect.com/science/article/pii/S016786552030204X).
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
A typical data point comprises a label which is out of 13 classes and a verse part of poem.
### Data Fields
[N/A]
### Data Splits
The data is split into a training and testing. The split is organized as the following
| | train | test |
|------------|-------:|------:|
| data split | 47,124 | 8,316 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
The dataset was collected from [Aldiwan](https://www.aldiwan.net/).
#### Who are the source language producers?
The poems are from different poets.
### 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
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
```
@article{metrec2020,
title={MetRec: A dataset for meter classification of arabic poetry},
author={Al-shaibani, Maged S and Alyafeai, Zaid and Ahmad, Irfan},
journal={Data in Brief},
year={2020},
publisher={Elsevier}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. |
false |
# 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://msropendata.com/datasets/f8ce2ec9-7fbd-48f7-a8bb-2d2279373563
- **Repository:**
- **Paper:** https://www.microsoft.com/en-us/research/wp-content/uploads/2016/09/Sentence_Compression_final-1.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains sentences and short paragraphs with corresponding shorter (compressed) versions. There are up to five compressions for each input text, together with quality judgements of their meaning preservation and grammaticality. The dataset is derived using source texts from the Open American National Corpus (ww.anc.org) and crowd-sourcing.
### Supported Tasks and Leaderboards
Text Summarization
### Languages
English
## Dataset Structure
### Data Instances
It contains approximately 6,000 source texts with multiple compressions (about 26,000 pairs of source and compressed texts), representing business letters, newswire, journals, and technical documents sampled from the Open American National Corpus (OANC1).
- Each source text is accompanied by up to five crowd-sourced rewrites constrained to a preset
compression ratio and annotated with quality judgments. Multiple rewrites permit study of the impact of operations on human compression quality and facilitate automatic evaluation.
- This dataset is the first to provide compressions at the multi-sentence (two-sentence paragraph)
level, which may present a stepping stone to whole document summarization.
- Many of these two-sentence paragraphs are compressed both as paragraphs and separately sentence-bysentence, offering data that may yield insights
into the impact of multi-sentence operations on human compression quality.
| Description | Source | Target | Average CPS | Meaning Quality | Grammar Quality |
| :------------- | :----------: | -----------: | -----------: | -----------: | -----------: |
| 1-Sentence | 3764 | 15523 | 4.12 | 2.78 | 2.81 |
| 2-Sentence | 2405 | 10900 | 4.53 | 2.78 | 2.83 |
**Note**: Average CPS = Average Compressions per Source Text
### Data Fields
```
{'domain': 'Newswire',
'source_id': '106',
'source_text': '" Except for this small vocal minority, we have just not gotten a lot of groundswell against this from members, " says APA president Philip G. Zimbardo of Stanford University.',
'targets': {'compressed_text': ['"Except for this small vocal minority, we have not gotten a lot of groundswell against this," says APA president Zimbardo.',
'"Except for a vocal minority, we haven\'t gotten much groundswell from members, " says Philip G. Zimbardo of Stanford University.',
'APA president of Stanford has stated that except for a vocal minority they have not gotten a lot of pushback from members.',
'APA president Philip G. Zimbardo of Stanford says they have not had much opposition against this.'],
'judge_id': ['2', '22', '10', '0'],
'num_ratings': [3, 3, 3, 3],
'ratings': [[6, 6, 6], [11, 6, 6], [6, 11, 6], [6, 11, 11]]}}
```
- source_id: index of article per original dataset
- source_text: uncompressed original text
- domain: source of the article
- targets:
- compressed_text: compressed version of `source_text`
- judge_id: anonymized ids of crowdworkers who proposed compression
- num_ratings: number of ratings available for each proposed compression
- ratings: see table below
Ratings system (excerpted from authors' README):
- 6 = Most important meaning Flawless language (3 on meaning and 3 on grammar as per the paper's terminology)
- 7 = Most important meaning Minor errors (3 on meaning and 2 on grammar)
- 9 = Most important meaning Disfluent or incomprehensible (3 on meaning and 1 on grammar)
- 11 = Much meaning Flawless language (2 on meaning and 3 on grammar)
- 12 = Much meaning Minor errors (2 on meaning and 2 on grammar)
- 14 = Much meaning Disfluent or incomprehensible (2 on meaning and 1 on grammar)
- 21 = Little or none meaning Flawless language (1 on meaning and 3 on grammar)
- 22 = Little or none meaning Minor errors (1 on meaning and 2 on grammar)
- 24 = Little or none meaning Disfluent or incomprehensible (1 on meaning and 1 on grammar)
See **README.txt** from data archive for additional details.
### Data Splits
There are 4,936 source texts in the training, 448 in the development, and 785 in the test set.
## Dataset Creation
### Annotations
#### Annotation process
Compressions were created using UHRS, an inhouse crowd-sourcing system similar to Amazon’s Mechanical Turk, in two annotation rounds, one for shortening and a second to rate compression quality:
1. In the first round, five workers were tasked with abridging each source text by at least 25%, while remaining grammatical and fluent, and retaining the meaning of the original.
2. In the second round, 3-5 judges (raters) were asked to evaluate the grammaticality of each compression on a scale from 1 (major errors, disfluent) through 3 (fluent), and again analogously for meaning preservation on a scale from 1 (orthogonal) through 3 (most important meaning-preserving).
## Additional Information
### Licensing Information
Microsoft Research Data License Agreement
### Citation Information
@inproceedings{Toutanova2016ADA,
title={A Dataset and Evaluation Metrics for Abstractive Compression of Sentences and Short Paragraphs},
author={Kristina Toutanova and Chris Brockett and Ke M. Tran and Saleema Amershi},
booktitle={EMNLP},
year={2016}
}
### Contributions
Thanks to [@jeromeku](https://github.com/jeromeku) for adding this dataset. |
false |
# Dataset Card for msr_zhen_translation_parity
## 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:**
[Translator Human Parity Data](https://msropendata.com/datasets/93f9aa87-9491-45ac-81c1-6498b6be0d0b)
- **Repository:**
- **Paper:**
[Achieving Human Parity on Automatic Chinese to English News Translation](https://www.microsoft.com/en-us/research/publication/achieving-human-parity-on-automatic-chinese-to-english-news-translation/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
> Human evaluation results and translation output for the Translator Human Parity Data release,
> as described in https://blogs.microsoft.com/ai/machine-translation-news-test-set-human-parity/
> The Translator Human Parity Data release contains all human evaluation results and translations
> related to our paper "Achieving Human Parity on Automatic Chinese to English News Translation",
> published on March 14, 2018. We have released this data to
> 1) allow external validation of our claim of having achieved human parity
> 2) to foster future research by releasing two additional human references
> for the Reference-WMT test set.
>
The dataset includes:
1) two new references for Chinese-English language pair of WMT17,
one based on human translation from scratch (Reference-HT),
the other based on human post-editing (Reference-PE);
2) human parity translations generated by our research systems Combo-4, Combo-5, and Combo-6,
as well as translation output from online machine translation service Online-A-1710,
collected on October 16, 2017;
The data package provided with the study also includes (but not parsed and provided as
workable features of this dataset) all data points collected in human evaluation campaigns.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
This dataset contains 6 extra English translations to Chinese-English language pair of WMT17.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
As mentioned in the summary, this dataset provides 6 extra English translations of
Chinese-English language pair of WMT17.
Data fields are named exactly like the associated paper for easier cross-referenceing.
- `Reference-HT`: human translation from scrach.
- `Reference-PE`: human post-editing.
- `Combo-4`, `Combo-5`, `Combo-6`: three translations by research systems.
- `Online-A-1710`: a translation from an anonymous online machine translation service.
All data fields of a record are translations for the same Chinese source sentence.
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
Citation information is available at this link [Achieving Human Parity on Automatic Chinese to English News Translation](https://www.microsoft.com/en-us/research/publication/achieving-human-parity-on-automatic-chinese-to-english-news-translation/)
### Contributions
Thanks to [@leoxzhao](https://github.com/leoxzhao) for adding this dataset. |
true |
# Dataset Card for Multi-Genre Natural Language Inference (Mismatched only)
## 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.nyu.edu/projects/bowman/multinli/](https://www.nyu.edu/projects/bowman/multinli/)
- **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:** 226.85 MB
- **Size of the generated dataset:** 77.62 MB
- **Total amount of disk used:** 304.46 MB
### Dataset Summary
The Multi-Genre Natural Language Inference (MultiNLI) corpus is a
crowd-sourced collection of 433k sentence pairs annotated with textual
entailment information. The corpus is modeled on the SNLI corpus, but differs in
that covers a range of genres of spoken and written text, and supports a
distinctive cross-genre generalization evaluation. The corpus served as the
basis for the shared task of the RepEval 2017 Workshop at EMNLP in Copenhagen.
### 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
#### plain_text
- **Size of downloaded dataset files:** 226.85 MB
- **Size of the generated dataset:** 77.62 MB
- **Total amount of disk used:** 304.46 MB
An example of 'train' looks as follows.
```
{
"hypothesis": "independence",
"label": "contradiction",
"premise": "correlation"
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `label`: a `string` feature.
### Data Splits
| name |train |validation|
|----------|-----:|---------:|
|plain_text|392702| 10000|
## 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{N18-1101,
author = "Williams, Adina
and Nangia, Nikita
and Bowman, Samuel",
title = "A Broad-Coverage Challenge Corpus for
Sentence Understanding through Inference",
booktitle = "Proceedings of the 2018 Conference of
the North American Chapter of the
Association for Computational Linguistics:
Human Language Technologies, Volume 1 (Long
Papers)",
year = "2018",
publisher = "Association for Computational Linguistics",
pages = "1112--1122",
location = "New Orleans, Louisiana",
url = "http://aclweb.org/anthology/N18-1101"
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. |
false |
# Dataset Card for OfisPublik
## 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://opus.nlpl.eu/OfisPublik.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
false |
# Dataset Card for PersiNLU (Reading Comprehension)
## 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:** [Github](https://github.com/persiannlp/parsinlu/)
- **Repository:** [Github](https://github.com/persiannlp/parsinlu/)
- **Paper:** [Arxiv](https://arxiv.org/abs/2012.06154)
- **Leaderboard:**
- **Point of Contact:** [email](d.khashabi@gmail.com)
### Dataset Summary
A Persian reading comprehenion task (generating an answer, given a question and a context paragraph).
The questions are mined using Google auto-complete, their answers and the corresponding evidence documents are manually annotated by native speakers.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text dataset is in Persian (`fa`).
## Dataset Structure
### Data Instances
Here is an example from the dataset:
```
{
'question': 'پیامبر در چه سالی به پیامبری رسید؟',
'url': 'https://fa.wikipedia.org/wiki/%D9%85%D8%AD%D9%85%D8%AF',
'passage': 'محمد که از روش زندگی مردم مکه ناخشنود بود، گهگاه در غار حرا در یکی از کوه\u200cهای اطراف آن دیار به تفکر و عبادت می\u200cپرداخت. به باور مسلمانان، محمد در همین مکان و در حدود ۴۰ سالگی از طرف خدا به پیامبری برگزیده، و وحی بر او فروفرستاده شد. در نظر آنان، دعوت محمد همانند دعوت دیگر پیامبرانِ کیش یکتاپرستی مبنی بر این بود که خداوند (الله) یکتاست و تسلیم شدن برابر خدا راه رسیدن به اوست.',
'answers': [
{'answer_start': 160, 'answer_text': 'حدود ۴۰ سالگی'}
]
}
```
### Data Fields
- `question`: the question, mined using Google auto-complete.
- `passage`: the passage that contains the answer.
- `url`: the url from which the passage was mined.
- `answers`: a list of answers, containing the string and the index of the answer with the fields `answer_start` and `answer_text`. Note that in the test set, some `answer_start` values are missing and replaced with `-1`
### Data Splits
The train/test split contains 600/575 samples.
## Dataset Creation
### Curation Rationale
The question were collected via Google auto-complete.
The answers were annotated by native speakers.
For more details, check [the corresponding draft](https://arxiv.org/abs/2012.06154).
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
CC BY-NC-SA 4.0 License
### Citation Information
```bibtex
@article{huggingface:dataset,
title = {ParsiNLU: A Suite of Language Understanding Challenges for Persian},
authors = {Khashabi, Daniel and Cohan, Arman and Shakeri, Siamak and Hosseini, Pedram and Pezeshkpour, Pouya and Alikhani, Malihe and Aminnaseri, Moin and Bitaab, Marzieh and Brahman, Faeze and Ghazarian, Sarik and others},
year={2020}
journal = {arXiv e-prints},
eprint = {2012.06154},
}
```
### Contributions
Thanks to [@danyaljj](https://github.com/danyaljj) for adding this dataset. |
false |
# Dataset Card for Reddit Webis-TLDR-17
## 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://webis.de/data/webis-tldr-17.html](https://webis.de/data/webis-tldr-17.html)
- **Repository:** [https://github.com/webis-de/webis-tldr-17-corpus](https://github.com/webis-de/webis-tldr-17-corpus)
- **Paper:** [https://aclanthology.org/W17-4508]
- **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:** 3.14 GB
- **Size of the generated dataset:** 18.94 GB
- **Total amount of disk used:** 22.08 GB
### Dataset Summary
This corpus contains preprocessed posts from the Reddit dataset (Webis-TLDR-17).
The dataset consists of 3,848,330 posts with an average length of 270 words for content,
and 28 words for the summary.
Features includes strings: author, body, normalizedBody, content, summary, subreddit, subreddit_id.
Content is used as document and summary is used as summary.
### Supported Tasks and Leaderboards
Summarization (abstractive)
Known ROUGE scores achieved for the Webis-TLDR-17:
| Model | ROUGE-1 | ROUGE-2 | ROUGE-L | Paper/Source |
|-------|-------|-------|-------|------:|
| Transformer + Copy (Gehrmann et al., 2019) | 22 | 6 | 17 | Generating Summaries with Finetuned Language Models |
| Unified VAE + PGN (Choi et al., 2019) | 19 | 4 | 15 | VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization |
(Source: https://github.com/sebastianruder/NLP-progress/blob/master/english/summarization.md)
### Languages
English
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 3.14 GB
- **Size of the generated dataset:** 18.94 GB
- **Total amount of disk used:** 22.08 GB
An example of 'train' looks as follows.
```
{
"author": "me",
"body": "<>",
"content": "input document.",
"id": "1",
"normalizedBody": "",
"subreddit": "machinelearning",
"subreddit_id": "2",
"summary": "output summary."
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `author`: a `string` feature.
- `body`: a `string` feature.
- `normalizedBody`: a `string` feature.
- `subreddit`: a `string` feature.
- `subreddit_id`: a `string` feature.
- `id`: a `string` feature.
- `content`: a `string` feature.
- `summary`: a `string` feature.
### Data Splits
| name | train |
|-------|------:|
|default|3848330|
This corpus does not contain a separate test set. Thus it is up to the users to divide the corpus into appropriate training, validation and test sets.
## Dataset Creation
### Curation Rationale
In the scope of the task of absractive summarization the creators of the Webis-TLDR-17 propose mining social media for author-provided summaries and taking advantage of the common practice of appending a "TL;DR" to long posts. A large Reddit crawl was used to yield the Webis-TLDR-17 corpus. This dataset intends to complement the existing summarization corpora primarily from the news genre.
### Source Data
Reddit subreddits posts (submissions & comments) containing "TL;DR" from 2006 to 2016. Multiple subreddits are included.
#### Initial Data Collection and Normalization
Initial data: a set of 286 million submissions and 1.6 billion comments posted to Reddit between 2006 and 2016.
Then a five-step pipeline of consecutive filtering steps was applied.
#### Who are the source language producers?
The contents of the dataset are produced by human authors, bot-generated content was eliminated by filtering out all bot accounts with the help of an extensive list provided by the Reddit community, as well as manual inspection of cases where the user name contained the substring "bot."
### 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
This dataset has been created to serve as a source of large-scale summarization training data. It is primarily geared towards the automatic abstractive summarization task, that can be considered one of the most challenging variants of automatic summarization. It also aims to tackle the lack of genre diversity in the summarization datasets (most are news-related).
### 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
Reddit users write TL;DRs with various intentions, such as providing a “true” summary, asking questions or for help, or forming judgments and conclusions. As noted in the paper introducing the dataset, although the first kind of TL;DR posts are most important for training summarization models, yet, the latter allow for various alternative summarization-related tasks.
Although filtering was performed abusive language maybe still be present.
## Additional Information
### Dataset Curators
Michael Völske, Martin Potthast, Shahbaz Syed, Benno Stein
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{volske-etal-2017-tl,
title = "{TL};{DR}: Mining {R}eddit to Learn Automatic Summarization",
author = {V{"o}lske, Michael and
Potthast, Martin and
Syed, Shahbaz and
Stein, Benno},
booktitle = "Proceedings of the Workshop on New Frontiers in Summarization",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/W17-4508",
doi = "10.18653/v1/W17-4508",
pages = "59--63",
abstract = "Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a {``}TL;DR{''} to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 dataset, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.",
}
```
### Contributions
Thanks to [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
true |
# Dataset Card for RO-STS
## 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:** [GitHub](https://github.com/dumitrescustefan/RO-STS)
- **Repository:** [GitHub](https://github.com/dumitrescustefan/RO-STS)
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [email](dumitrescu.stefan@gmail.com)
### Dataset Summary
We present RO-STS - the Semantic Textual Similarity dataset for the Romanian language. It is a high-quality translation of the [STS English dataset](https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark). RO-STS contains 8,628 sentence pairs with their similarity scores. The original English sentences were collected from news headlines, captions of images and user forums, and are categorized accordingly. The Romanian release follows this categorization and provides the same train/validation/test split with 5,749/1,500/1,379 sentence pairs in each subset.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The text dataset is in Romanian (`ro`)
## Dataset Structure
### Data Instances
An example looks like this:
```
{'score': 1.5,
'sentence1': 'Un bărbat cântă la harpă.',
'sentence2': 'Un bărbat cântă la claviatură.',
}
```
### Data Fields
- `score`: a float representing the semantic similarity score where 0.0 is the lowest score and 5.0 is the highest
- `sentence1`: a string representing a text
- `sentence2`: another string to compare the previous text with
### Data Splits
The train/validation/test split contain 5,749/1,500/1,379 sentence pairs.
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
[Needs More Information]
#### Initial Data Collection and Normalization
*To construct the dataset, we first obtained automatic translations using Google's translation engine. These were then manually checked, corrected, and cross-validated by human volunteers. *
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
CC BY-SA 4.0 License
### Citation Information
```
@inproceedings{dumitrescu2021liro,
title={Liro: Benchmark and leaderboard for romanian language tasks},
author={Dumitrescu, Stefan Daniel and Rebeja, Petru and Lorincz, Beata and Gaman, Mihaela and Avram, Andrei and Ilie, Mihai and Pruteanu, Andrei and Stan, Adriana and Rosia, Lorena and Iacobescu, Cristina and others},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)},
year={2021}
}
```
### Contributions
Thanks to [@lorinczb](https://github.com/lorinczb) for adding this dataset. |
false |
# Dataset Card for Sepedi 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:** [Sepedi Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/328)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za)
### Dataset Summary
The Sepedi Ner Corpus is a Sepedi 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 Sepedi language. The dataset uses CoNLL shared task annotation standards.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Sesotho sa Leboa (Sepedi).
