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fdd3e651eca987d5e0d62f2c7efe4594b952dd6a |
# Dataset Card for Recept
### 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
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed]
| amcoff/recept | [
"task_categories:text-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:sv",
"license:mit",
"region:us"
]
| 2023-02-25T15:45:45+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["sv"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": [], "pretty_name": "Recept", "tags": []} | 2023-02-25T16:19:01+00:00 | []
| [
"sv"
]
| TAGS
#task_categories-text-classification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Swedish #license-mit #region-us
|
# Dataset Card for Recept
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## 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
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Recept",
"### Dataset Summary",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## 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",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
"TAGS\n#task_categories-text-classification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Swedish #license-mit #region-us \n",
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"### 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",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
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"passage: TAGS\n#task_categories-text-classification #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-Swedish #license-mit #region-us \n# Dataset Card for Recept### Dataset Summary### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## 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### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
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|
97f101a4d6e903db82c8fde3762315f835665dec |
## Source
Source: [UCI](https://archive.ics.uci.edu/ml/datasets/BlogFeedback)
## Data Set Information:
This data originates from blog posts. The raw HTML-documents
of the blog posts were crawled and processed.
The prediction task associated with the data is the prediction
of the number of comments in the upcoming 24 hours. In order
to simulate this situation, we choose a basetime (in the past)
and select the blog posts that were published at most
72 hours before the selected base date/time. Then, we calculate
all the features of the selected blog posts from the information
that was available at the basetime, therefore each instance
corresponds to a blog post. The target is the number of
comments that the blog post received in the next 24 hours
relative to the basetime.
In the train data, the basetimes were in the years
2010 and 2011. In the test data the basetimes were
in February and March 2012. This simulates the real-world
situtation in which training data from the past is available
to predict events in the future.
The train data was generated from different basetimes that may
temporally overlap. Therefore, if you simply split the train
into disjoint partitions, the underlying time intervals may
overlap. Therefore, the you should use the provided, temporally
disjoint train and test splits in order to ensure that the
evaluation is fair.
## Attribute Information:
1...50:Average, standard deviation, min, max and median of them attributes 51...60 for the source of the current blog post. With source we mean the blog on which the post appeared.
For example, myblog.blog.org would be the source of the post myblog.blog.org/post_2010_09_10
51: Total number of comments before basetime
52: Number of comments in the last 24 hours before the
basetime
53: Let T1 denote the datetime 48 hours before basetime,
Let T2 denote the datetime 24 hours before basetime.
This attribute is the number of comments in the time period
between T1 and T2
54: Number of comments in the first 24 hours after the
publication of the blog post, but before basetime
55: The difference of Attribute 52 and Attribute 53
56...60:
The same features as the attributes 51...55, but
features 56...60 refer to the number of links (trackbacks),
while features 51...55 refer to the number of comments.
61: The length of time between the publication of the blog post
and basetime
62: The length of the blog post
63...262:
The 200 bag of words features for 200 frequent words of the
text of the blog post
263...269: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the basetime
270...276: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the date of publication of the blog
post
277: Number of parent pages: we consider a blog post P as a
parent of blog post B, if B is a reply (trackback) to
blog post P.
278...280:
Minimum, maximum, average number of comments that the
parents received
281: The target: the number of comments in the next 24 hours
(relative to basetime)
| wwydmanski/blog-feedback | [
"task_categories:tabular-regression",
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"tabular",
"region:us"
]
| 2023-02-25T15:57:14+00:00 | {"size_categories": ["10K<n<100K"], "task_categories": ["tabular-regression", "tabular-classification"], "tags": ["tabular"]} | 2023-02-25T16:03:19+00:00 | []
| []
| TAGS
#task_categories-tabular-regression #task_categories-tabular-classification #size_categories-10K<n<100K #tabular #region-us
|
## Source
Source: UCI
## Data Set Information:
This data originates from blog posts. The raw HTML-documents
of the blog posts were crawled and processed.
The prediction task associated with the data is the prediction
of the number of comments in the upcoming 24 hours. In order
to simulate this situation, we choose a basetime (in the past)
and select the blog posts that were published at most
72 hours before the selected base date/time. Then, we calculate
all the features of the selected blog posts from the information
that was available at the basetime, therefore each instance
corresponds to a blog post. The target is the number of
comments that the blog post received in the next 24 hours
relative to the basetime.
In the train data, the basetimes were in the years
2010 and 2011. In the test data the basetimes were
in February and March 2012. This simulates the real-world
situtation in which training data from the past is available
to predict events in the future.
The train data was generated from different basetimes that may
temporally overlap. Therefore, if you simply split the train
into disjoint partitions, the underlying time intervals may
overlap. Therefore, the you should use the provided, temporally
disjoint train and test splits in order to ensure that the
evaluation is fair.
## Attribute Information:
1...50:Average, standard deviation, min, max and median of them attributes 51...60 for the source of the current blog post. With source we mean the blog on which the post appeared.
For example, URL would be the source of the post URL
51: Total number of comments before basetime
52: Number of comments in the last 24 hours before the
basetime
53: Let T1 denote the datetime 48 hours before basetime,
Let T2 denote the datetime 24 hours before basetime.
This attribute is the number of comments in the time period
between T1 and T2
54: Number of comments in the first 24 hours after the
publication of the blog post, but before basetime
55: The difference of Attribute 52 and Attribute 53
56...60:
The same features as the attributes 51...55, but
features 56...60 refer to the number of links (trackbacks),
while features 51...55 refer to the number of comments.
61: The length of time between the publication of the blog post
and basetime
62: The length of the blog post
63...262:
The 200 bag of words features for 200 frequent words of the
text of the blog post
263...269: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the basetime
270...276: binary indicator features (0 or 1) for the weekday
(Monday...Sunday) of the date of publication of the blog
post
277: Number of parent pages: we consider a blog post P as a
parent of blog post B, if B is a reply (trackback) to
blog post P.
278...280:
Minimum, maximum, average number of comments that the
parents received
281: The target: the number of comments in the next 24 hours
(relative to basetime)
| [
"## Source\nSource: UCI",
"## Data Set Information:\n\nThis data originates from blog posts. The raw HTML-documents\nof the blog posts were crawled and processed.\nThe prediction task associated with the data is the prediction\nof the number of comments in the upcoming 24 hours. In order\nto simulate this situation, we choose a basetime (in the past)\nand select the blog posts that were published at most\n72 hours before the selected base date/time. Then, we calculate\nall the features of the selected blog posts from the information\nthat was available at the basetime, therefore each instance\ncorresponds to a blog post. The target is the number of\ncomments that the blog post received in the next 24 hours\nrelative to the basetime.\n\nIn the train data, the basetimes were in the years\n2010 and 2011. In the test data the basetimes were\nin February and March 2012. This simulates the real-world\nsitutation in which training data from the past is available\nto predict events in the future.\n\nThe train data was generated from different basetimes that may\ntemporally overlap. Therefore, if you simply split the train\ninto disjoint partitions, the underlying time intervals may\noverlap. Therefore, the you should use the provided, temporally\ndisjoint train and test splits in order to ensure that the\nevaluation is fair.",
"## Attribute Information:\n\n1...50:Average, standard deviation, min, max and median of them attributes 51...60 for the source of the current blog post. With source we mean the blog on which the post appeared.\nFor example, URL would be the source of the post URL\n\n51: Total number of comments before basetime\n\n52: Number of comments in the last 24 hours before the\nbasetime\n\n53: Let T1 denote the datetime 48 hours before basetime,\nLet T2 denote the datetime 24 hours before basetime.\nThis attribute is the number of comments in the time period\nbetween T1 and T2\n\n54: Number of comments in the first 24 hours after the\npublication of the blog post, but before basetime\n\n55: The difference of Attribute 52 and Attribute 53\n\n56...60:\nThe same features as the attributes 51...55, but\nfeatures 56...60 refer to the number of links (trackbacks),\nwhile features 51...55 refer to the number of comments.\n\n61: The length of time between the publication of the blog post\nand basetime\n\n62: The length of the blog post\n\n63...262:\nThe 200 bag of words features for 200 frequent words of the\ntext of the blog post\n\n263...269: binary indicator features (0 or 1) for the weekday\n(Monday...Sunday) of the basetime\n\n270...276: binary indicator features (0 or 1) for the weekday\n(Monday...Sunday) of the date of publication of the blog\npost\n\n277: Number of parent pages: we consider a blog post P as a\nparent of blog post B, if B is a reply (trackback) to\nblog post P.\n\n278...280:\nMinimum, maximum, average number of comments that the\nparents received\n\n281: The target: the number of comments in the next 24 hours\n(relative to basetime)"
]
| [
"TAGS\n#task_categories-tabular-regression #task_categories-tabular-classification #size_categories-10K<n<100K #tabular #region-us \n",
"## Source\nSource: UCI",
"## Data Set Information:\n\nThis data originates from blog posts. The raw HTML-documents\nof the blog posts were crawled and processed.\nThe prediction task associated with the data is the prediction\nof the number of comments in the upcoming 24 hours. In order\nto simulate this situation, we choose a basetime (in the past)\nand select the blog posts that were published at most\n72 hours before the selected base date/time. Then, we calculate\nall the features of the selected blog posts from the information\nthat was available at the basetime, therefore each instance\ncorresponds to a blog post. The target is the number of\ncomments that the blog post received in the next 24 hours\nrelative to the basetime.\n\nIn the train data, the basetimes were in the years\n2010 and 2011. In the test data the basetimes were\nin February and March 2012. This simulates the real-world\nsitutation in which training data from the past is available\nto predict events in the future.\n\nThe train data was generated from different basetimes that may\ntemporally overlap. Therefore, if you simply split the train\ninto disjoint partitions, the underlying time intervals may\noverlap. Therefore, the you should use the provided, temporally\ndisjoint train and test splits in order to ensure that the\nevaluation is fair.",
"## Attribute Information:\n\n1...50:Average, standard deviation, min, max and median of them attributes 51...60 for the source of the current blog post. With source we mean the blog on which the post appeared.\nFor example, URL would be the source of the post URL\n\n51: Total number of comments before basetime\n\n52: Number of comments in the last 24 hours before the\nbasetime\n\n53: Let T1 denote the datetime 48 hours before basetime,\nLet T2 denote the datetime 24 hours before basetime.\nThis attribute is the number of comments in the time period\nbetween T1 and T2\n\n54: Number of comments in the first 24 hours after the\npublication of the blog post, but before basetime\n\n55: The difference of Attribute 52 and Attribute 53\n\n56...60:\nThe same features as the attributes 51...55, but\nfeatures 56...60 refer to the number of links (trackbacks),\nwhile features 51...55 refer to the number of comments.\n\n61: The length of time between the publication of the blog post\nand basetime\n\n62: The length of the blog post\n\n63...262:\nThe 200 bag of words features for 200 frequent words of the\ntext of the blog post\n\n263...269: binary indicator features (0 or 1) for the weekday\n(Monday...Sunday) of the basetime\n\n270...276: binary indicator features (0 or 1) for the weekday\n(Monday...Sunday) of the date of publication of the blog\npost\n\n277: Number of parent pages: we consider a blog post P as a\nparent of blog post B, if B is a reply (trackback) to\nblog post P.\n\n278...280:\nMinimum, maximum, average number of comments that the\nparents received\n\n281: The target: the number of comments in the next 24 hours\n(relative to basetime)"
]
| [
45,
6,
275,
391
]
| [
"passage: TAGS\n#task_categories-tabular-regression #task_categories-tabular-classification #size_categories-10K<n<100K #tabular #region-us \n## Source\nSource: UCI## Data Set Information:\n\nThis data originates from blog posts. The raw HTML-documents\nof the blog posts were crawled and processed.\nThe prediction task associated with the data is the prediction\nof the number of comments in the upcoming 24 hours. In order\nto simulate this situation, we choose a basetime (in the past)\nand select the blog posts that were published at most\n72 hours before the selected base date/time. Then, we calculate\nall the features of the selected blog posts from the information\nthat was available at the basetime, therefore each instance\ncorresponds to a blog post. The target is the number of\ncomments that the blog post received in the next 24 hours\nrelative to the basetime.\n\nIn the train data, the basetimes were in the years\n2010 and 2011. In the test data the basetimes were\nin February and March 2012. This simulates the real-world\nsitutation in which training data from the past is available\nto predict events in the future.\n\nThe train data was generated from different basetimes that may\ntemporally overlap. Therefore, if you simply split the train\ninto disjoint partitions, the underlying time intervals may\noverlap. Therefore, the you should use the provided, temporally\ndisjoint train and test splits in order to ensure that the\nevaluation is fair."
]
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|
77d28245cc8586000028b3184c043dc66ec983e0 | # Dataset Card for "SROIE_layoutlmv2_sequence"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Ssunbell/SROIE_layoutlmv2_sequence | [
"region:us"
]
| 2023-02-25T17:09:59+00:00 | {"dataset_info": {"features": [{"name": "guid", "sequence": "string"}, {"name": "words", "sequence": "string"}, {"name": "labels", "sequence": "int64"}, {"name": "boxes", "sequence": {"sequence": "int64"}}, {"name": "actual_bboxes", "sequence": {"sequence": "int64"}}, {"name": "file_name", "dtype": "string"}, {"name": "image", "sequence": {"sequence": {"sequence": "uint8"}}}], "splits": [{"name": "train", "num_bytes": 98185080, "num_examples": 594}, {"name": "val", "num_bytes": 5264337, "num_examples": 32}, {"name": "test", "num_bytes": 57252300, "num_examples": 347}], "download_size": 67438968, "dataset_size": 160701717}} | 2023-02-25T17:10:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "SROIE_layoutlmv2_sequence"
More Information needed | [
"# Dataset Card for \"SROIE_layoutlmv2_sequence\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"SROIE_layoutlmv2_sequence\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"SROIE_layoutlmv2_sequence\"\n\nMore Information needed"
]
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|
de3cef7e4b317991a3a79cc4d7c7347026458aae | # Dataset Card for "stackoverflow_python_preprocessed"
This is a preprocessed version of the [stackoverflow_python] dataset.
Questions and answers were filtered to only include questions with more than 100 votes and answers with more than 5 votes.
The dataset has been converted from HTML to plain text and only includes the title, question, and answer columns.
## Additional Information
### License
All Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | KonradSzafer/stackoverflow_python_preprocessed | [
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"region:us"
]
| 2023-02-25T17:32:31+00:00 | {"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "pretty_name": "Stack Overflow Python - Preprocessed", "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5119086, "num_examples": 3296}], "download_size": 1939470, "dataset_size": 5119086}} | 2023-03-04T23:35:06+00:00 | []
| [
"en"
]
| TAGS
#task_categories-question-answering #size_categories-1K<n<10K #language-English #region-us
| # Dataset Card for "stackoverflow_python_preprocessed"
This is a preprocessed version of the [stackoverflow_python] dataset.
