text-classification bool 2
classes | text stringlengths 0 664k |
|---|---|
false |
# Dataset Card for "wmt18"
## 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.statmt.org/wmt18/translation-task.html](http://www.statmt.org/wmt18/translation-task.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 2.03 GB
- **Size of the generated dataset:** 1.46 GB
- **Total amount of disk used:** 3.49 GB
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p>
<ul>
<li>Non-English files contain many English sentences.</li>
<li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li>
</ul>
<p>We have contacted the WMT organizers.</p>
</div>
Translation dataset based on the data from statmt.org.
Versions exist for different years using a combination of data
sources. The base `wmt` allows you to create a custom dataset by choosing
your own data/language pair. This can be done as follows:
```python
from datasets import inspect_dataset, load_dataset_builder
inspect_dataset("wmt18", "path/to/scripts")
builder = load_dataset_builder(
"path/to/scripts/wmt_utils.py",
language_pair=("fr", "de"),
subsets={
datasets.Split.TRAIN: ["commoncrawl_frde"],
datasets.Split.VALIDATION: ["euelections_dev2019"],
},
)
# Standard version
builder.download_and_prepare()
ds = builder.as_dataset()
# Streamable version
ds = builder.as_streaming_dataset()
```
### 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
#### cs-en
- **Size of downloaded dataset files:** 2.03 GB
- **Size of the generated dataset:** 1.46 GB
- **Total amount of disk used:** 3.49 GB
An example of 'validation' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### cs-en
- `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`.
### Data Splits
|name | train |validation|test|
|-----|-------:|---------:|---:|
|cs-en|11046024| 3005|2983|
## 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{bojar-EtAl:2018:WMT1,
author = {Bojar, Ond
{r}ej and Federmann, Christian and Fishel, Mark
and Graham, Yvette and Haddow, Barry and Huck, Matthias and
Koehn, Philipp and Monz, Christof},
title = {Findings of the 2018 Conference on Machine Translation (WMT18)},
booktitle = {Proceedings of the Third Conference on Machine Translation,
Volume 2: Shared Task Papers},
month = {October},
year = {2018},
address = {Belgium, Brussels},
publisher = {Association for Computational Linguistics},
pages = {272--307},
url = {http://www.aclweb.org/anthology/W18-6401}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
false |
# Dataset Card for MGSM
## 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://openai.com/blog/grade-school-math/
- **Repository:** https://github.com/openai/grade-school-math
- **Paper:** https://arxiv.org/abs/2110.14168
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems, proposed in the paper [Language models are multilingual chain-of-thought reasoners](http://arxiv.org/abs/2210.03057).
The same 250 problems from [GSM8K](https://arxiv.org/abs/2110.14168) are each translated via human annotators in 10 languages. The 10 languages are:
- Spanish
- French
- German
- Russian
- Chinese
- Japanese
- Thai
- Swahili
- Bengali
- Telugu
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
You can find the input and targets for each of the ten languages (and English) as `.tsv` files.
We also include few-shot exemplars that are also manually translated from each language in `exemplars.py`.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The same 250 problems from [GSM8K](https://arxiv.org/abs/2110.14168) are each translated via human annotators in 10 languages. The 10 languages are:
- Spanish
- French
- German
- Russian
- Chinese
- Japanese
- Thai
- Swahili
- Bengali
- Telugu
## Dataset Structure
### Data Instances
Each instance in the train split contains:
- a string for the grade-school level math question
- a string for the corresponding answer with chain-of-thought steps.
- the numeric solution to the question
- the equation solution to the question
```python
{'question': 'Question: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?',
'answer': 'Step-by-Step Answer: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11.',
'answer_number': 11,
'equation_solution': '5 + 6 = 11.'}
```
Each instance in the test split contains:
- a string for the grade-school level math question
- the numeric solution to the question
```python
{'question': "Janet’s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?",
'answer': None,
'answer_number': 18,
'equation_solution': None}
```
### Data Fields
The data fields are the same among `train` and `test` splits.
- question: The question string to a grade school math problem.
- answer: The full solution string to the `question`. It contains multiple steps of reasoning with calculator annotations and the final numeric solution.
- answer_number: The numeric solution to the `question`.
- equation_solution: The equation solution to the `question`.
### Data Splits
- The train split includes 8 few-shot exemplars that are also manually translated from each language.
- The test split includes the same 250 problems from GSM8K translated via human annotators in 10 languages.
| name |train|test |
|--------|----:|---------:|
|en | 8 | 250 |
|es | 8 | 250 |
|fr | 8 | 250 |
|de | 8 | 250 |
|ru | 8 | 250 |
|zh | 8 | 250 |
|ja | 8 | 250 |
|th | 8 | 250 |
|sw | 8 | 250 |
|bn | 8 | 250 |
|te | 8 | 250 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
From the paper:
> We initially collected a starting set of a thousand problems and natural language solutions by hiring freelance contractors on Upwork (upwork.com). We then worked with Surge AI (surgehq.ai), an NLP data labeling platform, to scale up our data collection. After collecting the full dataset, we asked workers to re-solve all problems, with no workers re-solving problems they originally wrote. We checked whether their final answers agreed with the original solu- tions, and any problems that produced disagreements were either repaired or discarded. We then performed another round of agreement checks on a smaller subset of problems, finding that 1.7% of problems still produce disagreements among contractors. We estimate this to be the fraction of problems that con- tain breaking errors or ambiguities. It is possible that a larger percentage of problems contain subtle errors.
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
Surge AI (surgehq.ai)
### 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
The GSM8K dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT).
### Citation Information
```bibtex
@article{cobbe2021gsm8k,
title={Training Verifiers to Solve Math Word Problems},
author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John},
journal={arXiv preprint arXiv:2110.14168},
year={2021}
}
@misc{shi2022language,
title={Language Models are Multilingual Chain-of-Thought Reasoners},
author={Freda Shi and Mirac Suzgun and Markus Freitag and Xuezhi Wang and Suraj Srivats and Soroush Vosoughi and Hyung Won Chung and Yi Tay and Sebastian Ruder and Denny Zhou and Dipanjan Das and Jason Wei},
year={2022},
eprint={2210.03057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@juletx](https://github.com/juletx) for adding this dataset. |
true |
# Dataset Card for IMPPRES
## 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/facebookresearch/Imppres)
- **Repository:** [Github](https://github.com/facebookresearch/Imppres)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/2020.acl-main.768)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Over >25k semiautomatically generated sentence pairs illustrating well-studied pragmatic inference types. IMPPRES is an NLI dataset following the format of SNLI (Bowman et al., 2015), MultiNLI (Williams et al., 2018) and XNLI (Conneau et al., 2018), which was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
### Supported Tasks and Leaderboards
Natural Language Inference.
### Languages
English.
## Dataset Structure
### Data Instances
The data consists of 2 configurations: implicature and presupposition.
Each configuration consists of several different sub-datasets:
**Pressupposition**
- all_n_presupposition
- change_of_state
- cleft_uniqueness
- possessed_definites_existence
- question_presupposition
- both_presupposition
- cleft_existence
- only_presupposition
- possessed_definites_uniqueness
**Implicature**
- connectives
- gradable_adjective
- gradable_verb
- modals
- numerals_10_100
- numerals_2_3
- quantifiers
Each sentence type in IMPPRES is generated according to a template that specifies the linear order of the constituents in the sentence. The constituents are sampled from a vocabulary of over 3000 lexical items annotated with grammatical features needed to ensure wellformedness. We semiautomatically generate IMPPRES using a codebase developed by Warstadt et al. (2019a) and significantly expanded for the BLiMP dataset (Warstadt et al., 2019b).
Here is an instance of the raw presupposition data from any sub-dataset:
```buildoutcfg
{
"sentence1": "All ten guys that proved to boast might have been divorcing.",
"sentence2": "There are exactly ten guys that proved to boast.",
"trigger": "modal",
"presupposition": "positive",
"gold_label": "entailment",
"UID": "all_n_presupposition",
"pairID": "9e",
"paradigmID": 0
}
```
and the raw implicature data from any sub-dataset:
```buildoutcfg
{
"sentence1": "That teenager couldn't yell.",
"sentence2": "That teenager could yell.",
"gold_label_log": "contradiction",
"gold_label_prag": "contradiction",
"spec_relation": "negation",
"item_type": "control",
"trigger": "modal",
"lexemes": "can - have to"
}
```
### Data Fields
**Presupposition**
There is a slight mapping from the raw data fields in the presupposition sub-datasets and the fields appearing in the HuggingFace Datasets.
When dealing with the HF Dataset, the following mapping of fields happens:
```buildoutcfg
"premise" -> "sentence1"
"hypothesis"-> "sentence2"
"trigger" -> "trigger" or "Not_In_Example"
"trigger1" -> "trigger1" or "Not_In_Example"
"trigger2" -> "trigger2" or "Not_In_Example"
"presupposition" -> "presupposition" or "Not_In_Example"
"gold_label" -> "gold_label"
"UID" -> "UID"
"pairID" -> "pairID"
"paradigmID" -> "paradigmID"
```
For the most part, the majority of the raw fields remain unchanged. However, when it comes to the various `trigger` fields, a new mapping was introduced.
There are some examples in the dataset that only have the `trigger` field while other examples have the `trigger1` and `trigger2` field without the `trigger` or `presupposition` field.
Nominally, most examples look like the example in the Data Instances section above. Occassionally, however, some examples will look like:
```buildoutcfg
{
'sentence1': 'Did that committee know when Lissa walked through the cafe?',
'sentence2': 'That committee knew when Lissa walked through the cafe.',
'trigger1': 'interrogative',
'trigger2': 'unembedded',
'gold_label': 'neutral',
'control_item': True,
'UID': 'question_presupposition',
'pairID': '1821n',
'paradigmID': 95
}
```
In this example, `trigger1` and `trigger2` appear and `presupposition` and `trigger` are removed. This maintains the length of the dictionary.
To account for these examples, we have thus introduced the mapping above such that all examples accessed through the HF Datasets interface will have the same size as well as the same fields.
In the event that an example does not have a value for one of the fields, the field is maintained in the dictionary but given a value of `Not_In_Example`.
To illustrate this point, the example given in the Data Instances section above would look like the following in the HF Datasets:
```buildoutcfg
{
"premise": "All ten guys that proved to boast might have been divorcing.",
"hypothesis": "There are exactly ten guys that proved to boast.",
"trigger": "modal",
"trigger1": "Not_In_Example",
"trigger2": "Not_In_Example"
"presupposition": "positive",
"gold_label": "entailment",
"UID": "all_n_presupposition",
"pairID": "9e",
"paradigmID": 0
}
```
Below is description of the fields:
```buildoutcfg
"premise": The premise.
"hypothesis": The hypothesis.
"trigger": A detailed discussion of trigger types appears in the paper.
"trigger1": A detailed discussion of trigger types appears in the paper.
"trigger2": A detailed discussion of trigger types appears in the paper.
"presupposition": positive or negative.
"gold_label": Corresponds to entailment, contradiction, or neutral.
"UID": Unique id.
"pairID": Sentence pair ID.
"paradigmID": ?
```
It is not immediately clear what the difference is between `trigger`, `trigger1`, and `trigger2` is or what the `paradigmID` refers to.
**Implicature**
The `implicature` fields only have the mapping below:
```buildoutcfg
"premise" -> "sentence1"
"hypothesis"-> "sentence2"
```
Here is a description of the fields:
```buildoutcfg
"premise": The premise.
"hypothesis": The hypothesis.
"gold_label_log": Gold label for a logical reading of the sentence pair.
"gold_label_prag": Gold label for a pragmatic reading of the sentence pair.
"spec_relation": ?
"item_type": ?
"trigger": A detailed discussion of trigger types appears in the paper.
"lexemes": ?
```
### Data Splits
As the dataset was created to test already trained models, the only split that exists is for testing.
## Dataset Creation
### Curation Rationale
IMPPRES was created to evaluate how well trained NLI models recognize several classes of presuppositions and scalar implicatures.
### 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?
The annotations were generated semi-automatically.
### 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
IMPPRES is available under a Creative Commons Attribution-NonCommercial 4.0 International Public License ("The License"). You may not use these files except in compliance with the License. Please see the LICENSE file for more information before you use the dataset.
### Citation Information
```buildoutcfg
@inproceedings{jeretic-etal-2020-natural,
title = "Are Natural Language Inference Models {IMPPRESsive}? {L}earning {IMPlicature} and {PRESupposition}",
author = "Jereti\v{c}, Paloma and
Warstadt, Alex and
Bhooshan, Suvrat and
Williams, Adina",
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.768",
doi = "10.18653/v1/2020.acl-main.768",
pages = "8690--8705",
abstract = "Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer whether a sentence entails another. However, the ability of NLI models to make pragmatic inferences remains understudied. We create an IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of 32K semi-automatically generated sentence pairs illustrating well-studied pragmatic inference types. We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences. Although MultiNLI appears to contain very few pairs illustrating these inference types, we find that BERT learns to draw pragmatic inferences. It reliably treats scalar implicatures triggered by {``}some{''} as entailments. For some presupposition triggers like {``}only{''}, BERT reliably recognizes the presupposition as an entailment, even when the trigger is embedded under an entailment canceling operator like negation. BOW and InferSent show weaker evidence of pragmatic reasoning. We conclude that NLI training encourages models to learn some, but not all, pragmatic inferences.",
}
```
### Contributions
Thanks to [@aclifton314](https://github.com/aclifton314) for adding this dataset. |
false |
# Dataset Summary
Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking with learned dense representations.
This dataset stores documents of Mr. TyDi. To access the queries and judgments, please refer to [castorini/mr-tydi](https://huggingface.co/datasets/castorini/mr-tydi).
# Dataset Structure
The only configuration here is the `language`. As all three folds (train, dev and test) share the same corpus, there is only one fold 'train' under each language, unlike [castorini/mr-tydi](https://huggingface.co/datasets/castorini/mr-tydi).
An example of document data entry looks as follows:
```
{
'docid': '25#0',
'title': 'Autism',
'text': 'Autism is a developmental disorder characterized by difficulties with social interaction and communication, ...'
}
```
# Load Dataset
An example to load the dataset:
```
language = 'english'
dataset = load_dataset('castorini/mr-tydi-corpus', language, 'train')
```
# Citation Information
```
@article{mrtydi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
``` |
true |
# Dataset Card for IndoNLU
## 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:** [IndoNLU Website](https://www.indobenchmark.com/)
- **Repository:** [IndoNLU GitHub](https://github.com/indobenchmark/indonlu)
- **Paper:** [IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding](https://www.aclweb.org/anthology/2020aacl-main.85.pdf)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
The IndoNLU benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems for Bahasa Indonesia (Indonesian language).
There are 12 datasets in IndoNLU benchmark for Indonesian natural language understanding.
1. `EmoT`: An emotion classification dataset collected from the social media platform Twitter. The dataset consists of around 4000 Indonesian colloquial language tweets, covering five different emotion labels: anger, fear, happy, love, and sadness
2. `SmSA`: This sentence-level sentiment analysis dataset is a collection of comments and reviews in Indonesian obtained from multiple online platforms. The text was crawled and then annotated by several Indonesian linguists to construct this dataset. There are three possible sentiments on the `SmSA` dataset: positive, negative, and neutral
3. `CASA`: An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms. The dataset covers six aspects of car quality. We define the task to be a multi-label classification task, where each label represents a sentiment for a single aspect with three possible values: positive, negative, and neutral.
4. `HoASA`: An aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel aggregator platform, [AiryRooms](https://github.com/annisanurulazhar/absa-playground). The dataset covers ten different aspects of hotel quality. Similar to the `CASA` dataset, each review is labeled with a single sentiment label for each aspect. There are four possible sentiment classes for each sentiment label: positive, negative, neutral, and positive-negative. The positivenegative label is given to a review that contains multiple sentiments of the same aspect but for different objects (e.g., cleanliness of bed and toilet).
5. `WReTE`: The Wiki Revision Edits Textual Entailment dataset consists of 450 sentence pairs constructed from Wikipedia revision history. The dataset contains pairs of sentences and binary semantic relations between the pairs. The data are labeled as entailed when the meaning of the second sentence can be derived from the first one, and not entailed otherwise.
6. `POSP`: This Indonesian part-of-speech tagging (POS) dataset is collected from Indonesian news websites. The dataset consists of around 8000 sentences with 26 POS tags. The POS tag labels follow the [Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention](http://inacl.id/inacl/wp-content/uploads/2017/06/INACL-POS-Tagging-Convention-26-Mei.pdf).
7. `BaPOS`: This POS tagging dataset contains about 1000 sentences, collected from the [PAN Localization Project](http://www.panl10n.net/). In this dataset, each word is tagged by one of [23 POS tag classes](https://bahasa.cs.ui.ac.id/postag/downloads/Tagset.pdf). Data splitting used in this benchmark follows the experimental setting used by [Kurniawan and Aji (2018)](https://arxiv.org/abs/1809.03391).
8. `TermA`: This span-extraction dataset is collected from the hotel aggregator platform, [AiryRooms](https://github.com/jordhy97/final_project). The dataset consists of thousands of hotel reviews, which each contain a span label for aspect and sentiment words representing the opinion of the reviewer on the corresponding aspect. The labels use Inside-Outside-Beginning (IOB) tagging representation with two kinds of tags, aspect and sentiment.
9. `KEPS`: This keyphrase extraction dataset consists of text from Twitter discussing banking products and services and is written in the Indonesian language. A phrase containing important information is considered a keyphrase. Text may contain one or more keyphrases since important phrases can be located at different positions. The dataset follows the IOB chunking format, which represents the position of the keyphrase.
10. `NERGrit`: This NER dataset is taken from the [Grit-ID repository](https://github.com/grit-id/nergrit-corpus), and the labels are spans in IOB chunking representation. The dataset consists of three kinds of named entity tags, PERSON (name of person), PLACE (name of location), and ORGANIZATION (name of organization).
11. `NERP`: This NER dataset (Hoesen and Purwarianti, 2018) contains texts collected from several Indonesian news websites. There are five labels available in this dataset, PER (name of person), LOC (name of location), IND (name of product or brand), EVT (name of the event), and FNB (name of food and beverage). Similar to the `TermA` dataset, the `NERP` dataset uses the IOB chunking format.
12. `FacQA`: The goal of the FacQA dataset is to find the answer to a question from a provided short passage from a news article. Each row in the FacQA dataset consists of a question, a short passage, and a label phrase, which can be found inside the corresponding short passage. There are six categories of questions: date, location, name, organization, person, and quantitative.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
Indonesian
## Dataset Structure
### Data Instances
1. `EmoT` dataset
A data point consists of `tweet` and `label`. An example from the train set looks as follows:
```
{
'tweet': 'Ini adalah hal yang paling membahagiakan saat biasku foto bersama ELF #ReturnOfTheLittlePrince #HappyHeeChulDay'
'label': 4,
}
```
2. `SmSA` dataset
A data point consists of `text` and `label`. An example from the train set looks as follows:
```
{
'text': 'warung ini dimiliki oleh pengusaha pabrik tahu yang sudah puluhan tahun terkenal membuat tahu putih di bandung . tahu berkualitas , dipadu keahlian memasak , dipadu kretivitas , jadilah warung yang menyajikan menu utama berbahan tahu , ditambah menu umum lain seperti ayam . semuanya selera indonesia . harga cukup terjangkau . jangan lewatkan tahu bletoka nya , tidak kalah dengan yang asli dari tegal !'
'label': 0,
}
```
3. `CASA` dataset
A data point consists of `sentence` and multi-label `feature`, `machine`, `others`, `part`, `price`, and `service`. An example from the train set looks as follows:
```
{
'sentence': 'Saya memakai Honda Jazz GK5 tahun 2014 ( pertama meluncur ) . Mobil nya bagus dan enak sesuai moto nya menyenangkan untuk dikendarai',
'fuel': 1,
'machine': 1,
'others': 2,
'part': 1,
'price': 1,
'service': 1
}
```
4. `HoASA` dataset
A data point consists of `sentence` and multi-label `ac`, `air_panas`, `bau`, `general`, `kebersihan`, `linen`, `service`, `sunrise_meal`, `tv`, and `wifi`. An example from the train set looks as follows:
```
{
'sentence': 'kebersihan kurang...',
'ac': 1,
'air_panas': 1,
'bau': 1,
'general': 1,
'kebersihan': 0,
'linen': 1,
'service': 1,
'sunrise_meal': 1,
'tv': 1,
'wifi': 1
}
```
5. `WreTE` dataset
A data point consists of `premise`, `hypothesis`, `category`, and `label`. An example from the train set looks as follows:
```
{
'premise': 'Pada awalnya bangsa Israel hanya terdiri dari satu kelompok keluarga di antara banyak kelompok keluarga yang hidup di tanah Kanan pada abad 18 SM .',
'hypothesis': 'Pada awalnya bangsa Yahudi hanya terdiri dari satu kelompok keluarga di antara banyak kelompok keluarga yang hidup di tanah Kanan pada abad 18 SM .'
'category': 'menolak perubahan teks terakhir oleh istimewa kontribusi pengguna 141 109 98 87 141 109 98 87 dan mengembalikan revisi 6958053 oleh johnthorne',
'label': 0,
}
```
6. `POSP` dataset
A data point consists of `tokens` and `pos_tags`. An example from the train set looks as follows:
```
{
'tokens': ['kepala', 'dinas', 'tata', 'kota', 'manado', 'amos', 'kenda', 'menyatakan', 'tidak', 'tahu', '-', 'menahu', 'soal', 'pencabutan', 'baliho', '.', 'ia', 'enggan', 'berkomentar', 'banyak', 'karena', 'merasa', 'bukan', 'kewenangannya', '.'],
'pos_tags': [11, 6, 11, 11, 7, 7, 7, 9, 23, 4, 21, 9, 11, 11, 11, 21, 3, 2, 4, 1, 19, 9, 23, 11, 21]
}
```
7. `BaPOS` dataset
A data point consists of `tokens` and `pos_tags`. An example from the train set looks as follows:
```
{
'tokens': ['Kera', 'untuk', 'amankan', 'pesta', 'olahraga'],
'pos_tags': [27, 8, 26, 27, 30]
}
```
8. `TermA` dataset
A data point consists of `tokens` and `seq_label`. An example from the train set looks as follows:
```
{
'tokens': ['kamar', 'saya', 'ada', 'kendala', 'di', 'ac', 'tidak', 'berfungsi', 'optimal', '.', 'dan', 'juga', 'wifi', 'koneksi', 'kurang', 'stabil', '.'],
'seq_label': [1, 1, 1, 1, 1, 4, 3, 0, 0, 1, 1, 1, 4, 2, 3, 0, 1]
}
```
9. `KEPS` dataset
A data point consists of `tokens` and `seq_label`. An example from the train set looks as follows:
```
{
'tokens': ['Setelah', 'melalui', 'proses', 'telepon', 'yang', 'panjang', 'tutup', 'sudah', 'kartu', 'kredit', 'bca', 'Ribet'],
'seq_label': [0, 1, 1, 2, 0, 0, 1, 0, 1, 2, 2, 1]
}
```
10. `NERGrit` dataset
A data point consists of `tokens` and `ner_tags`. An example from the train set looks as follows:
```
{
'tokens': ['Kontribusinya', 'terhadap', 'industri', 'musik', 'telah', 'mengumpulkan', 'banyak', 'prestasi', 'termasuk', 'lima', 'Grammy', 'Awards', ',', 'serta', 'dua', 'belas', 'nominasi', ';', 'dua', 'Guinness', 'World', 'Records', ';', 'dan', 'penjualannya', 'diperkirakan', 'sekitar', '64', 'juta', 'rekaman', '.'],
'ner_tags': [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]}
```
11. `NERP` dataset
A data point consists of `tokens` and `ner_tags`. An example from the train set looks as follows:
```
{
'tokens': ['kepala', 'dinas', 'tata', 'kota', 'manado', 'amos', 'kenda', 'menyatakan', 'tidak', 'tahu', '-', 'menahu', 'soal', 'pencabutan', 'baliho', '.', 'ia', 'enggan', 'berkomentar', 'banyak', 'karena', 'merasa', 'bukan', 'kewenangannya', '.'],
'ner_tags': [9, 9, 9, 9, 2, 7, 0, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9]
}
```
12. `FacQA` dataset
A data point consists of `question`, `passage`, and `seq_label`. An example from the train set looks as follows:
```
{
'passage': ['Lewat', 'telepon', 'ke', 'kantor', 'berita', 'lokal', 'Current', 'News', 'Service', ',', 'Hezb-ul', 'Mujahedeen', ',', 'kelompok', 'militan', 'Kashmir', 'yang', 'terbesar', ',', 'menyatakan', 'bertanggung', 'jawab', 'atas', 'ledakan', 'di', 'Srinagar', '.'],
'question': ['Kelompok', 'apakah', 'yang', 'menyatakan', 'bertanggung', 'jawab', 'atas', 'ledakan', 'di', 'Srinagar', '?'],
'seq_label': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
}
```
### Data Fields
1. `EmoT` dataset
- `tweet`: a `string` feature.
- `label`: an emotion label, with possible values including `sadness`, `anger`, `love`, `fear`, `happy`.
2. `SmSA` dataset
- `text`: a `string` feature.
- `label`: a sentiment label, with possible values including `positive`, `neutral`, `negative`.
3. `CASA` dataset
- `sentence`: a `string` feature.
- `fuel`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
- `machine`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
- `others`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
- `part`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
- `price`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
- `service`: a sentiment label, with possible values including `negative`, `neutral`, `positive`.
4. `HoASA` dataset
- `sentence`: a `string` feature.
- `ac`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `air_panas`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `bau`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `general`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `kebersihan`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `linen`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `service`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `sunrise_meal`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `tv`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
- `wifi`: a sentiment label, with possible values including `neg`, `neut`, `pos`, `neg_pos`.
5. `WReTE` dataset
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `category`: a `string` feature.
- `label`: a classification label, with possible values including `NotEntail`, `Entail_or_Paraphrase`.
6. `POSP` dataset
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of POS tag labels, with possible values including `B-PPO`, `B-KUA`, `B-ADV`, `B-PRN`, `B-VBI`.
The POS tag labels follow the [Indonesian Association of Computational Linguistics (INACL) POS Tagging Convention](http://inacl.id/inacl/wp-content/uploads/2017/06/INACLPOS-Tagging-Convention-26-Mei.pdf).
7. `BaPOS` dataset
- `tokens`: a `list` of `string` features.
- `pos_tags`: a `list` of POS tag labels, with possible values including `B-PR`, `B-CD`, `I-PR`, `B-SYM`, `B-JJ`.
The POS tag labels from [Tagset UI](https://bahasa.cs.ui.ac.id/postag/downloads/Tagset.pdf).
8. `TermA` dataset
- `tokens`: a `list` of `string` features.
- `seq_label`: a `list` of classification labels, with possible values including `I-SENTIMENT`, `O`, `I-ASPECT`, `B-SENTIMENT`, `B-ASPECT`.
9. `KEPS` dataset
- `tokens`: a `list` of `string` features.
- `seq_label`: a `list` of classification labels, with possible values including `O`, `B`, `I`.
The labels use Inside-Outside-Beginning (IOB) tagging.
10. `NERGrit` dataset
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of NER tag labels, with possible values including `I-PERSON`, `B-ORGANISATION`, `I-ORGANISATION`, `B-PLACE`, `I-PLACE`.
The labels use Inside-Outside-Beginning (IOB) tagging.
11. `NERP` dataset
- `tokens`: a `list` of `string` features.
- `ner_tags`: a `list` of NER tag labels, with possible values including `I-PPL`, `B-EVT`, `B-PLC`, `I-IND`, `B-IND`.
12. `FacQA` dataset
- `question`: a `list` of `string` features.
- `passage`: a `list` of `string` features.
- `seq_label`: a `list` of classification labels, with possible values including `O`, `B`, `I`.
### Data Splits
The data is split into a training, validation and test set.
| | dataset | Train | Valid | Test |
|----|---------|-------|-------|------|
| 1 | EmoT | 3521 | 440 | 440 |
| 2 | SmSA | 11000 | 1260 | 500 |
| 3 | CASA | 810 | 90 | 180 |
| 4 | HoASA | 2283 | 285 | 286 |
| 5 | WReTE | 300 | 50 | 100 |
| 6 | POSP | 6720 | 840 | 840 |
| 7 | BaPOS | 8000 | 1000 | 1029 |
| 8 | TermA | 3000 | 1000 | 1000 |
| 9 | KEPS | 800 | 200 | 247 |
| 10 | NERGrit | 1672 | 209 | 209 |
| 11 | NERP | 6720 | 840 | 840 |
| 12 | FacQA | 2495 | 311 | 311 |
## 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
The licensing status of the IndoNLU benchmark datasets is under MIT License.
### Citation Information
IndoNLU citation
```
@inproceedings{wilie2020indonlu,
title={IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding},
author={Bryan Wilie and Karissa Vincentio and Genta Indra Winata and Samuel Cahyawijaya and X. Li and Zhi Yuan Lim and S. Soleman and R. Mahendra and Pascale Fung and Syafri Bahar and A. Purwarianti},
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},
year={2020}
}
```
`EmoT` dataset citation
```
@inproceedings{saputri2018emotion,
title={Emotion Classification on Indonesian Twitter Dataset},
author={Mei Silviana Saputri, Rahmad Mahendra, and Mirna Adriani},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing(IALP)},
pages={90--95},
year={2018},
organization={IEEE}
}
```
`SmSA` dataset citation
```
@inproceedings{purwarianti2019improving,
title={Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector},
author={Ayu Purwarianti and Ida Ayu Putu Ari Crisdayanti},
booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)},
pages={1--5},
year={2019},
organization={IEEE}
}
```
`CASA` dataset citation
```
@inproceedings{ilmania2018aspect,
title={Aspect Detection and Sentiment Classification Using Deep Neural Network for Indonesian Aspect-based Sentiment Analysis},
author={Arfinda Ilmania, Abdurrahman, Samuel Cahyawijaya, Ayu Purwarianti},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing(IALP)},
pages={62--67},
year={2018},
organization={IEEE}
}
```
`HoASA` dataset citation
```
@inproceedings{azhar2019multi,
title={Multi-label Aspect Categorization with Convolutional Neural Networks and Extreme Gradient Boosting},
author={A. N. Azhar, M. L. Khodra, and A. P. Sutiono}
booktitle={Proceedings of the 2019 International Conference on Electrical Engineering and Informatics (ICEEI)},
pages={35--40},
year={2019}
}
```
`WReTE` dataset citation
```
@inproceedings{setya2018semi,
title={Semi-supervised Textual Entailment on Indonesian Wikipedia Data},
author={Ken Nabila Setya and Rahmad Mahendra},
booktitle={Proceedings of the 2018 International Conference on Computational Linguistics and Intelligent Text Processing (CICLing)},
year={2018}
}
```
`POSP` dataset citation
```
@inproceedings{hoesen2018investigating,
title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger},
author={Devin Hoesen and Ayu Purwarianti},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
pages={35--38},
year={2018},
organization={IEEE}
}
```
`BaPOS` dataset citation
```
@inproceedings{dinakaramani2014designing,
title={Designing an Indonesian Part of Speech Tagset and Manually Tagged Indonesian Corpus},
author={Arawinda Dinakaramani, Fam Rashel, Andry Luthfi, and Ruli Manurung},
booktitle={Proceedings of the 2014 International Conference on Asian Language Processing (IALP)},
pages={66--69},
year={2014},
organization={IEEE}
}
@inproceedings{kurniawan2018toward,
title={Toward a Standardized and More Accurate Indonesian Part-of-Speech Tagging},
author={Kemal Kurniawan and Alham Fikri Aji},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
pages={303--307},
year={2018},
organization={IEEE}
}
```
`TermA` dataset citation
```
@article{winatmoko2019aspect,
title={Aspect and Opinion Term Extraction for Hotel Reviews Using Transfer Learning and Auxiliary Labels},
author={Yosef Ardhito Winatmoko, Ali Akbar Septiandri, Arie Pratama Sutiono},
journal={arXiv preprint arXiv:1909.11879},
year={2019}
}
@article{fernando2019aspect,
title={Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews},
author={Jordhy Fernando, Masayu Leylia Khodra, Ali Akbar Septiandri},
journal={arXiv preprint arXiv:1908.04899},
year={2019}
}
```
`KEPS` dataset citation
```
@inproceedings{mahfuzh2019improving,
title={Improving Joint Layer RNN based Keyphrase Extraction by Using Syntactical Features},
author={Miftahul Mahfuzh, Sidik Soleman, and Ayu Purwarianti},
booktitle={Proceedings of the 2019 International Conference of Advanced Informatics: Concepts, Theory and Applications (ICAICTA)},
pages={1--6},
year={2019},
organization={IEEE}
}
```
`NERGrit` dataset citation
```
@online{nergrit2019,
title={NERGrit Corpus},
author={NERGrit Developers},
year={2019},
url={https://github.com/grit-id/nergrit-corpus}
}
```
`NERP` dataset citation
```
@inproceedings{hoesen2018investigating,
title={Investigating Bi-LSTM and CRF with POS Tag Embedding for Indonesian Named Entity Tagger},
author={Devin Hoesen and Ayu Purwarianti},
booktitle={Proceedings of the 2018 International Conference on Asian Language Processing (IALP)},
pages={35--38},
year={2018},
organization={IEEE}
}
```
`FacQA` dataset citation
```
@inproceedings{purwarianti2007machine,
title={A Machine Learning Approach for Indonesian Question Answering System},
author={Ayu Purwarianti, Masatoshi Tsuchiya, and Seiichi Nakagawa},
booktitle={Proceedings of Artificial Intelligence and Applications },
pages={573--578},
year={2007}
}
```
### Contributions
Thanks to [@yasirabd](https://github.com/yasirabd) 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:** https://github.com/t-davidson/hate-speech-and-offensive-language
- **Repository:** https://github.com/t-davidson/hate-speech-and-offensive-language
- **Paper:** https://arxiv.org/abs/1703.04009
- **Leaderboard:**
- **Point of Contact:** https://docs.google.com/forms/d/e/1FAIpQLSdrPNlfVBlqxun2tivzAtsZaOoPC5YYMocn-xscCgeRakLXHg/viewform?usp=pp_url&entry.1506871634&entry.147453066&entry.1390333885&entry.516829772
### Dataset Summary
An annotated dataset for hate speech and offensive language detection on tweets.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (`en`)
## Dataset Structure
### Data Instances
```
{
"count": 3,
"hate_speech_annotation": 0,
"offensive_language_annotation": 0,
"neither_annotation": 3,
"label": 2, # "neither"
"tweet": "!!! RT @mayasolovely: As a woman you shouldn't complain about cleaning up your house. & as a man you should always take the trash out...")
}
```
### Data Fields
```
count: (Integer) number of users who coded each tweet (min is 3, sometimes more users coded a tweet when judgments were determined to be unreliable,
hate_speech_annotation: (Integer) number of users who judged the tweet to be hate speech,
offensive_language_annotation: (Integer) number of users who judged the tweet to be offensive,
neither_annotation: (Integer) number of users who judged the tweet to be neither offensive nor non-offensive,
label: (Class Label) class label for majority of CF users (0: 'hate-speech', 1: 'offensive-language' or 2: 'neither'),
tweet: (string)
```
### Data Splits
This dataset is not splitted, only the train split is 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
Usernames are not anonymized in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
MIT License
### Citation Information
@inproceedings{hateoffensive,
title = {Automated Hate Speech Detection and the Problem of Offensive Language},
author = {Davidson, Thomas and Warmsley, Dana and Macy, Michael and Weber, Ingmar},
booktitle = {Proceedings of the 11th International AAAI Conference on Web and Social Media},
series = {ICWSM '17},
year = {2017},
location = {Montreal, Canada},
pages = {512-515}
}
### Contributions
Thanks to [@hugoabonizio](https://github.com/hugoabonizio) for adding this dataset. |
false | # Dataset Card for "code_x_glue_tc_text_to_code"
## 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/Text-Code/text-to-code
### Dataset Summary
CodeXGLUE text-to-code dataset, available at https://github.com/microsoft/CodeXGLUE/tree/main/Text-Code/text-to-code
The dataset we use is crawled and filtered from Microsoft Documentation, whose document located at https://github.com/MicrosoftDocs/.
### Supported Tasks and Leaderboards
- `machine-translation`: The dataset can be used to train a model for generating Java code from an **English** natural language description.
### Languages
- Java **programming** language
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
"code": "boolean function ( ) { return isParsed ; }",
"id": 0,
"nl": "check if details are parsed . concode_field_sep Container parent concode_elem_sep boolean isParsed concode_elem_sep long offset concode_elem_sep long contentStartPosition concode_elem_sep ByteBuffer deadBytes concode_elem_sep boolean isRead concode_elem_sep long memMapSize concode_elem_sep Logger LOG concode_elem_sep byte[] userType concode_elem_sep String type concode_elem_sep ByteBuffer content concode_elem_sep FileChannel fileChannel concode_field_sep Container getParent concode_elem_sep byte[] getUserType concode_elem_sep void readContent concode_elem_sep long getOffset concode_elem_sep long getContentSize concode_elem_sep void getContent concode_elem_sep void setDeadBytes concode_elem_sep void parse concode_elem_sep void getHeader concode_elem_sep long getSize concode_elem_sep void parseDetails concode_elem_sep String getType concode_elem_sep void _parseDetails concode_elem_sep String getPath concode_elem_sep boolean verify concode_elem_sep void setParent concode_elem_sep void getBox concode_elem_sep boolean isSmallBox"
}
```
### 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 |
|nl |string| The natural language description of the task|
|code |string| The programming source code for the task |
### Data Splits
| name |train |validation|test|
|-------|-----:|---------:|---:|
|default|100000| 2000|2000|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
https://github.com/microsoft, https://github.com/madlag
### Licensing Information
Computational Use of Data Agreement (C-UDA) License.
### Citation Information
```
@article{iyer2018mapping,
title={Mapping language to code in programmatic context},
author={Iyer, Srinivasan and Konstas, Ioannis and Cheung, Alvin and Zettlemoyer, Luke},
journal={arXiv preprint arXiv:1808.09588},
year={2018}
}
```
### Contributions
Thanks to @madlag (and partly also @ncoop57) for adding this dataset. |
true |
# Dataset Card for Snips Built In Intents
## 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/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents
- **Repository:** https://github.com/sonos/nlu-benchmark/tree/master/2016-12-built-in-intents
- **Paper:** https://arxiv.org/abs/1805.10190
- **Point of Contact:** The Snips team has joined Sonos in November 2019. These open datasets remain available and their access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question.
### Dataset Summary
Snips' built in intents dataset was initially used to compare different voice assistants and released as a public dataset hosted at
https://github.com/sonos/nlu-benchmark in folder 2016-12-built-in-intents. The dataset contains 328 utterances over 10 intent classes.
A related Medium post is https://medium.com/snips-ai/benchmarking-natural-language-understanding-systems-d35be6ce568d.
### Supported Tasks and Leaderboards
There are no related shared tasks that we are aware of.
### Languages
English
## Dataset Structure
### Data Instances
The dataset contains 328 utterances over 10 intent classes. Each sample looks like:
`{'label': 8, 'text': 'Transit directions to Barcelona Pizza.'}`
### Data Fields
- `text`: The text utterance expressing some user intent.
- `label`: The intent label of the piece of text utterance.
### Data Splits
The source data is not split.
## Dataset Creation
### Curation Rationale
The dataset was originally created to compare the performance of a number of voice assistants. However, the labelled utterances are useful
for developing and benchmarking text chatbots as well.
### Source Data
#### Initial Data Collection and Normalization
It is not clear how the data was collected. From the Medium post: `The benchmark relies on a set of 328 queries built by the business team
at Snips, and kept secret from data scientists and engineers throughout the development of the solution.`
#### Who are the source language producers?
Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their
access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question.
### Annotations
#### Annotation process
It is not clear how the data was collected. From the Medium post: `The benchmark relies on a set of 328 queries built by the business team
at Snips, and kept secret from data scientists and engineers throughout the development of the solution.`
#### 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
Originally prepared by snips.ai. The Snips team has since joined Sonos in November 2019. These open datasets remain available and their
access is now managed by the Sonos Voice Experience Team. Please email sve-research@sonos.com with any question.
### Licensing Information
The source data is licensed under Creative Commons Zero v1.0 Universal.
### Citation Information
Any publication based on these datasets must include a full citation to the following paper in which the results were published by the Snips Team:
Coucke A. et al., "Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces." CoRR 2018,
https://arxiv.org/abs/1805.10190
### Contributions
Thanks to [@bduvenhage](https://github.com/bduvenhage) for adding this dataset. |
false |
# Dataset Card for CPPE - 5
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** https://github.com/Rishit-dagli/CPPE-Dataset
- **Paper:** [CPPE-5: Medical Personal Protective Equipment Dataset](https://arxiv.org/abs/2112.09569)
- **Leaderboard:** https://paperswithcode.com/sota/object-detection-on-cppe-5
- **Point of Contact:** rishit.dagli@gmail.com
### Dataset Summary
CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal to allow the study of subordinate categorization of medical personal protective equipments, which is not possible with other popular data sets that focus on broad level categories.
Some features of this dataset are:
* high quality images and annotations (~4.6 bounding boxes per image)
* real-life images unlike any current such dataset
* majority of non-iconic images (allowing easy deployment to real-world environments)
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection. This task has an active leaderboard which can be found at https://paperswithcode.com/sota/object-detection-on-cppe-5. The metrics for this task are adopted from the COCO detection evaluation criteria, and include the mean Average Precision (AP) across IoU thresholds ranging from 0.50 to 0.95 at different scales.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=943x663 at 0x2373B065C18>,
'width': 943,
'height': 663,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `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]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category, with possible values including `Coverall` (0),`Face_Shield` (1),`Gloves` (2),`Goggles` (3) and `Mask` (4)
### Data Splits
The data is split into training and testing set. The training set contains 1000 images and test set 29 images.
## Dataset Creation
### Curation Rationale
From the paper:
> With CPPE-5 dataset, we hope to facilitate research and use in applications at multiple public places to autonomously identify if a PPE (Personal Protective Equipment) kit has been worn and also which part of the PPE kit has been worn. One of the main aims with this dataset was to also capture a higher ratio of non-iconic images or non-canonical perspectives [5] of the objects in this dataset. We further hope to see high use of this dataset to aid in medical scenarios which would have a huge effect
worldwide.
### Source Data
#### Initial Data Collection and Normalization
The images in the CPPE-5 dataset were collected using the following process:
* Obtain Images from Flickr: Following the object categories we identified earlier, we first download images from Flickr and save them at the "Original" size. On Flickr, images are served at multiple different sizes (Square 75, Small 240, Large 1024, X-Large 4K etc.), the "Original" size is an exact copy of the image uploaded by author.
* Extract relevant metadata: Flickr contains images each with searchable metadata, we extract the following relevant
metadata:
* A direct link to the original image on Flickr
* Width and height of the image
* Title given to the image by the author
* Date and time the image was uploaded on
* Flickr username of the author of the image
* Flickr Name of the author of the image
* Flickr profile of the author of the image
* The License image is licensed under
* MD5 hash of the original image
* Obtain Images from Google Images: Due to the reasons we mention earlier, we only collect a very small proportion
of images from Google Images. For these set of images we extract the following metadata:
* A direct link to the original image
* Width and height of the image
* MD5 hash of the original image
* Filter inappropriate images: Though very rare in the collected images, we also remove images containing inappropriate content using the safety filters on Flickr and Google Safe Search.
* Filter near-similar images: We then remove near-duplicate images in the dataset using GIST descriptors
#### Who are the source language producers?
The images for this dataset were collected from Flickr and Google Images.
### Annotations
#### Annotation process
The dataset was labelled in two phases: the first phase included labelling 416 images and the second phase included labelling 613 images. For all the images in the dataset volunteers were provided the following table:
|Item |Description |
|------------|--------------------------------------------------------------------- |
|coveralls | Coveralls are hospital gowns worn by medical professionals as in order to provide a barrier between patient and professional, these usually cover most of the exposed skin surfaces of the professional medics.|
|mask | Mask prevents airborne transmission of infections between patients and/or treating personnel by blocking the movement of pathogens (primarily bacteria and viruses) shed in respiratory droplets and aerosols into and from the wearer’s mouth and nose.|
face shield | Face shield aims to protect the wearer’s entire face (or part of it) from hazards such as flying objects and road debris, chemical splashes (in laboratories or in industry), or potentially infectious materials (in medical and laboratory environments).|
gloves | Gloves are used during medical examinations and procedures to help prevent cross-contamination between caregivers and patients.|
|goggles | Goggles, or safety glasses, are forms of protective eye wear that usually enclose or protect the area surrounding the eye in order to prevent particulates, water or chemicals from striking the eyes.|
as well as examples of: correctly labelled images, incorrectly labelled images, and not applicable images. Before the labelling task, each volunteer was provided with an exercise to verify if the volunteer was able to correctly identify categories as well as identify if an annotated image is correctly labelled, incorrectly labelled, or not applicable. The labelling process first involved two volunteers independently labelling an image from the dataset. In any of the cases that: the number of bounding boxes are different, the labels for on or more of the bounding boxes are different or two volunteer annotations are sufficiently different; a third volunteer compiles the result from the two annotations to come up with a correctly labelled image. After this step, a volunteer verifies the bounding box annotations. Following this method of labelling the dataset we ensured that all images were labelled accurately and contained exhaustive
annotations. As a result of this, our dataset consists of 1029 high-quality, majorly non-iconic, and accurately annotated images.
#### Who are the annotators?
In both the phases crowd-sourcing techniques were used with multiple volunteers labelling the dataset using the open-source tool LabelImg.
### 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
Dagli, Rishit, and Ali Mustufa Shaikh.
### Licensing Information
[More Information Needed]
### Citation Information
```
@misc{dagli2021cppe5,
title={CPPE-5: Medical Personal Protective Equipment Dataset},
author={Rishit Dagli and Ali Mustufa Shaikh},
year={2021},
eprint={2112.09569},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
false |
# Dataset Card for NCBI Disease
## Table of Contents
- [Dataset Card for NCBI Disease](#dataset-card-for-ncbi-disease)
- [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)
- [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
- **Homepage:** [NCBI](https://www.ncbi.nlm.nih.gov/research/bionlp/Data/disease)
- **Repository:** [Github](https://github.com/spyysalo/ncbi-disease)
- **Paper:** [NCBI disease corpus: A resource for disease name recognition and concept normalization](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3951655)
- **Leaderboard:** [Named Entity Recognition on NCBI-disease](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ncbi-disease)
- **Point of Contact:** [email](zhiyong.lu@nih.gov)
### Dataset Summary
This dataset contains the disease name and concept annotations of the NCBI disease corpus, a collection of 793 PubMed abstracts fully annotated at the mention and concept level to serve as a research resource for the biomedical natural language processing community.
### Supported Tasks and Leaderboards
Named Entity Recognition: [Leaderboard](https://paperswithcode.com/sota/named-entity-recognition-ner-on-ncbi-disease)
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
Instances of the dataset contain an array of `tokens`, `ner_tags` and an `id`. An example of an instance of the dataset:
```
{
'tokens': ['Identification', 'of', 'APC2', ',', 'a', 'homologue', 'of', 'the', 'adenomatous', 'polyposis', 'coli', 'tumour', 'suppressor', '.'],
'ner_tags': [0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0],
'id': '0'
}
```
### Data Fields
- `id`: Sentence identifier.
- `tokens`: Array of tokens composing a sentence.
- `ner_tags`: Array of tags, where `0` indicates no disease mentioned, `1` signals the first token of a disease and `2` the subsequent disease tokens.
### Data Splits
The data is split into a train (5433 instances), validation (924 instances) and test set (941 instances).
## Dataset Creation
### Curation Rationale
The goal of the dataset consists on improving the state-of-the-art in disease name recognition and normalization research, by providing a high-quality gold standard thus enabling the development of machine-learning based approaches for such tasks.
### Source Data
#### Initial Data Collection and Normalization
The dataset consists on abstracts extracted from PubMed.
#### Who are the source language producers?
The source language producers are the authors of publication abstracts hosted in PubMed.
### Annotations
#### Annotation process
Each PubMed abstract was manually annotated by two annotators with disease mentions and their corresponding concepts in Medical Subject Headings (MeSH®) or Online Mendelian Inheritance in Man (OMIM®). Manual curation was performed using PubTator, which allowed the use of pre-annotations as a pre-step to manual annotations. Fourteen annotators were randomly paired and differing annotations were discussed for reaching a consensus in two annotation phases. Finally, all results were checked against annotations of the rest of the corpus to assure corpus-wide consistency.
#### Who are the annotators?
The annotator group consisted of 14 people with backgrounds in biomedical informatics research and experience in biomedical text corpus annotation.
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
Information encoded in natural language in biomedical literature publications is only useful if efficient and reliable ways of accessing and analyzing that information are available. Natural language processing and text mining tools are therefore essential for extracting valuable information. This dataset provides an annotated corpora that can be used to develop highly effective tools to automatically detect central biomedical concepts such as diseases.
### Discussion of Biases
To avoid annotator bias, pairs of annotators were chosen randomly for each set, so that each pair of annotators overlapped for at most two sets.
### Other Known Limitations
A handful of disease concepts were discovered that were not included in MEDIC. For those, we decided to include the appropriate OMIM identifiers.
In addition, certain disease mentions were found to not be easily represented using the standard categorizations.
Also, each PMID document was pre-annotated using the Inference Method developed for disease name normalization, which properly handles abbreviation recognition, robust string matching, etc. As such, human annotators were given the pre-annotated documents as a starting point and allowed to see each pre-annotation with a computed confidence.
## Additional Information
### Dataset Curators
Rezarta Islamaj Doğan, Robert Leaman, Zhiyong Lu
### Licensing Information
```
PUBLIC DOMAIN NOTICE
This work is a "United States Government Work" under the terms of the
United States Copyright Act. It was written as part of the authors'
official duties as a United States Government employee and thus cannot
be copyrighted within the United States. The data is freely available
to the public for use. The National Library of Medicine and the
U.S. Government have not placed any restriction on its use or
reproduction.
Although all reasonable efforts have been taken to ensure the accuracy
and reliability of the data and its source code, the NLM and the
U.S. Government do not and cannot warrant the performance or results
that may be obtained by using it. The NLM and the U.S. Government
disclaim all warranties, express or implied, including warranties of
performance, merchantability or fitness for any particular purpose.
Please cite the authors in any work or product based on this material:
An improved corpus of disease mentions in PubMed citations
http://aclweb.org/anthology-new/W/W12/W12-2411.pdf
NCBI Disease Corpus: A Resource for Disease Name Recognition and
Normalization http://www.ncbi.nlm.nih.gov/pubmed/24393765
Disease Name Normalization with Pairwise Learning to Rank
http://www.ncbi.nlm.nih.gov/pubmed/23969135
```
### Citation Information
```
@article{dougan2014ncbi,
title={NCBI disease corpus: a resource for disease name recognition and concept normalization},
author={Do{\u{g}}an, Rezarta Islamaj and Leaman, Robert and Lu, Zhiyong},
journal={Journal of biomedical informatics},
volume={47},
pages={1--10},
year={2014},
publisher={Elsevier}
}
```
### Contributions
Thanks to [@edugp](https://github.com/edugp) for adding this dataset. |
false |
# Dataset Card for Multi-XScience
## 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:** [Multi-XScience repository](https://github.com/yaolu/Multi-XScience)
- **Paper:** [Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://arxiv.org/abs/2010.14235)
### Dataset Summary
Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English
## Dataset Structure
### Data Instances
{'abstract': 'Author(s): Kuperberg, Greg; Thurston, Dylan P. | Abstract: We give a purely topological definition of the perturbative quantum invariants of links and 3-manifolds associated with Chern-Simons field theory. Our definition is as close as possible to one given by Kontsevich. We will also establish some basic properties of these invariants, in particular that they are universally finite type with respect to algebraically split surgery and with respect to Torelli surgery. Torelli surgery is a mutual generalization of blink surgery of Garoufalidis and Levine and clasper surgery of Habiro.',
'aid': 'math9912167',
'mid': '1631980677',
'ref_abstract': {'abstract': ['This note is a sequel to our earlier paper of the same title [4] and describes invariants of rational homology 3-spheres associated to acyclic orthogonal local systems. Our work is in the spirit of the Axelrod–Singer papers [1], generalizes some of their results, and furnishes a new setting for the purely topological implications of their work.',
'Recently, Mullins calculated the Casson-Walker invariant of the 2-fold cyclic branched cover of an oriented link in S^3 in terms of its Jones polynomial and its signature, under the assumption that the 2-fold branched cover is a rational homology 3-sphere. Using elementary principles, we provide a similar calculation for the general case. In addition, we calculate the LMO invariant of the p-fold branched cover of twisted knots in S^3 in terms of the Kontsevich integral of the knot.'],
'cite_N': ['@cite_16', '@cite_26'],
'mid': ['1481005306', '1641082372']},
'related_work': 'Two other generalizations that can be considered are invariants of graphs in 3-manifolds, and invariants associated to other flat connections @cite_16 . We will analyze these in future work. Among other things, there should be a general relation between flat bundles and links in 3-manifolds on the one hand and finite covers and branched covers on the other hand @cite_26 .'}
### Data Fields
{`abstract`: text of paper abstract \
`aid`: arxiv id \
`mid`: microsoft academic graph id \
`ref_abstract`: \
{ \
`abstract`: text of reference paper (cite_N) abstract \
`cite_N`: special cite symbol, \
`mid`: reference paper's (cite_N) microsoft academic graph id \
}, \
`related_work`: text of paper related work \
}
### Data Splits
The data is split into a training, validation and test.
| train | validation | test |
|------:|-----------:|-----:|
| 30369 | 5066 | 5093 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{lu2020multi,
title={Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
author={Lu, Yao and Dong, Yue and Charlin, Laurent},
journal={arXiv preprint arXiv:2010.14235},
year={2020}
}
```
### Contributions
Thanks to [@moussaKam](https://github.com/moussaKam) for adding this dataset. |
true |
# Dataset Card for SILICONE Benchmark
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [N/A]
- **Repository:** https://github.com/eusip/SILICONE-benchmark
- **Paper:** https://arxiv.org/abs/2009.11152
- **Leaderboard:** [N/A]
- **Point of Contact:** [Ebenge Usip](ebenge.usip@telecom-paris.fr)
### Dataset Summary
The Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE (SILICONE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems specifically designed for spoken language. All datasets are in the English language and covers a variety of domains including daily life, scripted scenarios, joint task completion, phone call conversations, and televsion dialogue. Some datasets additionally include emotion and/or sentimant labels.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English.
## Dataset Structure
### Data Instances
#### DailyDialog Act Corpus (Dialogue Act)
For the `dyda_da` configuration one example from the dataset is:
```
{
'Utterance': "the taxi drivers are on strike again .",
'Dialogue_Act': 2, # "inform"
'Dialogue_ID': "2"
}
```
#### DailyDialog Act Corpus (Emotion)
For the `dyda_e` configuration one example from the dataset is:
```
{
'Utterance': "'oh , breaktime flies .'",
'Emotion': 5, # "sadness"
'Dialogue_ID': "997"
}
```
#### Interactive Emotional Dyadic Motion Capture (IEMOCAP) database
For the `iemocap` configuration one example from the dataset is:
```
{
'Dialogue_ID': "Ses04F_script03_2",
'Utterance_ID': "Ses04F_script03_2_F025",
'Utterance': "You're quite insufferable. I expect it's because you're drunk.",
'Emotion': 0, # "ang"
}
```
#### HCRC MapTask Corpus
For the `maptask` configuration one example from the dataset is:
```
{
'Speaker': "f",
'Utterance': "i think that would bring me over the crevasse",
'Dialogue_Act': 4, # "explain"
}
```
#### Multimodal EmotionLines Dataset (Emotion)
For the `meld_e` configuration one example from the dataset is:
```
{
'Utterance': "'Push 'em out , push 'em out , harder , harder .'",
'Speaker': "Joey",
'Emotion': 3, # "joy"
'Dialogue_ID': "1",
'Utterance_ID': "2"
}
```
#### Multimodal EmotionLines Dataset (Sentiment)
For the `meld_s` configuration one example from the dataset is:
```
{
'Utterance': "'Okay , y'know what ? There is no more left , left !'",
'Speaker': "Rachel",
'Sentiment': 0, # "negative"
'Dialogue_ID': "2",
'Utterance_ID': "4"
}
```
#### ICSI MRDA Corpus
For the `mrda` configuration one example from the dataset is:
```
{
'Utterance_ID': "Bed006-c2_0073656_0076706",
'Dialogue_Act': 0, # "s"
'Channel_ID': "Bed006-c2",
'Speaker': "mn015",
'Dialogue_ID': "Bed006",
'Utterance': "keith is not technically one of us yet ."
}
```
#### BT OASIS Corpus
For the `oasis` configuration one example from the dataset is:
```
{
'Speaker': "b",
'Utterance': "when i rang up um when i rang to find out why she said oh well your card's been declined",
'Dialogue_Act': 21, # "inform"
}
```
#### SEMAINE database
For the `sem` configuration one example from the dataset is:
```
{
'Utterance': "can you think of somebody who is like that ?",
'NbPairInSession': "11",
'Dialogue_ID': "59",
'SpeechTurn': "674",
'Speaker': "Agent",
'Sentiment': 1, # "Neutral"
}
```
#### Switchboard Dialog Act (SwDA) Corpus
For the `swda` configuration one example from the dataset is:
```
{
'Utterance': "but i 'd probably say that 's roughly right .",
'Dialogue_Act': 33, # "aap_am"
'From_Caller': "1255",
'To_Caller': "1087",
'Topic': "CRIME",
'Dialogue_ID': "818",
'Conv_ID': "sw2836",
}
```
### Data Fields
For the `dyda_da` configuration, the different fields are:
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of "commissive" (0), "directive" (1), "inform" (2) or "question" (3).
- `Dialogue_ID`: identifier of the dialogue as a string.
For the `dyda_e` configuration, the different fields are:
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of "anger" (0), "disgust" (1), "fear" (2), "happiness" (3), "no emotion" (4), "sadness" (5) or "surprise" (6).
- `Dialogue_ID`: identifier of the dialogue as a string.
For the `iemocap` configuration, the different fields are:
- `Dialogue_ID`: identifier of the dialogue as a string.
- `Utterance_ID`: identifier of the utterance as a string.
- `Utterance`: Utterance as a string.
- `Emotion`: Emotion label of the utterance. It can be one of "Anger" (0), "Disgust" (1), "Excitement" (2), "Fear" (3), "Frustration" (4), "Happiness" (5), "Neutral" (6), "Other" (7), "Sadness" (8), "Surprise" (9) or "Unknown" (10).
For the `maptask` configuration, the different fields are:
- `Speaker`: identifier of the speaker as a string.
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of "acknowledge" (0), "align" (1), "check" (2), "clarify" (3), "explain" (4), "instruct" (5), "query_w" (6), "query_yn" (7), "ready" (8), "reply_n" (9), "reply_w" (10) or "reply_y" (11).
For the `meld_e` configuration, the different fields are:
- `Utterance`: Utterance as a string.
- `Speaker`: Speaker as a string.
- `Emotion`: Emotion label of the utterance. It can be one of "anger" (0), "disgust" (1), "fear" (2), "joy" (3), "neutral" (4), "sadness" (5) or "surprise" (6).
- `Dialogue_ID`: identifier of the dialogue as a string.
- `Utterance_ID`: identifier of the utterance as a string.
For the `meld_s` configuration, the different fields are:
- `Utterance`: Utterance as a string.
- `Speaker`: Speaker as a string.
- `Sentiment`: Sentiment label of the utterance. It can be one of "negative" (0), "neutral" (1) or "positive" (2).