## 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': ['Maikemišetšo', 'a', 'websaete', 'ya', 'ditirelo']
}
```
### 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 - sepedi.
[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.
#### 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{sepedi_ner_corpus,
author = {D.J. Prinsloo and
Roald Eiselen},
title = {NCHLT Sepedi 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/20.500.12185/328},
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset. |
false |
# Dataset Card for Siswati 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:** [Siswati Ner Corpus Homepage](https://repo.sadilar.org/handle/20.500.12185/346)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Martin Puttkammer](mailto:Martin.Puttkammer@nwu.ac.za)
### Dataset Summary
The Siswati Ner Corpus is a Siswati 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 Siswati language. The dataset uses CoNLL shared task annotation standards.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Siswati.
## 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': ['Tinsita', 'tebantfu', ':', 'tinsita', 'tetakhamiti']
}
```
### 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 - siswati.
[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.
#### 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{siswati_ner_corpus,
author = {B.B. Malangwane and
M.N. Kekana and
S.S. Sedibe and
B.C. Ndhlovu and
Roald Eiselen},
title = {NCHLT Siswati 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/20.500.12185/346},
}
```
### Contributions
Thanks to [@yvonnegitau](https://github.com/yvonnegitau) for adding this dataset. |
true |
# Dataset Card for Tunisian Sentiment Analysis 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:** None
- **Repository:** https://github.com/fbougares/TSAC
- **Paper:** https://www.aclweb.org/anthology/W17-1307
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** Salima Mdhaffar (firstname.lastname@univ-lemans.fr)
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
false |
# Dataset Card for turkish_shrinked_ner
## 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.kaggle.com/behcetsenturk/shrinked-twnertc-turkish-ner-data-by-kuzgunlar
- **Repository:** [Needs More Information]
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** https://www.kaggle.com/behcetsenturk
### Dataset Summary
Shrinked processed version (48 entity type) of the turkish_ner.
Original turkish_ner dataset: Automatically annotated Turkish corpus for named entity recognition and text categorization using large-scale gazetteers. The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 25 different domains.
Shrinked entity types are: academic, academic_person, aircraft, album_person, anatomy, animal, architect_person, capital, chemical, clothes, country, culture, currency, date, food, genre, government, government_person, language, location, material, measure, medical, military, military_person, nation, newspaper, organization, organization_person, person, production_art_music, production_art_music_person, quantity, religion, science, shape, ship, software, space, space_person, sport, sport_name, sport_person, structure, subject, tech, train, vehicle
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Turkish
## Dataset Structure
### Data Instances
[Needs More Information]
### Data Fields
[Needs More Information]
### Data Splits
There's only the training set.
## 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
Behcet Senturk
### Licensing Information
Creative Commons Attribution 4.0 International
### Citation Information
[Needs More Information]
### Contributions
Thanks to [@bhctsntrk](https://github.com/bhctsntrk) for adding this dataset. |
true |
# Dataset Card for wrbsc
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://clarin-pl.eu/dspace/handle/11321/305
- **Repository:** https://clarin-pl.eu/dspace/handle/11321/305
- **Paper:** [Needs More Information]
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
WUT Relations Between Sentences Corpus contains 2827 pairs of related sentences. Relationships are derived from Cross-document Structure Theory (CST), which enables multi-document summarization through identification of cross-document rhetorical relationships within a cluster of related documents. Every relation was marked by at least 3 annotators.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Polish
## Dataset Structure
### Data Instances
An example contains two related sentences and a class representing the type of relationship between those sentences.
```
{'relationship': 0,
'sentence1': 'Znajdujące się w Biurze Bezpieczeństwa Narodowego akta Komisji Weryfikacyjnej WSI zostały przewiezione do siedziby Służby Kontrwywiadu Wojskowego.',
'sentence2': '2008-07-03: Wywiezienie akt dotyczących WSI – sprawa dla prokuratury?'}
```
### Data Fields
- `sentence1`: the first sentence being compared (`string`)
- `sentence2`: the second sentence being compared (`string`)
- `relationship`: the type of relationship between those sentences. Can be one of 16 classes listed below:
- `Krzyżowanie_się`: crossing
- `Tło_historyczne`: historical background
- `Źródło`: source
- `Dalsze_informacje`: additional information
- `Zawieranie`: inclusion
- `Opis`: description
- `Uszczegółowienie`: further detail
- `Parafraza`: paraphrase
- `Spełnienie`: fulfillment
- `Mowa_zależna`: passive voice
- `Zmiana_poglądu`: change of opinion
- `Streszczenie`: summarization
- `Tożsamość`: identity
- `Sprzeczność`: conflict
- `Modalność`: modality
- `Cytowanie`: quotation
### Data Splits
Single train split
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
Attribution-ShareAlike 3.0 Unported (CC BY-SA 3.0)
### Citation Information
```
@misc{11321/305,
title = {{WUT} Relations Between Sentences Corpus},
author = {Oleksy, Marcin and Fikus, Dominika and Wolski, Micha{\l} and Podbielska, Ma{\l}gorzata and Turek, Agnieszka and Kędzia, Pawe{\l}},
url = {http://hdl.handle.net/11321/305},
note = {{CLARIN}-{PL} digital repository},
copyright = {Attribution-{ShareAlike} 3.0 Unported ({CC} {BY}-{SA} 3.0)},
year = {2016}
}
```
### Contributions
Thanks to [@kldarek](https://github.com/kldarek) for adding this dataset. |
true |
# Dataset Card for scientific-challenges-and-directions
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository: [repo](https://github.com/Dan-La/scientific-challenges-and-directions)**
- **Paper: [A Search Engine for Discovery of Scientific Challenges and Directions](https://arxiv.org/abs/2108.13751)**
- **Point of Contact: lahav@mail.tau.ac.il,tomh@allenai.org**
### Dataset Summary
The scientific challenges and directions dataset is a collection of 2894 sentences and their surrounding contexts, from 1786 full-text papers in the [CORD-19](https://arxiv.org/abs/2004.10706) corpus, labeled for classification of _challenges_ and _directions_ by expert annotators with biomedical and bioNLP backgrounds.
At a high level, our labels are defined as follows:
* **Challenge**: A sentence mentioning a problem, difficulty, flaw, limitation, failure, lack of clarity, or knowledge gap.
* **Research direction**: A sentence mentioning suggestions or needs for further research, hypotheses, speculations, indications or hints that an issue is worthy of exploration.
The dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature.
### Languages
The language in the dataset is English as written by authors of the scientific papers in the CORD-19 corpus.
## Dataset Structure
### Data Instances
For each instance, there is a unique id, a string for the text sentence, a string for the previous sentence, a string for the next sentence, and a list for the challenge and direction labels.
```
{'id': 'PMC7152165_152',
'label': [0.0, 0.0],
'next_sent': 'The railways brought a new technology and vast engineering and architectural structures into Britain’s rural and urban landscapes.',
'prev_sent': 'In Britain, improvements in coaching technologies and roads helped to increase stage coach speeds in the late eighteenth and early nineteenth centuries, while the railway construction boom of the 1830s and 1840s led to a massive reduction in journey times, and the emergence of distinctly new experiences and geographies.',
'text': 'Britain’s railway companies were among the nation’s largest employers in the nineteenth century, and they facilitated the mobility of passengers and important commodities.'}
```
### Data Fields
* id: A string as a unique id for the instance. The id is composed of the unique PMC id of the paper, an underscore, and the index of the sentence within the paper.
* next_sent_: A string of a sentence that is following the _text_ of the instance. If the text is the first in its paragraph the string is saved as '|'.
* prev_sent_: A string of a sentence that is preceding the _text_ of the instance. If the text is the first in its paragraph the string is saved as '|'.
* text: A string of the sentence we seek to classify.
* label: A list of 2 values - the first is the label for _challenge_ and the last of _direction_. Each value may be either 0, indicating that the _text_ is **not** _challenge_ or _direction_, or 1, indicating that the the _text_ is _challenge_ or _direction_. Each instance can be a _challenge_, a _direction_, both, or neither.
### Data Splits
The scientific-challenges-and-directions dataset has 3 splits: _train_, _dev_, and _test_. Each instances shows up in only one split. The splits are stratified with no overlap in papers.
| Labels | Train | Dev | Test | All |
|:----------------------------:|:------:|:-----:|:----:|:----:|
| Not Challenge, Not Direction | 602 | 146 | 745 | 1493 |
| Not Challenge, Direction | 106 | 25 | 122 | 253 |
| Challenge, Not Direction | 288 | 73 | 382 | 743 |
| Challenge, Direction | 155 | 40 | 210 | 405 |
## Dataset Creation
### Curation Rationale
The resource was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research.
### Source Data
#### Initial Data Collection and Normalization
See section 3.1 in our [paper](https://arxiv.org/abs/2108.13751).
#### Who are the source language producers?
The authors of the subset of full-text papers in the [CORD-19 dataset](https://arxiv.org/abs/2004.10706), which at the time of creating our dataset included roughly 180K documents.
### Annotations
#### Annotation process
See section 3.1 in our [paper](https://arxiv.org/abs/2108.13751).
#### Who are the annotators?
Four expert annotators with biomedical and bioNLP backgrounds. For more details see section 3.1 in our [paper](https://arxiv.org/abs/2108.13751).
### Personal and Sensitive Information
The dataset does not contain any personal information about the authors or annotators.
## Considerations for Using the Data
### Social Impact of Dataset
As mentioned, the dataset was developed to help scientists and medical professionals discover challenges and potential directions across scientific literature, focusing on a broad corpus pertaining to the COVID-19 pandemic and related historical research.
Studies were conducted to evaluate the utility of the dataset for researchers and medical professionals, in which a prototype based on the dataset was found to outperform other biomedical search tools. For more details see section 4 in our [paper](https://arxiv.org/abs/2108.13751).
This dataset was also developed for evaluating representational systems for scientific text classification and can be used as such.
### Discussion of Biases
The source of the dataset is the full-text papers in the [CORD-19 dataset](https://arxiv.org/abs/2004.10706), so biases in CORD-19 may be replicated to our dataset.
### Other Known Limitations
N/A
## Additional Information
### Dataset Curators
The dataset was developed by Dan Lahav, Jon Saad Falcon, Bailey Kuehl, Sophie Johnson, Sravanthi Parasa, Noam Shomron, Duen Horng Chau, Diyi Yang, Eric Horvitz, Daniel S. Weld and Tom Hope as part of _Tel Aviv University_, the _Allen Institute for AI_, _University of Washington_, _Georgia Institute of Technology_, _Microsoft_ and _Swedish Medical Group_.
It was supported by the Edmond J. Safra Center for Bioinformatics at Tel-Aviv University, ONR grant N00014-18-1-2193, NSF RAPID grant 2040196, the WR-F/Cable Professorship, and AI2.
### Licensing Information
[More Information Needed]
### Citation Information
If using our dataset and models, please cite:
```
@misc{lahav2021search,
title={A Search Engine for Discovery of Scientific Challenges and Directions},
author={Dan Lahav and Jon Saad Falcon and Bailey Kuehl and Sophie Johnson and Sravanthi Parasa and Noam Shomron and Duen Horng Chau and Diyi Yang and Eric Horvitz and Daniel S. Weld and Tom Hope},
year={2021},
eprint={2108.13751},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@Dan-La](https://github.com/Dan-La) and [@tomhoper](https://github.com/tomhoper) for adding this dataset.
|
false |
# Dataset Card for GEM/ART
## Dataset Description
- **Homepage:** http://abductivecommonsense.xyz/
- **Repository:** https://storage.googleapis.com/ai2-mosaic/public/abductive-commonsense-reasoning-iclr2020/anlg.zip
- **Paper:** https://openreview.net/pdf?id=Byg1v1HKDB
- **Leaderboard:** N/A
- **Point of Contact:** Chandra Bhagavatulla
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/ART).
### Dataset Summary
Abductive reasoning is inference to the most plausible explanation. For example, if Jenny finds her house in a mess when she returns from work, and remembers that she left a window open, she can hypothesize that a thief broke into her house and caused the mess, as the most plausible explanation.
This data loader focuses on abductive NLG: a conditional English generation task for explaining given observations in natural language.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/ART')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/ART).
#### website
[Website](http://abductivecommonsense.xyz/)
#### paper
[OpenReview](https://openreview.net/pdf?id=Byg1v1HKDB)
#### authors
Chandra Bhagavatula (AI2), Ronan Le Bras (AI2), Chaitanya Malaviya (AI2), Keisuke Sakaguchi (AI2), Ari Holtzman (AI2, UW), Hannah Rashkin (AI2, UW), Doug Downey (AI2), Wen-tau Yih (AI2), Yejin Choi (AI2, UW)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](http://abductivecommonsense.xyz/)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Google Storage](https://storage.googleapis.com/ai2-mosaic/public/abductive-commonsense-reasoning-iclr2020/anlg.zip)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[OpenReview](https://openreview.net/pdf?id=Byg1v1HKDB)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{
Bhagavatula2020Abductive,
title={Abductive Commonsense Reasoning},
author={Chandra Bhagavatula and Ronan Le Bras and Chaitanya Malaviya and Keisuke Sakaguchi and Ari Holtzman and Hannah Rashkin and Doug Downey and Wen-tau Yih and Yejin Choi},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=Byg1v1HKDB}
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Chandra Bhagavatulla
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
chandrab@allenai.org
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### Whose Language?
<!-- info: Whose language is in the dataset? -->
<!-- scope: periscope -->
Crowdworkers on the Amazon Mechanical Turk platform based in the U.S, Canada, U.K and Australia.
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
apache-2.0: Apache License 2.0
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
To study the viability of language-based abductive reasoning. Training and evaluating models to generate a plausible hypothesis to explain two given observations.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Reasoning
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Allen Institute for AI
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Chandra Bhagavatula (AI2), Ronan Le Bras (AI2), Chaitanya Malaviya (AI2), Keisuke Sakaguchi (AI2), Ari Holtzman (AI2, UW), Hannah Rashkin (AI2, UW), Doug Downey (AI2), Wen-tau Yih (AI2), Yejin Choi (AI2, UW)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Allen Institute for AI
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Chandra Bhagavatula (AI2), Ronan LeBras (AI2), Aman Madaan (CMU), Nico Daheim (RWTH Aachen University)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `observation_1`: A string describing an observation / event.
- `observation_2`: A string describing an observation / event.
- `label`: A string that plausibly explains why observation_1 and observation_2 might have happened.
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
Explanations were authored by crowdworkers on the Amazon Mechanical Turk platform using a custom template designed by the creators of the dataset.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
'gem_id': 'GEM-ART-validation-0',
'observation_1': 'Stephen was at a party.',
'observation_2': 'He checked it but it was completely broken.',
'label': 'Stephen knocked over a vase while drunk.'
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- `train`: Consists of training instances.
- `dev`: Consists of dev instances.
- `test`: Consists of test instances.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
Abductive reasoning is a crucial capability of humans and ART is the first dataset curated to study language-based abductive reasoning.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Whether models can reason abductively about a given pair of observations.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
- [Paper](https://arxiv.org/abs/1908.05739)
- [Code](https://github.com/allenai/abductive-commonsense-reasoning)
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Whether models can reason abductively about a given pair of observations.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `BERT-Score`, `ROUGE`
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
no
## Dataset Curation
### Original Curation
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Crowdsourced`
#### Where was it crowdsourced?
<!-- info: If crowdsourced, where from? -->
<!-- scope: periscope -->
`Amazon Mechanical Turk`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
Language producers were English speakers in U.S., Canada, U.K and Australia.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
No
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by crowdworker
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
Adversarial filtering algorithm as described in the [paper](https://arxiv.org/abs/1908.05739)
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
automatically created
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
Each observation is associated with a list of COMET (https://arxiv.org/abs/1906.05317) inferences.
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
none
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
no PII
#### Justification for no PII
<!-- info: Provide a justification for selecting `no PII` above. -->
<!-- scope: periscope -->
The dataset contains day-to-day events. It does not contain names, emails, addresses etc.
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
None
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`public domain`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`public domain`
### Known Technical Limitations
|
false |
# Dataset Card for GEM/mlb_data_to_text
## Dataset Description
- **Homepage:** https://github.com/ratishsp/mlb-data-scripts
- **Repository:** https://github.com/ratishsp/mlb-data-scripts
- **Paper:** https://aclanthology.org/P19-1195
- **Leaderboard:** N/A
- **Point of Contact:** Ratish Puduppully
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/mlb_data_to_text).
### Dataset Summary
The MLB dataset is an English sport-related data-to-text dataset in the baseball domain. The input is a large table with results of a game and the output is a description of the game.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/mlb_data_to_text')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/mlb_data_to_text).
#### website
[Github](https://github.com/ratishsp/mlb-data-scripts)
#### paper
[ACL Anthology](https://aclanthology.org/P19-1195)
#### authors
Ratish Puduppully, Li Dong, Mirella Lapata
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Github](https://github.com/ratishsp/mlb-data-scripts)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/ratishsp/mlb-data-scripts)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/P19-1195)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{puduppully-etal-2019-data,
title = "Data-to-text Generation with Entity Modeling",
author = "Puduppully, Ratish and
Dong, Li and
Lapata, Mirella",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P19-1195",
doi = "10.18653/v1/P19-1195",
pages = "2023--2035",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Ratish Puduppully
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
ratishpuduppully@gmail.com
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
other: Other license
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
The dataset can be used to study data-to-text generation. The dataset is in sports domain. It pairs statistics of Major League Baseball (MLB) game with its summary. The summary is in the form of a document containing an average of 540 tokens. Thus it is useful to study long document generation.
#### Add. License Info
<!-- info: What is the 'other' license of the dataset? -->
<!-- scope: periscope -->
Restricted to non-commercial research purposes.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Produce a summary of MLB game from its statistics.