Questions and answers were filtered to only include questions with more than 100 votes and answers with more than 5 votes.
The dataset has been converted from HTML to plain text and only includes the title, question, and answer columns.
## Additional Information
### License
All Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.
More Information needed | [
"# Dataset Card for \"stackoverflow_python_preprocessed\"\n\nThis is a preprocessed version of the [stackoverflow_python] dataset.\nQuestions and answers were filtered to only include questions with more than 100 votes and answers with more than 5 votes.\nThe dataset has been converted from HTML to plain text and only includes the title, question, and answer columns.",
"## Additional Information",
"### License\n\nAll Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #language-English #region-us \n",
"# Dataset Card for \"stackoverflow_python_preprocessed\"\n\nThis is a preprocessed version of the [stackoverflow_python] dataset.\nQuestions and answers were filtered to only include questions with more than 100 votes and answers with more than 5 votes.\nThe dataset has been converted from HTML to plain text and only includes the title, question, and answer columns.",
"## Additional Information",
"### License\n\nAll Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.\n\nMore Information needed"
]
| [
34,
93,
5,
29
]
| [
"passage: TAGS\n#task_categories-question-answering #size_categories-1K<n<10K #language-English #region-us \n# Dataset Card for \"stackoverflow_python_preprocessed\"\n\nThis is a preprocessed version of the [stackoverflow_python] dataset.\nQuestions and answers were filtered to only include questions with more than 100 votes and answers with more than 5 votes.\nThe dataset has been converted from HTML to plain text and only includes the title, question, and answer columns.## Additional Information### License\n\nAll Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.\n\nMore Information needed"
]
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|
6023919a617cdb7566894338012e9bde4b9a8a14 | # Dataset Card for "bioasqqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | lucadiliello/bioasqqa | [
"region:us"
]
| 2023-02-25T18:03:01+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "test", "num_bytes": 2478570, "num_examples": 1504}], "download_size": 1270845, "dataset_size": 2478570}} | 2023-06-06T07:36:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bioasqqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here. | [
"# Dataset Card for \"bioasqqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bioasqqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
6,
43
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bioasqqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
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]
|
1b78fe658eb31cb1323f5e715cbb8af3dace6eb3 | # Dataset Card for "dropqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | lucadiliello/dropqa | [
"region:us"
]
| 2023-02-25T18:03:07+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "test", "num_bytes": 1873397, "num_examples": 1503}], "download_size": 340899, "dataset_size": 1873397}} | 2023-06-06T07:35:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dropqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here. | [
"# Dataset Card for \"dropqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dropqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"dropqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
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|
6a6b3a20c7a24ab8ae3710665c9bed4281300500 | # Dataset Card for "duorc.paraphrasercqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | lucadiliello/duorc.paraphrasercqa | [
"region:us"
]
| 2023-02-25T18:03:12+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "test", "num_bytes": 5755609, "num_examples": 1501}], "download_size": 660068, "dataset_size": 5755609}} | 2023-06-06T07:36:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "duorc.paraphrasercqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here. | [
"# Dataset Card for \"duorc.paraphrasercqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"duorc.paraphrasercqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
6,
48
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"duorc.paraphrasercqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
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|
87a889a73da456a0888ab25d6e91aaf2ff6e9976 | # Dataset Card for "hotpotqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | lucadiliello/hotpotqa | [
"region:us"
]
| 2023-02-25T18:03:18+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 85224549, "num_examples": 72928}, {"name": "validation", "num_bytes": 8285153, "num_examples": 5901}], "download_size": 57326467, "dataset_size": 93509702}} | 2023-06-06T07:36:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "hotpotqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here. | [
"# Dataset Card for \"hotpotqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"hotpotqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
6,
43
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"hotpotqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
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|
49435450b0abb287be45639fdaa49a9b56512f9b | # Dataset Card for "naturalquestionsshortqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | lucadiliello/naturalquestionsshortqa | [
"region:us"
]
| 2023-02-25T18:03:29+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 100706304, "num_examples": 104071}, {"name": "validation", "num_bytes": 12941478, "num_examples": 12836}], "download_size": 61870589, "dataset_size": 113647782}} | 2023-06-06T07:35:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "naturalquestionsshortqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here. | [
"# Dataset Card for \"naturalquestionsshortqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"naturalquestionsshortqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
6,
45
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"naturalquestionsshortqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
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|
728e52920b8e4ffcfaad93fa47556f26a1d82546 | # Dataset Card for "newsqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | lucadiliello/newsqa | [
"region:us"
]
| 2023-02-25T18:03:41+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 234711053, "num_examples": 74160}, {"name": "validation", "num_bytes": 13234782, "num_examples": 4212}], "download_size": 31328809, "dataset_size": 247945835}} | 2023-06-06T07:36:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "newsqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here. | [
"# Dataset Card for \"newsqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"newsqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"newsqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
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|
20d542d2b3bb0dcfa828ba2116832ebdf11eedcf | # Dataset Card for "raceqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | lucadiliello/raceqa | [
"region:us"
]
| 2023-02-25T18:03:51+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "test", "num_bytes": 1272251, "num_examples": 674}], "download_size": 747482, "dataset_size": 1272251}} | 2023-06-06T07:33:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "raceqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here. | [
"# Dataset Card for \"raceqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"raceqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"raceqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
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|
84d634a1bc5ad3bdb084b170ecb5c8837219af2e | # Dataset Card for "relationextractionqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | lucadiliello/relationextractionqa | [
"region:us"
]
| 2023-02-25T18:03:57+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "test", "num_bytes": 863192, "num_examples": 2948}], "download_size": 527428, "dataset_size": 863192}} | 2023-06-06T07:33:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "relationextractionqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here. | [
"# Dataset Card for \"relationextractionqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"relationextractionqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
6,
45
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"relationextractionqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
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|
c1a979068ba118d85467179b704031d113d689cc | # Dataset Card for "searchqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | lucadiliello/searchqa | [
"region:us"
]
| 2023-02-25T18:04:03+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 483999103, "num_examples": 117384}, {"name": "validation", "num_bytes": 69647447, "num_examples": 16980}], "download_size": 325197949, "dataset_size": 553646550}} | 2023-06-06T07:34:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "searchqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here. | [
"# Dataset Card for \"searchqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"searchqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"searchqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
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|
6eb617820c532f5fab2d3b7aea89b55dc0b2ffde | # Dataset Card for "squadqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | lucadiliello/squadqa | [
"region:us"
]
| 2023-02-25T18:04:23+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 79320473, "num_examples": 86588}, {"name": "validation", "num_bytes": 10998734, "num_examples": 10507}], "download_size": 19381701, "dataset_size": 90319207}} | 2023-06-06T07:32:16+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "squadqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here. | [
"# Dataset Card for \"squadqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"squadqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
6,
43
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"squadqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
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|
ec81cbcf12163653deb26018f3377ec02948eca1 | # Dataset Card for "textbookqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | lucadiliello/textbookqa | [
"region:us"
]
| 2023-02-25T18:04:33+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "test", "num_bytes": 5371294, "num_examples": 1503}], "download_size": 802199, "dataset_size": 5371294}} | 2023-06-06T07:33:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "textbookqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here. | [
"# Dataset Card for \"textbookqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"textbookqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
6,
43
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"textbookqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
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|
b451053aeb939f0317f9f2df9ce2cfbbcb20ec05 | # Dataset Card for "triviaqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look [here](https://huggingface.co/datasets/mrqa). | lucadiliello/triviaqa | [
"region:us"
]
| 2023-02-25T18:04:39+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "key", "dtype": "string"}, {"name": "labels", "list": [{"name": "end", "sequence": "int64"}, {"name": "start", "sequence": "int64"}]}], "splits": [{"name": "train", "num_bytes": 270309068, "num_examples": 61688}, {"name": "validation", "num_bytes": 34067846, "num_examples": 7785}], "download_size": 178840166, "dataset_size": 304376914}} | 2023-06-06T07:31:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "triviaqa"
Split taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here. | [
"# Dataset Card for \"triviaqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"triviaqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
| [
6,
43
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"triviaqa\"\n\nSplit taken from the MRQA 2019 Shared Task, formatted and filtered for Question Answering. For the original dataset, have a look here."
]
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|
3dc7500505465d83c5e3fb01c8db3567c2d353e0 | # Dataset Card for "FontsLarge"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arbml/FontsLarge | [
"region:us"
]
| 2023-02-25T19:07:52+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 45180875.629, "num_examples": 9921}], "download_size": 28777247, "dataset_size": 45180875.629}} | 2023-02-25T19:07:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "FontsLarge"
More Information needed | [
"# Dataset Card for \"FontsLarge\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"FontsLarge\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"FontsLarge\"\n\nMore Information needed"
]
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|
094f17b0b2922e209a3807b1c0910ae8da567e42 | # Dataset Card for "wiki_book_corpus_processed_bert_dataset_medium"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | gokuls/wiki_book_corpus_processed_bert_dataset_medium | [
"region:us"
]
| 2023-02-25T19:22:17+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 11100805200.0, "num_examples": 3083557}], "download_size": 3272451599, "dataset_size": 11100805200.0}} | 2023-02-25T19:37:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wiki_book_corpus_processed_bert_dataset_medium"
More Information needed | [
"# Dataset Card for \"wiki_book_corpus_processed_bert_dataset_medium\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wiki_book_corpus_processed_bert_dataset_medium\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wiki_book_corpus_processed_bert_dataset_medium\"\n\nMore Information needed"
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|
eb3340c128e3715ba8224576f5a7449c0acd82b1 | # Dataset Card for "wiki_book_corpus_raw_dataset_medium"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | gokuls/wiki_book_corpus_raw_dataset_medium | [
"region:us"
]
| 2023-02-25T19:38:11+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 12250082590.5, "num_examples": 40231449}], "download_size": 7774316723, "dataset_size": 12250082590.5}} | 2023-02-25T20:10:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wiki_book_corpus_raw_dataset_medium"
More Information needed | [
"# Dataset Card for \"wiki_book_corpus_raw_dataset_medium\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wiki_book_corpus_raw_dataset_medium\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wiki_book_corpus_raw_dataset_medium\"\n\nMore Information needed"
]
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|
d355b3056a9ed9e0ed30cb981b962c05cd3663e6 | # AutoTrain Dataset for project: multifamily_v2
## Dataset Description
This dataset has been automatically processed by AutoTrain for project multifamily_v2.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<500x333 RGB PIL image>",
"target": 33
},
{
"image": "<500x667 RGB PIL image>",
"target": 11
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Balcony-Patio', 'Bathroom', 'Bedroom', 'Bike', 'Building', 'Business Center', 'Business Center Conference Room', 'Closet', 'Clubhouse', 'Clubhouse Dining Room', 'Construction', 'Dining Room', 'Dog Park', 'Fire Pit', 'Fitness Center', 'Floorplan', 'Fountain', 'Green Space', 'Grilling Area', 'Hallway', 'Headshot', 'Home Office', 'Hot Tub', 'Kitchen', 'Laundry Facility', 'Laundry Washer-Dryer', 'Leasing Office', 'Living Room', 'Living Room Fireplace', 'Logo', 'Lounge Area', 'Mail Box', 'Monument Sign', 'Neighborhood', 'Packages', 'Parking', 'Pet Washing', 'Picnic Area', 'Play Park', 'Pool', 'Pool Cabanas', 'Pool Table', 'Private Garage', 'Site-plan', 'Stock Photo', 'Tennis Court', 'View-Aerial', 'room'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 1748 |
| valid | 453 |
| lineups-io/autotrain-data-multifamily_v2 | [
"task_categories:image-classification",
"region:us"
]
| 2023-02-25T19:45:19+00:00 | {"task_categories": ["image-classification"]} | 2023-02-25T21:18:21+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: multifamily\_v2
==============================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project multifamily\_v2.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-image-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
17,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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|
ae76cf9f490ef0d9873e8e2a2cb1b72d1836641e | # Dataset Card for "wiki_book_corpus_processed_bert_dataset_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | gokuls/wiki_book_corpus_processed_bert_dataset_small | [
"region:us"
]
| 2023-02-25T20:10:23+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 5550400800.0, "num_examples": 1541778}], "download_size": 1636779213, "dataset_size": 5550400800.0}} | 2023-02-25T20:18:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wiki_book_corpus_processed_bert_dataset_small"
More Information needed | [
"# Dataset Card for \"wiki_book_corpus_processed_bert_dataset_small\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wiki_book_corpus_processed_bert_dataset_small\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wiki_book_corpus_processed_bert_dataset_small\"\n\nMore Information needed"
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|
3e6f67f6a83acd00699e97f8cf8b9b841491a2ee | # Dataset Card for "wiki_book_corpus_raw_dataset_small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | gokuls/wiki_book_corpus_raw_dataset_small | [
"region:us"
]
| 2023-02-25T20:18:08+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6125041143.004892, "num_examples": 20115724}], "download_size": 3887778273, "dataset_size": 6125041143.004892}} | 2023-02-25T20:34:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wiki_book_corpus_raw_dataset_small"
More Information needed | [
"# Dataset Card for \"wiki_book_corpus_raw_dataset_small\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wiki_book_corpus_raw_dataset_small\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wiki_book_corpus_raw_dataset_small\"\n\nMore Information needed"