- `Dialogue_ID`: identifier of the dialogue as a string.
- `Utterance_ID`: identifier of the utterance as a string.
For the `mrda` configuration, the different fields are:
- `Utterance_ID`: identifier of the utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of "s" (0) [Statement/Subjective Statement], "d" (1) [Declarative Question], "b" (2) [Backchannel], "f" (3) [Follow-me] or "q" (4) [Question].
- `Channel_ID`: identifier of the channel as a string.
- `Speaker`: identifier of the speaker as a string.
- `Dialogue_ID`: identifier of the channel as a string.
- `Utterance`: Utterance as a string.
For the `oasis` configuration, the different fields are:
- `Speaker`: identifier of the speaker as a string.
- `Utterance`: Utterance as a string.
- `Dialogue_Act`: Dialog act label of the utterance. It can be one of "accept" (0), "ackn" (1), "answ" (2), "answElab" (3), "appreciate" (4), "backch" (5), "bye" (6), "complete" (7), "confirm" (8), "correct" (9), "direct" (10), "directElab" (11), "echo" (12), "exclaim" (13), "expressOpinion"(14), "expressPossibility" (15), "expressRegret" (16), "expressWish" (17), "greet" (18), "hold" (19),
"identifySelf" (20), "inform" (21), "informCont" (22), "informDisc" (23), "informIntent" (24), "init" (25), "negate" (26), "offer" (27), "pardon" (28), "raiseIssue" (29), "refer" (30), "refuse" (31), "reqDirect" (32), "reqInfo" (33), "reqModal" (34), "selfTalk" (35), "suggest" (36), "thank" (37), "informIntent-hold" (38), "correctSelf" (39), "expressRegret-inform" (40) or "thank-identifySelf" (41).
For the `sem` configuration, the different fields are:
- `Utterance`: Utterance as a string.
- `NbPairInSession`: number of utterance pairs in a dialogue.
- `Dialogue_ID`: identifier of the dialogue as a string.
- `SpeechTurn`: SpeakerTurn as a string.
- `Speaker`: Speaker as a string.
- `Sentiment`: Sentiment label of the utterance. It can be "Negative", "Neutral" or "Positive".
For the `swda` configuration, the different fields are:
`Utterance`: Utterance as a string.
`Dialogue_Act`: Dialogue act label of the utterance. It can be "sd" (0) [Statement-non-opinion], "b" (1) [Acknowledge (Backchannel)], "sv" (2) [Statement-opinion], "%" (3) [Uninterpretable], "aa" (4) [Agree/Accept], "ba" (5) [Appreciation], "fc" (6) [Conventional-closing], "qw" (7) [Wh-Question], "nn" (8) [No Answers], "bk" (9) [Response Acknowledgement], "h" (10) [Hedge], "qy^d" (11) [Declarative Yes-No-Question], "bh" (12) [Backchannel in Question Form], "^q" (13) [Quotation], "bf" (14) [Summarize/Reformulate], 'fo_o_fw_"_by_bc' (15) [Other], 'fo_o_fw_by_bc_"' (16) [Other], "na" (17) [Affirmative Non-yes Answers], "ad" (18) [Action-directive], "^2" (19) [Collaborative Completion], "b^m" (20) [Repeat-phrase], "qo" (21) [Open-Question], "qh" (22) [Rhetorical-Question], "^h" (23) [Hold Before Answer/Agreement], "ar" (24) [Reject], "ng" (25) [Negative Non-no Answers], "br" (26) [Signal-non-understanding], "no" (27) [Other Answers], "fp" (28) [Conventional-opening], "qrr" (29) [Or-Clause], "arp_nd" (30) [Dispreferred Answers], "t3" (31) [3rd-party-talk], "oo_co_cc" (32) [Offers, Options Commits], "aap_am" (33) [Maybe/Accept-part], "t1" (34) [Downplayer], "bd" (35) [Self-talk], "^g" (36) [Tag-Question], "qw^d" (37) [Declarative Wh-Question], "fa" (38) [Apology], "ft" (39) [Thanking], "+" (40) [Unknown], "x" (41) [Unknown], "ny" (42) [Unknown], "sv_fx" (43) [Unknown], "qy_qr" (44) [Unknown] or "ba_fe" (45) [Unknown].
`From_Caller`: identifier of the from caller as a string.
`To_Caller`: identifier of the to caller as a string.
`Topic`: Topic as a string.
`Dialogue_ID`: identifier of the dialogue as a string.
`Conv_ID`: identifier of the conversation as a string.
### Data Splits
| Dataset name | Train | Valid | Test |
| ------------ | ----- | ----- | ---- |
| dyda_da | 87170 | 8069 | 7740 |
| dyda_e | 87170 | 8069 | 7740 |
| iemocap | 7213 | 805 | 2021 |
| maptask | 20905 | 2963 | 2894 |
| meld_e | 9989 | 1109 | 2610 |
| meld_s | 9989 | 1109 | 2610 |
| mrda | 83944 | 9815 | 15470 |
| oasis | 12076 | 1513 | 1478 |
| sem | 4264 | 485 | 878 |
| swda | 190709 | 21203 | 2714 |
## 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
### Benchmark Curators
Emile Chapuis, Pierre Colombo, Ebenge Usip.
### Licensing Information
This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 Unported License](https://creativecommons.org/licenses/by-sa/4.0/).
### Citation Information
```
@inproceedings{chapuis-etal-2020-hierarchical,
title = "Hierarchical Pre-training for Sequence Labelling in Spoken Dialog",
author = "Chapuis, Emile and
Colombo, Pierre and
Manica, Matteo and
Labeau, Matthieu and
Clavel, Chlo{\'e}",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.239",
doi = "10.18653/v1/2020.findings-emnlp.239",
pages = "2636--2648",
abstract = "Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. In this work, we propose a new approach to learn generic representations adapted to spoken dialog, which we evaluate on a new benchmark we call Sequence labellIng evaLuatIon benChmark fOr spoken laNguagE benchmark (SILICONE). SILICONE is model-agnostic and contains 10 different datasets of various sizes. We obtain our representations with a hierarchical encoder based on transformer architectures, for which we extend two well-known pre-training objectives. Pre-training is performed on OpenSubtitles: a large corpus of spoken dialog containing over 2.3 billion of tokens. We demonstrate how hierarchical encoders achieve competitive results with consistently fewer parameters compared to state-of-the-art models and we show their importance for both pre-training and fine-tuning.",
}
```
### Contributions
Thanks to [@eusip](https://github.com/eusip) and [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
false |
# Dataset Card for MedMCQA
## 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://medmcqa.github.io
- **Repository:** https://github.com/medmcqa/medmcqa
- **Paper:** [MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering](https://proceedings.mlr.press/v174/pal22a)
- **Leaderboard:** https://paperswithcode.com/dataset/medmcqa
- **Point of Contact:** [Aaditya Ura](mailto:aadityaura@gmail.com)
### Dataset Summary
MedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions.
MedMCQA has more than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity.
Each sample contains a question, correct answer(s), and other options which require a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects & topics. A detailed explanation of the solution, along with the above information, is provided in this study.
MedMCQA provides an open-source dataset for the Natural Language Processing community.
It is expected that this dataset would facilitate future research toward achieving better QA systems.
The dataset contains questions about the following topics:
- Anesthesia
- Anatomy
- Biochemistry
- Dental
- ENT
- Forensic Medicine (FM)
- Obstetrics and Gynecology (O&G)
- Medicine
- Microbiology
- Ophthalmology
- Orthopedics
- Pathology
- Pediatrics
- Pharmacology
- Physiology
- Psychiatry
- Radiology
- Skin
- Preventive & Social Medicine (PSM)
- Surgery
### Supported Tasks and Leaderboards
multiple-choice-QA, open-domain-QA: The dataset can be used to train a model for multi-choice questions answering, open domain questions answering. Questions in these exams are challenging and generally require deeper domain and language understanding as it tests the 10+ reasoning abilities across a wide range of medical subjects & topics.
### Languages
The questions and answers are available in English.
## Dataset Structure
### Data Instances
```
{
"question":"A 40-year-old man presents with 5 days of productive cough and fever. Pseudomonas aeruginosa is isolated from a pulmonary abscess. CBC shows an acute effect characterized by marked leukocytosis (50,000 mL) and the differential count reveals a shift to left in granulocytes. Which of the following terms best describes these hematologic findings?",
"exp": "Circulating levels of leukocytes and their precursors may occasionally reach very high levels (>50,000 WBC mL). These extreme elevations are sometimes called leukemoid reactions because they are similar to the white cell counts observed in leukemia, from which they must be distinguished. The leukocytosis occurs initially because of the accelerated release of granulocytes from the bone marrow (caused by cytokines, including TNF and IL-1) There is a rise in the number of both mature and immature neutrophils in the blood, referred to as a shift to the left. In contrast to bacterial infections, viral infections (including infectious mononucleosis) are characterized by lymphocytosis Parasitic infestations and certain allergic reactions cause eosinophilia, an increase in the number of circulating eosinophils. Leukopenia is defined as an absolute decrease in the circulating WBC count.",
"cop":1,
"opa":"Leukemoid reaction",
"opb":"Leukopenia",
"opc":"Myeloid metaplasia",
"opd":"Neutrophilia",
"subject_name":"Pathology",
"topic_name":"Basic Concepts and Vascular changes of Acute Inflammation",
"id":"4e1715fe-0bc3-494e-b6eb-2d4617245aef",
"choice_type":"single"
}
```
### Data Fields
- `id` : a string question identifier for each example
- `question` : question text (a string)
- `opa` : Option A
- `opb` : Option B
- `opc` : Option C
- `opd` : Option D
- `cop` : Correct option, i.e., 1,2,3,4
- `choice_type` ({"single", "multi"}): Question choice type.
- "single": Single-choice question, where each choice contains a single option.
- "multi": Multi-choice question, where each choice contains a combination of multiple suboptions.
- `exp` : Expert's explanation of the answer
- `subject_name` : Medical Subject name of the particular question
- `topic_name` : Medical topic name from the particular subject
### Data Splits
The goal of MedMCQA is to emulate the rigor of real word medical exams. To enable that, a predefined split of the dataset is provided. The split is by exams instead of the given questions. This also ensures the reusability and generalization ability of the models.
The training set of MedMCQA consists of all the collected mock & online test series, whereas the test set consists of all AIIMS PG exam MCQs (years 1991-present). The development set consists of NEET PG exam MCQs (years 2001-present) to approximate real exam evaluation.
Similar questions from train , test and dev set were removed based on similarity. The final split sizes are as follow:
| | Train | Test | Valid |
| ----- | ------ | ----- | ---- |
| Question #| 182,822 | 6,150 | 4,183|
| Vocab | 94,231 | 11,218 | 10,800 |
| Max Ques tokens | 220 | 135| 88 |
| Max Ans tokens | 38 | 21 | 25 |
## Dataset Creation
### Curation Rationale
Before this attempt, very few works have been done to construct biomedical MCQA datasets (Vilares and Gomez-Rodr, 2019), and they are (1) mostly small, containing up to few thousand questions, and (2) cover a limited number of Medical topics and Subjects. This paper addresses the aforementioned limitations by introducing MedMCQA, a new large-scale, Multiple-Choice Question Answering
(MCQA) dataset designed to address real-world medical entrance exam questions.
### Source Data
#### Initial Data Collection and Normalization
Historical Exam questions from official websites - AIIMS & NEET PG (1991- present)
The raw data is collected from open websites and books
#### Who are the source language producers?
The dataset was created by Ankit Pal, Logesh Kumar Umapathi and Malaikannan Sankarasubbu
### Annotations
#### Annotation process
The dataset does not contain any additional annotations.
#### 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
If you find this useful in your research, please consider citing the dataset paper
```
@InProceedings{pmlr-v174-pal22a,
title = {MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering},
author = {Pal, Ankit and Umapathi, Logesh Kumar and Sankarasubbu, Malaikannan},
booktitle = {Proceedings of the Conference on Health, Inference, and Learning},
pages = {248--260},
year = {2022},
editor = {Flores, Gerardo and Chen, George H and Pollard, Tom and Ho, Joyce C and Naumann, Tristan},
volume = {174},
series = {Proceedings of Machine Learning Research},
month = {07--08 Apr},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v174/pal22a/pal22a.pdf},
url = {https://proceedings.mlr.press/v174/pal22a.html},
abstract = {This paper introduces MedMCQA, a new large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. More than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which requires a deeper language understanding as it tests the 10+ reasoning abilities of a model across a wide range of medical subjects & topics. A detailed explanation of the solution, along with the above information, is provided in this study.}
}
```
### Contributions
Thanks to [@monk1337](https://github.com/monk1337) for adding this dataset. |
false |
# Dataset Card for "coqa"
## 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://stanfordnlp.github.io/coqa/](https://stanfordnlp.github.io/coqa/)
- **Repository:** https://github.com/stanfordnlp/coqa-baselines
- **Paper:** [CoQA: A Conversational Question Answering Challenge](https://arxiv.org/abs/1808.07042)
- **Point of Contact:** [Google Group](https://groups.google.com/forum/#!forum/coqa), [Siva Reddy](mailto:siva.reddy@mila.quebec), [Danqi Chen](mailto:danqic@cs.princeton.edu)
- **Size of downloaded dataset files:** 58.09 MB
- **Size of the generated dataset:** 19.24 MB
- **Total amount of disk used:** 77.33 MB
### Dataset Summary
CoQA is a large-scale dataset for building Conversational Question Answering systems.
Our dataset contains 127k questions with answers, obtained from 8k conversations about text passages from seven diverse domains. The questions are conversational, and the answers are free-form text with their corresponding evidence highlighted in the passage.
### 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:** 58.09 MB
- **Size of the generated dataset:** 19.24 MB
- **Total amount of disk used:** 77.33 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": "{\"answer_end\": [179, 494, 511, 545, 879, 1127, 1128, 94, 150, 412, 1009, 1046, 643, -1, 764, 724, 125, 1384, 881, 910], \"answer_...",
"questions": "[\"When was the Vat formally opened?\", \"what is the library for?\", \"for what subjects?\", \"and?\", \"what was started in 2014?\", \"ho...",
"source": "wikipedia",
"story": "\"The Vatican Apostolic Library (), more commonly called the Vatican Library or simply the Vat, is the library of the Holy See, l..."
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `source`: a `string` feature.
- `story`: a `string` feature.
- `questions`: a `list` of `string` features.
- `answers`: a dictionary feature containing:
- `input_text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `answer_end`: a `int32` feature.
### Data Splits
| name |train|validation|
|-------|----:|---------:|
|default| 7199| 500|
## 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
CoQA contains passages from seven domains. We make five of these public under the following licenses:
- Literature and Wikipedia passages are shared under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license.
- Children's stories are collected from [MCTest](https://www.microsoft.com/en-us/research/publication/mctest-challenge-dataset-open-domain-machine-comprehension-text/) which comes with [MSR-LA](https://github.com/mcobzarenco/mctest/blob/master/data/MCTest/LICENSE.pdf) license.
- Middle/High school exam passages are collected from [RACE](https://arxiv.org/abs/1704.04683) which comes with its [own](http://www.cs.cmu.edu/~glai1/data/race/) license.
- News passages are collected from the [DeepMind CNN dataset](https://arxiv.org/abs/1506.03340) which comes with [Apache](https://github.com/deepmind/rc-data/blob/master/LICENSE) license.
### Citation Information
```
@article{reddy-etal-2019-coqa,
title = "{C}o{QA}: A Conversational Question Answering Challenge",
author = "Reddy, Siva and
Chen, Danqi and
Manning, Christopher D.",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q19-1016",
doi = "10.1162/tacl_a_00266",
pages = "249--266",
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@ojasaar](https://github.com/ojasaar), [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
false |
# Dataset Card for TedIwlst2013
## 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/TED2013.php
- **Repository:** None
- **Paper:** hhttp://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** None
- **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 TabFact
## 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:** [TabFact](https://tabfact.github.io/index.html)
- **Repository:** [GitHub](https://github.com/wenhuchen/Table-Fact-Checking)
- **Paper:** [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://arxiv.org/abs/1909.02164)
- **Leaderboard:** [Leaderboard](https://competitions.codalab.org/competitions/21611)
- **Point of Contact:** [Wenhu Chen](wenhuchen@cs.ucsb.edu)
### Dataset Summary
The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation. However, existing studies are restricted to dealing with unstructured textual evidence (e.g., sentences and passages, a pool of passages), while verification using structured forms of evidence, such as tables, graphs, and databases, remains unexplored. TABFACT is large scale dataset with 16k Wikipedia tables as evidence for 118k human annotated statements designed for fact verification with semi-structured evidence. The statements are labeled as either ENTAILED or REFUTED. TABFACT is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning.
### 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
```
@inproceedings{2019TabFactA,
title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
booktitle = {International Conference on Learning Representations (ICLR)},
address = {Addis Ababa, Ethiopia},
month = {April},
year = {2020}
}
```
### Contributions
Thanks to [@patil-suraj](https://github.com/patil-suraj) for adding this dataset. |
false |
# Wikipedia (en) embedded with cohere.ai `multilingual-22-12` encoder
We encoded [Wikipedia (en)](https://en.wikipedia.org) using the [cohere.ai](https://txt.cohere.ai/multilingual/) `multilingual-22-12` embedding model.
To get an overview how this dataset was created and pre-processed, have a look at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Embeddings
We compute for `title+" "+text` the embeddings using our `multilingual-22-12` embedding model, a state-of-the-art model that works for semantic search in 100 languages. If you want to learn more about this model, have a look at [cohere.ai multilingual embedding model](https://txt.cohere.ai/multilingual/).
## Further languages
We provide embeddings of Wikipedia in many different languages:
[ar](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ar-embeddings), [de](https://huggingface.co/datasets/Cohere/wikipedia-22-12-de-embeddings), [en](https://huggingface.co/datasets/Cohere/wikipedia-22-12-en-embeddings), [es](https://huggingface.co/datasets/Cohere/wikipedia-22-12-es-embeddings), [fr](https://huggingface.co/datasets/Cohere/wikipedia-22-12-fr-embeddings), [hi](https://huggingface.co/datasets/Cohere/wikipedia-22-12-hi-embeddings), [it](https://huggingface.co/datasets/Cohere/wikipedia-22-12-it-embeddings), [ja](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ja-embeddings), [ko](https://huggingface.co/datasets/Cohere/wikipedia-22-12-ko-embeddings), [simple english](https://huggingface.co/datasets/Cohere/wikipedia-22-12-simple-embeddings), [zh](https://huggingface.co/datasets/Cohere/wikipedia-22-12-zh-embeddings),
You can find the Wikipedia datasets without embeddings at [Cohere/wikipedia-22-12](https://huggingface.co/datasets/Cohere/wikipedia-22-12).
## Loading the dataset
You can either load the dataset like this:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-en-embeddings", split="train")
```
Or you can also stream it without downloading it before:
```python
from datasets import load_dataset
docs = load_dataset(f"Cohere/wikipedia-22-12-en-embeddings", split="train", streaming=True)
for doc in docs:
docid = doc['id']
title = doc['title']
text = doc['text']
emb = doc['emb']
```
## Search
A full search example:
```python
#Run: pip install cohere datasets
from datasets import load_dataset
import torch
import cohere
co = cohere.Client(f"<<COHERE_API_KEY>>") # Add your cohere API key from www.cohere.com
#Load at max 1000 documents + embeddings
max_docs = 1000
docs_stream = load_dataset(f"Cohere/wikipedia-22-12-en-embeddings", split="train", streaming=True)
docs = []
doc_embeddings = []
for doc in docs_stream:
docs.append(doc)
doc_embeddings.append(doc['emb'])
if len(docs) >= max_docs:
break
doc_embeddings = torch.tensor(doc_embeddings)
query = 'Who founded Youtube'
response = co.embed(texts=[query], model='multilingual-22-12')
query_embedding = response.embeddings
query_embedding = torch.tensor(query_embedding)
# Compute dot score between query embedding and document embeddings
dot_scores = torch.mm(query_embedding, doc_embeddings.transpose(0, 1))
top_k = torch.topk(dot_scores, k=3)
# Print results
print("Query:", query)
for doc_id in top_k.indices[0].tolist():
print(docs[doc_id]['title'])
print(docs[doc_id]['text'], "\n")
```
## Performance
You can find performance on the MIRACL dataset (a semantic search evaluation dataset) here: [miracl-en-queries-22-12#performance](https://huggingface.co/datasets/Cohere/miracl-en-queries-22-12#performance) |
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://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection
- **Repository:**
- **Paper:** Almeida, T.A., Gomez Hidalgo, J.M., Yamakami, A. Contributions to the study of SMS Spam Filtering: New Collection and Results. Proceedings of the 2011 ACM Symposium on Document Engineering (ACM DOCENG'11), Mountain View, CA, USA, 2011.
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The SMS Spam Collection v.1 is a public set of SMS labeled messages that have been collected for mobile phone spam research.
It has one collection composed by 5,574 English, real and non-enconded messages, tagged according being legitimate (ham) or spam.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
- sms: the sms message
- label: indicating if the sms message is ham or spam, ham means it is not spam
### 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
@inproceedings{Almeida2011SpamFiltering,
title={Contributions to the Study of SMS Spam Filtering: New Collection and Results},
author={Tiago A. Almeida and Jose Maria Gomez Hidalgo and Akebo Yamakami},
year={2011},
booktitle = "Proceedings of the 2011 ACM Symposium on Document Engineering (DOCENG'11)",
}
### Contributions
Thanks to [@czabo](https://github.com/czabo) for adding this dataset. |
true |
# Dataset Card for "art"
## 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://leaderboard.allenai.org/anli/submissions/get-started](https://leaderboard.allenai.org/anli/submissions/get-started)
- **Repository:** https://github.com/allenai/abductive-commonsense-reasoning
- **Paper:** [Abductive Commonsense Reasoning](https://arxiv.org/abs/1908.05739)
- **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:** 5.12 MB
- **Size of the generated dataset:** 34.36 MB
- **Total amount of disk used:** 39.48 MB
### Dataset Summary
ART consists of over 20k commonsense narrative contexts and 200k explanations.
The Abductive Natural Language Inference Dataset from AI2.
### 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
#### anli
- **Size of downloaded dataset files:** 5.12 MB
- **Size of the generated dataset:** 34.36 MB
- **Total amount of disk used:** 39.48 MB
An example of 'train' looks as follows.
```
{
"hypothesis_1": "Chad's car had all sorts of other problems besides alignment.",
"hypothesis_2": "Chad's car had all sorts of benefits other than being sexy.",
"label": 1,
"observation_1": "Chad went to get the wheel alignment measured on his car.",
"observation_2": "The mechanic provided a working alignment with new body work."
}
```
### Data Fields
The data fields are the same among all splits.
#### anli
- `observation_1`: a `string` feature.
- `observation_2`: a `string` feature.
- `hypothesis_1`: a `string` feature.
- `hypothesis_2`: a `string` feature.
- `label`: a classification label, with possible values including `0` (0), `1` (1), `2` (2).
### Data Splits
|name|train |validation|
|----|-----:|---------:|
|anli|169654| 1532|
## 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{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}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
false |
# Dataset Card for "reddit_tifu"
## 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/ctr4si/MMN](https://github.com/ctr4si/MMN)
- **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:** 1.34 GB
- **Size of the generated dataset:** 229.76 MB
- **Total amount of disk used:** 1.57 GB
### Dataset Summary
Reddit dataset, where TIFU denotes the name of subbreddit /r/tifu.
As defined in the publication, style "short" uses title as summary and
"long" uses tldr as summary.
Features includes:
- document: post text without tldr.
- tldr: tldr line.
- title: trimmed title without tldr.
- ups: upvotes.
- score: score.
- num_comments: number of comments.
- upvote_ratio: upvote ratio.
### 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
#### long
- **Size of downloaded dataset files:** 670.61 MB
- **Size of the generated dataset:** 92.00 MB
- **Total amount of disk used:** 762.62 MB
An example of 'train' looks as follows.
```
{'ups': 115.0,
'num_comments': 23.0,
'upvote_ratio': 0.88,
'score': 115.0,
'documents': 'this actually happened a couple of years ago. i grew up in germany where i went to a german secondary school that went from 5th to 13th grade (we still had 13 grades then, they have since changed that). my school was named after anne frank and we had a club that i was very active in from 9th grade on, which was dedicated to teaching incoming 5th graders about anne franks life, discrimination, anti-semitism, hitler, the third reich and that whole spiel. basically a day where the students\' classes are cancelled and instead we give them an interactive history and social studies class with lots of activities and games. \n\nthis was my last year at school and i already had a lot of experience doing these project days with the kids. i was running the thing with a friend, so it was just the two of us and 30-something 5th graders. we start off with a brief introduction and brainstorming: what do they know about anne frank and the third reich? you\'d be surprised how much they know. anyway after the brainstorming we do a few activities, and then we take a short break. after the break we split the class into two groups to make it easier to handle. one group watches a short movie about anne frank while the other gets a tour through our poster presentation that our student group has been perfecting over the years. then the groups switch. \n\ni\'m in the classroom to show my group the movie and i take attendance to make sure no one decided to run away during break. i\'m going down the list when i come to the name sandra (name changed). a kid with a boyish haircut and a somewhat deeper voice, wearing clothes from the boy\'s section at a big clothing chain in germany, pipes up. \n\nnow keep in mind, these are all 11 year olds, they are all pre-pubescent, their bodies are not yet showing any sex specific features one would be able to see while they are fully clothed (e.g. boobs, beards,...). this being a 5th grade in the rather conservative (for german standards) bavaria, i was confused. i looked down at the list again making sure i had read the name right. look back up at the kid. \n\nme: "you\'re sandra?"\n\nkid: "yep."\n\nme: "oh, sorry. *thinking the kid must be from somewhere where sandra is both a girl\'s and boy\'s name* where are you from? i\'ve only ever heard that as a girl\'s name before."\n\nthe class starts laughing. sandra gets really quiet. "i am a girl..." she says. some of the other students start saying that their parents made the same mistake when they met sandra. i feel so sorry and stupid. i get the class to calm down and finish taking attendance. we watch the movie in silence. after the movie, when we walked down to where the poster presentation took place i apologised to sandra. i felt so incredibly terrible, i still do to this day. throughout the rest of the day i heard lots of whispers about sandra. i tried to stop them whenever they came up, but there was no stopping the 5th grade gossip i had set in motion.\n\nsandra, if you\'re out there, i am so incredibly sorry for humiliating you in front of your class. i hope you are happy and healthy and continue to live your life the way you like. don\'t let anyone tell you you have to dress or act a certain way just because of the body parts you were born with. i\'m sorry if i made you feel like you were wrong for dressing and acting differently. i\'m sorry i probably made that day hell for you. i\'m sorry for my ignorance.',
'tldr': 'confuse a 5th grade girl for a boy in front of half of her class. kids are mean. sorry sandra.**',
'title': 'gender-stereotyping'}
```
#### short
- **Size of downloaded dataset files:** 670.61 MB
- **Size of the generated dataset:** 137.75 MB
- **Total amount of disk used:** 808.37 MB
An example of 'train' looks as follows.
```
{'ups': 50.0,
'num_comments': 13.0,
'upvote_ratio': 0.77,
'score': 50.0,
'documents': "i was on skype on my tablet as i went to the toilet iming a friend. i don't multitask very well, so i forgot one of the most important things to do before pooping. i think the best part was when i realised and told my mate who just freaked out because i was talking to him on the john!",
'tldr': '',
'title': 'forgetting to pull my underwear down before i pooped.'}
```
### Data Fields
The data fields are the same among all splits.
#### long
- `ups`: a `float32` feature.
- `num_comments`: a `float32` feature.
- `upvote_ratio`: a `float32` feature.
- `score`: a `float32` feature.
- `documents`: a `string` feature.
- `tldr`: a `string` feature.
- `title`: a `string` feature.
#### short
- `ups`: a `float32` feature.
- `num_comments`: a `float32` feature.
- `upvote_ratio`: a `float32` feature.
- `score`: a `float32` feature.
- `documents`: a `string` feature.
- `tldr`: a `string` feature.
- `title`: a `string` feature.
### Data Splits
|name |train|
|-----|----:|
|long |42139|
|short|79740|
## 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
MIT License.
### Citation Information
```
@misc{kim2018abstractive,
title={Abstractive Summarization of Reddit Posts with Multi-level Memory Networks},
author={Byeongchang Kim and Hyunwoo Kim and Gunhee Kim},
year={2018},
eprint={1811.00783},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
false |
# Dataset Card for germanquad
## 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)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Citation Information](#citation-information)
## Dataset Description
- **Homepage:** https://deepset.ai/germanquad
- **Repository:** https://github.com/deepset-ai/haystack
- **Paper:** https://arxiv.org/abs/2104.12741
### Dataset Summary
In order to raise the bar for non-English QA, we are releasing a high-quality, human-labeled German QA dataset consisting of 13 722 questions, incl. a three-way annotated test set.
The creation of GermanQuAD is inspired by insights from existing datasets as well as our labeling experience from several industry projects. We combine the strengths of SQuAD, such as high out-of-domain performance, with self-sufficient questions that contain all relevant information for open-domain QA as in the NaturalQuestions dataset. Our training and test datasets do not overlap like other popular datasets and include complex questions that cannot be answered with a single entity or only a few words.
### Supported Tasks and Leaderboards
- `extractive-qa`, `closed-domain-qa`, `open-domain-qa`, `text-retrieval`: This dataset is intended to be used for `open-domain-qa`, but can also be used for information retrieval tasks.
### Languages
The sentences in the dataset are in German (de).
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
"paragraphs": [
{
"qas": [
{
"question": "Von welchem Gesetzt stammt das Amerikanische ab? ",
"id": 51870,
"answers": [
{
"answer_id": 53778,
"document_id": 43958,
"question_id": 51870,
"text": "britischen Common Laws",
"answer_start": 146,
"answer_category": "SHORT"
}
],
"is_impossible": false
}
],
"context": "Recht_der_Vereinigten_Staaten\
\
=== Amerikanisches Common Law ===\
Obwohl die Vereinigten Staaten wie auch viele Staaten des Commonwealth Erben des britischen Common Laws sind, setzt sich das amerikanische Recht bedeutend davon ab. Dies rührt größtenteils von dem langen Zeitraum her, in dem sich das amerikanische Recht unabhängig vom Britischen entwickelt hat. Entsprechend schauen die Gerichte in den Vereinigten Staaten bei der Analyse von eventuell zutreffenden britischen Rechtsprinzipien im Common Law gewöhnlich nur bis ins frühe 19. Jahrhundert.\
Während es in den Commonwealth-Staaten üblich ist, dass Gerichte sich Entscheidungen und Prinzipien aus anderen Commonwealth-Staaten importieren, ist das in der amerikanischen Rechtsprechung selten. Ausnahmen bestehen hier nur, wenn sich überhaupt keine relevanten amerikanischen Fälle finden lassen, die Fakten nahezu identisch sind und die Begründung außerordentlich überzeugend ist. Frühe amerikanische Entscheidungen zitierten oft britische Fälle, solche Zitate verschwanden aber während des 19. Jahrhunderts, als die Gerichte eindeutig amerikanische Lösungen zu lokalen Konflikten fanden. In der aktuellen Rechtsprechung beziehen sich fast alle Zitate auf amerikanische Fälle.\
Einige Anhänger des Originalismus und der strikten Gesetzestextauslegung (''strict constructionism''), wie zum Beispiel der verstorbene Bundesrichter am Obersten Gerichtshof, Antonin Scalia, vertreten die Meinung, dass amerikanische Gerichte ''nie'' ausländische Fälle überprüfen sollten, die nach dem Unabhängigkeitskrieg entschieden wurden, unabhängig davon, ob die Argumentation überzeugend ist oder nicht. Die einzige Ausnahme wird hier in Fällen gesehen, die durch die Vereinigten Staaten ratifizierte völkerrechtliche Verträge betreffen. Andere Richter, wie zum Beispiel Anthony Kennedy und Stephen Breyer vertreten eine andere Ansicht und benutzen ausländische Rechtsprechung, sofern ihre Argumentation für sie überzeugend, nützlich oder hilfreich ist.",
"document_id": 43958
}
]
},
```
### Data Fields
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
The dataset is split into a one-way annotated training set and a three-way annotated test set of German Wikipedia passages (paragraphs). Each passage is
from a different article.
| |passages|questions|answers|
|----------|----:|---------:|---------:|
|train|2540| 11518|11518|
|test|474| 2204|6536|
## Additional Information
### Dataset Curators
The dataset was initially created by Timo Möller, Julian Risch, Malte Pietsch, Julian Gutsch, Tom Hersperger, Luise Köhler, Iuliia Mozhina, and Justus Peter, during work done at deepset.ai
### Citation Information
```
@misc{möller2021germanquad,
title={GermanQuAD and GermanDPR: Improving Non-English Question Answering and Passage Retrieval},
author={Timo Möller and Julian Risch and Malte Pietsch},
year={2021},
eprint={2104.12741},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
false |
# Dataset Card for MusicCaps
## 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://kaggle.com/datasets/googleai/musiccaps
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The MusicCaps dataset contains **5,521 music examples, each of which is labeled with an English *aspect list* and a *free text caption* written by musicians**. An aspect list is for example *"pop, tinny wide hi hats, mellow piano melody, high pitched female vocal melody, sustained pulsating synth lead"*, while the caption consists of multiple sentences about the music, e.g.,
*"A low sounding male voice is rapping over a fast paced drums playing a reggaeton beat along with a bass. Something like a guitar is playing the melody along. This recording is of poor audio-quality. In the background a laughter can be noticed. This song may be playing in a bar."*
The text is solely focused on describing *how* the music sounds, not the metadata like the artist name.
The labeled examples are 10s music clips from the [**AudioSet**](https://research.google.com/audioset/) dataset (2,858 from the eval and 2,663 from the train split).
Please cite the corresponding paper, when using this dataset: http://arxiv.org/abs/2301.11325 (DOI: `10.48550/arXiv.2301.11325`)
### Dataset Usage
The published dataset takes the form of a `.csv` file that contains the ID of YouTube videos and their start/end stamps. In order to use this dataset, one must download the corresponding YouTube videos and chunk them according to the start/end times.
The following repository has an example script and notebook to load the clips. The notebook also includes a Gradio demo that helps explore some samples: https://github.com/nateraw/download-musiccaps-dataset
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
#### ytid
YT ID pointing to the YouTube video in which the labeled music segment appears. You can listen to the segment by opening https://youtu.be/watch?v={ytid}&start={start_s}
#### start_s
Position in the YouTube video at which the music starts.
#### end_s
Position in the YouTube video at which the music end. All clips are 10s long.
#### audioset_positive_labels
Labels for this segment from the AudioSet (https://research.google.com/audioset/) dataset.
#### aspect_list
A list of aspects describing the music.
#### caption
A multi-sentence free text caption describing the music.
#### author_id
An integer for grouping samples by who wrote them.
#### is_balanced_subset
If this value is true, the row is a part of the 1k subset which is genre-balanced.
#### is_audioset_eval
If this value is true, the clip is from the AudioSet eval split. Otherwise it is from the AudioSet train split.
### 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
This dataset was shared by [@googleai](https://ai.google/research/)
### Licensing Information
The license for this dataset is cc-by-sa-4.0
### Citation Information
```bibtex
[More Information Needed]
```
### Contributions
[More Information Needed] |
false |
## Dataset Description
- **Repository:** https://conala-corpus.github.io/
- **Paper:** [Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow](https://arxiv.org/pdf/1805.08949.pdf)
### Dataset Summary
[CoNaLa](https://conala-corpus.github.io/) is a benchmark of code and natural language pairs, for the evaluation of code generation tasks. The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators, split into 2,379 training and 500 test examples. The automatically mined dataset is also available with almost 600k examples.
### Supported Tasks and Leaderboards
This dataset is used to evaluate code generations.
### Languages
English - Python code.
## Dataset Structure
```python
dataset_curated = load_dataset("neulab/conala")
DatasetDict({
train: Dataset({
features: ['question_id', 'intent', 'rewritten_intent', 'snippet'],
num_rows: 2379
})
test: Dataset({
features: ['question_id', 'intent', 'rewritten_intent', 'snippet'],
num_rows: 500
})
})
dataset_mined = load_dataset("neulab/conala", "mined")
DatasetDict({
train: Dataset({
features: ['question_id', 'parent_answer_post_id', 'prob', 'snippet', 'intent', 'id'],
num_rows: 593891
})
})
```
### Data Instances
#### CoNaLa - curated
This is the curated dataset by annotators
```
{
'question_id': 41067960,
'intent': 'How to convert a list of multiple integers into a single integer?',
'rewritten_intent': "Concatenate elements of a list 'x' of multiple integers to a single integer",
'snippet': 'sum(d * 10 ** i for i, d in enumerate(x[::-1]))'
}
```
#### CoNaLa - mined
This is the automatically mined dataset before curation
```
{
'question_id': 34705205,
'parent_answer_post_id': 34705233,
'prob': 0.8690001442846342,
'snippet': 'sorted(l, key=lambda x: (-int(x[1]), x[0]))',
'intent': 'Sort a nested list by two elements',
'id': '34705205_34705233_0'
}
```
### Data Fields
Curated:
|Field|Type|Description|
|---|---|---|
|question_id|int64|Id of the Stack Overflow question|
|intent|string|Natural Language intent (i.e., the title of a Stack Overflow question)|
|rewritten_intent|string|Crowdsourced revised intents that try to better reflect the full meaning of the code|
|snippet|string| Code snippet that implements the intent|
Mined:
|Field|Type|Description|
|---|---|---|
|question_id|int64|Id of the Stack Overflow question|
|parent_answer_post_id|int64|Id of the answer post from which the candidate snippet is extracted|
|intent|string|Natural Language intent (i.e., the title of a Stack Overflow question)|
|snippet|string| Code snippet that implements the intent|
|id|string|Unique id for this intent/snippet pair|
|prob|float64|Probability given by the mining model|
### Data Splits
There are two version of the dataset (curated and mined), mined only has a train split and curated has two splits: train and test.
## Dataset Creation
The dataset was crawled from Stack Overflow, automatically filtered, then curated by annotators. For more details, please refer to the original [paper](https://arxiv.org/pdf/1805.08949.pdf)
### Citation Information
```
@inproceedings{yin2018learning,
title={Learning to mine aligned code and natural language pairs from stack overflow},
author={Yin, Pengcheng and Deng, Bowen and Chen, Edgar and Vasilescu, Bogdan and Neubig, Graham},
booktitle={2018 IEEE/ACM 15th international conference on mining software repositories (MSR)},
pages={476--486},
year={2018},
organization={IEEE}
}
``` |
false |
# Dataset Card for "wmt17"
## 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.statmt.org/wmt17/translation-task.html](http://www.statmt.org/wmt17/translation-task.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.78 GB
- **Size of the generated dataset:** 302.09 MB
- **Total amount of disk used:** 2.09 GB
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p>
<ul>
<li>Non-English files contain many English sentences.</li>
<li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li>
</ul>
<p>We have contacted the WMT organizers.</p>
</div>
Translation dataset based on the data from statmt.org.
Versions exist for different years using a combination of data
sources. The base `wmt` allows you to create a custom dataset by choosing
your own data/language pair. This can be done as follows:
```python
from datasets import inspect_dataset, load_dataset_builder
inspect_dataset("wmt17", "path/to/scripts")
builder = load_dataset_builder(
"path/to/scripts/wmt_utils.py",
language_pair=("fr", "de"),
subsets={
datasets.Split.TRAIN: ["commoncrawl_frde"],
datasets.Split.VALIDATION: ["euelections_dev2019"],
},
)
# Standard version
builder.download_and_prepare()
ds = builder.as_dataset()
# Streamable version
ds = builder.as_streaming_dataset()
```
### 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
#### cs-en
- **Size of downloaded dataset files:** 1.78 GB
- **Size of the generated dataset:** 302.09 MB
- **Total amount of disk used:** 2.09 GB
An example of 'train' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### cs-en
- `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`.
### Data Splits
|name | train |validation|test|
|-----|------:|---------:|---:|
|cs-en|1018291| 2999|3005|
## 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{bojar-EtAl:2017:WMT1,
author = {Bojar, Ond
{r}ej and Chatterjee, Rajen and Federmann, Christian and Graham, Yvette and Haddow, Barry and Huang, Shujian and Huck, Matthias and Koehn, Philipp and Liu, Qun and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Rubino, Raphael and Specia, Lucia and Turchi, Marco},
title = {Findings of the 2017 Conference on Machine Translation (WMT17)},
booktitle = {Proceedings of the Second Conference on Machine Translation, Volume 2: Shared Task Papers},
month = {September},
year = {2017},
address = {Copenhagen, Denmark},
publisher = {Association for Computational Linguistics},
pages = {169--214},
url = {http://www.aclweb.org/anthology/W17-4717}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
false |
# Dataset Card for JFLEG
## 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/keisks/jfleg)
- **Repository:** [Github](https://github.com/keisks/jfleg)
- **Paper:** [Napoles et al., 2020](https://www.aclweb.org/anthology/E17-2037/)
- **Leaderboard:** [Leaderboard](https://github.com/keisks/jfleg#leader-board-published-results)
- **Point of Contact:** Courtney Napoles, Keisuke Sakaguchi
### Dataset Summary
JFLEG (JHU FLuency-Extended GUG) is an English grammatical error correction (GEC) corpus. It is a gold standard benchmark for developing and evaluating GEC systems with respect to fluency (extent to which a text is native-sounding) as well as grammaticality. For each source document, there are four human-written corrections.
### Supported Tasks and Leaderboards
Grammatical error correction.
### Languages
English (native as well as L2 writers)
## Dataset Structure
### Data Instances
Each instance contains a source sentence and four corrections. For example:
```python
{
'sentence': "They are moved by solar energy ."
'corrections': [
"They are moving by solar energy .",
"They are moved by solar energy .",
"They are moved by solar energy .",
"They are propelled by solar energy ."
]
}
```
### Data Fields
- sentence: original sentence written by an English learner
- corrections: corrected versions by human annotators. The order of the annotations are consistent (eg first sentence will always be written by annotator "ref0").
### Data Splits
- This dataset contains 1511 examples in total and comprise a dev and test split.
- There are 754 and 747 source sentences for dev and test, respectively.
- Each sentence has 4 corresponding corrected versions.
## 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 work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
### Citation Information
This benchmark was proposed by [Napoles et al., 2020](https://www.aclweb.org/anthology/E17-2037/).
```
@InProceedings{napoles-sakaguchi-tetreault:2017:EACLshort,
author = {Napoles, Courtney and Sakaguchi, Keisuke and Tetreault, Joel},
title = {JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction},
booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
month = {April},
year = {2017},
address = {Valencia, Spain},
publisher = {Association for Computational Linguistics},
pages = {229--234},
url = {http://www.aclweb.org/anthology/E17-2037}
}
@InProceedings{heilman-EtAl:2014:P14-2,
author = {Heilman, Michael and Cahill, Aoife and Madnani, Nitin and Lopez, Melissa and Mulholland, Matthew and Tetreault, Joel},
title = {Predicting Grammaticality on an Ordinal Scale},
booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
month = {June},
year = {2014},
address = {Baltimore, Maryland},
publisher = {Association for Computational Linguistics},
pages = {174--180},
url = {http://www.aclweb.org/anthology/P14-2029}
}
```
### Contributions
Thanks to [@j-chim](https://github.com/j-chim) for adding this dataset. |
false |
# Dataset Card for OpusParaCrawl
## 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/ParaCrawl.php
- **Repository:** None
- **Paper:** [ParaCrawl: Web-Scale Acquisition of Parallel Corpora](https://aclanthology.org/2020.acl-main.417/)
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
Parallel corpora from Web Crawls collected in the ParaCrawl project.
Tha dataset contains:
- 42 languages, 43 bitexts
- total number of files: 59,996
- total number of tokens: 56.11G
- total number of sentence fragments: 3.13G
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs,
e.g.
```python
dataset = load_dataset("opus_paracrawl", lang1="en", lang2="so")
```
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/ParaCrawl.php
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The languages in the dataset are:
- bg
- ca
- cs
- da
- de
- el
- en
- es
- et
- eu
- fi
- fr
- ga
- gl
- hr
- hu
- is
- it
- km
- ko
- lt
- lv
- mt
- my
- nb
- ne
- nl
- nn
- pl
- pt
- ro
- ru
- si
- sk
- sl
- so
- sv
- sw
- tl
- uk
- zh
## Dataset Structure
### Data Instances
```
{
'id': '0',
'translation': {
"el": "Συνεχίστε ευθεία 300 μέτρα μέχρι να καταλήξουμε σε μια σωστή οδός (ul. Gagarina)? Περπατήστε περίπου 300 μέτρα μέχρι να φτάσετε το πρώτο ορθή οδός (ul Khotsa Namsaraeva)?",
"en": "Go straight 300 meters until you come to a proper street (ul. Gagarina); Walk approximately 300 meters until you reach the first proper street (ul Khotsa Namsaraeva);"
}
}
```
### Data Fields
- `id` (`str`): Unique identifier of the parallel sentence for the pair of languages.
- `translation` (`dict`): Parallel sentences for the pair of languages.
### Data Splits
The dataset contains a single `train` split.
## 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
- Creative commons CC0 (no rights reserved)
### Citation Information
```bibtex
@inproceedings{banon-etal-2020-paracrawl,
title = "{P}ara{C}rawl: Web-Scale Acquisition of Parallel Corpora",
author = "Ba{\~n}{\'o}n, Marta and
Chen, Pinzhen and
Haddow, Barry and
Heafield, Kenneth and
Hoang, Hieu and
Espl{\`a}-Gomis, Miquel and
Forcada, Mikel L. and
Kamran, Amir and
Kirefu, Faheem and
Koehn, Philipp and
Ortiz Rojas, Sergio and
Pla Sempere, Leopoldo and
Ram{\'\i}rez-S{\'a}nchez, Gema and
Sarr{\'\i}as, Elsa and
Strelec, Marek and
Thompson, Brian and
Waites, William and
Wiggins, Dion and
Zaragoza, Jaume",
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://aclanthology.org/2020.acl-main.417",
doi = "10.18653/v1/2020.acl-main.417",
pages = "4555--4567",
}
```
```bibtex
@InProceedings{TIEDEMANN12.463,
author = {Jörg 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 Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno 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. |
false |
# Dataset Card for The Winograd Schema Challenge
## 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://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WS.html
- **Repository:**
- **Paper:** https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.729.9814&rep=rep1&type=pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is
resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its
resolution. The schema takes its name from a well-known example by Terry Winograd:
> The city councilmen refused the demonstrators a permit because they [feared/advocated] violence.
If the word is ``feared'', then ``they'' presumably refers to the city council; if it is ``advocated'' then ``they''
presumably refers to the demonstrators.
### Supported Tasks and Leaderboards
From the official webpage:
> A contest, entitled the Winograd Schema Challenge was run once, in 2016. At that time, there was a cash prize
offered for achieving human-level performance in the contest. Since then, the sponsor has withdrawn; therefore NO
CASH PRIZES CAN BE OFFERED OR WILL BE AWARDED FOR ANY KIND OF PERFORMANCE OR ACHIEVEMENT ON THIS CHALLENGE.
### Languages
The dataset is in English.
[Translation of 12 WSs into Chinese ](https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/WSChinese.html)(translated by Wei Xu).
Translations into Japanese, by Soichiro Tanaka, Rafal Rzepka, and Shiho Katajima\
**Translation changing English names to Japanese **[PDF ](https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/collection_ja.pdf) [HTML](http://arakilab.media.eng.hokudai.ac.jp/~kabura/collection_ja.html)\
**Translation preserving English names** [PDF ](https://cs.nyu.edu/faculty/davise/papers/WinogradSchemas/collection_katakana.pdf) [HTML](http://arakilab.media.eng.hokudai.ac.jp/~kabura/collection_katakana.html)
[Translation into French, ](http://www.llf.cnrs.fr/winograd-fr)by Pascal Amsili and Olga Seminck
[Winograd Schemas in Portuguese](https://sol.sbc.org.br/index.php/eniac/article/view/9334) by Gabriela Melo, Vinicius Imaizumi, and Fábio Cozman.
[Mandarinograd: A Chinese Collection of Winograd Schemas](https://www.aclweb.org/anthology/2020.lrec-1.3) by Timothée Bernard and Ting Han, LREC-2020.
## Dataset Structure
### Data Instances
Each instance contains a text passage with a designated pronoun and two possible answers indicating which entity in
the passage the pronoun represents. An example instance looks like the following:
```python
{
'label': 0,
'options': ['The city councilmen', 'The demonstrators'],
'pronoun': 'they',
'pronoun_loc': 63,
'quote': 'they feared violence',
'quote_loc': 63,
'source': '(Winograd 1972)',
'text': 'The city councilmen refused the demonstrators a permit because they feared violence.'
}
```
### Data Fields
- `text` (str): The text sequence
- `options` (list[str]): The two entity options that the pronoun may be referring to
- `label` (int): The index of the correct option in the `options` field
- `pronoun` (str): The pronoun in the sequence to be resolved
- `pronoun_loc` (int): The starting position of the pronoun in the sequence
- `quote` (str): The substr with the key action or context surrounding the pronoun
- `quote_loc` (int): The starting position of the quote in the sequence
- `source` (str): A description of the source who contributed the example
### Data Splits
Only a test split is included.
## Dataset Creation
### Curation Rationale
The Winograd Schema Challenge was proposed as an automated evaluation of an AI system's commonsense linguistic
understanding. From the webpage:
> The strengths of the challenge are that it is clear-cut, in that the answer to each schema is a binary choice;
vivid, in that it is obvious to non-experts that a program that fails to get the right answers clearly has serious
gaps in its understanding; and difficult, in that it is far beyond the current state of the art.
### Source Data
#### Initial Data Collection and Normalization
This data was manually written by experts such that the schemas are:
- easily disambiguated by the human reader (ideally, so easily that the reader does not even notice that there is an ambiguity);
- not solvable by simple techniques such as selectional restrictions;
- Google-proof; that is, there is no obvious statistical test over text corpora that will reliably disambiguate these correctly.
#### Who are the source language producers?
This dataset has grown over time, and so was produced by a variety of lingustic and AI researchers. See the `source`
field for the source of each instance.
### Annotations
#### Annotation process
Annotations are produced by the experts who construct the examples.
#### Who are the annotators?
See above.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset has grown over time, and so was produced by a variety of lingustic and AI researchers. See the `source`
field for the source of each instance.
### Licensing Information
This work is licensed under a [Creative Commons Attribution 4.0 International
License](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
The Winograd Schema Challenge including many of the examples here was proposed by
[Levesque et al 2012](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.729.9814&rep=rep1&type=pdf):
```
@inproceedings{levesque2012winograd,
title={The winograd schema challenge},
author={Levesque, Hector and Davis, Ernest and Morgenstern, Leora},
booktitle={Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning},
year={2012},
organization={Citeseer}
}
```
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. |
false |
# Dataset Card for XStoryCloze MT
## 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://cs.rochester.edu/nlp/rocstories/](https://cs.rochester.edu/nlp/rocstories/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Few-shot Learning with Multilingual Generative Language Models](https://arxiv.org/pdf/2112.10668.pdf)
- **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:** 2.03 MB
- **Size of the generated dataset:** 2.03 MB
- **Total amount of disk used:** 2.05 MB
### Dataset Summary
XStoryCloze consists of the professionally translated version of the [English StoryCloze dataset](https://cs.rochester.edu/nlp/rocstories/) (Spring 2016 version) to 10 non-English languages. This dataset is released by Meta AI. This dataset is the machine-translated version of XstoryCloze to en from ru, zh, es, ar, hi, id, te, sw, eu, my.
### Supported Tasks and Leaderboards
commonsense reasoning
### Languages
This dataset is the machine-translated version of XstoryCloze to en from ru, zh (Simplified), es (Latin America), ar, hi, id, te, sw, eu, my.
## Dataset Structure
### Data Instances
- **Size of downloaded dataset files:** 2.03 MB
- **Size of the generated dataset:** 2.03 MB
- **Total amount of disk used:** 2.05 MB
An example of 'train' looks as follows.
```
{'answer_right_ending': 1,
'input_sentence_1': 'Rick grew up in a troubled household.',
'input_sentence_2': 'He never found good support in family, and turned to gangs.',
'input_sentence_3': "It wasn't long before Rick got shot in a robbery.",
'input_sentence_4': 'The incident caused him to turn a new leaf.',
'sentence_quiz1': 'He is happy now.',
'sentence_quiz2': 'He joined a gang.',
'story_id': '138d5bfb-05cc-41e3-bf2c-fa85ebad14e2'}
```
### Data Fields
The data fields are the same among all splits.
- `input_sentence_1`: The first statement in the story.
- `input_sentence_2`: The second statement in the story.
- `input_sentence_3`: The third statement in the story.
- `input_sentence_4`: The forth statement in the story.
- `sentence_quiz1`: first possible continuation of the story.
- `sentence_quiz2`: second possible continuation of the story.
- `answer_right_ending`: correct possible ending; either 1 or 2.
- `story_id`: story id.
### Data Splits
This dataset is intended to be used for evaluating the zero- and few-shot learning capabilities of multlingual language models. We split the data for each language into train and test (360 vs. 1510 examples, respectively). The released data files for different languages maintain a line-by-line alignment.
| name |test|
|-------|---:|
|ru|1510|
|zh|1510|
|es|1510|
|ar|1510|
|hi|1510|
|id|1510|
|te|1510|
|sw|1510|
|eu|1510|
|my|1510|
## 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
XStoryCloze is opensourced under [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode), the same license as the original English StoryCloze.
### Citation Information
```
@article{DBLP:journals/corr/abs-2112-10668,
author = {Xi Victoria Lin and
Todor Mihaylov and
Mikel Artetxe and
Tianlu Wang and
Shuohui Chen and
Daniel Simig and
Myle Ott and
Naman Goyal and
Shruti Bhosale and
Jingfei Du and
Ramakanth Pasunuru and
Sam Shleifer and
Punit Singh Koura and
Vishrav Chaudhary and
Brian O'Horo and
Jeff Wang and
Luke Zettlemoyer and
Zornitsa Kozareva and
Mona T. Diab and
Veselin Stoyanov and
Xian Li},
title = {Few-shot Learning with Multilingual Language Models},
journal = {CoRR},
volume = {abs/2112.10668},
year = {2021},
url = {https://arxiv.org/abs/2112.10668},
eprinttype = {arXiv},
eprint = {2112.10668},
timestamp = {Tue, 04 Jan 2022 15:59:27 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2112-10668.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@juletx](https://github.com/juletx). |
false |
# Dataset Card for Qasper
## 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)
- [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
- **Homepage:** [https://allenai.org/data/qasper](https://allenai.org/data/qasper)
- **Demo:** [https://qasper-demo.apps.allenai.org/](https://qasper-demo.apps.allenai.org/)
- **Paper:** [https://arxiv.org/abs/2105.03011](https://arxiv.org/abs/2105.03011)
- **Blogpost:** [https://medium.com/ai2-blog/question-answering-on-scientific-research-papers-f6d6da9fd55c](https://medium.com/ai2-blog/question-answering-on-scientific-research-papers-f6d6da9fd55c)
- **Leaderboards:** [https://paperswithcode.com/dataset/qasper](https://paperswithcode.com/dataset/qasper)
### Dataset Summary
QASPER is a dataset for question answering on scientific research papers. It consists of 5,049 questions over 1,585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers.
### Supported Tasks and Leaderboards
- `question-answering`: The dataset can be used to train a model for Question Answering. Success on this task is typically measured by achieving a *high* [F1 score](https://huggingface.co/metrics/f1). The [official baseline model](https://github.com/allenai/qasper-led-baseline) currently achieves 33.63 Token F1 score & uses [Longformer](https://huggingface.co/transformers/model_doc/longformer.html). This task has an active leaderboard which can be found [here](https://paperswithcode.com/sota/question-answering-on-qasper)
- `evidence-selection`: The dataset can be used to train a model for Evidence Selection. Success on this task is typically measured by achieving a *high* [F1 score](https://huggingface.co/metrics/f1). The [official baseline model](https://github.com/allenai/qasper-led-baseline) currently achieves 39.37 F1 score & uses [Longformer](https://huggingface.co/transformers/model_doc/longformer.html). This task has an active leaderboard which can be found [here](https://paperswithcode.com/sota/evidence-selection-on-qasper)
### Languages
English, as it is used in research papers.
## Dataset Structure
### Data Instances
A typical instance in the dataset:
```
{
'id': "Paper ID (string)",
'title': "Paper Title",
'abstract': "paper abstract ...",
'full_text': {
'paragraphs':[["section1_paragraph1_text","section1_paragraph2_text",...],["section2_paragraph1_text","section2_paragraph2_text",...]],
'section_name':["section1_title","section2_title"],...},
'qas': {
'answers':[{
'annotation_id': ["q1_answer1_annotation_id","q1_answer2_annotation_id"]
'answer': [{
'unanswerable':False,
'extractive_spans':["q1_answer1_extractive_span1","q1_answer1_extractive_span2"],
'yes_no':False,
'free_form_answer':"q1_answer1",
'evidence':["q1_answer1_evidence1","q1_answer1_evidence2",..],
'highlighted_evidence':["q1_answer1_highlighted_evidence1","q1_answer1_highlighted_evidence2",..]
},
{
'unanswerable':False,
'extractive_spans':["q1_answer2_extractive_span1","q1_answer2_extractive_span2"],
'yes_no':False,
'free_form_answer':"q1_answer2",
'evidence':["q1_answer2_evidence1","q1_answer2_evidence2",..],
'highlighted_evidence':["q1_answer2_highlighted_evidence1","q1_answer2_highlighted_evidence2",..]
}],
'worker_id':["q1_answer1_worker_id","q1_answer2_worker_id"]
},{...["question2's answers"]..},{...["question3's answers"]..}],
'question':["question1","question2","question3"...],
'question_id':["question1_id","question2_id","question3_id"...],
'question_writer':["question1_writer_id","question2_writer_id","question3_writer_id"...],
'nlp_background':["question1_writer_nlp_background","question2_writer_nlp_background",...],
'topic_background':["question1_writer_topic_background","question2_writer_topic_background",...],
'paper_read': ["question1_writer_paper_read_status","question2_writer_paper_read_status",...],
'search_query':["question1_search_query","question2_search_query","question3_search_query"...],
}
}
```
### Data Fields
The following is an excerpt from the dataset README:
Within "qas", some fields should be obvious. Here is some explanation about the others:
#### Fields specific to questions:
- "nlp_background" shows the experience the question writer had. The values can be "zero" (no experience), "two" (0 - 2 years of experience), "five" (2 - 5 years of experience), and "infinity" (> 5 years of experience). The field may be empty as well, indicating the writer has chosen not to share this information.
- "topic_background" shows how familiar the question writer was with the topic of the paper. The values are "unfamiliar", "familiar", "research" (meaning that the topic is the research area of the writer), or null.
- "paper_read", when specified shows whether the questionwriter has read the paper.
- "search_query", if not empty, is the query the question writer used to find the abstract of the paper from a large pool of abstracts we made available to them.
#### Fields specific to answers
Unanswerable answers have "unanswerable" set to true. The remaining answers have exactly one of the following fields being non-empty.
- "extractive_spans" are spans in the paper which serve as the answer.
- "free_form_answer" is a written out answer.
- "yes_no" is true iff the answer is Yes, and false iff the answer is No.
"evidence" is the set of paragraphs, figures or tables used to arrive at the answer. Tables or figures start with the string "FLOAT SELECTED"
"highlighted_evidence" is the set of sentences the answer providers selected as evidence if they chose textual evidence. The text in the "evidence" field is a mapping from these sentences to the paragraph level. That is, if you see textual evidence in the "evidence" field, it is guaranteed to be entire paragraphs, while that is not the case with "highlighted_evidence".
