
# Dataset Card for "lmqg/qa_harvesting_from_wikipedia_pseudo"

## Dataset Description
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
- **Point of Contact:** [Asahi Ushio](http://asahiushio.com/)

### Dataset Summary
This is a synthetic QA dataset generated with fine-tuned QG models over [`lmqg/qa_harvesting_from_wikipedia`](https://huggingface.co/datasets/lmqg/qa_harvesting_from_wikipedia), 1 million paragraph and answer pairs collected in [Du and Cardie, 2018](https://aclanthology.org/P18-1177/), made for question-answering based evaluation (QAE) for question generation model proposed by [Zhang and Bansal, 2019](https://aclanthology.org/D19-1253/).
The `train` split is the synthetic data and the `validation` split is the original validation set of [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/), where the model should be evaluate on.

This contains synthetic QA datasets created with the following QG models:
- [lmqg/bart-base-squad](https://huggingface.co/lmqg/bart-base-squad)
- [lmqg/bart-large-squad](https://huggingface.co/lmqg/bart-large-squad)
- [lmqg/t5-small-squad](https://huggingface.co/lmqg/t5-small-squad)
- [lmqg/t5-base-squad](https://huggingface.co/lmqg/t5-base-squad)
- [lmqg/t5-large-squad](https://huggingface.co/lmqg/t5-large-squad)

See more detail about the QAE at [https://github.com/asahi417/lm-question-generation/tree/master/misc/qa_based_evaluation](https://github.com/asahi417/lm-question-generation/tree/master/misc/emnlp_2022/qa_based_evaluation).

### Supported Tasks and Leaderboards
* `question-answering`

### Languages
English (en)

## Dataset Structure

### Data Fields
The data fields are the same among all splits.

#### plain_text

- `id`: a `string` feature of id
- `title`: a `string` feature of title of the paragraph 
- `context`: a `string` feature of paragraph 
- `question`: a `string` feature of question
- `answers`: a `json` feature of answers

### Data Splits

|train    |validation|
|--------:|---------:|
|1,092,142|   10,570 |

## Citation Information

```
@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
    author = "Ushio, Asahi  and
        Alva-Manchego, Fernando  and
        Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}
```
task_categories:question-answering task_ids:extractive-qa multilinguality:monolingual size_categories:10K<n<100K source_datasets:extended|wikipedia language:en license:cc-by-4.0 arxiv:2210.03992 region:us
{"language": "en", "license": "cc-by-4.0", "multilinguality": "monolingual", "size_categories": "10K<n<100K", "source_datasets": ["extended|wikipedia"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa"], "pretty_name": "Synthetic QA dataset."}
TAGS
#task_categories-question-answering #task_ids-extractive-qa #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|wikipedia #language-English #license-cc-by-4.0 #arxiv-2210.03992 #region-us 

Dataset Card for "lmqg/qa\_harvesting\_from\_wikipedia\_pseudo"
===============================================================


Dataset Description
-------------------


* Repository: URL
* Paper: URL
* Point of Contact: Asahi Ushio


### Dataset Summary


This is a synthetic QA dataset generated with fine-tuned QG models over 'lmqg/qa\_harvesting\_from\_wikipedia', 1 million paragraph and answer pairs collected in Du and Cardie, 2018, made for question-answering based evaluation (QAE) for question generation model proposed by Zhang and Bansal, 2019.
The 'train' split is the synthetic data and the 'validation' split is the original validation set of SQuAD, where the model should be evaluate on.


This contains synthetic QA datasets created with the following QG models:


* lmqg/bart-base-squad
* lmqg/bart-large-squad
* lmqg/t5-small-squad
* lmqg/t5-base-squad
* lmqg/t5-large-squad


See more detail about the QAE at URL


### Supported Tasks and Leaderboards


* 'question-answering'


### Languages


English (en)


Dataset Structure
-----------------


### Data Fields


The data fields are the same among all splits.