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
University of Edinburgh
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Ratish Puduppully, Li Dong, Mirella Lapata
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
```
features = datasets.Features(
{
"home_name": datasets.Value("string"),
"box_score": [
{
"p_l": datasets.Value("string"),
"last_name": datasets.Value("string"),
"p_h": datasets.Value("string"),
"sac": datasets.Value("string"),
"p_bb": datasets.Value("string"),
"pos": datasets.Value("string"),
"ao": datasets.Value("string"),
"p_bf": datasets.Value("string"),
"cs": datasets.Value("string"),
"hbp": datasets.Value("string"),
"ab": datasets.Value("string"),
"full_name": datasets.Value("string"),
"p_w": datasets.Value("string"),
"go": datasets.Value("string"),
"fldg": datasets.Value("string"),
"p_bs": datasets.Value("string"),
"avg": datasets.Value("string"),
"p_r": datasets.Value("string"),
"p_s": datasets.Value("string"),
"lob": datasets.Value("string"),
"first_name": datasets.Value("string"),
"p_sv": datasets.Value("string"),
"p_so": datasets.Value("string"),
"p_save": datasets.Value("string"),
"p_hr": datasets.Value("string"),
"po": datasets.Value("string"),
"p_ip1": datasets.Value("string"),
"p_ip2": datasets.Value("string"),
"bb": datasets.Value("string"),
"ops": datasets.Value("string"),
"p_hld": datasets.Value("string"),
"bo": datasets.Value("string"),
"p_loss": datasets.Value("string"),
"e": datasets.Value("string"),
"p_game_score": datasets.Value("string"),
"p_win": datasets.Value("string"),
"a": datasets.Value("string"),
"p_era": datasets.Value("string"),
"d": datasets.Value("string"),
"p_out": datasets.Value("string"),
"h": datasets.Value("string"),
"p_er": datasets.Value("string"),
"p_np": datasets.Value("string"),
"hr": datasets.Value("string"),
"r": datasets.Value("string"),
"so": datasets.Value("string"),
"t": datasets.Value("string"),
"rbi": datasets.Value("string"),
"team": datasets.Value("string"),
"sb": datasets.Value("string"),
"slg": datasets.Value("string"),
"sf": datasets.Value("string"),
"obp": datasets.Value("string"),
}
],
"home_city": datasets.Value("string"),
"vis_name": datasets.Value("string"),
"play_by_play": [{
"top": [{
"runs": datasets.Value("string"),
"scorers": [
datasets.Value("string")
],
"pitcher": datasets.Value("string"),
"o": datasets.Value("string"),
"b": datasets.Value("string"),
"s": datasets.Value("string"),
"batter": datasets.Value("string"),
"b1": [
datasets.Value("string")
],
"b2": [
datasets.Value("string")
],
"b3": [
datasets.Value("string")
],
"event": datasets.Value("string"),
"event2": datasets.Value("string"),
"home_team_runs": datasets.Value("string"),
"away_team_runs": datasets.Value("string"),
"rbi": datasets.Value("string"),
"error_runs": datasets.Value("string"),
"fielder_error": datasets.Value("string")
}
],
"bottom": [{
"runs": datasets.Value("string"),
"scorers": [
datasets.Value("string")
],
"pitcher": datasets.Value("string"),
"o": datasets.Value("string"),
"b": datasets.Value("string"),
"s": datasets.Value("string"),
"batter": datasets.Value("string"),
"b1": [
datasets.Value("string")
],
"b2": [
datasets.Value("string")
],
"b3": [
datasets.Value("string")
],
"event": datasets.Value("string"),
"event2": datasets.Value("string"),
"home_team_runs": datasets.Value("string"),
"away_team_runs": datasets.Value("string"),
"rbi": datasets.Value("string"),
"error_runs": datasets.Value("string"),
"fielder_error": datasets.Value("string")
}
],
"inning": datasets.Value("string")
}
],
"vis_line": {
"innings": [{
"inn": datasets.Value("string"),
"runs": datasets.Value("string")
}
],
"result": datasets.Value("string"),
"team_runs": datasets.Value("string"),
"team_hits": datasets.Value("string"),
"team_errors": datasets.Value("string"),
"team_name": datasets.Value("string"),
"team_city": datasets.Value("string")
},
"home_line": {
"innings": [{
"inn": datasets.Value("string"),
"runs": datasets.Value("string")
}
],
"result": datasets.Value("string"),
"team_runs": datasets.Value("string"),
"team_hits": datasets.Value("string"),
"team_errors": datasets.Value("string"),
"team_name": datasets.Value("string"),
"team_city": datasets.Value("string")
},
"vis_city": datasets.Value("string"),
"day": datasets.Value("string"),
"summary": [
datasets.Value("string"),
],
"gem_id": datasets.Value("string")
}
```
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
The high level structure contains the following attributes: home_name, vis_name, home_city, vis_city, summary, summary_eval, day, gem_id, box_score, play_by_play, home_line, vis_line.
The attributes home_name, vis_name, home_city, vis_city and day are string values.
The attribute "summary" contains the summary in the form of a list of tokens.
The attribute "summary_eval" contains the summary in the form of a string of tokens. The difference from "summary" field is that "summary_eval" doesn't contain "*NEWPARAGRAPH*" delimiters to separate the paragraphs. "summary_eval" field should be used to evaluate model outputs. "summary" field may be used during the training process.
box_score contains the box score statistics of the players in the game. It is in the form of a list (of average size 90), with each element describing the statistics of a player. The box score statistics contain 53 attributes.
The description of the attributes is given below. The descriptions of most of the attributes is obtained from [mlb.com](https://www.mlb.com/glossary/standard-stats).
- r : Runs scored by a player in the game.
- rbi Runs Batted In (RBI): action of a batter results in a run scored by other players in the team.
- pos Position of the player.
- avg Batting Average. It is an indicator of the hits in the players' career.
- bb A walk occurs when a pitcher throws four pitches out of the strike zone, none of which are swung at by the hitter.
- hr Batter hits the ball in the air over the outfield fence.
- p_r Runs given by a pitcher in the game.
- p_bb Walks allowed by pitcher in a game.
- p_h Hits allowed by pitcher in a game.
- p_hr Home runs allowed by pitcher in a game.
- p_er Earned Run (ER): An earned run is any run that scores against a pitcher.
- p_era Earned Run Average (ERA): Earned run average represents the number of earned runs a pitcher allows per nine innings.
- p_np Number of Pitches: A pitcher's total number of pitches is determined by all the pitches he throws in game.
- p_ip1 Innings Pitched (IP1): Innings pitched measures the number of innings a pitcher remains in a game. Because there are three outs in an inning, each out recorded represents one-third of an inning pitched.
- p_ip2 Innings Pitched (IP2): Innings pitched measures the number of innings a pitcher remains in a game. Because there are three outs in an inning, each out recorded represents one-third of an inning pitched.
- p_w A pitcher receives a win when he is the pitcher of record when his team takes the lead for good.
- p_l A pitcher receives a loss when a run that is charged to him proves to be the go-ahead run in the game, giving the opposing team a lead it never gives up.
- p_so A strikeout occurs when a pitcher throws any combination of three swinging or looking strikes to a hitter.
- p_save Save: A save is awarded to the relief pitcher who finishes a game for the winning team. A pitcher cannot receive a save and a win in the same game.
- p_sv Saves: The count of saves recorded by a pitcher in his career.
- sac A sacrifice fly occurs when a batter hits a fly-ball out to the outfield or foul territory that allows a runner to score.
- p_bf Batters faced is simply a count of the number of total plate appearances against a certain pitcher or team. In a perfect game -- with 27 outs -- a pitcher will record 27 batters faced.
- cs A caught stealing occurs when a runner attempts to steal but is tagged out before reaching second base, third base or home plate.
- hbp A hit-by-pitch occurs when a batter is struck by a pitched ball without swinging at it. He is awarded first base as a result.
- ab An official at-bat comes when a batter reaches base via a fielder's choice, hit or an error (not including catcher's interference) or when a batter is put out on a non-sacrifice.
- p_bs A blown save occurs when a relief pitcher enters a game in a save situation, but allows the tying run to score.
- p_s The count of strikes thrown by a pitcher
- lob Left on base can be viewed as both an individual statistic or as a team statistic. In an individual batter's case, it refers to how many men remain on base after that batter makes an out at the plate, as the batter has failed to do his job to score those runners -- or at least put himself in a position to score. In a team's case or in an individual pitcher's case, it refers to the number of men who remain on base at the end of an inning.
- po A fielder is credited with a putout when he is the fielder who physically records the act of completing an out -- whether it be by stepping on the base for a forceout, tagging a runner, catching a batted ball, or catching a third strike
- ops OPS adds on-base percentage and slugging percentage to get one number that unites the two. It's meant to combine how well a hitter can reach base, with how well he can hit for average and for power.
- p_hld A hold occurs when a relief pitcher enters the game in a save situation and maintains his team's lead for the next relief pitcher, while recording at least one out.
- p_loss True/False- Indicates losing pitcher
- e A fielder is given an error if, in the judgment of the official scorer, he fails to convert an out on a play that an average fielder should have made.
- p_win True/False- Indicates winning pitcher
- a An assist is awarded to a fielder who touches the ball before a putout is recorded by another fielder.
- h A hit occurs when a batter strikes the baseball into fair territory and reaches base without doing so via an error or a fielder's choice.
- so A strikeout of a batter
- team Team of the player
- sb A stolen base occurs when a baserunner advances by taking a base to which he isn't entitled.
- slg Slugging percentage represents the total number of bases a player records per at-bat. Unlike on-base percentage, slugging percentage deals only with hits and does not include walks and hit-by-pitches in its equation.
- sf A sacrifice fly occurs when a batter hits a fly-ball out to the outfield or foul territory that allows a runner to score.
- obp OBP refers to how frequently a batter reaches base per plate appearance. Times on base include hits, walks and hit-by-pitches, but do not include errors, times reached on a fielder's choice or a dropped third strike.
The description of attributes in play-by-play is below:
- batter Batter in the play.
- pitcher Pitcher in play.
- b1 Player/s at first base position.
- b2 Player/s at second base position.
- b3 Player/s at third base position.
- scorers Player/s scored in the play.
- fielder_error Player committed field error.
- event Event of the play such as single, double, home run etc.
- event2 Second event of the play such as wild pitch, error etc.
- inning Inning of the play.
- top/ bottom If home team is batting it is bottom and if away team is batting it is top.
- o Count of outs
- b Count of balls
- s Count of strikes
- r Count of runs
- rbi Count of runs batted in (rbi)
- error_runs Runs due to error
- home_team_runs Score of home team
- vis_team_runs Score of visiting team
`home_line` and `vis_line` contain string value pairs for `team_name`, `team_city`, `team_runs`, `team_hits`, `team_error`, `result`, and a list of runs scored in each inning.
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
There are three splits in the dataset: train, validation and test
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The splits are random.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
This dataset can verify if models are capable of long document generation. The challenges in long document generation conditioned on input tables include ensuring coherent output, staying faithful to the input, ensuring fluent output and avoiding repetition of text. Such aspects can be verified on models trained on this dataset
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
no
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
Compared to the existing RotoWire (Wiseman et al. 2017) dataset, MLB summaries are longer (approximately by 50%) and the input records are richer and more structured (with the addition of play-by-play). Moreover, the MLB dataset is five times larger in terms of data size (i.e., pairs of tables and game summaries).
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
Long document generation, coherent ordering of information, faithfulness to the input statistics, fluency in generation and avoiding repetition of text.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`data points removed`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
Some examples have been removed from training dataset which satisfied the below criteria:
1. The examples in training dataset which overlapped with validation/test.
2. Some examples which described washed out games.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
The [research paper](https://aclanthology.org/P19-1195) is a good resource
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
Automatic evaluation measure can evaluate the factuality, content selection, content ordering and the fluency of the model output. The factuality, content selection and content ordering is measured using an Information Extraction based evaluation approach introduced by Wiseman et al (2017). The fluency is measured using BLEU.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
Wiseman et al. (2017) define three metrics induced from the outputs of an Information Extraction model which is run on the model/human-written game summaries . Let ŷ be the gold summary and y the model output.
• Relation Generation (RG) measures the precision and count of relations extracted from y that also appear in records r.
• Content Selection (CS) measures the precision and recall of relations extracted from y that are also extracted from ŷ.
• Content Ordering (CO) measures the complement of the normalized Damerau-Levenshtein distance (Brill and Moore, 2000) between the sequences of relations extracted from y and ŷ
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
We have reused the automatic metrics based on Information Extraction evaluation introduced by Wiseman et al (2017). For human evaluation, we conducted studies to evaluate the factuality, coherence, grammaticality and conciseness.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
The most relevant previous results for dataset are in the TACL 2021 paper on [Data-to-text Generation with Macro Planning](https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00381/101876/Data-to-text-Generation-with-Macro-Planning)
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
This dataset was curated to complement an existing data-to-text generation dataset (RotoWire by Wiseman et al. 2017) which focuses on long document generation. Compared to RotoWire , MLB summaries are longer (approximately by 50%) and the input records are richer and more structured (with the addition of play-by-play). Moreover, the MLB dataset is five times larger in terms of data size (i.e., pairs of tables and game summaries)
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
The goal is to study automatic generation of long documents in a data-to-text setting. The generated summaries should exhibit coherent ordering of content, be faithful to the input statistics, be fluent and avoid repetition of text.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Found`
#### Where was it found?
<!-- info: If found, where from? -->
<!-- scope: telescope -->
`Single website`
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
The game summaries are produced by professional writers.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
The language focuses on the sports domain.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Game summaries were tokenized using NLTK (Bird et al., 2009) and hyphenated words were separated. Sentences containing quotes were removed as they included opinions and non-factual statements unrelated to the input tables. Sometimes MLB summaries contain a "Game notes" section with incidental information which was also removed.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
The copyright remains with the original data creators and the usage permission is restricted to non-commercial uses.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
yes/very likely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`sensitive information`, `generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`research use only`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`research use only`
### Known Technical Limitations
|
true | https://github.com/hendrycks/ethics |
true | |
false |
# preprocessed version of rcds/wikipedia-persons-masked
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data 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:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Contains ~70k pages from wikipedia, each describing a person. For each page, the person described in the text
is masked with a <mask> token. The ground truth for every mask is provided.
Each row contains a part of a wiki page, specified by the size parameter which limits the maximum size in number of tokens per text chunk.
for each chunk the expected name for each mask is given.
### Supported Tasks and Leaderboards
The dataset supports the tasks of fill-mask, but can also be used for other tasks such as question answering,
e.g. "Who is <mask>?"
### Languages
*english only*
## Dataset Structure
In /data find different versions of the full dataset, with original and paraphrased versions as well as chunked to 4096 and 512 tokens.
Use the dataset like this:
```python
from datasets import load_dataset
dataset = load_dataset('rcds/wikipedia-persons-masked', split='train', type='original', size='512')
```
### Data Fields
Columns are:
- texts: the text chunks
- masks: the names for each of the masks in the chunks
### Data Splits
There are no splits, only a default train.
## Dataset Creation
Created by using the tokenizer from allenai/longformer-base-4096 for the 4096 token per chunk version,
and the xml-roberta-large tokenizer for the 512 token version. Chunks are split to fit those token sizes,
with the splits ensuring no words are split in half.
Possible improvements: Last chunk of a page might be much shorter, could join part of the previous one to have more tokens
in the last chunk.
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
TODO add citation
```
### Contributions
Thanks to [@skatinger](https://github.com/skatinger) for adding this dataset.
|
false |
# Dataset Card for Czech Restaurant
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [Czech restaurants homepage](https://github.com/UFAL-DSG/cs_restaurant_dataset)
- **Paper:** [Czech restaurants on Arxiv](https://arxiv.org/abs/1910.05298)
### Dataset Summary
This is a dataset for NLG in task-oriented spoken dialogue systems with Czech as the target language. It originated as a translation of the [English San Francisco Restaurants dataset](https://www.repository.cam.ac.uk/handle/1810/251304) by Wen et al. (2015). The domain is restaurant information in Prague, with random/fictional values. It includes input dialogue acts and the corresponding outputs in Czech.
### Supported Tasks and Leaderboards
- `other-intent-to-text`: The dataset can be used to train a model for data-to-text generation: from a desired dialogue act, the model must produce textual output that conveys this intention.
### Languages
The entire dataset is in Czech, translated from the English San Francisco dataset by professional translators.
## Dataset Structure
### Data Instances
Example of a data instance:
```
{
"da": "?request(area)",
"delex_da": "?request(area)",
"text": "Jakou lokalitu hledáte ?",
"delex_text": "Jakou lokalitu hledáte ?"
}
```
### Data Fields
- `da`: input dialogue act
- `delex_da`: input dialogue act, delexicalized
- `text`: output text
- `delex_text`: output text, delexicalized
### Data Splits
The order of the instances is random; the split is roughly 3:1:1 between train, development, and test, ensuring that the different sections don't share the same DAs (so the generators need to generalize to unseen DAs), but they share as many generic different DA types as possible (e.g., confirm, inform_only_match etc.). DA types that only have a single corresponding DA (e.g., bye()) are included in the training set.
The training, development, and test set contain 3569, 781, and 842 instances, respectively.
## Dataset Creation
### Curation Rationale
While most current neural NLG systems do not explicitly contain language-specific components and are thus capable of multilingual generation in principle, there has been little work to test these capabilities experimentally. This goes hand in hand with the scarcity of non-English training datasets for NLG – the only data-to-text NLG set known to us is a small sportscasting Korean dataset (Chenet al., 2010), which only contains a limited number of named entities, reducing the need for their inflection. Since most generators are only tested on English, they do not need to handle grammar complexities not present in English. A prime example is the delexicalization technique used by most current generators. We create a novel dataset for Czech delexicalized generation; this extends the typical task of data-to-text NLG by requiring attribute value inflection. We choose Czech as an example of a morphologically complex language with a large set of NLP tools readily available.
### Source Data
#### Initial Data Collection and Normalization
The original data was collected from the [English San Francisco Restaurants dataset](https://www.repository.cam.ac.uk/handle/1810/251304) by Wen et al. (2015).
#### Who are the source language producers?
The original data was produced in interactions between Amazon Mechanical Turk workers and themed around San Francisco restaurants. This data was then translated into Czech and localized to Prague restaurants by professional translators.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
This data does not contain personal information.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Ondřej Dušek, Filip Jurčíček, Josef Dvořák, Petra Grycová, Matěj Hejda, Jana Olivová, Michal Starý, Eva Štichová, Charles University. This work was funded by the Ministry of Education, Youth and Sports of the Czech Republic under the grant agreement LK11221 and core research funding, SVV project 260 333, and GAUK grant 2058214 of Charles University in Prague. It used language resources stored and distributed by the LINDAT/CLARIN project of the Ministry of Education, Youth and Sports of the Czech Republic (project LM2015071).