### Data Splits
| | Train | Valid |
| ----- | ------ | ----- |
| Number of papers | 888 | 281 |
| Number of questions | 2593 | 1005 |
| Number of answers | 2675 | 1764 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
NLP papers: The full text of the papers is extracted from [S2ORC](https://huggingface.co/datasets/s2orc) (Lo et al., 2020)
#### 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?
"The annotators are NLP practitioners, not
expert researchers, and it is likely that an expert
would score higher"
### 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
Crowdsourced NLP practitioners
### Licensing Information
[CC BY 4.0](https://creativecommons.org/licenses/by/4.0)
### Citation Information
```
@inproceedings{Dasigi2021ADO,
title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner},
year={2021}
}
```
### Contributions
Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset.
|
true | # Dataset Card for NLI_zh
## 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:** [Chinese NLI dataset](https://github.com/shibing624/text2vec)
- **Leaderboard:** [NLI_zh leaderboard](https://github.com/shibing624/text2vec) (located on the homepage)
- **Size of downloaded dataset files:** 16 MB
- **Total amount of disk used:** 42 MB
### Dataset Summary
常见中文语义匹配数据集，包含[ATEC](https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC)、[BQ](http://icrc.hitsz.edu.cn/info/1037/1162.htm)、[LCQMC](http://icrc.hitsz.edu.cn/Article/show/171.html)、[PAWSX](https://arxiv.org/abs/1908.11828)、[STS-B](https://github.com/pluto-junzeng/CNSD)共5个任务。
数据源：
- ATEC: https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC
- BQ: http://icrc.hitsz.edu.cn/info/1037/1162.htm
- LCQMC: http://icrc.hitsz.edu.cn/Article/show/171.html
- PAWSX: https://arxiv.org/abs/1908.11828
- STS-B: https://github.com/pluto-junzeng/CNSD
### Supported Tasks and Leaderboards
Supported Tasks: 支持中文文本匹配任务，文本相似度计算等相关任务。
中文匹配任务的结果目前在顶会paper上出现较少，我罗列一个我自己训练的结果：
**Leaderboard:** [NLI_zh leaderboard](https://github.com/shibing624/text2vec)
### Languages
数据集均是简体中文文本。
## Dataset Structure
### Data Instances
An example of 'train' looks as follows.
```
{
"sentence1": "刘诗诗杨幂谁漂亮",
"sentence2": "刘诗诗和杨幂谁漂亮",
"label": 1,
}
{
"sentence1": "汇理财怎么样",
"sentence2": "怎么样去理财",
"label": 0,
}
```
### Data Fields
The data fields are the same among all splits.
- `sentence1`: a `string` feature.
- `sentence2`: a `string` feature.
- `label`: a classification label, with possible values including `similarity` (1), `dissimilarity` (0).
### Data Splits
#### ATEC
```shell
$ wc -l ATEC/*
20000 ATEC/ATEC.test.data
62477 ATEC/ATEC.train.data
20000 ATEC/ATEC.valid.data
102477 total
```
#### BQ
```shell
$ wc -l BQ/*
10000 BQ/BQ.test.data
100000 BQ/BQ.train.data
10000 BQ/BQ.valid.data
120000 total
```
#### LCQMC
```shell
$ wc -l LCQMC/*
12500 LCQMC/LCQMC.test.data
238766 LCQMC/LCQMC.train.data
8802 LCQMC/LCQMC.valid.data
260068 total
```
#### PAWSX
```shell
$ wc -l PAWSX/*
2000 PAWSX/PAWSX.test.data
49401 PAWSX/PAWSX.train.data
2000 PAWSX/PAWSX.valid.data
53401 total
```
#### STS-B
```shell
$ wc -l STS-B/*
1361 STS-B/STS-B.test.data
5231 STS-B/STS-B.train.data
1458 STS-B/STS-B.valid.data
8050 total
```
## Dataset Creation
### Curation Rationale
作为中文NLI(natural langauge inference)数据集，这里把这个数据集上传到huggingface的datasets，方便大家使用。
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
数据集的版权归原作者所有，使用各数据集时请尊重原数据集的版权。
BQ: Jing Chen, Qingcai Chen, Xin Liu, Haijun Yang, Daohe Lu, Buzhou Tang, The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification EMNLP2018.
### Annotations
#### Annotation process
#### Who are the annotators?
原作者。
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context.
Systems that are successful at such a task may be more successful in modeling semantic representations.
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
- 苏剑林对文件名称有整理
- 我上传到huggingface的datasets
### Licensing Information
用于学术研究。
The BQ corpus is free to the public for academic research.
### Contributions
Thanks to [@shibing624](https://github.com/shibing624) add this dataset.
|
false |
# Dataset Card for 'Adversarial Examples for SQuAD'
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- [**Homepage**](https://worksheets.codalab.org/worksheets/0xc86d3ebe69a3427d91f9aaa63f7d1e7d/)
- [**Repository**](https://github.com/robinjia/adversarial-squad/)
- [**Paper**](https://www.aclweb.org/anthology/D17-1215/)
### Dataset Summary
Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans.
### Supported Tasks and Leaderboards
`question-answering`, `adversarial attack`
### Languages
English
## Dataset Structure
Follows the standart SQuAD format.
### Data Instances
An example from the data set looks as follows:
```py
{'answers': {'answer_start': [334, 334, 334],
'text': ['February 7, 2016', 'February 7', 'February 7, 2016']},
'context': 'Super Bowl 50 was an American football game to determine the champion of the National Football League (NFL) for the 2015 season. The American Football Conference (AFC) champion Denver Broncos defeated the National Football Conference (NFC) champion Carolina Panthers 24–10 to earn their third Super Bowl title. The game was played on February 7, 2016, at Levi\'s Stadium in the San Francisco Bay Area at Santa Clara, California. As this was the 50th Super Bowl, the league emphasized the "golden anniversary" with various gold-themed initiatives, as well as temporarily suspending the tradition of naming each Super Bowl game with Roman numerals (under which the game would have been known as "Super Bowl L"), so that the logo could prominently feature the Arabic numerals 50. The Champ Bowl was played on August 18th,1991.',
'id': '56bea9923aeaaa14008c91bb-high-conf-turk2',
'question': 'What day was the Super Bowl played on?',
'title': 'Super_Bowl_50'}
```
`id` field is formed like: [original_squad_id]-[annotator_id]
### Data Fields
```py
{'id': Value(dtype='string', id=None), # id of example (same as SQuAD) OR SQuAD-id-[annotator_id] for adversarially modified examples
'title': Value(dtype='string', id=None), # title of document the context is from (same as SQuAD)
'context': Value(dtype='string', id=None), # the context (same as SQuAD) +adversarially added sentence
'question': Value(dtype='string', id=None), # the question (same as SQuAD)
'answers': Sequence(feature={'text': Value(dtype='string', id=None), # the answer (same as SQuAD)
'answer_start': Value(dtype='int32', id=None)}, length=-1, id=None) # the answer_start index (same as SQuAD)
}
```
### Data Splits
- AddSent: Has up to five candidate adversarial sentences that don't answer the question, but have a lot of words in common with the question. This adversary is does not query the model in any way.
- AddOneSent: Similar to AddSent, but just one candidate sentences was picked at random. This adversary is does not query the model in any way.
Number of Q&A pairs
- AddSent : 3560
- AddOneSent: 1787
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
SQuAD dev set (+with adversarial sentences added)
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[MIT License](https://github.com/robinjia/adversarial-squad/blob/master/LICENSE)
### Citation Information
```
@inproceedings{jia-liang-2017-adversarial,
title = "Adversarial Examples for Evaluating Reading Comprehension Systems",
author = "Jia, Robin and
Liang, Percy",
booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D17-1215",
doi = "10.18653/v1/D17-1215",
pages = "2021--2031",
abstract = "Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Our method tests whether systems can answer questions about paragraphs that contain adversarially inserted sentences, which are automatically generated to distract computer systems without changing the correct answer or misleading humans. In this adversarial setting, the accuracy of sixteen published models drops from an average of 75% F1 score to 36%; when the adversary is allowed to add ungrammatical sequences of words, average accuracy on four models decreases further to 7%. We hope our insights will motivate the development of new models that understand language more precisely.",
}
```
### Contributions
Thanks to [@cceyda](https://github.com/cceyda) for adding this dataset. |
true |
# Dataset Card for AmericasNLI
## 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/nala-cub/AmericasNLI
- **Paper:** https://arxiv.org/abs/2104.08726
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
AmericasNLI is an extension of XNLI (Conneau et al., 2018) a natural language inference (NLI) dataset covering 15 high-resource languages to 10 low-resource indigenous languages spoken in the Americas: Ashaninka, Aymara, Bribri, Guarani, Nahuatl, Otomi, Quechua, Raramuri, Shipibo-Konibo, and Wixarika. As with MNLI, the goal is to predict textual entailment (does sentence A imply/contradict/neither sentence B) and is a classification task (given two sentences, predict one of three labels).
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
- aym
- bzd
- cni
- gn
- hch
- nah
- oto
- quy
- shp
- tar
## Dataset Structure
### Data Instances
#### all_languages
An example of the test split looks as follows:
```
{'language': 'aym', 'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix p
arlxapxti.', 'label': 2}
```
#### aym
An example of the test split looks as follows:
```
{'premise': "Ukhamaxa, janiw ukatuqits lup'kayätti, ukhamarus wali phiñasitayätwa, ukatx jupampiw mayamp aruskipañ qallanttha.", 'hypothesis': 'Janiw mayamp jupampix parlxapxti.', 'label
': 2}
```
#### bzd
An example of the test split looks as follows:
```
{'premise': "Bua', kèq ye' kũ e' bikeitsök erë ye' chkénãwã tã ye' ujtémĩne ie' tã páxlĩnẽ.", 'hypothesis': "Kèq ye' ùtẽnẽ ie' tã páxlĩ.", 'label': 2}
```
#### cni
An example of the test split looks as follows:
```
{'premise': 'Kameetsa, tee nokenkeshireajeroji, iro kantaincha tee nomateroji aisati nintajaro noñanatajiri iroakera.', 'hypothesis': 'Tee noñatajeriji.', 'label': 2}
```
#### gn
An example of the test split looks as follows:
```
{'premise': "Néi, ni napensaikurihína upéva rehe, ajepichaiterei ha añepyrûjey añe'ê hendive.", 'hypothesis': "Nañe'êvéi hendive.", 'label': 2}
```
#### hch
An example of the test split looks as follows:
```
{'premise': 'mu hekwa.', 'hypothesis': 'neuka tita xatawe m+k+ mat+a.', 'label': 2}
```
#### nah
An example of the test split looks as follows:
```
{'premise': 'Cualtitoc, na axnimoihliaya ino, nicualaniztoya queh naha nicamohuihqui', 'hypothesis': 'Ayoc nicamohuihtoc', 'label': 2}
```
#### oto
An example of the test split looks as follows:
```
{'premise': 'mi-ga, nin mibⴘy mbô̮nitho ane guenu, guedi mibⴘy nho ⴘnmⴘy xi di mⴘdi o ñana nen nⴘua manaigui', 'hypothesis': 'hin din bi pengui nen nⴘa', 'label': 2}
```
#### quy
An example of the test split looks as follows:
``` {'premise': 'Allinmi, manam chaypiqa hamutachkarqanichu, ichaqa manam allinchu tarikurqani chaymi kaqllamanta paywan rimarqani.', 'hypothesis': 'Manam paywanqa kaqllamantaqa rimarqani
.', 'label': 2}
```
#### shp
An example of the test split looks as follows:
```
{'premise': 'Jakon riki, ja shinanamara ea ike, ikaxbi kikin frustradara ea ike jakopira ea jabe yoyo iribake.', 'hypothesis': 'Eara jabe yoyo iribiama iki.', 'label': 2}
```
#### tar
An example of the test split looks as follows:
```
{'premise': 'Ga’lá ju, ke tási newalayé nejé echi kítira, we ne majáli, a’lí ko uchécho ne yua ku ra’íchaki.', 'hypothesis': 'Tási ne uchecho yua ra’ícha échi rejói.', 'label': 2}
```
### Data Fields
#### all_languages
- language: a multilingual string variable, with languages including ar, bg, de, el, en.
- premise: a multilingual string variable, with languages including ar, bg, de, el, en.
- hypothesis: a multilingual string variable, with possible languages including ar, bg, de, el, en.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### aym
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### bzd
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### cni
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### hch
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### nah
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### oto
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### quy
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### shp
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
#### tar
- premise: a string feature.
- hypothesis: a string feature.
- label: a classification label, with possible values including entailment (0), neutral (1), contradiction (2).
### Data Splits
| Language | ISO | Family | Dev | Test |
|-------------------|-----|:-------------|-----:|-----:|
| all_languages | -- | -- | 6457 | 7486 |
| Aymara | aym | Aymaran | 743 | 750 |
| Ashaninka | cni | Arawak | 658 | 750 |
| Bribri | bzd | Chibchan | 743 | 750 |
| Guarani | gn | Tupi-Guarani | 743 | 750 |
| Nahuatl | nah | Uto-Aztecan | 376 | 738 |
| Otomi | oto | Oto-Manguean | 222 | 748 |
| Quechua | quy | Quechuan | 743 | 750 |
| Raramuri | tar | Uto-Aztecan | 743 | 750 |
| Shipibo-Konibo | shp | Panoan | 743 | 750 |
| Wixarika | hch | Uto-Aztecan | 743 | 750 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
The authors translate from the Spanish subset of XNLI.
> AmericasNLI is the translation of a subset of XNLI (Conneau et al., 2018). As translators between Spanish and the target languages are more frequently available than those for English, we translate from the Spanish version.
As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726).
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
The dataset comprises expert translations from Spanish XNLI.
> Additionally, some translators reported that code-switching is often used to describe certain topics, and, while many words without an exact equivalence in the target language are worked in through translation or interpretation, others are kept in Spanish. To minimize the amount of Spanish vocabulary in the translated examples, we choose sentences from genres that we judged to be relatively easy to translate into the target languages: “face-to-face,” “letters,” and “telephone.”
As per paragraph 3.1 of the [original paper](https://arxiv.org/abs/2104.08726).
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
[Needs More Information]
### Discussion of Biases
[Needs More Information]
### Other Known Limitations
[Needs More Information]
## Additional Information
### Dataset Curators
[Needs More Information]
### Licensing Information
[Needs More Information]
### Citation Information
```
@article{DBLP:journals/corr/abs-2104-08726,
author = {Abteen Ebrahimi and
Manuel Mager and
Arturo Oncevay and
Vishrav Chaudhary and
Luis Chiruzzo and
Angela Fan and
John Ortega and
Ricardo Ramos and
Annette Rios and
Ivan Vladimir and
Gustavo A. Gim{\'{e}}nez{-}Lugo and
Elisabeth Mager and
Graham Neubig and
Alexis Palmer and
Rolando A. Coto Solano and
Ngoc Thang Vu and
Katharina Kann},
title = {AmericasNLI: Evaluating Zero-shot Natural Language Understanding of
Pretrained Multilingual Models in Truly Low-resource Languages},
journal = {CoRR},
volume = {abs/2104.08726},
year = {2021},
url = {https://arxiv.org/abs/2104.08726},
eprinttype = {arXiv},
eprint = {2104.08726},
timestamp = {Mon, 26 Apr 2021 17:25:10 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2104-08726.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@fdschmidt93](https://github.com/fdschmidt93) for adding this dataset. |
false |
# Dataset Card for "empathetic_dialogues"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/facebookresearch/EmpatheticDialogues](https://github.com/facebookresearch/EmpatheticDialogues)
- **Repository:** https://github.com/facebookresearch/EmpatheticDialogues
- **Paper:** [Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset](https://arxiv.org/abs/1811.00207)
- **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:** 28.02 MB
- **Size of the generated dataset:** 25.13 MB
- **Total amount of disk used:** 53.15 MB
### Dataset Summary
PyTorch original implementation of Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
### 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:** 28.02 MB
- **Size of the generated dataset:** 25.13 MB
- **Total amount of disk used:** 53.15 MB
An example of 'train' looks as follows.
```
{
"context": "sentimental",
"conv_id": "hit:0_conv:1",
"prompt": "I remember going to the fireworks with my best friend. There was a lot of people_comma_ but it only felt like us in the world.",
"selfeval": "5|5|5_2|2|5",
"speaker_idx": 1,
"tags": "",
"utterance": "I remember going to see the fireworks with my best friend. It was the first time we ever spent time alone together. Although there was a lot of people_comma_ we felt like the only people in the world.",
"utterance_idx": 1
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `conv_id`: a `string` feature.
- `utterance_idx`: a `int32` feature.
- `context`: a `string` feature.
- `prompt`: a `string` feature.
- `speaker_idx`: a `int32` feature.
- `utterance`: a `string` feature.
- `selfeval`: a `string` feature.
- `tags`: a `string` feature.
### Data Splits
| name |train|validation|test |
|-------|----:|---------:|----:|
|default|76673| 12030|10943|
## 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
Creative Commons [Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
@inproceedings{rashkin-etal-2019-towards,
title = "Towards Empathetic Open-domain Conversation Models: A New Benchmark and Dataset",
author = "Rashkin, Hannah and
Smith, Eric Michael and
Li, Margaret and
Boureau, Y-Lan",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1534",
doi = "10.18653/v1/P19-1534",
pages = "5370--5381",
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun) for adding this dataset. |
false | # MBXP
## Table of Contents
- [MBXP](#MBXP)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#related-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Executional Correctness](#execution)
- [Execution Example](#execution-example)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
# MBXP
## Dataset Description
- **Repository:** [GitHub Repository](https://github.com/amazon-science/mbxp-exec-eval)
- **Paper:** [Multi-lingual Evaluation of Code Generation Models](https://openreview.net/forum?id=Bo7eeXm6An8)
### Dataset Summary
This repository contains data and code to perform execution-based multi-lingual evaluation of code generation capabilities and the corresponding data,
namely, a multi-lingual benchmark MBXP, multi-lingual MathQA and multi-lingual HumanEval.
<br>Results and findings can be found in the paper ["Multi-lingual Evaluation of Code Generation Models"](https://arxiv.org/abs/2210.14868).
### Related Tasks and Leaderboards
* [Multi-HumanEval](https://huggingface.co/datasets/mxeval/multi-humaneval)
* [MBXP](https://huggingface.co/datasets/mxeval/mbxp)
* [MathQA-X](https://huggingface.co/datasets/mxeval/mathqa-x)
### Languages
The programming problems are written in multiple programming languages and contain English natural text in comments and docstrings.
## Dataset Structure
To lookup currently supported datasets
```python
from datasets import get_dataset_config_names
get_dataset_config_names("mxeval/mbxp")
['python', 'csharp', 'go', 'java', 'javascript', 'kotlin', 'perl', 'php', 'ruby', 'scala', 'swift', 'typescript']
```
To load a specific dataset and language
```python
from datasets import load_dataset
load_dataset("mxeval/mbxp", "python")
DatasetDict({
test: Dataset({
features: ['task_id', 'language', 'prompt', 'test', 'entry_point', 'canonical_solution', 'description'],
num_rows: 974
})
})
```
### Data Instances
An example of a dataset instance:
```python
{
"task_id": "MBPP/1",
"language": "python",
"prompt": "\n\ndef min_cost(cost, m, n):\n\t\"\"\"\n\tWrite a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][].\n\t>>> min_cost([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2)\n\t8\n\t>>> min_cost([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2)\n\t12\n\t>>> min_cost([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2)\n\t16\n\t\"\"\"\n",
"test": "\n\nMETADATA = {}\n\n\ndef check(candidate):\n assert candidate([[1, 2, 3], [4, 8, 2], [1, 5, 3]], 2, 2) == 8\n assert candidate([[2, 3, 4], [5, 9, 3], [2, 6, 4]], 2, 2) == 12\n assert candidate([[3, 4, 5], [6, 10, 4], [3, 7, 5]], 2, 2) == 16\n\n",
"entry_point": "min_cost",
"canonical_solution": "\tR = 3\n\tC = 3\n\t \n\ttc = [[0 for x in range(C)] for x in range(R)] \n\ttc[0][0] = cost[0][0] \n\tfor i in range(1, m+1): \n\t\ttc[i][0] = tc[i-1][0] + cost[i][0] \n\tfor j in range(1, n+1): \n\t\ttc[0][j] = tc[0][j-1] + cost[0][j] \n\tfor i in range(1, m+1): \n\t\tfor j in range(1, n+1): \n\t\t\ttc[i][j] = min(tc[i-1][j-1], tc[i-1][j], tc[i][j-1]) + cost[i][j] \n\treturn tc[m][n]",
"description": "Write a function to find the minimum cost path to reach (m, n) from (0, 0) for the given cost matrix cost[][] and a position (m, n) in cost[][]."
}
```
### Data Fields
- `task_id`: identifier for the data sample
- `prompt`: input for the model containing function header and docstrings
- `canonical_solution`: solution for the problem in the `prompt`
- `description`: task description
- `test`: contains function to test generated code for correctness
- `entry_point`: entry point for test
- `language`: programming lanuage identifier to call the appropriate subprocess call for program execution
### Data Splits
- MBXP
- Python
- Java
- Javascript
- Typescript
- Kotlin
- Ruby
- Php
- Cpp
- Csharp
- Go
- Perl
- Scala
- Swift
## Dataset Creation
### Curation Rationale
Since code generation models are often trained on dumps of GitHub a dataset not included in the dump was necessary to properly evaluate the model. However, since this dataset was published on GitHub it is likely to be included in future dumps.
### Personal and Sensitive Information
None.
### Social Impact of Dataset
With this dataset code generating models can be better evaluated which leads to fewer issues introduced when using such models.
### Dataset Curators
AWS AI Labs
## Execution
### Execution Example
Install the repo [mbxp-exec-eval](https://github.com/amazon-science/mbxp-exec-eval) to execute generations or canonical solutions for the prompts from this dataset.
```python
>>> from datasets import load_dataset
>>> from mxeval.execution import check_correctness
>>> mbxp_python = load_dataset("mxeval/mbxp", "python", split="test")
>>> example_problem = mbxp_python[0]
>>> check_correctness(example_problem, example_problem["canonical_solution"], timeout=20.0)
{'task_id': 'MBPP/1', 'passed': True, 'result': 'passed', 'completion_id': None, 'time_elapsed': 10.314226150512695}
```
### Considerations for Using the Data
Make sure to sandbox the execution environment.
### Licensing Information
[LICENSE](https://huggingface.co/datasets/mxeval/mbxp/blob/main/mbxp-LICENSE) <br>
[THIRD PARTY LICENSES](https://huggingface.co/datasets/mxeval/mbxp/blob/main/THIRD_PARTY_LICENSES)
### Citation Information
```
@article{mbxp_athiwaratkun2022,
title = {Multi-lingual Evaluation of Code Generation Models},
author = {Athiwaratkun, Ben and
Gouda, Sanjay Krishna and
Wang, Zijian and
Li, Xiaopeng and
Tian, Yuchen and
Tan, Ming
and Ahmad, Wasi Uddin and
Wang, Shiqi and
Sun, Qing and
Shang, Mingyue and
Gonugondla, Sujan Kumar and
Ding, Hantian and
Kumar, Varun and
Fulton, Nathan and
Farahani, Arash and
Jain, Siddhartha and
Giaquinto, Robert and
Qian, Haifeng and
Ramanathan, Murali Krishna and
Nallapati, Ramesh and
Ray, Baishakhi and
Bhatia, Parminder and
Sengupta, Sudipta and
Roth, Dan and
Xiang, Bing},
doi = {10.48550/ARXIV.2210.14868},
url = {https://arxiv.org/abs/2210.14868},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
### Contributions
[skgouda@](https://github.com/sk-g) [benathi@](https://github.com/benathi) |
false |
# Dataset Card for "wmt15"
## 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.statmt.org/wmt15/translation-task.html](http://www.statmt.org/wmt15/translation-task.html)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 1.74 GB
- **Size of the generated dataset:** 284.34 MB
- **Total amount of disk used:** 2.02 GB
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Warning:</b> There are issues with the Common Crawl corpus data (<a href="https://www.statmt.org/wmt13/training-parallel-commoncrawl.tgz">training-parallel-commoncrawl.tgz</a>):</p>
<ul>
<li>Non-English files contain many English sentences.</li>
<li>Their "parallel" sentences in English are not aligned: they are uncorrelated with their counterpart.</li>
</ul>
<p>We have contacted the WMT organizers.</p>
</div>
Translation dataset based on the data from statmt.org.
Versions exist for different years using a combination of data
sources. The base `wmt` allows you to create a custom dataset by choosing
your own data/language pair. This can be done as follows:
```python
from datasets import inspect_dataset, load_dataset_builder
inspect_dataset("wmt15", "path/to/scripts")
builder = load_dataset_builder(
"path/to/scripts/wmt_utils.py",
language_pair=("fr", "de"),
subsets={
datasets.Split.TRAIN: ["commoncrawl_frde"],
datasets.Split.VALIDATION: ["euelections_dev2019"],
},
)
# Standard version
builder.download_and_prepare()
ds = builder.as_dataset()
# Streamable version
ds = builder.as_streaming_dataset()
```
### 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
#### cs-en
- **Size of downloaded dataset files:** 1.74 GB
- **Size of the generated dataset:** 284.34 MB
- **Total amount of disk used:** 2.02 GB
An example of 'validation' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### cs-en
- `translation`: a multilingual `string` variable, with possible languages including `cs`, `en`.
### Data Splits
|name |train |validation|test|
|-----|-----:|---------:|---:|
|cs-en|959768| 3003|2656|
## 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{bojar-EtAl:2015:WMT,
author = {Bojar, Ond
{r}ej and Chatterjee, Rajen and Federmann, Christian and Haddow, Barry and Huck, Matthias and Hokamp, Chris and Koehn, Philipp and Logacheva, Varvara and Monz, Christof and Negri, Matteo and Post, Matt and Scarton, Carolina and Specia, Lucia and Turchi, Marco},
title = {Findings of the 2015 Workshop on Statistical Machine Translation},
booktitle = {Proceedings of the Tenth Workshop on Statistical Machine Translation},
month = {September},
year = {2015},
address = {Lisbon, Portugal},
publisher = {Association for Computational Linguistics},
pages = {1--46},
url = {http://aclweb.org/anthology/W15-3001}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
false |
# Dataset Card for Crello
## Table of Contents
- [Dataset Card for Crello](#dataset-card-for-crello)
- [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)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [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:** [CanvasVAE github](https://github.com/CyberAgentAILab/canvas-vae)
- **Repository:**
- **Paper:** [CanvasVAE: Learning to Generate Vector Graphic Documents](https://arxiv.org/abs/2108.01249)
- **Leaderboard:**
- **Point of Contact:** [Kota Yamaguchi](https://github.com/kyamagu)
### Dataset Summary
The Crello dataset is compiled for the study of vector graphic documents. The dataset contains document meta-data such as canvas size and pre-rendered elements such as images or text boxes. The original templates were collected from [crello.com](https://crello.com) (now [create.vista.com](https://create.vista.com/)) and converted to a low-resolution format suitable for machine learning analysis.
### Supported Tasks and Leaderboards
[CanvasVAE](https://arxiv.org/abs/2108.01249) studies unsupervised document generation.
### Languages
Almost all design templates use English.
## Dataset Structure
### Data Instances
Each instance has scalar attributes (canvas) and sequence attributes (elements). Categorical values are stored as integer values. Check `ClassLabel` features of the dataset for the list of categorical labels.
```
{'id': '592d6c2c95a7a863ddcda140',
'length': 8,
'group': 4,
'format': 20,
'canvas_width': 3,
'canvas_height': 1,
'category': 0,
'title': 'Beauty Blog Ad Woman with Unusual Hairstyle',
'type': [1, 3, 3, 3, 3, 4, 4, 4],
'left': [0.0,
-0.0009259259095415473,
0.24444444477558136,
0.5712962746620178,
0.2657407522201538,
0.369228333234787,
0.2739444375038147,
0.44776931405067444],
'top': [0.0,
-0.0009259259095415473,
0.37037035822868347,
0.41296297311782837,
0.41296297311782837,
0.8946287035942078,
0.4549448788166046,
0.40591198205947876],
'width': [1.0,
1.0018517971038818,
0.510185182094574,
0.16296295821666718,
0.16296295821666718,
0.30000001192092896,
0.4990740716457367,
0.11388888955116272],
'height': [1.0,
1.0018517971038818,
0.25833332538604736,
0.004629629664123058,
0.004629629664123058,
0.016611294820904732,
0.12458471953868866,
0.02657807245850563],
'opacity': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
'text': ['', '', '', '', '', 'STAY WITH US', 'FOLLOW', 'PRESS'],
'font': [0, 0, 0, 0, 0, 152, 172, 152],
'font_size': [0.0, 0.0, 0.0, 0.0, 0.0, 18.0, 135.0, 30.0],
'text_align': [0, 0, 0, 0, 0, 2, 2, 2],
'angle': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
'capitalize': [0, 0, 0, 0, 0, 0, 0, 0],
'line_height': [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
'letter_spacing': [0.0, 0.0, 0.0, 0.0, 0.0, 14.0, 12.55813980102539, 3.0],
'suitability': [0],
'keywords': ['beautiful',
'beauty',
'blog',
'blogging',
'caucasian',
'cute',
'elegance',
'elegant',
'fashion',
'fashionable',
'femininity',
'glamour',
'hairstyle',
'luxury',
'model',
'stylish',
'vogue',
'website',
'woman',
'post',
'instagram',
'ig',
'insta',
'fashion',
'purple'],
'industries': [1, 8, 13],
'color': [[153.0, 118.0, 96.0],
[34.0, 23.0, 61.0],
[34.0, 23.0, 61.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0],
[255.0, 255.0, 255.0]],
'image': [<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>,
<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=256x256>]}
```
To get a label for categorical values, use the `int2str` method:
```python
key = "font"
example = dataset[0]
dataset.features[key].int2str(example[key])
```
### Data Fields
In the following, categorical fields are shown as `categorical` type, but the actual storage is `int64`.
**Canvas attributes**
| Field | Type | Shape | Description |
| ------------- | ----------- | ------- | --------------------------------------------------------------- |
| id | string | () | Template ID from crello.com |
| group | categorical | () | Broad design groups, such as social media posts or blog headers |
| format | categorical | () | Detailed design formats, such as Instagram post or postcard |
| category | categorical | () | Topic category of the design, such as holiday celebration |
| canvas_width | categorical | () | Canvas pixel width |
| canvas_height | categorical | () | Canvas pixel height |
| length | int64 | () | Length of elements |
| suitability | categorical | (None,) | List of display tags, only `mobile` tag exists |
| keywords | string | (None,) | List of keywords associated to this template |
| industries | categorical | (None,) | List of industry tags like `marketingAds` |
**Element attributes**
| Field | Type | Shape | Description |
| -------------- | ----------- | --------- | -------------------------------------------------------------------- |
| type | categorical | (None,) | Element type, such as vector shape, image, or text |
| left | float32 | (None,) | Element left position normalized to [0, 1] range w.r.t. canvas_width |
| top | float32 | (None,) | Element top position normalized to [0, 1] range w.r.t. canvas_height |
| width | float32 | (None,) | Element width normalized to [0, 1] range w.r.t. canvas_width |
| height | float32 | (None,) | Element height normalized to [0, 1] range w.r.t. canvas_height |
| color | int64 | (None, 3) | Extracted main RGB color of the element |
| opacity | float32 | (None,) | Opacity in [0, 1] range |
| image | image | (None,) | Pre-rendered 256x256 preview of the element encoded in PNG format |
| text | string | (None,) | Text content in UTF-8 encoding for text element |
| font | categorical | (None,) | Font family name for text element |
| font_size | float32 | (None,) | Font size (height) in pixels |
| text_align | categorical | (None,) | Horizontal text alignment, left, center, right for text element |
| angle | float32 | (None,) | Element rotation angle (radian) w.r.t. the center of the element |
| capitalize | categorical | (None,) | Binary flag to capitalize letters |
| line_height | float32 | (None,) | Scaling parameter to line height, default is 1.0 |
| letter_spacing | float32 | (None,) | Adjustment parameter for letter spacing, default is 0.0 |
Note that the color and pre-rendered images do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available.
```
https://create.vista.com/artboard/?template=<template_id>
```
`left` and `top` can be negative because elements can be bigger than the canvas size.
### Data Splits
The Crello dataset has 3 splits: train, validation, and test. The current split is generated such that the same title of the original template shows up in only in one split.
| Split | Count |
| --------- | ----- |
| train | 18659 |
| validaton | 2391 |
| test | 2371 |
### Visualization
Each example can be visualized in the following approach using [`skia-python`](https://kyamagu.github.io/skia-python/). Note the following does not guarantee a similar appearance to the original template. Currently, the quality of text rendering is far from perfect.
```python
import io
from typing import Any, Dict
import numpy as np
import skia
def render(features: datasets.Features, example: Dict[str, Any], max_size: float=512.) -> bytes:
"""Render parsed sequence example onto an image and return as PNG bytes."""
canvas_width = int(features["canvas_width"].int2str(example["canvas_width"]))
canvas_height = int(features["canvas_height"].int2str(example["canvas_height"]))
scale = min(1.0, max_size / canvas_width, max_size / canvas_height)
surface = skia.Surface(int(scale * canvas_width), int(scale * canvas_height))
with surface as canvas:
canvas.scale(scale, scale)
for index in range(example["length"]):
pil_image = example["image"][index]
image = skia.Image.frombytes(
pil_image.convert('RGBA').tobytes(),
pil_image.size,
skia.kRGBA_8888_ColorType)
left = example["left"][index] * canvas_width
top = example["top"][index] * canvas_height
width = example["width"][index] * canvas_width
height = example["height"][index] * canvas_height
rect = skia.Rect.MakeXYWH(left, top, width, height)
paint = skia.Paint(Alphaf=example["opacity"][index], AntiAlias=True)
angle = example["angle"][index]
with skia.AutoCanvasRestore(canvas):
if angle != 0:
degree = 180. * angle / np.pi
canvas.rotate(degree, left + width / 2., top + height / 2.)
canvas.drawImageRect(image, rect, paint=paint)
image = surface.makeImageSnapshot()
with io.BytesIO() as f:
image.save(f, skia.kPNG)
return f.getvalue()
```
## Dataset Creation
### Curation Rationale
The Crello dataset is compiled for the general study of vector graphic documents, with the goal of producing a dataset that offers complete vector graphic information suitable for neural methodologies.
### Source Data
#### Initial Data Collection and Normalization
The dataset is initially scraped from the former `crello.com` and pre-processed to the above format.
#### Who are the source language producers?
While [create.vista.com](https://create.vista.com/) owns those templates, the templates seem to be originally created by a specific group of design studios.
### Personal and Sensitive Information
The dataset does not contain any personal information about the creator but may contain a picture of people in the design template.
## Considerations for Using the Data
### Social Impact of Dataset
This dataset was developed for advancing the general study of vector graphic documents, especially for generative systems of graphic design. Successful utilization might enable the automation of creative workflow that human designers get involved in.
### Discussion of Biases
The templates contained in the dataset reflect the biases appearing in the source data, which could present gender biases in specific design categories.
### Other Known Limitations
Due to the unknown data specification of the source data, the color and pre-rendered images do not necessarily accurately reproduce the original design templates. The original template is accessible at the following URL if still available.
https://create.vista.com/artboard/?template=<template_id>
## Additional Information
### Dataset Curators
The Crello dataset was developed by [Kota Yamaguchi](https://github.com/kyamagu).
### Licensing Information
The origin of the dataset is [create.vista.com](https://create.vista.com) (formally, `crello.com`).
The distributor ("We") do not own the copyrights of the original design templates.
By using the Crello dataset, the user of this dataset ("You") must agree to the
[VistaCreate License Agreements](https://create.vista.com/faq/legal/licensing/license_agreements/).
The dataset is distributed under [CDLA-Permissive-2.0 license](https://cdla.dev/permissive-2-0/).
**Note**
We do not re-distribute the original files as we are not allowed by terms.
### Citation Information
@article{yamaguchi2021canvasvae,
title={CanvasVAE: Learning to Generate Vector Graphic Documents},
author={Yamaguchi, Kota},
journal={ICCV},
year={2021}
}
### Releases
3.1: bugfix release (Feb 16, 2023)
- Fix a bug that ignores newline characters in some of the texts
3.0: v3 release (Feb 13, 2023)
- Migrate to Hugging Face Hub.
- Fix various text rendering bugs.
- Change split generation criteria for avoiding near-duplicates: no compatibility with v2 splits.
- Incorporate a motion picture thumbnail in templates.
- Add `title`, `keywords`, `suitability`, and `industries` canvas attributes.
- Add `capitalize`, `line_height`, and `letter_spacing` element attributes.
2.0: v2 release (May 26, 2022)
- Add `text`, `font`, `font_size`, `text_align`, and `angle` element attributes.
- Include rendered text element in `image_bytes`.
1.0: v1 release (Aug 24, 2021)
### Contributions
Thanks to [@kyamagu](https://github.com/kyamagu) for adding this dataset. |
false |
# Dataset Card for "tner/wnut2017"
## Dataset Description
- **Repository:** [T-NER](https://github.com/asahi417/tner)
- **Paper:** [https://aclanthology.org/W17-4418/](https://aclanthology.org/W17-4418/)
- **Dataset:** WNUT 2017
- **Domain:** Twitter, Reddit, YouTube, and StackExchange
- **Number of Entity:** 6
### Dataset Summary
WNUT 2017 NER dataset formatted in a part of [TNER](https://github.com/asahi417/tner) project.
- Entity Types: `creative-work`, `corporation`, `group`, `location`, `person`, `product`
## Dataset Structure
### Data Instances
An example of `train` looks as follows.
```
{
'tokens': ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.'],
'tags': [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 3, 9, 9, 12, 3, 12, 12, 12, 12, 12, 12, 12, 12]
}
```
### Label ID
The label2id dictionary can be found at [here](https://huggingface.co/datasets/tner/wnut2017/raw/main/dataset/label.json).
```python
{
"B-corporation": 0,
"B-creative-work": 1,
"B-group": 2,
"B-location": 3,
"B-person": 4,
"B-product": 5,
"I-corporation": 6,
"I-creative-work": 7,
"I-group": 8,
"I-location": 9,
"I-person": 10,
"I-product": 11,
"O": 12
}
```
### Data Splits
| name |train|validation|test|
|---------|----:|---------:|---:|
|wnut2017 | 2395| 1009|1287|
### Citation Information
```
@inproceedings{derczynski-etal-2017-results,
title = "Results of the {WNUT}2017 Shared Task on Novel and Emerging Entity Recognition",
author = "Derczynski, Leon and
Nichols, Eric and
van Erp, Marieke and
Limsopatham, Nut",
booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-4418",
doi = "10.18653/v1/W17-4418",
pages = "140--147",
abstract = "This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms. Take for example the tweet {``}so.. kktny in 30 mins?!{''} {--} even human experts find the entity {`}kktny{'} hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the ability of participating entries to detect and classify novel and emerging named entities in noisy text.",
}
``` |
false |
# Dataset Card for MDD
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[The bAbI project](https://research.fb.com/downloads/babi/)
- **Repository:**
- **Paper:** [arXiv Paper](https://arxiv.org/pdf/1511.06931.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The Movie Dialog dataset (MDD) is designed to measure how well models can perform at goal and non-goal orientated dialog centered around the topic of movies (question answering, recommendation and discussion), from various movie reviews sources such as MovieLens and OMDb.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The data is present in English language as written by users on OMDb and MovieLens websites.
## Dataset Structure
### Data Instances
An instance from the `task3_qarecs` config's `train` split:
```
{'dialogue_turns': {'speaker': [0, 1, 0, 1, 0, 1], 'utterance': ["I really like Jaws, Bottle Rocket, Saving Private Ryan, Tommy Boy, The Muppet Movie, Face/Off, and Cool Hand Luke. I'm looking for a Documentary movie.", 'Beyond the Mat', 'Who is that directed by?', 'Barry W. Blaustein', 'I like Jon Fauer movies more. Do you know anything else?', 'Cinematographer Style']}}
```
An instance from the `task4_reddit` config's `cand-valid` split:
```
{'dialogue_turns': {'speaker': [0], 'utterance': ['MORTAL KOMBAT !']}}
```
### Data Fields
For all configurations:
- `dialogue_turns`: a dictionary feature containing:
- `speaker`: an integer with possible values including `0`, `1`, indicating which speaker wrote the utterance.
- `utterance`: a `string` feature containing the text utterance.
### Data Splits
The splits and corresponding sizes are:
|config |train |test |validation|cand_valid|cand_test|
|:--|------:|----:|---------:|----:|----:|
|task1_qa|96185|9952|9968|-|-|
|task2_recs|1000000|10000|10000|-|-|
|task3_qarecs|952125|4915|5052|-|-|
|task4_reddit|945198|10000|10000|10000|10000|
The `cand_valid` and `cand_test` are negative candidates for the `task4_reddit` configuration which is used in ranking true positive against these candidates and hits@k (or another ranking metric) is reported. (See paper)
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
The construction of the tasks depended on some existing datasets:
1) MovieLens. The data was downloaded from: http://grouplens.org/datasets/movielens/20m/ on May 27th, 2015.
2) OMDB. The data was downloaded from: http://beforethecode.com/projects/omdb/download.aspx on May 28th, 2015.
3) For `task4_reddit`, the data is a processed subset (movie subreddit only) of the data available at:
https://www.reddit.com/r/datasets/comments/3bxlg7
#### Who are the source language producers?
Users on MovieLens, OMDB website and reddit websites, among others.
### 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
Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston (at Facebook Research).
### Licensing Information
```
Creative Commons Attribution 3.0 License
```
### Citation Information
```
@misc{dodge2016evaluating,
title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems},
author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston},
year={2016},
eprint={1511.06931},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset. |
false |
# Dataset Card for CodeContests
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/deepmind/code_contests/
- **Paper:** [Competition-Level Code Generation with AlphaCode](https://arxiv.org/abs/2203.07814v1)
- **Leaderboard:** [Code Generation on CodeContests](https://paperswithcode.com/sota/code-generation-on-codecontests)
- **Point of Contact:** [David Choi](mailto:david.hu.choi@gmail.com)
### Dataset Summary
CodeContests is a competitive programming dataset for machine-learning. This
dataset was used when training [AlphaCode](https://deepmind.com/blog/article/Competitive-programming-with-AlphaCode).
It consists of programming problems, from a variety of sources:
Site | URL | Source
----------- | --------------------------- | ------
Aizu | https://judge.u-aizu.ac.jp | [CodeNet](https://github.com/IBM/Project_CodeNet)
AtCoder | https://atcoder.jp | [CodeNet](https://github.com/IBM/Project_CodeNet)
CodeChef | https://www.codechef.com | [description2code](https://github.com/ethancaballero/description2code)
Codeforces | https://codeforces.com | [description2code](https://github.com/ethancaballero/description2code) and Codeforces
HackerEarth | https://www.hackerearth.com | [description2code](https://github.com/ethancaballero/description2code)
Problems include test cases in the form of paired inputs and outputs, as well as both correct and incorrect human solutions in a variety of languages.
### Supported Tasks and Leaderboards
- `translation` - the competitive programming code generation problem can be viewed as a sequence-to-sequence translation task: given a problem description 𝑋 in natural language, produce a corresponding solution 𝑌 in a programming language. The metric used for evaluation is "percentage of problems solved using 𝑛 submissions from 𝑘 samples per problem", denoted as 𝑛@𝑘. More information on the evaluation of AlphaCode can be found in Section 2.2. and Appendix A.3. of the paper. The leaderboard for this task is available [here](https://paperswithcode.com/sota/code-generation-on-codecontests).
### Languages
English.
## Dataset Structure
### Data Instances
A data point corresponds to a singular contest problem:
```
{
'name': '76_B. Mice',
'description': 'Modern researches has shown that a flock of hungry mice '
'searching for a piece of...',
'public_tests': {'input': ['3 2 0 2\n0 1 3\n2 5\n'], 'output': ['1\n']},
'private_tests': {'input': ['20 18 1 2\n'
'-9999944 -9999861 -9999850 -9999763 -9999656 '
'-9999517 -9999375 -999927...',
...,
'7 11 10 20\n'
'6 18 32 63 66 68 87\n'
'6 8 15 23 25 41 53 59 60 75 90\n'],
'output': ['2\n', ..., '1\n']},
'generated_tests': {'input': ['7 11 10 5\n'
'6 18 32 63 66 68 87\n'
'6 8 15 23 25 41 53 59 60 75 90\n',
...,
'7 11 10 4\n'
'6 18 46 63 85 84 87\n'
'6 8 15 18 25 41 53 59 60 75 90\n'],
'output': ['1\n', ..., '2\n']},
'source': 2,
'difficulty': 8,
'solutions': {'language': [2, ..., 2],
'solution': ['#include <bits/stdc++.h>\n'
'using namespace std;\n'
'int n, m;\n'
'int data[2][100010], t[1...',
...,
'#include <bits/stdc++.h>\n'
'using namespace std;\n'
'int n, m, pos[100100], food[100100...']},
'incorrect_solutions': {'language': [2, ..., 2],
'solution': ['#include <bits/stdc++.h>\n'
'using namespace std;\n'
'vector<pair<int, int> > v[100010];...',
...,
'#include <bits/stdc++.h>\n'
'using namespace std;\n'
'vector<pair<int, int> > v[100010];...']},
'cf_contest_id': 76,
'cf_index': 'B',
'cf_points': 0.0,
'cf_rating': 2100,
'cf_tags': ['greedy', 'two pointers'],
'is_description_translated': False,
'untranslated_description': '',
'time_limit': {'seconds': 0, 'nanos': 500000000},
'memory_limit_bytes': 256000000,
'input_file': '',
'output_file': ''
}
```
### Data Fields
- `name`: The name of the contest. Note that names could agree between different sources.
- `description`: A natural language description of a programming problem.
- `public_tests`: Public tests are those that are available before submitting a solution, typically as part of the description itself. Represented as a paired `input` and `output` that can be used to test potential solutions. They are therefore acceptable inputs to a model.
- `private_tests`: Private tests are not visible before submitting a solution, so should not be made available as inputs to a model.
- `generated_tests`: Generated tests are automatically generated by modifying inputs from public and private tests and validating using known correct solutions.
- `source`: The original source of the problem, with possible values including `UNKNOWN_SOURCE` (0),`CODECHEF` (1), `CODEFORCES` (2), `HACKEREARTH` (3), `CODEJAM` (4), `ATCODER` (5) and `AIZU` (6).
- `difficulty`: A representation of the difficulty of the problem with possible values including `UNKNOWN_DIFFICULTY` (0), `EASY` (1), `MEDIUM` (2), `HARD` (3), `HARDER` (4), `HARDEST` (5), `EXTERNAL` (6), `A` (7), `B` (8), `C` (9), `D` (10), `E` (11), `F` (12), `G` (13), `H` (14), `I` (15), `J` (16), `K` (17), `L` (18), `M` (19), `N` (20), `O` (21), `P` (22), `Q` (23), `R` (24), `S` (25), `T` (26), `U` (27) and `V` (28). Note that different sources use different, non-comparable gradings. For Codeforces problems, `cf_rating` is a more reliable measure of difficulty when available.
- `solutions`: Correct solutions to the problem. Contrast with `incorrect_solutions` below.
- `incorrect_solutions`: Incorrect solutions.
- `cf_contest_id`: The Contest ID. Note that Contest ID is not monotonic with respect to time.
- `cf_index`: Problem index, e.g. `"A"` or `"B"` or `"C"`.
- `cf_points`: Points for the problem, e.g. `1000.0`
- `cf_rating`: Problem rating (difficulty), e.g. `1100`
- `cf_tags`: Problem tags, e.g. `['greedy', 'math']`
- `is_description_translated`: Whether the problem was translated to English.
- `untranslated_description`: The untranslated description is only available for translated problems.
- `time_limit`: The time limit constraint to use when executing solutions. Represented as a dictionary with two keys, `seconds` and `nanos`. This field is None if not defined.
- `memory_limit_bytes`: The memory limit constraint to use when executing solutions.
- `input_file`: Most problems use stdin for IO. Some problems expect specific files to be used instead.
- `output_file`: Most problems use stdout for IO. Some problems expect specific files to be used instead.
All tests are represented as a paired `input` and `output` that can be used to test potential solutions and all solutions comprise a `language`, with possible values including `UNKNOWN_LANGUAGE` (0), `PYTHON` (1) (solutions written in PYTHON2), `CPP` (2), `PYTHON3` (3) and `JAVA` (4), and a `solution` string written in that `language`. The fields preceded with `cf_` denote extra meta-data for Codeforces problems.
### Data Splits
The data is split into training, validation and test set. The training set contains 13328 samples, the validation set 117 samples and the test set 165 samples.
## Dataset Creation
### Curation Rationale
This dataset was created for fine-tuning AlphaCode models:
> Models pre-trained on GitHub can generate good code and solve simple programming problems, but
as shown in Appendix B.3 they can solve very few competitive programming problems. Fine-tuning
the model on a dedicated competitive programming dataset is critical for performance.
### Source Data
#### Initial Data Collection and Normalization
The information on the data collection and normalization procedures can found in Section 3.2. and Appendinx B.2. of the paper.
#### Who are the source language producers?
The problems are scraped from the following platforms: [Aizu](https://judge.u-aizu.ac.jp), [AtCoder](https://atcoder.jp ), [CodeChef](https://www.codechef.com), [Codeforces](https://codeforces.com) and [HackerEarch](https://www.hackerearth.com). Additionally, some data from the existing public competitive programming dataset Description2Code ([Caballero et al., 2016](https://github.com/ethancaballero/description2code)) and CodeNet ([(Puri et al., 2021](https://arxiv.org/pdf/2105.12655.pdf)) is mixed into the training set.
### Annotations
#### Annotation process
The solutions are scapred alongside the problem descriptions.
#### Who are the annotators?
Same as the source data creators.
### 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
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d'Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu and Oriol Vinyals.
### Licensing Information
This dataset is made available under the terms of the CC BY
4.0 license ([Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/legalcode)).
Additional acknowledged contributions:
* Codeforces materials are sourced from http://codeforces.com.
* Description2Code materials are sourced from:
[Description2Code Dataset](https://github.com/ethancaballero/description2code),
licensed under the
[MIT open source license](https://opensource.org/licenses/MIT), copyright
not specified.
* CodeNet materials are sourced from:
[Project_CodeNet](https://github.com/IBM/Project_CodeNet), licensed under
[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), copyright not
specified.
### Citation Information
```bibtex
@article{li2022competition,
title={Competition-Level Code Generation with AlphaCode},
author={Li, Yujia and Choi, David and Chung, Junyoung and Kushman, Nate and
Schrittwieser, Julian and Leblond, R{\'e}mi and Eccles, Tom and
Keeling, James and Gimeno, Felix and Dal Lago, Agustin and
Hubert, Thomas and Choy, Peter and de Masson d'Autume, Cyprien and
Babuschkin, Igor and Chen, Xinyun and Huang, Po-Sen and Welbl, Johannes and
Gowal, Sven and Cherepanov, Alexey and Molloy, James and
Mankowitz, Daniel and Sutherland Robson, Esme and Kohli, Pushmeet and
de Freitas, Nando and Kavukcuoglu, Koray and Vinyals, Oriol},
journal={arXiv preprint arXiv:2203.07814},
year={2022}
}
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
false |
# Dataset Card for MGSM MT
## 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://openai.com/blog/grade-school-math/
- **Repository:** https://github.com/openai/grade-school-math
- **Paper:** https://arxiv.org/abs/2110.14168
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
Multilingual Grade School Math Benchmark (MGSM) is a benchmark of grade-school math problems, proposed in the paper [Language models are multilingual chain-of-thought reasoners](http://arxiv.org/abs/2210.03057). This dataset is the machine-translated version of MGSM in English from each language.
The same 250 problems from [GSM8K](https://arxiv.org/abs/2110.14168) are each translated via human annotators in 10 languages. The 10 languages are:
- Spanish
- French
- German
- Russian
- Chinese
- Japanese
- Thai
- Swahili
- Bengali
- Telugu
GSM8K (Grade School Math 8K) is a dataset of 8.5K high quality linguistically diverse grade school math word problems. The dataset was created to support the task of question answering on basic mathematical problems that require multi-step reasoning.
You can find the input and targets for each of the ten languages (and English) as `.tsv` files.
We also include few-shot exemplars that are also manually translated from each language in `exemplars.py`.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The same 250 problems from [GSM8K](https://arxiv.org/abs/2110.14168) are each translated via human annotators in 10 languages. The 10 languages are:
- Spanish
- French
- German
- Russian
- Chinese
- Japanese
- Thai
- Swahili
- Bengali
- Telugu
This dataset is the machine-translated version of MGSM in English from each language.
## Dataset Structure
### Data Instances
Each instance in the train split contains:
- a string for the grade-school level math question
- a string for the corresponding answer with chain-of-thought steps.
- the numeric solution to the question
- the equation solution to the question
```python
{'question': 'Question: Roger has 5 tennis balls. He buys 2 more cans of tennis balls. Each can has 3 tennis balls. How many tennis balls does he have now?',
'answer': 'Step-by-Step Answer: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 tennis balls. 5 + 6 = 11. The answer is 11.',
'answer_number': 11,
'equation_solution': '5 + 6 = 11.'}
```
Each instance in the test split contains:
- a string for the grade-school level math question
- the numeric solution to the question
```python
{'question': "Janet’s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?",
'answer': None,
'answer_number': 18,
'equation_solution': None}
```
### Data Fields
The data fields are the same among `train` and `test` splits.
- question: The question string to a grade school math problem.
- answer: The full solution string to the `question`. It contains multiple steps of reasoning with calculator annotations and the final numeric solution.
- answer_number: The numeric solution to the `question`.
- equation_solution: The equation solution to the `question`.
### Data Splits
- The train split includes 8 few-shot exemplars that are also manually translated from each language.
- The test split includes the same 250 problems from GSM8K translated via human annotators in 10 languages.
| name |train|test |
|--------|----:|---------:|
|en | 8 | 250 |
|es | 8 | 250 |
|fr | 8 | 250 |
|de | 8 | 250 |
|ru | 8 | 250 |
|zh | 8 | 250 |
|ja | 8 | 250 |
|th | 8 | 250 |
|sw | 8 | 250 |
|bn | 8 | 250 |
|te | 8 | 250 |
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
From the paper:
> We initially collected a starting set of a thousand problems and natural language solutions by hiring freelance contractors on Upwork (upwork.com). We then worked with Surge AI (surgehq.ai), an NLP data labeling platform, to scale up our data collection. After collecting the full dataset, we asked workers to re-solve all problems, with no workers re-solving problems they originally wrote. We checked whether their final answers agreed with the original solu- tions, and any problems that produced disagreements were either repaired or discarded. We then performed another round of agreement checks on a smaller subset of problems, finding that 1.7% of problems still produce disagreements among contractors. We estimate this to be the fraction of problems that con- tain breaking errors or ambiguities. It is possible that a larger percentage of problems contain subtle errors.
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
Surge AI (surgehq.ai)
### 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
The GSM8K dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT).
### Citation Information
```bibtex
@article{cobbe2021gsm8k,
title={Training Verifiers to Solve Math Word Problems},
author={Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and Hesse, Christopher and Schulman, John},
journal={arXiv preprint arXiv:2110.14168},
year={2021}
}
@misc{shi2022language,
title={Language Models are Multilingual Chain-of-Thought Reasoners},
author={Freda Shi and Mirac Suzgun and Markus Freitag and Xuezhi Wang and Suraj Srivats and Soroush Vosoughi and Hyung Won Chung and Yi Tay and Sebastian Ruder and Denny Zhou and Dipanjan Das and Jason Wei},
year={2022},
eprint={2210.03057},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@juletx](https://github.com/juletx) for adding this dataset. |
true |
# Dataset Card for "emo"
## 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/S19-2005/](https://www.aclweb.org/anthology/S19-2005/)
- **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:** 3.37 MB
- **Size of the generated dataset:** 2.85 MB
- **Total amount of disk used:** 6.22 MB
### Dataset Summary
In this dataset, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others.
### 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
#### emo2019
- **Size of downloaded dataset files:** 3.37 MB
- **Size of the generated dataset:** 2.85 MB
- **Total amount of disk used:** 6.22 MB
An example of 'train' looks as follows.
```
{
"label": 0,
"text": "don't worry i'm girl hmm how do i know if you are what's ur name"
}
```
### Data Fields
The data fields are the same among all splits.
#### emo2019
- `text`: a `string` feature.
- `label`: a classification label, with possible values including `others` (0), `happy` (1), `sad` (2), `angry` (3).
### Data Splits
| name |train|test|
|-------|----:|---:|
|emo2019|30160|5509|
## 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{chatterjee-etal-2019-semeval,
title={SemEval-2019 Task 3: EmoContext Contextual Emotion Detection in Text},
author={Ankush Chatterjee and Kedhar Nath Narahari and Meghana Joshi and Puneet Agrawal},
booktitle={Proceedings of the 13th International Workshop on Semantic Evaluation},
year={2019},
address={Minneapolis, Minnesota, USA},
publisher={Association for Computational Linguistics},
url={https://www.aclweb.org/anthology/S19-2005},
doi={10.18653/v1/S19-2005},
pages={39--48},
abstract={In this paper, we present the SemEval-2019 Task 3 - EmoContext: Contextual Emotion Detection in Text. Lack of facial expressions and voice modulations make detecting emotions in text a challenging problem. For instance, as humans, on reading ''Why don't you ever text me!'' we can either interpret it as a sad or angry emotion and the same ambiguity exists for machines. However, the context of dialogue can prove helpful in detection of the emotion. In this task, given a textual dialogue i.e. an utterance along with two previous turns of context, the goal was to infer the underlying emotion of the utterance by choosing from four emotion classes - Happy, Sad, Angry and Others. To facilitate the participation in this task, textual dialogues from user interaction with a conversational agent were taken and annotated for emotion classes after several data processing steps. A training data set of 30160 dialogues, and two evaluation data sets, Test1 and Test2, containing 2755 and 5509 dialogues respectively were released to the participants. A total of 311 teams made submissions to this task. The final leader-board was evaluated on Test2 data set, and the highest ranked submission achieved 79.59 micro-averaged F1 score. Our analysis of systems submitted to the task indicate that Bi-directional LSTM was the most common choice of neural architecture used, and most of the systems had the best performance for the Sad emotion class, and the worst for the Happy emotion class}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lordtt13](https://github.com/lordtt13), [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
false |
# Dataset Card for MultiDoc2Dial
## 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://doc2dial.github.io/multidoc2dial/
- **Repository:** https://github.com/IBM/multidoc2dial
- **Paper:** https://arxiv.org/pdf/2109.12595.pdf
- **Leaderboard:**
- **Point of Contact:** sngfng@gmail.com
### Dataset Summary
MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents.
Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a
single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking
conversation involves multiple topics, and hence is grounded on different documents.