]
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|
a3fd459176a64ac5ae46366d8c006e329ce6e874 | # Dataset Card for "SORIE_layoutlmv2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Ssunbell/SORIE_layoutlmv2 | [
"region:us"
]
| 2023-02-25T20:32:38+00:00 | {"dataset_info": {"features": [{"name": "words", "sequence": "string"}, {"name": "labels", "sequence": "int64"}, {"name": "boxes", "sequence": {"sequence": "int64"}}, {"name": "file_name", "dtype": "string"}, {"name": "actual_bboxes", "sequence": {"sequence": "int64"}}, {"name": "image", "sequence": {"sequence": {"sequence": "uint8"}}}], "splits": [{"name": "train", "num_bytes": 9991089032, "num_examples": 65151}, {"name": "val", "num_bytes": 1110120734, "num_examples": 7239}, {"name": "test", "num_bytes": 6085345877, "num_examples": 39682}], "download_size": 7559946719, "dataset_size": 17186555643}} | 2023-02-25T20:58:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "SORIE_layoutlmv2"
More Information needed | [
"# Dataset Card for \"SORIE_layoutlmv2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"SORIE_layoutlmv2\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"SORIE_layoutlmv2\"\n\nMore Information needed"
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|
5dec05f73088dcb7ce54bd9f2b7ffe3a57a3a582 | # Dataset Card for "wiki_book_corpus_processed_bert_dataset_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | gokuls/wiki_book_corpus_processed_bert_dataset_tiny | [
"region:us"
]
| 2023-02-25T20:34:27+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "special_tokens_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 2775200400.0, "num_examples": 770889}], "download_size": 818434567, "dataset_size": 2775200400.0}} | 2023-02-25T20:38:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wiki_book_corpus_processed_bert_dataset_tiny"
More Information needed | [
"# Dataset Card for \"wiki_book_corpus_processed_bert_dataset_tiny\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wiki_book_corpus_processed_bert_dataset_tiny\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wiki_book_corpus_processed_bert_dataset_tiny\"\n\nMore Information needed"
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|
17b5aaf2d1494fdbf99c6ae85026ed24a787686b | # Dataset Card for "wiki_book_corpus_raw_dataset_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | gokuls/wiki_book_corpus_raw_dataset_tiny | [
"region:us"
]
| 2023-02-25T20:38:09+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3062520571.502446, "num_examples": 10057862}], "download_size": 1942096237, "dataset_size": 3062520571.502446}} | 2023-02-25T20:45:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wiki_book_corpus_raw_dataset_tiny"
More Information needed | [
"# Dataset Card for \"wiki_book_corpus_raw_dataset_tiny\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wiki_book_corpus_raw_dataset_tiny\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wiki_book_corpus_raw_dataset_tiny\"\n\nMore Information needed"
]
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|
b433cbb3f74899f7118bbe9689f38ddf27f1e089 | # AutoTrain Dataset for project: weather-classification
## Dataset Description
This dataset has been automatically processed by AutoTrain for project weather-classification.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<771x514 RGB PIL image>",
"target": 2
},
{
"image": "<269x254 RGB PIL image>",
"target": 8
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['dew', 'fogsmog', 'frost', 'glaze', 'hail', 'lightning', 'rain', 'rainbow', 'rime', 'sandstorm', 'snow'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 5484 |
| valid | 1378 |
| dazzle-nu/autotrain-data-weather-classification | [
"task_categories:image-classification",
"region:us"
]
| 2023-02-25T22:54:10+00:00 | {"task_categories": ["image-classification"]} | 2023-02-26T00:02:09+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: weather-classification
=====================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project weather-classification.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-image-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
17,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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|
a5af251bbab6979f9fa86cb41fb8919beca1b016 | # Dataset Card for "reorder.natural.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/reorder.natural.es | [
"region:us"
]
| 2023-02-25T23:19:01+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 504857351, "dataset_size": 1091423892}} | 2023-02-25T23:19:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
9b9fdc2e2ca31f5f24d6026faedca4500c858b6c | # Dataset Card for "reorder.svo.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/reorder.svo.es | [
"region:us"
]
| 2023-02-25T23:20:10+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 511493512, "dataset_size": 1091423892}} | 2023-02-25T23:20:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
323f556849c2f756fb4795504d7c940726b4cf61 | # Dataset Card for "reorder.vos.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/reorder.vos.es | [
"region:us"
]
| 2023-02-25T23:21:17+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 514360793, "dataset_size": 1091423892}} | 2023-02-25T23:21:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
b871a0c1ae098d708c43d0d953f792da8c1e51a7 | # Dataset Card for "reorder.osv.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/reorder.osv.es | [
"region:us"
]
| 2023-02-25T23:22:21+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 514726308, "dataset_size": 1091423892}} | 2023-02-25T23:23:01+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
9ff11a8b83872fda132f8152d532a8be307ae8fc | # Dataset Card for "reorder.sov.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/reorder.sov.es | [
"region:us"
]
| 2023-02-25T23:23:25+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 514178300, "dataset_size": 1091423892}} | 2023-02-25T23:24:05+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
f5c70c4c447134efea7bca24ad683743a9fa3742 | # Dataset Card for "reorder.vso.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/reorder.vso.es | [
"region:us"
]
| 2023-02-25T23:24:32+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 512310606, "dataset_size": 1091423892}} | 2023-02-25T23:25:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
0deb9d0ff99942e681f80f9bb4cb68ffb9b50667 | # Dataset Card for "reorder.ovs.es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/reorder.ovs.es | [
"region:us"
]
| 2023-02-25T23:25:38+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1091423892, "num_examples": 566216}], "download_size": 513562772, "dataset_size": 1091423892}} | 2023-02-25T23:26:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
89c2cabb1a8905793d0a24ceaa434ef8bd0186d1 | # Dataset Card for "zambezivoice_bem_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zambezivoice/zambezivoice_bem_text | [
"region:us"
]
| 2023-02-26T00:16:36+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 937260, "num_examples": 14121}], "download_size": 629604, "dataset_size": 937260}} | 2023-02-26T00:16:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "zambezivoice_bem_text"
More Information needed | [
"# Dataset Card for \"zambezivoice_bem_text\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"zambezivoice_bem_text\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"zambezivoice_bem_text\"\n\nMore Information needed"
]
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|
8d9baf1f5d4092ff480de7ddf3f0256ee0bf83fc | Concatenated and edited collection of fairy tales taken from Project Gutenberg.
Texts:
https://www.gutenberg.org/files/2591/2591-0.txt
https://www.gutenberg.org/files/503/503-0.txt
https://www.gutenberg.org/files/7277/7277-0.txt
https://www.gutenberg.org/cache/epub/35862/pg35862.txt
https://www.gutenberg.org/cache/epub/69739/pg69739.txt
https://www.gutenberg.org/files/2435/2435-0.txt
https://www.gutenberg.org/cache/epub/7871/pg7871.txt
https://www.gutenberg.org/files/8933/8933-0.txt
gutenberg.org/cache/epub/30834/pg30834.txt
https://www.gutenberg.org/cache/epub/68589/pg68589.txt
https://www.gutenberg.org/cache/epub/34453/pg34453.txt
gutenberg.org/cache/epub/8653/pg8653.txt | vicclab/fairy_tales | [
"task_categories:text-generation",
"language:en",
"region:us"
]
| 2023-02-26T01:18:41+00:00 | {"language": ["en"], "task_categories": ["text-generation"]} | 2023-02-27T10:35:24+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #language-English #region-us
| Concatenated and edited collection of fairy tales taken from Project Gutenberg.
Texts:
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL
URL | []
| [
"TAGS\n#task_categories-text-generation #language-English #region-us \n"
]
| [
21
]
| [
"passage: TAGS\n#task_categories-text-generation #language-English #region-us \n"
]
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fd4a6fa3297645ffbb8c019f0a35d29d0c673a17 | # Dataset Card for "the-stack-dedup-python-docstrings-1.0-percent-unified"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | trelent/the-stack-dedup-python-docstrings-1.0-percent-unified | [
"region:us"
]
| 2023-02-26T01:31:05+00:00 | {"dataset_info": {"features": [{"name": "body_hash", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "docstring", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "repository_name", "dtype": "string"}, {"name": "repository_stars", "dtype": "float64"}, {"name": "lang", "dtype": "string"}, {"name": "body_without_docstring", "dtype": "string"}, {"name": "unified", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 680876286, "num_examples": 237074}], "download_size": 247316903, "dataset_size": 680876286}} | 2023-02-26T01:33:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "the-stack-dedup-python-docstrings-1.0-percent-unified"
More Information needed | [
"# Dataset Card for \"the-stack-dedup-python-docstrings-1.0-percent-unified\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"the-stack-dedup-python-docstrings-1.0-percent-unified\"\n\nMore Information needed"
]
| [
6,
32
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"the-stack-dedup-python-docstrings-1.0-percent-unified\"\n\nMore Information needed"
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|
4c83fedf8e0249494c534ba65998d76ca09fba93 | # Dataset Card for "switchboard-ner-non-normalized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | anonymoussubmissions/switchboard-ner-non-normalized | [
"region:us"
]
| 2023-02-26T02:12:37+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}, {"name": "labels_orig", "sequence": "string"}, {"name": "tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-CARDINAL", "2": "B-DATE", "3": "B-EVENT", "4": "B-FAC", "5": "B-GPE", "6": "B-LANGUAGE", "7": "B-LAW", "8": "B-LOC", "9": "B-MONEY", "10": "B-NORP", "11": "B-ORDINAL", "12": "B-ORG", "13": "B-PERCENT", "14": "B-PERSON", "15": "B-PRODUCT", "16": "B-QUANTITY", "17": "B-TIME", "18": "B-WORK_OF_ART", "19": "I-CARDINAL", "20": "I-DATE", "21": "I-EVENT", "22": "I-FAC", "23": "I-GPE", "24": "I-LANGUAGE", "25": "I-LAW", "26": "I-LOC", "27": "I-MONEY", "28": "I-NORP", "29": "I-ORDINAL", "30": "I-ORG", "31": "I-PERCENT", "32": "I-PERSON", "33": "I-PRODUCT", "34": "I-QUANTITY", "35": "I-TIME", "36": "I-WORK_OF_ART"}}}}, {"name": "swbd_id", "dtype": "string"}, {"name": "swne_sentence_no", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2309183, "num_examples": 7713}, {"name": "validation", "num_bytes": 1539969, "num_examples": 5143}, {"name": "test", "num_bytes": 2577677, "num_examples": 8571}], "download_size": 913903, "dataset_size": 6426829}} | 2023-02-26T02:12:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "switchboard-ner-non-normalized"
More Information needed | [
"# Dataset Card for \"switchboard-ner-non-normalized\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"switchboard-ner-non-normalized\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"switchboard-ner-non-normalized\"\n\nMore Information needed"
]
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|
8b0096acbcdadbb773213c123249b7735003de79 | # Dataset Card for "switchboard-ner-normalized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | anonymoussubmissions/switchboard-ner-normalized | [
"region:us"
]
| 2023-02-26T02:12:43+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}, {"name": "labels_orig", "sequence": "string"}, {"name": "tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-CARDINAL", "2": "B-DATE", "3": "B-EVENT", "4": "B-FAC", "5": "B-GPE", "6": "B-LANGUAGE", "7": "B-LAW", "8": "B-LOC", "9": "B-MONEY", "10": "B-NORP", "11": "B-ORDINAL", "12": "B-ORG", "13": "B-PERCENT", "14": "B-PERSON", "15": "B-PRODUCT", "16": "B-QUANTITY", "17": "B-TIME", "18": "B-WORK_OF_ART", "19": "I-CARDINAL", "20": "I-DATE", "21": "I-EVENT", "22": "I-FAC", "23": "I-GPE", "24": "I-LANGUAGE", "25": "I-LAW", "26": "I-LOC", "27": "I-MONEY", "28": "I-NORP", "29": "I-ORDINAL", "30": "I-ORG", "31": "I-PERCENT", "32": "I-PERSON", "33": "I-PRODUCT", "34": "I-QUANTITY", "35": "I-TIME", "36": "I-WORK_OF_ART"}}}}, {"name": "swbd_id", "dtype": "string"}, {"name": "swne_sentence_no", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2651450, "num_examples": 7721}, {"name": "validation", "num_bytes": 1778041, "num_examples": 5148}, {"name": "test", "num_bytes": 2962454, "num_examples": 8580}], "download_size": 1002621, "dataset_size": 7391945}} | 2023-02-26T02:12:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "switchboard-ner-normalized"
More Information needed | [
"# Dataset Card for \"switchboard-ner-normalized\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"switchboard-ner-normalized\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"switchboard-ner-normalized\"\n\nMore Information needed"
]
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6d86872b1b1b43bcc2fcae9a9bb6f0ca19a1023e | # Dataset Card for "patched_test_p_200_f_membrane_m1_predictions"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_200_f_membrane_m1_predictions | [
"region:us"
]
| 2023-02-26T04:16:03+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 1379688652, "num_examples": 2057621}], "download_size": 120998098, "dataset_size": 1379688652}} | 2023-02-26T04:16:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_200_f_membrane_m1_predictions"
More Information needed | [
"# Dataset Card for \"patched_test_p_200_f_membrane_m1_predictions\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_200_f_membrane_m1_predictions\"\n\nMore Information needed"
]
| [
6,
31
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_200_f_membrane_m1_predictions\"\n\nMore Information needed"
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ef27843a62e9673eb9703eb895e7717108cb99be | # Dataset Card for "squad_v2_factuality_v2_top5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | kenhktsui/squad_v2_factuality_v2_top5 | [
"region:us"
]
| 2023-02-26T04:18:37+00:00 | {"dataset_info": {"features": [{"name": "context", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "entity_modified", "dtype": "string"}, {"name": "new_entity", "dtype": "string"}, {"name": "ner_type", "dtype": "string"}, {"name": "top_k", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 13582791, "num_examples": 17250}], "download_size": 8600847, "dataset_size": 13582791}} | 2023-02-26T04:18:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "squad_v2_factuality_v2_top5"
More Information needed | [
"# Dataset Card for \"squad_v2_factuality_v2_top5\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"squad_v2_factuality_v2_top5\"\n\nMore Information needed"
]
| [
6,
25
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"squad_v2_factuality_v2_top5\"\n\nMore Information needed"
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|
786522515790b00ae4156278b768f610e481c4f3 | # AutoTrain Dataset for project: guitarsproject
## Dataset Description
This dataset has been automatically processed by AutoTrain for project guitarsproject.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<1990x2520 RGB PIL image>",
"target": 1
},
{
"image": "<6000x4000 RGB PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['LesPaul', 'Stratocaster'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 80 |
| valid | 21 |
| rpereira90/autotrain-data-guitarsproject | [
"task_categories:image-classification",
"region:us"
]
| 2023-02-26T05:49:44+00:00 | {"task_categories": ["image-classification"]} | 2023-02-26T05:50:55+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: guitarsproject
=============================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project guitarsproject.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-image-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
17,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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|
0e58105e93f0255a388f198a75e619ecc1e4f474 | # Dataset Card for "har"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | hazardous/har | [
"region:us"
]
| 2023-02-26T08:48:31+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "calling", "1": "clapping", "2": "cycling", "3": "dancing", "4": "drinking", "5": "eating", "6": "fighting", "7": "hugging", "8": "laughing", "9": "listening_to_music", "10": "running", "11": "sitting", "12": "sleeping", "13": "texting", "14": "using_laptop"}}}}], "splits": [{"name": "train", "num_bytes": 208908112.2, "num_examples": 12600}], "download_size": 227817680, "dataset_size": 208908112.2}} | 2023-02-26T09:18:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "har"
More Information needed | [
"# Dataset Card for \"har\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"har\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"har\"\n\nMore Information needed"
]
| [
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89e85f5c829c7cc31afbbcfae82d543ac237fead | # Dataset Card for Dataset Name
## Dataset Description
- **https://duskfallcrew.carrd.co/:**
- **https://discord.gg/Da7s8d3KJ7**
### Dataset Summary
A mixture of photography and other goods from Dusfkallcrew that has been either curated or taken by duskfall crew. Some may or may not be AI generated.