### Licensing Information
[Creative Commons 4.0 BY-SA](https://creativecommons.org/licenses/by-sa/4.0/)
### Citation Information
```
@article{DBLP:journals/corr/abs-1910-05298,
author = {Ondrej Dusek and
Filip Jurcicek},
title = {Neural Generation for Czech: Data and Baselines},
journal = {CoRR},
volume = {abs/1910.05298},
year = {2019},
url = {http://arxiv.org/abs/1910.05298},
archivePrefix = {arXiv},
eprint = {1910.05298},
timestamp = {Wed, 16 Oct 2019 16:25:53 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1910-05298.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@TevenLeScao](https://github.com/TevenLeScao) for adding this dataset. |
true |
# 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:** [Hippocorpus](https://msropendata.com/datasets/0a83fb6f-a759-4a17-aaa2-fbac84577318)
- **Repository:** [Hippocorpus](https://msropendata.com/datasets/0a83fb6f-a759-4a17-aaa2-fbac84577318)
- **Paper:** [Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models](http://erichorvitz.com/cognitive_studies_narrative.pdf)
- **Point of Contact:** [Eric Horvitz](mailto:horvitz@microsoft.com)
### Dataset Summary
To examine the cognitive processes of remembering and imagining and their traces in language, we introduce Hippocorpus, a dataset of 6,854 English diary-like short stories about recalled and imagined events. Using a crowdsourcing framework, we first collect recalled stories and summaries from workers, then provide these summaries to other workers who write imagined stories. Finally, months later, we collect a retold version of the recalled stories from a subset of recalled authors. Our dataset comes paired with author demographics (age, gender, race), their openness to experience, as well as some variables regarding the author's relationship to the event (e.g., how personal the event is, how often they tell its story, etc.).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset can be found in English
## Dataset Structure
[More Information Needed]
### Data Instances
[More Information Needed]
### Data Fields
This CSV file contains all the stories in Hippcorpus v2 (6854 stories)
These are the columns in the file:
- `AssignmentId`: Unique ID of this story
- `WorkTimeInSeconds`: Time in seconds that it took the worker to do the entire HIT (reading instructions, storywriting, questions)
- `WorkerId`: Unique ID of the worker (random string, not MTurk worker ID)
- `annotatorAge`: Lower limit of the age bucket of the worker. Buckets are: 18-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55+
- `annotatorGender`: Gender of the worker
- `annotatorRace`: Race/ethnicity of the worker
- `distracted`: How distracted were you while writing your story? (5-point Likert)
- `draining`: How taxing/draining was writing for you emotionally? (5-point Likert)
- `frequency`: How often do you think about or talk about this event? (5-point Likert)
- `importance`: How impactful, important, or personal is this story/this event to you? (5-point Likert)
- `logTimeSinceEvent`: Log of time (days) since the recalled event happened
- `mainEvent`: Short phrase describing the main event described
- `memType`: Type of story (recalled, imagined, retold)
- `mostSurprising`: Short phrase describing what the most surpring aspect of the story was
- `openness`: Continuous variable representing the openness to experience of the worker
- `recAgnPairId`: ID of the recalled story that corresponds to this retold story (null for imagined stories). Group on this variable to get the recalled-retold pairs.
- `recImgPairId`: ID of the recalled story that corresponds to this imagined story (null for retold stories). Group on this variable to get the recalled-imagined pairs.
- `similarity`: How similar to your life does this event/story feel to you? (5-point Likert)
- `similarityReason`: Free text annotation of similarity
- `story`: Story about the imagined or recalled event (15-25 sentences)
- `stressful`: How stressful was this writing task? (5-point Likert)
- `summary`: Summary of the events in the story (1-3 sentences)
- `timeSinceEvent`: Time (num. days) since the recalled event happened
### Data Splits
[More Information Needed]
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
[More Information Needed]
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
[More Information Needed]
### Dataset Curators
The dataset was initially created by Maarten Sap, Eric Horvitz, Yejin Choi, Noah A. Smith, James W. Pennebaker, during work done at Microsoft Research.
### Licensing Information
Hippocorpus is distributed under the [Open Use of Data Agreement v1.0](https://msropendata-web-api.azurewebsites.net/licenses/f1f352a6-243f-4905-8e00-389edbca9e83/view).
### Citation Information
```
@inproceedings{sap-etal-2020-recollection,
title = "Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models",
author = "Sap, Maarten and
Horvitz, Eric and
Choi, Yejin and
Smith, Noah A. and
Pennebaker, James",
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.178",
doi = "10.18653/v1/2020.acl-main.178",
pages = "1970--1978",
abstract = "We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release Hippocorpus, a dataset of 7,000 stories about imagined and recalled events. We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory (Tulving, 1972) for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events. Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly; Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language.",
}
```
### Contributions
Thanks to [@manandey](https://github.com/manandey) for adding this dataset. |
false |
# Dataset Card for hrenwac_para
## 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://nlp.ffzg.hr/resources/corpora/hrenwac/
- **Repository:** http://nlp.ffzg.hr/data/corpora/hrenwac/hrenwac.en-hr.txt.gz
- **Paper:** http://workshop2013.iwslt.org/downloads/IWSLT-2013-Cettolo.pdf
- **Leaderboard:**
- **Point of Contact:** [Nikola Ljubešič](mailto:nikola.ljubesic@ffzg.hr)
### Dataset Summary
The hrenWaC corpus version 2.0 consists of parallel Croatian-English texts crawled from the .hr top-level domain for Croatia. The corpus was built with Spidextor (https://github.com/abumatran/spidextor), a tool that glues together the output of SpiderLing used for crawling and Bitextor used for bitext extraction. The accuracy of the extracted bitext on the segment level is around 80% and on the word level around 84%.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Dataset is bilingual with Croatian and English languages.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Dataset is under the [CC-BY-SA 3.0](http://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```
@misc{11356/1058,
title = {Croatian-English parallel corpus {hrenWaC} 2.0},
author = {Ljube{\v s}i{\'c}, Nikola and Espl{\`a}-Gomis, Miquel and Ortiz Rojas, Sergio and Klubi{\v c}ka, Filip and Toral, Antonio},
url = {http://hdl.handle.net/11356/1058},
note = {Slovenian language resource repository {CLARIN}.{SI}},
copyright = {{CLARIN}.{SI} User Licence for Internet Corpora},
year = {2016} }
```
### Contributions
Thanks to [@IvanZidov](https://github.com/IvanZidov) for adding this dataset. |
true |
# Dataset Card for journalists_questions
## 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://qufaculty.qu.edu.qa/telsayed/datasets/
- **Repository:** [Needs More Information]
- **Paper:** https://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/download/13221/12856
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Maram Hasanain]
maram.hasanain@qu.edu.qa
### Dataset Summary
The journalists_questions dataset supports question identification over Arabic tweets of journalists.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Arabic
## Dataset Structure
### Data Instances
Our dataset supports question identification task. It includes 10K Arabic tweets crawled from journalists accounts. Tweets were labelled by crowdsourcing. Each tweet is associated with one label: question tweet or not. A question tweet is a tweet that has at least one interrogative question. Each label is associated with a number that represents the confidence in the label, given that each tweet was labelled by 3 annotators and an aggregation method was followed to choose the final label.
Below is an example:
{
'tweet_id': '493235142128074753',
'label': 'yes',
'label_confidence':0.6359
}
### Data Fields
tweet_id: the Twitter assigned ID for the tweet object.
label: annotation of the tweet by whether it is a question or not
label_confidence: confidence score for the label given annotations of multiple annotators per tweet
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
The dataset includes tweet IDs only due to Twitter content re-distribution policy. It was created and shared for research purposes for parties interested in understanding questions expecting answers by Arab journalists on Twitter.
### Source Data
#### Initial Data Collection and Normalization
To construct our dataset of question tweets posted by journalists, we first acquire a list of Twitter accounts of 389 Arab journalists. We use the Twitter API to crawl their available tweets, keeping only those that are identified by Twitter to be both Arabic, and not retweets (as these would contain content that was not originally authored by journalists). We apply a rule-based question filter to this dataset of 465,599 tweets, extracting 49,119 (10.6%) potential question tweets from 363 (93.3%) Arab journalists.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@MaramHasanain](https://github.com/MaramHasanain) for adding this dataset. |
false |
# Dataset Card for LiveQA
## 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:** [Github](https://github.com/PKU-TANGENT/LiveQA)
- **Repository:** [Github](https://github.com/PKU-TANGENT/LiveQA)
- **Paper:** [Liu et al., 2020](https://www.aclweb.org/anthology/2020.ccl-1.98.pdf)
- **Leaderboard:** N/A
- **Point of Contact:** Qianying Liu
### Dataset Summary
The LiveQA dataset is a Chinese question-answering resource constructed from playby-play live broadcasts. It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, which are collected from the Chinese Hupu website.
### Supported Tasks and Leaderboards
Question Answering.
[More Information Needed]
### Languages
Chinese.
## Dataset Structure
### Data Instances
Each instance represents a timeline (i.e., a game) with an identifier. The passages field comprise an array of text or question segments. In the following truncated example, user comments about the game is followed by a question about which team will be the first to reach 60 points.
```python
{
'id': 1,
'passages': [
{
"is_question": False,
"text": "'我希望两位球员都能做到！！",
"candidate1": "",
"candidate2": "",
"answer": "",
},
{
"is_question": False,
"text": "新年给我们送上精彩比赛！",
"candidate1": "",
"candidate2": "",
"answer": "",
},
{
"is_question": True,
"text": "先达到60分？",
"candidate1": "火箭",
"candidate2": "勇士",
"answer": "勇士",
},
{
"is_question": False,
"text": "自己急停跳投！！！",
"candidate1": "",
"candidate2": "",
"answer": "",
}
]
}
```
### Data Fields
- id: identifier for the game
- passages: collection of text/question segments
- text: real-time text comment or binary question related to the context
- candidate1/2: one of the two answer options to the question
- answer: correct answer to the question in text
### Data Splits
There is no predefined split in this dataset.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
This resource is developed by [Liu et al., 2020](https://www.aclweb.org/anthology/2020.ccl-1.98.pdf).
```
@inproceedings{qianying-etal-2020-liveqa,
title = "{L}ive{QA}: A Question Answering Dataset over Sports Live",
author = "Qianying, Liu and
Sicong, Jiang and
Yizhong, Wang and
Sujian, Li",
booktitle = "Proceedings of the 19th Chinese National Conference on Computational Linguistics",
month = oct,
year = "2020",
address = "Haikou, China",
publisher = "Chinese Information Processing Society of China",
url = "https://www.aclweb.org/anthology/2020.ccl-1.98",
pages = "1057--1067"
}
```
### Contributions
Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset. |
true |
# Dataset Card for #MeTooMA 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://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
- **Repository:** https://github.com/midas-research/MeTooMA
- **Paper:** https://ojs.aaai.org//index.php/ICWSM/article/view/7292
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
- The dataset consists of tweets belonging to #MeToo movement on Twitter, labelled into different categories.
- This dataset includes more data points and has more labels than any of the previous datasets that contain social media
posts about sexual abuse discloures. Please refer to the Related Datasets of the publication for a detailed information about this.
- Due to Twitters development policies, the authors provide only the tweet IDs and corresponding labels,
other data can be fetched via Twitter API.
- The data has been labelled by experts, with the majority taken into the account for deciding the final label.
- The authors provide these labels for each of the tweets.
- Relevance
- Directed Hate
- Generalized Hate
- Sarcasm
- Allegation
- Justification
- Refutation
- Support
- Oppose
- The definitions for each task/label is in the main publication.
- Please refer to the accompanying paper https://aaai.org/ojs/index.php/ICWSM/article/view/7292 for statistical analysis on the textual data
extracted from this dataset.
- The language of all the tweets in this dataset is English
- Time period: October 2018 - December 2018
- Suggested Use Cases of this dataset:
- Evaluating usage of linguistic acts such as: hate-spech and sarcasm in the incontext of public sexual abuse discloures.
- Extracting actionable insights and virtual dynamics of gender roles in sexual abuse revelations.
- Identifying how influential people were potrayed on public platform in the
events of mass social movements.
- Polarization analysis based on graph simulations of social nodes of users involved
in the #MeToo movement.
### Supported Tasks and Leaderboards
Multi Label and Multi-Class Classification
### Languages
English
## Dataset Structure
- The dataset is structured into CSV format with TweetID and accompanying labels.
- Train and Test sets are split into respective files.
### Data Instances
Tweet ID and the appropriate labels
### Data Fields
Tweet ID and appropriate labels (binary label applicable for a data point) and multiple labels for each Tweet ID
### Data Splits
- Train: 7979
- Test: 1996
## Dataset Creation
### Curation Rationale
- Twitter was the major source of all the public discloures of sexual abuse incidents during the #MeToo movement.
- People expressed their opinions over issues which were previously missing from the social media space.
- This provides an option to study the linguistic behaviours of social media users in an informal setting,
therefore the authors decide to curate this annotated dataset.
- The authors expect this dataset would be of great interest and use to both computational and socio-linguists.
- For computational linguists, it provides an opportunity to model three new complex dialogue acts (allegation, refutation, and justification) and also to study how these acts interact with some of the other linguistic components like stance, hate, and sarcasm. For socio-linguists, it provides an opportunity to explore how a movement manifests in social media.
### Source Data
- Source of all the data points in this dataset is Twitter social media platform.
#### Initial Data Collection and Normalization
- All the tweets are mined from Twitter with initial search paramters identified using keywords from the #MeToo movement.
- Redundant keywords were removed based on manual inspection.
- Public streaming APIs of Twitter were used for querying with the selected keywords.
- Based on text de-duplication and cosine similarity score, the set of tweets were pruned.
- Non english tweets were removed.
- The final set was labelled by experts with the majority label taken into the account for deciding the final label.
- Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292
#### Who are the source language producers?
Please refer to this paper for detailed information: https://ojs.aaai.org//index.php/ICWSM/article/view/7292
### Annotations
#### Annotation process
- The authors chose against crowd sourcing for labeling this dataset due to its highly sensitive nature.
- The annotators are domain experts having degress in advanced clinical psychology and gender studies.
- They were provided a guidelines document with instructions about each task and its definitions, labels and examples.
- They studied the document, worked a few examples to get used to this annotation task.
- They also provided feedback for improving the class definitions.
- The annotation process is not mutually exclusive, implying that presence of one label does not mean the
absence of the other one.
#### Who are the annotators?
- The annotators are domain experts having a degree in clinical psychology and gender studies.
- Please refer to the accompnaying paper for a detailed annotation process.
### Personal and Sensitive Information
- Considering Twitters policy for distribution of data, only Tweet ID and applicable labels are shared for the public use.
- It is highly encouraged to use this dataset for scientific purposes only.
- This dataset collection completely follows the Twitter mandated guidelines for distribution and usage.
## Considerations for Using the Data
### Social Impact of Dataset
- The authors of this dataset do not intend to conduct a population centric analysis of #MeToo movement on Twitter.
- The authors acknowledge that findings from this dataset cannot be used as-is for any direct social intervention, these
should be used to assist already existing human intervention tools and therapies.
- Enough care has been taken to ensure that this work comes of as trying to target a specific person for their
personal stance of issues pertaining to the #MeToo movement.
- The authors of this work do not aim to vilify anyone accused in the #MeToo movement in any manner.
- Please refer to the ethics and discussion section of the mentioned publication for appropriate sharing of this dataset
and social impact of this work.
### Discussion of Biases
- The #MeToo movement acted as a catalyst for implementing social policy changes to benefit the members of
community affected by sexual abuse.
- Any work undertaken on this dataset should aim to minimize the bias against minority groups which
might amplified in cases of sudden outburst of public reactions over sensitive social media discussions.
### Other Known Limitations
- Considering privacy concerns, social media practitioners should be aware of making automated interventions
to aid the victims of sexual abuse as some people might not prefer to disclose their notions.
- Concerned social media users might also repeal their social information, if they found out that their
information is being used for computational purposes, hence it is important seek subtle individual consent
before trying to profile authors involved in online discussions to uphold personal privacy.
## Additional Information
Please refer to this link: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/JN4EYU
### Dataset Curators
- If you use the corpus in a product or application, then please credit the authors
and [Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi]
(http://midas.iiitd.edu.in) appropriately.
Also, if you send us an email, we will be thrilled to know about how you have used the corpus.
- If interested in commercial use of the corpus, send email to midas@iiitd.ac.in.
- Multimodal Digital Media Analysis Lab - Indraprastha Institute of Information Technology, New Delhi, India
disclaims any responsibility for the use of the corpus and does not provide technical support.
However, the contact listed above will be happy to respond to queries and clarifications
- Please feel free to send us an email:
- with feedback regarding the corpus.
- with information on how you have used the corpus.
- if interested in having us analyze your social media data.
- if interested in a collaborative research project.
### Licensing Information
[More Information Needed]
### Citation Information
Please cite the following publication if you make use of the dataset: https://ojs.aaai.org/index.php/ICWSM/article/view/7292
```
@article{Gautam_Mathur_Gosangi_Mahata_Sawhney_Shah_2020, title={#MeTooMA: Multi-Aspect Annotations of Tweets Related to the MeToo Movement}, volume={14}, url={https://aaai.org/ojs/index.php/ICWSM/article/view/7292}, abstractNote={<p>In this paper, we present a dataset containing 9,973 tweets related to the MeToo movement that were manually annotated for five different linguistic aspects: relevance, stance, hate speech, sarcasm, and dialogue acts. We present a detailed account of the data collection and annotation processes. The annotations have a very high inter-annotator agreement (0.79 to 0.93 k-alpha) due to the domain expertise of the annotators and clear annotation instructions. We analyze the data in terms of geographical distribution, label correlations, and keywords. Lastly, we present some potential use cases of this dataset. We expect this dataset would be of great interest to psycholinguists, socio-linguists, and computational linguists to study the discursive space of digitally mobilized social movements on sensitive issues like sexual harassment.</p&gt;}, number={1}, journal={Proceedings of the International AAAI Conference on Web and Social Media}, author={Gautam, Akash and Mathur, Puneet and Gosangi, Rakesh and Mahata, Debanjan and Sawhney, Ramit and Shah, Rajiv Ratn}, year={2020}, month={May}, pages={209-216} }
```
### Contributions
Thanks to [@akash418](https://github.com/akash418) for adding this dataset. |
false |
# 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:** [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](https://msropendata.com/datasets/80b4f6e8-5d7c-4abc-9c79-2e51dfedd791)
- **Repository:** [NCI-PID-PubMed Genomics Knowledge Base Completion Dataset](NCI-PID-PubMed Genomics Knowledge Base Completion Dataset)
- **Paper:** [Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text](https://www.aclweb.org/anthology/P16-1136/)
- **Point of Contact:** [Kristina Toutanova](mailto:kristout@google.com)
### Dataset Summary
The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome (Poon et al. 2014). This dataset was used in the paper “Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text” (Toutanova, Lin, Yih, Poon, and Quirk, 2016). More details can be found in the included README.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
NCI-PID-PubMed Genomics Knowledge Base Completion Dataset
This dataset includes a database of regulation relationships among genes and corresponding textual mentions of pairs of genes in PubMed article abstracts.
The database is derived from the NCI PID Pathway Interaction Database, and the textual mentions are extracted from cooccurring pairs of genes in PubMed abstracts, processed and annotated by Literome. This dataset was used in the paper "Compositional Learning of Embeddings for Relation Paths in Knowledge Bases and Text".
FILE FORMAT DETAILS
The files train.txt, valid.txt, and test.text contain the training, development, and test set knowledge base (database of regulation relationships) triples used in.
The file text.txt contains the textual triples derived from PubMed via entity linking and processing with Literome. The textual mentions were used for knowledge base completion in.
The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set).
The format is:
| GENE1 | relation | GENE2 |
Example:
ABL1 Positive_regulation CDK2
The separator is a tab character; the relations are Positive_regulation, Negative_regulation, and Family (Family relationships occur only in the training set).
### Data Instances
[More Information Needed]
### Data Fields
The format is:
| GENE1 | relation | GENE2 |
### Data Splits
[More Information Needed]
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
[More Information Needed]
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
[More Information Needed]
### Dataset Curators
The dataset was initially created by Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon and Chris Quirk, during work done at Microsoft Research.