### Supported Tasks and Leaderboards
> Supported Task: Open domain question answering, document-grounded dialogue, passage retrieval
> Leaderboard:
### Languages
English
## Dataset Structure
### Data Instances
Sample data instance for `multidoc2dial` :
```
{
"id": "8df07b7a98990db27c395cb1f68a962e_1",
"title": "Top 5 DMV Mistakes and How to Avoid Them#3_0",
"context": "Many DMV customers make easily avoidable mistakes that cause them significant problems, including encounters with law enforcement and impounded vehicles. Because we see customers make these mistakes over and over again , we are issuing this list of the top five DMV mistakes and how to avoid them. \n\n1. Forgetting to Update Address \nBy statute , you must report a change of address to DMV within ten days of moving. That is the case for the address associated with your license, as well as all the addresses associated with each registered vehicle, which may differ. It is not sufficient to only: write your new address on the back of your old license; tell the United States Postal Service; or inform the police officer writing you a ticket. If you fail to keep your address current , you will miss a suspension order and may be charged with operating an unregistered vehicle and/or aggravated unlicensed operation, both misdemeanors. This really happens , but the good news is this is a problem that is easily avoidable. Learn more about how to change the address on your license and registrations [1 ] \n\n2. Leaving the State Without Notifying DMV \nStates communicate with each other , so when you move to another state, be sure to tie up any loose ends regarding your New York State license or registration. That means resolving any unanswered tickets, suspensions or revocations, and surrendering your license plates to NYS when you get to your new home state. A license suspension or revocation here could mean that your new home state will not issue you a license there. Remember , it is important to notify DMV of your new address so that any possible mail correspondence can reach you. Also , turning in your plates is important to avoid an insurance lapse. \n\n3. Letting Insurance Lapse \nBecause we all pay indirectly for crashes involving uninsured motorists , New York State requires every motorist to maintain auto insurance every single day a vehicle is registered. DMV works with insurance companies to electronically monitor your insurance coverage , and we know when coverage is dropped for any reason. When that happens , we mail you an insurance inquiry letter to allow you to clear up the problem. We send 500,000 inquiry letters a year. If the inquiry letter does not resolve the problem , we must suspend the vehicle registration and , if it persists, your driver license!We suspend 300,000 registrations a year for failure to maintain insurance. If you fail to maintain an updated address with us , you won t learn that you have an insurance problem , and we will suspend your registration and license. Make sure you turn in your vehicle s license plates at DMV before you cancel your insurance policy. Insurance policies must be from a company licensed in New York State. Learn more about Insurances Lapes [2] and How to Surrender your Plates [3 ] \n\n4. Understanding how Much Traffic Points Cost \nDMV maintains a point system to track dangerous drivers. Often , motorists convicted of a traffic ticket feel they have resolved all their motoring issues with the local court, but later learn that the Driver Responsibility Assessment DRA is a separate DMV charge based on the total points they accumulate. The $300 DRA fee can be paid in $100 annual installments over three years. Motorists who fail to maintain an updated address with DMV may resolve their tickets with the court, but never receive their DRA assessment because we do not have their new address on record. Failure to pay the DRA will result in a suspended license. Learn more about About the NYS Driver Point System [4] and how to Pay Driver Responsibility Assessment [5 ] \n\n5. Not Bringing Proper Documentation to DMV Office \nAbout ten percent of customers visiting a DMV office do not bring what they need to complete their transaction, and have to come back a second time to finish their business. This can be as simple as not bringing sufficient funds to pay for a license renewal or not having the proof of auto insurance required to register a car. Better yet , don t visit a DMV office at all, and see if your transaction can be performed online, like an address change, registration renewal, license renewal, replacing a lost title, paying a DRA or scheduling a road test. Our award - winning website is recognized as one of the best in the nation. It has all the answers you need to efficiently perform any DMV transaction. Consider signing up for our MyDMV service, which offers even more benefits. Sign up or log into MyDMV [6 ] ",
"question": "Hello, I forgot o update my address, can you help me with that?[SEP]",
"da": "query_condition",
"answers":
{
"text": ['you must report a change of address to DMV within ten days of moving. That is the case for the address associated with your license, as well as all the addresses associated with each registered vehicle, which may differ. "],
"answer_start": [346]
},
"utterance": "hi, you have to report any change of address to DMV within 10 days after moving. You should do this both for the address associated with your license and all the addresses associated with all your vehicles.",
"domain": "dmv"
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Sample data instance for `document_domain` :
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"domain": "ssa",
"doc_id": "Benefits Planner: Survivors | Planning For Your Survivors | Social Security Administration#1_0",
"title": "Benefits Planner: Survivors | Planning For Your Survivors | Social Security Administration#1",
"doc_text": "\n\nBenefits Planner: Survivors | Planning For Your Survivors \nAs you plan for the future , you'll want to think about what your family would need if you should die now. Social Security can help your family if you have earned enough Social Security credits through your work. You can earn up to four credits each year. In 2019 , for example , you earn one credit for each $1,360 of wages or self - employment income. When you have earned $5,440 , you have earned your four credits for the year. The number of credits needed to provide benefits for your survivors depends on your age when you die. No one needs more than 40 credits 10 years of work to be eligible for any Social Security benefit. But , the younger a person is , the fewer credits they must have for family members to receive survivors benefits. Benefits can be paid to your children and your spouse who is caring for the children even if you don't have the required number of credits. They can get benefits if you have credit for one and one - half years of work 6 credits in the three years just before your death. \n\nFor Your Widow Or Widower \nThere are about five million widows and widowers receiving monthly Social Security benefits based on their deceased spouse's earnings record. And , for many of those survivors, particularly aged women, those benefits are keeping them out of poverty. Widows and widowers can receive : reduced benefits as early as age 60 or full benefits at full retirement age or older. benefits as early as age 50 if they're disabled AND their disability started before or within seven years of your death. benefits at any age , if they have not remarried , and if they take care of your child who is under age 16 or disabled and receives benefits on your record. If applying for disability benefits on a deceased worker s record , they can speed up the application process if they complete an Adult Disability Report and have it available at the time of their appointment. We use the same definition of disability for widows and widowers as we do for workers. \n\nFor Your Surviving Divorced Spouse \nIf you have a surviving divorced spouse , they could get the same benefits as your widow or widower provided that your marriage lasted 10 years or more. Benefits paid to a surviving divorced spouse won't affect the benefit amounts your other survivors will receive based on your earnings record. If your former spouse is caring for your child who is under age 16 or disabled and gets benefits on your record , they will not have to meet the length - of - marriage rule. The child must be your natural or legally adopted child. \n\nFor Your Children \nYour unmarried children who are under 18 up to age 19 if attending elementary or secondary school full time can be eligible to receive Social Security benefits when you die. And your child can get benefits at any age if they were disabled before age 22 and remain disabled. Besides your natural children , your stepchildren, grandchildren, step grandchildren or adopted children may receive benefits under certain circumstances. For further information , view our publication. \n\nFor Your Parents \nYou must have been providing at least half of your parent s support and your parent must not be eligible to receive a retirement benefit that is higher than the benefit we could pay on your record. Generally, your parent also must not have married after your death ; however, there are some exceptions. In addition to your natural parent , your stepparent or adoptive parent may receive benefits if they became your parent before you were age 16. \n\nHow Much Would Your Survivors Receive \nHow much your family could receive in benefits depends on your average lifetime earnings. The higher your earnings were , the higher their benefits would be. We calculate a basic amount as if you had reached full retirement age at the time you die. These are examples of monthly benefit payments : Widow or widower, full retirement age or older 100 percent of your benefit amount ; Widow or widower , age 60 to full retirement age 71 to 99 percent of your basic amount ; Disabled widow or widower , age 50 through 59 71 percent ; Widow or widower , any age, caring for a child under age 16 75 percent ; A child under age 18 19 if still in elementary or secondary school or disabled 75 percent ; and Your dependent parent , age 62 or older : One surviving parent 82 percent. Two surviving parents 75 percent to each parent. Percentages for a surviving divorced spouse would be the same as above. There may also be a special lump - sum death payment. \n\nMaximum Family Amount \nThere's a limit to the amount that family members can receive each month. The limit varies , but it is generally equal to between 150 and 180 percent of the basic benefit rate. If the sum of the benefits payable to family members is greater than this limit , the benefits will be reduced proportionately. Any benefits paid to a surviving divorced spouse based on disability or age won't count toward this maximum amount. Get your online or check our Benefit Calculators for an estimate of the benefits your family could receive if you died right now. \n\nOther Things You Need To Know \nThere are limits on how much survivors may earn while they receive benefits. Benefits for a widow, widower, or surviving divorced spouse may be affected by several additional factors : If your widow, widower, or surviving divorced spouse remarries before they reach age 60 age 50 if disabled , they cannot receive benefits as a surviving spouse while they're married. If your widow, widower, or surviving divorced spouse remarries after they reach age 60 age 50 if disabled , they will continue to qualify for benefits on your Social Security record. However , if their current spouse is a Social Security beneficiary , they may want to apply for spouse's benefits on their record. If that amount is more than the widow's or widower's benefit on your record , they will receive a combination of benefits that equals the higher amount. If your widow, widower, or surviving divorced spouse receives benefits on your record , they can switch to their own retirement benefit as early as age 62. This assumes they're eligible for retirement benefits and their retirement rate is higher than their rate as a widow, widower, or surviving divorced spouse. In many cases , a widow or widower can begin receiving one benefit at a reduced rate and then, at full retirement age, switch to the other benefit at an unreduced rate. If your widow, widower, or surviving divorced spouse will also receive a pension based on work not covered by Social Security, such as government or foreign work , their Social Security benefits as a survivor may be affected. ",
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"text_sec": "As you plan for the future , you'll want to think about what your family would need if you should die now. Social Security can help your family if you have earned enough Social Security credits through your work. ",
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"doc_html_ts": "<main><section><div><h2 sent_id=\"1\" text_id=\"1\">Benefits Planner: Survivors | Planning For Your Survivors</h2></div></section><section><div><article><section><div tag_id=\"1\"><u sent_id=\"2\" tag_id=\"1\"><u sent_id=\"2\" tag_id=\"1\" text_id=\"2\">As you plan for the future ,</u><u sent_id=\"2\" tag_id=\"1\" text_id=\"3\">you 'll want to think about what your family would need if you should die now .</u></u><u sent_id=\"3\" tag_id=\"1\"><u sent_id=\"3\" tag_id=\"1\" text_id=\"4\">Social Security can help your family if you have earned enough Social Security credits through your work .</u></u></div><div tag_id=\"2\"><u sent_id=\"4\" tag_id=\"2\"><u sent_id=\"4\" tag_id=\"2\" text_id=\"5\">You can earn up to four credits each year .</u></u><u sent_id=\"5\" tag_id=\"2\"><u sent_id=\"5\" tag_id=\"2\" text_id=\"6\">In 2019 ,</u><u sent_id=\"5\" tag_id=\"2\" text_id=\"7\">for example ,</u><u sent_id=\"5\" tag_id=\"2\" text_id=\"8\">you earn one credit for each $ 1,360 of wages or self - employment income .</u></u><u sent_id=\"6\" tag_id=\"2\"><u sent_id=\"6\" tag_id=\"2\" text_id=\"9\">When you have earned $ 5,440 ,</u><u sent_id=\"6\" tag_id=\"2\" text_id=\"10\">you have earned your four credits for the year .</u></u></div><div tag_id=\"3\"><u sent_id=\"7\" tag_id=\"3\"><u sent_id=\"7\" tag_id=\"3\" text_id=\"11\">The number of credits needed to provide benefits for your survivors depends on your age when you die .</u></u><u sent_id=\"8\" tag_id=\"3\"><u sent_id=\"8\" tag_id=\"3\" text_id=\"12\">No one needs more than 40 credits 10 years of work to be eligible for any Social Security benefit .</u></u><u sent_id=\"9\" tag_id=\"3\"><u sent_id=\"9\" tag_id=\"3\" text_id=\"13\">But ,</u><u sent_id=\"9\" tag_id=\"3\" text_id=\"14\">the younger a person is ,</u><u sent_id=\"9\" tag_id=\"3\" text_id=\"15\">the fewer credits they must have for family members to receive survivors benefits .</u></u></div><div tag_id=\"4\"><u sent_id=\"10\" tag_id=\"4\"><u sent_id=\"10\" tag_id=\"4\" text_id=\"16\">Benefits can be paid to your children and your spouse who is caring for the children even if you do n't have the required number of credits .</u></u><u sent_id=\"11\" tag_id=\"4\"><u sent_id=\"11\" tag_id=\"4\" text_id=\"17\">They can get benefits if you have credit for one and one - half years of work 6 credits in the three years just before your death .</u></u></div></section><section><h3 sent_id=\"12\" text_id=\"18\">For Your Widow Or Widower</h3><div tag_id=\"5\"><u sent_id=\"13\" tag_id=\"5\"><u sent_id=\"13\" tag_id=\"5\" text_id=\"19\">There are about five million widows and widowers receiving monthly Social Security benefits based on their deceased spouse 's earnings record .</u></u><u sent_id=\"14\" tag_id=\"5\"><u sent_id=\"14\" tag_id=\"5\" text_id=\"20\">And ,</u><u sent_id=\"14\" tag_id=\"5\" text_id=\"21\">for many of those survivors , particularly aged women , those benefits are keeping them out of poverty .</u></u></div><div tag_id=\"6\"><u sent_id=\"15\" tag_id=\"6\"><u sent_id=\"15\" tag_id=\"6\" text_id=\"22\">Widows and widowers can receive :</u></u></div><ul class=\"browser-default\" tag_id=\"6\"><li tag_id=\"6\"><u sent_id=\"16\" tag_id=\"6\"><u sent_id=\"16\" tag_id=\"6\" text_id=\"23\">reduced benefits as early as age 60 or full benefits at full retirement age or older .</u></u></li><div>If widows or widowers qualify for retirement benefits on their own record, they can switch to their own retirement benefit as early as age 62.</div><li tag_id=\"6\"><u sent_id=\"17\" tag_id=\"6\"><u sent_id=\"17\" tag_id=\"6\" text_id=\"24\">benefits as early as age 50 if they 're disabled AND their disability started before or within seven years of your death .</u></u></li><div>If a widow or widower who is caring for your children receives Social Security benefits, they're still eligible if their disability starts before those payments end or within seven years after they end.</div><li tag_id=\"6\"><u sent_id=\"18\" tag_id=\"6\"><u sent_id=\"18\" tag_id=\"6\" text_id=\"25\">benefits at any age ,</u><u sent_id=\"18\" tag_id=\"6\" text_id=\"26\">if they have not remarried ,</u><u sent_id=\"18\" tag_id=\"6\" text_id=\"27\">and if they take care of your child who is under age 16 or disabled and receives benefits on your record .</u></u></li><div>If a widow or widower remarries <strong>after they reach age 60</strong> (age 50 if disabled), the remarriage will not affect their eligibility for survivors benefits.</div></ul><div>Widows, widowers, and surviving divorced spouses cannot apply online for survivors benefits. They should <a>contact Social Security</a> at <nobr><strong>1-800-772-1213</strong></nobr> (TTY <nobr><strong>1-800-325-0778</strong>) to request an appointment.</nobr></div><div tag_id=\"7\"><u sent_id=\"19\" tag_id=\"7\"><u sent_id=\"19\" tag_id=\"7\" text_id=\"28\">If applying for disability benefits on a deceased worker s record ,</u><u sent_id=\"19\" tag_id=\"7\" text_id=\"29\">they can speed up the application process if they complete an Adult Disability Report and have it available at the time of their appointment .</u></u></div><div tag_id=\"8\"><u sent_id=\"20\" tag_id=\"8\"><u sent_id=\"20\" tag_id=\"8\" text_id=\"30\">We use the same definition of disability for widows and widowers as we do for workers .</u></u></div></section><section><h3 sent_id=\"21\" text_id=\"31\">For Your Surviving Divorced Spouse</h3><div tag_id=\"9\"><u sent_id=\"22\" tag_id=\"9\"><u sent_id=\"22\" tag_id=\"9\" text_id=\"32\">If you have a surviving divorced spouse ,</u><u sent_id=\"22\" tag_id=\"9\" text_id=\"33\">they could get the same benefits as your widow or widower provided that your marriage lasted 10 years or more .</u></u></div><div>If your surviving divorced spouse qualifies for retirement benefits on their own record they can switch to their own retirement benefit as early as age 62.</div><div tag_id=\"10\"><u sent_id=\"23\" tag_id=\"10\"><u sent_id=\"23\" tag_id=\"10\" text_id=\"34\">Benefits paid to a surviving divorced spouse wo n't affect the benefit amounts your other survivors will receive based on your earnings record .</u></u></div><div>If your surviving divorced spouse remarries <strong>after they reach age 60</strong> (age 50 if disabled), the remarriage will not affect their eligibility for survivors benefits.</div><div tag_id=\"11\"><u sent_id=\"24\" tag_id=\"11\"><u sent_id=\"24\" tag_id=\"11\" text_id=\"35\">If your former spouse is caring for your child who is under age 16 or disabled and gets benefits on your record ,</u><u sent_id=\"24\" tag_id=\"11\" text_id=\"36\">they will not have to meet the length - of - marriage rule .</u></u><u sent_id=\"25\" tag_id=\"11\"><u sent_id=\"25\" tag_id=\"11\" text_id=\"37\">The child must be your natural or legally adopted child .</u></u></div><div>However, if they qualify for benefits as a surviving divorced mother or father who is caring for your child, their benefits may affect the amount of benefits your other survivors will receive based on your earnings record.</div></section><section><h3 sent_id=\"26\" text_id=\"38\">For Your Children</h3><div tag_id=\"12\"><u sent_id=\"27\" tag_id=\"12\"><u sent_id=\"27\" tag_id=\"12\" text_id=\"39\">Your unmarried children who are under 18 up to age 19 if attending elementary or secondary school full time can be eligible to receive Social Security benefits when you die .</u></u></div><div tag_id=\"13\"><u sent_id=\"28\" tag_id=\"13\"><u sent_id=\"28\" tag_id=\"13\" text_id=\"40\">And your child can get benefits at any age if they were disabled before age 22 and remain disabled .</u></u></div><div tag_id=\"14\"><u sent_id=\"29\" tag_id=\"14\"><u sent_id=\"29\" tag_id=\"14\" text_id=\"41\">Besides your natural children ,</u><u sent_id=\"29\" tag_id=\"14\" text_id=\"42\">your stepchildren , grandchildren , step grandchildren or adopted children may receive benefits under certain circumstances .</u></u><u sent_id=\"30\" tag_id=\"14\"><u sent_id=\"30\" tag_id=\"14\" text_id=\"43\">For further information ,</u><u sent_id=\"30\" tag_id=\"14\" text_id=\"44\">view our publication .</u></u></div></section><section><h3 sent_id=\"31\" text_id=\"45\">For Your Parents</h3><div tag_id=\"15\"><u sent_id=\"32\" tag_id=\"15\"><u sent_id=\"32\" tag_id=\"15\" text_id=\"46\">You must have been providing at least half of your parent s support and your parent must not be eligible to receive a retirement benefit that is higher than the benefit we could pay on your record .</u></u><u sent_id=\"33\" tag_id=\"15\"><u sent_id=\"33\" tag_id=\"15\" text_id=\"47\">Generally , your parent also must not have married after your death ;</u><u sent_id=\"33\" tag_id=\"15\" text_id=\"48\">however , there are some exceptions .</u></u></div><div tag_id=\"16\"><u sent_id=\"34\" tag_id=\"16\"><u sent_id=\"34\" tag_id=\"16\" text_id=\"49\">In addition to your natural parent ,</u><u sent_id=\"34\" tag_id=\"16\" text_id=\"50\">your stepparent or adoptive parent may receive benefits if they became your parent before you were age 16 .</u></u></div></section><section><h3 sent_id=\"35\" text_id=\"51\">How Much Would Your Survivors Receive</h3><div tag_id=\"17\"><u sent_id=\"36\" tag_id=\"17\"><u sent_id=\"36\" tag_id=\"17\" text_id=\"52\">How much your family could receive in benefits</u><u sent_id=\"36\" tag_id=\"17\" text_id=\"53\">depends on your average lifetime earnings .</u></u><u sent_id=\"37\" tag_id=\"17\"><u sent_id=\"37\" tag_id=\"17\" text_id=\"54\">The higher your earnings were ,</u><u sent_id=\"37\" tag_id=\"17\" text_id=\"55\">the higher their benefits would be .</u></u><u sent_id=\"38\" tag_id=\"17\"><u sent_id=\"38\" tag_id=\"17\" text_id=\"56\">We calculate a basic amount as if you had reached full retirement age at the time you die .</u></u></div><div>If you are already receiving reduced benefits when you die, survivors benefits are based on that amount.</div><div tag_id=\"18\"><u sent_id=\"39\" tag_id=\"18\"><u sent_id=\"39\" tag_id=\"18\" text_id=\"57\">These are examples of monthly benefit payments :</u></u></div><ul class=\"browser-default\" tag_id=\"18\"><li tag_id=\"18\"><u sent_id=\"40\" tag_id=\"18\"><u sent_id=\"40\" tag_id=\"18\" text_id=\"58\">Widow or widower , full retirement age or older 100 percent of your benefit amount ;</u></u></li><li tag_id=\"18\"><u sent_id=\"41\" tag_id=\"18\"><u sent_id=\"41\" tag_id=\"18\" text_id=\"59\">Widow or widower ,</u><u sent_id=\"41\" tag_id=\"18\" text_id=\"60\">age 60 to full retirement age 71 to 99 percent of your basic amount ;</u></u></li><li tag_id=\"18\"><u sent_id=\"42\" tag_id=\"18\"><u sent_id=\"42\" tag_id=\"18\" text_id=\"61\">Disabled widow or widower ,</u><u sent_id=\"42\" tag_id=\"18\" text_id=\"62\">age 50 through 59 71 percent ;</u></u></li><li tag_id=\"18\"><u sent_id=\"43\" tag_id=\"18\"><u sent_id=\"43\" tag_id=\"18\" text_id=\"63\">Widow or widower ,</u><u sent_id=\"43\" tag_id=\"18\" text_id=\"64\">any age , caring for a child under age 16 75 percent ;</u></u></li><li tag_id=\"18\"><u sent_id=\"44\" tag_id=\"18\"><u sent_id=\"44\" tag_id=\"18\" text_id=\"65\">A child under age 18 19 if still in elementary or secondary school or disabled 75 percent ;</u><u sent_id=\"44\" tag_id=\"18\" text_id=\"66\">and</u></u></li><li tag_id=\"18\"><div tag_id=\"18\"><u sent_id=\"48\" tag_id=\"18\"><u sent_id=\"48\" tag_id=\"18\" text_id=\"67\">Your dependent parent ,</u><u sent_id=\"48\" tag_id=\"18\" text_id=\"68\">age 62 or older :</u></u></div><ul class=\"browser-default\" tag_id=\"18\"><li tag_id=\"18\"><u sent_id=\"49\" tag_id=\"18\"><u sent_id=\"49\" tag_id=\"18\" text_id=\"69\">One surviving parent 82 percent .</u></u></li><li tag_id=\"18\"><u sent_id=\"50\" tag_id=\"18\"><u sent_id=\"50\" tag_id=\"18\" text_id=\"70\">Two surviving parents 75 percent to each parent .</u></u></li></ul></li></ul><div tag_id=\"19\"><u sent_id=\"51\" tag_id=\"19\"><u sent_id=\"51\" tag_id=\"19\" text_id=\"71\">Percentages for a surviving divorced spouse would be the same as above .</u></u></div><div tag_id=\"20\"><u sent_id=\"52\" tag_id=\"20\"><u sent_id=\"52\" tag_id=\"20\" text_id=\"72\">There may also be a special lump - sum death payment .</u></u></div><h3 sent_id=\"53\" text_id=\"73\">Maximum Family Amount</h3><div tag_id=\"21\"><u sent_id=\"54\" tag_id=\"21\"><u sent_id=\"54\" tag_id=\"21\" text_id=\"74\">There 's a limit to the amount that family members can receive each month .</u></u><u sent_id=\"55\" tag_id=\"21\"><u sent_id=\"55\" tag_id=\"21\" text_id=\"75\">The limit varies ,</u><u sent_id=\"55\" tag_id=\"21\" text_id=\"76\">but it is generally equal to between 150 and 180 percent of the basic benefit rate .</u></u></div><div tag_id=\"22\"><u sent_id=\"56\" tag_id=\"22\"><u sent_id=\"56\" tag_id=\"22\" text_id=\"77\">If the sum of the benefits payable to family members is greater than this limit ,</u><u sent_id=\"56\" tag_id=\"22\" text_id=\"78\">the benefits will be reduced proportionately .</u></u><u sent_id=\"57\" tag_id=\"22\"><u sent_id=\"57\" tag_id=\"22\" text_id=\"79\">Any benefits paid to a surviving divorced spouse based on disability or age wo n't count toward this maximum amount .</u></u></div><div tag_id=\"23\"><u sent_id=\"58\" tag_id=\"23\"><u sent_id=\"58\" tag_id=\"23\" text_id=\"80\">Get your online or check our Benefit Calculators for an estimate of the benefits your family could receive if you died right now .</u></u></div><h3 sent_id=\"59\" text_id=\"81\">Other Things You Need To Know</h3><div tag_id=\"24\"><u sent_id=\"60\" tag_id=\"24\"><u sent_id=\"60\" tag_id=\"24\" text_id=\"82\">There are limits on how much survivors may earn while they receive benefits .</u></u></div><div tag_id=\"25\"><u sent_id=\"61\" tag_id=\"25\"><u sent_id=\"61\" tag_id=\"25\" text_id=\"83\">Benefits for a widow , widower , or surviving divorced spouse may be affected by several additional factors :</u></u></div><div><a>If they remarry</a><section><div tag_id=\"26\"><u sent_id=\"62\" tag_id=\"26\"><u sent_id=\"62\" tag_id=\"26\" text_id=\"84\">If your widow , widower , or surviving divorced spouse remarries before they reach age 60 age 50 if disabled ,</u><u sent_id=\"62\" tag_id=\"26\" text_id=\"85\">they can not receive benefits as a surviving spouse while they 're married .</u></u></div><div tag_id=\"27\"><u sent_id=\"63\" tag_id=\"27\"><u sent_id=\"63\" tag_id=\"27\" text_id=\"86\">If your widow , widower , or surviving divorced spouse remarries after they reach age 60 age 50 if disabled ,</u><u sent_id=\"63\" tag_id=\"27\" text_id=\"87\">they will continue to qualify for benefits on your Social Security record .</u></u></div><div tag_id=\"28\"><u sent_id=\"64\" tag_id=\"28\"><u sent_id=\"64\" tag_id=\"28\" text_id=\"88\">However ,</u><u sent_id=\"64\" tag_id=\"28\" text_id=\"89\">if their current spouse is a Social Security beneficiary ,</u><u sent_id=\"64\" tag_id=\"28\" text_id=\"90\">they may want to apply for spouse 's benefits on their record .</u></u><u sent_id=\"65\" tag_id=\"28\"><u sent_id=\"65\" tag_id=\"28\" text_id=\"91\">If that amount is more than the widow 's or widower 's benefit on your record ,</u><u sent_id=\"65\" tag_id=\"28\" text_id=\"92\">they will receive a combination of benefits that equals the higher amount .</u></u></div></section></div><div><a>If they're eligible for retirement benefits on their own record</a><section><div tag_id=\"29\"><u sent_id=\"66\" tag_id=\"29\"><u sent_id=\"66\" tag_id=\"29\" text_id=\"93\">If your widow , widower , or surviving divorced spouse receives benefits on your record ,</u><u sent_id=\"66\" tag_id=\"29\" text_id=\"94\">they can switch to their own retirement benefit as early as age 62 .</u></u><u sent_id=\"67\" tag_id=\"29\"><u sent_id=\"67\" tag_id=\"29\" text_id=\"95\">This assumes they 're eligible for retirement benefits and their retirement rate is higher than their rate as a widow , widower , or surviving divorced spouse .</u></u></div><div tag_id=\"30\"><u sent_id=\"68\" tag_id=\"30\"><u sent_id=\"68\" tag_id=\"30\" text_id=\"96\">In many cases ,</u><u sent_id=\"68\" tag_id=\"30\" text_id=\"97\">a widow or widower can begin receiving one benefit at a reduced rate and then , at full retirement age , switch to the other benefit at an unreduced rate .</u></u></div><div><a>Full retirement age for retirement benefits</a> may not match full retirement age for survivors benefits.</div></section></div><div><a>If they will also receive a pension based on work not covered by Social Security</a><section><div tag_id=\"31\"><u sent_id=\"69\" tag_id=\"31\"><u sent_id=\"69\" tag_id=\"31\" text_id=\"98\">If your widow , widower , or surviving divorced spouse will also receive a pension based on work not covered by Social Security , such as government or foreign work ,</u><u sent_id=\"69\" tag_id=\"31\" text_id=\"99\">their Social Security benefits as a survivor may be affected .</u></u></div></section></div></section></article></div></section></main>",
"doc_html_raw": "<main class=\"content\" id=\"content\" role=\"main\">\n\n<section>\n\n<div>\n<h2>Benefits Planner: Survivors | Planning For Your Survivors</h2>\n</div>\n</section>\n\n<section>\n\n<div>\n\n<div>\n\n\n</div>\n\n\n\n<article>\n<section>\n<p>As you plan for the future, you'll want to think about what your family would need if you should die now. Social Security can help your family if you have earned enough Social Security credits through your work.</p>\n<p><a>You can earn up to four credits each year</a>. In 2019, for example, you earn one credit for each $1,360 of wages or <a>self-employment</a> income. When you have earned $5,440, you have earned your four credits for the year.</p>\n<p>The number of credits needed to provide benefits for your survivors depends on your age when you die. No one needs more than 40 credits (10 years of work) to be eligible for any Social Security benefit. But, the younger a person is, the fewer credits they must have for family members to receive survivors benefits.</p>\n<p>Benefits can be paid to your children and your spouse who is caring for the children even if you don't have the required number of credits. They can get benefits if you have credit for one and one-half years of work (6 credits) in the three years just before your death.</p>\n</section>\n<section>\n<h3>For Your Widow Or Widower</h3>\n<p>There are about five million widows and widowers receiving monthly Social Security benefits based on their deceased spouse's earnings record. And, for many of those survivors, particularly aged women, those benefits are keeping them out of poverty. </p>\n<p>Widows and widowers can receive:</p>\n<ul class=\"browser-default\">\n<li>reduced benefits as early as age 60 or full benefits at <a>full retirement age</a> or older.</li>\n<div>\n If widows or widowers qualify for retirement benefits on their own record, they can switch to their own retirement benefit as early as age 62.\n </div>\n<li>benefits as early as age 50 if they're disabled AND their disability started before or within seven years of your death.</li>\n<div>\n If a widow or widower who is caring for your children receives Social Security benefits, they're still eligible if their disability starts before those payments end or within seven years after they end.\n </div>\n<li>benefits at any age, if they have not remarried, and if they take care of your child who is under age 16 or disabled and receives benefits on your record.</li>\n<div>\n If a widow or widower remarries <strong>after they reach age 60</strong> (age 50 if disabled), the remarriage will not affect their eligibility for survivors benefits.\n </div>\n</ul>\n<div>\n Widows, widowers, and surviving divorced spouses cannot apply online for survivors benefits. They should <a>contact Social Security</a> at <nobr><strong>1-800-772-1213</strong></nobr> (TTY <nobr><strong>1-800-325-0778</strong>) to request an appointment.</nobr>\n</div>\n<p>If applying for disability benefits on a deceased worker s record, they can speed up the application process if they complete an <a>Adult Disability Report</a> and have it available at the time of their appointment.</p>\n<p>We use the same <a>definition of disability</a> for widows and widowers as we do for workers.</p>\n</section>\n<section>\n<h3>For Your Surviving Divorced Spouse</h3>\n<p>If you have a surviving divorced spouse, they could get the same benefits as your widow or widower provided that your marriage lasted 10 years or more.</p>\n<div>\n If your surviving divorced spouse qualifies for retirement benefits on their own record they can switch to their own retirement benefit as early as age 62.\n </div>\n<p>Benefits paid to a surviving divorced spouse won't affect the benefit amounts your other survivors will receive based on your earnings record.</p>\n<div>\n If your surviving divorced spouse remarries <strong>after they reach age 60</strong> (age 50 if disabled), the remarriage will not affect their eligibility for survivors benefits.\n </div>\n<p>If your former spouse is caring for your child who is under age 16 or disabled and gets benefits on your record, they will not have to meet the length-of-marriage rule. The child must be your natural or legally adopted child.</p>\n<div>\n However, if they qualify for benefits as a surviving divorced mother or father who is caring for your child, their benefits may affect the amount of benefits your other survivors will receive based on your earnings record.\n </div>\n</section>\n<section>\n<h3>For Your Children</h3>\n<p>Your unmarried children who are under 18 (up to age 19 if attending elementary or secondary school full time) can be eligible to receive Social Security benefits when you die.</p>\n<p>And your child can get benefits at any age if they were disabled before age 22 and remain disabled.</p>\n<p>Besides your natural children, your stepchildren, grandchildren, step grandchildren or adopted children may receive benefits under certain circumstances. For further information, view our <a>publication</a>.</p>\n</section>\n<section>\n<h3>For Your Parents</h3>\n<p>You must have been providing at least half of your parent s support and your parent must not be eligible to receive a retirement benefit that is higher than the benefit we could pay on your record. Generally, your parent also must not have married after your death; however, there are some exceptions.</p>\n<p>In addition to your natural parent, your stepparent or adoptive parent may receive benefits if they became your parent before you were age 16.</p>\n</section>\n<section>\n<h3>How Much Would Your Survivors Receive</h3>\n<p>How much your family could receive in benefits depends on your average lifetime earnings. The higher your earnings were, the higher their benefits would be. We calculate a basic amount as if you had reached full retirement age at the time you die.</p>\n<div>\n If you are already receiving reduced benefits when you die, survivors benefits are based on that amount.\n </div>\n<p>These are examples of monthly benefit payments:</p>\n<ul class=\"browser-default\">\n<li>Widow or widower, <a>full retirement age</a> or older 100 percent of your benefit amount;</li>\n<li>Widow or widower, age 60 to <a>full retirement age</a> 71 to 99 percent of your basic amount;</li>\n<li>Disabled widow or widower, age 50 through 59 71 percent;</li>\n<li>Widow or widower, any age, caring for a child under age 16 75 percent;</li>\n<li>A child under age 18 (19 if still in elementary or secondary school) or disabled 75 percent; and</li>\n<li>Your dependent parent(s), age 62 or older:\n <ul class=\"browser-default\">\n<li>One surviving parent 82 percent.</li>\n<li>Two surviving parents 75 percent to each parent.</li>\n</ul>\n</li>\n</ul>\n<p>Percentages for a surviving divorced spouse would be the same as above.</p>\n<p>There may also be a <a>special lump-sum death payment</a>.</p>\n<h3>Maximum Family Amount</h3>\n<p>There's a limit to the amount that family members can receive each month. <a>The limit varies</a>, but it is generally equal to between 150 and 180 percent of the basic benefit rate.</p>\n<p>If the sum of the benefits payable to family members is greater than this limit, the benefits will be reduced proportionately. (Any benefits paid to a surviving divorced spouse based on disability or age won't count toward this maximum amount.)</p>\n<p>Get your <a></a> online or check our <a>Benefit Calculators</a> for an estimate of the benefits your family could receive if you died right now.</p>\n<h3>Other Things You Need To Know</h3>\n<p>There are <a>limits on how much survivors may earn</a> while they receive benefits.</p>\n<p>Benefits for a widow, widower, or surviving divorced spouse may be affected by several additional factors:</p>\n<div>\n<a>If they remarry</a>\n<section>\n<p>If your widow, widower, or surviving divorced spouse remarries before they reach age 60 (age 50 if disabled), they cannot receive benefits as a surviving spouse while they're married.</p>\n<p>If your widow, widower, or surviving divorced spouse remarries after they reach age 60 (age 50 if disabled), they will continue to qualify for benefits on your Social Security record.</p>\n<p>However, if their current spouse is a Social Security beneficiary, they may want to apply for spouse's benefits on their record. If that amount is more than the widow's or widower's benefit on your record, they will receive a combination of benefits that equals the higher amount.</p>\n</section>\n</div>\n<div>\n<a>If they're eligible for retirement benefits on their own record</a>\n<section>\n<p>If your widow, widower, or surviving divorced spouse receives benefits on your record, they can switch to their own retirement benefit as early as age 62. This assumes they're eligible for retirement benefits and their retirement rate is higher than their rate as a widow, widower, or surviving divorced spouse.</p>\n<p>In many cases, a widow or widower can begin receiving one benefit at a reduced rate and then, at full retirement age, switch to the other benefit at an unreduced rate.</p>\n<div>\n<a>Full retirement age for retirement benefits</a> may not match full retirement age for survivors benefits.\n </div>\n</section>\n</div>\n<div>\n<a>If they will also receive a pension based on work not covered by Social Security</a>\n<section>\n<p>If your widow, widower, or surviving divorced spouse will also receive a pension based on work not covered by Social Security, such as government or foreign work, <a>their Social Security benefits as a survivor may be affected</a>.</p>\n</section>\n</div>\n</section>\n</article>\n</div>\n</section>\n</main>"
}
```
Sample data instance for `dialogue_domain` :
```
{
"dial_id": "8df07b7a98990db27c395cb1f68a962e",
"domain": "dmv",
"turns": [
{
"turn_id": 1,
"role": "user",
"da": "query_condition",
"references": [
{
"id_sp": "4",
"label": "precondition",
"doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0"
}
],
"utterance": "Hello, I forgot o update my address, can you help me with that?"
},
{
"turn_id": 2,
"role": "agent",
"da": "respond_solution",
"references": [
{
"id_sp": "6",
"label": "solution",
"doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0"
},
{
"id_sp": "7",
"label": "solution",
"doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0"
}
],
"utterance": "hi, you have to report any change of address to DMV within 10 days after moving. You should do this both for the address associated with your license and all the addresses associated with all your vehicles."
},
{
"turn_id": 3,
"role": "user",
"da": "query_solution",
"references": [
{
"id_sp": "56",
"label": "solution",
"doc_id": "Top 5 DMV Mistakes and How to Avoid Them#3_0"
}
],
"utterance": "Can I do my DMV transactions online?"
}
]
}
```
### Data Fields
- `document_domain` contains the documents that are indexed by key `domain` and `doc_id` . Each document instance includes the following,
- `domain`: the domain of the document;
- `doc_id`: the ID of a document;
- `title`: the title of the document;
- `doc_text`: the text content of the document (without HTML markups);
- `spans`: key-value pairs of all spans in the document, with `id_sp` as key. Each span includes the following,
- `id_sp`: the id of a span as noted by `text_id` in `doc_html_ts`;
- `start_sp`/ `end_sp`: the start/end position of the text span in `doc_text`;
- `text_sp`: the text content of the span.
- `id_sec`: the id of the (sub)section (e.g. `<p>`) or title (`<h2>`) that contains the span.
- `start_sec` / `end_sec`: the start/end position of the (sub)section in `doc_text`.
- `text_sec`: the text of the (sub)section.
- `title`: the title of the (sub)section.
- `parent_titles`: the parent titles of the `title`.
- `doc_html_ts`: the document content with HTML markups and the annotated spans that are indicated by `text_id` attribute, which corresponds to `id_sp`.
- `doc_html_raw`: the document content with HTML markups and without span annotations.
- `dialogue_domain`
Each dialogue instance includes the following,
- `dial_id`: the ID of a dialogue;
- `domain`: the domain of the document;
- `turns`: a list of dialogue turns. Each turn includes,
- `turn_id`: the time order of the turn;
- `role`: either "agent" or "user";
- `da`: dialogue act;
- `references`: a list of spans with `id_sp` , `label` and `doc_id`. `references` is empty if a turn is for indicating previous user query not answerable or irrelevant to the document. **Note** that labels "*precondition*"/"*solution*" are fuzzy annotations that indicate whether a span is for describing a conditional context or a solution.
- `utterance`: the human-generated utterance based on the dialogue scene.
- `multidoc2dial`
Each dialogue instance includes the following,
- `id`: the ID of a QA instance
- `title`: the title of the relevant document;
- `context`: the text content of the relevant document (without HTML markups).
- `question`: user query;
- `da`: dialogue act;
- `answers`: the answers that are grounded in the associated document;
- `text`: the text content of the grounding span;
- `answer_start`: the start position of the grounding span in the associated document (context);
- `utterance`: the human-generated utterance based on the dialogue scene.
- `domain`: domain of the relevant document;
### Data Splits
Training, dev and test split for default configuration `multidoc2dial`, with respectively 21451, 4201 and 5 examples,
- Training & dev split for dialogue domain, with 3474 and 661 examples,
- Training split only for document domain, with 488 examples.
## 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
Song Feng, Siva Sankalp Patel, Hui Wan, Sachindra Joshi
### Licensing Information
Creative Commons Attribution 3.0 Unported
### Citation Information
```bibtex
@inproceedings{feng2021multidoc2dial,
title={MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents},
author={Feng, Song and Patel, Siva Sankalp and Wan, Hui and Joshi, Sachindra},
booktitle={EMNLP},
year={2021}
}
```
### Contributions
Thanks to [@songfeng](https://github.com/songfeng) and [@sivasankalpp](https://github.com/sivasankalpp) for adding this dataset. |
true |
# Dataset Card for blbooksgenre
## Table of Contents
- [Dataset Card for blbooksgenre](#dataset-card-for-blbooksgenre)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Supervised tasks](#supervised-tasks)
- [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)
- [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)
- [Colonialism](#colonialism)
- [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://doi.org/10.23636/BKHQ-0312](https://doi.org/10.23636/BKHQ-0312)
- **Repository:** [https://doi.org/10.23636/BKHQ-0312](https://doi.org/10.23636/BKHQ-0312)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset consists of metadata relating to books [digitised by the British Library in partnership with Microsoft](https://www.bl.uk/collection-guides/google-books-digitised-printed-heritage). Some of this metadata was exported from the British Library catalogue whilst others was generated as part of a crowdsourcing project. The text of this book and other metadata can be found on the [date.bl](https://data.bl.uk/bl_labs_datasets/#3) website.
The majority of the books in this collection were published in the 18th and 19th Century but the collection also includes a smaller number of books from earlier periods. Items within this collection cover a wide range of subject areas including geography, philosophy, history, poetry and literature and are published in a variety of languages.
For the subsection of the data which contains additional crowsourced annotations the date of publication breakdown is as follows:
| | Date of publication |
| ---- | ------------------- |
| 1630 | 8 |
| 1690 | 4 |
| 1760 | 10 |
| 1770 | 5 |
| 1780 | 5 |
| 1790 | 18 |
| 1800 | 45 |
| 1810 | 96 |
| 1820 | 152 |
| 1830 | 182 |
| 1840 | 259 |
| 1850 | 400 |
| 1860 | 377 |
| 1870 | 548 |
| 1880 | 776 |
| 1890 | 1484 |
| 1900 | 17 |
| 1910 | 1 |
| 1970 | 1 |
[More Information Needed]
### Supported Tasks and Leaderboards
The digitised books collection which this dataset describes has been used in a variety of digital history and humanities projects since being published.
This dataset is suitable for a variety of unsupervised tasks and for a 'genre classification task'.
#### Supervised tasks
The main possible use case for this dataset is to develop and evaluate 'genre classification' models. The dataset includes human generated labels for whether a book is 'fiction' or 'non-fiction'. This has been used to train models for genre classifcation which predict whether a book is 'fiction' or 'non-fiction' based on its title.
### Languages
[More Information Needed]
## Dataset Structure
The dataset currently has three configurations intended to support a range of tasks for which this dataset could be used for:
- `title_genre_classifiction` : this creates a de-duplicated version of the dataset with the `BL record`, `title` and `label`.
- `annotated_raw`: This version of the dataset includes all fields from the original dataset which are annotated. This includes duplication from different annotators"
- `raw`: This version of the dataset includes all the data from the original data including data without annotations.
### Data Instances
An example data instance from the `title_genre_classifiction` config:
```python
{'BL record ID': '014603046',
'title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]',
'label': 0}
```
An example data instance from the `annotated_raw` config:
```python
{'BL record ID': '014603046',
'Name': 'Yates, William Joseph H.',
'Dates associated with name': '',
'Type of name': 'person',
'Role': '',
'All names': ['Yates, William Joseph H. [person] ', ' Y, W. J. H. [person]'],
'Title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]',
'Variant titles': '',
'Series title': '',
'Number within series': '',
'Country of publication': ['England'],
'Place of publication': ['London'],
'Publisher': '',
'Date of publication': '1879',
'Edition': '',
'Physical description': 'pages not numbered, 21 cm',
'Dewey classification': '',
'BL shelfmark': 'Digital Store 11601.f.36. (1.)',
'Topics': '',
'Genre': '',
'Languages': ['English'],
'Notes': 'In verse',
'BL record ID for physical resource': '004079262',
'classification_id': '267476823.0',
'user_id': '15.0',
'subject_ids': '44369003.0',
'annotator_date_pub': '1879',
'annotator_normalised_date_pub': '1879',
'annotator_edition_statement': 'NONE',
'annotator_FAST_genre_terms': '655 7 ‡aPoetry‡2fast‡0(OCoLC)fst01423828',
'annotator_FAST_subject_terms': '60007 ‡aAlice,‡cGrand Duchess, consort of Ludwig IV, Grand Duke of Hesse-Darmstadt,‡d1843-1878‡2fast‡0(OCoLC)fst00093827',
'annotator_comments': '',
'annotator_main_language': '',
'annotator_other_languages_summaries': 'No',
'annotator_summaries_language': '',
'annotator_translation': 'No',
'annotator_original_language': '',
'annotator_publisher': 'NONE',
'annotator_place_pub': 'London',
'annotator_country': 'enk',
'annotator_title': 'The Canadian farmer. A missionary incident [Signed: W. J. H. Y, i.e. William J. H. Yates.]',
'Link to digitised book': 'http://access.bl.uk/item/viewer/ark:/81055/vdc_00000002842E',
'annotated': True,
'Type of resource': 0,
'created_at': datetime.datetime(2020, 8, 11, 14, 30, 33),
'annotator_genre': 0}
```
### Data Fields
The data fields differ slightly between configs. All possible fields for the `annotated_raw` config are listed below. For the `raw` version of the dataset datatypes are usually string to avoid errors when processing missing values.
- `BL record ID`: an internal ID used by the British Library, this can be useful for linking this data to other BL collections.
- `Name`: name associated with the item (usually author)
- `Dates associated with name`: dates associated with above e.g. DOB
- `Type of name`: whether `Name` is a person or an organization etc.
- `Role`: i.e. whether `Name` is `author`, `publisher` etc.
- `All names`: a fuller list of names associated with the item.
- `Title`: The title of the work
- `Variant titles`
- `Series title`
- `Number within series`
- `Country of publication`: encoded as a list of countries listed in the metadata
- `Place of publication`: encoded as a list of places listed in the metadata
- `Publisher`
- `Date of publication`: this is encoded as a string since this field can include data ranges i.e.`1850-1855`.
- `Edition`
- `Physical description`: encoded as a string since the format of this field varies
- `Dewey classification`
- `BL shelfmark`: a British Library shelf mark
- `Topics`: topics included in the catalogue record
- `Genre` the genre information included in the original catalogue record note that this is often missing
- `Languages`; encoded as a list of languages
- `Notes`: notes from the catalogue record
- `BL record ID for physical resource`
The following fields are all generated via the crowdsourcing task (discussed in more detail below)
- `classification_id`: ID for the classification in the annotation task
- `user_id` ID for the annotator
- `subject_ids`: internal annotation task ID
- `annotator_date_pub`: an updated publication data
- `annotator_normalised_date_pub`: normalized version of the above
- `annotator_edition_statement` updated edition
- `annotator_FAST_genre_terms`: [FAST classification genre terms](https://www.oclc.org/research/areas/data-science/fast.html)
- `annotator_FAST_subject_terms`: [FAST subject terms](https://www.oclc.org/research/areas/data-science/fast.html)
- `annotator_comments`: free form comments
- `annotator_main_language`
- `annotator_other_languages_summaries`
- `'annotator_summaries_language`
- `annotator_translation`
- `annotator_original_language`
- `annotator_publisher`
- `annotator_place_pub`
- `annotator_country`
- `annotator_title`
- `Link to digitised book`
- `annotated`: `bool` flag to indicate if row has annotations or not
- `created_at`: when the annotation was created
- `annotator_genre`: the updated annotation for the `genre` of the book.
Finally the `label` field of the `title_genre_classifiction` configuration is a class label with values 0 (Fiction) or 1 (Non-fiction).
[More Information Needed]
### Data Splits
This dataset contains a single split `train`.
## Dataset Creation
**Note** this section is a work in progress.
### Curation Rationale
The books in this collection were digitised as part of a project partnership between the British Library and Microsoft. [Mass digitisation](https://en.wikipedia.org/wiki/Category:Mass_digitization) i.e. projects where there is a goal to quickly digitise large volumes of materials shape the selection of materials to include in a number of ways. Some consideratoins which are often involved in the decision of whether to include items for digitization include (but are not limited to):
- copyright status
- preservation needs- the size of an item, very large and very small items are often hard to digitize quickly
These criteria can have knock-on effects on the makeup of a collection. For example systematically excluding large books may result in some types of book content not being digitized. Large volumes are likely to be correlated to content to at least some extent so excluding them from digitization will mean that material is under represented. Similarly copyright status is often (but not only) determined by publication data. This can often lead to a rapid fall in the number of items in a collection after a certain cut-off date.
All of the above is largely to make clear that this collection was not curated with the aim of creating a representative sample of the British Library's holdings. Some material will be over-represented and other under-represented. Similarly, the collection should not be considered a representative sample of what was published across the time period covered by the dataset (nor that that the relative proportions of the data for each time period represent a proportional sample of publications from that period).
[More Information Needed]
### Source Data
The original source data (physical items) includes a variety of resources (predominantly monographs) held by the [British Library](bl.uk/](https://bl.uk/). The British Library is a [Legal Deposit](https://www.bl.uk/legal-deposit/about-legal-deposit) library. "Legal deposit requires publishers to provide a copy of every work they publish in the UK to the British Library. It's existed in English law since 1662."[source](https://www.bl.uk/legal-deposit/about-legal-deposit).
[More Information Needed]
#### Initial Data Collection and Normalization
This version of the dataset was created partially from data exported from British Library catalogue records and partially via data generated from a crowdsourcing task involving British Library staff.
#### Who are the source language producers?
[More Information Needed]
### Annotations
The data does includes metadata associated with the books these are produced by British Library staff. The additional annotations were carried out during 2020 as part of an internal crowdsourcing task.
#### Annotation process
New annotations were produced via a crowdsourcing tasks. Annotators have the option to pick titles from a particular language subset from the broader digitized 19th century books collection. As a result the annotations are not random and overrepresent some languages.
[More Information Needed]
#### Who are the annotators?
Staff working at the British Library. Most of these staff work with metadata as part of their jobs and so could be considered expert annotators.
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
There a range of considerations around using the data. These include the representativeness of the dataset, the bias towards particular languages etc.
It is also important to note that library metadata is not static. The metadata held in library catalogues is updated and changed over time for a variety of reasons.
The way in which different institutions catalogue items also varies. As a result it is important to evaluate the performance of any models trained on this data before applying to a new collection.
[More Information Needed]
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
The text in this collection is derived from historic text. As a result the text will reflect to social beliefs and attitudes of this time period. The titles of the book give some sense of their content. Examples of book titles which appear in the data (these are randomly sampled from all titles):
- 'Rhymes and Dreams, Legends of Pendle Forest, and other poems',
- "Précis of Information concerning the Zulu Country, with a map. Prepared in the Intelligence Branch of the Quarter-Master-General's Department, Horse Guards, War Office, etc",
- 'The fan. A poem',
- 'Grif; a story of Australian Life',
- 'Calypso; a masque: in three acts, etc',
- 'Tales Uncle told [With illustrative woodcuts.]',
- 'Questings',
- 'Home Life on an Ostrich Farm. With ... illustrations',
- 'Bulgarya i Bulgarowie',
- 'Εἰς τα βαθη της Ἀφρικης [In darkest Africa.] ... Μεταφρασις Γεωρ. Σ. Βουτσινα, etc',
- 'The Corsair, a tale',
'Poems ... With notes [With a portrait.]',
- 'Report of the Librarian for the year 1898 (1899, 1901, 1909)',
- "The World of Thought. A novel. By the author of 'Before I began to speak.'",
- 'Amleto; tragedia ... recata in versi italiani da M. Leoni, etc']
Whilst using titles alone, is obviously insufficient to integrate bias in this collection it gives some insight into the topics covered by books in the corpus. Further looking into the tiles highlight some particular types of bias we might find in the collection. This should in no way be considered an exhaustive list.
#### Colonialism
We can see even in the above random sample of titles examples of colonial attitudes. We can try and interrogate this further by searching for the name of countries which were part of the British Empire at the time many of these books were published.
Searching for the string `India` in the titles and randomly sampling 10 titles returns:
- "Travels in India in the Seventeenth Century: by Sir Thomas Roe and Dr. John Fryer. Reprinted from the 'Calcutta Weekly Englishman.'",
- 'A Winter in India and Malaysia among the Methodist Missions',
- "The Tourist's Guide to all the principal stations on the railways of Northern India [By W. W.] ... Fifth edition",
- 'Records of Sport and Military Life in Western India ... With an introduction by ... G. B. Malleson',
- "Lakhmi, the Rájpút's Bride. A tale of Gujarát in Western India [A poem.]",
- 'The West India Commonplace Book: compiled from parliamentary and official documents; shewing the interest of Great Britain in its Sugar Colonies',
- "From Tonkin to India : by the sources of the Irawadi, January '95-January '96",
- 'Case of the Ameers of Sinde : speeches of Mr. John Sullivan, and Captain William Eastwick, at a special court held at the India House, ... 26th January, 1844',
- 'The Andaman Islands; their colonization, etc. A correspondence addressed to the India Office',
- 'Ancient India as described by Ptolemy; being a translation of the chapters which describe India and Eastern Asia in the treatise on Geography written by Klaudios Ptolemaios ... with introduction, commentary, map of India according to Ptolemy, and ... index, by J. W. McCrindle']
Searching form the string `Africa` in the titles and randomly sampling 10 titles returns:
- ['De Benguella ás Terras de Iácca. Descripção de uma viagem na Africa Central e Occidental ... Expedição organisada nos annos de 1877-1880. Edição illustrada',
- 'To the New Geographical Society of Edinburgh [An address on Africa by H. M. Stanley.]',
- 'Diamonds and Gold in South Africa ... With maps, etc',
- 'Missionary Travels and Researches in South Africa ... With notes by F. S. Arnot. With map and illustrations. New edition',
- 'A Narrative of a Visit to the Mauritius and South Africa ... Illustrated by two maps, sixteen etchings and twenty-eight wood-cuts',
- 'Side Lights on South Africa ... With a map, etc',
- 'My Second Journey through Equatorial Africa ... in ... 1886 and 1887 ... Translated ... by M. J. A. Bergmann. With a map ... and ... illustrations, etc',
- 'Missionary Travels and Researches in South Africa ... With portrait and fullpage illustrations',
- '[African sketches.] Narrative of a residence in South Africa ... A new edition. To which is prefixed a biographical sketch of the author by J. Conder',
- 'Lake Ngami; or, Explorations and discoveries during four years wandering in the wilds of South Western Africa ... With a map, and numerous illustrations, etc']
Whilst this dataset doesn't include the underlying text it is important to consider the potential attitudes represented in the title of the books, or the full text if you are using this dataset in conjunction with the full text.
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The books are licensed under the [CC Public Domain Mark 1.0](https://creativecommons.org/publicdomain/mark/1.0/) license.
### Citation Information
```bibtex
@misc{british library_genre,
title={ 19th Century Books - metadata with additional crowdsourced annotations},
url={https://doi.org/10.23636/BKHQ-0312},
author={{British Library} and Morris, Victoria and van Strien, Daniel and Tolfo, Giorgia and Afric, Lora and Robertson, Stewart and Tiney, Patricia and Dogterom, Annelies and Wollner, Ildi},
year={2021}}
```
### Contributions
Thanks to [@davanstrien](https://github.com/davanstrien) for adding this dataset. |
false |
# Dataset Card for MultiLingual LibriSpeech
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [How to use](#how-to-use)
- [Dataset Structure](#dataset-structure)
- [Data 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:** [MultiLingual LibriSpeech ASR corpus](http://www.openslr.org/94)
- **Repository:** [Needs More Information]
- **Paper:** [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://arxiv.org/abs/2012.03411)
- **Leaderboard:** [🤗 Autoevaluate Leaderboard](https://huggingface.co/spaces/autoevaluate/leaderboards?dataset=facebook%2Fmultilingual_librispeech&only_verified=0&task=automatic-speech-recognition&config=-unspecified-&split=-unspecified-&metric=wer)
### Dataset Summary
This is a streamable version of the Multilingual LibriSpeech (MLS) dataset.
The data archives were restructured from the original ones from [OpenSLR](http://www.openslr.org/94) to make it easier to stream.
MLS dataset is a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of
8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish.
### Supported Tasks and Leaderboards
- `automatic-speech-recognition`, `speaker-identification`: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER). The task has an active leaderboard which can be found at https://paperswithcode.com/dataset/multilingual-librispeech and ranks models based on their WER.
### Languages
The dataset is derived from read audiobooks from LibriVox and consists of 8 languages - English, German, Dutch, Spanish, French, Italian, Portuguese, Polish
### How to use
The `datasets` library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the `load_dataset` function.
For example, to download the German config, simply specify the corresponding language config name (i.e., "german" for German):
```python
from datasets import load_dataset
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
```
Using the datasets library, you can also stream the dataset on-the-fly by adding a `streaming=True` argument to the `load_dataset` function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
```python
from datasets import load_dataset
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
print(next(iter(mls)))
```
*Bonus*: create a [PyTorch dataloader](https://huggingface.co/docs/datasets/use_with_pytorch) directly with your own datasets (local/streamed).
Local:
```python
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train")
batch_sampler = BatchSampler(RandomSampler(mls), batch_size=32, drop_last=False)
dataloader = DataLoader(mls, batch_sampler=batch_sampler)
```
Streaming:
```python
from datasets import load_dataset
from torch.utils.data import DataLoader
mls = load_dataset("facebook/multilingual_librispeech", "german", split="train", streaming=True)
dataloader = DataLoader(mls, batch_size=32)
```
To find out more about loading and preparing audio datasets, head over to [hf.co/blog/audio-datasets](https://huggingface.co/blog/audio-datasets).
### Example scripts
Train your own CTC or Seq2Seq Automatic Speech Recognition models on MultiLingual Librispeech with `transformers` - [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/speech-recognition).
## Dataset Structure
### Data Instances
A typical data point comprises the path to the audio file, usually called `file` and its transcription, called `text`. Some additional information about the speaker and the passage which contains the transcription is provided.
```
{'file': '10900_6473_000030.flac',
'audio': {'path': '10900_6473_000030.flac',
'array': array([-1.52587891e-04, 6.10351562e-05, 0.00000000e+00, ...,
4.27246094e-04, 5.49316406e-04, 4.57763672e-04]),
'sampling_rate': 16000},
'text': 'więc czego chcecie odemnie spytałem wysłuchawszy tego zadziwiającego opowiadania broń nas stary człowieku broń zakrzyknęli równocześnie obaj posłowie\n',
'speaker_id': 10900,
'chapter_id': 6473,
'id': '10900_6473_000030'}
```
### Data Fields
- file: A filename .flac format.
- audio: A dictionary containing the audio filename, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
- text: the transcription of the audio file.
- id: unique id of the data sample.
- speaker_id: unique id of the speaker. The same speaker id can be found for multiple data samples.
- chapter_id: id of the audiobook chapter which includes the transcription.