#### plain\_text


* 'id': a 'string' feature of id
* 'title': a 'string' feature of title of the paragraph
* 'context': a 'string' feature of paragraph
* 'question': a 'string' feature of question
* 'answers': a 'json' feature of answers


### Data Splits




### Dataset Summary


This is a synthetic QA dataset generated with fine-tuned QG models over 'lmqg/qa\_harvesting\_from\_wikipedia', 1 million paragraph and answer pairs collected in Du and Cardie, 2018, made for question-answering based evaluation (QAE) for question generation model proposed by Zhang and Bansal, 2019.
The 'train' split is the synthetic data and the 'validation' split is the original validation set of SQuAD, where the model should be evaluate on.


This contains synthetic QA datasets created with the following QG models:


* lmqg/bart-base-squad
* lmqg/bart-large-squad
* lmqg/t5-small-squad
* lmqg/t5-base-squad
* lmqg/t5-large-squad


See more detail about the QAE at URL ### Supported Tasks and Leaderboards


* 'question-answering' ### Languages


English (en)


Dataset Structure
----------------- ### Data Fields


The data fields are the same among all splits. #### plain\_text


* 'id': a 'string' feature of id
* 'title': a 'string' feature of title of the paragraph
* 'context': a 'string' feature of paragraph
* 'question': a 'string' feature of question
* 'answers': a 'json' feature of answers ### Data Splits
TAGS
#task_categories-question-answering #task_ids-extractive-qa #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|wikipedia #language-English #license-cc-by-4.0 #arxiv-2210.03992 #region-us 
 ### Dataset Summary


This is a synthetic QA dataset generated with fine-tuned QG models over 'lmqg/qa\_harvesting\_from\_wikipedia', 1 million paragraph and answer pairs collected in Du and Cardie, 2018, made for question-answering based evaluation (QAE) for question generation model proposed by Zhang and Bansal, 2019.
The 'train' split is the synthetic data and the 'validation' split is the original validation set of SQuAD, where the model should be evaluate on.


This contains synthetic QA datasets created with the following QG models:


* lmqg/bart-base-squad
* lmqg/bart-large-squad
* lmqg/t5-small-squad
* lmqg/t5-base-squad
* lmqg/t5-large-squad


See more detail about the QAE at URL ### Supported Tasks and Leaderboards


* 'question-answering' ### Languages


English (en)


Dataset Structure
----------------- ### Data Fields


The data fields are the same among all splits. #### plain\_text


* 'id': a 'string' feature of id
* 'title': a 'string' feature of title of the paragraph
* 'context': a 'string' feature of paragraph
* 'question': a 'string' feature of question
* 'answers': a 'json' feature of answers ### Data Splits
passage: TAGS
#task_categories-question-answering #task_ids-extractive-qa #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended|wikipedia #language-English #license-cc-by-4.0 #arxiv-2210.03992 #region-us 
### Dataset Summary


This is a synthetic QA dataset generated with fine-tuned QG models over 'lmqg/qa\_harvesting\_from\_wikipedia', 1 million paragraph and answer pairs collected in Du and Cardie, 2018, made for question-answering based evaluation (QAE) for question generation model proposed by Zhang and Bansal, 2019.
The 'train' split is the synthetic data and the 'validation' split is the original validation set of SQuAD, where the model should be evaluate on.


This contains synthetic QA datasets created with the following QG models:


* lmqg/bart-base-squad
* lmqg/bart-large-squad
* lmqg/t5-small-squad
* lmqg/t5-base-squad
* lmqg/t5-large-squad


See more detail about the QAE at URL### Supported Tasks and Leaderboards


* 'question-answering'### Languages


English (en)


Dataset Structure
-----------------### Data Fields


The data fields are the same among all splits.#### plain\_text


* 'id': a 'string' feature of id
* 'title': a 'string' feature of title of the paragraph
* 'context': a 'string' feature of paragraph
* 'question': a 'string' feature of question
* 'answers': a 'json' feature of answers### Data Splits