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{toutanova-etal-2016-compositional,
title = "Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text",
author = "Toutanova, Kristina and
Lin, Victoria and
Yih, Wen-tau and
Poon, Hoifung and
Quirk, Chris",
booktitle = "Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2016",
address = "Berlin, Germany",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/P16-1136",
doi = "10.18653/v1/P16-1136",
pages = "1434--1444",
}
```
### Contributions
Thanks to [@manandey](https://github.com/manandey) for adding this dataset. |
false |
# Dataset Card for [opus_elhuyar]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[Opus Elhuyar](http://opus.nlpl.eu/Elhuyar.php)
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Dataset provided by the foundation Elhuyar (http://webcorpusak.elhuyar.eus/sarrera_paraleloa.html) and submitted to OPUS by Joseba Garcia Beaumont
### Supported Tasks and Leaderboards
The underlying task is machine translation from Spanish to Basque
### Languages
Spanish to Basque
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
J. Tiedemann, 2012, Parallel Data, Tools and Interfaces in OPUS. In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC 2012)
### Contributions
Thanks to [@spatil6](https://github.com/spatil6) for adding this dataset. |
false |
# Dataset Card for Polish Summaries Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://zil.ipipan.waw.pl/PolishSummariesCorpus
- **Repository:** http://zil.ipipan.waw.pl/PolishSummariesCorpus
- **Paper:** http://nlp.ipipan.waw.pl/Bib/ogro:kop:14:lrec.pdf
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Mateusz Kopeć](http://zil.ipipan.waw.pl/MateuszKopec)
### Dataset Summary
The Corpus contains a large number of manual summaries of news articles,
with many independently created summaries for a single text. Such approach is supposed to overcome the annotator bias, which is often described as a problem during the evaluation of the summarization algorithms against a single gold standard.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Polish
## Dataset Structure
### Data Instances
See below an example from the dataset. Detailed descriptions of the fields are provided in the following section.
```
{'authors': 'Krystyna Forowicz',
'body': "ROZMOWA\n\nProf. Krzysztof Ernst, kierownik Zakładu Optyki Instytutu Fizyki Doświadczalnej Uniwersytetu Warszawskiego\n\nLidarowe oczy\n\nRYS. MAREK KONECKI\n\nJutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.\n\nCzy to kosztowne urządzenie będzie służyło tylko naukowcom?\n\nTego typu lidar jest rzeczywiście drogi, kosztuje około miliona marek niemieckich. Jest to najnowsza generacja tego typu lidarów. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem, staramy się m.in. rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Nad lidarem pracują specjaliści od laserów i od komputerów. Współpracujemy z doskonałym laboratorium prof. Ludgera Wöste z Freie Universitat Berlin rozwijającym m.in. problematykę lidarową. Pakiet software'u wzbogacamy o nowe algorytmy, które potrafią lepiej i dokładniej rozszyfrowywać sygnał lidarowy, a w konsekwencji skażenia. Żeby przetworzyć tzw. sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać rozsądne dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji. \n\nBadania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Zasadniczy koszt jego budowy pokryła uzyskana od Fundacji dotacja. Część pieniędzy przekazał też Narodowy Fundusz Ochrony Środowiska i Gospodarki Wodnej oraz Komitet Badań Naukowych.\n\nCzy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?\n\nNie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze łącznie z dostarczeniem informacji o ich rozkładzie. Ale np. obecnie prowadzimy badania mające na celu rozszerzenie możliwości lidaru o taką substancję jak fosgen. Tym szkodliwym gazem może być skażone powietrze w miastach, w których zlokalizowane są zakłady chemiczne, np. w Bydgoszczy pewne ilości fosgenu emitują Zakłady Chemiczne Organika- Zachem. \n\nLidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć. Cząsteczki, które wykrywamy mają pasma absorbcji w bliskim nadfiolecie. Możemy np. badać zawartość ozonu w troposferze. Okazuje się bowiem, że o ile brak tego gazu w wysokich warstwach atmosfery powoduje groźny efekt cieplarniany, to jego nadmiar tuż nad Ziemią jest szkodliwy. Groźne są też substancje gazowe, jak np. tlenki azotu, będące następstwem spalin samochodowych. A samochodów przybywa.\n\nCzy stać nas będzie na prowadzenie pomiarów ozonu w miastach? \n\nKoszt jednego dnia kampanii pomiarowej firmy zachodnie szacują na kilka tysięcy DM. Potrzebne są pieniądze na utrzymanie lidaru, na prowadzenie badań. Nasze przedsięwzięcie nie ma charakteru komercyjnego. Koszt pomiarów będzie znacznie niższy. Chcemy np. mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta. Chcielibyśmy rozwinąć tutaj współpracę z państwowymi i wojewódzkimi służbami ochrony środowiska. Tego typu badania były prowadzone np. w Lyonie. Okazało się, że najwięcej tlenków azotu występuje niekoniecznie tam gdzie są one produkowane, to znaczy nie przy najruchliwszych ulicach, jeśli są one dobrze wentylowane a gromadzą się one w małych uliczkach. Przede wszystkim jednak do końca tego roku zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu trzech granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie. Prowadziliśmy pomiary w samym Turowie, gdzie elektrownia Turoszowska jest głównym źródłem emisji. W planie mamy Bogatynię, zagłębie miedziowe. \n\nW Czarnym Trójkącie istnieje wiele stacjonarnych stacji monitoringowych.\n\nNasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów. Możemy zatem śledzić ewolucję rozprzestrzeniania się tych zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. Wyniki naszych pomiarów porównujemy z danymi uzyskanymi ze stacji monitoringowych. \n\nJak wypadł Czarny Trójkąt?\n\nKiedy występowaliśmy o finansowanie tego projektu do Fundacji Współpracy Polsko-Niemieckiej zanieczyszczenie powietrza w Czarnym Trójkącie było dużo większe niż obecnie i wszystko wskazuje na to, że będzie dalej spadać. Obecnie stężenie dwutlenku siarki jest na granicy naszych możliwości pomiarowych. Dla regionu Turoszowskiego to dobra wiadomość i dla stosunków polsko-niemieckich też.\n\nTypów lidarów jest wiele \n\nTen lidar pracuje w obszarze bliskiego nadfioletu i promieniowania widzialnego, które jest wynikiem wykorzystania drugiej lub trzeciej harmonicznej lasera szafirowego, pracującego na granicy czerwieni i podczerwieni. DIAL jest tym typem lidara, który dzisiaj ma zdecydowanie największe wzięcie w ochronie środowiska. Z lidarów korzysta meteorologia. W Stanach Zjednoczonych lidary umieszcza się na satelitach (program NASA). Określają na przestrzeni kilkudziesięciu kilometrów rozkłady temperatury, wilgotności, ciśnienia, a także prędkości wiatru. Wykrywają pojawianie się huraganów, a nawet mogą określać rozmiary oka tajfunu.\n\nIle takich urządzeń jest w Europie?\n\n- W Europie takich lidarów jak nasz jest zaledwie kilka. Większość z nich mierzy ozon, dwutlenek siarki i tlenek azotu. Wykrywanie toluenu i benzenu jest oryginalnym rozwiązaniem. Długość fali dla benzenu jest już na skraju możliwości widmowych. Nasz lidar typu DIAL jest najnowocześniejszym w Polsce. Ponadto jest lidarem ruchomym, zainstalowanym na samochodzie. Ale historia lidarów w naszym kraju jest dłuższa i zaczęła się na początku lat 60. Pierwsze próby prowadzone były w stacji geofizycznej PAN w Belsku, niedługo po skonstruowaniu pierwszego w świecie lasera rubinowego. Potem powstał lidar stacjonarny, również typu DIAL, w Gdańsku, a w Krakowie sodary - urządzenia oparte na falach akustycznych, wygodne np. do pomiarów szybkości wiatru. Lidar umieszczony na samochodzie i zbudowany w latach 80 na Politechnice Poznańskiej w perspektywie miał być lidarem typu DIAL.\n\nFizycy dotychczas nie zajmowali się ochroną środowiska?\n\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji (zdjęć satelitarnych) Instytutu Geofizyki i, co bardzo ważne, współpraca z Freie Universität Berlin. Mamy również na UW Międzywydziałowe Studia Ochrony Środowiska i studentom przekazujemy informacje o lidarze i fizycznych metodach badania środowiska. Nasze działania dydaktyczne bardzo efektywnie wspiera NFOŚ.\n\nRozmawiała Krystyna Forowicz",
'date': '1997-04-21',
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'body': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.Czy to kosztowne urządzenie będzie służyło tylko naukowcom? Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad tym urządzeniem. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.Czy to kosztowne urządzenie będzie służyło tylko naukowcom? Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad tym urządzeniem. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Czy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?Nie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze łącznie z dostarczeniem informacji o ich rozkładzie. Możemy np. badać zawartość ozonu w troposferze. W Europie takich lidarów jak nasz jest zaledwie kilka. Większość z nich mierzy ozon, dwutlenek siarki i tlenek azotu. Fizycy dotychczas nie zajmowali się ochroną środowiska?Taka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji Instytutu Geofizyki i współpraca z Freie Universität Berlin.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. lidar Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, naukową I dydaktyczną. Żeby przetworzyć sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji. muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. lidar Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym. Jest to najnowsza generacja tego typu lidarów. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska. Żeby przetworzyć tzw. sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać rozsądne dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Zasadniczy koszt jego budowy pokryła uzyskana od Fundacji dotacja. Część pieniędzy przekazał też Narodowy Fundusz Ochrony Środowiska i Gospodarki Wodnej oraz Komitet Badań Naukowych. Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. lidar Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, naukową I dydaktyczną.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.Tego typu lidar jest drogi, kosztuje około miliona marek niemieckich. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem, staramy się m.in. rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć. Cząsteczki, które wykrywamy mają pasma absorbcji w bliskim nadfiolecie.Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów. Możemy zatem śledzić ewolucję rozprzestrzeniania się tych zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. Wyniki naszych pomiarów porównujemy z danymi uzyskanymi ze stacji monitoringowych.',
'Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową i dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.\nto najnowsza generacja tego typu lidarów. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. korzyść mamy potrójną: użyteczną, przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad urządzeniem I dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.\nNasze przedsięwzięcie nie ma charakteru komercyjnego. Chcemy np. mierzyć w Warszawie rozkłady koncentracji tlenków azotu. Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie.',
'Jutro odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.\n\nto kosztowne urządzenie będzie służyło tylko naukowcom?\n\nlidar jest rzeczywiście drogi. to najnowsza generacja tego typu lidarów. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. korzyść mamy potrójną: użyteczną, przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad tym urządzeniem I dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.\n\nCzy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?\n\nNie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze. Ale prowadzimy badania mające na celu rozszerzenie możliwości lidaru o taką substancję jak fosgen.\n\nstać nas będzie na prowadzenie pomiarów ozonu w miastach? \n\nNasze przedsięwzięcie nie ma charakteru komercyjnego. Chcemy np. mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta. Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie. zanieczyszczenie było dużo większe niż obecnie i wszystko wskazuje na to, że będzie dalej spadać.\nDIAL dzisiaj ma zdecydowanie największe wzięcie w ochronie środowiska. \n\nFizycy dotychczas nie zajmowali się ochroną środowiska?\n\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu.',
'Co to jest lidar? \n\nPROF. KRZYSZTOF ERNST: urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.\nto najnowsza generacja tego typu lidarów. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. korzyść mamy potrójną: użyteczną, wykonujemy pomiary skażeń atmosferycznych, naukową - rozwijamy badania nad urządzeniem I dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. staramy się rozszerzyć jego zastosowanie na inne substancje występujące w atmosferze. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej. zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Nasz lidar ma większe możliwości niż stacje monitoringowe. Możemy śledzić ewolucję rozprzestrzeniania się zanieczyszczeń, ich kierunek i zmiany. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji Instytutu Geofizyki i współpraca z Freie Universität Berlin.',
"Co to jest lidar? \nPROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. DIAL - lidar absorbcji różnicowej potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. staramy się rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze. Pakiet software'u wzbogacamy o nowe algorytmy, które potrafią dokładniej rozszyfrowywać sygnał lidarowy, a w konsekwencji skażenia. Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej. \n\nChcemy mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta. zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Nasz lidar ma większe możliwości niż stacje monitoringowe. Możemy śledzić ewolucję rozprzestrzeniania się zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. \n\nDIAL jest tym typem lidara, który dzisiaj ma największe wzięcie w ochronie środowiska. Z lidarów korzysta meteorologia. W Europie takich lidarów jak nasz jest zaledwie kilka. Nasz lidar jest najnowocześniejszym w Polsce. Ponadto jest lidarem ruchomym, zainstalowanym na samochodzie. \n\nFizycy dotychczas nie zajmowali się ochroną środowiska?\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji Instytutu Geofizyki i współpraca z Freie Universität Berlin.",
'Co to jest lidar? \nPROF. KRZYSZTOF ERNST: to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką. Nasz lidar ma większe możliwości niż stacje monitoringowe. Możemy śledzić ewolucję rozprzestrzeniania się zanieczyszczeń, ich kierunek i zmiany.'],
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'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.',
'Czy to kosztowne urządzenie będzie służyło tylko naukowcom?',
'Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad tym urządzeniem',
'.',
'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą.'],
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'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.',
'Czy to kosztowne urządzenie będzie służyło tylko naukowcom?',
'Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad tym urządzeniem',
'.',
'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą.',
'Czy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?',
'Nie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze łącznie z dostarczeniem informacji o ich rozkładzie.',
'Możemy np. badać zawartość ozonu w troposferze.',
'W Europie takich lidarów jak nasz jest zaledwie kilka. Większość z nich mierzy ozon, dwutlenek siarki i tlenek azotu.',
'',
'Fizycy dotychczas nie zajmowali się ochroną środowiska?',
'Taka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji',
'Instytutu Geofizyki i',
'współpraca z Freie Universität Berlin.'],
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'',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał',
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'span_text': ['Jutro',
'odbędzie sie pokaz nowego polskiego lidara typu DIAL.',
'lidar',
'Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną,',
'naukową',
'I',
'dydaktyczną',
'.',
'Żeby przetworzyć',
'sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać',
'dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji.',
'muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.'],
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'span_text': ['Jutro',
'odbędzie sie pokaz nowego polskiego lidara typu DIAL.',
'lidar',
'Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.',
'Jest to najnowsza generacja tego typu lidarów. DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem',
'. I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Żeby przetworzyć tzw. sygnał lidarowy, czyli to co wraca po rozproszeniu światła do układu, i otrzymać rozsądne dane dotyczące rozkładu koncentracji - trzeba dokonać skomplikowanych operacji.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej, dzięki której ten lidar u nas zaistniał i dla której w ramach naszych zobowiązań wykonujemy pomiary zanieczyszczeń nad naszą wspólną granicą. Zasadniczy koszt jego budowy pokryła uzyskana od Fundacji dotacja. Część pieniędzy przekazał też Narodowy Fundusz Ochrony Środowiska i Gospodarki Wodnej oraz Komitet Badań Naukowych.',
'',
'Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć.'],
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'span_text': ['Jutro',
'odbędzie sie pokaz nowego polskiego lidara typu DIAL.',
'lidar',
'Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną,',
'naukową',
'I',
'dydaktyczną',
'.'],
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'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.',
'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem',
'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów.'],
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'span_text': ['Jutro w Instytucie odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi. Nazywane też jest radarem laserowym.',
'Tego typu lidar jest',
'drogi, kosztuje około miliona marek niemieckich.',
'DIAL - lidar absorbcji różnicowej jest urządzeniem inteligentnym, to znaczy potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen. Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową - rozwijamy badania nad tym urządzeniem, staramy się m.in. rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze.',
'I korzyść dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Lidar typu DIAL jest oparty na pomiarze absorbcji różnicowej, czyli muszą być zastosowane dwie wiązki laserowe o dwóch różnych długościach fali, z których jedna jest absorbowana, a druga nie jest absorbowana przez substancję, którą chcemy wykryć. Cząsteczki, które wykrywamy mają pasma absorbcji w bliskim nadfiolecie.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe. Mierzy zanieczyszczenia nie tylko lokalnie, ale też ich rozkład w przestrzeni, z wysoką rozdzielczością przestrzenną i na odległość kilku kilometrów. Możemy zatem śledzić ewolucję rozprzestrzeniania się tych zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi. Wyniki naszych pomiarów porównujemy z danymi uzyskanymi ze stacji monitoringowych.'],
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'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'',
'Z lidara korzyść mamy potrójną: użyteczną, bo przy jego pomocy wykonujemy pomiary skażeń atmosferycznych, korzyść naukową',
'i',
'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.'],
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'span_text': ['Jutro',
'odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF',
'ERNST:',
'urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'',
'to najnowsza generacja tego typu lidarów.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'korzyść mamy potrójną: użyteczną,',
'przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad',
'urządzeniem',
'I',
'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'',
'Nasze przedsięwzięcie nie ma charakteru komercyjnego.',
'Chcemy np. mierzyć w Warszawie rozkłady',
'koncentracji tlenków azotu',
'.',
'Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu',
'granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie.'],
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'span_text': ['Jutro',
'odbędzie sie pokaz nowego polskiego lidara typu DIAL. Co to jest lidar? \n\nPROF. KRZYSZTOF',
'ERNST:',
'urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'',
'to kosztowne urządzenie będzie służyło tylko naukowcom?',
'lidar jest rzeczywiście drogi',
'.',
'to najnowsza generacja tego typu lidarów.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'korzyść mamy potrójną: użyteczną,',
'przy jego pomocy wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad tym urządzeniem',
'I',
'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.',
'Czy wszystkie zanieczyszczenia będzie można wykryć za pomocą lidaru?\n\nNie ma takiego jednostkowego urządzenia, które by wykrywało i mierzyło wszystkie szkodliwe gazy w atmosferze',
'. Ale',
'prowadzimy badania mające na celu rozszerzenie możliwości lidaru o taką substancję jak fosgen.',
'',
'stać nas będzie na prowadzenie pomiarów ozonu w miastach?',
'Nasze przedsięwzięcie nie ma charakteru komercyjnego.',
'Chcemy np. mierzyć w Warszawie rozkłady',
'koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta.',
'Koncentrujemy się głównie na Czarnym Trójkącie - obszarze u zbiegu',
'granic: Polski, Niemiec i Czech, do niedawna uważanym za najbardziej zdegradowany region w Europie.',
'zanieczyszczenie',
'było dużo większe niż obecnie i wszystko wskazuje na to, że będzie dalej spadać.',
'',
'DIAL',
'dzisiaj ma zdecydowanie największe wzięcie w ochronie środowiska.',
'Fizycy dotychczas nie zajmowali się ochroną środowiska?\n\nTaka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu.'],
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'span_text': ['Co to jest lidar? \n\nPROF. KRZYSZTOF',
'ERNST:',
'urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'',
'to najnowsza generacja tego typu lidarów.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'korzyść mamy potrójną: użyteczną,',
'',
'wykonujemy pomiary skażeń atmosferycznych,',
'naukową - rozwijamy badania nad',
'urządzeniem',
'I',
'dydaktyczną - szkolimy studentów zainteresowanych ochroną środowiska.'],
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'span_text': ['Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'staramy się',
'rozszerzyć jego zastosowanie',
'na inne substancje występujące w atmosferze.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej',
'.',
'zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe.',
'Możemy',
'śledzić ewolucję rozprzestrzeniania się',
'zanieczyszczeń, ich kierunek i zmiany',
'.',
'Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji',
'Instytutu Geofizyki i',
'współpraca z Freie Universität Berlin.'],
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'span_text': ['Co to jest lidar?',
'PROF. KRZYSZTOF ERNST: Jest to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'DIAL - lidar absorbcji różnicowej',
'potrafi rozróżnić, co mierzy. Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'staramy się',
'rozszerzyć jego zastosowanie także na inne substancje występujące w atmosferze.',
"Pakiet software'u",
'wzbogacamy o nowe algorytmy, które potrafią',
'dokładniej rozszyfrowywać sygnał lidarowy, a w konsekwencji skażenia.',
'Badania, które prowadzimy, są zainicjowane i finansowane przez Fundację Współpracy Polsko-Niemieckiej',
'.',
'',
'',
'Chcemy',
'mierzyć w Warszawie rozkłady koncentracji tlenków azotu, ich ewolucję czasową nad różnymi arteriami miasta.',
'zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe.',
'Możemy',
'śledzić ewolucję rozprzestrzeniania się',
'zanieczyszczeń, ich kierunek i zmiany spowodowane m.in. warunkami atmosferycznymi.',
'',
'',
'DIAL jest tym typem lidara, który dzisiaj ma',
'największe wzięcie w ochronie środowiska. Z lidarów korzysta meteorologia.',
'W Europie takich lidarów jak nasz jest zaledwie kilka.',
'Nasz lidar',
'jest najnowocześniejszym w Polsce. Ponadto jest lidarem ruchomym, zainstalowanym na samochodzie.',
'Fizycy dotychczas nie zajmowali się ochroną środowiska?',
'Taka specjalizacja powstala na Wydziale Fizyki UW dwa lata temu. Gwarancją sukcesu naszego programu dydaktyczno-badawczego jest udział w nim zakładów należących do Instytutu Fizyki Doświadczalnej UW, Pracowni Przetwarzania Informacji',
'Instytutu Geofizyki i',
'współpraca z Freie Universität Berlin.'],
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7305,
7344]},
{'end': [245, 271, 360, 761, 4159, 4614, 4772, 4818, 4860, 4905],
'span_text': ['Co to jest lidar?',
'PROF. KRZYSZTOF ERNST:',
'to urządzenie pozwalające wyznaczać zanieczyszczenia atmosfery metodami optycznymi.',
'Wykrywa ozon, dwutlenek siarki, tlenki azotu, benzen, toluen.',
'zamierzamy zakończyć pomiary skażeń atmosferycznych nad granicą polsko-niemiecką.',
'Nasz lidar ma większe możliwości niż stacje monitoringowe.',
'Możemy',
'śledzić ewolucję rozprzestrzeniania się',
'zanieczyszczeń, ich kierunek i zmiany',
'.'],
'start': [227, 246, 276, 699, 4076, 4555, 4765, 4778, 4823, 4904]}],
'type': ['extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract',
'extract']},
'title': 'Lidarowe oczy'}
```
### Data Fields
- `id`: a `string` example identifier
- `date`: date of the original article (`string`)
- `title`: title of the original article (`string`)
- `section`: the section of the newspaper the original article belonged to (`string`)
- `authors`: original article authors (`string`)
- `body`: original article body (list of `string`s)
- `summaries`: a dictionary feature containing summaries of the original article with the following attributes:
- `ratio`: ratio of summary - percentage of the original article (list of `int32`s)
- `type`: type of summary - extractive (`extract`) or abstractive (`abstract`) (list of `string`s)
- `author`: acronym of summary author (list of `string`)
- `body`: body of summary (list of `string`)
- `spans`: a list containing spans for extractive summaries (empty for abstractive summaries):
- `start`: start of span (`int32`)
- `end`: end of span (`int32`)
- `span_text`: span text (`string`)
### Data Splits
Single train split
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@inproceedings{
ogro:kop:14:lrec,
author = "Ogrodniczuk, Maciej and Kopeć, Mateusz",
pdf = "http://nlp.ipipan.waw.pl/Bib/ogro:kop:14:lrec.pdf",
title = "The {P}olish {S}ummaries {C}orpus",
pages = "3712--3715",
crossref = "lrec:14"
}
@proceedings{
lrec:14,
editor = "Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Loftsson, Hrafn and Maegaard, Bente and Mariani, Joseph and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios",
isbn = "978-2-9517408-8-4",
title = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014",
url = "http://www.lrec-conf.org/proceedings/lrec2014/index.html",
booktitle = "Proceedings of the Ninth International {C}onference on {L}anguage {R}esources and {E}valuation, {LREC}~2014",
address = "Reykjavík, Iceland",
key = "LREC",
year = "2014",
organization = "European Language Resources Association (ELRA)"
}
```
### Contributions
Thanks to [@kldarek](https://github.com/kldarek) for adding this dataset. |
false | # Dataset Card for [py_ast]
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **homepage**: [py150](https://www.sri.inf.ethz.ch/py150)
- **Paper**: [Probabilistic Model for Code with Decision Trees](https://www.semanticscholar.org/paper/Probabilistic-model-for-code-with-decision-trees-Raychev-Bielik/62e176977d439aac2e2d7eca834a7a99016dfcaf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The dataset consists of parsed ASTs that were used to train and evaluate the DeepSyn tool.