### Data Splits
| | Train | Train.9h | Train.1h | Dev | Test |
| ----- | ------ | ----- | ---- | ---- | ---- |
| german | 469942 | 2194 | 241 | 3469 | 3394 |
| dutch | 374287 | 2153 | 234 | 3095 | 3075 |
| french | 258213 | 2167 | 241 | 2416 | 2426 |
| spanish | 220701 | 2110 | 233 | 2408 | 2385 |
| italian | 59623 | 2173 | 240 | 1248 | 1262 |
| portuguese | 37533 | 2116 | 236 | 826 | 871 |
| polish | 25043 | 2173 | 238 | 512 | 520 |
## 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
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
## 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
Public Domain, Creative Commons Attribution 4.0 International Public License ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/legalcode))
### Citation Information
```
@article{Pratap2020MLSAL,
title={MLS: A Large-Scale Multilingual Dataset for Speech Research},
author={Vineel Pratap and Qiantong Xu and Anuroop Sriram and Gabriel Synnaeve and Ronan Collobert},
journal={ArXiv},
year={2020},
volume={abs/2012.03411}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten)
and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
|
false |
# Dataset Card for Arabic Billion Words 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://www.abuelkhair.net/index.php/en/arabic/abu-el-khair-corpus
- **Repository:**
- **Paper:** https://arxiv.org/pdf/1611.04033
- **Leaderboard:**
- **Point of Contact:**[Ibrahim Abu El-Khair](iabuelkhair@gmail.com)
### Dataset Summary
Abu El-Khair Corpus is an Arabic text corpus, that includes more than five million newspaper articles.
It contains over a billion and a half words in total, out of which, there are about three million unique words.
The corpus is encoded with two types of encoding, namely: UTF-8, and Windows CP-1256.
Also it was marked with two mark-up languages, namely: SGML, and XML.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Arabic
## Dataset Structure
### Data Instances
This is an example of the "Almasryalyoum" configuration subset:
```python
{
"url": "http://today.almasryalyoum.com/printerfriendly.aspx?ArticleID=61300",
"head_line": "رئيس وزراء المجر: عنصرية جماهير أوجبيست جلبت العار للبلاد",
"date": "19/5/2007",
"text": """قال متحدث باسم الحكومة المجرية: إن رئيس الوزراء فيرنك جيوركساني رحب بقرار اتحاد كرة القدم المجري بخصم ثلاث نقاط من نادي أوجبيست بسبب السلوك العنصري الذي صدر من جماهيره.
وعاقب الاتحاد المجري فريق أوجبيست بعد أن سخرت جماهيره من إبراهيم سيديبي مهاجم فريق ديبرينسين الأسود أثناء مباراة الفريقين أوائل مايو الجاري.
يذكر أن الاتحاد فرض أيضا غرامة مالية قدرها 20 ألف دولار علي أوجبيست في عام 2005 بعد أن رددت جماهيره شعارات معادية للسامية خلال مباراة بالدوري المجري.
وأوضح جيوركساني في خطاب إلي إيستفان كيستليكي رئيس الاتحاد المجري لكرة القدم، أن هذا السلوك العنصري من الجماهير «جلب العار لكرة القدم وللمجر». يذكر أن المجر بها مجموعة من مشجعي كرة القدم المشاغبين «الهوليجانز»، وشارك الكثير منهم في أعمال شغب معادية للحكومة في العام الماضي.""",
}
```
### Data Fields
The data fields are:
- "url": string, original url of the article,
- "head_line": string, headline of the article,
- "date": string, date of the article,
- "text": string, text content of the article,
### Data Splits
There is only one "training" split for all configuration subsets, containing the following number of examples:
| | Number of examples |
|:---------------|-------------------:|
| Alittihad | 349342 |
| Almasryalyoum | 291723 |
| Almustaqbal | 446873 |
| Alqabas | 817274 |
| Echoroukonline | 139732 |
| Ryiadh | 858188 |
| Sabanews | 92149 |
| SaudiYoum | 888068 |
| Techreen | 314597 |
| Youm7 | 1172136 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{el20161,
title={1.5 billion words arabic corpus},
author={El-Khair, Ibrahim Abu},
journal={arXiv preprint arXiv:1611.04033},
year={2016}
}
```
### Contributions
Thanks to [@zaidalyafeai](https://github.com/zaidalyafeai) and [@albertvillanova](https://github.com/albertvillanova) for adding this dataset. |
false |
# Dataset Card for Samanantar
## 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://indicnlp.ai4bharat.org/samanantar/
- **Repository:**
- **Paper:** [Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages](https://arxiv.org/abs/2104.05596)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Samanantar is the largest publicly available parallel corpora collection for Indic language: Assamese, Bengali,
Gujarati, Hindi, Kannada, Malayalam, Marathi, Oriya, Punjabi, Tamil, Telugu.
The corpus has 49.6M sentence pairs between English to Indian Languages.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Samanantar contains parallel sentences between English (`en`) and 11 Indic language:
- Assamese (`as`),
- Bengali (`bn`),
- Gujarati (`gu`),
- Hindi (`hi`),
- Kannada (`kn`),
- Malayalam (`ml`),
- Marathi (`mr`),
- Odia (`or`),
- Punjabi (`pa`),
- Tamil (`ta`) and
- Telugu (`te`).
## Dataset Structure
### Data Instances
```
{
'idx': 0,
'src': 'Prime Minister Narendra Modi met Her Majesty Queen Maxima of the Kingdom of the Netherlands today.',
'tgt': 'নতুন দিল্লিতে সোমবার প্রধানমন্ত্রী শ্রী নরেন্দ্র মোদীর সঙ্গে নেদারন্যান্ডসের মহারানী ম্যাক্সিমা সাক্ষাৎ করেন।',
'data_source': 'pmi'
}
```
### Data Fields
- `idx` (int): ID.
- `src` (string): Sentence in source language (English).
- `tgt` (string): Sentence in destination language (one of the 11 Indic languages).
- `data_source` (string): Source of the data.
For created data sources, depending on the destination language, it might be one of:
- anuvaad_catchnews
- anuvaad_DD_National
- anuvaad_DD_sports
- anuvaad_drivespark
- anuvaad_dw
- anuvaad_financialexpress
- anuvaad-general_corpus
- anuvaad_goodreturns
- anuvaad_indianexpress
- anuvaad_mykhel
- anuvaad_nativeplanet
- anuvaad_newsonair
- anuvaad_nouns_dictionary
- anuvaad_ocr
- anuvaad_oneindia
- anuvaad_pib
- anuvaad_pib_archives
- anuvaad_prothomalo
- anuvaad_timesofindia
- asianetnews
- betterindia
- bridge
- business_standard
- catchnews
- coursera
- dd_national
- dd_sports
- dwnews
- drivespark
- fin_express
- goodreturns
- gu_govt
- jagran-business
- jagran-education
- jagran-sports
- ie_business
- ie_education
- ie_entertainment
- ie_general
- ie_lifestyle
- ie_news
- ie_sports
- ie_tech
- indiccorp
- jagran-entertainment
- jagran-lifestyle
- jagran-news
- jagran-tech
- khan_academy
- Kurzgesagt
- marketfeed
- mykhel
- nativeplanet
- nptel
- ocr
- oneindia
- pa_govt
- pmi
- pranabmukherjee
- sakshi
- sentinel
- thewire
- toi
- tribune
- vsauce
- wikipedia
- zeebiz
### 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
[Creative Commons Attribution-NonCommercial 4.0 International](https://creativecommons.org/licenses/by-nc/4.0/).
### Citation Information
```
@misc{ramesh2021samanantar,
title={Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages},
author={Gowtham Ramesh and Sumanth Doddapaneni and Aravinth Bheemaraj and Mayank Jobanputra and Raghavan AK and Ajitesh Sharma and Sujit Sahoo and Harshita Diddee and Mahalakshmi J and Divyanshu Kakwani and Navneet Kumar and Aswin Pradeep and Srihari Nagaraj and Kumar Deepak and Vivek Raghavan and Anoop Kunchukuttan and Pratyush Kumar and Mitesh Shantadevi Khapra},
year={2021},
eprint={2104.05596},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@albertvillanova](https://github.com/albertvillanova) for adding this dataset.
|
false |
# Dataset Card for CLEVR-Math
## 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)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [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/dali-does/clevr-math*
- **Paper:*https://arxiv.org/abs/2208.05358*
- **Leaderboard:**
- **Point of Contact:*dali@cs.umu.se*
### Dataset Summary
Dataset for compositional multimodal mathematical reasoning based on CLEVR.
#### Loading the data, preprocessing text with CLIP
```
from transformers import CLIPPreprocessor
from datasets import load_dataset, DownloadConfig
dl_config = DownloadConfig(resume_download=True,
num_proc=8,
force_download=True)
# Load 'general' instance of dataset
dataset = load_dataset('dali-does/clevr-math', download_config=dl_config)
# Load version with only multihop in test data
dataset_multihop = load_dataset('dali-does/clevr-math', 'multihop',
download_config=dl_config)
model_path = "openai/clip-vit-base-patch32"
extractor = CLIPProcessor.from_pretrained(model_path)
def transform_tokenize(e):
e['image'] = [image.convert('RGB') for image in e['image']]
return extractor(text=e['question'],
images=e['image'],
padding=True)
dataset = dataset.map(transform_tokenize,
batched=True,
num_proc=8,
padding='max_length')
dataset_subtraction = dataset.filter(lambda e:
e['template'].startswith('subtraction'), num_proc=4)
```
### Supported Tasks and Leaderboards
Leaderboard will be announced at a later date.
### Languages
The dataset is currently only available in English. To extend the dataset to other languages, the CLEVR templates must be rewritten in the target language.
## Dataset Structure
### Data Instances
* `general` containing the default version with multihop questions in train and test
* `multihop` containing multihop questions only in test data to test generalisation of reasoning
### Data Fields
```
features = datasets.Features(
{
"template": datasets.Value("string"),
"id": datasets.Value("string"),
"question": datasets.Value("string"),
"image": datasets.Image(),
"label": datasets.Value("int64")
}
)
```
### Data Splits
train/val/test
## Dataset Creation
Data is generated using code provided with the CLEVR-dataset, using blender and templates constructed by the dataset curators.
## Considerations for Using the Data
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Adam Dahlgren Lindström - dali@cs.umu.se
### Licensing Information
Licensed under Creative Commons Attribution Share Alike 4.0 International (CC-by 4.0).
### Citation Information
[More Information Needed]
```
@misc{https://doi.org/10.48550/arxiv.2208.05358,
doi = {10.48550/ARXIV.2208.05358},
url = {https://arxiv.org/abs/2208.05358},
author = {Lindström, Adam Dahlgren and Abraham, Savitha Sam},
keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences, I.2.7; I.2.10; I.2.6; I.4.8; I.1.4},
title = {CLEVR-Math: A Dataset for Compositional Language, Visual, and Mathematical Reasoning},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
}
```
### Contributions
Thanks to [@dali-does](https://github.com/dali-does) for adding this dataset.
|
true |
# Dataset Card for SuperLim
## 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 Structure/Creation/Use/Additional Information](#dataset-structurecreationuseadditional-information)
- [Dalaj](#dalaj)
- [SweAna](#sweana)
- [SweDiag](#swediag)
- [SweFaq](#swefaq)
- [SweFracas](#swefracas)
- [SwePar](#swepar)
- [SweSat](#swesat)
- [SweSim](#swesim)
- [SweWgr](#swewgr)
- [SweWic](#swewic)
- [SweWsc](#swewsc)
## Dataset Description
- **Homepage:** [Språkbanken](https://spraakbanken.gu.se/en/resources/superlim)
- **Repository:** /
- **Paper:** /
- **Leaderboard:** /
- **Point of Contact:** [Contact Us](mailto:severine.verlinden@ai.se)
### Dataset Summary
A standardized suite for evaluation and analysis of Swedish natural language understanding systems.
### Supported Tasks and Leaderboards
Work in progress
### Languages
Swedish
## Dataset Structure/Creation/Use/Additional Information
### Dalaj
[dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/dalaj/dalaj_documentation.tsv)
### SweAna
[dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/swedish_analogy/analogy_documentation_sheet.tsv)
#### SweDiag
work in progress
### SweFaq
[dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/faq/faq_documentation_sheet.tsv)
### SweFracas
[dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/swefracas/swefracas_documentation_sheet.tsv)
### SwePar
[dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/sweparaphrase/sweparaphrase_documentation.tsv)
### SweSat
[dataset documentation](https://svn.spraakdata.gu.se/sb-arkiv/pub/swesat/swesat-synonyms_documentation_sheet.tsv)
### SweSim
[dataset documentation](https://demo.spraakbanken.gu.se/gerlof/SuperSim/supersim-superlim_documentation_sheet.txt)
### SweWgr
[dataset documentation](https://demo.spraakbanken.gu.se/gerlof/SweWinogender/swewinogender_documentation_sheet.txt)
### SweWic
[dataset documentation](https://demo.spraakbanken.gu.se/gerlof/SweWiC/swewic_documentation_sheet.txt)
### SweWsc
[dataset documentation](https://demo.spraakbanken.gu.se/gerlof/SweWinograd/swewinograd_documentation_sheet.txt)
|
true |
# Dataset Card for [Russian SuperGLUE]
## 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://russiansuperglue.com/
- **Repository:** https://github.com/RussianNLP/RussianSuperGLUE
- **Paper:** https://russiansuperglue.com/download/main_article
- **Leaderboard:** https://russiansuperglue.com/leaderboard/2
- **Point of Contact:** [More Information Needed]
### Dataset Summary
Modern universal language models and transformers such as BERT, ELMo, XLNet, RoBERTa and others need to be properly
compared and evaluated. In the last year, new models and methods for pretraining and transfer learning have driven
striking performance improvements across a range of language understanding tasks.
We offer testing methodology based on tasks, typically proposed for “strong AI” — logic, commonsense, reasoning.
Adhering to the GLUE and SuperGLUE methodology, we present a set of test tasks for general language understanding
and leaderboard models.
For the first time a complete test for Russian language was developed, which is similar to its English analog.
Many datasets were composed for the first time, and a leaderboard of models for the Russian language with comparable
results is also presented.
### Supported Tasks and Leaderboards
Supported tasks, barring a few additions, are equivalent to the original SuperGLUE tasks.
|Task Name|Equiv. to|
|----|---:|
|Linguistic Diagnostic for Russian|Broadcoverage Diagnostics (AX-b)|
|Russian Commitment Bank (RCB)|CommitmentBank (CB)|
|Choice of Plausible Alternatives for Russian language (PARus)|Choice of Plausible Alternatives (COPA)|
|Russian Multi-Sentence Reading Comprehension (MuSeRC)|Multi-Sentence Reading Comprehension (MultiRC)|
|Textual Entailment Recognition for Russian (TERRa)|Recognizing Textual Entailment (RTE)|
|Russian Words in Context (based on RUSSE)|Words in Context (WiC)|
|The Winograd Schema Challenge (Russian)|The Winograd Schema Challenge (WSC)|
|Yes/no Question Answering Dataset for the Russian (DaNetQA)|BoolQ|
|Russian Reading Comprehension with Commonsense Reasoning (RuCoS)|Reading Comprehension with Commonsense Reasoning (ReCoRD)|
### Languages
All tasks are in Russian.
## Dataset Structure
### Data Instances
Note that there are no labels in the `test` splits. This is signified by the `-1` value.
#### LiDiRus
- **Size of downloaded dataset files:** 0.05 MB
- **Size of the generated dataset:** 0.49 MB
- **Total amount of disk used:** 0.54 MB
An example of 'test' looks as follows
```
{
"sentence1": "Новая игровая консоль доступна по цене.",
"sentence2": "Новая игровая консоль недоступна по цене.",
"knowledge": "",
"lexical-semantics": "Morphological negation",
"logic": "Negation",
"predicate-argument-structure": "",
"idx": 10,
"label": 1
}
```
#### RCB
- **Size of downloaded dataset files:** 0.14 MB
- **Size of the generated dataset:** 0.53 MB
- **Total amount of disk used:** 0.67 MB
An example of 'train'/'dev' looks as follows
```
{
"premise": "— Пойдём пообедаем. Я с утра ничего не ел. Отель, как видишь, весьма посредственный, но мне сказали,
что в здешнем ресторане отлично готовят.",
"hypothesis": "В здешнем ресторане отлично готовят.",
"verb": "сказать",
"negation": "no_negation",
"idx": 10,
"label": 2
}
```
An example of 'test' looks as follows
```
{
"premise": "Я уверен, что вместе мы победим. Да, парламентское большинство думает иначе.",
"hypothesis": "Вместе мы проиграем.",
"verb": "думать",
"negation": "no_negation",
"idx": 10,
"label": -1
}
```
#### PARus
- **Size of downloaded dataset files:** 0.06 MB
- **Size of the generated dataset:** 0.20 MB
- **Total amount of disk used:** 0.245 MB
An example of 'train'/'dev' looks as follows
```
{
"premise": "Женщина чинила кран.",
"choice1": "Кран подтекал.",
"choice2": "Кран был выключен.",
"question": "cause",
"idx": 10,
"label": 0
}
```
An example of 'test' looks as follows
```
{
"premise": "Ребятам было страшно.",
"choice1": "Их вожатый рассказал им историю про призрака.",
"choice2": "Они жарили маршмеллоу на костре.",
"question": "cause",
"idx": 10,
"label": -1
}
```
#### MuSeRC
- **Size of downloaded dataset files:** 1.26 MB
- **Size of the generated dataset:** 59.77 MB
- **Total amount of disk used:** 61.87 MB
An example of 'train'/'dev' looks as follows
```
{
"paragraph": "(1) Но люди не могут существовать без природы, поэтому в парке стояли железобетонные скамейки —
деревянные моментально ломали. (2) В парке бегали ребятишки, водилась шпана, которая развлекалась игрой в карты,
пьянкой, драками, «иногда насмерть». (3) «Имали они тут и девок...» (4) Верховодил шпаной Артемка-мыло, с
вспененной белой головой. (5) Людочка сколько ни пыталась усмирить лохмотья на буйной голове Артемки, ничего у
неё не получалось. (6) Его «кудри, издали напоминавшие мыльную пену, изблизя оказались что липкие рожки из
вокзальной столовой — сварили их, бросили комком в пустую тарелку, так они, слипшиеся, неподъёмно и лежали.
(7) Да и не ради причёски приходил парень к Людочке. (8) Как только её руки становились занятыми ножницами
и расчёской, Артемка начинал хватать её за разные места. (9) Людочка сначала увёртывалась от хватких рук Артемки,
а когда не помогло, стукнула его машинкой по голове и пробила до крови, пришлось лить йод на голову «ухажористого
человека». (10) Артемка заулюлюкал и со свистом стал ловить воздух. (11) С тех пор «домогания свои хулиганские
прекратил», более того, шпане повелел Людочку не трогать.",
"question": "Как развлекались в парке ребята?",
"answer": "Развлекались игрой в карты, пьянкой, драками, снимали они тут и девок.",
"idx":
{
"paragraph": 0,
"question": 2,
"answer": 10
},
"label": 1
}
```
An example of 'test' looks as follows
```
{
"paragraph": "\"(1) Издательство Viking Press совместно с компанией TradeMobile выпустят мобильное приложение,
посвященное Анне Франк, передает The Daily Telegraph. (2) Программа будет включать в себя фрагменты из дневника
Анны, озвученные британской актрисой Хеленой Бонэм Картер. (3) Помимо этого, в приложение войдут фотографии
и видеозаписи, документы из архива Фонда Анны Франк, план здания в Амстердаме, где Анна с семьей скрывались от
нацистов, и факсимильные копии страниц дневника. (4) Приложение, которое получит название Anne Frank App, выйдет
18 октября. (5) Интерфейс программы будет англоязычным. (6) На каких платформах будет доступно Anne Frank App,
не уточняется. Анна Франк родилась в Германии в 1929 году. (7) Когда в стране начались гонения на евреев, Анна с
семьей перебрались в Нидерланды. (8) С 1942 года члены семьи Франк и еще несколько человек скрывались от нацистов
в потайных комнатах дома в Амстердаме, который занимала компания отца Анны. (9) В 1944 году группу по доносу
обнаружили гестаповцы. (10) Обитатели \"Убежища\" (так Анна называла дом в дневнике) были отправлены в концлагеря;
выжить удалось только отцу девочки Отто Франку. (11) Находясь в \"Убежище\", Анна вела дневник, в котором описывала
свою жизнь и жизнь своих близких. (12) После ареста книгу с записями сохранила подруга семьи Франк и впоследствии
передала ее отцу Анны. (13) Дневник был впервые опубликован в 1947 году. (14) Сейчас он переведен более
чем на 60 языков.\"",
"question": "Какая информация войдет в новой мобильное приложение?",
"answer": "Видеозаписи Анны Франк.",
"idx":
{
"paragraph": 0,
"question": 2,
"answer": 10
},
"label": -1
}
```
#### TERRa
- **Size of downloaded dataset files:** 0.93 MB
- **Size of the generated dataset:** 3.44 MB
- **Total amount of disk used:** 4.39 MB
An example of 'train'/'dev' looks as follows
```
{
"premise": "Музей, расположенный в Королевских воротах, меняет экспозицию. На смену выставке, рассказывающей об
истории ворот и их реставрации, придет «Аптека трех королей». Как рассказали в музее, посетители попадут в
традиционный интерьер аптеки.",
"hypothesis": "Музей закроется навсегда.",
"idx": 10,
"label": 1
}
```
An example of 'test' looks as follows
```
{
"premise": "Маршрутка полыхала несколько минут. Свидетели утверждают, что приезду пожарных салон «Газели» выгорел полностью. К счастью, пассажиров внутри не было, а водитель успел выскочить из кабины.",
"hypothesis": "Маршрутка выгорела.",
"idx": 10,
"label": -1
}
```
#### RUSSE
- **Size of downloaded dataset files:** 3.88 MB
- **Size of the generated dataset:** 20.97 MB
- **Total amount of disk used:** 25.17 MB
An example of 'train'/'dev' looks as follows
```
{
"word": "дух",
"sentence1": "Завертелась в доме веселая коловерть: праздничный стол, праздничный дух, шумные разговоры",
"sentence2": "Вижу: духи собралися / Средь белеющих равнин. // Бесконечны, безобразны, / В мутной месяца игре / Закружились бесы разны, / Будто листья в ноябре",
"start1": 68,
"start2": 6,
"end1": 72,
"end2": 11,
"gold_sense1": 3,
"gold_sense2": 4,
"idx": 10,
"label": 0
}
```
An example of 'test' looks as follows
```
{
"word": "доска",
"sentence1": "На 40-й день после трагедии в переходе была установлена мемориальная доска, надпись на которой гласит: «В память о погибших и пострадавших от террористического акта 8 августа 2000 года».",
"sentence2": "Фото с 36-летним миллиардером привлекло сеть его необычной фигурой при стойке на доске и кремом на лице.",
"start1": 69,
"start2": 81,
"end1": 73,
"end2": 85,
"gold_sense1": -1,
"gold_sense2": -1,
"idx": 10,
"label": -1
}
```
#### RWSD
- **Size of downloaded dataset files:** 0.04 MB
- **Size of the generated dataset:** 0.29 MB
- **Total amount of disk used:** 0.320 MB
An example of 'train'/'dev' looks as follows
```
{
"text": "Женя поблагодарила Сашу за помощь, которую она оказала.",
"span1_index": 0,
"span2_index": 6,
"span1_text": "Женя",
"span2_text": "она оказала",
"idx": 10,
"label": 0
}
```
An example of 'test' looks as follows
```
{
"text": "Мод и Дора видели, как через прерию несутся поезда, из двигателей тянулись клубы черного дыма. Ревущие
звуки их моторов и дикие, яростные свистки можно было услышать издалека. Лошади убежали, когда они увидели
приближающийся поезд.",
"span1_index": 22,
"span2_index": 30,
"span1_text": "свистки",
"span2_text": "они увидели",
"idx": 10,
"label": -1
}
```
#### DaNetQA
- **Size of downloaded dataset files:** 1.36 MB
- **Size of the generated dataset:** 4.82 MB
- **Total amount of disk used:** 5.9 MB
An example of 'train'/'dev' looks as follows
```
{
"question": "Вреден ли алкоголь на первых неделях беременности?",
"passage": "А Бакингем-Хоуз и её коллеги суммировали последствия, найденные в обзорных статьях ранее. Частые случаи
задержки роста плода, результатом чего является укороченный средний срок беременности и сниженный вес при рождении.
По сравнению с нормальными детьми, дети 3-4-недельного возраста демонстрируют «менее оптимальную» двигательную
активность, рефлексы, и ориентацию в пространстве, а дети 4-6 лет показывают низкий уровень работы
нейроповеденческих функций, внимания, эмоциональной экспрессии, и развития речи и языка. Величина этих влияний
часто небольшая, частично в связи с независимыми переменными: включая употребление во время беременности
алкоголя/табака, а также факторы среды . У детей школьного возраста проблемы с устойчивым вниманием и контролем
своего поведения, а также незначительные с ростом, познавательными и языковыми способностями.",
"idx": 10,
"label": 1
}
```
An example of 'test' looks as follows
```
{
"question": "Вредна ли жесткая вода?",
"passage": "Различают временную жёсткость, обусловленную гидрокарбонатами кальция и магния Са2; Mg2, и постоянную
жёсткость, вызванную присутствием других солей, не выделяющихся при кипячении воды: в основном, сульфатов и
хлоридов Са и Mg. Жёсткая вода при умывании сушит кожу, в ней плохо образуется пена при использовании мыла.
Использование жёсткой воды вызывает появление осадка на стенках котлов, в трубах и т. п. В то же время,
использование слишком мягкой воды может приводить к коррозии труб, так как, в этом случае отсутствует
кислотно-щелочная буферность, которую обеспечивает гидрокарбонатная жёсткость. Потребление жёсткой или мягкой
воды обычно не является опасным для здоровья, однако есть данные о том, что высокая жёсткость способствует
образованию мочевых камней, а низкая — незначительно увеличивает риск сердечно-сосудистых заболеваний. Вкус
природной питьевой воды, например, воды родников, обусловлен именно присутствием солей жёсткости.",
"idx": 100,
"label": -1
}
```
#### RuCoS
- **Size of downloaded dataset files:** 56.62 MB
- **Size of the generated dataset:** 202.38 MB
- **Total amount of disk used:** 261.10 MB
An example of 'train'/'dev' looks as follows
```
{
"passage": "В Абхазии 24 августа на досрочных выборах выбирают нового президента. Кто бы ни стал победителем,
возможности его будут ограничены, говорят эксперты, опрошенные DW. В Абхазии 24 августа проходят досрочные выборы
президента не признанной международным сообществом республики. Толчком к их проведению стали массовые протесты в
конце мая 2014 года, в результате которых со своего поста был вынужден уйти действующий президент Абхазии Александр
Анкваб. Эксперты называют среди наиболее перспективных кандидатов находящегося в оппозиции политика Рауля Хаджимбу,
экс-главу службы безопасности Аслана Бжанию и генерала Мираба Кишмарию, исполняющего обязанности министра обороны.
У кого больше шансов\n\"Ставки делаются на победу Хаджимбы.\n@highlight\nВ Швеции задержаны двое граждан РФ в связи
с нападением на чеченского блогера\n@highlight\nТуризм в эпоху коронавируса: куда поехать? И ехать ли
вообще?\n@highlight\nКомментарий: Россия накануне эпидемии - виноватые назначены заранее",
"query": "Несмотря на то, что Кремль вложил много денег как в @placeholder, так и в Южную Осетию, об экономическом
восстановлении данных регионов говорить не приходится, считает Хальбах: \"Многие по-прежнему живут в
полуразрушенных домах и временных жилищах\".",
"entities":
[
"DW.",
"Абхазии ",
"Александр Анкваб.",
"Аслана Бжанию ",
"Мираба Кишмарию,",
"РФ ",
"Рауля Хаджимбу,",
"Россия ",
"Хаджимбы.",
"Швеции "
],
"answers":
[
"Абхазии"
],
"idx":
{
"passage": 500,
"query": 500
}
}
```
An example of 'test' looks as follows
```
{
"passage": "Почему и как изменится курс белорусского рубля? Какие инструменты следует предпочесть населению, чтобы
сохранить сбережения, DW рассказали финансовые аналитики Беларуси. На последних валютных торгах БВФБ 2015 года в
среду, 30 декабря, курс белорусского рубля к доллару - 18569, к евро - 20300, к российскому рублю - 255. В 2016
году белорусскому рублю пророчат падение как минимум на 12 процентов к корзине валют, к которой привязан его курс.
А чтобы избежать потерь, белорусам советуют диверсифицировать инвестиционные портфели. Чем обусловлены прогнозные
изменения котировок белорусского рубля, и какие финансовые инструменты стоит предпочесть, чтобы минимизировать риск
потерь?\n@highlight\nВ Германии за сутки выявлено более 100 новых заражений коронавирусом\n@highlight\nРыночные цены
на нефть рухнули из-за провала переговоров ОПЕК+\n@highlight\nВ Италии за сутки произошел резкий скачок смертей от
COVID-19",
"query": "Последнее, убежден аналитик, инструмент для узкого круга профессиональных инвесторов, культуры следить за
финансовым состоянием предприятий - такой, чтобы играть на рынке корпоративных облигаций, - в @placeholder пока нет.",
"entities":
[
"DW ",
"Беларуси.",
"Германии ",
"Италии ",
"ОПЕК+"
],
"answers": [],
"idx":
{
"passage": 500,
"query": 500
}
}
```
### Data Fields
#### LiDiRus
- `idx`: an `int32` feature
- `label`: a classification label, with possible values `entailment` (0), `not_entailment` (1)
- `sentence1`: a `string` feature
- `sentence2`: a `string` feature
- `knowledge`: a `string` feature with possible values `''`, `'World knowledge'`, `'Common sense'`
- `lexical-semantics`: a `string` feature
- `logic`: a `string` feature
- `predicate-argument-structure`: a `string` feature
#### RCB
- `idx`: an `int32` feature
- `label`: a classification label, with possible values `entailment` (0), `contradiction` (1), `neutral` (2)
- `premise`: a `string` feature
- `hypothesis`: a `string` feature
- `verb`: a `string` feature
- `negation`: a `string` feature with possible values `'no_negation'`, `'negation'`, `''`, `'double_negation'`
#### PARus
- `idx`: an `int32` feature
- `label`: a classification label, with possible values `choice1` (0), `choice2` (1)
- `premise`: a `string` feature
- `choice1`: a `string` feature
- `choice2`: a `string` feature
- `question`: a `string` feature with possible values `'cause'`, `'effect'`
#### MuSeRC
- `idx`: an `int32` feature
- `label` : a classification label, with possible values `false` (0) , `true` (1) (does the provided `answer` contain
a factual response to the `question`)
- `paragraph`: a `string` feature
- `question`: a `string` feature
- `answer`: a `string` feature
#### TERRa
- `idx`: an `int32` feature
- `label`: a classification label, with possible values `entailment` (0), `not_entailment` (1)
- `premise`: a `string` feature
- `hypothesis`: a `string` feature
#### RUSSE
- `idx`: an `int32` feature
- `label` : a classification label, with possible values `false` (0), `true` (1) (whether the given `word` used in the
same sense in both sentences)
- `word`: a `string` feature
- `sentence1`: a `string` feature
- `sentence2`: a `string` feature
- `gold_sense1`: an `int32` feature
- `gold_sense2`: an `int32` feature
- `start1`: an `int32` feature
- `start2`: an `int32` feature
- `end1`: an `int32` feature
- `end2`: an `int32` feature
#### RWSD
- `idx`: an `int32` feature
- `label` : a classification label, with possible values `false` (0), `true` (1) (whether the given spans are
coreferential)
- `text`: a `string` feature
- `span1_index`: an `int32` feature
- `span2_index`: an `int32` feature
- `span1_text`: a `string` feature
- `span2_text`: a `string` feature
#### DaNetQA
- `idx`: an `int32` feature
- `label` : a classification label, with possible values `false` (0), `true` (1) (yes/no answer to the `question` found
in the `passage`)
- `question`: a `string` feature
- `passage`: a `string` feature
#### RuCoS
- `idx`: an `int32` feature
- `passage`: a `string` feature
- `query`: a `string` feature
- `entities`: a `list of strings` feature
- `answers`: a `list of strings` feature
[More Information Needed]
### Data Splits
#### LiDiRus
| |test|
|---|---:|
|LiDiRus|1104|
#### RCB
| |train|validation|test|
|----|---:|----:|---:|
|RCB|438|220|438|
#### PARus
| |train|validation|test|
|----|---:|----:|---:|
|PARus|400|100|500|
#### MuSeRC
| |train|validation|test|
|----|---:|----:|---:|
|MuSeRC|500|100|322|
#### TERRa
| |train|validation|test|
|----|---:|----:|---:|
|TERRa|2616|307|3198|
#### RUSSE
| |train|validation|test|
|----|---:|----:|---:|
|RUSSE|19845|8508|18892|
#### RWSD
| |train|validation|test|
|----|---:|----:|---:|
|RWSD|606|204|154|
#### DaNetQA
| |train|validation|test|
|----|---:|----:|---:|
|DaNetQA|1749|821|805|
#### RuCoS
| |train|validation|test|
|----|---:|----:|---:|
|RuCoS|72193|7577|7257|
## 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
All our datasets are published by MIT License.
### Citation Information
```
@article{shavrina2020russiansuperglue,
title={RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark},
author={Shavrina, Tatiana and Fenogenova, Alena and Emelyanov, Anton and Shevelev, Denis and Artemova, Ekaterina and Malykh, Valentin and Mikhailov, Vladislav and Tikhonova, Maria and Chertok, Andrey and Evlampiev, Andrey},
journal={arXiv preprint arXiv:2010.15925},
year={2020}
}
@misc{fenogenova2022russian,
title={Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models},
author={Alena Fenogenova and Maria Tikhonova and Vladislav Mikhailov and Tatiana Shavrina and Anton Emelyanov and Denis Shevelev and Alexandr Kukushkin and Valentin Malykh and Ekaterina Artemova},
year={2022},
eprint={2202.07791},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@slowwavesleep](https://github.com/slowwavesleep) for adding this dataset. |
false |
# Dataset Card for Imagenette
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/fastai/imagenette
- **Repository:** https://github.com/fastai/imagenette
- **Leaderboard:** https://paperswithcode.com/sota/image-classification-on-imagenette
### Dataset Summary
A smaller subset of 10 easily classified classes from [Imagenet](https://huggingface.co/datasets/imagenet-1k#dataset-summary), and a little more French.
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward), and this repository is only there to share his work on this platform. The repository owner takes no credit of any kind in the creation, curation or packaging of the dataset.
### Supported Tasks and Leaderboards
- `image-classification`: The dataset can be used to train a model for Image Classification.
### Languages
The class labels in the dataset are in English.
## Dataset Structure
### Data Instances
A data point comprises an image URL and its classification label.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=320x320 at 0x19FA12186D8>,
'label': 'tench',
}
```
### Data Fields
- `image`: A `PIL.Image.Image` object containing the image.
- `label`: the expected class label of the image.
### Data Splits
| |train|validation|
|----------|----:|---------:|
|imagenette| 9469| 3925|
## Dataset Creation
### Curation Rationale
cf. https://huggingface.co/datasets/imagenet-1k#curation-rationale
### Source Data
#### Initial Data Collection and Normalization
Imagenette is a subset of [ImageNet](https://huggingface.co/datasets/imagenet-1k). Information about data collection of the source data can be found [here](https://huggingface.co/datasets/imagenet-1k#initial-data-collection-and-normalization).
### Annotations
#### Annotation process
cf. https://huggingface.co/datasets/imagenet-1k#annotation-process
#### Who are the annotators?
cf. https://huggingface.co/datasets/imagenet-1k#who-are-the-annotators
### Personal and Sensitive Information
cf. https://huggingface.co/datasets/imagenet-1k#personal-and-sensitive-information
## Considerations for Using the Data
### Social Impact of Dataset
cf. https://huggingface.co/datasets/imagenet-1k#social-impact-of-dataset
### Discussion of Biases
cf. https://huggingface.co/datasets/imagenet-1k#discussion-of-biases
### Other Known Limitations
cf. https://huggingface.co/datasets/imagenet-1k#other-known-limitations
## Additional Information
### Dataset Curators
cf. https://huggingface.co/datasets/imagenet-1k#dataset-curators
and Jeremy Howard
### Licensing Information
[Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
```
@software{Howard_Imagenette_2019,
title={Imagenette: A smaller subset of 10 easily classified classes from Imagenet},
author={Jeremy Howard},
year={2019},
month={March},
publisher = {GitHub},
url = {https://github.com/fastai/imagenette}
}
```
### Contributions
This dataset was created by [Jeremy Howard](https://twitter.com/jeremyphoward) and published on [Github](https://github.com/fastai/imagenette). It was then only integrated into HuggingFace Datasets by [@frgfm](https://huggingface.co/frgfm).
|
false |
# Dataset Card for CCMatrix v1
## 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://opus.nlpl.eu/CCMatrix.php
- **Repository:** None
- **Paper:** https://arxiv.org/abs/1911.04944
### Dataset Summary
This corpus has been extracted from web crawls using the margin-based bitext mining techniques described at https://github.com/facebookresearch/LASER/tree/master/tasks/CCMatrix.
* 90 languages, 1,197 bitexts
* total number of files: 90
* total number of tokens: 112.14G
* total number of sentence fragments: 7.37G
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Configs are generated for all language pairs in both directions.
You can find the valid pairs in Homepage section of Dataset Description: https://opus.nlpl.eu/CCMatrix.php
E.g.
```
from datasets import load_dataset
dataset = load_dataset("yhavinga/ccmatrix", "en-nl", streaming=True)
```
This will open the `en-nl` dataset in streaming mode. Without streaming, download and prepare will take tens of minutes.
You can inspect elements with:
```
print(next(iter(dataset['train'])))
{'id': 0, 'score': 1.2499677, 'translation': {'en': 'They come from all parts of Egypt, just like they will at the day of His coming.', 'nl': 'Zij kwamen uit alle delen van Egypte, evenals zij op de dag van Zijn komst zullen doen.'}}
```
## Dataset Structure
### Data Instances
For example:
```json
{
"id": 1,
"score": 1.2498379,
"translation": {
"nl": "En we moeten elke waarheid vals noemen die niet minstens door een lach vergezeld ging.”",
"en": "And we should call every truth false which was not accompanied by at least one laugh.”"
}
}
```
### Data Fields
Each example contains an integer id starting with 0, a score, and a translation dictionary with the language 1 and
language 2 texts.
### Data Splits
Only a `train` split is provided.
## 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
IMPORTANT: Please cite reference [2][3] if you use this data.
1. **[CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data](https://arxiv.org/abs/1911.00359)**
by *Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, Vishrav Chaudhary, Francisco Guzmán, Armand Jouli
and Edouard Grave*.
2. **[CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEB](https://arxiv.org/abs/1911.04944)** by *Holger Schwenk, Guillaume Wenzek, Sergey Edunov, Edouard Grave and Armand Joulin*.
3. **[Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125)** by *Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines,
Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky,
Sergey Edunov, Edouard Grave, Michael Auli, and Armand Joulin.*
This HuggingFace CCMatrix dataset is a wrapper around the service and files prepared and hosted by OPUS:
* **[Parallel Data, Tools and Interfaces in OPUS](https://www.aclweb.org/anthology/L12-1246/)** by *Jörg Tiedemann*.
### Contributions
|
false |
# Dataset Card for SelQA
## 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/emorynlp/selqa
- **Repository:** https://github.com/emorynlp/selqa
- **Paper:** https://arxiv.org/abs/1606.00851
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** Tomasz Jurczyk <http://tomaszjurczyk.com/>, Jinho D. Choi <http://www.mathcs.emory.edu/~choi/home.html>
### Dataset Summary
SelQA: A New Benchmark for Selection-Based Question Answering
### Supported Tasks and Leaderboards
Question Answering
### Languages
English
## Dataset Structure
### Data Instances
An example from the `answer selection` set:
```
{
"section": "Museums",
"question": "Where are Rockefeller Museum and LA Mayer Institute for Islamic Art?",
"article": "Israel",
"is_paraphrase": true,
"topic": "COUNTRY",
"answers": [
5
],
"candidates": [
"The Israel Museum in Jerusalem is one of Israel's most important cultural institutions and houses the Dead Sea scrolls, along with an extensive collection of Judaica and European art.",
"Israel's national Holocaust museum, Yad Vashem, is the world central archive of Holocaust-related information.",
"Beth Hatefutsoth (the Diaspora Museum), on the campus of Tel Aviv University, is an interactive museum devoted to the history of Jewish communities around the world.",
"Apart from the major museums in large cities, there are high-quality artspaces in many towns and \"kibbutzim\".",
"\"Mishkan Le'Omanut\" on Kibbutz Ein Harod Meuhad is the largest art museum in the north of the country.",
"Several Israeli museums are devoted to Islamic culture, including the Rockefeller Museum and the L. A. Mayer Institute for Islamic Art, both in Jerusalem.",
"The Rockefeller specializes in archaeological remains from the Ottoman and other periods of Middle East history.",
"It is also the home of the first hominid fossil skull found in Western Asia called Galilee Man.",
"A cast of the skull is on display at the Israel Museum."
],
"q_types": [
"where"
]
}
```
An example from the `answer triggering` set:
```
{
"section": "Museums",
"question": "Where are Rockefeller Museum and LA Mayer Institute for Islamic Art?",
"article": "Israel",
"is_paraphrase": true,
"topic": "COUNTRY",
"candidate_list": [
{
"article": "List of places in Jerusalem",
"section": "List_of_places_in_Jerusalem-Museums",
"answers": [],
"candidates": [
" Israel Museum *Shrine of the Book *Rockefeller Museum of Archeology Bible Lands Museum Jerusalem Yad Vashem Holocaust Museum L.A. Mayer Institute for Islamic Art Bloomfield Science Museum Natural History Museum Museum of Italian Jewish Art Ticho House Tower of David Jerusalem Tax Museum Herzl Museum Siebenberg House Museums.",
"Museum on the Seam "
]
},
{
"article": "Israel",
"section": "Israel-Museums",
"answers": [
5
],
"candidates": [
"The Israel Museum in Jerusalem is one of Israel's most important cultural institutions and houses the Dead Sea scrolls, along with an extensive collection of Judaica and European art.",
"Israel's national Holocaust museum, Yad Vashem, is the world central archive of Holocaust-related information.",
"Beth Hatefutsoth (the Diaspora Museum), on the campus of Tel Aviv University, is an interactive museum devoted to the history of Jewish communities around the world.",
"Apart from the major museums in large cities, there are high-quality artspaces in many towns and \"kibbutzim\".",
"\"Mishkan Le'Omanut\" on Kibbutz Ein Harod Meuhad is the largest art museum in the north of the country.",
"Several Israeli museums are devoted to Islamic culture, including the Rockefeller Museum and the L. A. Mayer Institute for Islamic Art, both in Jerusalem.",
"The Rockefeller specializes in archaeological remains from the Ottoman and other periods of Middle East history.",
"It is also the home of the first hominid fossil skull found in Western Asia called Galilee Man.",
"A cast of the skull is on display at the Israel Museum."
]
},
{
"article": "L. A. Mayer Institute for Islamic Art",
"section": "L._A._Mayer_Institute_for_Islamic_Art-Abstract",
"answers": [],
"candidates": [
"The L.A. Mayer Institute for Islamic Art (Hebrew: \u05de\u05d5\u05d6\u05d9\u05d0\u05d5\u05df \u05dc.",
"\u05d0.",
"\u05de\u05d0\u05d9\u05e8 \u05dc\u05d0\u05de\u05e0\u05d5\u05ea \u05d4\u05d0\u05e1\u05dc\u05d0\u05dd) is a museum in Jerusalem, Israel, established in 1974.",
"It is located in Katamon, down the road from the Jerusalem Theater.",
"The museum houses Islamic pottery, textiles, jewelry, ceremonial objects and other Islamic cultural artifacts.",
"It is not to be confused with the Islamic Museum, Jerusalem. "
]
},
{
"article": "Islamic Museum, Jerusalem",
"section": "Islamic_Museum,_Jerusalem-Abstract",
"answers": [],
"candidates": [
"The Islamic Museum is a museum on the Temple Mount in the Old City section of Jerusalem.",
"On display are exhibits from ten periods of Islamic history encompassing several Muslim regions.",
"The museum is located adjacent to al-Aqsa Mosque.",
"It is not to be confused with the L. A. Mayer Institute for Islamic Art, also a museum in Jerusalem. "
]
},
{
"article": "L. A. Mayer Institute for Islamic Art",
"section": "L._A._Mayer_Institute_for_Islamic_Art-Contemporary_Arab_art",
"answers": [],
"candidates": [
"In 2008, a group exhibit of contemporary Arab art opened at L.A. Mayer Institute, the first show of local Arab art in an Israeli museum and the first to be mounted by an Arab curator.",
"Thirteen Arab artists participated in the show. "
]
}
],
"q_types": [
"where"
]
}
```
An example from any of the `experiments` data:
```
Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ? The Israel Museum in Jerusalem is one of Israel 's most important cultural institutions and houses the Dead Sea scrolls , along with an extensive collection of Judaica and European art . 0
Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ? Israel 's national Holocaust museum , Yad Vashem , is the world central archive of Holocaust - related information . 0
Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ? Beth Hatefutsoth ( the Diaspora Museum ) , on the campus of Tel Aviv University , is an interactive museum devoted to the history of Jewish communities around the world . 0
Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ? Apart from the major museums in large cities , there are high - quality artspaces in many towns and " kibbutzim " . 0
Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ? " Mishkan Le'Omanut " on Kibbutz Ein Harod Meuhad is the largest art museum in the north of the country . 0
Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ? Several Israeli museums are devoted to Islamic culture , including the Rockefeller Museum and the L. A. Mayer Institute for Islamic Art , both in Jerusalem . 1
Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ? The Rockefeller specializes in archaeological remains from the Ottoman and other periods of Middle East history . 0
Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ? It is also the home of the first hominid fossil skull found in Western Asia called Galilee Man . 0
Where are Rockefeller Museum and LA Mayer Institute for Islamic Art ? A cast of the skull is on display at the Israel Museum . 0
```
### Data Fields
#### Answer Selection
##### Data for Analysis
for analysis, the columns are:
* `question`: the question.
* `article`: the Wikipedia article related to this question.
* `section`: the section in the Wikipedia article related to this question.
* `topic`: the topic of this question, where the topics are *MUSIC*, *TV*, *TRAVEL*, *ART*, *SPORT*, *COUNTRY*, *MOVIES*, *HISTORICAL EVENTS*, *SCIENCE*, *FOOD*.
* `q_types`: the list of question types, where the types are *what*, *why*, *when*, *who*, *where*, and *how*. If empty, none of the those types are recognized in this question.
* `is_paraphrase`: *True* if this question is a paragraph of some other question in this dataset; otherwise, *False*.
* `candidates`: the list of sentences in the related section.
* `answers`: the list of candidate indices containing the answer context of this question.
##### Data for Experiments
for experiments, each column gives:
* `0`: a question where all tokens are separated.
* `1`: a candidate of the question where all tokens are separated.
* `2`: the label where `0` implies no answer to the question is found in this candidate and `1` implies the answer is found.
#### Answer Triggering
##### Data for Analysis
for analysis, the columns are:
* `question`: the question.
* `article`: the Wikipedia article related to this question.
* `section`: the section in the Wikipedia article related to this question.
* `topic`: the topic of this question, where the topics are *MUSIC*, *TV*, *TRAVEL*, *ART*, *SPORT*, *COUNTRY*, *MOVIES*, *HISTORICAL EVENTS*, *SCIENCE*, *FOOD*.
* `q_types`: the list of question types, where the types are *what*, *why*, *when*, *who*, *where*, and *how*. If empty, none of the those types are recognized in this question.
* `is_paraphrase`: *True* if this question is a paragraph of some other question in this dataset; otherwise, *False*.
* `candidate_list`: the list of 5 candidate sections:
* `article`: the title of the candidate article.
* `section`: the section in the candidate article.
* `candidates`: the list of sentences in this candidate section.
* `answers`: the list of candidate indices containing the answer context of this question (can be empty).
##### Data for Experiments
for experiments, each column gives:
* `0`: a question where all tokens are separated.
* `1`: a candidate of the question where all tokens are separated.
* `2`: the label where `0` implies no answer to the question is found in this candidate and `1` implies the answer is found.
### Data Splits
| |Train| Valid| Test|
| --- | --- | --- | --- |
| Answer Selection | 5529 | 785 | 1590 |
| Answer Triggering | 27645 | 3925 | 7950 |
## Dataset Creation
### Curation Rationale
To encourage research and provide an initial benchmark for selection based question answering and answer triggering tasks
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
Crowdsourced
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
[Needs More Information]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop better selection-based question answering systems.
### 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
@InProceedings{7814688,
author={T. {Jurczyk} and M. {Zhai} and J. D. {Choi}},
booktitle={2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)},
title={SelQA: A New Benchmark for Selection-Based Question Answering},
year={2016},
volume={},
number={},
pages={820-827},
doi={10.1109/ICTAI.2016.0128}
}
### Contributions
Thanks to [@Bharat123rox](https://github.com/Bharat123rox) for adding this dataset. |
false | ### Getting Started
The dataset consists of 2084 jsonl files.
You can download the dataset using HuggingFace:
```python
from datasets import load_dataset
ds = load_dataset("togethercomputer/RedPajama-Data-1T")
```
Or you can directly download the files using the following command:
```
wget 'https://data.together.xyz/redpajama-data-1T/v1.0.0/urls.txt'
while read line; do
dload_loc=${line#https://data.together.xyz/redpajama-data-1T/v1.0.0/}
mkdir -p $(dirname $dload_loc)
wget "$line" -O "$dload_loc"
done < urls.txt
```
A smaller 1B-token sample of the dataset can be found [here](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T-Sample).
A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/togethercomputer/RedPajama-Data).
### Dataset Summary
RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset.
| Dataset | Token Count |
|---------------|-------------|
| Commoncrawl | 878 Billion |
| C4 | 175 Billion |
| GitHub | 59 Billion |
| Books | 26 Billion |
| ArXiv | 28 Billion |
| Wikipedia | 24 Billion |
| StackExchange | 20 Billion |
| Total | 1.2 Trillion |
### Languages
Primarily English, though the Wikipedia slice contains multiple languages.
## Dataset Structure
The dataset structure is as follows:
```json
{
"text": ...,
"meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...},
"red_pajama_subset": "common_crawl" | "c4" | "github" | "books" | "arxiv" | "wikipedia" | "stackexchange"
}
```
## Dataset Creation
This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe.
### Source Data
#### Commoncrawl
We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline.
We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to
classify paragraphs as Wikipedia references or random Commoncrawl samples.
#### C4
C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format.
#### GitHub
The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality
files and only keep projects that are distributed under the MIT, BSD, or Apache license.
#### Wikipedia
We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains
text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other
formatting boilerplate has been removed.
#### Gutenberg and Books3
The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use
simhash to remove near duplicates.
#### ArXiv
ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and
remove preambles, comments, macros and bibliographies.
#### Stackexchange
The Stack Exchange split of the dataset is download from the
[Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites,
remove html tags, group the posts into question-answer pairs, and order answers by their score.
### SHA256 Checksums
SHA256 checksums for the dataset files for each data source are available here:
```
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/arxiv_SHA256SUMS.txt
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/book_SHA256SUMS.txt
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/c4_SHA256SUMS.txt
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/common_crawl_SHA256SUMS.txt
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/github_SHA256SUMS.txt
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/stackexchange_SHA256SUMS.txt
https://data.together.xyz/redpajama-data-1T/v1.0.0/sha256/wikipedia_SHA256SUMS.txt
```
To cite RedPajama, please use:
```
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama: An Open Source Recipe to Reproduce LLaMA training dataset},
month = April,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
```
### License
Please refer to the licenses of the data subsets you use.
* [Common Crawl Foundation Terms of Use](https://commoncrawl.org/terms-of-use/full/)
* [C4 license](https://huggingface.co/datasets/allenai/c4#license)
* GitHub was limited to MIT, BSD, or Apache licenses only
* Books: [the_pile_books3 license](https://huggingface.co/datasets/the_pile_books3#licensing-information) and [pg19 license](https://huggingface.co/datasets/pg19#licensing-information)
* [ArXiv Terms of Use](https://info.arxiv.org/help/api/tou.html)
* [Wikipedia License](https://huggingface.co/datasets/wikipedia#licensing-information)
* [StackExchange license on the Internet Archive](https://archive.org/details/stackexchange)
<!--
### 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
[More Information Needed]
--> |
true |
# Dataset Card for ASSIN 2
## 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:** [ASSIN 2 homepage](https://sites.google.com/view/assin2)
- **Repository:** [ASSIN 2 repository](https://sites.google.com/view/assin2)
- **Paper:** [The ASSIN 2 shared task: a quick overview](https://drive.google.com/file/d/1ft1VU6xiVm-N58dfAp6FHWjQ4IvcXgqp/view)
- **Point of Contact:** [Livy Real](mailto:livyreal@gmail.com)
### Dataset Summary
The ASSIN 2 corpus is composed of rather simple sentences. Following the procedures of SemEval 2014 Task 1.
The training and validation data are composed, respectively, of 6,500 and 500 sentence pairs in Brazilian Portuguese,
annotated for entailment and semantic similarity. Semantic similarity values range from 1 to 5, and text entailment
classes are either entailment or none. The test data are composed of approximately 3,000 sentence pairs with the same
annotation. All data were manually annotated.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language supported is Portuguese.
## Dataset Structure
### Data Instances
An example from the ASSIN 2 dataset looks as follows:
```
{
"entailment_judgment": 1,
"hypothesis": "Uma criança está segurando uma pistola de água",
"premise": "Uma criança risonha está segurando uma pistola de água e sendo espirrada com água",
"relatedness_score": 4.5,
"sentence_pair_id": 1
}
```
### Data Fields
- `sentence_pair_id`: a `int64` feature.
- `premise`: a `string` feature.
- `hypothesis`: a `string` feature.
- `relatedness_score`: a `float32` feature.
- `entailment_judgment`: a classification label, with possible values including `NONE`, `ENTAILMENT`.
### Data Splits
The data is split into train, validation and test set. The split sizes are as follow:
| Train | Val | Test |
| ------ | ----- | ---- |
| 6500 | 500 | 2448 |
## 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
```
@inproceedings{real2020assin,
title={The assin 2 shared task: a quick overview},
author={Real, Livy and Fonseca, Erick and Oliveira, Hugo Goncalo},
booktitle={International Conference on Computational Processing of the Portuguese Language},
pages={406--412},
year={2020},
organization={Springer}
}
```
### Contributions
Thanks to [@jonatasgrosman](https://github.com/jonatasgrosman) for adding this dataset. |
false |
# Dataset Card for MNBVC
## Table of Contents
- [Dataset Card for MNBVC](#dataset-card-for-mnbvc)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [数据集介绍](#数据集介绍)
- [数据子集](#数据子集)
- [数据格式](#数据格式)
- [文本数据](#文本数据)
- [问答数据](#问答数据)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://mnbvc.253874.net/
- **Repository:** https://github.com/esbatmop/MNBVC
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** N/A
### 数据集介绍
中文互联网上最古老最神秘(没有之一)的里屋社区于2023.1.1庄重宣布:
在英明神武的里屋管子带领下，决心发挥社区所长(哪都长)，帮助开源社区长期更新一份最大的中文互联网语料集。
Huggingface上的MNBVC数据集在逐渐更新中，请到[https://github.com/esbatmop/MNBVC](https://github.com/esbatmop/MNBVC) 获取未完成清洗的更多数据。
可以使用如下脚本加载：
```python
from datasets import load_dataset
dataset = load_dataset("liwu/MNBVC", 'law_judgement', split='train', streaming=True)
next(iter(dataset)) # get the first line
```
## 数据子集
MNBVC数据集包含数个子集：
- `law_judgement`: 来自法律文书的文本。
- `gov_xuexiqiangguo`: 来自学习强国的文本。
- `gov_report`: 来自政府工作报告的文本。
- `co_ann_report`: 企业年报文本。
- `code_metadata`: 代码元数据。
- `qa_zhihu`: 来自知乎的问答数据。
- `qa_wikihow`: 来自wikihow的问答数据。
- `qa_mfa`: 外交部问答数据。
- `news_peoples_daily`: 来自人民日报的文本数据。
- `wikipedia`: 来自维基百科的文本数据。
## 数据格式
目前MNBVC数据集包含如下几类数据：
### 文本数据
文本数据使用如下格式组织：
```json
{
"文件名": datasets.Value("string"),
"是否待查文件": datasets.Value("bool"),
"是否重复文件": datasets.Value("bool"),
"文件大小": datasets.Value("int32"),
"simhash": datasets.Value("uint64"),
"最长段落长度": datasets.Value("int32"),
"段落数": datasets.Value("int32"),
"去重段落数": datasets.Value("int32"),
"低质量段落数": datasets.Value("int32"),
"段落": [
datasets.Features(
{
"行号": datasets.Value("int32"),
"是否重复": datasets.Value("bool"),
"是否跨文件重复": datasets.Value("bool"),
"md5": datasets.Value("string"),
"内容": datasets.Value("string"),
}
)
]
}
```
### 问答数据
问答数据使用如下格式组织：
```json
{
"id": datasets.Value("int32"),
"问": datasets.Value("string"),
"答": datasets.Value("string"),
"来源": datasets.Value("string"),
"元数据": {
"create_time": datasets.Value("string"),
"问题明细": datasets.Value("string"),
"回答明细": datasets.Value("string"),
"扩展字段": datasets.Value("string"),
}
}
```
项目早期所上传的数据使用如下格式，以后这一格式会被废弃，相应数据也会重新上传：
```json
{
"text": datasets.Value("string"),
"meta": datasets.Value("string")
}
```
### Contributions
Thanks to the [Liwu community](http://mnbvc.253874.net/) for constructing this dataset.
Thanks to [silver](https://github.com/silverriver) for adding this dataset. |
false |
# Dataset Card for Opus Gnome
## 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/GNOME.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
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/GNOME.php
E.g.
`dataset = load_dataset("opus_gnome", lang1="it", lang2="pl")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
```
{
'id': '0',
'translation': {
'ar': 'إعداد سياسة القفل',
'bal': 'تنظیم کتن سیاست کبل'
}
}
```
### Data Fields
Each instance has two fields:
- **id**: the id of the example
- **translation**: a dictionary containing translated texts in two languages.
### Data Splits
Each subset simply consists in a train set. We provide the number of examples for certain language pairs:
| | train |
|:---------|--------:|
| ar-bal | 60 |
| bg-csb | 10 |
| ca-en_GB | 7982 |
| cs-eo | 73 |
| de-ha | 216 |
| cs-tk | 18686 |
| da-vi | 149 |
| en_GB-my | 28232 |
| el-sk | 150 |
| de-tt | 2169 |
## 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
@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. |
false |
# Dataset Card for Wikipedia
This repo is a fork of the original Hugging Face Wikipedia repo [here](https://huggingface.co/datasets/wikipedia).
The difference is that this fork does away with the need for `apache-beam`, and this fork is very fast if you have a lot of CPUs on your machine.
It will use all CPUs available to create a clean Wikipedia pretraining dataset. It takes less than an hour to process all of English wikipedia on a GCP n1-standard-96.
This fork is also used in the [OLM Project](https://github.com/huggingface/olm-datasets) to pull and process up-to-date wikipedia snapshots.
## 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://dumps.wikimedia.org](https://dumps.wikimedia.org)
- **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)
### Dataset Summary
Wikipedia dataset containing cleaned articles of all languages.
The datasets are built from the Wikipedia dump
(https://dumps.wikimedia.org/) with one split per language. Each example
contains the content of one full Wikipedia article with cleaning to strip
markdown and unwanted sections (references, etc.).
The articles are parsed using the ``mwparserfromhell`` tool, and we use ``multiprocess`` for parallelization.