This template was generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Languages
English mainly, but that's because the data is largely of New Zealand.
### Source Data
### Personal and Sensitive Information
No personal data has been included in this data, it is ALL a mixture of AI generated and personally created photography.
If data is not from what is said, then the data set will be cleaned of any errors.
## Considerations for Using the Data
### Social Impact of Dataset
Too much time on my hands.
### Discussion of Biases
It's a DSLR, it's a samsung phne - its' a BIRD ITS A - you get my point. There shoudl be no bias other than where I can actually take photos.
### Licensing Information
Do not sell this dataset, however you may use it as you see fit in TEXT TO IMAGE stable diffusion models.
Your outputs are your own, and the datawithin is free to be used for AI generation models.
### Citation Information
None needed.
### Contributions
If you'd like to contribute please do so! | Capsekai/DuskfallcrewPhotography | [
"task_categories:text-to-image",
"task_categories:image-classification",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"new zealand",
"photography",
"region:us"
]
| 2023-02-26T09:05:18+00:00 | {"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image", "image-classification"], "pretty_name": "Duskfall Photography", "tags": ["new zealand", "photography"]} | 2023-02-26T10:25:35+00:00 | []
| [
"en"
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| TAGS
#task_categories-text-to-image #task_categories-image-classification #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #new zealand #photography #region-us
| # Dataset Card for Dataset Name
## Dataset Description
- URL
- URL
### Dataset Summary
A mixture of photography and other goods from Dusfkallcrew that has been either curated or taken by duskfall crew. Some may or may not be AI generated.
This template was generated using this raw template.
### Languages
English mainly, but that's because the data is largely of New Zealand.
### Source Data
### Personal and Sensitive Information
No personal data has been included in this data, it is ALL a mixture of AI generated and personally created photography.
If data is not from what is said, then the data set will be cleaned of any errors.
## Considerations for Using the Data
### Social Impact of Dataset
Too much time on my hands.
### Discussion of Biases
It's a DSLR, it's a samsung phne - its' a BIRD ITS A - you get my point. There shoudl be no bias other than where I can actually take photos.
### Licensing Information
Do not sell this dataset, however you may use it as you see fit in TEXT TO IMAGE stable diffusion models.
Your outputs are your own, and the datawithin is free to be used for AI generation models.
None needed.
### Contributions
If you'd like to contribute please do so! | [
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"## Dataset Description\n\n- URL \n\n- URL",
"### Dataset Summary\n\nA mixture of photography and other goods from Dusfkallcrew that has been either curated or taken by duskfall crew. Some may or may not be AI generated. \nThis template was generated using this raw template.",
"### Languages\n\nEnglish mainly, but that's because the data is largely of New Zealand.",
"### Source Data",
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"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nToo much time on my hands.",
"### Discussion of Biases\n\nIt's a DSLR, it's a samsung phne - its' a BIRD ITS A - you get my point. There shoudl be no bias other than where I can actually take photos.",
"### Licensing Information\n\nDo not sell this dataset, however you may use it as you see fit in TEXT TO IMAGE stable diffusion models. \nYour outputs are your own, and the datawithin is free to be used for AI generation models. \n\n\n\nNone needed.",
"### Contributions\n\nIf you'd like to contribute please do so!"
]
| [
"TAGS\n#task_categories-text-to-image #task_categories-image-classification #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #new zealand #photography #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- URL \n\n- URL",
"### Dataset Summary\n\nA mixture of photography and other goods from Dusfkallcrew that has been either curated or taken by duskfall crew. Some may or may not be AI generated. \nThis template was generated using this raw template.",
"### Languages\n\nEnglish mainly, but that's because the data is largely of New Zealand.",
"### Source Data",
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"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nToo much time on my hands.",
"### Discussion of Biases\n\nIt's a DSLR, it's a samsung phne - its' a BIRD ITS A - you get my point. There shoudl be no bias other than where I can actually take photos.",
"### Licensing Information\n\nDo not sell this dataset, however you may use it as you see fit in TEXT TO IMAGE stable diffusion models. \nYour outputs are your own, and the datawithin is free to be used for AI generation models. \n\n\n\nNone needed.",
"### Contributions\n\nIf you'd like to contribute please do so!"
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|
fdfcf30fa2b95639efe19f19d69143490a773e9a | # AutoTrain Dataset for project: phototest
## Dataset Description
This dataset has been automatically processed by AutoTrain for project phototest.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<768x768 RGB PIL image>",
"target": 0
},
{
"image": "<768x768 RGB PIL image>",
"target": 3
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Row 1', 'Row 2', 'Row 3', 'Row 4', 'Row 5'], id=None)"
}
```
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| Split name | Num samples |
| ------------ | ------------------- |
| train | 72 |
| valid | 19 | | Duskfallcrew/autotrain-data-phototest | [
"task_categories:image-classification",
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"region:us"
]
| 2023-02-26T09:13:52+00:00 | {"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["image-classification", "text-to-image"], "pretty_name": "Phototest"} | 2023-02-26T10:46:20+00:00 | []
| [
"en"
]
| TAGS
#task_categories-image-classification #task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #region-us
| AutoTrain Dataset for project: phototest
========================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project phototest.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-image-classification #task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
57,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-image-classification #task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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|
c6d79c54153a671ed0fd1d6fc9407b3120b5f592 | # Dataset Card for "kek"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | BrennanGambling/kek | [
"region:us"
]
| 2023-02-26T09:45:10+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 22400683756, "num_examples": 536512826}], "download_size": 14348823259, "dataset_size": 22400683756}} | 2023-02-26T11:00:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "kek"
More Information needed | [
"# Dataset Card for \"kek\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"kek\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"kek\"\n\nMore Information needed"
]
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|
34576c0fbb3a4cbf021676ead6f38fb4197f237d | # Dataset Card for "mgb2_audios_transcriptions_non_overlap"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | BelalElhossany/mgb2_audios_transcriptions_non_overlap | [
"region:us"
]
| 2023-02-26T10:08:30+00:00 | {"dataset_info": {"features": [{"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 901857303.92, "num_examples": 4972}], "download_size": 965382804, "dataset_size": 901857303.92}} | 2023-02-26T10:09:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mgb2_audios_transcriptions_non_overlap"
More Information needed | [
"# Dataset Card for \"mgb2_audios_transcriptions_non_overlap\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mgb2_audios_transcriptions_non_overlap\"\n\nMore Information needed"
]
| [
6,
25
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mgb2_audios_transcriptions_non_overlap\"\n\nMore Information needed"
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|
948263bbab398417ff90f4253149a779c0493daa |
# Do not resell the data, you don't own the data but you do your own outputs of your training. See main lisc for details | Capsekai/80sCartoons | [
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"text",
"text to image",
"stable diffusion",
"80s",
"region:us"
]
| 2023-02-26T10:36:18+00:00 | {"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "pretty_name": "Eighties Cartoons", "tags": ["text", "text to image", "stable diffusion", "80s"]} | 2023-02-26T10:40:31+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #text #text to image #stable diffusion #80s #region-us
|
# Do not resell the data, you don't own the data but you do your own outputs of your training. See main lisc for details | [
"# Do not resell the data, you don't own the data but you do your own outputs of your training. See main lisc for details"
]
| [
"TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #text #text to image #stable diffusion #80s #region-us \n",
"# Do not resell the data, you don't own the data but you do your own outputs of your training. See main lisc for details"
]
| [
60,
32
]
| [
"passage: TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #text #text to image #stable diffusion #80s #region-us \n# Do not resell the data, you don't own the data but you do your own outputs of your training. See main lisc for details"
]
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|
961c58a6aab1f4736f6a34be968d672a515a886e |
## Source
https://www.kaggle.com/datasets/dhoogla/unswnb15?resource=download
## Dataset
This is an academic intrusion detection dataset. All the credit goes to the original authors: dr. Nour Moustafa and dr. Jill Slay.
Please cite their original paper and all other appropriate articles listed on the UNSW-NB15 page.
The full dataset also offers the pcap, BRO and Argus files along with additional documentation.
The modifications to the predesignated train-test sets are minimal and designed to decrease disk storage and increase performance & reliability.
Exploratory Data Analysis (EDA) through classification with very simple models to .877 AUROC. | wwydmanski/UNSW-NB15 | [
"task_categories:tabular-classification",
"size_categories:1M<n<10M",
"tabular",
"network",
"region:us"
]
| 2023-02-26T11:07:57+00:00 | {"size_categories": ["1M<n<10M"], "task_categories": ["tabular-classification"], "tags": ["tabular", "network"]} | 2023-02-26T11:14:46+00:00 | []
| []
| TAGS
#task_categories-tabular-classification #size_categories-1M<n<10M #tabular #network #region-us
|
## Source
URL
## Dataset
This is an academic intrusion detection dataset. All the credit goes to the original authors: dr. Nour Moustafa and dr. Jill Slay.
Please cite their original paper and all other appropriate articles listed on the UNSW-NB15 page.
The full dataset also offers the pcap, BRO and Argus files along with additional documentation.
The modifications to the predesignated train-test sets are minimal and designed to decrease disk storage and increase performance & reliability.
Exploratory Data Analysis (EDA) through classification with very simple models to .877 AUROC. | [
"## Source\nURL",
"## Dataset\n\nThis is an academic intrusion detection dataset. All the credit goes to the original authors: dr. Nour Moustafa and dr. Jill Slay.\n\nPlease cite their original paper and all other appropriate articles listed on the UNSW-NB15 page.\n\nThe full dataset also offers the pcap, BRO and Argus files along with additional documentation.\n\nThe modifications to the predesignated train-test sets are minimal and designed to decrease disk storage and increase performance & reliability.\n\nExploratory Data Analysis (EDA) through classification with very simple models to .877 AUROC."
]
| [
"TAGS\n#task_categories-tabular-classification #size_categories-1M<n<10M #tabular #network #region-us \n",
"## Source\nURL",
"## Dataset\n\nThis is an academic intrusion detection dataset. All the credit goes to the original authors: dr. Nour Moustafa and dr. Jill Slay.\n\nPlease cite their original paper and all other appropriate articles listed on the UNSW-NB15 page.\n\nThe full dataset also offers the pcap, BRO and Argus files along with additional documentation.\n\nThe modifications to the predesignated train-test sets are minimal and designed to decrease disk storage and increase performance & reliability.\n\nExploratory Data Analysis (EDA) through classification with very simple models to .877 AUROC."
]
| [
36,
3,
131
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-1M<n<10M #tabular #network #region-us \n## Source\nURL## Dataset\n\nThis is an academic intrusion detection dataset. All the credit goes to the original authors: dr. Nour Moustafa and dr. Jill Slay.\n\nPlease cite their original paper and all other appropriate articles listed on the UNSW-NB15 page.\n\nThe full dataset also offers the pcap, BRO and Argus files along with additional documentation.\n\nThe modifications to the predesignated train-test sets are minimal and designed to decrease disk storage and increase performance & reliability.\n\nExploratory Data Analysis (EDA) through classification with very simple models to .877 AUROC."
]
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|
23650bbaa638faab05e0aeb48a91b3c72333e274 | # Dataset Card for "zambezivoice_bem_train_text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zambezivoice/zambezivoice_bem_train_text | [
"region:us"
]
| 2023-02-26T12:07:53+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 835072, "num_examples": 12421}], "download_size": 563571, "dataset_size": 835072}} | 2023-02-26T12:07:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "zambezivoice_bem_train_text"
More Information needed | [
"# Dataset Card for \"zambezivoice_bem_train_text\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"zambezivoice_bem_train_text\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"zambezivoice_bem_train_text\"\n\nMore Information needed"
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|
a2b1c76b01f160672ce495008aecf0b036f47df4 | # Dataset Card for "stackoverflow_linux"
Dataset information:
- Source: Stack Overflow
- Category: Linux
- Number of samples: 300
- Train/Test split: 270/30
- Quality: Data come from the top 1k most upvoted questions
## Additional Information
### License
All Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | KonradSzafer/stackoverflow_linux | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"region:us"
]
| 2023-02-26T12:48:36+00:00 | {"language": ["en"], "size_categories": ["n<1K"], "task_categories": ["question-answering"], "pretty_name": "Stack Overflow Linux", "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 303464, "num_examples": 270}, {"name": "test", "num_bytes": 37456, "num_examples": 30}], "download_size": 172425, "dataset_size": 340920}} | 2023-03-04T23:23:28+00:00 | []
| [
"en"
]
| TAGS
#task_categories-question-answering #size_categories-n<1K #language-English #region-us
| # Dataset Card for "stackoverflow_linux"
Dataset information:
- Source: Stack Overflow
- Category: Linux
- Number of samples: 300
- Train/Test split: 270/30
- Quality: Data come from the top 1k most upvoted questions
## Additional Information
### License
All Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.