The Python programs are collected from GitHub repositories
by removing duplicate files, removing project forks (copy of another existing repository),
keeping only programs that parse and have at most 30'000 nodes in the AST and
we aim to remove obfuscated files
### Supported Tasks and Leaderboards
Code Representation, Unsupervised Learning
### Languages
Python
## Dataset Structure
### Data Instances
A typical datapoint contains an AST of a python program, parsed.
The main key is `ast` wherein every program's AST is stored.
Each children would have,
`type` which will formulate the type of the node.
`children` which enumerates if a given node has children(non-empty list).
`value`, if the given node has any hardcoded value(else "N/A").
An example would be,
'''
[ {"type":"Module","children":[1,4]},{"type":"Assign","children":[2,3]},{"type":"NameStore","value":"x"},{"type":"Num","value":"7"}, {"type":"Print","children":[5]}, {"type":"BinOpAdd","children":[6,7]}, {"type":"NameLoad","value":"x"}, {"type":"Num","value":"1"} ]
'''
### Data Fields
- `ast`: a list of dictionaries, wherein every dictionary is a node in the Abstract Syntax Tree.
- `type`: explains the type of the node.
- `children`: list of nodes which are children under the given
- `value`: hardcoded value, if the node holds an hardcoded value.
### Data Splits
The data is split into a training and test set.
The final split sizes are as follows:
| | train | validation |
|------------------|--------:|------------:|
| py_ast examples | 100000 | 50000 |
## Dataset Creation
[More Information Needed]
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Raychev, V., Bielik, P., and Vechev, M
### Licensing Information
MIT, BSD and Apache
### Citation Information
@InProceedings{OOPSLA ’16, ACM,
title = {Probabilistic Model for Code with Decision Trees.},
authors={Raychev, V., Bielik, P., and Vechev, M.},
year={2016}
}
```
@inproceedings{10.1145/2983990.2984041,
author = {Raychev, Veselin and Bielik, Pavol and Vechev, Martin},
title = {Probabilistic Model for Code with Decision Trees},
year = {2016},
isbn = {9781450344449},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2983990.2984041},
doi = {10.1145/2983990.2984041},
booktitle = {Proceedings of the 2016 ACM SIGPLAN International Conference on Object-Oriented Programming, Systems, Languages, and Applications},
pages = {731–747},
numpages = {17},
keywords = {Code Completion, Decision Trees, Probabilistic Models of Code},
location = {Amsterdam, Netherlands},
series = {OOPSLA 2016}
}
```
### Contributions
Thanks to [@reshinthadithyan](https://github.com/reshinthadithyan) for adding this dataset. |
false |
# 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:**[sanskrit_classic](https://github.com/parmarsuraj99/hf_datasets/tree/master/sanskrit_classic)
- **Repository:**[GitHub](https://github.com/parmarsuraj99/hf_datasets/tree/master/sanskrit_classic)
- **Paper:**N/A
- **Leaderboard:**N/A
- **Point of Contact:**[parmarsuraj99](parmarsuraj99@gmail.com)
### Dataset Summary
A collection of classical sanskrit texts
### Supported Tasks and Leaderboards
Language modeling
### Languages
Sanskrit
## Dataset Structure
### Data Instances
{'text': 'मा कर्मफलहेतुर्भूर्मा ते सङ्गोऽस्त्वकर्मणि॥'}
### Data Fields
`text`: a line
### Data Splits
| | Train |
|-------------------|--------|
| n_instances | 342033 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@Misc{johnsonetal2014,
author = {Johnson, Kyle P. and Patrick Burns and John Stewart and Todd Cook},
title = {CLTK: The Classical Language Toolkit},
url = {https://github.com/cltk/cltk},
year = {2014--2020},
}
```
### Contributions
Thanks to [@parmarsuraj99](https://github.com/parmarsuraj99) for adding this dataset. |
false |
# Dataset Card for "saudinewsnet"
## 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:** [SaudiNewsNet](https://github.com/parallelfold/SaudiNewsNet)
- **Repository:** [Website](https://github.com/parallelfold/SaudiNewsNet)
- **Paper:** [More Information Needed]
- **Point of Contact:** [Mazen Abdulaziz](mailto:mazen.abdulaziz@gmail.com)
- **Size of downloaded dataset files:** 29.01 MB
- **Size of the generated dataset:** 103.65 MB
- **Total amount of disk used:** 132.67 MB
### Dataset Summary
The dataset contains a set of 31,030 Arabic newspaper articles alongwith metadata, extracted from various online Saudi newspapers and written in MSA.
The dataset currently contains **31,030** Arabic articles (with a total number of **8,758,976 words**). The articles were extracted from the following Saudi newspapers (sorted by number of articles):
- [Al-Riyadh](http://www.alriyadh.com/) (4,852 articles)
- [Al-Jazirah](http://al-jazirah.com/) (3,690 articles)
- [Al-Yaum](http://alyaum.com/) (3,065 articles)
- [Al-Eqtisadiya](http://aleqt.com/) (2,964 articles)
- [Al-Sharq Al-Awsat](http://aawsat.com/) (2,947 articles)
- [Okaz](http://www.okaz.com.sa/) (2,846 articles)
- [Al-Watan](http://alwatan.com.sa/) (2,279 articles)
- [Al-Madina](http://www.al-madina.com/) (2,252 articles)
- [Al-Weeam](http://alweeam.com.sa/) (2,090 articles)
- [Ain Alyoum](http://3alyoum.com/) (2,080 articles)
- [Sabq](http://sabq.org/) (1,411 articles)
- [Saudi Press Agency](http://www.spa.gov.sa) (369 articles)
- [Arreyadi](http://www.arreyadi.com.sa/) (133 articles)
- [Arreyadiyah](http://www.arreyadiyah.com/) (52 articles)
### 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:** 29.01 MB
- **Size of the generated dataset:** 103.65 MB
- **Total amount of disk used:** 132.67 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"author": "الرياض: محمد الحميدي",
"content": "\"في وقت تتهيأ فيه السعودية لإطلاق الإصدار الثاني من العملات المعدنية، لا تزال التداول بمبالغ النقود المصنوعة من المعدن مستقرة عن...",
"date_extracted": "2015-07-22 01:18:37",
"source": "aawsat",
"title": "\"«العملة المعدنية» السعودية تسجل انحسارًا تاريخيًا وسط تهيؤ لإطلاق الإصدار الثاني\"...",
"url": "\"http://aawsat.com/home/article/411671/«العملة-المعدنية»-السعودية-تسجل-انحسارًا-تاريخيًا-وسط-تهيؤ-لإطلاق-الإصدار-الثاني\"..."
}
```
### Data Fields
The data fields are the same among all splits.
- **`source`** (str): The source newspaper.
- **`url`** (str): The full URL from which the article was extracted.
- **`date_extracted`** (str): The timestamp of the date on which the article was extracted. It has the format `YYYY-MM-DD hh:mm:ss`. Notice that this field does not necessarily represent the date on which the article was authored (or made available online), however for articles stamped with a date of extraction after August 1, 2015, this field most probably represents the date of authoring.
- **`title`** (str): The title of the article. Contains missing values that were replaced with an empty string.
- **`author`** (str): The author of the article. Contains missing values that were replaced with an empty string.
- **`content`** (str): The content of the article.
### Data Splits
| name |train|
|-------|----:|
|default|31030|
## 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
| String Identifier | Newspaper |
| ------------------ | --------- |
| aawsat | [Al-Sharq Al-Awsat](http://aawsat.com/) |
| aleqtisadiya | [Al-Eqtisadiya](http://aleqt.com/) |
| aljazirah | [Al-Jazirah](http://al-jazirah.com/) |
| almadina | [Al-Madina](http://www.al-madina.com/) |
| alriyadh | [Al-Riyadh](http://www.alriyadh.com/) |
| alwatan | [Al-Watan](http://alwatan.com.sa/) |
| alweeam | [Al-Weeam](http://alweeam.com.sa/) |
| alyaum | [Al-Yaum](http://alyaum.com/) |
| arreyadi | [Arreyadi](http://www.arreyadi.com.sa/) |
| arreyadiyah | [Arreyadi](http://www.arreyadiyah.com/) |
| okaz | [Okaz](http://www.okaz.com.sa/) |
| sabq | [Sabq](http://sabq.org/) |
| was | [Saudi Press Agency](http://www.spa.gov.sa/) |
| 3alyoum | [Ain Alyoum](http://3alyoum.com/) |
#### Initial Data Collection and Normalization
The Modern Standard Arabic texts crawled from the Internet.
#### Who are the source language producers?
Newspaper Websites.
### Annotations
The dataset does not contain any additional annotations.
### 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
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
### Citation Information
```
@misc{hagrima2015,
author = "M. Alhagri",
title = "Saudi Newspapers Arabic Corpus (SaudiNewsNet)",
year = 2015,
url = "http://github.com/ParallelMazen/SaudiNewsNet"
}
```
### Contributions
Thanks to [@abdulelahsm](https://github.com/abdulelahsm) for adding this dataset. |
false |
# Dataset Card for Tashkeela
## 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:** [Tashkeela](https://sourceforge.net/projects/tashkeela/)
- **Repository:** [Tashkeela](https://sourceforge.net/projects/tashkeela/)
- **Paper:** [Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems](https://www.sciencedirect.com/science/article/pii/S2352340917300112)
- **Point of Contact:** [Taha Zerrouki](mailto:t_zerrouki@esi.dz)
### Dataset Summary
It contains 75 million of fully vocalized words mainly
97 books from classical and modern Arabic language.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The dataset is based on Arabic.