To load this dataset you need to install these first:
```
pip install mwparserfromhell==0.6.4 multiprocess==0.70.13
```
Then, you can load any subset of Wikipedia per language and per date this way:
```python
from datasets import load_dataset
load_dataset("olm/wikipedia", language="en", date="20220920")
```
You can find the full list of languages and dates [here](https://dumps.wikimedia.org/backup-index.html).
### Supported Tasks and Leaderboards
The dataset is generally used for Language Modeling.
### Languages
You can find the list of languages [here](https://meta.wikimedia.org/wiki/List_of_Wikipedias).
## Dataset Structure
### Data Instances
An example looks as follows:
```
{'id': '1',
'url': 'https://simple.wikipedia.org/wiki/April',
'title': 'April',
'text': 'April is the fourth month...'
}
```
### Data Fields
The data fields are the same among all configurations:
- `id` (`str`): ID of the article.
- `url` (`str`): URL of the article.
- `title` (`str`): Title of the article.
- `text` (`str`): Text content of the article.
### 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
Most of Wikipedia's text and many of its images are co-licensed under the
[Creative Commons Attribution-ShareAlike 3.0 Unported License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_Creative_Commons_Attribution-ShareAlike_3.0_Unported_License)
(CC BY-SA) and the [GNU Free Documentation License](https://en.wikipedia.org/wiki/Wikipedia:Text_of_the_GNU_Free_Documentation_License)
(GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts).
Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such
text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes
the text.
### Citation Information
```
@ONLINE{wikidump,
author = "Wikimedia Foundation",
title = "Wikimedia Downloads",
url = "https://dumps.wikimedia.org"
}
```
|
true |
# Dataset Card for "SwissJudgmentPrediction"
## 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/JoelNiklaus/SwissCourtRulingCorpus
- **Repository:** https://github.com/JoelNiklaus/SwissCourtRulingCorpus
- **Paper:** https://arxiv.org/abs/2110.00806
- **Leaderboard:** N/A
- **Point of Contact:** [Joel Niklaus](mailto:joel.niklaus@inf.unibe.ch)
### Dataset Summary
**Documents**
Swiss-Judgment-Prediction is a multilingual, diachronic dataset of 85K Swiss Federal Supreme Court (FSCS) cases annotated with the respective binarized judgment outcome (approval/dismissal), posing a challenging text classification task. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
### Supported Tasks and Leaderboards
SwissJudgmentPrediction can be used for the legal judgment prediction task.
The dataset is not yet part of an established benchmark.
### Languages
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
## Dataset Structure
In version 2 we added machine translated data using [EasyNMT](https://github.com/UKPLab/EasyNMT) for all documents into German, French, Italian and English as an additional training set.
### Data Instances
**Multilingual use of the dataset**
When the dataset is used in a multilingual setting selecting the the 'all_languages' flag:
```python
from datasets import load_dataset
dataset = load_dataset('swiss_judgment_prediction', 'all_languages')
```
```
{
"id": 48757,
"year": 2015,
"facts": "Sachverhalt: A. X._ war bei der Krankenversicherung C._ taggeldversichert. Infolge einer Arbeitsunf\u00e4higkeit leistete ihm die C._ vom 30. Juni 2011 bis am 28. Juni 2013 Krankentaggelder, wobei die Leistungen bis am 30. September 2012 auf Grundlage einer Arbeitsunf\u00e4higkeit von 100% und danach basierend auf einer Arbeitsunf\u00e4higkeit von 55% erbracht wurden. Die Neueinsch\u00e4tzung der Arbeitsf\u00e4higkeit erfolgte anhand eines Gutachtens der D._ AG vom 27. August 2012, welches im Auftrag der C._ erstellt wurde. X._ machte daraufhin gegen\u00fcber der C._ geltend, er sei entgegen dem Gutachten auch nach dem 30. September 2012 zu 100% arbeitsunf\u00e4hig gewesen. Ferner verlangte er von der D._ AG zwecks externer \u00dcberpr\u00fcfung des Gutachtens die Herausgabe s\u00e4mtlicher diesbez\u00fcglicher Notizen, Auswertungen und Unterlagen. A._ (als Gesch\u00e4ftsf\u00fchrer der D._ AG) und B._ (als f\u00fcr das Gutachten medizinisch Verantwortliche) antworteten ihm, dass sie alle Unterlagen der C._ zugestellt h\u00e4tten und dass allf\u00e4llige Fragen zum Gutachten direkt der C._ zu stellen seien. X._ reichte am 2. Januar 2014 eine Strafanzeige gegen A._ und B._ ein. Er wirft diesen vor, ihn durch die Nichtherausgabe der Dokumente und durch Behinderung des IV-Verfahrens gen\u00f6tigt, Daten besch\u00e4digt bzw. vernichtet und ein falsches \u00e4rztliches Zeugnis ausgestellt zu haben. Zudem h\u00e4tten sie durch die Verz\u00f6gerung des IV-Verfahrens und insbesondere durch das falsche \u00e4rztliche Zeugnis sein Verm\u00f6gen arglistig gesch\u00e4digt. B. Die Staatsanwaltschaft des Kantons Bern, Region Oberland, nahm das Verfahren wegen N\u00f6tigung, Datenbesch\u00e4digung, falschem \u00e4rztlichem Zeugnis und arglistiger Verm\u00f6genssch\u00e4digung mit Verf\u00fcgung vom 10. November 2014 nicht an die Hand. Das Obergericht des Kantons Bern wies die von X._ dagegen erhobene Beschwerde am 27. April 2015 ab, soweit darauf einzutreten war. C. X._ beantragt mit Beschwerde in Strafsachen, der Beschluss vom 27. April 2015 sei aufzuheben und die Angelegenheit zur korrekten Ermittlung des Sachverhalts an die Staatsanwaltschaft zur\u00fcckzuweisen. Er stellt zudem den sinngem\u00e4ssen Antrag, das bundesgerichtliche Verfahren sei w\u00e4hrend der Dauer des konnexen Strafverfahrens gegen eine Teilgutachterin und des ebenfalls konnexen Zivil- oder Strafverfahrens gegen die C._ wegen Einsichtsverweigerung in das mutmasslich gef\u00e4lschte Originalgutachten zu sistieren. X._ ersucht um unentgeltliche Rechtspflege. ",
"labels": 0, # dismissal
"language": "de",
"region": "Espace Mittelland",
"canton": "be",
"legal area": "penal law"
}
```
**Monolingual use of the dataset**
When the dataset is used in a monolingual setting selecting the ISO language code for one of the 3 supported languages. For example:
```python
from datasets import load_dataset
dataset = load_dataset('swiss_judgment_prediction', 'de')
```
```
{
"id": 48757,
"year": 2015,
"facts": "Sachverhalt: A. X._ war bei der Krankenversicherung C._ taggeldversichert. Infolge einer Arbeitsunf\u00e4higkeit leistete ihm die C._ vom 30. Juni 2011 bis am 28. Juni 2013 Krankentaggelder, wobei die Leistungen bis am 30. September 2012 auf Grundlage einer Arbeitsunf\u00e4higkeit von 100% und danach basierend auf einer Arbeitsunf\u00e4higkeit von 55% erbracht wurden. Die Neueinsch\u00e4tzung der Arbeitsf\u00e4higkeit erfolgte anhand eines Gutachtens der D._ AG vom 27. August 2012, welches im Auftrag der C._ erstellt wurde. X._ machte daraufhin gegen\u00fcber der C._ geltend, er sei entgegen dem Gutachten auch nach dem 30. September 2012 zu 100% arbeitsunf\u00e4hig gewesen. Ferner verlangte er von der D._ AG zwecks externer \u00dcberpr\u00fcfung des Gutachtens die Herausgabe s\u00e4mtlicher diesbez\u00fcglicher Notizen, Auswertungen und Unterlagen. A._ (als Gesch\u00e4ftsf\u00fchrer der D._ AG) und B._ (als f\u00fcr das Gutachten medizinisch Verantwortliche) antworteten ihm, dass sie alle Unterlagen der C._ zugestellt h\u00e4tten und dass allf\u00e4llige Fragen zum Gutachten direkt der C._ zu stellen seien. X._ reichte am 2. Januar 2014 eine Strafanzeige gegen A._ und B._ ein. Er wirft diesen vor, ihn durch die Nichtherausgabe der Dokumente und durch Behinderung des IV-Verfahrens gen\u00f6tigt, Daten besch\u00e4digt bzw. vernichtet und ein falsches \u00e4rztliches Zeugnis ausgestellt zu haben. Zudem h\u00e4tten sie durch die Verz\u00f6gerung des IV-Verfahrens und insbesondere durch das falsche \u00e4rztliche Zeugnis sein Verm\u00f6gen arglistig gesch\u00e4digt. B. Die Staatsanwaltschaft des Kantons Bern, Region Oberland, nahm das Verfahren wegen N\u00f6tigung, Datenbesch\u00e4digung, falschem \u00e4rztlichem Zeugnis und arglistiger Verm\u00f6genssch\u00e4digung mit Verf\u00fcgung vom 10. November 2014 nicht an die Hand. Das Obergericht des Kantons Bern wies die von X._ dagegen erhobene Beschwerde am 27. April 2015 ab, soweit darauf einzutreten war. C. X._ beantragt mit Beschwerde in Strafsachen, der Beschluss vom 27. April 2015 sei aufzuheben und die Angelegenheit zur korrekten Ermittlung des Sachverhalts an die Staatsanwaltschaft zur\u00fcckzuweisen. Er stellt zudem den sinngem\u00e4ssen Antrag, das bundesgerichtliche Verfahren sei w\u00e4hrend der Dauer des konnexen Strafverfahrens gegen eine Teilgutachterin und des ebenfalls konnexen Zivil- oder Strafverfahrens gegen die C._ wegen Einsichtsverweigerung in das mutmasslich gef\u00e4lschte Originalgutachten zu sistieren. X._ ersucht um unentgeltliche Rechtspflege. ",
"labels": 0, # dismissal
"language": "de",
"region": "Espace Mittelland",
"canton": "be",
"legal area": "penal law"
}
```
### Data Fields
**Multilingual use of the dataset**
The following data fields are provided for documents (`train`, `validation`, `test`):
`id`: (**int**) a unique identifier of the for the document \
`year`: (**int**) the publication year \
`text`: (**str**) the facts of the case \
`label`: (**class label**) the judgment outcome: 0 (dismissal) or 1 (approval) \
`language`: (**str**) one of (de, fr, it) \
`region`: (**str**) the region of the lower court \
`canton`: (**str**) the canton of the lower court \
`legal area`: (**str**) the legal area of the case
**Monolingual use of the dataset**
The following data fields are provided for documents (`train`, `validation`, `test`):
`id`: (**int**) a unique identifier of the for the document \
`year`: (**int**) the publication year \
`text`: (**str**) the facts of the case \
`label`: (**class label**) the judgment outcome: 0 (dismissal) or 1 (approval) \
`language`: (**str**) one of (de, fr, it) \
`region`: (**str**) the region of the lower court \
`canton`: (**str**) the canton of the lower court \
`legal area`: (**str**) the legal area of the case
### Data Splits
| Language | Subset | Number of Documents (Training/Validation/Test) |
|------------|------------|------------------------------------------------|
| German | **de** | 35'452 / 4'705 / 9'725 |
| French | **fr** | 21'179 / 3'095 / 6'820 |
| Italian | **it** | 3'072 / 408 / 812 |
| All | **all** | 59'709 / 8'208 / 17'357 |
| MT German | **mt_de** | 24'251 / 0 / 0 |
| MT French | **mt_fr** | 38'524 / 0 / 0 |
| MT Italian | **mt_it** | 56'631 / 0 / 0 |
| MT All | **all+mt** | 238'818 / 8'208 / 17'357 |
## Dataset Creation
### Curation Rationale
The dataset was curated by Niklaus et al. (2021).
### Source Data
#### Initial Data Collection and Normalization
The original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML.
#### Who are the source language producers?
Switzerland has four official languages with 3 languages (German, French and Italian) being represented in more than 1000 Swiss Federal Supreme court decisions. The decisions are written by the judges and clerks in the language of the proceedings.
### Annotations
#### Annotation process
The decisions have been annotated with the binarized judgment outcome using parsers and regular expressions.
#### Who are the annotators?
Joel Niklaus and Adrian Jörg annotated the binarized judgment outcomes.
Metadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.
## 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
Niklaus et al. (2021)
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)
© Swiss Federal Supreme Court, 2000-2020
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf
### Citation Information
*Joel Niklaus, Ilias Chalkidis, and Matthias Stürmer.*
*Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark*
*Proceedings of the 2021 Natural Legal Language Processing Workshop. Punta Cana, Dominican Republic. 2021*
```
@InProceedings{niklaus-etal-2021-swiss,
author = {Niklaus, Joel
and Chalkidis, Ilias
and Stürmer, Matthias},
title = {Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark},
booktitle = {Proceedings of the 2021 Natural Legal Language Processing Workshop},
year = {2021},
location = {Punta Cana, Dominican Republic},
}
```
and the new citation
```
@misc{niklaus2022empirical,
title={An Empirical Study on Cross-X Transfer for Legal Judgment Prediction},
author={Joel Niklaus and Matthias Stürmer and Ilias Chalkidis},
year={2022},
eprint={2209.12325},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@joelniklaus](https://github.com/joelniklaus) for adding this dataset. |
false |
# Dataset Card for Asian Language Treebank (ALT)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/
- **Leaderboard:**
- **Paper:** [Introduction of the Asian Language Treebank](https://ieeexplore.ieee.org/abstract/document/7918974)
- **Point of Contact:** [ALT info](alt-info@khn.nict.go.jp)
### Dataset Summary
The ALT project aims to advance the state-of-the-art Asian natural language processing (NLP) techniques through the open collaboration for developing and using ALT. It was first conducted by NICT and UCSY as described in Ye Kyaw Thu, Win Pa Pa, Masao Utiyama, Andrew Finch and Eiichiro Sumita (2016). Then, it was developed under [ASEAN IVO](https://www.nict.go.jp/en/asean_ivo/index.html) as described in this Web page.
The process of building ALT began with sampling about 20,000 sentences from English Wikinews, and then these sentences were translated into the other languages.
### Supported Tasks and Leaderboards
Machine Translation, Dependency Parsing
### Languages
It supports 13 language:
* Bengali
* English
* Filipino
* Hindi
* Bahasa Indonesia
* Japanese
* Khmer
* Lao
* Malay
* Myanmar (Burmese)
* Thai
* Vietnamese
* Chinese (Simplified Chinese).
## Dataset Structure
### Data Instances
#### ALT Parallel Corpus
```
{
"SNT.URLID": "80188",
"SNT.URLID.SNTID": "1",
"url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal",
"bg": "[translated sentence]",
"en": "[translated sentence]",
"en_tok": "[translated sentence]",
"fil": "[translated sentence]",
"hi": "[translated sentence]",
"id": "[translated sentence]",
"ja": "[translated sentence]",
"khm": "[translated sentence]",
"lo": "[translated sentence]",
"ms": "[translated sentence]",
"my": "[translated sentence]",
"th": "[translated sentence]",
"vi": "[translated sentence]",
"zh": "[translated sentence]"
}
```
#### ALT Treebank
```
{
"SNT.URLID": "80188",
"SNT.URLID.SNTID": "1",
"url": "http://en.wikinews.org/wiki/2007_Rugby_World_Cup:_Italy_31_-_5_Portugal",
"status": "draft/reviewed",
"value": "(S (S (BASENP (NNP Italy)) (VP (VBP have) (VP (VP (VP (VBN defeated) (BASENP (NNP Portugal))) (ADVP (RB 31-5))) (PP (IN in) (NP (BASENP (NNP Pool) (NNP C)) (PP (IN of) (NP (BASENP (DT the) (NN 2007) (NNP Rugby) (NNP World) (NNP Cup)) (PP (IN at) (NP (BASENP (NNP Parc) (FW des) (NNP Princes)) (COMMA ,) (BASENP (NNP Paris) (COMMA ,) (NNP France))))))))))) (PERIOD .))"
}
```
#### ALT Myanmar transliteration
```
{
"en": "CASINO",
"my": [
"ကက်စီနို",
"ကစီနို",
"ကာစီနို",
"ကာဆီနို"
]
}
```
### Data Fields
#### ALT Parallel Corpus
- SNT.URLID: URL link to the source article listed in [URL.txt](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/ALT-Parallel-Corpus-20191206/URL.txt)
- SNT.URLID.SNTID: index number from 1 to 20000. It is a seletected sentence from `SNT.URLID`
and bg, en, fil, hi, id, ja, khm, lo, ms, my, th, vi, zh correspond to the target language
#### ALT Treebank
- status: it indicates how a sentence is annotated; `draft` sentences are annotated by one annotater and `reviewed` sentences are annotated by two annotater
The annotatation is different from language to language, please see [their guildlines](https://www2.nict.go.jp/astrec-att/member/mutiyama/ALT/) for more detail.
### Data Splits
| | train | valid | test |
|-----------|-------|-------|-------|
| # articles | 1698 | 98 | 97 |
| # sentences | 18088 | 1000 | 1018 |
## Dataset Creation
### Curation Rationale
The ALT project was initiated by the [National Institute of Information and Communications Technology, Japan](https://www.nict.go.jp/en/) (NICT) in 2014. NICT started to build Japanese and English ALT and worked with the University of Computer Studies, Yangon, Myanmar (UCSY) to build Myanmar ALT in 2014. Then, the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT), the Institute for Infocomm Research, Singapore (I2R), the Institute of Information Technology, Vietnam (IOIT), and the National Institute of Posts, Telecoms and ICT, Cambodia (NIPTICT) joined to make ALT for Indonesian, Malay, Vietnamese, and Khmer in 2015.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The dataset is sampled from the English Wikinews in 2014. These will be annotated with word segmentation, POS tags, and syntax information, in addition to the word alignment information by linguistic experts from
* National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English
* University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar
* the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian
* the Institute for Infocomm Research, Singapore (I2R) for Malay
* the Institute of Information Technology, Vietnam (IOIT) for Vietnamese
* the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
* National Institute of Information and Communications Technology, Japan (NICT) for Japanses and English
* University of Computer Studies, Yangon, Myanmar (UCSY) for Myanmar
* the Badan Pengkajian dan Penerapan Teknologi, Indonesia (BPPT) for Indonesian
* the Institute for Infocomm Research, Singapore (I2R) for Malay
* the Institute of Information Technology, Vietnam (IOIT) for Vietnamese
* the National Institute of Posts, Telecoms and ICT, Cambodia for Khmer
### Licensing Information
[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)
### Citation Information
Please cite the following if you make use of the dataset:
Hammam Riza, Michael Purwoadi, Gunarso, Teduh Uliniansyah, Aw Ai Ti, Sharifah Mahani Aljunied, Luong Chi Mai, Vu Tat Thang, Nguyen Phuong Thai, Vichet Chea, Rapid Sun, Sethserey Sam, Sopheap Seng, Khin Mar Soe, Khin Thandar Nwet, Masao Utiyama, Chenchen Ding. (2016) "Introduction of the Asian Language Treebank" Oriental COCOSDA.
BibTeX:
```
@inproceedings{riza2016introduction,
title={Introduction of the asian language treebank},
author={Riza, Hammam and Purwoadi, Michael and Uliniansyah, Teduh and Ti, Aw Ai and Aljunied, Sharifah Mahani and Mai, Luong Chi and Thang, Vu Tat and Thai, Nguyen Phuong and Chea, Vichet and Sam, Sethserey and others},
booktitle={2016 Conference of The Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Techniques (O-COCOSDA)},
pages={1--6},
year={2016},
organization={IEEE}
}
```
### Contributions
Thanks to [@chameleonTK](https://github.com/chameleonTK) for adding this dataset. |
false |
# Dataset Card for CoNLL-2002
## 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:** [homepage](https://www.clips.uantwerpen.be/conll2002/ner/)
- **Repository:** [github](https://github.com/teropa/nlp/tree/master/resources/corpora/conll2002)
- **Paper:** [paper](https://www.aclweb.org/anthology/W02-2024/)
- **Point of Contact:** [Erik Tjong Kim Sang](erikt@uia.ua.ac.be)
### Dataset Summary
Named entities are phrases that contain the names of persons, organizations, locations, times and quantities. Example:
[PER Wolff] , currently a journalist in [LOC Argentina] , played with [PER Del Bosque] in the final years of the seventies in [ORG Real Madrid] .
The shared task of CoNLL-2002 concerns language-independent named entity recognition. We will concentrate on four types of named entities: persons, locations, organizations and names of miscellaneous entities that do not belong to the previous three groups. The participants of the shared task will be offered training and test data for at least two languages. They will use the data for developing a named-entity recognition system that includes a machine learning component. Information sources other than the training data may be used in this shared task. We are especially interested in methods that can use additional unannotated data for improving their performance (for example co-training).
### Supported Tasks and Leaderboards
Named Entity Recognition (NER) is a subtask of Information Extraction. Different NER systems were evaluated as a part of the Sixth Message Understanding Conference in 1995 (MUC6). The target language was English. The participating systems performed well. However, many of them used language-specific resources for performing the task and it is unknown how they would have performed on another language than English.
After 1995 NER systems have been developed for some European languages and a few Asian languages. There have been at least two studies that have applied one NER system to different languages. Palmer and Day [PD97] have used statistical methods for finding named entities in newswire articles in Chinese, English, French, Japanese, Portuguese and Spanish. They found that the difficulty of the NER task was different for the six languages but that a large part of the task could be performed with simple methods. Cucerzan and Yarowsky [CY99] used both morphological and contextual clues for identifying named entities in English, Greek, Hindi, Rumanian and Turkish. With minimal supervision, they obtained overall F measures between 40 and 70, depending on the languages used.
- `named-entity-recognition`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A named entity is correct only if it is an exact match of the corresponding entity in the data.
- `parsing`: The performance in this task is measured with [F1](https://huggingface.co/metrics/f1) (higher is better). A part-of-speech tag is correct only if it is equal to the corresponding tag in the data.
### Languages
There are two languages available : Spanish (es) and Dutch (nl).
## Dataset Structure
### Data Instances
The examples look like this :
```
{'id': '0',
'ner_tags': [5, 6, 0, 0, 0, 0, 3, 0, 0],
'pos_tags': [4, 28, 13, 59, 28, 21, 29, 22, 20],
'tokens': ['La', 'Coruña', ',', '23', 'may', '(', 'EFECOM', ')', '.']
}
```
The original data files within the Dutch sub-dataset have `-DOCSTART-` lines used to separate documents, but these lines are removed here.
Indeed `-DOCSTART-` is a special line that acts as a boundary between two different documents, and it is filtered out in this implementation.
### Data Fields
- `id`: id of the sample
- `tokens`: the tokens of the example text
- `ner_tags`: the NER tags of each token
- `pos_tags`: the POS tags of each token
The POS tags correspond to this list for Spanish:
```
'AO', 'AQ', 'CC', 'CS', 'DA', 'DE', 'DD', 'DI', 'DN', 'DP', 'DT', 'Faa', 'Fat', 'Fc', 'Fd', 'Fe', 'Fg', 'Fh', 'Fia', 'Fit', 'Fp', 'Fpa', 'Fpt', 'Fs', 'Ft', 'Fx', 'Fz', 'I', 'NC', 'NP', 'P0', 'PD', 'PI', 'PN', 'PP', 'PR', 'PT', 'PX', 'RG', 'RN', 'SP', 'VAI', 'VAM', 'VAN', 'VAP', 'VAS', 'VMG', 'VMI', 'VMM', 'VMN', 'VMP', 'VMS', 'VSG', 'VSI', 'VSM', 'VSN', 'VSP', 'VSS', 'Y', 'Z'
```
And this list for Dutch:
```
'Adj', 'Adv', 'Art', 'Conj', 'Int', 'Misc', 'N', 'Num', 'Prep', 'Pron', 'Punc', 'V'
```
The NER tags correspond to this list:
```
"O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "B-MISC", "I-MISC",
```
The NER tags have the same format as in the chunking 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).
It is assumed that named entities are non-recursive and non-overlapping. In case a named entity is embedded in another named entity usually, only the top level entity is marked.
### Data Splits
For both configurations (Spanish and Dutch), there are three splits.
The original splits were named `train`, `testa` and `testb` and they correspond to the `train`, `validation` and `test` splits.
The splits have the following sizes :
| | train | validation | test |
| ----- |-------:|------------:|------:|
| N. Examples (Spanish) | 8324 | 1916 | 1518 |
| N. Examples (Dutch) | 15807 | 2896 | 5196 |
## Dataset Creation
### Curation Rationale
The dataset was introduced to introduce new resources to two languages that were under-served for statistical machine learning at the time, Dutch and Spanish.
[More Information Needed]
### Source Data
The Spanish data is a collection of news wire articles made available by the Spanish EFE News Agency. The articles are from May 2000.
The Dutch data consist of four editions of the Belgian newspaper "De Morgen" of 2000 (June 2, July 1, August 1 and September 1).
#### Initial Data Collection and Normalization
The articles were word-tokenized, information on the exact pre-processing pipeline is unavailable.
#### Who are the source language producers?
The source language was produced by journalists and writers employed by the news agency and newspaper mentioned above.
### Annotations
#### Annotation process
For the Dutch data, the annotator has followed the MITRE and SAIC guidelines for named entity recognition (Chinchor et al., 1999) as well as possible.
#### Who are the annotators?
The Spanish data annotation was carried out by the TALP Research Center of the Technical University of Catalonia (UPC) and the Center of Language and Computation (CLiC) of the University of Barcelona (UB).
The Dutch data was annotated as a part of the Atranos project at the University of Antwerp.
### Personal and Sensitive Information
The data is sourced from newspaper source and only contains mentions of public figures or individuals
## Considerations for Using the Data
### Social Impact of Dataset
Named Entity Recognition systems can be used to efficiently index news text, allowing to easily gather all information pertaining to an organization or individual. Making such resources widely available in languages other than English can support better research and user experience for a larger part of the world's population. At the same time, better indexing and discoverability can also enable surveillance by state actors.
### Discussion of Biases
News text reproduces the biases of society, and any system trained on news data should be cognizant of these limitations and the risk for models to learn spurious correlations in this context, for example between a person's gender and their occupation.
### Other Known Limitations
Users should keep in mind that the dataset only contains news text, which might limit the applicability of the developed systems to other domains.
## Additional Information
### Dataset Curators
The annotation of the Spanish data was funded by the European Commission through the NAMIC project (IST-1999-12392).
### Licensing Information
The licensing status of the data, especially the news source text, is unknown.
### Citation Information
Provide the [BibTex](http://www.bibtex.org/)-formatted reference for the dataset. For example:
```
@inproceedings{tjong-kim-sang-2002-introduction,
title = "Introduction to the {C}o{NLL}-2002 Shared Task: Language-Independent Named Entity Recognition",
author = "Tjong Kim Sang, Erik F.",
booktitle = "{COLING}-02: The 6th Conference on Natural Language Learning 2002 ({C}o{NLL}-2002)",
year = "2002",
url = "https://www.aclweb.org/anthology/W02-2024",
}
```
### Contributions
Thanks to [@lhoestq](https://github.com/lhoestq) for adding this dataset. |
false |
# Dataset Card for Cats Vs. Dogs
## 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:** [Cats vs Dogs Dataset](https://www.microsoft.com/en-us/download/details.aspx?id=54765)
- **Repository:**
- **Paper:** [Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization](https://www.microsoft.com/en-us/research/wp-content/uploads/2007/10/CCS2007.pdf)
- **Leaderboard:** [Dogs vs. Cats](https://www.kaggle.com/competitions/dogs-vs-cats)
- **Point of Contact:**
### Dataset Summary
A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset.
From the competition page:
> The Asirra data set
>
> Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a [CAPTCHA](http://www.captcha.net/) (Completely Automated Public Turing test to tell Computers and Humans Apart) or HIP (Human Interactive Proof). HIPs are used for many purposes, such as to reduce email and blog spam and prevent brute-force attacks on web site passwords.
>
> Asirra (Animal Species Image Recognition for Restricting Access) is a HIP that works by asking users to identify photographs of cats and dogs. This task is difficult for computers, but studies have shown that people can accomplish it quickly and accurately. Many even think it's fun! Here is an example of the Asirra interface:
>
> Asirra is unique because of its partnership with [Petfinder.com](https://www.petfinder.com/), the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States. Kaggle is fortunate to offer a subset of this data for fun and research.
### Supported Tasks and Leaderboards
- `image-classification`: The goal of this task is to classify a given image as either containing a cat or a dog. The leaderboard is available [here](https://paperswithcode.com/sota/image-classification-on-cats-vs-dogs).
### Languages
English.
## Dataset Structure
### Data Instances
A sample from the training set is provided below:
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=500x375 at 0x29CEAD71780>,
'labels': 0
}
```
### Data Fields
The data instances have the following 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]`.
- `labels`: an `int` classification label.
Class Label Mappings:
```
{
"cat": 0,
"dog": 1,
}
```
### Data Splits
| | train |
|---------------|------:|
| # of examples | 23410 |
## Dataset Creation
### Curation Rationale
This subset was to built to test whether computer vision algorithms can beat the Asirra CAPTCHA:
From the competition page:
> Image recognition attacks
>
> While random guessing is the easiest form of attack, various forms of image recognition can allow an attacker to make guesses that are better than random. There is enormous diversity in the photo database (a wide variety of backgrounds, angles, poses, lighting, etc.), making accurate automatic classification difficult. In an informal poll conducted many years ago, computer vision experts posited that a classifier with better than 60% accuracy would be difficult without a major advance in the state of the art. For reference, a 60% classifier improves the guessing probability of a 12-image HIP from 1/4096 to 1/459.
### Source Data
#### Initial Data Collection and Normalization
This dataset is a subset of the Asirra dataset.
From the competition page:
> Asirra is unique because of its partnership with Petfinder.com, the world's largest site devoted to finding homes for homeless pets. They've provided Microsoft Research with over three million images of cats and dogs, manually classified by people at thousands of animal shelters across the United States.
#### Who are the source language producers?
The users of [Petfinder.com](https://www.petfinder.com/).
### Annotations
#### Annotation process
The images were annotated by selecting a pet category on [Petfinder.com](https://www.petfinder.com/).
#### Who are the annotators?
The users of [Petfinder.com](https://www.petfinder.com/).
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
From the paper:
> Unlike many image-based CAPTCHAs which are abstract or subjective, Asirra’s challenges are concrete, inoffensive (cute, by some accounts), require no specialized or culturally biased knowledge, and have definite ground truth. This
makes Asirra less frustrating for humans. Some beta-testers found it fun. The four-year-old child of one asked several times to “play the cat and dog game again.”
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```bibtex
@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
year = {2007},
month = {October},
publisher = {Association for Computing Machinery, Inc.},
url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},
edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
}
```
### Contributions
Thanks to [@nateraw](https://github.com/nateraw) for adding this dataset. |
false |
# Dataset Card for "drop"
## 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://allennlp.org/drop](https://allennlp.org/drop)
- **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:** 8.30 MB
- **Size of the generated dataset:** 110.91 MB
- **Total amount of disk used:** 119.21 MB
### Dataset Summary
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs.
. DROP is a crowdsourced, adversarially-created, 96k-question benchmark, in which a system must resolve references in a
question, perhaps to multiple input positions, and perform discrete operations over them (such as addition, counting, or
sorting). These operations require a much more comprehensive understanding of the content of paragraphs than what was
necessary for prior datasets.
### 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:** 8.30 MB
- **Size of the generated dataset:** 110.91 MB
- **Total amount of disk used:** 119.21 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers_spans": {
"spans": ["Chaz Schilens"]
},
"passage": "\" Hoping to rebound from their loss to the Patriots, the Raiders stayed at home for a Week 16 duel with the Houston Texans. Oak...",
"question": "Who scored the first touchdown of the game?"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `passage`: a `string` feature.
- `question`: a `string` feature.
- `answers_spans`: a dictionary feature containing:
- `spans`: a `string` feature.
### Data Splits
| name |train|validation|
|-------|----:|---------:|
|default|77409| 9536|
## 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{Dua2019DROP,
author={Dheeru Dua and Yizhong Wang and Pradeep Dasigi and Gabriel Stanovsky and Sameer Singh and Matt Gardner},
title={ {DROP}: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs},
booktitle={Proc. of NAACL},
year={2019}
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@thomwolf](https://github.com/thomwolf), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun) for adding this dataset. |
false |
# Dataset Card for MIRACL Corpus
## Dataset Description
* **Homepage:** http://miracl.ai
* **Repository:** https://github.com/project-miracl/miracl
* **Paper:** https://arxiv.org/abs/2210.09984
MIRACL 🌍🙌🌏 (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual retrieval dataset that focuses on search across 18 different languages, which collectively encompass over three billion native speakers around the world.
This dataset contains the collection data of the 16 "known languages". The remaining 2 "surprise languages" will not be released until later.
The corpus for each language is prepared from a Wikipedia dump, where we keep only the plain text and discard images, tables, etc. Each article is segmented into multiple passages using WikiExtractor based on natural discourse units (e.g., `\n\n` in the wiki markup). Each of these passages comprises a "document" or unit of retrieval. We preserve the Wikipedia article title of each passage.
## Dataset Structure
Each retrieval unit contains three fields: `docid`, `title`, and `text`. Consider an example from the English corpus:
```
{
"docid": "39#0",
"title": "Albedo",
"text": "Albedo (meaning 'whiteness') is the measure of the diffuse reflection of solar radiation out of the total solar radiation received by an astronomical body (e.g. a planet like Earth). It is dimensionless and measured on a scale from 0 (corresponding to a black body that absorbs all incident radiation) to 1 (corresponding to a body that reflects all incident radiation)."
}
```
The `docid` has the schema `X#Y`, where all passages with the same `X` come from the same Wikipedia article, whereas `Y` denotes the passage within that article, numbered sequentially. The text field contains the text of the passage. The title field contains the name of the article the passage comes from.
The collection can be loaded using:
```
lang='ar' # or any of the 16 languages
miracl_corpus = datasets.load_dataset('miracl/miracl-corpus', lang)['train']
for doc in miracl_corpus:
docid = doc['docid']
title = doc['title']
text = doc['text']
```
## Dataset Statistics and Links
The following table contains the number of passage and Wikipedia articles in the collection of each language, along with the links to the datasets and raw Wikipedia dumps.
| Language | # of Passages | # of Articles | Links | Raw Wiki Dump |
|:----------------|--------------:|--------------:|:------|:------|
| Arabic (ar) | 2,061,414 | 656,982 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ar) | [🌏](https://archive.org/download/arwiki-20190201/arwiki-20190201-pages-articles-multistream.xml.bz2)
| Bengali (bn) | 297,265 | 63,762 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-bn) | [🌏](https://archive.org/download/bnwiki-20190201/bnwiki-20190201-pages-articles-multistream.xml.bz2)
| English (en) | 32,893,221 | 5,758,285 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-en) | [🌏](https://archive.org/download/enwiki-20190201/enwiki-20190201-pages-articles-multistream.xml.bz2)
| Spanish (es) | 10,373,953 | 1,669,181 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-es) | [🌏](https://archive.org/download/eswiki-20220301/eswiki-20220301-pages-articles-multistream.xml.bz2)
| Persian (fa) | 2,207,172 | 857,827 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fa) | [🌏](https://archive.org/download/fawiki-20220301/fawiki-20220301-pages-articles-multistream.xml.bz2)
| Finnish (fi) | 1,883,509 | 447,815 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fi) | [🌏](https://archive.org/download/fiwiki-20190201/fiwiki-20190201-pages-articles-multistream.xml.bz2)
| French (fr) | 14,636,953 | 2,325,608 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-fr) | [🌏](https://archive.org/download/frwiki-20220301/frwiki-20220301-pages-articles-multistream.xml.bz2)
| Hindi (hi) | 506,264 | 148,107 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-hi) | [🌏](https://archive.org/download/hiwiki-20220301/hiwiki-20220301-pages-articles-multistream.xml.bz2)
| Indonesian (id) | 1,446,315 | 446,330 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-id) | [🌏](https://archive.org/download/idwiki-20190201/idwiki-20190201-pages-articles-multistream.xml.bz2)
| Japanese (ja) | 6,953,614 | 1,133,444 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ja) | [🌏](https://archive.org/download/jawiki-20190201/jawiki-20190201-pages-articles-multistream.xml.bz2)
| Korean (ko) | 1,486,752 | 437,373 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ko) | [🌏](https://archive.org/download/kowiki-20190201/kowiki-20190201-pages-articles-multistream.xml.bz2)
| Russian (ru) | 9,543,918 | 1,476,045 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-ru) | [🌏](https://archive.org/download/ruwiki-20190201/ruwiki-20190201-pages-articles-multistream.xml.bz2)
| Swahili (sw) | 131,924 | 47,793 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-sw) | [🌏](https://archive.org/download/swwiki-20190201/swwiki-20190201-pages-articles-multistream.xml.bz2)
| Telugu (te) | 518,079 | 66,353 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-te) | [🌏](https://archive.org/download/tewiki-20190201/tewiki-20190201-pages-articles-multistream.xml.bz2)
| Thai (th) | 542,166 | 128,179 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-th) | [🌏](https://archive.org/download/thwiki-20190101/thwiki-20190101-pages-articles-multistream.xml.bz2)
| Chinese (zh) | 4,934,368 | 1,246,389 | [🤗](https://huggingface.co/datasets/miracl/miracl-corpus/tree/main/miracl-corpus-v1.0-zh) | [🌏](https://archive.org/download/zhwiki-20220301/zhwiki-20220301-pages-articles-multistream.xml.bz2)
|
false |
# Dataset Card for "cmrc2018"
## 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/ymcui/cmrc2018](https://github.com/ymcui/cmrc2018)
- **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:** 11.50 MB
- **Size of the generated dataset:** 22.31 MB
- **Total amount of disk used:** 33.83 MB
### Dataset Summary
A Span-Extraction dataset for Chinese machine reading comprehension to add language
diversities in this area. The dataset is composed by near 20,000 real questions annotated
on Wikipedia paragraphs by human experts. We also annotated a challenge set which
contains the questions that need comprehensive understanding and multi-sentence
inference throughout the context.
### 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:** 11.50 MB
- **Size of the generated dataset:** 22.31 MB
- **Total amount of disk used:** 33.83 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"answers": {
"answer_start": [11, 11],
"text": ["光荣和ω-force", "光荣和ω-force"]
},
"context": "\"《战国无双3》（）是由光荣和ω-force开发的战国无双系列的正统第三续作。本作以三大故事为主轴，分别是以武田信玄等人为主的《关东三国志》，织田信长等人为主的《战国三杰》，石田三成等人为主的《关原的年轻武者》，丰富游戏内的剧情。此部份专门介绍角色，欲知武...",
"id": "DEV_0_QUERY_0",
"question": "《战国无双3》是由哪两个公司合作开发的？"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name | train | validation | test |
| ------- | ----: | ---------: | ---: |
| default | 10142 | 3219 | 1002 |
## 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{cui-emnlp2019-cmrc2018,
title = "A Span-Extraction Dataset for {C}hinese Machine Reading Comprehension",
author = "Cui, Yiming and
Liu, Ting and
Che, Wanxiang and
Xiao, Li and
Chen, Zhipeng and
Ma, Wentao and
Wang, Shijin and
Hu, Guoping",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1600",
doi = "10.18653/v1/D19-1600",
pages = "5886--5891",
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
true |
# Dataset Card for CIRCA
## 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:** [CIRCA homepage](https://github.com/google-research-datasets/circa)
- **Repository:** [CIRCA repository](https://github.com/google-research-datasets/circa)
- **Paper:** ["I’d rather just go to bed”: Understanding Indirect Answers](https://arxiv.org/abs/2010.03450)
- **Point of Contact:** [Circa team, Google](circa@google.com)
### Dataset Summary
The Circa (meaning ‘approximately’) dataset aims to help machine learning systems to solve the problem of interpreting indirect answers to polar questions.
The dataset contains pairs of yes/no questions and indirect answers, together with annotations for the interpretation of the answer. The data is collected in 10 different social conversational situations (eg. food preferences of a friend).
The following are the situational contexts for the dialogs in the data.
```
1. X wants to know about Y’s food preferences
2. X wants to know what activities Y likes to do during weekends.
3. X wants to know what sorts of books Y likes to read.
4. Y has just moved into a neighbourhood and meets his/her new neighbour X.
5. X and Y are colleagues who are leaving work on a Friday at the same time.
6. X wants to know about Y's music preferences.
7. Y has just travelled from a different city to meet X.
8. X and Y are childhood neighbours who unexpectedly run into each other at a cafe.
9. Y has just told X that he/she is thinking of buying a flat in New York.
10. Y has just told X that he/she is considering switching his/her job.
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
The columns indicate:
```
1. id : unique id for the question-answer pair
2. context : the social situation for the dialogue. One of 10 situations (see next section). Each
situation is a dialogue between a person who poses the question (X) and the person who
answers (Y).
3. question-X : the question posed by X
4. canquestion-X : a (automatically) rewritten version of question into declarative form
Eg. Do you like Italian? --> I like Italian. See the paper for details.
5. answer-Y : the answer given by Y to X
6. judgements : the interpretations for the QA pair from 5 annotators. The value is a list of 5 strings,
separated by the token ‘#’
7. goldstandard1 : a gold standard majority judgement from the annotators. The value is the most common
interpretation and picked by at least 3 (out of 5 annotators). When a majority
judgement was not reached by the above criteria, the value is ‘NA’
8. goldstandard2 : Here the labels ‘Probably yes / sometimes yes’, ‘Probably no', and 'I am not sure how
X will interpret Y’s answer' are mapped respectively to ‘Yes’, ‘No’, and 'In the
middle, neither yes nor no’ before computing the majority. Still the label must be given
at least 3 times to become the majority choice. This method represents a less strict way
of analyzing the interpretations.
```
### Data Fields
```
id : 1
context : X wants to know about Y's food preferences.
question-X : Are you vegan?
canquestion-X : I am vegan.
answer-Y : I love burgers too much.
judgements : no#no#no#no#no
goldstandard1 : no (label(s) used for the classification task)
goldstandard2 : no (label(s) used for the classification task)
```
### Data Splits
There are no explicit train/val/test splits in this dataset.
## Dataset Creation
### Curation Rationale
They revisited a pragmatic inference problem in dialog: Understanding indirect responses to questions. Humans can interpret ‘I’m starving.’ in response to ‘Hungry?’, even without direct cue words such as ‘yes’ and ‘no’. In dialog systems, allowing natural responses rather than closed vocabularies would be similarly beneficial. However, today’s systems are only as sensitive to these pragmatic moves as their language model allows. They create and release the first large-scale English language corpus ‘Circa’ with 34,268 (polar question, indirect answer) pairs to enable progress on this task.
### Source Data
#### Initial Data Collection and Normalization
The QA pairs and judgements were collected using crowd annotations in three phases. They recruited English native speakers. The full descriptions of the data collection and quality control are present in [EMNLP 2020 paper](https://arxiv.org/pdf/2010.03450.pdf). Below is a brief overview only.
Phase 1: In the first phase, they collected questions only. They designed 10 imaginary social situations which give the annotator a context for the conversation. Examples are:
```
‘asking a friend for food preferences’
‘meeting your childhood neighbour’
‘your friend wants to buy a flat in New York’
```
Annotators were asked to suggest questions which could be asked in each situation, such that each question only requires a ‘yes’ or ‘no’ answer. 100 annotators produced 5 questions each for the 10 situations, resulting in 5000 questions.
Phase 2: Here they focused on eliciting answers to the questions. They sampled 3500 questions from our previous set. For each question, They collected possible answers from 10 different annotators. The annotators were instructed to provide a natural phrase or a sentence as the answer and to avoid the use of explicit ‘yes’ and ‘no’ words.
Phase 3: Finally the QA pairs (34,268) were given to a third set of annotators who were asked how the question seeker would likely interpret a particular answer. These annotators had the following options to choose from:
```
* 'Yes'
* 'Probably yes' / 'sometimes yes'
* 'Yes, subject to some conditions'
* 'No'
* 'Probably no'
* 'In the middle, neither yes nor no'
* 'I am not sure how X will interpret Y's answer'
```
#### Who are the source language producers?
The rest of the data apart from 10 initial questions was collected using crowd workers. They ran pilots for each step of data collection, and perused their results manually to ensure clarity in guidelines, and quality of the data. They also recruited native English speakers, mostly from the USA, and a few from the UK and Canada. They did not collect any further information about the crowd workers.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
The rest of the data apart from 10 initial questions was collected using crowd workers. They ran pilots for each step of data collection, and perused their results manually to ensure clarity in guidelines, and quality of the data. They also recruited native English speakers, mostly from the USA, and a few from the UK and Canada. They did not collect any further information about the crowd workers.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
This dataset is the work of Annie Louis, Dan Roth, and Filip Radlinski from Google LLC.
### Licensing Information
This dataset was made available under the Creative Commons Attribution 4.0 License. A full copy of the license can be found at https://creativecommons.org/licenses/by-sa/4.0/e and link to the license webpage if available.
### Citation Information
```
@InProceedings{louis_emnlp2020,
author = "Annie Louis and Dan Roth and Filip Radlinski",
title = ""{I}'d rather just go to bed": {U}nderstanding {I}ndirect {A}nswers",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
year = "2020",
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. |
false |
# Dataset Card for MRQA 2019
## 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:** [MRQA 2019 Shared Task](https://mrqa.github.io/2019/shared.html)
- **Repository:** [MRQA 2019 Github repository](https://github.com/mrqa/MRQA-Shared-Task-2019)
- **Paper:** [MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension
](https://arxiv.org/abs/1910.09753)
- **Leaderboard:** [Shared task](https://mrqa.github.io/2019/shared.html)
- **Point of Contact:** [mrforqa@gmail.com](mrforqa@gmail.com)
### Dataset Summary
The MRQA 2019 Shared Task focuses on generalization in question answering. An effective question answering system should do more than merely interpolate from the training set to answer test examples drawn from the same distribution: it should also be able to extrapolate to out-of-distribution examples — a significantly harder challenge.
The dataset is a collection of 18 existing QA dataset (carefully selected subset of them) and converted to the same format (SQuAD format). Among these 18 datasets, six datasets were made available for training, six datasets were made available for development, and the final six for testing. The dataset is released as part of the MRQA 2019 Shared Task.
### Supported Tasks and Leaderboards
From the official repository:
*The format of the task is extractive question answering. Given a question and context passage, systems must find the word or phrase in the document that best answers the question. While this format is somewhat restrictive, it allows us to leverage many existing datasets, and its simplicity helps us focus on out-of-domain generalization, instead of other important but orthogonal challenges.*
*We have adapted several existing datasets from their original formats and settings to conform to our unified extractive setting. Most notably:*
- *We provide only a single, length-limited context.*
- *There are no unanswerable or non-span answer questions.*
- *All questions have at least one accepted answer that is found exactly in the context.*
*A span is judged to be an exact match if it matches the answer string after performing normalization consistent with the SQuAD dataset. Specifically:*
- *The text is uncased.*
- *All punctuation is stripped.*
- *All articles `{a, an, the}` are removed.*
- *All consecutive whitespace markers are compressed to just a single normal space `' '`.*
Answers are evaluated using exact match and token-level F1 metrics. One can refer to the [mrqa_official_eval.py](https://github.com/mrqa/MRQA-Shared-Task-2019/blob/master/mrqa_official_eval.py) for evaluation.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
An examples looks like this:
```
{
'qid': 'f43c83e38d1e424ea00f8ad3c77ec999',
'subset': 'SQuAD'
'context': 'CBS broadcast Super Bowl 50 in the U.S., and charged an average of $5 million for a 30-second commercial during the game. The Super Bowl 50 halftime show was headlined by the British rock group Coldplay with special guest performers Beyoncé and Bruno Mars, who headlined the Super Bowl XLVII and Super Bowl XLVIII halftime shows, respectively. It was the third-most watched U.S. broadcast ever.',
'context_tokens': {
'offsets': [0, 4, 14, 20, 25, 28, 31, 35, 39, 41, 45, 53, 56, 64, 67, 68, 70, 78, 82, 84, 94, 105, 112, 116, 120, 122, 126, 132, 137, 140, 149, 154, 158, 168, 171, 175, 183, 188, 194, 203, 208, 216, 222, 233, 241, 245, 251, 255, 257, 261, 271, 275, 281, 286, 292, 296, 302, 307, 314, 323, 328, 330, 342, 344, 347, 351, 355, 360, 361, 366, 374, 379, 389, 393],
'tokens': ['CBS', 'broadcast', 'Super', 'Bowl', '50', 'in', 'the', 'U.S.', ',', 'and', 'charged', 'an', 'average', 'of', '$', '5', 'million', 'for', 'a', '30-second', 'commercial', 'during', 'the', 'game', '.', 'The', 'Super', 'Bowl', '50', 'halftime', 'show', 'was', 'headlined', 'by', 'the', 'British', 'rock', 'group', 'Coldplay', 'with', 'special', 'guest', 'performers', 'Beyoncé', 'and', 'Bruno', 'Mars', ',', 'who', 'headlined', 'the', 'Super', 'Bowl', 'XLVII', 'and', 'Super', 'Bowl', 'XLVIII', 'halftime', 'shows', ',', 'respectively', '.', 'It', 'was', 'the', 'third', '-', 'most', 'watched', 'U.S.', 'broadcast', 'ever', '.']
},
'question': "Who was the main performer at this year's halftime show?",
'question_tokens': {
'offsets': [0, 4, 8, 12, 17, 27, 30, 35, 39, 42, 51, 55],
'tokens': ['Who', 'was', 'the', 'main', 'performer', 'at', 'this', 'year', "'s", 'halftime', 'show', '?']
},
'detected_answers': {
'char_spans': [
{
'end': [201],
'start': [194]
}, {
'end': [201],
'start': [194]
}, {
'end': [201],
'start': [194]
}
],
'text': ['Coldplay', 'Coldplay', 'Coldplay'],
'token_spans': [
{
'end': [38],
'start': [38]
}, {
'end': [38],
'start': [38]
}, {
'end': [38],
'start': [38]
}
]
},
'answers': ['Coldplay', 'Coldplay', 'Coldplay'],
}
```
### Data Fields
- `subset`: which of the dataset does this examples come from?
- `context`: This is the raw text of the supporting passage. Three special token types have been inserted: `[TLE]` precedes document titles, `[DOC]` denotes document breaks, and `[PAR]` denotes paragraph breaks. The maximum length of the context is 800 tokens.
- `context_tokens`: A tokenized version of the supporting passage, using spaCy. Each token is a tuple of the token string and token character offset. The maximum number of tokens is 800.
- `tokens`: list of tokens.
- `offets`: list of offsets.
- `qas`: A list of questions for the given context.
- `qid`: A unique identifier for the question. The `qid` is unique across all datasets.
- `question`: The raw text of the question.
- `question_tokens`: A tokenized version of the question. The tokenizer and token format is the same as for the context.
- `tokens`: list of tokens.
- `offets`: list of offsets.
- `detected_answers`: A list of answer spans for the given question that index into the context. For some datasets these spans have been automatically detected using searching heuristics. The same answer may appear multiple times in the text --- each of these occurrences is recorded. For example, if `42` is the answer, the context `"The answer is 42. 42 is the answer."`, has two occurrences marked.
- `text`: The raw text of the detected answer.
- `char_spans`: Inclusive (start, end) character spans (indexing into the raw context).
- `start`: start (single element)
- `end`: end (single element)
- `token_spans`: Inclusive (start, end) token spans (indexing into the tokenized context).
- `start`: start (single element)
- `end`: end (single element)
### Data Splits
**Training data**
| Dataset | Number of Examples |
| :-----: | :------: |
| [SQuAD](https://arxiv.org/abs/1606.05250) | 86,588 |
| [NewsQA](https://arxiv.org/abs/1611.09830) | 74,160 |
| [TriviaQA](https://arxiv.org/abs/1705.03551)| 61,688 |
| [SearchQA](https://arxiv.org/abs/1704.05179)| 117,384 |
| [HotpotQA](https://arxiv.org/abs/1809.09600)| 72,928 |
| [NaturalQuestions](https://ai.google/research/pubs/pub47761)| 104,071 |
**Development data**
This in-domain data may be used for helping develop models.
| Dataset | Examples |
| :-----: | :------: |
| [SQuAD](https://arxiv.org/abs/1606.05250) | 10,507 |
| [NewsQA](https://arxiv.org/abs/1611.09830) | 4,212 |
| [TriviaQA](https://arxiv.org/abs/1705.03551)| 7,785|
| [SearchQA](https://arxiv.org/abs/1704.05179)| 16,980 |
| [HotpotQA](https://arxiv.org/abs/1809.09600)| 5,904 |
| [NaturalQuestions](https://ai.google/research/pubs/pub47761)| 12,836 |
**Test data**
The final testing data only contain out-of-domain data.
| Dataset | Examples |
| :-----: | :------: |
| [BioASQ](http://bioasq.org/) | 1,504 |
| [DROP](https://arxiv.org/abs/1903.00161) | 1,503 |
| [DuoRC](https://arxiv.org/abs/1804.07927)| 1,501 |
| [RACE](https://arxiv.org/abs/1704.04683) | 674 |
| [RelationExtraction](https://arxiv.org/abs/1706.04115) | 2,948|
| [TextbookQA](http://ai2-website.s3.amazonaws.com/publications/CVPR17_TQA.pdf)| 1,503 |
From the official repository:
***Note:** As previously mentioned, the out-of-domain dataset have been modified from their original settings to fit the unified MRQA Shared Task paradigm. At a high level, the following two major modifications have been made:*
*1. All QA-context pairs are extractive. That is, the answer is selected from the context and not via, e.g., multiple-choice.*
*2. All contexts are capped at a maximum of `800` tokens. As a result, for longer contexts like Wikipedia articles, we only consider examples where the answer appears in the first `800` tokens.*
*As a result, some splits are harder than the original datasets (e.g., removal of multiple-choice in RACE), while some are easier (e.g., restricted context length in NaturalQuestions --- we use the short answer selection). Thus one should expect different performance ranges if comparing to previous work on these datasets.*
## Dataset Creation
### Curation Rationale
From the official repository:
*Both train and test datasets have the same format described above, but may differ in some of the following ways:*
- *Passage distribution: Test examples may involve passages from different sources (e.g., science, news, novels, medical abstracts, etc) with pronounced syntactic and lexical differences.*
- *Question distribution: Test examples may emphasize different styles of questions (e.g., entity-centric, relational, other tasks reformulated as QA, etc) which may come from different sources (e.g., crowdworkers, domain experts, exam writers, etc.)*
- *Joint distribution: Test examples may vary according to the relationship of the question to the passage (e.g., collected independent vs. dependent of evidence, multi-hop, etc)*
### 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
```
@inproceedings{fisch2019mrqa,
title={{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension},
author={Adam Fisch and Alon Talmor and Robin Jia and Minjoon Seo and Eunsol Choi and Danqi Chen},
booktitle={Proceedings of 2nd Machine Reading for Reading Comprehension (MRQA) Workshop at EMNLP},
year={2019},
}
```
### Contributions
Thanks to [@jimmycode](https://github.com/jimmycode), [@VictorSanh](https://github.com/VictorSanh) for adding this dataset. |
true | # Dataset Card for WikiBio GPT-3 Hallucination Dataset
- GitHub repository: https://github.com/potsawee/selfcheckgpt
- Paper: [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://arxiv.org/abs/2303.08896)
### Dataset Summary
- We generate Wikipedia-like passages using GPT-3 (text-davinci-003) using the prompt: ```This is a Wikipedia passage about {concept}``` where `concept` represents an individual from the WikiBio dataset.
- We split the generated passages into sentences, and we annotate each sentence into one of the 3 options: (1) accurate (2) minor_inaccurate (3) major_inaccurate.
- We report the data statistics, annotation process, and inter-annotator agreement in our paper.
## Update
- v3 (5 May 2023): 238 test IDs have been annotated in total.
- v2 (6 April 2023): 142 test IDs have been annotated, GPT-3 sampled passages are now included in this dataset.
- v1 (15 March 2023): 65 test IDs -- here is `wiki_bio_test_idx` of the documents in v1 [[Link]](https://drive.google.com/file/d/1N3_ZQmr9yBbsOP2JCpgiea9oiNIu78Xw/view?usp=sharing)
## Dataset Structure
Each instance consists of:
- `gpt3_text`: GPT-3 generated passage
- `wiki_bio_text`: Actual Wikipedia passage (first paragraph)
- `gpt3_sentences`: `gpt3_text` split into sentences using `spacy`
- `annotation`: human annotation at the sentence level
- `wiki_bio_test_idx`: ID of the concept/individual from the original wikibio dataset (testset)
- `gpt3_text_samples`: list of 20 sampled passages (do_sample = True & temperature = 1.0)
### Citation Information
```
@misc{manakul2023selfcheckgpt,
title={SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models},
author={Potsawee Manakul and Adian Liusie and Mark J. F. Gales},
year={2023},
eprint={2303.08896},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
false | # Dataset Card for "index-constituents-sp500"
## 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://edarchimbaud.substack.com
- **Repository:** https://github.com/edarchimbaud
- **Point of Contact:** contact@edarchimbaud.com
### Dataset Summary
The index-constituents-sp500 dataset provides information about the constituents of the S&P 500 index. It contains several features that describe each constituent company.
### Supported Tasks and Leaderboards
[N/A]
### Languages
[N/A]
## Dataset Structure
### Data Instances
[N/A]
### Data Fields
- symbol (string): A string representing the ticker symbol or abbreviation used to identify the company.
- security (string): A string specifying the name or title of the security.
- gics_sector (string): A string indicating the Global Industry Classification Standard (GICS) sector to which the company belongs. GICS is a widely used classification system for categorizing companies based on their primary business activities.
- gics_sub_industry (string): A string specifying the GICS sub-industry of the company, which provides further granularity within the sector classification.
- headquarters_location (string): A string representing the location of the company's headquarters.
- date_added (string): A string indicating the date when the company was added to the S&P 500 index.
- cik (string): A string representing the Central Index Key (CIK) assigned to the company by the United States Securities and Exchange Commission (SEC). The CIK is a unique identifier used for regulatory filings.
- founded (string): A string indicating the year or date of the company's founding.
### Data Splits
[N/A]
## Dataset Creation
### Curation Rationale
The index-constituents-sp500 dataset was developed to support the development of low-frequency trading algorithms.
### Source Data
#### Initial Data Collection and Normalization
This data was sourced from the web, and aggregated.
### Annotations
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
[N/A]
## Considerations for Using the Data
### Social Impact of Dataset
[N/A]
### Discussion of Biases
[N/A]
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
The index-constituents-sp500 dataset was collected by https://edarchimbaud.substack.com.
### Licensing Information
The index-constituents-sp500 dataset is licensed under the MIT License.
### Citation Information
> https://edarchimbaud.substack.com, index-constituents-sp500 dataset, GitHub repository, https://github.com/edarchimbaud
### Contributions
Thanks to [@edarchimbaud](https://github.com/edarchimbaud) for adding this dataset. |
false |
This dataset splits the original [CodeAlpaca dataset](https://huggingface.co/datasets/sahil2801/CodeAlpaca-20k) into train and test splits. |
false |
# Dataset Card for MedDialog
## 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/UCSD-AI4H/Medical-Dialogue-System
- **Paper:** [MedDialog: Two Large-scale Medical Dialogue Datasets](https://arxiv.org/abs/2004.03329)
[//]: # (- **Leaderboard:** )
[//]: # (- **Point of Contact:** )
### Dataset Summary
The MedDialog dataset (Chinese) contains conversations (in Chinese) between doctors and patients. It has 1.1 million dialogues and 4 million utterances. The data is continuously growing and more dialogues will be added. The raw dialogues are from haodf.com. All copyrights of the data belong to haodf.com.
The MedDialog dataset (English) contains conversations (in English) between doctors and patients. It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com. All copyrights of the data belong to healthcaremagic.com and icliniq.com.