More Information needed | [
"# Dataset Card for \"stackoverflow_linux\"\n\nDataset information:\n- Source: Stack Overflow\n- Category: Linux\n- Number of samples: 300\n- Train/Test split: 270/30\n- Quality: Data come from the top 1k most upvoted questions",
"## Additional Information",
"### License\n\nAll Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-question-answering #size_categories-n<1K #language-English #region-us \n",
"# Dataset Card for \"stackoverflow_linux\"\n\nDataset information:\n- Source: Stack Overflow\n- Category: Linux\n- Number of samples: 300\n- Train/Test split: 270/30\n- Quality: Data come from the top 1k most upvoted questions",
"## Additional Information",
"### License\n\nAll Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.\n\nMore Information needed"
]
| [
32,
59,
5,
29
]
| [
"passage: TAGS\n#task_categories-question-answering #size_categories-n<1K #language-English #region-us \n# Dataset Card for \"stackoverflow_linux\"\n\nDataset information:\n- Source: Stack Overflow\n- Category: Linux\n- Number of samples: 300\n- Train/Test split: 270/30\n- Quality: Data come from the top 1k most upvoted questions## Additional Information### License\n\nAll Stack Overflow user contributions are licensed under CC-BY-SA 3.0 with attribution required.\n\nMore Information needed"
]
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|
b7a121d997c31139f7e507d09f245ee24faea68b | # Dataset Card for "earnings21-gold-transcripts-non-normalized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | anonymoussubmissions/earnings21-gold-transcripts-non-normalized | [
"region:us"
]
| 2023-02-26T14:35:16+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}, {"name": "tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-CARDINAL", "2": "B-DATE", "3": "B-EVENT", "4": "B-FAC", "5": "B-GPE", "6": "B-LANGUAGE", "7": "B-LAW", "8": "B-LOC", "9": "B-MONEY", "10": "B-NORP", "11": "B-ORDINAL", "12": "B-ORG", "13": "B-PERCENT", "14": "B-PERSON", "15": "B-PRODUCT", "16": "B-QUANTITY", "17": "B-TIME", "18": "B-WORK_OF_ART", "19": "I-CARDINAL", "20": "I-DATE", "21": "I-EVENT", "22": "I-FAC", "23": "I-GPE", "24": "I-LANGUAGE", "25": "I-LAW", "26": "I-LOC", "27": "I-MONEY", "28": "I-NORP", "29": "I-ORDINAL", "30": "I-ORG", "31": "I-PERCENT", "32": "I-PERSON", "33": "I-PRODUCT", "34": "I-QUANTITY", "35": "I-TIME", "36": "I-WORK_OF_ART"}}}}, {"name": "labels_orig", "sequence": "string"}, {"name": "file_id", "dtype": "string"}, {"name": "sentence_no", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4503755, "num_examples": 6955}, {"name": "validation", "num_bytes": 3019922, "num_examples": 4637}, {"name": "test", "num_bytes": 4948836, "num_examples": 7729}], "download_size": 1677962, "dataset_size": 12472513}} | 2023-02-27T14:38:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "earnings21-gold-transcripts-non-normalized"
More Information needed | [
"# Dataset Card for \"earnings21-gold-transcripts-non-normalized\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"earnings21-gold-transcripts-non-normalized\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"earnings21-gold-transcripts-non-normalized\"\n\nMore Information needed"
]
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|
03a383205ee0212f8129bf7663abb5baa4f161f9 | # Dataset Card for "earnings21-gold-transcripts-normalized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | anonymoussubmissions/earnings21-gold-transcripts-normalized | [
"region:us"
]
| 2023-02-26T14:35:23+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "labels", "sequence": "string"}, {"name": "tags", "sequence": {"class_label": {"names": {"0": "O", "1": "B-CARDINAL", "2": "B-DATE", "3": "B-EVENT", "4": "B-FAC", "5": "B-GPE", "6": "B-LANGUAGE", "7": "B-LAW", "8": "B-LOC", "9": "B-MONEY", "10": "B-NORP", "11": "B-ORDINAL", "12": "B-ORG", "13": "B-PERCENT", "14": "B-PERSON", "15": "B-PRODUCT", "16": "B-QUANTITY", "17": "B-TIME", "18": "B-WORK_OF_ART", "19": "I-CARDINAL", "20": "I-DATE", "21": "I-EVENT", "22": "I-FAC", "23": "I-GPE", "24": "I-LANGUAGE", "25": "I-LAW", "26": "I-LOC", "27": "I-MONEY", "28": "I-NORP", "29": "I-ORDINAL", "30": "I-ORG", "31": "I-PERCENT", "32": "I-PERSON", "33": "I-PRODUCT", "34": "I-QUANTITY", "35": "I-TIME", "36": "I-WORK_OF_ART"}}}}, {"name": "labels_orig", "sequence": "string"}, {"name": "file_id", "dtype": "string"}, {"name": "sentence_no", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4503755, "num_examples": 6955}, {"name": "validation", "num_bytes": 3019922, "num_examples": 4637}, {"name": "test", "num_bytes": 4948836, "num_examples": 7729}], "download_size": 1708904, "dataset_size": 12472513}} | 2023-02-27T14:38:37+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "earnings21-gold-transcripts-normalized"
More Information needed | [
"# Dataset Card for \"earnings21-gold-transcripts-normalized\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"earnings21-gold-transcripts-normalized\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"earnings21-gold-transcripts-normalized\"\n\nMore Information needed"
]
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|
bd7ec22690de51972bd49708a4c72fe50379c591 | # Dataset Card for "humaneval-mbpp-codegen-qa"
This dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python function which satisfies the functionality described in a specified docstring. The responses are then the generated functions. | OllieStanley/humaneval-mbpp-codegen-qa | [
"region:us"
]
| 2023-02-26T14:59:10+00:00 | {"dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 225572, "num_examples": 591}], "download_size": 89931, "dataset_size": 225572}} | 2023-03-15T15:13:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "humaneval-mbpp-codegen-qa"
This dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python function which satisfies the functionality described in a specified docstring. The responses are then the generated functions. | [
"# Dataset Card for \"humaneval-mbpp-codegen-qa\"\n\nThis dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python function which satisfies the functionality described in a specified docstring. The responses are then the generated functions."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"humaneval-mbpp-codegen-qa\"\n\nThis dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python function which satisfies the functionality described in a specified docstring. The responses are then the generated functions."
]
| [
6,
70
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"humaneval-mbpp-codegen-qa\"\n\nThis dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python function which satisfies the functionality described in a specified docstring. The responses are then the generated functions."
]
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|
eb69d2f1c650ae8d98ba37d986a763057ee53893 | # Dataset Card for "humaneval-mbpp-testgen-qa"
This dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python unit tests which tests for the functionality described in a specific docstring. The responses are then the generated unit tests. | OllieStanley/humaneval-mbpp-testgen-qa | [
"region:us"
]
| 2023-02-26T15:01:49+00:00 | {"dataset_info": {"features": [{"name": "INSTRUCTION", "dtype": "string"}, {"name": "RESPONSE", "dtype": "string"}, {"name": "SOURCE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 304315, "num_examples": 591}], "download_size": 0, "dataset_size": 304315}} | 2023-03-15T15:12:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "humaneval-mbpp-testgen-qa"
This dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python unit tests which tests for the functionality described in a specific docstring. The responses are then the generated unit tests. | [
"# Dataset Card for \"humaneval-mbpp-testgen-qa\"\n\nThis dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python unit tests which tests for the functionality described in a specific docstring. The responses are then the generated unit tests."
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"humaneval-mbpp-testgen-qa\"\n\nThis dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python unit tests which tests for the functionality described in a specific docstring. The responses are then the generated unit tests."
]
| [
6,
70
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"humaneval-mbpp-testgen-qa\"\n\nThis dataset contains prompt-reply (question-answer) pairs where the prompt is to create a Python unit tests which tests for the functionality described in a specific docstring. The responses are then the generated unit tests."
]
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|
fbdcdd3b0ea685924ab0c262ec51e420db0eee01 | # Dataset Card for "kdqb_0227"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | daxiangpanda/kdqb_0227 | [
"region:us"
]
| 2023-02-26T15:26:09+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 51563593.0, "num_examples": 198}], "download_size": 51554554, "dataset_size": 51563593.0}} | 2023-02-26T15:26:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "kdqb_0227"
More Information needed | [
"# Dataset Card for \"kdqb_0227\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"kdqb_0227\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"kdqb_0227\"\n\nMore Information needed"
]
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79922b63139c9fda62d30f777ffff9d06b8e2a3f | # Dataset Card for "70000_method2test_tokonized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Minata/70000_method2test_tokonized | [
"region:us"
]
| 2023-02-26T17:06:47+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 466760000, "num_examples": 70000}], "download_size": 27648900, "dataset_size": 466760000}} | 2023-02-26T17:06:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "70000_method2test_tokonized"
More Information needed | [
"# Dataset Card for \"70000_method2test_tokonized\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"70000_method2test_tokonized\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"70000_method2test_tokonized\"\n\nMore Information needed"
]
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|
a0d10bb991c1cbe6ab011e96742b41ae8a3a2a4d | # Dataset Card for "FontsLargeSpaced"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arbml/FontsLargeSpaced | [
"region:us"
]
| 2023-02-26T18:30:12+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 43557017.054, "num_examples": 9899}], "download_size": 28289205, "dataset_size": 43557017.054}} | 2023-02-26T18:30:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "FontsLargeSpaced"
More Information needed | [
"# Dataset Card for \"FontsLargeSpaced\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"FontsLargeSpaced\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"FontsLargeSpaced\"\n\nMore Information needed"
]
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|
ff73c48799c629fdec465eee3ba4918fee7e1cef | # Dataset Card for "VISBank"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Yamei/VISBank | [
"region:us"
]
| 2023-02-26T19:54:58+00:00 | {"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "paper_id", "dtype": "int64"}, {"name": "abstract", "dtype": "string"}, {"name": "authors", "list": [{"name": "first", "dtype": "string"}, {"name": "middle", "sequence": "string"}, {"name": "last", "dtype": "string"}, {"name": "suffix", "dtype": "string"}]}, {"name": "year", "dtype": "float64"}, {"name": "arxiv_id", "dtype": "string"}, {"name": "acl_id", "dtype": "string"}, {"name": "pmc_id", "dtype": "string"}, {"name": "pubmed_id", "dtype": "string"}, {"name": "doi", "dtype": "string"}, {"name": "venue", "dtype": "string"}, {"name": "journal", "dtype": "string"}, {"name": "mag_id", "dtype": "string"}, {"name": "outbound_citations", "sequence": "string"}, {"name": "inbound_citations", "sequence": "string"}, {"name": "has_outbound_citations", "dtype": "bool"}, {"name": "has_inbound_citations", "dtype": "bool"}, {"name": "has_pdf_parse", "dtype": "bool"}, {"name": "s2_url", "dtype": "string"}, {"name": "has_pdf_body_text", "dtype": "float64"}, {"name": "has_pdf_parsed_abstract", "dtype": "float64"}, {"name": "has_pdf_parsed_body_text", "dtype": "float64"}, {"name": "has_pdf_parsed_bib_entries", "dtype": "float64"}, {"name": "has_pdf_parsed_ref_entries", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 194394520, "num_examples": 125745}], "download_size": 109992297, "dataset_size": 194394520}} | 2023-02-26T19:55:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VISBank"
More Information needed | [
"# Dataset Card for \"VISBank\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VISBank\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VISBank\"\n\nMore Information needed"
]
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|
2e481c54790763f1efdc1d00d7bd5e0c8a7c6b42 | # Dataset Card for "smart-lights-en-close-field"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | qmeeus/smart-lights-en-close-field | [
"region:us"
]
| 2023-02-26T20:50:28+00:00 | {"dataset_info": {"features": [{"name": "uttid", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text", "dtype": "string"}, {"name": "intent", "dtype": {"class_label": {"names": {"0": "DecreaseBrightness", "1": "IncreaseBrightness", "2": "SetLightBrightness", "3": "SetLightColor", "4": "SwitchLightOff", "5": "SwitchLightOn"}}}}, {"name": "entities", "sequence": {"class_label": {"names": {"0": "B-LOC", "1": "I-LOC", "2": "B-COL", "3": "I-COL", "4": "B-NUM", "5": "I-NUM", "6": "O"}}}}, {"name": "speaker", "struct": [{"name": "age", "dtype": "int64"}, {"name": "country", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "id", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 124895101.58399998, "num_examples": 1328}, {"name": "validation", "num_bytes": 15339937.9, "num_examples": 166}, {"name": "test", "num_bytes": 15496384.9, "num_examples": 166}], "download_size": 129906544, "dataset_size": 155731424.38399997}} | 2023-03-04T02:42:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "smart-lights-en-close-field"
More Information needed | [
"# Dataset Card for \"smart-lights-en-close-field\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"smart-lights-en-close-field\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"smart-lights-en-close-field\"\n\nMore Information needed"
]
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|
7cda082983e146261ec7d87fb1562d2a1a3e9f80 | # Dataset Card for "avatar-lite"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/avatar-lite | [
"region:us"
]
| 2023-02-26T21:46:11+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1595867500.125, "num_examples": 2031}], "download_size": 1595634006, "dataset_size": 1595867500.125}} | 2023-02-26T21:46:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "avatar-lite"
More Information needed | [
"# Dataset Card for \"avatar-lite\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"avatar-lite\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"avatar-lite\"\n\nMore Information needed"
]
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|
49068c54a4551304208aea80749f1a4d5ab893d2 | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- https://github.com/AARSynth/Dataset
- **Repository:**
- https://github.com/AARSynth/Dataset
- **Paper:**
- App-Aware Response Synthesis for User Reviews.
Umar Farooq, A.B. Siddique, Fuad Jamour, Zahijia Zhao and Vagelis Hristidis, “App-Aware Response Synthesis for User Reviews,” 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 699-708, DOI: https://doi.org/10.1109/BigData50022.2020.9377983.
- **Point of Contact:**
- Umar Farooq ([email protected])
- Abubakar Siddique ([email protected])
### Dataset Summary
AARSynth is a large-scale app review dataset. There are 570K review-response pairs and more than 2 million user
reviews for 103 popular applications.
### Supported Tasks and Leaderboards
Question Answer
Response Generation
### Languages
English
## How to use the dataset?
```
from datasets import load_dataset
import pandas as pd
# load the dataset
mbr_data = load_dataset('recmeapp/AARSynth', data_dir='replies')
# Save dataset to .csv file for creating pandas dataframe
mbr_data['train'].to_csv('./mbr_data.csv', sep='***')
# Convert to pandas dataframe
aarsynth_df = pd.read_csv('./mbr_data.csv', sep='***')
# How many interactions are there in the AARSynth dataset?
print(f'There are {len(aarsynth_df)} interactions in AARSynth dataset.')
```
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Umar Farooq and A.B. Siddique
### Licensing Information
[More Information Needed]
### Citation Information
- App-Aware Response Synthesis for User Reviews.
Umar Farooq, A.B. Siddique, Fuad Jamour, Zahijia Zhao and Vagelis Hristidis, “App-Aware Response Synthesis for User Reviews,” 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 699-708, DOI: https://doi.org/10.1109/BigData50022.2020.9377983.
### Contributions
[More Information Needed] | recmeapp/AARSynth | [
"license:cc-by-4.0",
"region:us"
]
| 2023-02-26T23:02:54+00:00 | {"license": "cc-by-4.0"} | 2023-03-11T19:00:19+00:00 | []
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| TAGS
#license-cc-by-4.0 #region-us
| ---
# For reference on model card metadata, see the spec: URL
# Doc / guide: URL
{}
---
# Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- URL
- Repository:
- URL
- Paper:
- App-Aware Response Synthesis for User Reviews.
Umar Farooq, A.B. Siddique, Fuad Jamour, Zahijia Zhao and Vagelis Hristidis, “App-Aware Response Synthesis for User Reviews,” 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 699-708, DOI: URL
- Point of Contact:
- Umar Farooq (URL@URL)
- Abubakar Siddique (URL@URL)
### Dataset Summary
AARSynth is a large-scale app review dataset. There are 570K review-response pairs and more than 2 million user
reviews for 103 popular applications.
### Supported Tasks and Leaderboards
Question Answer
Response Generation
### Languages
English
## How to use the dataset?
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
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#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
Umar Farooq and A.B. Siddique
### Licensing Information
- App-Aware Response Synthesis for User Reviews.