## Dataset Structure
### Data Instances
```
{'book': 'zip://Tashkeela-arabic-diacritized-text-utf8-0.3/texts.txt/msa/al-kalema.org/أشكال-التجارب-في-مَثَل-الزارع.htm.txt::https://sourceforge.net/projects/tashkeela/files/latest/download',
'text': 'الكلمة\n\n\nصفحه اصلی\nاشترك\nالكتاب المقدس\nجميع المقالات\nالترتيب بالموضوع\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nهذا المقال على نسخة PDF\n\n\nأشكال التجارب في مَثَل الزارع\n\n\tقد رأينا في مقال " \nوسائل واشكال التجارب" الأشكال التي من الممكن أن تتخذها التجارب (وخاصة الاختبارات التي تأتي من خلال الآلام والاضطهاد وأشراك إطاعة شهوات الإنسان العتيق، الجسد)، نستطيع أيضاً أن نرى هذه الأقسام عاملة في مثال الزارع. هناك مجموعتين في مثال الزارع أنه برغم من سماعهم واستقبالهم للكلمة، إلا أنهم لم يجلبوا ثماراً. والسؤال هو لماذا؟\n\n1. التجارب في القسم الثاني من مثال الزارع\n\nفيما يخص القسم الثاني من مثال الزارع، تخبرنا عنها متى 13: 20- 21 ولوقا 8: 13 \nمتى 13: 20- 21\n" وَالْمَزْرُوعُ عَلَى الأَمَاكِنِ الْمُحْجِرَةِ هُوَ الَّذِي يَسْمَعُ الْكَلِمَةَ، وَحَالاً يَقْبَلُهَا بِفَرَحٍ، وَلكِنْ لَيْسَ لَهُ أَصْلٌ فِي ذَاتِهِ، بَلْ هُوَ إِلَى حِينٍ. فَإِذَا حَدَثَ ضِيقٌ أَوِ اضْطِهَادٌ مِنْ أَجْلِ الْكَلِمَةِ فَحَالاً يَعْثُرُ."\nلوقا 8: 13\n" وَالَّذِينَ عَلَى الصَّخْرِ هُمُ الَّذِينَ مَتَى سَمِعُوا يَقْبَلُونَ الْكَلِمَةَ بِفَرَحٍ، وَهؤُلاَءِ لَيْسَ لَهُمْ أَصْلٌ، فَيُؤْمِنُونَ إِلَى حِينٍ، وَفِي وَقْتِ التَّجْرِبَةِ يَرْتَدُّونَ."\n\nكما نرى، الناس في هذا القسم سمعوا الكلمة وحالاً قبلوها بفرح! بمعنى آخر، لقد كانوا متحمسين جداً تجاه الكلمة. ثم جاءت التجارب والاختبارات في شكل ضيق واضطهاد من أجل الكلمة، أي أنه بسبب الكلمة، اضطهد هؤلاء الناس. وعندئذ توقفوا. عوضاً عن أن يحفظوا ويتمسكوا بالكلمة التي قد حدث واستقبلوها بفرح، تراجعوا وسقطوا بعيداً، إن كنت مؤمناً صغيراً مليء بالحماسة تجاه الله، وبالرغم من أنه قد يبدو أنه لا يوجد شيطان من حولك، فهذا لن يستمر إلى الأبد. فالتجارب والاختبارات آتية. ستحتاج إلى أن تحفظ وتتمسك بالإيمان وبالكلمة التي قد حدث واستقبلتها بفرح. كما تقول لنا الكلمة:\nعبرانيين 10: 35- 39\n" فَلاَ تَطْرَحُوا ثِقَتَكُمُ الَّتِي لَهَا مُجَازَاةٌ عَظِيمَةٌ. لأَنَّكُمْ تَحْتَاجُونَ إِلَى الصَّبْرِ، حَتَّى إِذَا صَنَعْتُمْ مَشِيئَةَ اللهِ تَنَالُونَ الْمَوْعِدَ. لأَنَّهُ بَعْدَ قَلِيل جِدًّا «سَيَأْتِي الآتِي وَلاَ يُبْطِئُ. أَمَّا الْبَارُّ فَبِالإِيمَانِ يَحْيَا، وَإِنِ ارْتَدَّ لاَ تُسَرُّ بِهِ نَفْسِي». وَأَمَّا نَحْنُ فَلَسْنَا مِنَ الارْتِدَادِ لِلْهَلاَكِ، بَلْ مِنَ الإِيمَانِ لاقْتِنَاءِ النَّفْسِ."\n\nوالضيق قد يأخذ أشكالاً عديدة. رأيت أناساً يسقطون، تاركين الإيمان لأن آبائهم أو أقاربهم وأصدقائهم قد عارضوهم ورفضوهم بسبب إيمانهم. بالطبع قد يأخذ الاضطهاد أشكالاً أكثر من ذلك أيضاً، مثل أن تلقى في سجن أو أن تعذب لأجل إيمانك. قد يسبب الموت كذلك، كما حدث مع اسطفانوس ويعقوب أخو يوحنا. وتقول الكلمة من أجلك ومن أجل كل الذين حوكموا:\nرومية 16: 19- 20\n" لأَنَّ طَاعَتَكُمْ ذَاعَتْ إِلَى الْجَمِيعِ، فَأَفْرَحُ أَنَا بِكُمْ، وَأُرِيدُ أَنْ تَكُونُوا حُكَمَاءَ لِلْخَيْرِ وَبُسَطَاءَ لِلشَّرِّ. وَإِلهُ السَّلاَمِ سَيَسْحَقُ الشَّيْطَانَ تَحْتَ أَرْجُلِكُمْ سَرِيعًا."\nو بطرس الأولى 5: 8- 10\n" اُصْحُوا وَاسْهَرُوا. لأَنَّ إِبْلِيسَ خَصْمَكُمْ كَأَسَدٍ زَائِرٍ، يَجُولُ مُلْتَمِسًا مَنْ يَبْتَلِعُهُ هُوَ. فَقَاوِمُوهُ، رَاسِخِينَ فِي الإِيمَانِ، عَالِمِينَ أَنَّ نَفْسَ هذِهِ الآلاَمِ تُجْرَى عَلَى إِخْوَتِكُمُ الَّذِينَ فِي الْعَالَمِ. وَإِلهُ كُلِّ نِعْمَةٍ الَّذِي دَعَانَا إِلَى مَجْدِهِ الأَبَدِيِّ فِي الْمَسِيحِ يَسُوعَ، بَعْدَمَا تَأَلَّمْتُمْ يَسِيرًا، هُوَ يُكَمِّلُكُمْ، وَيُثَبِّتُكُمْ، وَيُقَوِّيكُمْ، وَيُمَكِّنُكُمْ."\n\nتمسك بالإيمان حتى النهاية. ضع حياتك ووضعك بين يدي الله وكن مستعداً لمواجهة أي شيء قد يحدث، أجل وحتى السخرية والعذاب. الله معك، سيقويك وسيعينك تماماً مثلما فعل مع يسوع في بستان جسثيماني. وتماماً مثلما فعل مع بولس في السجن عندما اضطهد من قِبَل اليهود (أعمال الرسل 23: 11). وكما قال بولس في كورنثوس الثانية 1: 7:" عَالِمِينَ أَنَّكُمْ كَمَا أَنْتُمْ شُرَكَاءُ فِي الآلاَمِ، كَذلِكَ فِي التَّعْزِيَةِ أَيْضًا." فالعزاء الآتي من الله يوازن أي سخرية أو أي عذاب قد يأتي إلينا من أي إنسان.\n\n2. التجارب في القسم الثالث من مثال الزارع\n\nبخصوص القسم الثالث من مثال الزارع، فنقرأ عنه في مرقس 4: 18- 19\n\n" وَهؤُلاَءِ هُمُ الَّذِينَ زُرِعُوا بَيْنَ الشَّوْكِ: هؤُلاَءِ هُمُ الَّذِينَ يَسْمَعُونَ الْكَلِمَةَ، وَهُمُومُ هذَا الْعَالَمِ وَغُرُورُ الْغِنَى وَشَهَوَاتُ سَائِرِ الأَشْيَاءِ تَدْخُلُ وَتَخْنُقُ الْكَلِمَةَ فَتَصِيرُ بِلاَ ثَمَرٍ."\nو لوقا 8: 14\n" وَالَّذِي سَقَطَ بَيْنَ الشَّوْكِ هُمُ الَّذِينَ يَسْمَعُونَ، ثُمَّ يَذْهَبُونَ فَيَخْتَنِقُونَ مِنْ هُمُومِ الْحَيَاةِ وَغِنَاهَا وَلَذَّاتِهَا، وَلاَ يُنْضِجُونَ ثَمَرًا."\n\nهؤلاء قد سمعوا الكلمة وفهموها ولكنهم صاروا بلا ثمر، وما هو السبب؟ السبب هو لأنهم تركوا أبواب قلوبهم مفتوحة لأشواك " وَهُمُومُ هذَا الْعَالَمِ وَغُرُورُ الْغِنَى وَشَهَوَاتُ سَائِرِ الأَشْيَاءِ" (مرقس 4: 19)، والتي تدخل فتخنق الكلمة، كما رأينا يعقوب دائماً ما يقول:\nيعقوب 1: 13- 15\n" لاَ يَقُلْ أَحَدٌ إِذَا جُرِّبَ: «إِنِّي أُجَرَّبُ مِنْ قِبَلِ اللهِ»، لأَنَّ اللهَ غَيْرُ مُجَرَّبٍ بِالشُّرُورِ، وَهُوَ لاَ يُجَرِّبُ أَحَدًا. وَلكِنَّ كُلَّ وَاحِدٍ يُجَرَّبُ إِذَا انْجَذَبَ وَانْخَدَعَ مِنْ شَهْوَتِهِ. ثُمَّ الشَّهْوَةُ إِذَا حَبِلَتْ تَلِدُ خَطِيَّةً، وَالْخَطِيَّةُ إِذَا كَمَلَتْ تُنْتِجُ مَوْتًا."\nوتيموثاوس الأولى 6: 9 تقول لنا\n" وَأَمَّا الَّذِينَ يُرِيدُونَ أَنْ يَكُونُوا أَغْنِيَاءَ، فَيَسْقُطُونَ فِي تَجْرِبَةٍ وَفَخٍّ وَشَهَوَاتٍ كَثِيرَةٍ غَبِيَّةٍ وَمُضِرَّةٍ، تُغَرِّقُ النَّاسَ فِي الْعَطَبِ وَالْهَلاَكِ."\n\nيجب أن نلاحظ شيئاً هنا: أن تأثير هموم الحياة هو نفس التأثير الذي لتجارب الغنى وشهوات الأشياء الأخرى. فهموم الحياة أيضاً لا تجلب الثمار، إذاً فإن اردت أن تكون مسيحياً مثمراً، أي مسيحي حقيقي وليس فقط مسيحي اسمي، فيجب عليك أن تزيل أشواك الهموم والغنى وملذات الحياة وأن تمنعهم من العودة مرة أخرى. تحتاج إلى أن تفعل شيئاً، تحتاج إلى أن تتغير والله سيعينك في هذا إن كنت حقاً تريده. التجارب في القسم الثالث من مثال الزارع لا تأتي من خلال الاضطهاد والآلام عن طريق الشيطان. ولكن هنا تأخذ التجارب صوراً أكثر مكراً والتي مع هذا تتطلب مقاومتنا. الاهتمام بما يهتم به هذا العالم ("هموم هذا العالم")، الرغبة في الغنى أو اشتهاء الأشياء الأخرى هي أمور خطيرة جداً. إنها أشواك يجب إزالتها. كما رأينا بولس يقول:\nرومية 13: 14\n" بَلِ الْبَسُوا الرَّبَّ يَسُوعَ الْمَسِيحَ، وَلاَ تَصْنَعُوا تَدْبِيرًا لِلْجَسَدِ لأَجْلِ الشَّهَوَاتِ."\n\n" لاَ تَصْنَعُوا تَدْبِيرًا لِلْجَسَدِ" والتي تعني أنه يجب علينا أن لا نهتم بالجسد وشهواته. ولكن عوضاً عن ذلك ينبغي لنا أن نطعم أنفسنا بلبن الكلمة الصافي الذي ننمو بواستطه (بطرس الأولى 2: 2).\n\n\nتاسوس كيولاشوجلو'}
```
### Data Fields
- `book` (str): Book filename.
- `text` (str): Text of the book.
### Data Splits
The dataset is not split.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
The Modern Standard Arabic texts crawled from the Internet.
#### Who are the source language producers?
Websites.
### 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
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[GNU General Public License, version 2 (GPLv2)](https://opensource.org/licenses/GPL-2.0).
### Citation Information
The dataset was published on this [paper](https://www.sciencedirect.com/science/article/pii/S2352340917300112#!):
```
@article{zerrouki2017tashkeela,
title={Tashkeela: Novel corpus of Arabic vocalized texts, data for auto-diacritization systems},
author={Zerrouki, Taha and Balla, Amar},
journal={Data in brief},
volume={11},
pages={147},
year={2017},
publisher={Elsevier}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) for adding this dataset. |
true |
# Dataset Card for TurkishMovieSentiment: This dataset contains turkish movie reviews.
## 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.kaggle.com/mustfkeskin/turkish-movie-sentiment-analysis-dataset/tasks](https://www.kaggle.com/mustfkeskin/turkish-movie-sentiment-analysis-dataset/tasks)
- **Point of Contact:** [Mustafa Keskin](https://www.linkedin.com/in/mustfkeskin/)
### Dataset Summary
This data set is a dataset from kaggle consisting of Turkish movie reviews and scored between 0-5.
### Languages
The dataset is based on Turkish.
## Dataset Structure
### Data Instances
**Example 1:**
**Comment:** Jean Reno denince zaten leon filmi gelir akla izlemeyen kalmamıştır ama kaldıysada ee ne duruyorsun hemen izle :),
**Film_name:** Sevginin Gücü,
**Point:** 5,0
**Example 2:**
**Comment:** Bence güzel bi film olmush.İzlenmeli.İnsana şükretmek gerektini hatırlatıyor.Ama cok da poh pohlanacak bi sey yapmamıslar,
**Film_name:** Cinderella Man,
**Point:** 2,5
### Data Fields
- **comment**(string) : Contatins turkish movie review
- **film_name**(string) : Film name in Turkish.
- **point**(float) : [0-5] floating point
### Data Splits
It is not divided into Train set and Test set.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### 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
### Discussion of Social Impact and Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The dataset was created by [Mustafa Keskin](https://www.linkedin.com/in/mustfkeskin/).
### Licensing Information
The data is under the [CC0: Public Domain](https://creativecommons.org/publicdomain/zero/1.0/)
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@yavuzKomecoglu](https://github.com/yavuzKomecoglu) for adding this dataset. |
false |
# Dataset Card for Twi Text C3
## 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.aclweb.org/anthology/2020.lrec-1.335
- **Repository:** https://github.com/ajesujoba/YorubaTwi-Embedding/
- **Paper:** https://www.aclweb.org/anthology/2020.lrec-1.335
- **Leaderboard:**
- **Point of Contact:** [Kwabena Amponsah-Kaakyire](mailto:s8kwampo@stud.uni-saarland.de)
### Dataset Summary
Twi Text C3 was collected from various sources from the web (Bible, JW300, wikipedia, etc)
to compare pre-trained word embeddings (Fasttext) and embeddings and embeddings trained on curated Twi Texts.
The dataset consists of clean texts (i.e the Bible) and noisy texts (with incorrect orthography and mixed dialects)
from other online sources like Wikipedia and JW300
### Supported Tasks and Leaderboards
For training word embeddings and language models on Twi texts.
### Languages
The language supported is Twi.
## Dataset Structure
### Data Instances
A data point is a sentence in each line.
{
'text': 'mfitiaseɛ no onyankopɔn bɔɔ ɔsoro ne asaase'
}
### Data Fields
- `text`: a `string` feature.
a sentence text per line
### Data Splits
Contains only the training split.
## Dataset Creation
### Curation Rationale
The data was created to help introduce resources to new language - Twi.
### Source Data
#### Initial Data Collection and Normalization
The dataset comes from various sources of the web: Bible, JW300, and wikipedia.
See Table 1 in the [paper](https://www.aclweb.org/anthology/2020.lrec-1.335/) for the summary of the dataset and statistics
#### Who are the source language producers?
[Jehovah Witness](https://www.jw.org/) (JW300)
[Twi Bible](http://www.bible.com/)
[Yorùbá Wikipedia](dumps.wikimedia.org/twwiki)
### 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
The dataset is biased to the religion domain (Christianity) because of the inclusion of JW300 and the Bible.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
The data sets were curated by Kwabena Amponsah-Kaakyire, Jesujoba Alabi, and David Adelani, students of Saarland University, Saarbrücken, Germany .
### Licensing Information
The data is under the [Creative Commons Attribution-NonCommercial 4.0 ](https://creativecommons.org/licenses/by-nc/4.0/legalcode)
### Citation Information
```
@inproceedings{alabi-etal-2020-massive,
title = "Massive vs. Curated Embeddings for Low-Resourced Languages: the Case of {Y}or{\`u}b{\'a} and {T}wi",
author = "Alabi, Jesujoba and
Amponsah-Kaakyire, Kwabena and
Adelani, David and
Espa{\~n}a-Bonet, Cristina",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.335",
pages = "2754--2762",
abstract = "The success of several architectures to learn semantic representations from unannotated text and the availability of these kind of texts in online multilingual resources such as Wikipedia has facilitated the massive and automatic creation of resources for multiple languages. The evaluation of such resources is usually done for the high-resourced languages, where one has a smorgasbord of tasks and test sets to evaluate on. For low-resourced languages, the evaluation is more difficult and normally ignored, with the hope that the impressive capability of deep learning architectures to learn (multilingual) representations in the high-resourced setting holds in the low-resourced setting too. In this paper we focus on two African languages, Yor{\`u}b{\'a} and Twi, and compare the word embeddings obtained in this way, with word embeddings obtained from curated corpora and a language-dependent processing. We analyse the noise in the publicly available corpora, collect high quality and noisy data for the two languages and quantify the improvements that depend not only on the amount of data but on the quality too. We also use different architectures that learn word representations both from surface forms and characters to further exploit all the available information which showed to be important for these languages. For the evaluation, we manually translate the wordsim-353 word pairs dataset from English into Yor{\`u}b{\'a} and Twi. We extend the analysis to contextual word embeddings and evaluate multilingual BERT on a named entity recognition task. For this, we annotate with named entities the Global Voices corpus for Yor{\`u}b{\'a}. As output of the work, we provide corpora, embeddings and the test suits for both languages.",
language = "English",
ISBN = "979-10-95546-34-4",
}
```
### Contributions
Thanks to [@dadelani](https://github.com/dadelani) for adding this dataset. |
false |
# Dataset Card for WikiSource
## 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://opus.nlpl.eu/WikiSource.php
- **Repository:** None
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
false |
# Dataset Card for GEM/RotoWire_English-German
## Dataset Description
- **Homepage:** https://sites.google.com/view/wngt19/dgt-task
- **Repository:** https://github.com/neulab/dgt
- **Paper:** https://www.aclweb.org/anthology/D19-5601/
- **Leaderboard:** N/A
- **Point of Contact:** Hiroaki Hayashi
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/RotoWire_English-German).
### Dataset Summary
This dataset is a data-to-text dataset in the basketball domain. The input are tables in a fixed format with statistics about a game (in English) and the target is a German translation of the originally English description. The translations were done by professional translators with basketball experience. The dataset can be used to evaluate the cross-lingual data-to-text capabilities of a model with complex inputs.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/RotoWire_English-German')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/RotoWire_English-German).
#### website
[Website](https://sites.google.com/view/wngt19/dgt-task)
#### paper
[ACL Anthology](https://www.aclweb.org/anthology/D19-5601/)
#### authors
Graham Neubig (Carnegie Mellon University), Hiroaki Hayashi (Carnegie Mellon University)
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Website](https://sites.google.com/view/wngt19/dgt-task)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/neulab/dgt)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://www.aclweb.org/anthology/D19-5601/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{hayashi-etal-2019-findings,
title = "Findings of the Third Workshop on Neural Generation and Translation",
author = "Hayashi, Hiroaki and
Oda, Yusuke and
Birch, Alexandra and
Konstas, Ioannis and
Finch, Andrew and
Luong, Minh-Thang and
Neubig, Graham and
Sudoh, Katsuhito",
booktitle = "Proceedings of the 3rd Workshop on Neural Generation and Translation",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5601",
doi = "10.18653/v1/D19-5601",
pages = "1--14",
abstract = "This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language.",
}
```
#### Contact Name
<!-- quick -->
<!-- info: If known, provide the name of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
Hiroaki Hayashi
#### Contact Email
<!-- info: If known, provide the email of at least one person the reader can contact for questions about the dataset. -->
<!-- scope: periscope -->
hiroakih@andrew.cmu.edu
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
yes
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`, `German`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
cc-by-4.0: Creative Commons Attribution 4.0 International
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Foster the research on document-level generation technology and contrast the methods for different types of inputs.
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Data-to-Text
#### Communicative Goal
<!-- quick -->
<!-- info: Provide a short description of the communicative goal of a model trained for this task on this dataset. -->
<!-- scope: periscope -->
Describe a basketball game given its box score table (and possibly a summary in a foreign language).
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`academic`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
Carnegie Mellon University
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Graham Neubig (Carnegie Mellon University), Hiroaki Hayashi (Carnegie Mellon University)
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
Graham Neubig
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Hiroaki Hayashi (Carnegie Mellon University)
### Dataset Structure
#### Data Fields
<!-- info: List and describe the fields present in the dataset. -->
<!-- scope: telescope -->
- `id` (`string`): The identifier from the original dataset.
- `gem_id` (`string`): The identifier from GEMv2.
- `day` (`string`): Date of the game (Format: `MM_DD_YY`)
- `home_name` (`string`): Home team name.
- `home_city` (`string`): Home team city name.
- `vis_name` (`string`): Visiting (Away) team name.
- `vis_city` (`string`): Visiting team (Away) city name.
- `home_line` (`Dict[str, str]`): Home team statistics (e.g., team free throw percentage).
- `vis_line` (`Dict[str, str]`): Visiting team statistics (e.g., team free throw percentage).
- `box_score` (`Dict[str, Dict[str, str]]`): Box score table. (Stat_name to [player ID to stat_value].)
- `summary_en` (`List[string]`): Tokenized target summary in English.
- `sentence_end_index_en` (`List[int]`): Sentence end indices for `summary_en`.
- `summary_de` (`List[string]`): Tokenized target summary in German.
- `sentence_end_index_de` (`List[int]`): ): Sentence end indices for `summary_de`.
- (Unused) `detok_summary_org` (`string`): Original summary provided by RotoWire dataset.
- (Unused) `summary` (`List[string]`): Tokenized summary of `detok_summary_org`.
- (Unused) `detok_summary` (`string`): Detokenized (with organizer's detokenizer) summary of `summary`.
#### Reason for Structure
<!-- info: How was the dataset structure determined? -->
<!-- scope: microscope -->
- Structured data are directly imported from the original RotoWire dataset.