Directions for using the pre-trained model using BERT using PyTorch is available in the Homepage.
### Supported Tasks and Leaderboards
Closed domain qa
### Languages
Monolingual. The datasets are in English (EN) and Chinese (ZH)
## Dataset Structure
### Data Instances
There are 4 configurations:
- Raw data:
- en
- zh
- Processed data:
- processed.en
- processed.zh
#### en
Each consultation consists of the below:
- ID
- URL
- Description of patient’s medical condition
- Dialogue
The dataset is built from [icliniq.com](https://www.icliniq.com/), [healthcaremagic.com](https://www.healthcaremagic.com/), [healthtap.com](https://www.healthtap.com/) and all copyrights of the data belong to these websites.
#### zh
Each consultation consists of the below:
- ID
- URL
- Description of patient’s medical condition
- Dialogue
- (Optional) Diagnosis and suggestions.
The dataset is built from [Haodf.com](https://www.haodf.com/) and all copyrights of the data belong to [Haodf.com](https://www.haodf.com/).
One example for chinese is
```
{
{'dialogue_id': 2,
'dialogue_turns': [{'speaker': '病人',
'utterance': '孩子哭闹时，鸡鸡旁边会肿起，情绪平静时肿块会消失，去一个私人诊所看过，说是疝气.如果确定是疝气，是不是一定要手术治疗？我孩子只有1岁10月，自愈的可能性大吗？如果一定要手术，这么小的孩子风险大吗？术后的恢复困难吗？谢谢.'},
{'speaker': '医生', 'utterance': '南方医的B超说得不清楚，可能是鞘膜积液，可到我医院复查一个B超。'}],
'dialogue_url': 'https://www.haodf.com/doctorteam/flow_team_6477251152.htm',
'file_name': '2020.txt'},
}
```
#### processed.en
```
{
'description': 'throat a bit sore and want to get a good imune booster, especially in light of the virus. please advise. have not been in contact with nyone with the virus.',
'utterances': [
'patient: throat a bit sore and want to get a good imune booster, especially in light of the virus. please advise. have not been in contact with nyone with the virus.',
"doctor: during this pandemic. throat pain can be from a strep throat infection (antibiotics needed), a cold or influenza or other virus, or from some other cause such as allergies or irritants. usually, a person sees the doctor (call first) if the sore throat is bothersome, recurrent, or doesn't go away quickly. covid-19 infections tend to have cough, whereas strep throat usually lacks cough but has more throat pain. (3/21/20)"
]
}
```
#### processed.zh
```
{
'utterances': [
'病人：强制性脊柱炎，晚上睡觉翻身时腰骶骨区域疼痛，其他身体任何部位均不疼痛。',
'医生：应该没有问题，但最好把图像上传看看。'
]
}
```
### Data Fields
For generating the QA only the below fields have been considered:
- ID : Consultatation Identifier (restarts for each file)
- URL: The url link of the extracted conversation
- Dialogue : The conversation between the doctor and the patient.
These are arranged as below in the prepared dataset. Each item will be represented with these parameters.
- "file_name": string - signifies the file from which the conversation was extracted
- "dialogue_id": int32 - the dialogue id
- "dialogue_url": string - url of the conversation
- "dialogue_turns": datasets.Sequence - sequence of dialogues between patient and the doctor.Consists ClassLabel(names=["病人", "医生"]), and "utterance"(string) for each turn. (ClassLable(names=["Patient", "Doctor"]) for english)
#### processed.en
- `description` (str): Description of the dialog.
- `utterances` (list of str): Dialog utterances between patient and doctor.
#### processed.zh
- `utterances` (list of str): Dialog utterances between patient and doctor.
### Data Splits
There are no data splits on the original raw data. The "train" split for each language contains:
- en: 229674 examples
- zh: 1921127 examples
For processed configurations, data is split into train, validation and test, with the following number of examples:
| | train | validation | test |
|--------------|--------:|-----------:|-------:|
| processed.en | 482 | 60 | 61 |
| processed.zh | 2725989 | 340748 | 340754 |
## Dataset Creation
### Curation Rationale
Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs.
### 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
Unknow.
### Citation Information
```
@article{chen2020meddiag,
title={MedDialog: a large-scale medical dialogue dataset},
author={Chen, Shu and Ju, Zeqian and Dong, Xiangyu and Fang, Hongchao and Wang, Sicheng and Yang, Yue and Zeng, Jiaqi and Zhang, Ruisi and Zhang, Ruoyu and Zhou, Meng and Zhu, Penghui and Xie, Pengtao},
journal={arXiv preprint arXiv:2004.03329},
year={2020}
}
```
### Contributions
Thanks to [@vrindaprabhu](https://github.com/vrindaprabhu) for adding this dataset. |
false |
# Dataset Card for AfriBERTa's Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Loading Dataset](#loading-dataset)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Discussion of Biases](#discussion-of-biases)
- [Additional Information](#additional-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
### Dataset Summary
This is the corpus on which AfriBERTa was trained on.
The dataset is mostly from the BBC news website, but some languages also have data from Common Crawl.
- **Homepage:** https://github.com/keleog/afriberta
- **Models:**
- https://huggingface.co/castorini/afriberta_small
- https://huggingface.co/castorini/afriberta_base
- https://huggingface.co/castorini/afriberta_large
- **Paper:** https://aclanthology.org/2021.mrl-1.11/
- **Point of Contact:** kelechi.ogueji@uwaterloo.ca
### Supported Tasks and Leaderboards
The AfriBERTa corpus was mostly intended to pre-train language models.
### Languages
```
afaanoromoo
amharic
gahuza
hausa
igbo
pidgin
somali
swahili
tigrinya
yoruba
```
### Loading Dataset
An example to load the train split of the Somali corpus:
```
dataset = load_dataset("castorini/afriberta-corpus", "somali", split="train")
```
An example to load the test split of the Pidgin corpus:
```
dataset = load_dataset("castorini/afriberta-corpus", "pidgin", split="test")
```
## Dataset Structure
### Data Instances
Each data point is a line of text.
An example from the `igbo` dataset:
```
{"id": "6", "text": "Ngwá ọrụ na-echebe ma na-ebuli gị na kọmputa."}
```
### Data Fields
The data fields are:
- id: id of the example
- text: content as a string
### Data Splits
Each language has a train and test split, with varying sizes.
## Considerations for Using the Data
### Discussion of Biases
Since majority of the data is obtained from the BBC's news website, models trained on this dataset are likely going to
be biased towards the news domain.
Also, since some of the data is obtained from Common Crawl, care should be taken (especially for text generation models) since personal and sensitive information might be present.
## Additional Information
### Citation Information
```
@inproceedings{ogueji-etal-2021-small,
title = "Small Data? No Problem! Exploring the Viability of Pretrained Multilingual Language Models for Low-resourced Languages",
author = "Ogueji, Kelechi and
Zhu, Yuxin and
Lin, Jimmy",
booktitle = "Proceedings of the 1st Workshop on Multilingual Representation Learning",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.mrl-1.11",
pages = "116--126",
}
```
### Contributions
Thanks to [Kelechi Ogueji](https://github.com/keleog) for adding this dataset. |
true |
# Dataset Card for Discovery
## 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/sileod/Discovery
- **Repository:** https://github.com/sileod/Discovery
- **Paper:** https://www.aclweb.org/anthology/N19-1351/
- **Leaderboard:**
- **Point of Contact:** damien.sileo at inria.fr
### Dataset Summary
Discourse marker prediction with 174 markers
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
input : sentence1, sentence2,
label: marker originally between sentence1 and sentence2
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
Train/Val/Test
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
Aranea english web corpus
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
Self supervised (see paper)
#### 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
```
@inproceedings{sileo-etal-2019-mining,
title = "Mining Discourse Markers for Unsupervised Sentence Representation Learning",
author = "Sileo, Damien and
Van De Cruys, Tim and
Pradel, Camille and
Muller, Philippe",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/N19-1351",
pages = "3477--3486",
abstract = "Current state of the art systems in NLP heavily rely on manually annotated datasets, which are expensive to construct. Very little work adequately exploits unannotated data {--} such as discourse markers between sentences {--} mainly because of data sparseness and ineffective extraction methods. In the present work, we propose a method to automatically discover sentence pairs with relevant discourse markers, and apply it to massive amounts of data. Our resulting dataset contains 174 discourse markers with at least 10k examples each, even for rare markers such as {``}coincidentally{''} or {``}amazingly{''}. We use the resulting data as supervision for learning transferable sentence embeddings. In addition, we show that even though sentence representation learning through prediction of discourse marker yields state of the art results across different transfer tasks, it{'}s not clear that our models made use of the semantic relation between sentences, thus leaving room for further improvements.",
}
```
### Contributions
Thanks to [@sileod](https://github.com/sileod) for adding this dataset. |
false |
## 内容
包含约50万条由[BELLE](https://github.com/LianjiaTech/BELLE)项目生成的中文指令数据。
## 样例
```
{
"instruction": "给定一个文字输入，将其中的所有数字加1。\n“明天的会议在9点开始，记得准时到达。”\n",
"input": "",
"output": "“明天的会议在10点开始，记得准时到达。”"
}
```
### 字段：
```
instruction: 指令
input: 输入（本数据集均为空）
output: 输出
```
## 使用限制
仅允许将此数据集及使用此数据集生成的衍生物用于研究目的，不得用于商业，以及其他会对社会带来危害的用途。
本数据集不代表任何一方的立场、利益或想法，无关任何团体的任何类型的主张。因使用本数据集带来的任何损害、纠纷，本项目不承担任何责任。
|
true | Imppres, but it works
https://github.com/facebookresearch/Imppres
```
@inproceedings{jeretic-etal-2020-natural,
title = "Are Natural Language Inference Models {IMPPRESsive}? {L}earning {IMPlicature} and {PRESupposition}",
author = "Jereti\v{c}, Paloma and
Warstadt, Alex and
Bhooshan, Suvrat and
Williams, Adina",
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.768",
doi = "10.18653/v1/2020.acl-main.768",
pages = "8690--8705",
abstract = "Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer whether a sentence entails another. However, the ability of NLI models to make pragmatic inferences remains understudied. We create an IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of 32K semi-automatically generated sentence pairs illustrating well-studied pragmatic inference types. We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences. Although MultiNLI appears to contain very few pairs illustrating these inference types, we find that BERT learns to draw pragmatic inferences. It reliably treats scalar implicatures triggered by {``}some{''} as entailments. For some presupposition triggers like {``}only{''}, BERT reliably recognizes the presupposition as an entailment, even when the trigger is embedded under an entailment canceling operator like negation. BOW and InferSent show weaker evidence of pragmatic reasoning. We conclude that NLI training encourages models to learn some, but not all, pragmatic inferences.",
}
``` |
false |
# Dataset Card for Opus Ubuntu
## 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/Ubuntu.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
These are translations of the Ubuntu software package messages, donated by the Ubuntu community.
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/Ubuntu.php
E.g.
`dataset = load_dataset("opus_ubuntu", lang1="it", lang2="pl")`
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Example instance:
```
{
'id': '0',
'translation': {
'it': 'Comprende Gmail, Google Docs, Google+, YouTube e Picasa',
'pl': 'Zawiera Gmail, Google Docs, Google+, YouTube oraz Picasa'
}
}
```
### Data Fields
Each instance has two fields:
- **id**: the id of the example
- **translation**: a dictionary containing translated texts in two languages.
### Data Splits
Each subset simply consists in a train set. We provide the number of examples for certain language pairs:
| | train |
|:---------|--------:|
| as-bs | 8583 |
| az-cs | 293 |
| bg-de | 184 |
| br-es_PR | 125 |
| bn-ga | 7324 |
| br-hi | 15551 |
| br-la | 527 |
| bs-szl | 646 |
| br-uz | 1416 |
| br-yi | 2799 |
## 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
BSD "Revised" license (see (https://help.launchpad.net/Legal#Translations_copyright)[https://help.launchpad.net/Legal#Translations_copyright])
### Citation Information
```bibtex
@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. |
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:** http://opus.nlpl.eu/RF.php
- **Repository:**
- **Paper:** http://www.lrec-conf.org/proceedings/lrec2012/pdf/463_Paper.pdf
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
RF is a tiny parallel corpus of the Declarations of the Swedish Government and its translations.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English (en), Spanish (es), German (de), French (fr), Swedish (sv)
## 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
```
@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 [@akshayb7](https://github.com/akshayb7) for adding this dataset. |
false |
# Dataset Card for OpusDgt
## 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/DGT.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
A collection of translation memories provided by the Joint Research Centre (JRC) Directorate-General for Translation (DGT): https://ec.europa.eu/jrc/en/language-technologies/dgt-translation-memory
Tha dataset contains 25 languages and 299 bitexts.
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs,
e.g.
```python
dataset = load_dataset("opus_dgt", lang1="it", lang2="pl")
```
You can find the valid pairs in Homepage section of Dataset Description: http://opus.nlpl.eu/DGT.php
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The languages in the dataset are:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- ga
- hr
- hu
- it
- lt
- lv
- mt
- nl
- pl
- pt
- ro
- sh
- sk
- sl
- sv
## Dataset Structure
### Data Instances
```
{
'id': '0',
'translation': {
"bg": "Протокол за поправка на Конвенцията относно компетентността, признаването и изпълнението на съдебни решения по граждански и търговски дела, подписана в Лугано на 30 октомври 2007 г.",
"ga": "Miontuairisc cheartaitheach maidir le Coinbhinsiún ar dhlínse agus ar aithint agus ar fhorghníomhú breithiúnas in ábhair shibhialta agus tráchtála, a siníodh in Lugano an 30 Deireadh Fómhair 2007"
}
}
```
### Data Fields
- `id` (`str`): Unique identifier of the parallel sentence for the pair of languages.
- `translation` (`dict`): Parallel sentences for the pair of languages.
### Data Splits
The dataset contains a single `train` split.
## 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
```bibtex
@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. |
false | # Dataset Card for NCHLT
## 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:** [link](https://repo.sadilar.org/handle/20.500.12185/7/discover?filtertype_0=database&filtertype_1=title&filter_relational_operator_1=contains&filter_relational_operator_0=equals&filter_1=&filter_0=Monolingual+Text+Corpora%3A+Annotated&filtertype=project&filter_relational_operator=equals&filter=NCHLT+Text+II)
- **Repository:** []()
- **Paper:** []()
- **Leaderboard:** []()
- **Point of Contact:** []()
### Dataset Summary
The development of linguistic resources for use in natural language processingis of utmost importance for the continued growth of research anddevelopment in the field, especially for resource-scarce languages. In this paper we describe the process and challenges of simultaneouslydevelopingmultiple linguistic resources for ten of the official languages of South Africa. The project focussed on establishing a set of foundational resources that can foster further development of both resources and technologies for the NLP industry in South Africa. The development efforts during the project included creating monolingual unannotated corpora, of which a subset of the corpora for each language was annotated on token, orthographic, morphological and morphosyntactic layers. The annotated subsetsincludes both development and test setsand were used in the creation of five core-technologies, viz. atokeniser, sentenciser,lemmatiser, part of speech tagger and morphological decomposer for each language. We report on the quality of these tools for each language and provide some more context of the importance of the resources within the South African context.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
[More Information Needed]
### 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
Martin.Puttkammer@nwu.ac.za
### Licensing Information
[More Information Needed]
### Citation Information
```
@inproceedings{eiselen2014developing,
title={Developing Text Resources for Ten South African Languages.},
author={Eiselen, Roald and Puttkammer, Martin J},
booktitle={LREC},
pages={3698--3703},
year={2014}
}
```
### Contributions
Thanks to [@Narsil](https://github.com/Narsil) 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:**[Humicroedit](https://www.cs.rochester.edu/u/nhossain/humicroedit.html)
- **Repository:**
- **Paper:**["President Vows to Cut Taxes Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines.](http://cs.rochester.edu/~nhossain/humicroedit-naacl-19.pdf)
- **Leaderboard:**
- **Point of Contact:**[nhossain@cs.rochester.edu]
### Dataset Summary
This is the task dataset for SemEval-2020 Task 7: Assessing Humor in Edited News Headlines.
### Supported Tasks and Leaderboards
[Task Description Page](https://competitions.codalab.org/competitions/20970)
- Regression Task: In this task, given the original and the edited headline, the participant is required to predict the mean funniness of the edited headline. Success on this task is typically measured by achieving a *low* Mean Square Error.
- Predict the funnier of the two edited headlines: Given the original headline and two edited versions, the participant has to predict which edited version is the funnier of the two. Success on this task is typically measured by achieving a *high* accuracy.
### Languages
English
## Dataset Structure
### Data Instances
For subtask-1, i.e Given the original and the edited headline, predict the mean funniness of the edited headline.
```
{
'id': 1183,
'original': 'Kushner to visit <Mexico/> following latest trump tirades.',
'edit': 'therapist',
'grades': '33332',
'meanGrade': 2.8
}
```
For subtask-2, i.e Given the original headline and two edited versions, predict which edited version is the funnier of the two.
```
{
'id': 1183,
'original1': 'Gene Cernan , Last <Astronaut/> on the Moon , Dies at 82',
'edit1': 'Dancer',
'grades1': '1113',
'meanGrade1': 1.2,
'original2': 'Gene Cernan , Last Astronaut on the Moon , <Dies/> at 82',
'edit2': 'impregnated',
'grades2': '30001',
'meanGrade2': 0.8,
'label': 1
}
```
### Data Fields
For subtask-1
- `id`: Unique identifier of an edited headline.
- `original`: The headline with replaced word(s) identified with the </> tag.
- `edit`: The new word which replaces the word marked in </> tag in the original field.
- `grades`: 'grades' are the concatenation of all the grades by different annotators.
- `mean` is the mean of all the judges scores.
For subtask-2
- `id`: Unique identifier of an edited headline.
- `original1`: The original headline with replaced word(s) identified with </> tag.
- `edit1`: The new word which replaces the word marked in </> tag in the `original1` field.
- `grades1`: The concatenation of all the grades annotated by different annotators for sentence1.
- `meanGrade1` is the mean of all the judges scores for sentence1.
- `original2`: The original headline with replaced word(s) identified with </> tag.
- `edit2`: The new word which replaces the word marked in </> tag in the `original1` field.
- `grades2`: The concatenation of all the grades annotated by different annotators for the sentence2.
- `meanGrade2` is the mean of all the judges scores for sentence2.
- `label` is 1 if sentence1 is more humourous than sentence2,
2 if sentence 2 is more humorous than sentence1,
0 if both the sentences are equally humorous
### Data Splits
| Sub Task | Train | Dev | Test | Funlines|
| ----- | ------ | ---- | ---- |-----|
| Subtask-1:Regression | 9652 | 2419 | 3024| 8248 |
| Subtask-2: Funnier headline prediction| 9381 | 2355 | 2960| 1958 |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Crowd-sourced the data by gamifying it as on the website funlines.co. Players rate the headlines on a scale of 0-4.
Players are scored based on their editing and rating, and they
are ranked on the game’s leaderboard page.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{hossain2019president,
title={" President Vows to Cut< Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines},
author={Hossain, Nabil and Krumm, John and Gamon, Michael},
journal={arXiv preprint arXiv:1906.00274},
year={2019}
}```
### Contributions
Thanks to [@saradhix](https://github.com/saradhix) for adding this dataset. |
true |
[github](https://github.com/ntunlp/xCodeEval)
# xCodeEval
[xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval](https://arxiv.org/abs/2303.03004)
We introduce **xCodeEval**, the largest executable multilingual multitask benchmark to date consisting of 25 M document-level coding examples from about 7.5 K unique problems covering up to 17 programming languages with execution-level parallelism. It features a total of seven tasks involving code understanding, generation, translation and retrieval, and it employs an execution-based evaluation. We develop a test-case based multilingual code execution engine, [**ExecEval**](https://github.com/ntunlp/ExecEval) that supports all the programming languages in **xCodeEval**. We also propose a novel data splitting and a data selection schema for balancing data distributions over multiple attributes based on geometric mean and graph-theoretic principle.
This repository contains the sample code and data link for xCodeEval [paper](https://arxiv.org/abs/2303.03004).
# Data Download
Currently this repository supports huggingface [`load_dataset()`](https://huggingface.co/docs/datasets/v1.11.0/package_reference/loading_methods.html#datasets.load_dataset) api. Follow the following example to load dataset for individual examples.
```
import datasets
prog_synthesis_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "program_synthesis")
code_translation_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "code_translation")
tag_classification_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "tag_classification")
apr_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "apr")
pcode_compilation_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "code_compilation")
retrieval_code_code_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "retrieval_code_code")
retrieval_nl_code_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "retrieval_nl_code")
retrieval_corpus_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "retrieval_corpus")
```
## Hf large data download tricks.
If you are facing long delay with data processing, add a `ignore_verifications=True`.
```
prog_synthesis_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "program_synthesis", ignore_verifications=True)
```
If you are facing long delay with data downloading, use huggingface streaming mode.
```
prog_synthesis_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "program_synthesis", streaming=True)
```
## Just Give me the raw data (😠)
Data can be also downloaded as a git LFS repo from huggingface.
You can download the full data using the following command.
```
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval
cd xCodeEval
git lfs pull
```
To download a specific part of the dataset,
```
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval
cd xCodeEval
git lfs pull --include "apr/test/*"
```
We propose 7 Tasks.
1. [Tag Classification](https://github.com/ntunlp/xCodeEval/blob/main/apr.md)
2. [Code Compilation](https://github.com/ntunlp/xCodeEval/blob/main/code_compilation.md)
3. [Program Synthesis](https://github.com/ntunlp/xCodeEval/blob/main/program_synthesis.md)
4. [Code Translation](https://github.com/ntunlp/xCodeEval/blob/main/code_translation.md)
5. [Automatic Program Repair](https://github.com/ntunlp/xCodeEval/blob/main/apr.md)
6. [Code-Code Retrieval](https://github.com/ntunlp/xCodeEval/blob/main/retrieval.md)
7. [NL-Code Retrieval](https://github.com/ntunlp/xCodeEval/blob/main/retrieval.md)
# Common Data for different tasks
If you are not using huggingface [`load_dataset()`](https://huggingface.co/docs/datasets/v1.11.0/package_reference/loading_methods.html#datasets.load_dataset) api, you may need to link some data with different tasks.
We have two data files that are required for multiple tasks.
1. `problem_descriptions.jsonl`
2. `unittest_db.json`
You can find these two files in the root directory of the [main](https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/tree/main) branch of huggingface dataset repository. To avoid data redundancy we didn't include these data with the relevant tasks, rather we add a unique id `src_uid` to retrieve these data.
## Structure of `problem_descriptions.jsonl`
A sample,
```json
{
"description": "There are $$$n$$$ positive integers $$$a_1, a_2, \\dots, a_n$$$. For the one move you can choose any even value $$$c$$$ and divide by two all elements that equal $$$c$$$.For example, if $$$a=[6,8,12,6,3,12]$$$ and you choose $$$c=6$$$, and $$$a$$$ is transformed into $$$a=[3,8,12,3,3,12]$$$ after the move.You need to find the minimal number of moves for transforming $$$a$$$ to an array of only odd integers (each element shouldn't be divisible by $$$2$$$).",
"input_from": "standard input",
"output_to": "standard output",
"time_limit": "3 seconds",
"memory_limit": "256 megabytes",
"input_spec": "The first line of the input contains one integer $$$t$$$ ($$$1 \\le t \\le 10^4$$$) \u2014 the number of test cases in the input. Then $$$t$$$ test cases follow. The first line of a test case contains $$$n$$$ ($$$1 \\le n \\le 2\\cdot10^5$$$) \u2014 the number of integers in the sequence $$$a$$$. The second line contains positive integers $$$a_1, a_2, \\dots, a_n$$$ ($$$1 \\le a_i \\le 10^9$$$). The sum of $$$n$$$ for all test cases in the input doesn't exceed $$$2\\cdot10^5$$$.",
"output_spec": "For $$$t$$$ test cases print the answers in the order of test cases in the input. The answer for the test case is the minimal number of moves needed to make all numbers in the test case odd (i.e. not divisible by $$$2$$$).",
"notes": "NoteIn the first test case of the example, the optimal sequence of moves can be as follows: before making moves $$$a=[40, 6, 40, 3, 20, 1]$$$; choose $$$c=6$$$; now $$$a=[40, 3, 40, 3, 20, 1]$$$; choose $$$c=40$$$; now $$$a=[20, 3, 20, 3, 20, 1]$$$; choose $$$c=20$$$; now $$$a=[10, 3, 10, 3, 10, 1]$$$; choose $$$c=10$$$; now $$$a=[5, 3, 5, 3, 5, 1]$$$ \u2014 all numbers are odd. Thus, all numbers became odd after $$$4$$$ moves. In $$$3$$$ or fewer moves, you cannot make them all odd.",
"sample_inputs": [
"4\n6\n40 6 40 3 20 1\n1\n1024\n4\n2 4 8 16\n3\n3 1 7"
],
"sample_outputs": [
"4\n10\n4\n0"
],
"tags": [
"number theory",
"greedy"
],
"src_uid": "afcd41492158e68095b01ff1e88c3dd4",
"difficulty": 1200,
"created_at": 1576321500
}
```
### Key Definitions
1. `description`: Problem description in textual format, math operations are written in latex.
2. `input_from`: How the program should take the unit test.
3. `output_to`: Where the program should output the result of the unit test.
4. `time_limit`: Time limit to solve the problem.
5. `memory_limit`: Memory limit to solve the problem.
6. `input_spec`: How and in what order the input will be given to the program? It also includes the date range, types, and sizes.
7. `output_spec`: How the outputs should be printed. Most of the time the unit test results are matched with an *exact string match* or *floating point comparison* with a precision boundary.
8. `sample_inputs`: A sample input for the code that is expected to solve the problem described in `description`.
9. `sample_outputs`: The expected output for the `sample_input` that is expected to solve the problem described in `description`.
10. `notes`: Explanation of `sample_inputs` & `sample_outputs`.
11. `tags`: The problem categories.
12. `src_uid`: The unique id of the problem. This ID is referred to in the task data samples instead of putting all this information.
13. `difficulty`: How difficult is it to solve the problem for a human (annotated by an expert human)?
14. `created_at`: The Unix timestamp when the problem was released. Use `datetime` lib in Python to parse it to a human-readable format.
## Structure of `unittest_db.json`
The structure of the `json` file,
```python
unittest_db = {
"db884d679d9cfb1dc4bc511f83beedda" : [
{
"input": "4\r\n3 2 3 2\r\n",
"output": [
"1"
],
},
{
...
},
...
]
"3bc096d8cd3418948d5be6bf297aa9b5":[
...
],
...
}
```
### Key Definitions
1. `unittest_db.json` dict keys i.e., `db884d679d9cfb1dc4bc511f83beedda` are the `src_uid` from `problem_descriptions.jsonl`.
2. `input`: Input of the unit test.
3. `output`: List of expected outputs for the unit test.
# Citation
```
@misc{khan2023xcodeeval,
title={xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval},
author={Mohammad Abdullah Matin Khan and M Saiful Bari and Xuan Long Do and Weishi Wang and Md Rizwan Parvez and Shafiq Joty},
year={2023},
eprint={2303.03004},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
false |
# Dataset Card for "blended_skill_talk"
## 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://parl.ai/projects/bst/](https://parl.ai/projects/bst/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills](https://arxiv.org/abs/2004.08449v1)
- **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:** 38.11 MB
- **Size of the generated dataset:** 15.08 MB
- **Total amount of disk used:** 53.17 MB
### Dataset Summary
A dataset of 7k conversations explicitly designed to exhibit multiple conversation modes: displaying personality, having empathy, and demonstrating knowledge.
### 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:** 38.11 MB
- **Size of the generated dataset:** 15.08 MB
- **Total amount of disk used:** 53.17 MB
An example of 'train' looks as follows.
```
{
'personas': ['my parents don t really speak english , but i speak italian and english.', 'i have three children.'],
'additional_context': 'Backstreet Boys',
'previous_utterance': ['Oh, I am a BIG fan of the Backstreet Boys! Have you ever seen them performing live?', "No,I listen to their music a lot, mainly the unbreakable which is the Backstreet Boys' sixth studio album. "],
'context': 'wizard_of_wikipedia',
'free_messages': ['you are very knowledgeable, do you prefer nsync or bsb?', "haha kids of this days don't know them, i'm 46 and i still enjoying them, my kids only listen k-pop", "italian?haha that's strange, i only talk english and a little spanish "],
'guided_messages': ["i don't have a preference, they are both great. All 3 of my kids get annoyed when I listen to them though.", 'Sometimes I sing their songs in Italian, that really annoys them lol.', 'My parents barely speak English, so I was taught both. By the way, what is k-pop?'],
'suggestions': {'convai2': ["i don't have a preference , both are pretty . do you have any hobbies ?", "do they the backstreet boys ? that's my favorite group .", 'are your kids interested in music ?'], 'empathetic_dialogues': ['I actually just discovered Imagine Dragons. I love them!', "Hahaha that just goes to show ya, age is just a umber!'", 'That would be hard! Do you now Spanish well?'], 'wizard_of_wikipedia': ['NSYNC Also had Lance Bass and Joey Fatone, sometimes called the Fat One.', 'Yes, there are a few K-Pop songs that I have heard good big in the USA. It is the most popular in South Korea and has Western elements of pop.', 'English, beleive it or not.']},
'guided_chosen_suggestions': ['convai2', '', ''],
'label_candidates': []}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `personas`: a `list` of `string` features.
- `additional_context`: a `string` feature.
- `previous_utterance`: a `list` of `string` features.
- `context`: a `string` feature.
- `free_messages`: a `list` of `string` features.
- `guided_messgaes`: a `list` of `string` features.
- `suggestions`: a dictionary feature containing:
- `convai2`: a `string` feature.
- `empathetic_dialogues`: a `string` feature.
- `wizard_of_wikipedia`: a `string` feature.
- `guided_chosen_suggestions`: a `list` of `string` features.
- `label_candidates`: a `list` of `lists` of `string` features.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default| 4819| 1009| 980|
## 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
```
@misc{smith2020evaluating,
title={Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills},
author={Eric Michael Smith and Mary Williamson and Kurt Shuster and Jason Weston and Y-Lan Boureau},
year={2020},
eprint={2004.08449},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf), [@lhoestq](https://github.com/lhoestq), [@patrickvonplaten](https://github.com/patrickvonplaten), [@mariamabarham](https://github.com/mariamabarham) for adding this dataset. |
false |
# Dataset Card for the_pile_books3
## Table of Contents
- [Dataset Card for the_pile_books3](#dataset-card-for-the_pile_books3)
- [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
- **Homepage:** [GitHub](https://github.com/soskek/bookcorpus/issues/27#issuecomment-716104208)
- **Repository:** [Needs More Information]
- **Paper:** [arXiv](https://arxiv.org/abs/2101.00027)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
This dataset is Shawn Presser's work and is part of EleutherAi/The Pile dataset.
This dataset contains all of bibliotik in plain .txt form, aka 197,000 books processed in exactly the same way as did for bookcorpusopen (a.k.a. books1). seems to be similar to OpenAI's mysterious "books2" dataset referenced in their papers. Unfortunately OpenAI will not give details, so we know very little about any differences. People suspect it's "all of libgen", but it's purely conjecture.
|download_size|36.8 Gib|
|dataset_size|100.9 Gib|
### Supported Tasks and Leaderboards
This dataset is used for Language Modeling.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
```
{'title': '07 LEGO Ninjago - The Search For Zane (Scholastic) - Kate Howard (retail)'
'text': '\n\nTITLE PAGE\n\nFROM THE JOURNAL OF SENSEI GARMADON\n\nCHAPTER 1\n\nCHAPTER 2\n\nCHAPTER 3\n\nCHAPTER 4\n\nCHAPTER 5\n\nCHAPTER 6\n\nCHAPTER 7\n\nCHAPTER 8\n\nCHAPTER 9\n\nCOPYRIGHT\n\nThroughout Ninjago", five ninja are well-known for their speed, strength, and of course the elemental powers that help them protect our world from evil. But there are others who possess some of the same powers as the ninja. Others who may not always use their powers for good.\n\nBefore now, the ninja believed they were special. They di.......'}
```
### Data Fields
- `title`: title of the book
- `text`: text content of the book
### Data Splits
|split|num examples|
--------------------------------
|train|196640|
## 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
MIT
### Citation Information
```
@article{pile,
title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
journal={arXiv preprint arXiv:2101.00027},
year={2020}
}
```
### Contributions
Thanks to [@shawwn](https://github.com/shawwn) for creating this dataset.
Thanks to [@richarddwang](https://github.com/richarddwang) for adding this dataset. |
false |
#### Overview
This dataset builds from [WikiSQL](https://huggingface.co/datasets/wikisql) and [Spider](https://huggingface.co/datasets/spider).
There are 78,577 examples of natural language queries, SQL CREATE TABLE statements, and SQL Query answering the question using the CREATE statement as context. This dataset was built with text-to-sql LLMs in mind, intending to prevent hallucination of column and table names often seen when trained on text-to-sql datasets. The CREATE TABLE statement can often be copy and pasted from different DBMS and provides table names, column names and their data types. By providing just the CREATE TABLE statement as context, we can hopefully provide better grounding for models without having to provide actual rows of data, limiting token usage and exposure to private, sensitive, or proprietary data.
#### Cleansing and Augmentation
Cleansing and data augmentation has been done on the combined WikiSQL and Spider data. I used [SQLGlot](https://github.com/tobymao/sqlglot) on queries from Spider and WikiSQL and parsed them into different tables and columns, I then inferred column data types based on usage of `>` `<` operators as well as the use of `MIN()` `MAX()` `AVG()` `SUM()` on columns. While this isn't perfect, it increases the likelihood of inferring the correct datatype for a column, the columns otherwise default to VARCHAR type. These tables and columns are then used to generate CREATE TABLE statements using the inferred types. SQLGlot is used again to ensure both the SQL queries and CREATE TABLE statements parse without errors.
Some queries that do not have column names, e.g. SELECT * FROM table, have a default Id column added to the CREATE TABLE statement. Some other queries which use the generic `table` as the FROM table have instead been changed to a variation of `table_name_1` or some other number which is also reflected in the CREATE TABLE statement.
#### TODO
- Further augment the data by converting queries and CREATE TABLE statements into different SQL dialects, this can be done with SQLGlot. Reference to the dialect might also be added to the question.
- Support other informative contexts beyond CREATE TABLE
Random sample:
```json
{
"question": "Please show the themes of competitions with host cities having populations larger than 1000.",
"context": "CREATE TABLE city (City_ID VARCHAR, Population INTEGER); CREATE TABLE farm_competition (Theme VARCHAR, Host_city_ID VARCHAR)",
"answer": "SELECT T2.Theme FROM city AS T1 JOIN farm_competition AS T2 ON T1.City_ID = T2.Host_city_ID WHERE T1.Population > 1000"
},
{
"question": "Please show the different statuses of cities and the average population of cities with each status.",
"context": "CREATE TABLE city (Status VARCHAR, Population INTEGER)",
"answer": "SELECT Status, AVG(Population) FROM city GROUP BY Status"
},
``` |
false |
# Dataset Card for ToTTo
## 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/google-research-datasets/ToTTo
- **Paper:** https://arxiv.org/abs/2004.14373
- **Leaderboard:** https://github.com/google-research-datasets/ToTTo#leaderboard
- **Point of Contact:** [totto@google.com](mailto:totto@google.com)
### Dataset Summary
ToTTo is an open-domain English table-to-text dataset with over 120,000 training examples that proposes a controlled
generation task: given a Wikipedia table and a set of highlighted table cells, produce a one-sentence description.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
A sample training set is provided below
```
{'example_id': '1762238357686640028',
'highlighted_cells': [[13, 2]],
'id': 0,
'overlap_subset': 'none',
'sentence_annotations': {'final_sentence': ['A Favorita is the telenovela aired in the 9 pm timeslot.'],
'original_sentence': ['It is also the first telenovela by the writer to air in the 9 pm timeslot.'],
'sentence_after_ambiguity': ['A Favorita is the telenovela aired in the 9 pm timeslot.'],
'sentence_after_deletion': ['It is the telenovela air in the 9 pm timeslot.']},
'table': [[{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': '#'},
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Run'},
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Title'},
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Chapters'},
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Author'},
{'column_span': 1, 'is_header': True, 'row_span': 1, 'value': 'Director'},
{'column_span': 1,
'is_header': True,
'row_span': 1,
'value': 'Ibope Rating'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '59'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'June 5, 2000— February 2, 2001'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Laços de Família'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '209'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Manoel Carlos'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Ricardo Waddington'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '44.9'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '60'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'February 5, 2001— September 28, 2001'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Porto dos Milagres'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Aguinaldo Silva Ricardo Linhares'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Marcos Paulo Simões'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '44.6'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '61'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'October 1, 2001— June 14, 2002'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'O Clone'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '221'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Glória Perez'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Jayme Monjardim'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '47.0'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '62'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'June 17, 2002— February 14, 2003'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Esperança'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '209'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Benedito Ruy Barbosa'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Luiz Fernando'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '37.7'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '63'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'February 17, 2003— October 10, 2003'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Mulheres Apaixonadas'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Manoel Carlos'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Ricardo Waddington'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '46.6'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '64'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'October 13, 2003— June 25, 2004'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Celebridade'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '221'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Gilberto Braga'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Dennis Carvalho'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '46.0'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '65'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'June 28, 2004— March 11, 2005'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Senhora do Destino'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '221'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Aguinaldo Silva'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Wolf Maya'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '50.4'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '66'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'March 14, 2005— November 4, 2005'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'América'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Glória Perez'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Jayme Monjardim Marcos Schechtman'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '49.4'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '67'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'November 7, 2005— July 7, 2006'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Belíssima'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '209'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Sílvio de Abreu'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Denise Saraceni'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '48.5'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '68'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'July 10, 2006— March 2, 2007'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Páginas da Vida'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Manoel Carlos'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Jayme Monjardim'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '46.8'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '69'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'March 5, 2007— September 28, 2007'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Paraíso Tropical'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '179'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Gilberto Braga Ricardo Linhares'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Dennis Carvalho'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '42.8'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '70'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'October 1, 2007— May 31, 2008'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Duas Caras'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '210'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Aguinaldo Silva'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': 'Wolf Maya'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '41.1'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '71'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'June 2, 2008— January 16, 2009'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'A Favorita'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '197'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'João Emanuel Carneiro'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Ricardo Waddington'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '39.5'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '72'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'January 19, 2009— September 11, 2009'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Caminho das Índias'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '203'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Glória Perez'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Marcos Schechtman'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '38.8'}],
[{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '73'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'September 14, 2009— May 14, 2010'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Viver a Vida'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '209'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Manoel Carlos'},
{'column_span': 1,
'is_header': False,
'row_span': 1,
'value': 'Jayme Monjardim'},
{'column_span': 1, 'is_header': False, 'row_span': 1, 'value': '35.6'}]],
'table_page_title': 'List of 8/9 PM telenovelas of Rede Globo',
'table_section_text': '',
'table_section_title': '2000s',
'table_webpage_url': 'http://en.wikipedia.org/wiki/List_of_8/9_PM_telenovelas_of_Rede_Globo'}
```
Please note that in test set sentence annotations are not available and thus values inside `sentence_annotations` can be safely ignored.
### Data Fields
- `table_webpage_url` (`str`): Table webpage URL.
- `table_page_title` (`str`): Table metadata with context about the table.
- `table_section_title` (`str`): Table metadata with context about the table.
- `table_section_text` (`str`): Table metadata with context about the table.
- `table` (`List[List[Dict]]`): The outer lists represents rows and the inner lists columns. Each Dict has the fields:
- `column_span` (`int`)
- `is_header` (`bool`)
- `row_span` (`int`)
- `value` (`str`)
- `highlighted_cells` (`List[[row_index, column_index]]`): Where each `[row_index, column_index]` pair indicates that `table[row_index][column_index]` is highlighted.
- `example_id` (`int`): A unique id for this example.
- `sentence_annotations`: Consists of the `original_sentence` and the sequence of revised sentences performed in order to produce the `final_sentence`.
### Data Splits
```
DatasetDict({
train: Dataset({
features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
num_rows: 120761
})
validation: Dataset({
features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
num_rows: 7700
})
test: Dataset({
features: ['id', 'table_page_title', 'table_webpage_url', 'table_section_title', 'table_section_text', 'table', 'highlighted_cells', 'example_id', 'sentence_annotations', 'overlap_subset'],
num_rows: 7700
})
})
```
## 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
```
@inproceedings{parikh2020totto,
title={{ToTTo}: A Controlled Table-To-Text Generation Dataset},
author={Parikh, Ankur P and Wang, Xuezhi and Gehrmann, Sebastian and Faruqui, Manaal and Dhingra, Bhuwan and Yang, Diyi and Das, Dipanjan},
booktitle={Proceedings of EMNLP},
year={2020}
}
```
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
false |
# Dataset Card for Conceptual Captions
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [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:** [Conceptual Captions homepage](https://ai.google.com/research/ConceptualCaptions/)
- **Repository:** [Conceptual Captions repository](https://github.com/google-research-datasets/conceptual-captions)
- **Paper:** [Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning](https://www.aclweb.org/anthology/P18-1238/)
- **Leaderboard:** [Conceptual Captions leaderboard](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard)https://ai.google.com/research/ConceptualCaptions/leaderboard?active_tab=leaderboard
- **Point of Contact:** [Conceptual Captions e-mail](mailto:conceptual-captions@google.com)
### Dataset Summary
Conceptual Captions is a dataset consisting of ~3.3M images annotated with captions. In contrast with the curated style of other image caption annotations, Conceptual Caption images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles. More precisely, the raw descriptions are harvested from the Alt-text HTML attribute associated with web images. To arrive at the current version of the captions, we have developed an automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions.
### Dataset Preprocessing
This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
USER_AGENT = get_datasets_user_agent()
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": USER_AGENT},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
return batch
num_threads = 20
dset = load_dataset("conceptual_captions")
dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
```
### Supported Tasks and Leaderboards
- `image-captioning`: This dataset can be used to train model for the Image Captioning task. The leaderboard for this task is available [here](https://ai.google.com/research/ConceptualCaptions/competition?active_tab=leaderboard). Official submission output captions are scored against the reference captions from the hidden test set using [this](https://github.com/tylin/coco-caption) implementation of the CIDEr (primary), ROUGE-L and SPICE metrics.
### Languages
All captions are in English.
## Dataset Structure
### Data Instances
#### `unlabeled`
Each instance in this configuration represents a single image with a caption:
```
{
'image_url': 'http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800',
'caption': 'a very typical bus station'
}
```
#### `labeled`
Each instance in this configuration represents a single image with a caption with addtional machine-generated image labels and confidence scores:
```
{
'image_url': 'https://thumb1.shutterstock.com/display_pic_with_logo/261388/223876810/stock-vector-christmas-tree-on-a-black-background-vector-223876810.jpg',
'caption': 'christmas tree on a black background .',
'labels': ['christmas tree', 'christmas decoration', 'font', 'text', 'graphic design', 'illustration','interior design', 'tree', 'christmas eve', 'ornament', 'fir', 'plant', 'pine', 'pine family', 'graphics'],
'MIDs': ['/m/025nd', '/m/05fc9mj', '/m/03gq5hm', '/m/07s6nbt', '/m/03c31', '/m/01kr8f', '/m/0h8nzzj', '/m/07j7r', '/m/014r1s', '/m/05ykl4', '/m/016x4z', '/m/05s2s', '/m/09t57', '/m/01tfm0', '/m/021sdg'],
'confidence_scores': [0.9818305373191833, 0.952756941318512, 0.9227379560470581, 0.8524878621101379, 0.7597672343254089, 0.7493422031402588, 0.7332468628883362, 0.6869218349456787, 0.6552258133888245, 0.6357356309890747, 0.5992692708969116, 0.585474967956543, 0.5222904086112976, 0.5113164782524109, 0.5036579966545105]
}
```
### Data Fields
#### `unlabeled`
- `image_url`: Static URL for downloading the image associated with the post.
- `caption`: Textual description of the image.
#### `labeled`
- `image_url`: Static URL for downloading the image associated with the post.
- `caption`: Textual description of the image.
- `labels`: A sequence of machine-generated labels obtained using the [Google Cloud Vision API](https://cloud.google.com/vision).
- `MIDs`: A sequence of machine-generated identifiers (MID) corresponding to the label's Google Knowledge Graph entry.
- `confidence_scores`: A sequence of confidence scores denoting how likely the corresponing labels are present on the image.
### Data Splits
#### `unlabeled`
The basic version of the dataset split into Training and Validation splits. The Training split consists of 3,318,333 image-URL/caption pairs and the Validation split consists of 15,840 image-URL/caption pairs.
#### `labeled`
The labeled version of the dataset with a single. The entire data is contained in Training split, which is a subset of 2,007,090 image-URL/caption pairs from the Training set of the `unlabeled` config.
## Dataset Creation
### Curation Rationale
From the paper:
> In this paper, we make contributions to both the data and modeling categories. First, we present a new dataset of caption annotations Conceptual Captions (Fig. 1), which has an order of magnitude more images than the COCO dataset. Conceptual Captions consists of about 3.3M himage, descriptioni pairs. In contrast with the curated style of the COCO images, Conceptual Captions images and their raw descriptions are harvested from the web, and therefore represent a wider variety of styles.
### Source Data
#### Initial Data Collection and Normalization
From the homepage:
>For Conceptual Captions, we developed a fully automatic pipeline that extracts, filters, and transforms candidate image/caption pairs, with the goal of achieving a balance of cleanliness, informativeness, fluency, and learnability of the resulting captions. Because no human annotators are involved, the Conceptual Captions dataset generation process is highly scalable.
>
>To generate this dataset, we started with a Flume pipeline that processes billions of Internet webpages, extracting, filtering, and processing candidate image and caption pairs, and keeping those that pass through several filters.
>
>We first screen for certain properties like size, aspect ratio, adult content scores. These filters discard more than 65% of the candidates. Next, we use Alt-Texts for text-based filtering, removing captions with non-descriptive text (such as SEO tags or hashtags); we also discard texts with high sentiment polarity or adult content scores, resulting in just 3% of the incoming candidates passing through.
>
>In the next step, we filter out candidates for which none of the text tokens can be mapped to the visual content of the image. We use image classifiers (e.g., Google Cloud Vision APIs) to assign class labels to images and match these labels against the candidate text (allowing morphological transformations), discarding >around 60% of the candidates that reach this stage.
>
>The candidates passing the above filters tend to be good Alt-text image descriptions. However, a large majority of these use proper names (for people, venues, locations, etc.), brands, dates, quotes, etc. This creates two distinct problems. First, some of these cannot be inferred based on the image pixels alone. This is problematic because unless the image has the necessary visual information it is not useful for training. Second, even if the proper names could be inferred from the image it is extremely difficult for a model to learn to perform both fine-grained classification and natural-language descriptions simultaneously. We posit that if automatic determination of names, locations, brands, etc. is needed, it should be done as a separate task that may leverage image meta-information (e.g. GPS info), or complementary techniques such as OCR.
>
>We address the above problems with the insight that proper names should be replaced by words that represent the same general notion, i.e., by their concept. For example, we remove locations (“Crowd at a concert in Los Angeles“ becomes “Crowd at a concert”), names (e.g., “Former Miss World Priyanka Chopra on the red carpet” becomes “actor on the red carpet”), proper noun modifiers (e.g., “Italian cuisine” becomes just “cuisine”) and noun phrases (e.g., “actor and actor” becomes “actors”). Around 20% of the samples are discarded during this transformation because it can leave sentences too short, or otherwise inconsistent.
>
>Finally, we perform another round of filtering to identify concepts with low-count. We cluster all resolved entities (e.g., “actor”, “dog”, “neighborhood”, etc.) and keep only the candidate types which have a count of over 100 mentions. This retains around 16K entity concepts such as: “person”, “actor”, “artist”, “player” and “illustration”. The less frequent ones that we dropped include “baguette”, “bridle”, “deadline”, “ministry” and “funnel”.
#### Who are the source language producers?
Not specified.
### Annotations
#### Annotation process
Annotations are extracted jointly with the images using the automatic pipeline.
#### Who are the annotators?
Not specified.
### 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
Piyush Sharma, Nan Ding, Sebastian Goodman and Radu Soricut.
### Licensing Information
The dataset may be freely used for any purpose, although acknowledgement of
Google LLC ("Google") as the data source would be appreciated. The dataset is
provided "AS IS" without any warranty, express or implied. Google disclaims all
liability for any damages, direct or indirect, resulting from the use of the
dataset.
### Citation Information
```bibtex
@inproceedings{sharma2018conceptual,
title = {Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning},
author = {Sharma, Piyush and Ding, Nan and Goodman, Sebastian and Soricut, Radu},
booktitle = {Proceedings of ACL},
year = {2018},
}
```
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) and [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
false |
# Dataset Card for WebNLG
## 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:** [WebNLG challenge website](https://webnlg-challenge.loria.fr/)
- **Repository:** [Enriched WebNLG Github repository](https://github.com/ThiagoCF05/webnlg)
- **Paper:** [Enriching the WebNLG corpus](https://www.aclweb.org/anthology/W18-6521/)
### Dataset Summary
The WebNLG challenge consists in mapping data to text. The training data consists of Data/Text pairs where the data is a
set of triples extracted from DBpedia and the text is a verbalisation of these triples. For instance, given the 3
DBpedia triples shown in (a), the aim is to generate a text such as (b). It is a valuable resource and benchmark for the Natural Language Generation (NLG) community. However, as other NLG benchmarks, it only consists of a collection of parallel raw representations and their corresponding textual realizations. This work aimed to provide intermediate representations of the data for the development and evaluation of popular tasks in the NLG pipeline architecture, such as Discourse Ordering, Lexicalization, Aggregation and Referring Expression Generation.
### Supported Tasks and Leaderboards
The dataset supports a `other-rdf-to-text` task which requires a model takes a set of RDF (Resource Description
Format) triples from a database (DBpedia) of the form (subject, property, object) as input and write out a natural
language sentence expressing the information contained in the triples.
### Languages
The dataset is presented in two versions: English (config `en`) and German (config `de`)
## Dataset Structure
### Data Instances
A typical example contains the original RDF triples in the set, a modified version which presented to crowd workers, and
a set of possible verbalizations for this set of triples:
```
{ 'category': 'Politician',
'eid': 'Id10',
'lex': {'comment': ['good', 'good', 'good'],
'lid': ['Id1', 'Id2', 'Id3'],
'text': ['World War II had Chiang Kai-shek as a commander and United States Army soldier Abner W. Sibal.',
'Abner W. Sibal served in the United States Army during the Second World War and during that war Chiang Kai-shek was one of the commanders.',
'Abner W. Sibal, served in the United States Army and fought in World War II, one of the commanders of which, was Chiang Kai-shek.']},
'modified_triple_sets': {'mtriple_set': [['Abner_W._Sibal | battle | World_War_II',
'World_War_II | commander | Chiang_Kai-shek',
'Abner_W._Sibal | militaryBranch | United_States_Army']]},
'original_triple_sets': {'otriple_set': [['Abner_W._Sibal | battles | World_War_II', 'World_War_II | commander | Chiang_Kai-shek', 'Abner_W._Sibal | branch | United_States_Army'],
['Abner_W._Sibal | militaryBranch | United_States_Army',
'Abner_W._Sibal | battles | World_War_II',
'World_War_II | commander | Chiang_Kai-shek']]},
'shape': '(X (X) (X (X)))',
'shape_type': 'mixed',
'size': 3}
```
### Data Fields
The following fields can be found in the instances:
- `category`: the category of the DBpedia entites present in the RDF triples.
- `eid`: an example ID, only unique per split per category.
- `size`: number of RDF triples in the set.
- `shape`: (for v3 only) Each set of RDF-triples is a tree, which is characterised by its shape and shape type. `shape`
is a string representation of the tree with nested parentheses where X is a node (
see [Newick tree format](https://en.wikipedia.org/wiki/Newick_format))
- `shape_type`: (for v3 only) is a type of the tree shape, which can be: `chain` (the object of one triple is the
subject of the other); `sibling` (triples with a shared subject); `mixed` (both chain and sibling types present).
- `2017_test_category`: (for `webnlg_challenge_2017`) tells whether the set of RDF triples was present in the training
set or not.
- `lex`: the lexicalizations, with:
- `text`: the text to be predicted.
- `lid`: a lexicalizayion ID, unique per example.
- `comment`: the lexicalizations were rated by crowd workers are either `good` or `bad`
### Data Splits
The `en` version has `train`, `test` and `dev` splits; the `de` version, only `train` and `dev`.
## Dataset Creation
### Curation Rationale
Natural Language Generation (NLG) is the process of automatically converting non-linguistic data into a linguistic output format (Reiter andDale, 2000; Gatt and Krahmer, 2018). Recently, the field has seen an increase in the number of available focused data resources as E2E (Novikova et al., 2017), ROTOWIRE(Wise-man et al., 2017) and WebNLG (Gardent et al.,2017a,b) corpora. Although theses recent releases are highly valuable resources for the NLG community in general,nall of them were designed to work with end-to-end NLG models. Hence, they consist of a collection of parallel raw representations and their corresponding textual realizations. No intermediate representations are available so researchersncan straight-forwardly use them to develop or evaluate popular tasks in NLG pipelines (Reiter and Dale, 2000), such as Discourse Ordering, Lexicalization, Aggregation, Referring Expression Generation, among others. Moreover, these new corpora, like many other resources in Computational Linguistics more in general, are only available in English, limiting the development of NLG-applications to other languages.
### 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 uses the `cc-by-nc-sa-4.0` license. The source DBpedia project uses the `cc-by-sa-3.0` and `gfdl-1.1`
licenses.
### Citation Information
- If you use the Enriched WebNLG corpus, cite:
```
@InProceedings{ferreiraetal2018,
author = "Castro Ferreira, Thiago
and Moussallem, Diego
and Wubben, Sander
and Krahmer, Emiel",
title = "Enriching the WebNLG corpus",
booktitle = "Proceedings of the 11th International Conference on Natural Language Generation",
year = "2018",
series = {INLG'18},
publisher = "Association for Computational Linguistics",
address = "Tilburg, The Netherlands",
}
@inproceedings{web_nlg,
author = {Claire Gardent and
Anastasia Shimorina and
Shashi Narayan and
Laura Perez{-}Beltrachini},
editor = {Regina Barzilay and
Min{-}Yen Kan},
title = {Creating Training Corpora for {NLG} Micro-Planners},
booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational
Linguistics, {ACL} 2017, Vancouver, Canada, July 30 - August 4, Volume
1: Long Papers},
pages = {179--188},
publisher = {Association for Computational Linguistics},
year = {2017},
url = {https://doi.org/10.18653/v1/P17-1017},
doi = {10.18653/v1/P17-1017}
}
```
### Contributions
Thanks to [@TevenLeScao](https://github.com/TevenLeScao) for adding this dataset. |
true |
# Dataset Card for OneStopEnglish 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:** https://github.com/nishkalavallabhi/OneStopEnglishCorpus
- **Repository:** https://github.com/purvimisal/OneStopCorpus-Compiled/raw/main/Texts-SeparatedByReadingLevel.zip
- **Paper:** https://www.aclweb.org/anthology/W18-0535.pdf
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
OneStopEnglish is a corpus of texts written at three reading levels, and demonstrates its usefulness for through two applications - automatic readability assessment and automatic text simplification.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
An instance example:
```
{
"text": "When you see the word Amazon, what’s the first thing you think...",
"label": 0
}
```
Note that each instance contains the full text of the document.
### Data Fields
- `text`: Full document text.
- `label`: Reading level of the document- ele/int/adv (Elementary/Intermediate/Advance).
### Data Splits
The OneStopEnglish dataset has a single _train_ split.
| Split | Number of instances |
|-------|--------------------:|
| train | 567 |
## 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-ShareAlike 4.0 International License
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@purvimisal](https://github.com/purvimisal) for adding this dataset. |
false |
## Dataset Description
- **Homepage:** [ZeroSCROLLS](https://www.zero.scrolls-benchmark.com/)
- **Leaderboard:** [Leaderboard](https://www.zero.scrolls-benchmark.com/leaderboard)
- **Point of Contact:** [scrolls-benchmark-contact@googlegroups.com](scrolls-benchmark-contact@googlegroups.com)
# Dataset Card for ZeroSCROLLS
## Overview
ZeroSCROLLS is a zero-shot benchmark for natural language understanding over long texts.
## Leaderboard
The ZeroSCROLLS benchmark leaderboard can be found [here](https://www.zero.scrolls-benchmark.com/leaderboard).
## Tasks
ZeroSCROLLS contains the following tasks:
#### GovReport ([Huang et al., 2021](https://arxiv.org/pdf/2104.02112.pdf))
GovReport is a summarization dataset of reports addressing various national policy issues published by the
Congressional Research Service and the U.S. Government Accountability Office, where each document is paired with a hand-written executive summary.
The reports and their summaries are longer than their equivalents in other popular long-document summarization datasets;
for example, GovReport's documents are approximately 1.5 and 2.5 times longer than the documents in Arxiv and PubMed, respectively.
#### SummScreenFD ([Chen et al., 2022](https://arxiv.org/pdf/2104.07091.pdf))
SummScreenFD is a summarization dataset in the domain of TV shows (e.g. Friends, Game of Thrones).
Given a transcript of a specific episode, the goal is to produce the episode's recap.
The original dataset is divided into two complementary subsets, based on the source of its community contributed transcripts.
For SCROLLS, we use the ForeverDreaming (FD) subset, as it incorporates 88 different shows,
making it a more diverse alternative to the TV MegaSite (TMS) subset, which has only 10 shows.
Community-authored recaps for the ForeverDreaming transcripts were collected from English Wikipedia and TVMaze.
#### QMSum ([Zhong et al., 2021](https://arxiv.org/pdf/2104.05938.pdf))
QMSum is a query-based summarization dataset, consisting of 232 meetings transcripts from multiple domains.
The corpus covers academic group meetings at the International Computer Science Institute and their summaries, industrial product meetings for designing a remote control,
and committee meetings of the Welsh and Canadian Parliaments, dealing with a variety of public policy issues.
Annotators were tasked with writing queries about the broad contents of the meetings, as well as specific questions about certain topics or decisions,
while ensuring that the relevant text for answering each query spans at least 200 words or 10 turns.
#### SQuALITY ([Wang et al., 2022](https://arxiv.org/pdf/2205.11465.pdf))
SQuALITY (Wang et al., 2022) is a question-focused summarization dataset, where given a story from Project Gutenberg,
the task is to produce a summary of the story or aspects of it based on a guiding question.
The questions and summaries are original and crowdsourced; experienced writers were guided to design questions that require reading significant parts of the story to answer correctly.
#### Qasper ([Dasigi et al., 2021](https://arxiv.org/pdf/2105.03011.pdf))
Qasper is a question answering dataset over NLP papers filtered from the Semantic Scholar Open Research Corpus (S2ORC).
Questions were written by NLP practitioners after reading only the title and abstract of the papers,
while another set of NLP practitioners annotated the answers given the entire document.
Qasper contains abstractive, extractive, and yes/no questions, as well as unanswerable ones.
#### NarrativeQA ([Kočiský et al., 2018](https://arxiv.org/pdf/1712.07040.pdf))
NarrativeQA (Kočiský et al., 2021) is an established question answering dataset over entire books from Project Gutenberg and movie scripts from different websites.
Annotators were given summaries of the books and scripts obtained from Wikipedia, and asked to generate question-answer pairs,
resulting in about 30 questions and answers for each of the 1,567 books and scripts.
They were encouraged to use their own words rather then copying, and avoid asking yes/no questions or ones about the cast.