Umar Farooq, A.B. Siddique, Fuad Jamour, Zahijia Zhao and Vagelis Hristidis, “App-Aware Response Synthesis for User Reviews,” 2020 IEEE International Conference on Big Data (Big Data), 2020, pp. 699-708, DOI: URL
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|
d3532e18de38ff26219279b3d0e187ccd33f8288 | # Dataset Card for "avatar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/avatar | [
"region:us"
]
| 2023-02-26T23:10:19+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 5855144505.625, "num_examples": 7355}], "download_size": 5854241061, "dataset_size": 5855144505.625}} | 2023-02-26T23:12:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "avatar"
More Information needed | [
"# Dataset Card for \"avatar\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"avatar\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"avatar\"\n\nMore Information needed"
]
| [
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|
50d665a3069696b00be4aafe6888371a5d96d283 | # Dataset Card for "VISBank_Parsed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Yamei/VISBank_Parsed | [
"region:us"
]
| 2023-02-27T01:06:48+00:00 | {"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "paper_id", "dtype": "int64"}, {"name": "abstract", "dtype": "string"}, {"name": "authors", "list": [{"name": "first", "dtype": "string"}, {"name": "middle", "sequence": "string"}, {"name": "last", "dtype": "string"}, {"name": "suffix", "dtype": "string"}]}, {"name": "year", "dtype": "float64"}, {"name": "arxiv_id", "dtype": "string"}, {"name": "acl_id", "dtype": "string"}, {"name": "pmc_id", "dtype": "string"}, {"name": "pubmed_id", "dtype": "string"}, {"name": "doi", "dtype": "string"}, {"name": "venue", "dtype": "string"}, {"name": "journal", "dtype": "string"}, {"name": "mag_id", "dtype": "string"}, {"name": "outbound_citations", "sequence": "string"}, {"name": "inbound_citations", "sequence": "string"}, {"name": "has_outbound_citations", "dtype": "bool"}, {"name": "has_inbound_citations", "dtype": "bool"}, {"name": "has_pdf_parse", "dtype": "bool"}, {"name": "s2_url", "dtype": "string"}, {"name": "has_pdf_body_text", "dtype": "float64"}, {"name": "has_pdf_parsed_abstract", "dtype": "float64"}, {"name": "has_pdf_parsed_body_text", "dtype": "float64"}, {"name": "has_pdf_parsed_bib_entries", "dtype": "float64"}, {"name": "has_pdf_parsed_ref_entries", "dtype": "float64"}, {"name": "entities", "sequence": {"sequence": "string"}}], "splits": [{"name": "train", "num_bytes": 260857853, "num_examples": 125745}], "download_size": 135186490, "dataset_size": 260857853}} | 2023-03-04T19:58:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VISBank_Parsed"
More Information needed | [
"# Dataset Card for \"VISBank_Parsed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VISBank_Parsed\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VISBank_Parsed\"\n\nMore Information needed"
]
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|
15a062c32ea2f51465374a6a0cb4dd20423da990 | # Negative outputs from various models of Stable Diffusion - use at your will to train textual inversons or other things. | Capsekai/Negative_Images | [
"task_categories:text-to-image",
"size_categories:1K<n<10K",
"language:en",
"license:creativeml-openrail-m",
"dataset",
"stable diffusion",
"region:us"
]
| 2023-02-27T02:04:08+00:00 | {"language": ["en"], "license": "creativeml-openrail-m", "size_categories": ["1K<n<10K"], "task_categories": ["text-to-image"], "pretty_name": "Negative Outputs", "tags": ["dataset", "stable diffusion"]} | 2023-02-27T02:19:57+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #dataset #stable diffusion #region-us
| # Negative outputs from various models of Stable Diffusion - use at your will to train textual inversons or other things. | [
"# Negative outputs from various models of Stable Diffusion - use at your will to train textual inversons or other things."
]
| [
"TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #dataset #stable diffusion #region-us \n",
"# Negative outputs from various models of Stable Diffusion - use at your will to train textual inversons or other things."
]
| [
54,
29
]
| [
"passage: TAGS\n#task_categories-text-to-image #size_categories-1K<n<10K #language-English #license-creativeml-openrail-m #dataset #stable diffusion #region-us \n# Negative outputs from various models of Stable Diffusion - use at your will to train textual inversons or other things."
]
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|
aaaabe386a88dafa8de1fa42c0fbee0ad5d48b6a |
# Dataset Card for AI Text Dectection Pile
## Dataset Description
- **Point of Contact:[email protected]
### Dataset Summary
This is a large scale dataset intended for AI Text Detection tasks, geared toward long-form text and essays. It contains samples of both human text and AI-generated text from GPT2, GPT3, ChatGPT, GPTJ.
Here is the (tentative) breakdown:
#### Human Text
| Dataset | Num Samples | Link |
| ----------- | ----------- | ----------- |
| Reddit WritingPromps | 570k | [Link](https://www.kaggle.com/datasets/ratthachat/writing-prompts) |
| OpenAI Webtext | 260k | [Link](https://github.com/openai/gpt-2-output-dataset) |
| HC3 (Human Responses) | 58k | [Link](https://huggingface.co/datasets/Hello-SimpleAI/HC3) |
| ivypanda-essays | TODO | TODO |
| **Total** | **990k** | **-** |
#### AI-Generated Text
| Model | Dataset | Num Samples | Link |
| ----------- | ----------- | ----------- | ----------- |
| GPT2 | OpenAI gpt2-output-dataset | 260k | [Link](https://github.com/openai/gpt-2-output-dataset) |
| GPT3 | pairwise-davinci | 44k | TODO |
| GPT3 | synthetic-instruct-davinci-pairwise | 30k | [Link](https://huggingface.co/datasets/Dahoas/instruct-synthetic-prompt-responses) |
| GPTJ | synthetic-instruct-gptj-pairwise | 44k | [Link](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) |
| ChatGPT | Scraped from twitter | 5k | **-** |
| ChatGPT | HC3 (ChatGPT Responses) | 27k | [Link](https://huggingface.co/datasets/Hello-SimpleAI/HC3) |
| ChatGPT | ChatGPT Prompts/emergentmind | 500 | [Link](https://huggingface.co/datasets/MohamedRashad/ChatGPT-prompts/tree/main) |
| **Total** | **340k** | **-** | **-** |
### Supported Tasks and Leaderboards
Text Classification, AI Text Detection.
### Languages
English.
### Data Fields
TEXT: The text of the sample.
SOURCE: either "human" or "ai" | artem9k/ai-text-detection-pile | [
"license:mit",
"region:us"
]
| 2023-02-27T02:52:29+00:00 | {"license": "mit"} | 2023-02-27T03:37:54+00:00 | []
| []
| TAGS
#license-mit #region-us
| Dataset Card for AI Text Dectection Pile
========================================
Dataset Description
-------------------
* Point of Contact:artem9k@URL
### Dataset Summary
This is a large scale dataset intended for AI Text Detection tasks, geared toward long-form text and essays. It contains samples of both human text and AI-generated text from GPT2, GPT3, ChatGPT, GPTJ.
Here is the (tentative) breakdown:
#### Human Text
Dataset: Reddit WritingPromps, Num Samples: 570k, Link: Link
Dataset: OpenAI Webtext, Num Samples: 260k, Link: Link
Dataset: HC3 (Human Responses), Num Samples: 58k, Link: Link
Dataset: ivypanda-essays, Num Samples: TODO, Link: TODO
Dataset: Total, Num Samples: 990k, Link: -
#### AI-Generated Text
### Supported Tasks and Leaderboards
Text Classification, AI Text Detection.
### Languages
English.
### Data Fields
TEXT: The text of the sample.
SOURCE: either "human" or "ai"
| [
"### Dataset Summary\n\n\nThis is a large scale dataset intended for AI Text Detection tasks, geared toward long-form text and essays. It contains samples of both human text and AI-generated text from GPT2, GPT3, ChatGPT, GPTJ.\n\n\nHere is the (tentative) breakdown:",
"#### Human Text\n\n\nDataset: Reddit WritingPromps, Num Samples: 570k, Link: Link\nDataset: OpenAI Webtext, Num Samples: 260k, Link: Link\nDataset: HC3 (Human Responses), Num Samples: 58k, Link: Link\nDataset: ivypanda-essays, Num Samples: TODO, Link: TODO\nDataset: Total, Num Samples: 990k, Link: -",
"#### AI-Generated Text",
"### Supported Tasks and Leaderboards\n\n\nText Classification, AI Text Detection.",
"### Languages\n\n\nEnglish.",
"### Data Fields\n\n\nTEXT: The text of the sample.\nSOURCE: either \"human\" or \"ai\""
]
| [
"TAGS\n#license-mit #region-us \n",
"### Dataset Summary\n\n\nThis is a large scale dataset intended for AI Text Detection tasks, geared toward long-form text and essays. It contains samples of both human text and AI-generated text from GPT2, GPT3, ChatGPT, GPTJ.\n\n\nHere is the (tentative) breakdown:",
"#### Human Text\n\n\nDataset: Reddit WritingPromps, Num Samples: 570k, Link: Link\nDataset: OpenAI Webtext, Num Samples: 260k, Link: Link\nDataset: HC3 (Human Responses), Num Samples: 58k, Link: Link\nDataset: ivypanda-essays, Num Samples: TODO, Link: TODO\nDataset: Total, Num Samples: 990k, Link: -",
"#### AI-Generated Text",
"### Supported Tasks and Leaderboards\n\n\nText Classification, AI Text Detection.",
"### Languages\n\n\nEnglish.",
"### Data Fields\n\n\nTEXT: The text of the sample.\nSOURCE: either \"human\" or \"ai\""
]
| [
11,
75,
99,
8,
19,
6,
26
]
| [
"passage: TAGS\n#license-mit #region-us \n### Dataset Summary\n\n\nThis is a large scale dataset intended for AI Text Detection tasks, geared toward long-form text and essays. It contains samples of both human text and AI-generated text from GPT2, GPT3, ChatGPT, GPTJ.\n\n\nHere is the (tentative) breakdown:#### Human Text\n\n\nDataset: Reddit WritingPromps, Num Samples: 570k, Link: Link\nDataset: OpenAI Webtext, Num Samples: 260k, Link: Link\nDataset: HC3 (Human Responses), Num Samples: 58k, Link: Link\nDataset: ivypanda-essays, Num Samples: TODO, Link: TODO\nDataset: Total, Num Samples: 990k, Link: -#### AI-Generated Text### Supported Tasks and Leaderboards\n\n\nText Classification, AI Text Detection.### Languages\n\n\nEnglish.### Data Fields\n\n\nTEXT: The text of the sample.\nSOURCE: either \"human\" or \"ai\""
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|
50b76face3b2a46e5091efc55939db9b5a16b63f | # Dataset Card for "avatar-lite-augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/avatar-lite-augmented | [
"region:us"
]
| 2023-02-27T03:35:31+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 27395268.125, "num_examples": 2031}], "download_size": 27171589, "dataset_size": 27395268.125}} | 2023-02-27T03:35:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "avatar-lite-augmented"
More Information needed | [
"# Dataset Card for \"avatar-lite-augmented\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"avatar-lite-augmented\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"avatar-lite-augmented\"\n\nMore Information needed"
]
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|
0c50d6c850fb36a98f0a27712e8273cd3f8d5c82 |
# The 3oloum corpus of scientific titles and abstracts
This is a corpus of titles and abstracts of 147,673 scientific articles that were scraped from the following scientific journals:
- Nature: from year 1870 to 2021
- Science: from year 1960 to 2020
- Science Advances: from year 2015 to 2020
The corpus has been created for the purpose of contributing to natural language processing (NLP) projects.
There are currently *21,309,015* words in the corpus, including the text of titles and abstracts, and excluding non-ASCII strings.
Every non-ASCII character has been replaced by the string `<non_ascii>` to facilitate text processing.
It is being continuously updated.