- Textual data (English, German) are associated to each sample.
#### Example Instance
<!-- info: Provide a JSON formatted example of a typical instance in the dataset. -->
<!-- scope: periscope -->
```
{
'id': '11_02_16-Jazz-Mavericks-TheUtahJazzdefeatedthe',
'gem_id': 'GEM-RotoWire_English-German-train-0'
'day': '11_02_16',
'home_city': 'Utah',
'home_name': 'Jazz',
'vis_city': 'Dallas',
'vis_name': 'Mavericks',
'home_line': {
'TEAM-FT_PCT': '58', ...
},
'vis_line': {
'TEAM-FT_PCT': '80', ...
},
'box_score': {
'PLAYER_NAME': {
'0': 'Harrison Barnes', ...
}, ...
'summary_en': ['The', 'Utah', 'Jazz', 'defeated', 'the', 'Dallas', 'Mavericks', ...],
'sentence_end_index_en': [16, 52, 100, 137, 177, 215, 241, 256, 288],
'summary_de': ['Die', 'Utah', 'Jazz', 'besiegten', 'am', 'Mittwoch', 'in', 'der', ...],
'sentence_end_index_de': [19, 57, 107, 134, 170, 203, 229, 239, 266],
'detok_summary_org': "The Utah Jazz defeated the Dallas Mavericks 97 - 81 ...",
'detok_summary': "The Utah Jazz defeated the Dallas Mavericks 97-81 ...",
'summary': ['The', 'Utah', 'Jazz', 'defeated', 'the', 'Dallas', 'Mavericks', ...],
}
```
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
- Train
- Validation
- Test
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
- English summaries are provided sentence-by-sentence to professional German translators with basketball knowledge to obtain sentence-level German translations.
- Split criteria follows the original RotoWire dataset.
####
<!-- info: What does an outlier of the dataset in terms of length/perplexity/embedding look like? -->
<!-- scope: microscope -->
- The (English) summary length in the training set varies from 145 to 650 words, with an average of 323 words.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
The use of two modalities (data, foreign text) to generate a document-level text summary.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
yes
#### Unique Language Coverage
<!-- info: Does this dataset cover other languages than other datasets for the same task? -->
<!-- scope: periscope -->
yes
#### Difference from other GEM datasets
<!-- info: What else sets this dataset apart from other similar datasets in GEM? -->
<!-- scope: microscope -->
The potential use of two modalities (data, foreign text) as input.
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
- Translation
- Data-to-text verbalization
- Aggregation of the two above.
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
yes
#### GEM Modifications
<!-- info: What changes have been made to he original dataset? -->
<!-- scope: periscope -->
`other`
#### Modification Details
<!-- info: For each of these changes, described them in more details and provided the intended purpose of the modification -->
<!-- scope: microscope -->
- Added GEM ID in each sample.
- Normalize the number of players in each sample with "N/A" for consistent data loading.
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
#### Pointers to Resources
<!-- info: Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task. -->
<!-- scope: microscope -->
- [Challenges in Data-to-Document Generation](https://aclanthology.org/D17-1239)
- [Data-to-Text Generation with Content Selection and Planning](https://ojs.aaai.org//index.php/AAAI/article/view/4668)
- [Findings of the Third Workshop on Neural Generation and Translation](https://aclanthology.org/D19-5601)
#### Technical Terms
<!-- info: Technical terms used in this card and the dataset and their definitions -->
<!-- scope: microscope -->
- Data-to-text
- Neural machine translation (NMT)
- Document-level generation and translation (DGT)
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
- Textual accuracy towards the gold-standard summary.
- Content faithfulness to the input structured data.
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`BLEU`, `ROUGE`, `Other: Other Metrics`
#### Other Metrics
<!-- info: Definitions of other metrics -->
<!-- scope: periscope -->
Model-based measures proposed by (Wiseman et al., 2017):
- Relation Generation
- Content Selection
- Content Ordering
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
To evaluate the fidelity of the generated content to the input data.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
N/A.
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
See Table 2 to 7 of (https://aclanthology.org/D19-5601) for previous results for this dataset.
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
A random subset of RotoWire dataset was chosen for German translation annotation.
#### Communicative Goal
<!-- info: What was the communicative goal? -->
<!-- scope: periscope -->
Foster the research on document-level generation technology and contrast the methods for different types of inputs.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
yes
#### Source Details
<!-- info: List the sources (one per line) -->
<!-- scope: periscope -->
RotoWire
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Created for the dataset`
#### Creation Process
<!-- info: If created for the dataset, describe the creation process. -->
<!-- scope: microscope -->
Professional German language translators were hired to translate basketball summaries from a subset of RotoWire dataset.
#### Language Producers
<!-- info: What further information do we have on the language producers? -->
<!-- scope: microscope -->
Translators are familiar with basketball terminology.
#### Topics Covered
<!-- info: Does the language in the dataset focus on specific topics? How would you describe them? -->
<!-- scope: periscope -->
Basketball (NBA) game summaries.
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
validated by data curator
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Sentence-level translations were aligned back to the original English summary sentences.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
not filtered
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
automatically created
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
#### Annotation Values
<!-- info: Purpose and values for each annotation -->
<!-- scope: microscope -->
Sentence-end indices for the tokenized summaries. Sentence boundaries can help users accurately identify aligned sentences in both languages, as well as allowing an accurate evaluation that involves sentence boundaries (ROUGE-L).
#### Any Quality Control?
<!-- info: Quality control measures? -->
<!-- scope: telescope -->
validated through automated script
#### Quality Control Details
<!-- info: Describe the quality control measures that were taken. -->
<!-- scope: microscope -->
Token and number overlaps between pairs of aligned sentences are measured.
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
no
#### Justification for Using the Data
<!-- info: If not, what is the justification for reusing the data? -->
<!-- scope: microscope -->
Reusing by citing the original papers:
- Sam Wiseman, Stuart M. Shieber, Alexander M. Rush:
Challenges in Data-to-Document Generation. EMNLP 2017.
- Hiroaki Hayashi, Yusuke Oda, Alexandra Birch, Ioannis Konstas, Andrew Finch, Minh-Thang Luong, Graham Neubig, Katsuhito Sudoh. Findings of the Third Workshop on Neural Generation and Translation. WNGT 2019.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
unlikely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
no
#### Are the Language Producers Representative of the Language?
<!-- info: Does the distribution of language producers in the dataset accurately represent the full distribution of speakers of the language world-wide? If not, how does it differ? -->
<!-- scope: periscope -->
- English text in this dataset is from Rotowire, originally written by writers at Rotowire.com that are likely US-based.
- German text is produced by professional translators proficient in both English and German.
## Considerations for Using the Data
### PII Risks and Liability
#### Potential PII Risk
<!-- info: Considering your answers to the PII part of the Data Curation Section, describe any potential privacy to the data subjects and creators risks when using the dataset. -->
<!-- scope: microscope -->
- Structured data contain real National Basketball Association player and organization names.
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`open license - commercial use allowed`
### Known Technical Limitations
#### Technical Limitations
<!-- info: Describe any known technical limitations, such as spurrious correlations, train/test overlap, annotation biases, or mis-annotations, and cite the works that first identified these limitations when possible. -->
<!-- scope: microscope -->
Potential overlap of box score tables between splits. This was extensively studied and pointed out by [1].
[1]: Thomson, Craig, Ehud Reiter, and Somayajulu Sripada. "SportSett: Basketball-A robust and maintainable data-set for Natural Language Generation." Proceedings of the Workshop on Intelligent Information Processing and Natural Language Generation. 2020.
#### Unsuited Applications
<!-- info: When using a model trained on this dataset in a setting where users or the public may interact with its predictions, what are some pitfalls to look out for? In particular, describe some applications of the general task featured in this dataset that its curation or properties make it less suitable for. -->
<!-- scope: microscope -->
Users may interact with a trained model to learn about a NBA game in a textual manner. On generated texts, they may observe factual errors that contradicts the actual data that the model conditions on.
Factual errors include wrong statistics of a player (e.g., 3PT), non-existent injury information.
#### Discouraged Use Cases
<!-- info: What are some discouraged use cases of a model trained to maximize the proposed metrics on this dataset? In particular, think about settings where decisions made by a model that performs reasonably well on the metric my still have strong negative consequences for user or members of the public. -->
<!-- scope: microscope -->
Publishing the generated text as is. Even if the model achieves high scores on the evaluation metrics, there is a risk of factual errors mentioned above.
|
false |
# Dataset Card for GEM/SciDuet
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/GEM/SciDuet
- **Repository:** https://github.com/IBM/document2slides/tree/main/SciDuet-ACL
- **Paper:** https://aclanthology.org/2021.naacl-main.111/
- **Leaderboard:** N/A
- **Point of Contact:** N/A
### Link to Main Data Card
You can find the main data card on the [GEM Website](https://gem-benchmark.com/data_cards/SciDuet).
### Dataset Summary
This dataset supports the document-to-slide generation task where a model has to generate presentation slide content from the text of a document.
You can load the dataset via:
```
import datasets
data = datasets.load_dataset('GEM/SciDuet')
```
The data loader can be found [here](https://huggingface.co/datasets/GEM/SciDuet).
#### website
[Huggingface](https://huggingface.co/datasets/GEM/SciDuet)
#### paper
[ACL Anthology](https://aclanthology.org/2021.naacl-main.111/)
#### authors
Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, Nancy Wang
## Dataset Overview
### Where to find the Data and its Documentation
#### Webpage
<!-- info: What is the webpage for the dataset (if it exists)? -->
<!-- scope: telescope -->
[Huggingface](https://huggingface.co/datasets/GEM/SciDuet)
#### Download
<!-- info: What is the link to where the original dataset is hosted? -->
<!-- scope: telescope -->
[Github](https://github.com/IBM/document2slides/tree/main/SciDuet-ACL)
#### Paper
<!-- info: What is the link to the paper describing the dataset (open access preferred)? -->
<!-- scope: telescope -->
[ACL Anthology](https://aclanthology.org/2021.naacl-main.111/)
#### BibTex
<!-- info: Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created Bibtex. -->
<!-- scope: microscope -->
```
@inproceedings{sun-etal-2021-d2s,
title = "{D}2{S}: Document-to-Slide Generation Via Query-Based Text Summarization",
author = "Sun, Edward and
Hou, Yufang and
Wang, Dakuo and
Zhang, Yunfeng and
Wang, Nancy X. R.",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.111",
doi = "10.18653/v1/2021.naacl-main.111",
pages = "1405--1418",
abstract = "Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming. There has been limited research aiming to automate the document-to-slides generation process and all face a critical challenge: no publicly available dataset for training and benchmarking. In this work, we first contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years{'} NLP and ML conferences (e.g., ACL). Secondly, we present D2S, a novel system that tackles the document-to-slides task with a two-step approach: 1) Use slide titles to retrieve relevant and engaging text, figures, and tables; 2) Summarize the retrieved context into bullet points with long-form question answering. Our evaluation suggests that long-form QA outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation.",
}
```
#### Has a Leaderboard?
<!-- info: Does the dataset have an active leaderboard? -->
<!-- scope: telescope -->
no
### Languages and Intended Use
#### Multilingual?
<!-- quick -->
<!-- info: Is the dataset multilingual? -->
<!-- scope: telescope -->
no
#### Covered Languages
<!-- quick -->
<!-- info: What languages/dialects are covered in the dataset? -->
<!-- scope: telescope -->
`English`
#### License
<!-- quick -->
<!-- info: What is the license of the dataset? -->
<!-- scope: telescope -->
apache-2.0: Apache License 2.0
#### Intended Use
<!-- info: What is the intended use of the dataset? -->
<!-- scope: microscope -->
Promote research on the task of document-to-slides generation
#### Primary Task
<!-- info: What primary task does the dataset support? -->
<!-- scope: telescope -->
Text-to-Slide
### Credit
#### Curation Organization Type(s)
<!-- info: In what kind of organization did the dataset curation happen? -->
<!-- scope: telescope -->
`industry`
#### Curation Organization(s)
<!-- info: Name the organization(s). -->
<!-- scope: periscope -->
IBM Research
#### Dataset Creators
<!-- info: Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s). -->
<!-- scope: microscope -->
Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, Nancy Wang
#### Funding
<!-- info: Who funded the data creation? -->
<!-- scope: microscope -->
IBM Research
#### Who added the Dataset to GEM?
<!-- info: Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM. -->
<!-- scope: microscope -->
Yufang Hou (IBM Research), Dakuo Wang (IBM Research)
### Dataset Structure
#### How were labels chosen?
<!-- info: How were the labels chosen? -->
<!-- scope: microscope -->
The original papers and slides (both are in PDF format) are carefully processed by a combination of PDF/Image processing tookits. The text contents from multiple slides that correspond to the same slide title are mreged.
#### Data Splits
<!-- info: Describe and name the splits in the dataset if there are more than one. -->
<!-- scope: periscope -->
Training, validation and testing data contain 136, 55, and 81 papers from ACL Anthology and their corresponding slides, respectively.
#### Splitting Criteria
<!-- info: Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here. -->
<!-- scope: microscope -->
The dataset integrated into GEM is the ACL portion of the whole dataset described in the [paper](https://aclanthology.org/2021.naacl-main.111), It contains the full Dev and Test sets, and a portion of the Train dataset.
Note that although we cannot release the whole training dataset due to copyright issues, researchers can still use our released data procurement code to generate the training dataset from the online ICML/NeurIPS anthologies.
## Dataset in GEM
### Rationale for Inclusion in GEM
#### Why is the Dataset in GEM?
<!-- info: What does this dataset contribute toward better generation evaluation and why is it part of GEM? -->
<!-- scope: microscope -->
SciDuet is the first publicaly available dataset for the challenging task of document2slides generation, which requires a model has a good ability to "understand" long-form text, choose appropriate content and generate key points.
#### Similar Datasets
<!-- info: Do other datasets for the high level task exist? -->
<!-- scope: telescope -->
no
#### Ability that the Dataset measures
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: periscope -->
content selection, long-form text undersanding and generation
### GEM-Specific Curation
#### Modificatied for GEM?
<!-- info: Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data? -->
<!-- scope: telescope -->
no
#### Additional Splits?
<!-- info: Does GEM provide additional splits to the dataset? -->
<!-- scope: telescope -->
no
### Getting Started with the Task
## Previous Results
### Previous Results
#### Measured Model Abilities
<!-- info: What aspect of model ability can be measured with this dataset? -->
<!-- scope: telescope -->
content selection, long-form text undersanding and key points generation
#### Metrics
<!-- info: What metrics are typically used for this task? -->
<!-- scope: periscope -->
`ROUGE`
#### Proposed Evaluation
<!-- info: List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task. -->
<!-- scope: microscope -->
Automatical Evaluation Metric: ROUGE
Human Evaluation: (Readability, Informativeness, Consistency)
1) Readability: The generated slide content is coherent, concise, and grammatically correct;
2) Informativeness: The generated slide provides sufficient and necessary information that corresponds to the given slide title, regardless of its similarity to the original slide;
3) Consistency: The generated slide content is similar to the original author’s reference slide.
#### Previous results available?
<!-- info: Are previous results available? -->
<!-- scope: telescope -->
yes
#### Other Evaluation Approaches
<!-- info: What evaluation approaches have others used? -->
<!-- scope: periscope -->
ROUGE + Human Evaluation
#### Relevant Previous Results
<!-- info: What are the most relevant previous results for this task/dataset? -->
<!-- scope: microscope -->
Paper "D2S: Document-to-Slide Generation Via Query-Based
Text Summarization" reports 20.47, 5.26 and 19.08 for ROUGE-1, ROUGE-2 and ROUGE-L (f-score).
## Dataset Curation
### Original Curation
#### Original Curation Rationale
<!-- info: Original curation rationale -->
<!-- scope: telescope -->
Provide a benchmark dataset for the document-to-slides task.
#### Sourced from Different Sources
<!-- info: Is the dataset aggregated from different data sources? -->
<!-- scope: telescope -->
no
### Language Data
#### How was Language Data Obtained?
<!-- info: How was the language data obtained? -->
<!-- scope: telescope -->
`Other`
#### Data Validation
<!-- info: Was the text validated by a different worker or a data curator? -->
<!-- scope: telescope -->
not validated
#### Data Preprocessing
<!-- info: How was the text data pre-processed? (Enter N/A if the text was not pre-processed) -->
<!-- scope: microscope -->
Text on papers was extracted through Grobid. Figures andcaptions were extracted through pdffigures. Text on slides was extracted through IBM Watson Discovery package and OCR by pytesseract. Figures and tables that appear on slides and papers were linked through multiscale template matching by OpenCV. Further dataset
cleaning was performed with standard string-based
heuristics on sentence building, equation and floating caption removal, and duplicate line deletion.
#### Was Data Filtered?
<!-- info: Were text instances selected or filtered? -->
<!-- scope: telescope -->
algorithmically
#### Filter Criteria
<!-- info: What were the selection criteria? -->
<!-- scope: microscope -->
the slide context text shouldn't contain additional format information such as "*** University"
### Structured Annotations
#### Additional Annotations?
<!-- quick -->
<!-- info: Does the dataset have additional annotations for each instance? -->
<!-- scope: telescope -->
none
#### Annotation Service?
<!-- info: Was an annotation service used? -->
<!-- scope: telescope -->
no
### Consent
#### Any Consent Policy?
<!-- info: Was there a consent policy involved when gathering the data? -->
<!-- scope: telescope -->
yes
#### Consent Policy Details
<!-- info: What was the consent policy? -->
<!-- scope: microscope -->
The original dataset was open-sourced under Apache-2.0.
Some of the original dataset creators are part of the GEM v2 dataset infrastructure team and take care of integrating this dataset into GEM.
### Private Identifying Information (PII)
#### Contains PII?
<!-- quick -->
<!-- info: Does the source language data likely contain Personal Identifying Information about the data creators or subjects? -->
<!-- scope: telescope -->
yes/very likely
#### Categories of PII
<!-- info: What categories of PII are present or suspected in the data? -->
<!-- scope: periscope -->
`generic PII`
#### Any PII Identification?
<!-- info: Did the curators use any automatic/manual method to identify PII in the dataset? -->
<!-- scope: periscope -->
no identification
### Maintenance
#### Any Maintenance Plan?
<!-- info: Does the original dataset have a maintenance plan? -->
<!-- scope: telescope -->
no
## Broader Social Context
### Previous Work on the Social Impact of the Dataset
#### Usage of Models based on the Data
<!-- info: Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems? -->
<!-- scope: telescope -->
no
### Impact on Under-Served Communities
#### Addresses needs of underserved Communities?
<!-- info: Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models). -->
<!-- scope: telescope -->
no
### Discussion of Biases
#### Any Documented Social Biases?
<!-- info: Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group. -->
<!-- scope: telescope -->
unsure
## Considerations for Using the Data
### PII Risks and Liability
### Licenses
#### Copyright Restrictions on the Dataset
<!-- info: Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset? -->
<!-- scope: periscope -->
`non-commercial use only`
#### Copyright Restrictions on the Language Data
<!-- info: Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data? -->
<!-- scope: periscope -->
`research use only`
### Known Technical Limitations
|
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