Each question was then answered by an additional annotator, providing each question with two reference answers (unless both answers are identical).
#### QuALITY ([Pang et al., 2022](https://arxiv.org/pdf/2112.08608.pdf))
QuALITY is a multiple-choice question answering dataset over articles and stories sourced from Project Gutenberg,
the Open American National Corpus, and more.
Experienced writers wrote questions and distractors, and were incentivized to write answerable, unambiguous questions such that in order to correctly answer them,
human annotators must read large portions of the given document.
Reference answers were then calculated using the majority vote between of the annotators and writer's answers.
To measure the difficulty of their questions, Pang et al. conducted a speed validation process,
where another set of annotators were asked to answer questions given only a short period of time to skim through the document.
As a result, 50% of the questions in QuALITY are labeled as hard, i.e. the majority of the annotators in the speed validation setting chose the wrong answer.
#### MuSiQue ([Trivedi et al., 2022](https://arxiv.org/pdf/2108.00573.pdf))
MuSiQue is a multi-hop question answering dataset, where the inputs are 20 Wikipedia paragraphs and a question that requires multiple hops between different paragraphs.
In the original dataset, each question also has an unanswerable twin question, where the correct answer is not present in the paragraphs.
#### SpaceDigest (New)
SpaceDigest is a new sentiment aggregation task. Given 50 hotel reviews (without their ratings) from the Space dataset (Angelidis et al., 2021), the task is to determine the percentage of positive reviews.
#### BookSumSort (New)
BookSumSort is a new task based on the BookSum dataset (Kry ́sci ́nski et al., 2022), which contains summaries of chapters (or parts) of novels, plays, and long poems from various sources.
Given a shuffled list of chapter summaries, the task is to reorder them according to the original order of summaries in BookSum.
## Data Fields
Most datasets in the benchmark are in the same input-output format
- `input`: a `string` feature. The input document.
- `output`: this feature is always None, as ZeroSCROLLS contains only test sets.
- `id`: a `string` feature. Unique per input.
- `pid`: a `string` feature, identical to 'id`. Facilitates evaluating tasks with multiple refrences per input.
- `document_start_index`: an `int32` feature. Character index that enables easy parsing of the context document.
- `document_end_index`: an `int32` feature. Character index that enables easy parsing of the context document.
- `query_start_index`: an `int32` feature. Character index that enables easy parsing of the query, if exists.
- `query_end_index`: an `int32` feature. Character index that enables easy parsing of the query, if exists.
- `truncation_seperator`: a `string` feature. The string used to append to a trimmed context document, mentioning the context was trimmed.
Datasets containing multiple documents inside the `input` feature are MuSiQue, SpaceDigest, and BookSumSort. They also have the following feature:
- `inner_docs_start_indices`: a sequence of `int32` feature. Character indexes that enables easy parsing of the the inner documents, e.g. Reviews, of Summaries.
## Citation
If you use the ZeroSCROLLS data, **please make sure to cite all of the original dataset papers.** [[bibtex](https://zero-scrolls-tau.s3.us-east-2.amazonaws.com/zero_scrolls_datasets.bib)]
```
@misc{shaham2023zeroscrolls,
title={ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding},
author={Uri Shaham and Maor Ivgi and Avia Efrat and Jonathan Berant and Omer Levy},
year={2023},
eprint={2305.14196},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
false |
# Dataset Card for europarl-bilingual
## 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:** [Statmt](http://www.statmt.org/europarl/)
- **Repository:** [OPUS Europarl](https://opus.nlpl.eu/Europarl.php)
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/L12-1246/)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Needs More Information]
### Dataset Summary
A parallel corpus extracted from the European Parliament web site by Philipp Koehn (University of Edinburgh). The main intended use is to aid statistical machine translation research.
To load a language pair which isn't part of the config, all you need to do is specify the language code as pairs.
You can find the valid pairs in Homepage section of Dataset Description: https://opus.nlpl.eu/Europarl.php
E.g.
`dataset = load_dataset("europarl_bilingual", lang1="fi", lang2="fr")`
### Supported Tasks and Leaderboards
Tasks: Machine Translation, Cross Lingual Word Embeddings (CWLE) Alignment
### Languages
- 21 languages, 211 bitexts
- total number of files: 207,775
- total number of tokens: 759.05M
- total number of sentence fragments: 30.32M
Every pair of the following languages is available:
- bg
- cs
- da
- de
- el
- en
- es
- et
- fi
- fr
- hu
- it
- lt
- lv
- nl
- pl
- pt
- ro
- sk
- sl
- sv
## Dataset Structure
### Data Instances
Here is an example from the en-fr pair:
```
{
'translation': {
'en': 'Resumption of the session',
'fr': 'Reprise de la session'
}
}
```
### Data Fields
- `translation`: a dictionary containing two strings paired with a key indicating the corresponding language.
### Data Splits
- `train`: only train split is provided. Authors did not provide a separation of examples in `train`, `dev` and `test`.
## 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
The data set comes with the same license
as the original sources.
Please, check the information about the source
that is given on
http://opus.nlpl.eu/Europarl-v8.php
### Citation Information
```
@InProceedings{TIEDEMANN12.463,
author = {J�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 [@lucadiliello](https://github.com/lucadiliello) for adding this dataset. |
true |
# Dataset Card for ClimateFever
## 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:** [CLIMATE-FEVER homepage](http://climatefever.ai)
- **Repository:** [CLIMATE-FEVER repository](https://github.com/tdiggelm/climate-fever-dataset)
- **Paper:** [CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims](https://arxiv.org/abs/2012.00614)
- **Leaderboard:** [Needs More Information]
- **Point of Contact:** [Thomas Diggelmann](mailto:thomasdi@student.ethz.ch)
### Dataset Summary
A dataset adopting the FEVER methodology that consists of 1,535 real-world claims regarding climate-change collected on the internet. Each claim is accompanied by five manually annotated evidence sentences retrieved from the English Wikipedia that support, refute or do not give enough information to validate the claim totalling in 7,675 claim-evidence pairs. The dataset features challenging claims that relate multiple facets and disputed cases of claims where both supporting and refuting evidence are present.
### Supported Tasks and Leaderboards
[Needs More Information]
### Languages
The text in the dataset is in English, as found in real-world claims about climate-change on the Internet. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
```
{
"claim_id": "0",
"claim": "Global warming is driving polar bears toward extinction",
"claim_label": 0, # "SUPPORTS"
"evidences": [
{
"evidence_id": "Extinction risk from global warming:170",
"evidence_label": 2, # "NOT_ENOUGH_INFO"
"article": "Extinction risk from global warming",
"evidence": "\"Recent Research Shows Human Activity Driving Earth Towards Global Extinction Event\".",
"entropy": 0.6931471805599453,
"votes": [
"SUPPORTS",
"NOT_ENOUGH_INFO",
null,
null,
null
]
},
{
"evidence_id": "Global warming:14",
"evidence_label": 0, # "SUPPORTS"
"article": "Global warming",
"evidence": "Environmental impacts include the extinction or relocation of many species as their ecosystems change, most immediately the environments of coral reefs, mountains, and the Arctic.",
"entropy": 0.0,
"votes": [
"SUPPORTS",
"SUPPORTS",
null,
null,
null
]
},
{
"evidence_id": "Global warming:178",
"evidence_label": 2, # "NOT_ENOUGH_INFO"
"article": "Global warming",
"evidence": "Rising temperatures push bees to their physiological limits, and could cause the extinction of bee populations.",
"entropy": 0.6931471805599453,
"votes": [
"SUPPORTS",
"NOT_ENOUGH_INFO",
null,
null,
null
]
},
{
"evidence_id": "Habitat destruction:61",
"evidence_label": 0, # "SUPPORTS"
"article": "Habitat destruction",
"evidence": "Rising global temperatures, caused by the greenhouse effect, contribute to habitat destruction, endangering various species, such as the polar bear.",
"entropy": 0.0,
"votes": [
"SUPPORTS",
"SUPPORTS",
null,
null,
null
]
},
{
"evidence_id": "Polar bear:1328",
"evidence_label": 2, # "NOT_ENOUGH_INFO"
"article": "Polar bear",
"evidence": "\"Bear hunting caught in global warming debate\".",
"entropy": 0.6931471805599453,
"votes": [
"SUPPORTS",
"NOT_ENOUGH_INFO",
null,
null,
null
]
}
]
}
```
### Data Fields
- `claim_id`: a `string` feature, unique claim identifier.
- `claim`: a `string` feature, claim text.
- `claim_label`: a `int` feature, overall label assigned to claim (based on evidence majority vote). The label correspond to 0: "supports", 1: "refutes", 2: "not enough info" and 3: "disputed".
- `evidences`: a list of evidences with fields:
- `evidence_id`: a `string` feature, unique evidence identifier.
- `evidence_label`: a `int` feature, micro-verdict label. The label correspond to 0: "supports", 1: "refutes" and 2: "not enough info".
- `article`: a `string` feature, title of source article (Wikipedia page).
- `evidence`: a `string` feature, evidence sentence.
- `entropy`: a `float32` feature, entropy reflecting uncertainty of `evidence_label`.
- `votes`: a `list` of `string` features, corresponding to individual votes.
### Data Splits
This benchmark dataset currently consists of a single data split `test` that consists of 1,535 claims or 7,675 claim-evidence pairs.
## 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
```bibtex
@misc{diggelmann2020climatefever,
title={CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims},
author={Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold},
year={2020},
eprint={2012.00614},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@tdiggelm](https://github.com/tdiggelm) for adding this dataset. |
true |
# Dataset Card for PUBHEALTH
## 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:** [PUBHEALTH homepage](https://github.com/neemakot/Health-Fact-Checking)
- **Repository:** [PUBHEALTH repository](https://github.com/neemakot/Health-Fact-Checking/blob/master/data/DATASHEET.md)
- **Paper:** [Explainable Automated Fact-Checking for Public Health Claims"](https://arxiv.org/abs/2010.09926)
- **Point of Contact:**[Neema Kotonya](mailto:nk2418@ic.ac.uk)
### Dataset Summary
PUBHEALTH is a comprehensive dataset for explainable automated fact-checking of public health claims. Each instance in the PUBHEALTH dataset has an associated veracity label (true, false, unproven, mixture). Furthermore each instance in the dataset has an explanation text field. The explanation is a justification for which the claim has been assigned a particular veracity label.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The text in the dataset is in English.
## Dataset Structure
### Data Instances
The following is an example instance of the PUBHEALTH dataset:
| Field | Example |
| ----------------- | -------------------------------------------------------------|
| __claim__ | Expired boxes of cake and pancake mix are dangerously toxic. |
| __explanation__ | What's True: Pancake and cake mixes that contain mold can cause life-threatening allergic reactions. What's False: Pancake and cake mixes that have passed their expiration dates are not inherently dangerous to ordinarily healthy people, and the yeast in packaged baking products does not "over time develops spores." |
| __label__ | mixture |
| __author(s)__ | David Mikkelson |
| __date published__ | April 19, 2006 |
| __tags__ | food, allergies, baking, cake |
| __main_text__ | In April 2006, the experience of a 14-year-old who had eaten pancakes made from a mix that had gone moldy was described in the popular newspaper column Dear Abby. The account has since been circulated widely on the Internet as scores of concerned homemakers ponder the safety of the pancake and other baking mixes lurking in their larders [...] |
| __evidence sources__ | [1] Bennett, Allan and Kim Collins. “An Unusual Case of Anaphylaxis: Mold in Pancake Mix.” American Journal of Forensic Medicine & Pathology. September 2001 (pp. 292-295). [2] Phillips, Jeanne. “Dear Abby.” 14 April 2006 [syndicated column]. |
### Data Fields
Mentioned above in data instances.
### Data Splits
| | # Instances |
|-----------|-------------|
| train.tsv | 9832 |
| dev.tsv | 1221 |
| test.tsv | 1235 |
| total | 12288 |
## Dataset Creation
### Curation Rationale
The dataset was created to explore fact-checking of difficult to verify claims i.e., those which require expertise from outside of the journalistics domain, in this case biomedical and public health expertise.
It was also created in response to the lack of fact-checking datasets which provide gold standard natural language explanations for verdicts/labels.
### Source Data
#### Initial Data Collection and Normalization
The dataset was retrieved from the following fact-checking, news reviews and news websites:
| URL | Type |
|-----------------------------------|--------------------|
| http://snopes.com/ | fact-checking |
| http://politifact.com/ | fact-checking |
| http://truthorfiction.com/ | fact-checking |
| https://www.factcheck.org/ | fact-checking |
| https://fullfact.org/ | fact-checking |
| https://apnews.com/ | news |
| https://uk.reuters.com/ | news |
| https://www.healthnewsreview.org/ | health news review |
#### 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
Not to our knowledge, but if it is brought to our attention that we are mistaken we will make the appropriate corrections to the dataset.
## 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 Neema Kotonya, and Francesca Toni, for their research paper "Explainable Automated Fact-Checking for Public Health Claims" presented at EMNLP 2020.
### Licensing Information
MIT License
### Citation Information
```
@inproceedings{kotonya-toni-2020-explainable,
title = "Explainable Automated Fact-Checking for Public Health Claims",
author = "Kotonya, Neema and
Toni, Francesca",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.623",
pages = "7740--7754",
}
```
### Contributions
Thanks to [@bhavitvyamalik](https://github.com/bhavitvyamalik) for adding this dataset. |
false |
# Dataset Card for bAbi QA
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**[The bAbI project](https://research.fb.com/downloads/babi/)
- **Repository:**
- **Paper:** [arXiv Paper](https://arxiv.org/pdf/1502.05698.pdf)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The (20) QA bAbI tasks are a set of proxy tasks that evaluate reading comprehension via question answering. Our tasks measure understanding in several ways: whether a system is able to answer questions via chaining facts, simple induction, deduction and many more. The tasks are designed to be prerequisites for any system that aims to be capable of conversing with a human. The aim is to classify these tasks into skill sets,so that researchers can identify (and then rectify) the failings of their systems.
### Supported Tasks and Leaderboards
The dataset supports a set of 20 proxy story-based question answering tasks for various "types" in English and Hindi. The tasks are:
|task_no|task_name|
|----|------------|
|qa1 |single-supporting-fact|
|qa2 |two-supporting-facts|
|qa3 |three-supporting-facts|
|qa4 |two-arg-relations|
|qa5 |three-arg-relations|
|qa6 |yes-no-questions|
|qa7 |counting|
|qa8 |lists-sets|
|qa9 |simple-negation|
|qa10| indefinite-knowledge|
|qa11| basic-coreference|
|qa12| conjunction|
|qa13| compound-coreference|
|qa14| time-reasoning|
|qa15| basic-deduction|
|qa16| basic-induction|
|qa17| positional-reasoning|
|qa18| size-reasoning|
|qa19| path-finding|
|qa20| agents-motivations|
The "types" are are:
- `en`
- the tasks in English, readable by humans.
- `hn`
- the tasks in Hindi, readable by humans.
- `shuffled`
- the same tasks with shuffled letters so they are not readable by humans, and for existing parsers and taggers cannot be used in a straight-forward fashion to leverage extra resources-- in this case the learner is more forced to rely on the given training data. This mimics a learner being first presented a language and having to learn from scratch.
- `en-10k`, `shuffled-10k` and `hn-10k`
- the same tasks in the three formats, but with 10,000 training examples, rather than 1000 training examples.
- `en-valid` and `en-valid-10k`
- are the same as `en` and `en10k` except the train sets have been conveniently split into train and valid portions (90% and 10% split).
To get a particular dataset, use `load_dataset('babi_qa',type=f'{type}',task_no=f'{task_no}')` where `type` is one of the types, and `task_no` is one of the task numbers. For example, `load_dataset('babi_qa', type='en', task_no='qa1')`.
### Languages
## Dataset Structure
### Data Instances
An instance from the `en-qa1` config's `train` split:
```
{'story': {'answer': ['', '', 'bathroom', '', '', 'hallway', '', '', 'hallway', '', '', 'office', '', '', 'bathroom'], 'id': ['1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14', '15'], 'supporting_ids': [[], [], ['1'], [], [], ['4'], [], [], ['4'], [], [], ['11'], [], [], ['8']], 'text': ['Mary moved to the bathroom.', 'John went to the hallway.', 'Where is Mary?', 'Daniel went back to the hallway.', 'Sandra moved to the garden.', 'Where is Daniel?', 'John moved to the office.', 'Sandra journeyed to the bathroom.', 'Where is Daniel?', 'Mary moved to the hallway.', 'Daniel travelled to the office.', 'Where is Daniel?', 'John went back to the garden.', 'John moved to the bedroom.', 'Where is Sandra?'], 'type': [0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1]}}
```
### Data Fields
- `story`: a dictionary feature containing:
- `id`: a `string` feature, which denotes the line number in the example.
- `type`: a classification label, with possible values including `context`, `question`, denoting whether the text is context or a question.
- `text`: a `string` feature the text present, whether it is a question or context.
- `supporting_ids`: a `list` of `string` features containing the line numbers of the lines in the example which support the answer.
- `answer`: a `string` feature containing the answer to the question, or an empty string if the `type`s is not `question`.
### Data Splits
The splits and corresponding sizes are:
| | train | test | validation |
|-------------------|---------|--------|--------------|
| en-qa1 | 200 | 200 | - |
| en-qa2 | 200 | 200 | - |
| en-qa3 | 200 | 200 | - |
| en-qa4 | 1000 | 1000 | - |
| en-qa5 | 200 | 200 | - |
| en-qa6 | 200 | 200 | - |
| en-qa7 | 200 | 200 | - |
| en-qa8 | 200 | 200 | - |
| en-qa9 | 200 | 200 | - |
| en-qa10 | 200 | 200 | - |
| en-qa11 | 200 | 200 | - |
| en-qa12 | 200 | 200 | - |
| en-qa13 | 200 | 200 | - |
| en-qa14 | 200 | 200 | - |
| en-qa15 | 250 | 250 | - |
| en-qa16 | 1000 | 1000 | - |
| en-qa17 | 125 | 125 | - |
| en-qa18 | 198 | 199 | - |
| en-qa19 | 1000 | 1000 | - |
| en-qa20 | 94 | 93 | - |
| en-10k-qa1 | 2000 | 200 | - |
| en-10k-qa2 | 2000 | 200 | - |
| en-10k-qa3 | 2000 | 200 | - |
| en-10k-qa4 | 10000 | 1000 | - |
| en-10k-qa5 | 2000 | 200 | - |
| en-10k-qa6 | 2000 | 200 | - |
| en-10k-qa7 | 2000 | 200 | - |
| en-10k-qa8 | 2000 | 200 | - |
| en-10k-qa9 | 2000 | 200 | - |
| en-10k-qa10 | 2000 | 200 | - |
| en-10k-qa11 | 2000 | 200 | - |
| en-10k-qa12 | 2000 | 200 | - |
| en-10k-qa13 | 2000 | 200 | - |
| en-10k-qa14 | 2000 | 200 | - |
| en-10k-qa15 | 2500 | 250 | - |
| en-10k-qa16 | 10000 | 1000 | - |
| en-10k-qa17 | 1250 | 125 | - |
| en-10k-qa18 | 1978 | 199 | - |
| en-10k-qa19 | 10000 | 1000 | - |
| en-10k-qa20 | 933 | 93 | - |
| en-valid-qa1 | 180 | 200 | 20 |
| en-valid-qa2 | 180 | 200 | 20 |
| en-valid-qa3 | 180 | 200 | 20 |
| en-valid-qa4 | 900 | 1000 | 100 |
| en-valid-qa5 | 180 | 200 | 20 |
| en-valid-qa6 | 180 | 200 | 20 |
| en-valid-qa7 | 180 | 200 | 20 |
| en-valid-qa8 | 180 | 200 | 20 |
| en-valid-qa9 | 180 | 200 | 20 |
| en-valid-qa10 | 180 | 200 | 20 |
| en-valid-qa11 | 180 | 200 | 20 |
| en-valid-qa12 | 180 | 200 | 20 |
| en-valid-qa13 | 180 | 200 | 20 |
| en-valid-qa14 | 180 | 200 | 20 |
| en-valid-qa15 | 225 | 250 | 25 |
| en-valid-qa16 | 900 | 1000 | 100 |
| en-valid-qa17 | 113 | 125 | 12 |
| en-valid-qa18 | 179 | 199 | 19 |
| en-valid-qa19 | 900 | 1000 | 100 |
| en-valid-qa20 | 85 | 93 | 9 |
| en-valid-10k-qa1 | 1800 | 200 | 200 |
| en-valid-10k-qa2 | 1800 | 200 | 200 |
| en-valid-10k-qa3 | 1800 | 200 | 200 |
| en-valid-10k-qa4 | 9000 | 1000 | 1000 |
| en-valid-10k-qa5 | 1800 | 200 | 200 |
| en-valid-10k-qa6 | 1800 | 200 | 200 |
| en-valid-10k-qa7 | 1800 | 200 | 200 |
| en-valid-10k-qa8 | 1800 | 200 | 200 |
| en-valid-10k-qa9 | 1800 | 200 | 200 |
| en-valid-10k-qa10 | 1800 | 200 | 200 |
| en-valid-10k-qa11 | 1800 | 200 | 200 |
| en-valid-10k-qa12 | 1800 | 200 | 200 |
| en-valid-10k-qa13 | 1800 | 200 | 200 |
| en-valid-10k-qa14 | 1800 | 200 | 200 |
| en-valid-10k-qa15 | 2250 | 250 | 250 |
| en-valid-10k-qa16 | 9000 | 1000 | 1000 |
| en-valid-10k-qa17 | 1125 | 125 | 125 |
| en-valid-10k-qa18 | 1781 | 199 | 197 |
| en-valid-10k-qa19 | 9000 | 1000 | 1000 |
| en-valid-10k-qa20 | 840 | 93 | 93 |
| hn-qa1 | 200 | 200 | - |
| hn-qa2 | 200 | 200 | - |
| hn-qa3 | 167 | 167 | - |
| hn-qa4 | 1000 | 1000 | - |
| hn-qa5 | 200 | 200 | - |
| hn-qa6 | 200 | 200 | - |
| hn-qa7 | 200 | 200 | - |
| hn-qa8 | 200 | 200 | - |
| hn-qa9 | 200 | 200 | - |
| hn-qa10 | 200 | 200 | - |
| hn-qa11 | 200 | 200 | - |
| hn-qa12 | 200 | 200 | - |
| hn-qa13 | 125 | 125 | - |
| hn-qa14 | 200 | 200 | - |
| hn-qa15 | 250 | 250 | - |
| hn-qa16 | 1000 | 1000 | - |
| hn-qa17 | 125 | 125 | - |
| hn-qa18 | 198 | 198 | - |
| hn-qa19 | 1000 | 1000 | - |
| hn-qa20 | 93 | 94 | - |
| hn-10k-qa1 | 2000 | 200 | - |
| hn-10k-qa2 | 2000 | 200 | - |
| hn-10k-qa3 | 1667 | 167 | - |
| hn-10k-qa4 | 10000 | 1000 | - |
| hn-10k-qa5 | 2000 | 200 | - |
| hn-10k-qa6 | 2000 | 200 | - |
| hn-10k-qa7 | 2000 | 200 | - |
| hn-10k-qa8 | 2000 | 200 | - |
| hn-10k-qa9 | 2000 | 200 | - |
| hn-10k-qa10 | 2000 | 200 | - |
| hn-10k-qa11 | 2000 | 200 | - |
| hn-10k-qa12 | 2000 | 200 | - |
| hn-10k-qa13 | 1250 | 125 | - |
| hn-10k-qa14 | 2000 | 200 | - |
| hn-10k-qa15 | 2500 | 250 | - |
| hn-10k-qa16 | 10000 | 1000 | - |
| hn-10k-qa17 | 1250 | 125 | - |
| hn-10k-qa18 | 1977 | 198 | - |
| hn-10k-qa19 | 10000 | 1000 | - |
| hn-10k-qa20 | 934 | 94 | - |
| shuffled-qa1 | 200 | 200 | - |
| shuffled-qa2 | 200 | 200 | - |
| shuffled-qa3 | 200 | 200 | - |
| shuffled-qa4 | 1000 | 1000 | - |
| shuffled-qa5 | 200 | 200 | - |
| shuffled-qa6 | 200 | 200 | - |
| shuffled-qa7 | 200 | 200 | - |
| shuffled-qa8 | 200 | 200 | - |
| shuffled-qa9 | 200 | 200 | - |
| shuffled-qa10 | 200 | 200 | - |
| shuffled-qa11 | 200 | 200 | - |
| shuffled-qa12 | 200 | 200 | - |
| shuffled-qa13 | 200 | 200 | - |
| shuffled-qa14 | 200 | 200 | - |
| shuffled-qa15 | 250 | 250 | - |
| shuffled-qa16 | 1000 | 1000 | - |
| shuffled-qa17 | 125 | 125 | - |
| shuffled-qa18 | 198 | 199 | - |
| shuffled-qa19 | 1000 | 1000 | - |
| shuffled-qa20 | 94 | 93 | - |
| shuffled-10k-qa1 | 2000 | 200 | - |
| shuffled-10k-qa2 | 2000 | 200 | - |
| shuffled-10k-qa3 | 2000 | 200 | - |
| shuffled-10k-qa4 | 10000 | 1000 | - |
| shuffled-10k-qa5 | 2000 | 200 | - |
| shuffled-10k-qa6 | 2000 | 200 | - |
| shuffled-10k-qa7 | 2000 | 200 | - |
| shuffled-10k-qa8 | 2000 | 200 | - |
| shuffled-10k-qa9 | 2000 | 200 | - |
| shuffled-10k-qa10 | 2000 | 200 | - |
| shuffled-10k-qa11 | 2000 | 200 | - |
| shuffled-10k-qa12 | 2000 | 200 | - |
| shuffled-10k-qa13 | 2000 | 200 | - |
| shuffled-10k-qa14 | 2000 | 200 | - |
| shuffled-10k-qa15 | 2500 | 250 | - |
| shuffled-10k-qa16 | 10000 | 1000 | - |
| shuffled-10k-qa17 | 1250 | 125 | - |
| shuffled-10k-qa18 | 1978 | 199 | - |
| shuffled-10k-qa19 | 10000 | 1000 | - |
| shuffled-10k-qa20 | 933 | 93 | - |
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
Code to generate tasks is available on [github](https://github.com/facebook/bAbI-tasks)
#### 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
Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston, at Facebook Research.
### Licensing Information
```
Creative Commons Attribution 3.0 License
```
### Citation Information
```
@misc{dodge2016evaluating,
title={Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems},
author={Jesse Dodge and Andreea Gane and Xiang Zhang and Antoine Bordes and Sumit Chopra and Alexander Miller and Arthur Szlam and Jason Weston},
year={2016},
eprint={1511.06931},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@gchhablani](https://github.com/gchhablani) for adding this dataset. |
true |
# Dataset Card for Scientific Exaggeration Detection
## Dataset Description
- **Homepage:** https://github.com/copenlu/scientific-exaggeration-detection
- **Repository:** https://github.com/copenlu/scientific-exaggeration-detection
- **Paper:** https://aclanthology.org/2021.emnlp-main.845.pdf
### Dataset Summary
Public trust in science depends on honest and factual communication of scientific papers. However, recent studies have demonstrated a tendency of news media to misrepresent scientific papers by exaggerating their findings. Given this, we present a formalization of and study into the problem of exaggeration detection in science communication. While there are an abundance of scientific papers and popular media articles written about them, very rarely do the articles include a direct link to the original paper, making data collection challenging. We address this by curating a set of labeled press release/abstract pairs from existing expert annotated studies on exaggeration in press releases of scientific papers suitable for benchmarking the performance of machine learning models on the task. Using limited data from this and previous studies on exaggeration detection in science, we introduce MT-PET, a multi-task version of Pattern Exploiting Training (PET), which leverages knowledge from complementary cloze-style QA tasks to improve few-shot learning. We demonstrate that MT-PET outperforms PET and supervised learning both when data is limited, as well as when there is an abundance of data for the main task.
## Dataset Structure
The training and test data are derived from the InSciOut studies from [Sumner et al. 2014](https://www.bmj.com/content/349/bmj.g7015) and [Bratton et al. 2019](https://pubmed.ncbi.nlm.nih.gov/31728413/#:~:text=Results%3A%20We%20found%20that%20the,inference%20from%20non%2Dhuman%20studies.). The splits have the following fields:
```
original_file_id: The ID of the original spreadsheet in the Sumner/Bratton data where the annotations are derived from
press_release_conclusion: The conclusion sentence from the press release
press_release_strength: The strength label for the press release
abstract_conclusion: The conclusion sentence from the abstract
abstract_strength: The strength label for the abstract
exaggeration_label: The final exaggeration label
```
The exaggeration label is one of `same`, `exaggerates`, or `downplays`. The strength label is one of the following:
```
0: Statement of no relationship
1: Statement of correlation
2: Conditional statement of causation
3: Statement of causation
```
## Dataset Creation
See section 4 of the [paper](https://aclanthology.org/2021.emnlp-main.845.pdf) for details on how the dataset was curated. The original InSciOut data can be found [here](https://figshare.com/articles/dataset/InSciOut/903704)
## Citation
```
@inproceedings{wright2021exaggeration,
title={{Semi-Supervised Exaggeration Detection of Health Science Press Releases}},
author={Dustin Wright and Isabelle Augenstein},
booktitle = {Proceedings of EMNLP},
publisher = {Association for Computational Linguistics},
year = 2021
}
```
Thanks to [@dwright37](https://github.com/dwright37) for adding this dataset. |
true |
# Dataset Card for Google Query-wellformedness 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/google-research-datasets/query-wellformedness)
- **Repository:** [GitHub](https://github.com/google-research-datasets/query-wellformedness)
- **Paper:** [ARXIV](https://arxiv.org/abs/1808.09419)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
English
## Dataset Structure
### Data Instances
```
{'rating': 0.2, 'content': 'The European Union includes how many ?'}
```
### Data Fields
- `rating`: a `float` between 0-1
- `sentence`: query which you want to rate
### Data Splits
| | Train | Valid | Test |
| ----- | ------ | ----- | ---- |
| Input Sentences | 17500 | 3750 | 3850 |
## Dataset Creation
### Curation Rationale
Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. This dataset introduce a new task of identifying a well-formed natural language question.
### Source Data
Used the Paralex corpus (Fader et al., 2013) that contains pairs of noisy paraphrase questions. These questions were issued by users in WikiAnswers (a Question-Answer forum) and consist of both web-search query like constructs (“5 parts of chloroplast?”) and well-formed questions (“What is the punishment for grand theft?”).
#### Initial Data Collection and Normalization
Selected 25,100 queries from the unique list of queries extracted from the corpus such that no two queries in the selected set are paraphrases.
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
The queries are annotated into well-formed or non-wellformed questions if it satisfies the following:
1. Query is grammatical.
2. Query is an explicit question.
3. Query does not contain spelling errors.
#### Who are the annotators?
Every query was labeled by five different crowdworkers with a binary label indicating whether a query is well-formed or not. And average of the ratings of the five annotators was reported, to get the probability of a query being well-formed.
### 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
Query-wellformedness dataset is licensed under CC BY-SA 4.0. Any third party content or data is provided “As Is” without any warranty, express or implied.
### Citation Information
```
@InProceedings{FaruquiDas2018,
title = {{Identifying Well-formed Natural Language Questions}},
author = {Faruqui, Manaal and Das, Dipanjan},
booktitle = {Proc. of EMNLP},
year = {2018}
}
```
### Contributions
Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset. |
false |
# Dataset Card for Legal Documents Entity Recognition
## 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/elenanereiss/Legal-Entity-Recognition
- **Repository:** None
- **Paper:** https://link.springer.com/chapter/10.1007/978-3-030-33220-4_20
- **Leaderboard:** [If the dataset supports an active leaderboard, add link here]()
- **Point of Contact:** Georg Rehm (georg.rehm@dfki.de)
### Dataset Summary
<div class="course-tip course-tip-orange bg-gradient-to-br dark:bg-gradient-to-r before:border-orange-500 dark:before:border-orange-800 from-orange-50 dark:from-gray-900 to-white dark:to-gray-950 border border-orange-50 text-orange-700 dark:text-gray-400">
<p><b>Deprecated:</b> Dataset "german_legal_entity_recognition" is deprecated and will be deleted. Use <a href="https://huggingface.co/datasets/elenanereiss/german-ler">"elenanereiss/german-ler"</a> instead.</p>
</div>
### 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 |
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Statistics](#dataset-statistics)
- [Usage](#usage)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** [FSoft-AI4Code/TheVault](https://github.com/FSoft-AI4Code/TheVault)
- **Paper:** [The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation](https://arxiv.org/abs/2305.06156)
- **Contact:** support.ailab@fpt.com
- **Website:** https://www.fpt-aicenter.com/ai-residency/
<p align="center">
<img src="https://raw.githubusercontent.com/FSoft-AI4Code/TheVault/main/assets/the-vault-4-logo-png.png" width="300px" alt="logo">
</p>
<div align="center">
# The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation
</div>
## Dataset Summary
The Vault is a multilingual code-text dataset with over 40 million pairs covering 10 popular programming languages. It is the largest corpus containing parallel code-text data. By building upon [The Stack](https://huggingface.co/datasets/bigcode/the-stack), a massive raw code sample collection, the Vault offers a comprehensive and clean resource for advancing research in code understanding and generation. It provides a high-quality dataset that includes code-text pairs at multiple levels, such as class and inline-level, in addition to the function level. The Vault can serve many purposes at multiple levels.
## Supported Tasks
The Vault can be used for pretraining LLMs or downstream code-text interaction tasks. A number of tasks related to code understanding and geneartion can be constructed using The Vault such as *code summarization*, *text-to-code generation* and *code search*.
## Languages
The natural language text (docstring) is in English.
10 programming languages are supported in The Vault: `Python`, `Java`, `JavaScript`, `PHP`, `C`, `C#`, `C++`, `Go`, `Ruby`, `Rust`
## Dataset Structure
### Data Instances
```
{
"hexsha": "5c47f0b4c173a8fd03e4e633d9b3dd8211e67ad0",
"repo": "neumanna94/beepboop",
"path": "js/scripts.js",
"license": [
"MIT"
],
"language": "JavaScript",
"identifier": "beepBoopSelector",
"return_type": "<not_specific>",
"original_string": "function beepBoopSelector(inputString, bbFunction){\n if(bbFunction==1){\n return beepBoop(inputString);\n } else if(bbFunction==2){\n return beepBoop2(inputString);\n } else if(bbFunction==3){\n return beepBoop3(inputString);\n } else {\n }\n}",
"original_docstring": "//Determines what beepBoop function to use",
"docstring": "Determines what beepBoop function to use",
"docstring_tokens": [
"Determines",
"what",
"beepBoop",
"function",
"to",
"use"
],
"code": "function beepBoopSelector(inputString, bbFunction){\n if(bbFunction==1){\n return beepBoop(inputString);\n } else if(bbFunction==2){\n return beepBoop2(inputString);\n } else if(bbFunction==3){\n return beepBoop3(inputString);\n } else {\n }\n}",
"code_tokens": [
"function",
"beepBoopSelector",
"(",
"inputString",
",",
"bbFunction",
")",
"{",
"if",
"(",
"bbFunction",
"==",
"1",
")",
"{",
"return",
"beepBoop",
"(",
"inputString",
")",
";",
"}",
"else",
"if",
"(",
"bbFunction",
"==",
"2",
")",
"{",
"return",
"beepBoop2",
"(",
"inputString",
")",
";",
"}",
"else",
"if",
"(",
"bbFunction",
"==",
"3",
")",
"{",
"return",
"beepBoop3",
"(",
"inputString",
")",
";",
"}",
"else",
"{",
"}",
"}"
],
"short_docstring": "Determines what beepBoop function to use",
"short_docstring_tokens": [
"Determines",
"what",
"beepBoop",
"function",
"to",
"use"
],
"comment": [],
"parameters": [
{
"param": "inputString",
"type": null
},
{
"param": "bbFunction",
"type": null
}
],
"docstring_params": {
"returns": [],
"raises": [],
"params": [
{
"identifier": "inputString",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
},
{
"identifier": "bbFunction",
"type": null,
"docstring": null,
"docstring_tokens": [],
"default": null,
"is_optional": null
}
],
"outlier_params": [],
"others": []
}
}
```
### Data Fields
Data fields for function level:
- **hexsha** (string): the unique git hash of file
- **repo** (string): the owner/repo
- **path** (string): the full path to the original file
- **license** (list): licenses in the repo
- **language** (string): the programming language
- **identifier** (string): the function or method name
- **return_type** (string): the type returned by the function
- **original_string** (string): original version of function/class node
- **original_docstring** (string): the raw string before tokenization or parsing
- **code** (string): the part of the original that is code
- **code_tokens** (list): tokenized version of `code`
- **short_docstring** (string): short, brief summarization (first line of the docstring)
- **short_docstring_tokens** (list): tokenized version of `short_docstring
- **docstring** (string): the top-level comment or docstring (docstring version without param’s doc, return, exception fields, etc)
- **docstring_tokens** (list): tokenized version of docstring
- **comment** (list): list of comments (line) inside the function/class
- **parameters** (list): List of parameters and its type (type can be None)
- **docstring_params** (dict): Dictionary of the parsed information from docstring
See [here](https://github.com/FSoft-AI4Code/TheVault/blob/main/data/README.md) for more details and examples.
### Data Splits
In this repo, The Vault is divided into 5 subsets, where three training versions are split based on size of the full training set, and the remains are validation set and test set (approximate 20,000 samples in each). The statistic for languages in each split set is illustrated in the following section.
Before split, the dataset is deduplicated. There are 3 versions of training set that are small (5%), medium (20%) and large (100%).
## Dataset Statistics
- Compare to other benchmarks
| Dataset | #Language | #Code-text pair |
|:--------------------------|----------:|-----------------:|
| PyMT5 | 1 | ≈ 7,700,000 |
| CoDesc | 1 | 4,211,516 |
| CodeSearchNet | 6 | 2,326,976 |
| CodeSearchNet (CodeXGLUE) | 6 | 1,005,474 |
| Deepcom | 1 | 424,028 |
| CONCODE | 1 | 2,184,310 |
| Funcom | 1 | 2,149,121 |
| CodeT5 | 8 | 3,158,313 |
| **The Vault** | **10** | **40,993,893** |
- Statistic for split sets
| | train/small | train/medium | train/full | validation | test | total |
|:-----------|------------:|-------------:|-----------:|-----------:|-------:|--------------:|
|Python | 370,657 | 1,952,110 | 7,772,647 | 30,992 | 21,652 | 7,825,291 |
|Java | 351,213 | 1,612,366 | 6,629,193 | 22,677 | 15,552 | 6,667,422 |
|JavaScript | 82,931 | 404,729 | 1,640,416 | 22,044 | 21,108 | 1,683,568 |
|PHP | 236,638 | 1,155,476 | 4,656,371 | 21,375 | 19,010 | 4,696,756 |
|C | 105,978 | 381,207 | 1,639,319 | 27,525 | 19,122 | 1,685,966 |
|C# | 141,090 | 783,166 | 3,305,891 | 24,787 | 19,638 | 3,350,316 |
|C++ | 87,420 | 410,907 | 1,671,268 | 20,011 | 18,169 | 1,709,448 |
|Go | 267,535 | 1,319,547 | 5,109,020 | 19,102 | 25,314 | 5,153,436 |
|Ruby | 23,921 | 112,574 | 424,339 | 17,338 | 19,908 | 461,585 |
|Rust | 35,367 | 224,015 | 825,130 | 16,716 | 23,141 | 864,987 |
|TOTAL | 1,702,750 | 8,356,097 |33,673,594 |222,567 |202,614 |**34,098,775** |
## Usage
You can load The Vault dataset using datasets library: ```pip install datasets```
```python
from datasets import load_dataset
# Load full function level dataset (40M samples)
dataset = load_dataset("Fsoft-AIC/the-vault-function")
# Load function level train/validation/test set
dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"])
# Load "small" (or "medium", "full") version of function level training set
dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train/small"])
# specific language (e.g. Python)
dataset = load_dataset("Fsoft-AIC/the-vault-function", split_set=["train"], languages=['Python'])
# dataset streaming
data = load_dataset("Fsoft-AIC/the-vault-function", split_set= ["train"], streaming= True)
for sample in iter(data['train']):
print(sample)
```
A back up dataset can be downloaded in azure storage. See [Download The Vault from Azure blob storage](https://github.com/FSoft-AI4Code/TheVault#download-via-link).
## Additional information
### Licensing Information
MIT License
### Citation Information
```
@article{manh2023vault,
title={The Vault: A Comprehensive Multilingual Dataset for Advancing Code Understanding and Generation},
author={Manh, Dung Nguyen and Hai, Nam Le and Dau, Anh TV and Nguyen, Anh Minh and Nghiem, Khanh and Guo, Jin and Bui, Nghi DQ},
journal={arXiv preprint arXiv:2305.06156},
year={2023}
}
```
### Contributions
This dataset is developed by [FSOFT AI4Code team](https://github.com/FSoft-AI4Code). |
false |
# Dataset Card for AMI
## 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)
- [Terms of Usage](#terms-of-usage)
## Dataset Description
- **Homepage:** https://groups.inf.ed.ac.uk/ami/corpus/
- **Repository:** https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [jonathan@ed.ac.uk](mailto:jonathan@ed.ac.uk)
## Dataset Description
The AMI Meeting Corpus consists of 100 hours of meeting recordings. The recordings use a range of signals
synchronized to a common timeline. These include close-talking and far-field microphones, individual and
room-view video cameras, and output from a slide projector and an electronic whiteboard. During the meetings,
the participants also have unsynchronized pens available to them that record what is written. The meetings
were recorded in English using three different rooms with different acoustic properties, and include mostly
non-native speakers.
**Note**: This dataset corresponds to the data-processing of [KALDI's AMI S5 recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5).
This means text is normalized and the audio data is chunked according to the scripts above!
To make the user experience as simply as possible, we provide the already chunked data to the user here so that the following can be done:
### Example Usage
```python
from datasets import load_dataset
ds = load_dataset("edinburghcstr/ami", "ihm")
print(ds)
```
gives:
```
DatasetDict({
train: Dataset({
features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
num_rows: 108502
})
validation: Dataset({
features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
num_rows: 13098
})
test: Dataset({
features: ['meeting_id', 'audio_id', 'text', 'audio', 'begin_time', 'end_time', 'microphone_id', 'speaker_id'],
num_rows: 12643
})
})
```
```py
ds["train"][0]
```
automatically loads the audio into memory:
```
{'meeting_id': 'EN2001a',
'audio_id': 'AMI_EN2001a_H00_MEE068_0000557_0000594',
'text': 'OKAY',
'audio': {'path': '/cache/dir/path/downloads/extracted/2d75d5b3e8a91f44692e2973f08b4cac53698f92c2567bd43b41d19c313a5280/EN2001a/train_ami_en2001a_h00_mee068_0000557_0000594.wav',
'array': array([0. , 0. , 0. , ..., 0.00033569, 0.00030518,
0.00030518], dtype=float32),
'sampling_rate': 16000},
'begin_time': 5.570000171661377,
'end_time': 5.940000057220459,
'microphone_id': 'H00',
'speaker_id': 'MEE068'}
```
The dataset was tested for correctness by fine-tuning a Wav2Vec2-Large model on it, more explicitly [the `wav2vec2-large-lv60` checkpoint](https://huggingface.co/facebook/wav2vec2-large-lv60).
As can be seen in this experiments, training the model for less than 2 epochs gives
*Result (WER)*:
| "dev" | "eval" |
|---|---|
| 25.27 | 25.21 |
as can be seen [here](https://huggingface.co/patrickvonplaten/ami-wav2vec2-large-lv60).
The results are in-line with results of published papers:
- [*Hybrid acoustic models for distant and multichannel large vocabulary speech recognition*](https://www.researchgate.net/publication/258075865_Hybrid_acoustic_models_for_distant_and_multichannel_large_vocabulary_speech_recognition)
- [Multi-Span Acoustic Modelling using Raw Waveform Signals](https://arxiv.org/abs/1906.11047)
You can run [run.sh](https://huggingface.co/patrickvonplaten/ami-wav2vec2-large-lv60/blob/main/run.sh) to reproduce the result.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
#### Transcribed Subsets Size
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Citation Information
### Contributions
Thanks to [@sanchit-gandhi](https://github.com/sanchit-gandhi), [@patrickvonplaten](https://github.com/patrickvonplaten),
and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.
## Terms of Usage
|
true |
## Dataset Description
- **Homepage:** [CUB 200 2011](http://www.vision.caltech.edu/datasets/cub_200_2011/)
- **Repository:** [Caltech Vision Lab](http://www.vision.caltech.edu/datasets/cub_200_2011/)
- **Paper:** [The Caltech-UCSD Birds-200-2011 Dataset](https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf)
- **Leaderboard:** [Paperswithcode](https://paperswithcode.com/dataset/cub-200-2011)
- **Point of Contact:** [Catherine Wah](https://scholar.google.com/citations?user=rCDdLUsAAAAJ&hl=en)
# CC6204: Hackaton Deep Learning 2022
**Nota:** esta fue un actividad del curso CC6204: Deep Learning, Universidad de Chile, año 2022. Dictado por el profesor Iván Sipiran, material del curso [aquí](https://github.com/ivansipiran/CC6204-Deep-Learning).
En esta actividad intentaremos resolver un problema de clasificación multimodal. En un problema de clasificación multimodal, cada pieza de información viene en diferentes representaciones (imágenes, texto, audios, etc) y la idea es determinar cómo usar esos datos para un problema de clasificación.
En este caso trabajaremos con un dataset que contiene datos sobre especies de pájaros.
## Dataset
### Data Instances
Una muestra del _dataset_ se encuentra a continuación:
```
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=334x500 at 0x7F59DE348AF0>,
'description': 'this bird has a short orange bill, white breast and body and white eyes.\na medium sized bird with a orange bill and a black crown and white eyes\nthis white-breasted bird has a short, squat, orange bill, a black head and wings, and small white eyes above a white stripe.\nthis bird has a white breast, a black head, a short red beak, and webbed feet.\nthis bird is white with black on its neck and has a long, pointy beak.\nthis bird has wings that are black and has a white belly\nthis bird has wings that are black and has a long bill\nthis is a medium sized bird, with a white belly, and a grey head and wings, with a short yellow bill.\nthis bird is white and gray in color, and has a bright orange beak.\nthis bird has a blunt orange beak with mostly black above the neck, the belly is solid white.\n',
'label': 6,
'file_name': 'Parakeet_Auklet_0048_795980.jpg'}
```
### Data Fields
Cada instancia de datos tiene los siguientes campos:
- `image`: imagen RGB de un pájaro
- `description`: texto con 10 descripciones del pájaro en la foto, cada descripción esta separado por un salto de linea (i.e. `\n`)
- `label`: un número entero que representa el id de la especie a la que pertenece el pájaro
<details>
<summary>Id2String</summary>
```bash
1 001.Black_footed_Albatross
2 002.Laysan_Albatross
3 003.Sooty_Albatross
4 004.Groove_billed_Ani
5 005.Crested_Auklet
6 006.Least_Auklet
7 007.Parakeet_Auklet
8 008.Rhinoceros_Auklet
9 009.Brewer_Blackbird
10 010.Red_winged_Blackbird
11 011.Rusty_Blackbird
12 012.Yellow_headed_Blackbird
13 013.Bobolink
14 014.Indigo_Bunting
15 015.Lazuli_Bunting
16 016.Painted_Bunting
17 017.Cardinal
18 018.Spotted_Catbird
19 019.Gray_Catbird
20 020.Yellow_breasted_Chat
21 021.Eastern_Towhee
22 022.Chuck_will_Widow
23 023.Brandt_Cormorant
24 024.Red_faced_Cormorant
25 025.Pelagic_Cormorant
26 026.Bronzed_Cowbird
27 027.Shiny_Cowbird
28 028.Brown_Creeper
29 029.American_Crow
30 030.Fish_Crow
31 031.Black_billed_Cuckoo
32 032.Mangrove_Cuckoo
33 033.Yellow_billed_Cuckoo
34 034.Gray_crowned_Rosy_Finch
35 035.Purple_Finch
36 036.Northern_Flicker
37 037.Acadian_Flycatcher
38 038.Great_Crested_Flycatcher
39 039.Least_Flycatcher
40 040.Olive_sided_Flycatcher
41 041.Scissor_tailed_Flycatcher
42 042.Vermilion_Flycatcher
43 043.Yellow_bellied_Flycatcher
44 044.Frigatebird
45 045.Northern_Fulmar
46 046.Gadwall
47 047.American_Goldfinch
48 048.European_Goldfinch
49 049.Boat_tailed_Grackle
50 050.Eared_Grebe
51 051.Horned_Grebe
52 052.Pied_billed_Grebe
53 053.Western_Grebe
54 054.Blue_Grosbeak
55 055.Evening_Grosbeak
56 056.Pine_Grosbeak
57 057.Rose_breasted_Grosbeak
58 058.Pigeon_Guillemot
59 059.California_Gull
60 060.Glaucous_winged_Gull
61 061.Heermann_Gull
62 062.Herring_Gull
63 063.Ivory_Gull
64 064.Ring_billed_Gull
65 065.Slaty_backed_Gull
66 066.Western_Gull
67 067.Anna_Hummingbird
68 068.Ruby_throated_Hummingbird
69 069.Rufous_Hummingbird
70 070.Green_Violetear
71 071.Long_tailed_Jaeger
72 072.Pomarine_Jaeger
73 073.Blue_Jay
74 074.Florida_Jay
75 075.Green_Jay
76 076.Dark_eyed_Junco
77 077.Tropical_Kingbird
78 078.Gray_Kingbird
79 079.Belted_Kingfisher
80 080.Green_Kingfisher
81 081.Pied_Kingfisher
82 082.Ringed_Kingfisher
83 083.White_breasted_Kingfisher
84 084.Red_legged_Kittiwake
85 085.Horned_Lark
86 086.Pacific_Loon
87 087.Mallard
88 088.Western_Meadowlark
89 089.Hooded_Merganser
90 090.Red_breasted_Merganser
91 091.Mockingbird
92 092.Nighthawk
93 093.Clark_Nutcracker
94 094.White_breasted_Nuthatch
95 095.Baltimore_Oriole
96 096.Hooded_Oriole
97 097.Orchard_Oriole
98 098.Scott_Oriole
99 099.Ovenbird
100 100.Brown_Pelican
101 101.White_Pelican
102 102.Western_Wood_Pewee
103 103.Sayornis
104 104.American_Pipit
105 105.Whip_poor_Will
106 106.Horned_Puffin
107 107.Common_Raven
108 108.White_necked_Raven
109 109.American_Redstart
110 110.Geococcyx
111 111.Loggerhead_Shrike
112 112.Great_Grey_Shrike
113 113.Baird_Sparrow
114 114.Black_throated_Sparrow
115 115.Brewer_Sparrow
116 116.Chipping_Sparrow
117 117.Clay_colored_Sparrow
118 118.House_Sparrow
119 119.Field_Sparrow
120 120.Fox_Sparrow
121 121.Grasshopper_Sparrow
122 122.Harris_Sparrow
123 123.Henslow_Sparrow
124 124.Le_Conte_Sparrow
125 125.Lincoln_Sparrow
126 126.Nelson_Sharp_tailed_Sparrow
127 127.Savannah_Sparrow
128 128.Seaside_Sparrow
129 129.Song_Sparrow
130 130.Tree_Sparrow
131 131.Vesper_Sparrow
132 132.White_crowned_Sparrow
133 133.White_throated_Sparrow
134 134.Cape_Glossy_Starling
135 135.Bank_Swallow
136 136.Barn_Swallow
137 137.Cliff_Swallow
138 138.Tree_Swallow
139 139.Scarlet_Tanager
140 140.Summer_Tanager
141 141.Artic_Tern
142 142.Black_Tern
143 143.Caspian_Tern
144 144.Common_Tern
145 145.Elegant_Tern
146 146.Forsters_Tern
147 147.Least_Tern
148 148.Green_tailed_Towhee
149 149.Brown_Thrasher
150 150.Sage_Thrasher
151 151.Black_capped_Vireo
152 152.Blue_headed_Vireo
153 153.Philadelphia_Vireo
154 154.Red_eyed_Vireo
155 155.Warbling_Vireo
156 156.White_eyed_Vireo
157 157.Yellow_throated_Vireo
158 158.Bay_breasted_Warbler
159 159.Black_and_white_Warbler
160 160.Black_throated_Blue_Warbler
161 161.Blue_winged_Warbler
162 162.Canada_Warbler
163 163.Cape_May_Warbler
164 164.Cerulean_Warbler
165 165.Chestnut_sided_Warbler
166 166.Golden_winged_Warbler
167 167.Hooded_Warbler
168 168.Kentucky_Warbler
169 169.Magnolia_Warbler
170 170.Mourning_Warbler
171 171.Myrtle_Warbler
172 172.Nashville_Warbler
173 173.Orange_crowned_Warbler
174 174.Palm_Warbler
175 175.Pine_Warbler
176 176.Prairie_Warbler
177 177.Prothonotary_Warbler
178 178.Swainson_Warbler
179 179.Tennessee_Warbler
180 180.Wilson_Warbler
181 181.Worm_eating_Warbler
182 182.Yellow_Warbler
183 183.Northern_Waterthrush
184 184.Louisiana_Waterthrush
185 185.Bohemian_Waxwing
186 186.Cedar_Waxwing
187 187.American_Three_toed_Woodpecker
188 188.Pileated_Woodpecker
189 189.Red_bellied_Woodpecker
190 190.Red_cockaded_Woodpecker
191 191.Red_headed_Woodpecker
192 192.Downy_Woodpecker
193 193.Bewick_Wren
194 194.Cactus_Wren
195 195.Carolina_Wren
196 196.House_Wren
197 197.Marsh_Wren
198 198.Rock_Wren
199 199.Winter_Wren
200 200.Common_Yellowthroat
```
</details>
- `file_name`: nombre del archivo que tiene la imagen
### Data Splits
| |train| test|
|------------------|----:|----:|
|# de observaciones|5994 |5794 |
## Problema
El problema consiste en entrenar un modelo que clasifique instancias del dataset CUB de la mejor manera posible. Algunas preguntas que podrían guiar nuestro desarrollo son:
* Se podrá obtener un buen _performance_ de clasificación solo usando las imágenes del dataset? Este tipo de problema sería el clásico problema de clasificar imágenes.
* Se podrá obtener un buen _performance_ de clasificación solo usando los textos del dataset? Este tipo de problema sería el clásico problema de clasificar texto.
* Se podrá obtener un mejor _performance_ si combino la información en un modelo multimodal? Cómo construyo un modelo multimodal que reciba una imagen y un texto y clasifique la instancia con su respectiva especie? Hint: piense en cómo una red neuronal (la que sea) es simplemente una función que recibe un dato y genera una representación de alto nivel (vector característico) de ese dato. Una red CNN podría hacerse cargo de calcular la representación de una imagen y una red RNN podría hacerse cargo de calcular la representación del texto. Finalmente concateno ambas representaciones y entreno un MLP final que hace la clasificación.
## Experimentación
Como el dataset es grande y los recursos de computación son muy limitados, una estrategia para hacer los experimentos es tomar una muestra más pequeña de datos para ir probando las ideas. Para esta estrategia, éstas son dos ideas válidas:
* Tomar menos instancias por cada clase para el desarrollo y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing.
* Tomar menos clases para el desarrollo inicial y solo dejar el dataset final para hacer el entrenamiento final y la evaluación final con testing.
Ambas estrategias nos permiten lidiar con los recursos limitados que tenemos, pero cuáles son sus ventajas o desventajas? Si usas alguna de estas estrategias, puedes comentar este punto en tu desarrollo final.
## Métrica de Evaluación
La métrica que se debe reportar es el accuracy en conjunto de test.
## Citation Information
Sitio web del [_dataset_ CUB200](http://www.vision.caltech.edu/datasets/cub_200_2011/), y reporte técnico [aquí](https://authors.library.caltech.edu/27452/1/CUB_200_2011.pdf).
```
@techreport{WahCUB_200_2011,
Title = The Caltech-UCSD Birds-200-2011 Dataset,
Author = {Wah, C. and Branson, S. and Welinder, P. and Perona, P. and Belongie, S.},
Year = {2011}
Institution = {California Institute of Technology},
Number = {CNS-TR-2011-001}
}
```
## Contributions
Creación y adaptación del material de la actividad en un Hugging Face dataset por Cristóbal Alcázar.
|
false | # Dataset Card for "TALI-large"
## Table of Contents
1. Dataset Description
1. Abstract
2. Brief Description
2. Dataset Information
1. Modalities
2. Dataset Variants
3. Dataset Statistics
4. Data Fields
5. Data Splits
3. Dataset Creation
4. Dataset Use
5. Additional Information
## Dataset Description
### Abstract
TALI is a large-scale, tetramodal dataset designed to facilitate a shift from unimodal and duomodal to tetramodal research in deep learning. It aligns text, video, images, and audio, providing a rich resource for innovative self-supervised learning tasks and multimodal research. TALI enables exploration of how different modalities and data/model scaling affect downstream performance, with the aim of inspiring diverse research ideas and enhancing understanding of model capabilities and robustness in deep learning.
### Brief Description
TALI (Temporally and semantically Aligned Audio, Language and Images) is a dataset that uses the Wikipedia Image Text (WIT) captions and article titles to search Youtube for videos that match the captions. It then downloads the video, audio, and subtitles from these videos. The result is a rich multimodal dataset that has multiple caption types related to both the WiT Images, and the Youtube videos. This enables learning to take place between either temporally or semantically aligned text, images, audio and video.
## Dataset Information
### Modalities
The TALI dataset consists of the following modalities:
1. Image:
1. Wikipedia caption image
2. Randomly sampled image from youtube video
2. Text
1. Wikipedia Caption Text
2. Wikipedia Title Text
3. Wikipedia Main Body Text
4. YouTube Subtitle Text
5. YouTube Description Text
6. YouTube Title Text
3. Audio
1. YouTube Content Audio
4. Video
1. YouTube Content Video
### Dataset Variants
The TALI dataset comes in three variants that differ in the training set size:
- TALI-small: Contains about 1.3 million 30-second video clips, aligned with 120K WiT entries.
- TALI-base: Contains about 6.5 million 30-second video clips, aligned with 120K WiT entries.
- TALI-big: Contains about 13 million 30-second video clips, aligned with 120K WiT entries.
The validation and test sets remain consistent across all three variants at about 80K Videos aligned to 8K wikipedia entries (10 subclips for each Wikipedia entry) each.
### Dataset Statistics
TBA
## Dataset Creation
The TALI dataset was created by starting from the WiT dataset and using either the context_page_description or page_title as a source-query to search YouTube for video that were creative commons opted-in, and, not age restricted. The top 100 result titles were returned and compared with the source-query using the CLIP text embeddings of the largest CLIP model available. The top-1 title’s video based on the CLIP ranking was chosen and downloaded. The video was broken into 30-second segments and the top-10 segments for eachvideo were chosen based on the distance between the CLIP image embedding of the first image of each segment and the video’s title text. The image, audio, and subtitle frames were extracted from these segments. At sampling time, one of these 10 segments is randomly selected, and a 10-second segment is chosen out of the 30-second clip. The result is 200 video frames (spread throughout the 10-second segment), and 160000 audio frames (10 seconds).
## Dataset Use
TALI is designed for use in a wide range of multimodal research tasks, including but not limited to:
- Multimodal understanding and reasoning
- Self-supervised learning
- Multimodal alignment and translation
- Multimodal summarization
- Multimodal question answering
## Dataset Curators: Antreas Antoniou
Citation Information: TBA
Contributions: Thanks to all contributors including data curators, annotators, and software developers. |
false |
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