## Sample data
|Title|Abstract|
| :--- | :--- |
| Velocity of Light and Measurement of Interplanetary Distances | The combined availability of atomic clocks and of instrumented planetoids traveling in their own solar orbits will offer the possibility of determining their distance from us, and hence interplanetary distances, in terms of the wavelength of the radiation of atomic frequency standards. It can be anticipated that the accuracy of these measurements will be very high and will not depend upon our less accurate knowledge of the velocity of light in terms of the standard meter, the sidereal second, and so on. |
| High-Resolution Density Gradient Sedimentation Analysis | The principle of stability for a sample layered in a density-gradient liquid column is discussed, and a method for separating ribonucleoprotein particles by means of sedimentation in the ultracentrifuge is described. |
| Daily Light Sensitivity Rhythm in a Rodent | Single 10-minute light periods can cause a phase shift in the rhythm of the daily locomotor activity of flying squirrels otherwise maintained in constant darkness. A daily rhythm of sensitivity to these standard light periods was found. |
| Heat-Labile Serum Systems in Fresh-Water Fish | Serum specimens from 18 specimens of 12 different species of freshwater fish were examined for their ability to kill <i>Toxoplasma</i> nonspecifically. This ability was present in all sera except those of two of three great northern pike. The effect was destroyed by exposure to 53<non_ascii><non_ascii>C, 56<non_ascii><non_ascii>C, or zymosan. Complement was demonstrated in all sera except that from one great northern pike, when rabbit erythrocytes were used in the indicator system. |
| Chemically Induced Phenocopy of a Tomato Mutant | Lanceolate, a spontaneous leaf-shape mutant which fails to produce cotyledons and plumule in the homozygous condition, shows development if supplied with either adenine or a diffusate obtained from normal seeds. Similar development occurs in a different genetic background. |
| Mitotic Arrest by Deuterium Oxide | In marine invertebrate eggs, where cell divisions occur without growth, deuterium oxide produces arrest of, or serious delay in, mitosis and cytokinesis. All stages requiring assembly or operation of mechanical structures in the cytoplasm are sensitive to D.O. The block is reversible in some cells. |
| Nonlogarithmic Linear Titration Curves | Titration curves can be based on linear nonlogarithmic forms of the equilibrium equation of a dissociation reaction. From such curves, in contrast to those based on logarithmic transformations, both the end point of the titration and the dissociation constant can be derived. |
| On the Function of Corticothalamic Neurons | The effect of the synchronous discharge of a large population of corticothalamic neurons on activity within the somatosensory relay nuclei has been studied. Thalamic responses to peripheral nerve stimulation are depressed by activity in corticothalamic neurons. A subconvulsive dose of strychnine, given intravenously, changes this depression to enhancement. |
| Occurrence of Scandium-46 and Cesium-134 in Radioactive Fallout | Two hitherto unreported induced radionuclides, scandium-46 and cesium-134, have been detected in fallout material. Identification was made by chemical separation and gamma scintillation spectrometry. While the origin of these materials is not known, possible routes of formation from stable elements are suggested. |
| Degree of Obesity and Serum Cholesterol Level | No significant correlation was found between the serum cholesterol level and weight, weight corrected for frame size, or thickness of the fat shadow in medical students (mean age, 22 years). |
| Neural and Hypophyseal Colloid Deposition in the Collared Lemming | Feral and captive lemmings from Churchill, Manitoba, are subject to a unique pathological process in which colloidal material is deposited in bloodvessel walls at scattered points through the central nervous system. Destruction of nervous tissue at these foci is progressive, and colloidal masses in the vascular lumina of the hypothalamus appear to become fixed in the capillaries of the hypophyseal anterior lobe. Inflammatory reactions are never associated with the lesions, and the latter are larger and more numerous in older animals in warmer environments. |
| On Pleistocene Surface Temperatures of the North Atlantic and Arctic Oceans | Two additional interpretations are given for the important data of D. B. Ericson on the correlation of coiling directions of <i>Globigerina pachyderma</i> in late Pleistocene North Atlantic sediments with ocean surface temperatures. One interpretation relates the distribution of this species to the distribution and circulation of ocean water masses. On the basis of our ice-age theory, our second interpretation uses the data and correlations of Ericson to establish temperature limits of a thermal node, a line on which glacial and interglacial temperatures were equal, for the North Atlantic Ocean. This line crosses the strait between Greenland and Scandinavia. Further, Ericson9s interpretation of the 7.2<non_ascii><non_ascii>C isotherm implies that the glacial-stage surface waters of the Arctic Ocean were between 0<non_ascii><non_ascii> and 3.5<non_ascii><non_ascii>C. |
| Genetic and Environmental Control of Flowering in Trifolium repens in the Tropics | <i>Trifolium repens</i> at low elevations expressed wide genetic variation in tendency to flower. Clones classified as flowering or nonflowering were subjected to temperatures associated with high elevations. Flowering in "nonflowering" clones was induced under warm-day-cool-night treatments. It is proposed that in the tropics, low temperatures associated with high elevations are an important factor in determining flowering, and therefore ability to persist, in plants which are long-day and temperature sensitive. |
| Mammalian Liver <non_ascii><non_ascii>-Glucuronidase for Hydrolysis of Steroidal Conjugates | Although the rate of hydrolysis by mammalian <non_ascii><non_ascii>-glucuronidase appears to be inhibited by methylene chloride or carbon tetrachloride with the standard technique (phenolphthalein glucuronide as a substrate), the release of steroidal conjugates under conditions generally employed does not appear to be affected. |
| Glucuronidase Activation: Enzyme Action at an Interface | The potentiating action of chloroform on bacterial <non_ascii><non_ascii>-glucuronidase has been shown to increase as the interface area between the two liquid phases increases. Prior extraction of the enzyme with chloroform causes a loss rather than an increase in activity. It is tentatively suggested that the correlation between activity and interface area may reflect a phenomenon of enzyme action at a liquid/liquid interface. |
| Characterization of Endogenous Ethanol in the Mammal | Ethanol has been isolated from the tissues of several animal species in amounts ranging from 23 to 145 <non_ascii><non_ascii>mole/100 gm of tissue. Intestinal bacterial flora appear to be excluded as a source of this ethanol. Radioactivity from pyruvate-2-C<sup>14</sup> appeared in ethanol after incubation with liver slices; this finding indicates an endogenous synthesis. |
| Reciprocal Inhibition as Indicated by a Differential Staining Reaction | Neurohistological and neurophysiological studies have shown that the bilaterally represented Mauthner9s cells in teleosts are related both structurally and functionally. The VIIIth nerve afferents, as well as the axoaxonal collaterals, display a distribution pattern which supports the concept of polar function of the neuron. Inasmuch as it is possible to alter the staining reaction of both the Mauthner9s cells by unilateral stimulation of the entering VIIIth nerve roots, it is proposed that the synaptic endings serve principally as activators and that neuronal excitation or inhibition is determined by the chemical state of the dendrites, the cell body, and the axon hillock region. |
| Orientation of Migratory Restlessness in the White-Crowned Sparrow | Individuals of two migratory races of white-crowned sparrows (<i>Zonotrichia leucophrys</i>) caged under an open sky showed a pronounced orientation in their night restlessness during normal periods of migration for the species. In August and September 1958 most birds showed a southerly orientation at night; daytime activity was random to somewhat northerly. In April and May 1959 most birds showed a strong northerly orientation at night; daytime activity was random to somewhat southerly (<i>1</i>). |
| State of Dynamic Equilibrium in Protein of Mammalian Cells | Labeled strain L cells in suspension tissue culture showed no degradation of protein when maintained in logarithmic growth. Although the protein of these cells was not in dynamic equilibrium, the conclusions cannot be transferred to the intact mammalian organism. |
| Mosses as Possible Sources of Antibiotics | An examination of 12 species of mosses has indicated that three produce substances capable of inhibiting the growth of various bacteria and other fungi. The method of extraction included several solvents. The extracts were not consistent in their antagonistic activity against the various species of microorganisms, nor were those that displayed antibiotic action always effective against the same organisms. Results indicate unstable products as well as physiological variation in the mosses. |
| sheriftawfikabbas/3oloum_corpus | [
"language:en",
"license:gpl-3.0",
"science abstracts",
"English",
"region:us"
]
| 2023-02-27T03:37:35+00:00 | {"language": ["en"], "license": "gpl-3.0", "pretty_name": "3oloum corpus for scientific abstract", "tags": ["science abstracts", "English"]} | 2023-04-12T05:56:18+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-gpl-3.0 #science abstracts #English #region-us
| The 3oloum corpus of scientific titles and abstracts
====================================================
This is a corpus of titles and abstracts of 147,673 scientific articles that were scraped from the following scientific journals:
* Nature: from year 1870 to 2021
* Science: from year 1960 to 2020
* Science Advances: from year 2015 to 2020
The corpus has been created for the purpose of contributing to natural language processing (NLP) projects.
There are currently *21,309,015* words in the corpus, including the text of titles and abstracts, and excluding non-ASCII strings.
Every non-ASCII character has been replaced by the string '<non\_ascii>' to facilitate text processing.
It is being continuously updated.
Sample data
-----------
| []
| [
"TAGS\n#language-English #license-gpl-3.0 #science abstracts #English #region-us \n"
]
| [
24
]
| [
"passage: TAGS\n#language-English #license-gpl-3.0 #science abstracts #English #region-us \n"
]
| [
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|
91ccae8af14e13929ecb6d745ea6b5aa18e609c7 | # Dataset Card for "avatar-augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/avatar-augmented | [
"region:us"
]
| 2023-02-27T04:16:33+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 100596505.625, "num_examples": 7355}], "download_size": 99698053, "dataset_size": 100596505.625}} | 2023-02-27T04:16:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "avatar-augmented"
More Information needed | [
"# Dataset Card for \"avatar-augmented\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"avatar-augmented\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"avatar-augmented\"\n\nMore Information needed"
]
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|
c57e832af56db74341f5bac4d244d6ae113ab3c5 | # Dataset Card for "summarization-sft-heirarchical"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dmayhem93/summarization-sft-heirarchical | [
"region:us"
]
| 2023-02-27T06:11:06+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "125M", "dtype": "string"}, {"name": "1B", "dtype": "string"}, {"name": "6B", "dtype": "string"}, {"name": "20B", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 223409, "num_examples": 112}], "download_size": 144701, "dataset_size": 223409}} | 2023-02-27T06:12:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "summarization-sft-heirarchical"
More Information needed | [
"# Dataset Card for \"summarization-sft-heirarchical\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"summarization-sft-heirarchical\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"summarization-sft-heirarchical\"\n\nMore Information needed"
]
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|
259ca2f2d4aca7782d56694fb5a1466ff1d72098 | # Dataset Card for "Sudanese_Dialect"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MohammadJamalaldeen/Sudanese_Dialect | [
"region:us"
]
| 2023-02-27T08:10:45+00:00 | {"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 4639654208, "num_examples": 4830}, {"name": "test", "num_bytes": 511020520, "num_examples": 532}], "download_size": 988631878, "dataset_size": 5150674728}} | 2023-02-27T08:12:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Sudanese_Dialect"
More Information needed | [
"# Dataset Card for \"Sudanese_Dialect\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Sudanese_Dialect\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Sudanese_Dialect\"\n\nMore Information needed"
]
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|
278f7450f6117be9376c15a7179b9c156be8ce84 | A collection of large-scale, high-quality datasets of URL links of up to 650,000 video clips that cover 400 human action classes.
The videos include human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands and hugging.
Each action class has at least 400.
Each clip is human annotated with a single action class and lasts around 10 seconds.
Originally created by Google Inc. and the dataset has been uploaded as is without any changes. | AlexFierro9/Kinetics400 | [
"license:cc-by-4.0",
"region:us"
]
| 2023-02-27T08:16:25+00:00 | {"license": "cc-by-4.0"} | 2023-02-27T12:45:06+00:00 | []
| []
| TAGS
#license-cc-by-4.0 #region-us
| A collection of large-scale, high-quality datasets of URL links of up to 650,000 video clips that cover 400 human action classes.
The videos include human-object interactions such as playing instruments, as well as human-human interactions such as shaking hands and hugging.
Each action class has at least 400.
Each clip is human annotated with a single action class and lasts around 10 seconds.
Originally created by Google Inc. and the dataset has been uploaded as is without any changes. | []
| [
"TAGS\n#license-cc-by-4.0 #region-us \n"
]
| [
15
]
| [
"passage: TAGS\n#license-cc-by-4.0 #region-us \n"
]
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|
627904d5ff8b0e4101a485c349956a6056c31601 |
# SQuALITY - v1.3
> Original paper [here](https://arxiv.org/abs/2205.11465)
This is v1.3, the 'text' edition `.jsonl` files. See description from the [original repo](https://github.com/nyu-mll/SQuALITY):
> v1.3 fixes some bugs in v1.2. In v1.2, 10 out of 127 articles (each ~5k-word-long) are missing a few hundreds words each, so summaries may not be fully contained in the article. To fix this issue, we have updated the 10 articles.
## contents
> again, this is taken from the repo
Each data file ({train/dev/test}.jsonl) is formatted as a JSON lines file. Each row in the data file is a JSON dictionary with the following fields:
- metadata: the Gutenberg story ID, an internal UID, and the Project Gutenberg license
- document: the Gutenberg story
questions: a list of questions and accompanying responses
- question text
- question number: the order in which that question was answered by the writers
- responses: list of worker's response, where each response is a dictionary containing the (anonymized) worker ID, an internal UID, and their response to the question
### dataset contents
```python
DatasetDict({
train: Dataset({
features: ['metadata', 'document', 'questions'],
num_rows: 50
})
test: Dataset({
features: ['metadata', 'document', 'questions'],
num_rows: 52
})
validation: Dataset({
features: ['metadata', 'document', 'questions'],
num_rows: 25
})
})
```
| pszemraj/SQuALITY-v1.3 | [
"task_categories:summarization",
"task_categories:text2text-generation",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"summarization",
"long-document",
"arxiv:2205.11465",
"region:us"
]
| 2023-02-27T08:25:50+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["summarization", "text2text-generation"], "pretty_name": "SQuALITY v1.3", "tags": ["summarization", "long-document"]} | 2023-02-27T08:42:44+00:00 | [
"2205.11465"
]
| [
"en"
]
| TAGS
#task_categories-summarization #task_categories-text2text-generation #size_categories-n<1K #language-English #license-apache-2.0 #summarization #long-document #arxiv-2205.11465 #region-us
|
# SQuALITY - v1.3
> Original paper here
This is v1.3, the 'text' edition '.jsonl' files. See description from the original repo:
> v1.3 fixes some bugs in v1.2. In v1.2, 10 out of 127 articles (each ~5k-word-long) are missing a few hundreds words each, so summaries may not be fully contained in the article. To fix this issue, we have updated the 10 articles.
## contents
> again, this is taken from the repo
Each data file ({train/dev/test}.jsonl) is formatted as a JSON lines file. Each row in the data file is a JSON dictionary with the following fields:
- metadata: the Gutenberg story ID, an internal UID, and the Project Gutenberg license
- document: the Gutenberg story
questions: a list of questions and accompanying responses
- question text
- question number: the order in which that question was answered by the writers
- responses: list of worker's response, where each response is a dictionary containing the (anonymized) worker ID, an internal UID, and their response to the question
### dataset contents
| [
"# SQuALITY - v1.3\n\n> Original paper here\n\nThis is v1.3, the 'text' edition '.jsonl' files. See description from the original repo:\n\n> v1.3 fixes some bugs in v1.2. In v1.2, 10 out of 127 articles (each ~5k-word-long) are missing a few hundreds words each, so summaries may not be fully contained in the article. To fix this issue, we have updated the 10 articles.",
"## contents \n\n> again, this is taken from the repo\n\nEach data file ({train/dev/test}.jsonl) is formatted as a JSON lines file. Each row in the data file is a JSON dictionary with the following fields:\n\n- metadata: the Gutenberg story ID, an internal UID, and the Project Gutenberg license\n- document: the Gutenberg story\nquestions: a list of questions and accompanying responses\n - question text\n - question number: the order in which that question was answered by the writers\n - responses: list of worker's response, where each response is a dictionary containing the (anonymized) worker ID, an internal UID, and their response to the question",
"### dataset contents"
]
| [
"TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-n<1K #language-English #license-apache-2.0 #summarization #long-document #arxiv-2205.11465 #region-us \n",
"# SQuALITY - v1.3\n\n> Original paper here\n\nThis is v1.3, the 'text' edition '.jsonl' files. See description from the original repo:\n\n> v1.3 fixes some bugs in v1.2. In v1.2, 10 out of 127 articles (each ~5k-word-long) are missing a few hundreds words each, so summaries may not be fully contained in the article. To fix this issue, we have updated the 10 articles.",
"## contents \n\n> again, this is taken from the repo\n\nEach data file ({train/dev/test}.jsonl) is formatted as a JSON lines file. Each row in the data file is a JSON dictionary with the following fields:\n\n- metadata: the Gutenberg story ID, an internal UID, and the Project Gutenberg license\n- document: the Gutenberg story\nquestions: a list of questions and accompanying responses\n - question text\n - question number: the order in which that question was answered by the writers\n - responses: list of worker's response, where each response is a dictionary containing the (anonymized) worker ID, an internal UID, and their response to the question",
"### dataset contents"
]
| [
68,
102,
162,
6
]
| [
"passage: TAGS\n#task_categories-summarization #task_categories-text2text-generation #size_categories-n<1K #language-English #license-apache-2.0 #summarization #long-document #arxiv-2205.11465 #region-us \n# SQuALITY - v1.3\n\n> Original paper here\n\nThis is v1.3, the 'text' edition '.jsonl' files. See description from the original repo:\n\n> v1.3 fixes some bugs in v1.2. In v1.2, 10 out of 127 articles (each ~5k-word-long) are missing a few hundreds words each, so summaries may not be fully contained in the article. To fix this issue, we have updated the 10 articles.## contents \n\n> again, this is taken from the repo\n\nEach data file ({train/dev/test}.jsonl) is formatted as a JSON lines file. Each row in the data file is a JSON dictionary with the following fields:\n\n- metadata: the Gutenberg story ID, an internal UID, and the Project Gutenberg license\n- document: the Gutenberg story\nquestions: a list of questions and accompanying responses\n - question text\n - question number: the order in which that question was answered by the writers\n - responses: list of worker's response, where each response is a dictionary containing the (anonymized) worker ID, an internal UID, and their response to the question### dataset contents"
]
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|
50e79c314a7603ebc92236b66a0973d51a00ed8c |
# Dataset Card for JGLUE
[](https://github.com/shunk031/huggingface-datasets_JGLUE/actions/workflows/ci.yaml)
[](https://aclanthology.org/2022.lrec-1.317)
This dataset loading script is developed on [GitHub](https://github.com/shunk031/huggingface-datasets_JGLUE).
Please feel free to open an [issue](https://github.com/shunk031/huggingface-datasets_JGLUE/issues/new/choose) or [pull request](https://github.com/shunk031/huggingface-datasets_JGLUE/pulls).
## 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/yahoojapan/JGLUE
- **Repository:** https://github.com/shunk031/huggingface-datasets_JGLUE
### Dataset Summary
From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#jglue-japanese-general-language-understanding-evaluation):
> JGLUE, Japanese General Language Understanding Evaluation, is built to measure the general NLU ability in Japanese. JGLUE has been constructed from scratch without translation. We hope that JGLUE will facilitate NLU research in Japanese.
> JGLUE has been constructed by a joint research project of Yahoo Japan Corporation and Kawahara Lab at Waseda University.
### Supported Tasks and Leaderboards
From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#tasksdatasets):
> JGLUE consists of the tasks of text classification, sentence pair classification, and QA. Each task consists of multiple datasets.
#### Supported Tasks
##### MARC-ja
From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#marc-ja):
> MARC-ja is a dataset of the text classification task. This dataset is based on the Japanese portion of [Multilingual Amazon Reviews Corpus (MARC)](https://docs.opendata.aws/amazon-reviews-ml/readme.html) ([Keung+, 2020](https://aclanthology.org/2020.emnlp-main.369/)).
##### JCoLA
From [JCoLA's README.md](https://github.com/osekilab/JCoLA#jcola-japanese-corpus-of-linguistic-acceptability)
> JCoLA (Japanese Corpus of Linguistic Accept010 ability) is a novel dataset for targeted syntactic evaluations of language models in Japanese, which consists of 10,020 sentences with acceptability judgments by linguists. The sentences are manually extracted from linguistics journals, handbooks and textbooks. JCoLA is included in [JGLUE benchmark](https://github.com/yahoojapan/JGLUE) (Kurihara et al., 2022).
##### JSTS
From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#jsts):
> JSTS is a Japanese version of the STS (Semantic Textual Similarity) dataset. STS is a task to estimate the semantic similarity of a sentence pair. The sentences in JSTS and JNLI (described below) are extracted from the Japanese version of the MS COCO Caption Dataset, [the YJ Captions Dataset](https://github.com/yahoojapan/YJCaptions) ([Miyazaki and Shimizu, 2016](https://aclanthology.org/P16-1168/)).
##### JNLI
From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#jnli):
> JNLI is a Japanese version of the NLI (Natural Language Inference) dataset. NLI is a task to recognize the inference relation that a premise sentence has to a hypothesis sentence. The inference relations are entailment, contradiction, and neutral.
##### JSQuAD
From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#jsquad):
> JSQuAD is a Japanese version of [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) ([Rajpurkar+, 2018](https://aclanthology.org/P18-2124/)), one of the datasets of reading comprehension. Each instance in the dataset consists of a question regarding a given context (Wikipedia article) and its answer. JSQuAD is based on SQuAD 1.1 (there are no unanswerable questions). We used [the Japanese Wikipedia dump](https://dumps.wikimedia.org/jawiki/) as of 20211101.
##### JCommonsenseQA
From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#jcommonsenseqa):
> JCommonsenseQA is a Japanese version of [CommonsenseQA](https://www.tau-nlp.org/commonsenseqa) ([Talmor+, 2019](https://aclanthology.org/N19-1421/)), which is a multiple-choice question answering dataset that requires commonsense reasoning ability. It is built using crowdsourcing with seeds extracted from the knowledge base [ConceptNet](https://conceptnet.io/).
#### Leaderboard
From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#leaderboard):
> A leaderboard will be made public soon. The test set will be released at that time.
### Languages
The language data in JGLUE is in Japanese ([BCP-47 ja-JP](https://www.rfc-editor.org/info/bcp47)).
## Dataset Structure
### Data Instances
When loading a specific configuration, users has to append a version dependent suffix:
#### MARC-ja
```python
from datasets import load_dataset
dataset = load_dataset("shunk031/JGLUE", name="MARC-ja")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['sentence', 'label', 'review_id'],
# num_rows: 187528
# })
# validation: Dataset({
# features: ['sentence', 'label', 'review_id'],
# num_rows: 5654
# })
# })
```
#### JCoLA
```python
from datasets import load_dataset
dataset = load_dataset("shunk031/JGLUE", name="JCoLA")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['uid', 'source', 'label', 'diacritic', 'sentence', 'original', 'translation', 'gloss', 'simple', 'linguistic_phenomenon'],
# num_rows: 6919
# })
# validation: Dataset({
# features: ['uid', 'source', 'label', 'diacritic', 'sentence', 'original', 'translation', 'gloss', 'simple', 'linguistic_phenomenon'],
# num_rows: 865
# })
# validation_out_of_domain: Dataset({
# features: ['uid', 'source', 'label', 'diacritic', 'sentence', 'original', 'translation', 'gloss', 'simple', 'linguistic_phenomenon'],
# num_rows: 685
# })
# validation_out_of_domain_annotated: Dataset({
# features: ['uid', 'source', 'label', 'diacritic', 'sentence', 'original', 'translation', 'gloss', 'simple', 'linguistic_phenomenon'],
# num_rows: 685
# })
# })
```
An example of the JCoLA dataset (validation - out of domain annotated) looks as follows:
```json
{
"uid": 9109,
"source": "Asano_and_Ura_2010",
"label": 1,
"diacritic": "g",
"sentence": "太郎のゴミの捨て方について話した。",
"original": "太郎のゴミの捨て方",
"translation": "‘The way (for Taro) to throw out garbage’",
"gloss": true,
"linguistic_phenomenon": {
"argument_structure": true,
"binding": false,
"control_raising": false,
"ellipsis": false,
"filler_gap": false,
"island_effects": false,
"morphology": false,
"nominal_structure": false,
"negative_polarity_concord_items": false,
"quantifier": false,
"verbal_agreement": false,
"simple": false
}
}
```
#### JSTS
```python
from datasets import load_dataset
dataset = load_dataset("shunk031/JGLUE", name="JSTS")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['sentence_pair_id', 'yjcaptions_id', 'sentence1', 'sentence2', 'label'],
# num_rows: 12451
# })
# validation: Dataset({
# features: ['sentence_pair_id', 'yjcaptions_id', 'sentence1', 'sentence2', 'label'],
# num_rows: 1457
# })
# })
```
An example of the JSTS dataset looks as follows:
```json
{
"sentence_pair_id": "691",
"yjcaptions_id": "127202-129817-129818",
"sentence1": "街中の道路を大きなバスが走っています。 (A big bus is running on the road in the city.)",
"sentence2": "道路を大きなバスが走っています。 (There is a big bus running on the road.)",
"label": 4.4
}
```
#### JNLI
```python
from datasets import load_dataset
dataset = load_dataset("shunk031/JGLUE", name="JNLI")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['sentence_pair_id', 'yjcaptions_id', 'sentence1', 'sentence2', 'label'],
# num_rows: 20073
# })
# validation: Dataset({
# features: ['sentence_pair_id', 'yjcaptions_id', 'sentence1', 'sentence2', 'label'],
# num_rows: 2434
# })
# })
```
An example of the JNLI dataset looks as follows:
```json
{
"sentence_pair_id": "1157",
"yjcaptions_id": "127202-129817-129818",
"sentence1": "街中の道路を大きなバスが走っています。 (A big bus is running on the road in the city.)",
"sentence2": "道路を大きなバスが走っています。 (There is a big bus running on the road.)",
"label": "entailment"
}
```
#### JSQuAD
```python
from datasets import load_dataset
dataset = load_dataset("shunk031/JGLUE", name="JSQuAD")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['id', 'title', 'context', 'question', 'answers', 'is_impossible'],
# num_rows: 62859
# })
# validation: Dataset({
# features: ['id', 'title', 'context', 'question', 'answers', 'is_impossible'],
# num_rows: 4442
# })
# })
```
An example of the JSQuAD looks as follows:
```json
{
"id": "a1531320p0q0",
"title": "東海道新幹線",
"context": "東海道新幹線 [SEP] 1987 年（昭和 62 年）4 月 1 日の国鉄分割民営化により、JR 東海が運営を継承した。西日本旅客鉄道（JR 西日本）が継承した山陽新幹線とは相互乗り入れが行われており、東海道新幹線区間のみで運転される列車にも JR 西日本所有の車両が使用されることがある。2020 年（令和 2 年）3 月現在、東京駅 - 新大阪駅間の所要時間は最速 2 時間 21 分、最高速度 285 km/h で運行されている。",
"question": "2020 年（令和 2 年）3 月現在、東京駅 - 新大阪駅間の最高速度はどのくらいか。",
"answers": {
"text": ["285 km/h"],
"answer_start": [182]
},
"is_impossible": false
}
```
#### JCommonsenseQA
```python
from datasets import load_dataset
dataset = load_dataset("shunk031/JGLUE", name="JCommonsenseQA")
print(dataset)
# DatasetDict({
# train: Dataset({
# features: ['q_id', 'question', 'choice0', 'choice1', 'choice2', 'choice3', 'choice4', 'label'],
# num_rows: 8939
# })
# validation: Dataset({
# features: ['q_id', 'question', 'choice0', 'choice1', 'choice2', 'choice3', 'choice4', 'label'],
# num_rows: 1119
# })
# })
```
An example of the JCommonsenseQA looks as follows:
```json
{
"q_id": 3016,
"question": "会社の最高責任者を何というか？ (What do you call the chief executive officer of a company?)",
"choice0": "社長 (president)",
"choice1": "教師 (teacher)",
"choice2": "部長 (manager)",
"choice3": "バイト (part-time worker)",
"choice4": "部下 (subordinate)",
"label": 0
}
```
### Data Fields
#### MARC-ja
- `sentence_pair_id`: ID of the sentence pair
- `yjcaptions_id`: sentence ids in yjcaptions (explained below)
- `sentence1`: first sentence
- `sentence2`: second sentence
- `label`: sentence similarity: 5 (equivalent meaning) - 0 (completely different meaning)
##### Explanation for `yjcaptions_id`
From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE#explanation-for-yjcaptions_id), there are the following two cases:
1. sentence pairs in one image: `(image id)-(sentence1 id)-(sentence2 id)`
- e.g., 723-844-847
- a sentence id starting with "g" means a sentence generated by a crowdworker (e.g., 69501-75698-g103): only for JNLI
2. sentence pairs in two images: `(image id of sentence1)_(image id of sentence2)-(sentence1 id)-(sentence2 id)`
- e.g., 91337_217583-96105-91680
#### JCoLA
From [JCoLA's README.md](https://github.com/osekilab/JCoLA#data-description) and [JCoLA's paper](https://arxiv.org/abs/2309.12676)
- `uid`: unique id of the sentence
- `source`: author and the year of publication of the source article
- `label`: acceptability judgement label (0 for unacceptable, 1 for acceptable)
- `diacritic`: acceptability judgement as originally notated in the source article
- `sentence`: sentence (modified by the author if needed)
- `original`: original sentence as presented in the source article
- `translation`: English translation of the sentence as presentend in the source article (if any)
- `gloss`: gloss of the sentence as presented in the source article (if any)
- `linguistic_phenomenon`
- `argument_structure`: acceptability judgements based on the order of arguments and case marking
- `binding`: acceptability judgements based on the binding of noun phrases
- `control_raising`: acceptability judgements based on predicates that are categorized as control or raising
- `ellipsis`: acceptability judgements based on the possibility of omitting elements in the sentences
- `filler_gap`: acceptability judgements based on the dependency between the moved element and the gap
- `island effects`: acceptability judgements based on the restrictions on filler-gap dependencies such as wh-movements
- `morphology`: acceptability judgements based on the morphology
- `nominal_structure`: acceptability judgements based on the internal structure of noun phrases
- `negative_polarity_concord_items`: acceptability judgements based on the restrictions on where negative polarity/concord items (NPIs/NCIs) can appear
- `quantifiers`: acceptability judgements based on the distribution of quantifiers such as floating quantifiers
- `verbal_agreement`: acceptability judgements based on the dependency between subjects and verbs
- `simple`: acceptability judgements that do not have marked syntactic structures
#### JNLI
- `sentence_pair_id`: ID of the sentence pair
- `yjcaptions_id`: sentence ids in the yjcaptions
- `sentence1`: premise sentence
- `sentence2`: hypothesis sentence
- `label`: inference relation
#### JSQuAD
- `title`: title of a Wikipedia article
- `paragraphs`: a set of paragraphs
- `qas`: a set of pairs of a question and its answer
- `question`: question
- `id`: id of a question
- `answers`: a set of answers
- `text`: answer text
- `answer_start`: start position (character index)
- `is_impossible`: all the values are false
- `context`: a concatenation of the title and paragraph
#### JCommonsenseQA
- `q_id`: ID of the question
- `question`: question
- `choice{0..4}`: choice
- `label`: correct choice id
### Data Splits
From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE/blob/main/README.md#tasksdatasets):
> Only train/dev sets are available now, and the test set will be available after the leaderboard is made public.
From [JCoLA's paper](https://arxiv.org/abs/2309.12676):
> The in-domain data is split into training data (6,919 instances), development data (865 instances), and test data (865 instances). On the other hand, the out-of-domain data is only used for evaluation, and divided into development data (685 instances) and test data (686 instances).
| Task | Dataset | Train | Dev | Test |
|------------------------------|----------------|--------:|------:|------:|
| Text Classification | MARC-ja | 187,528 | 5,654 | 5,639 |
| | JCoLA | 6,919 | 865† / 685‡ | 865† / 685‡ |
| Sentence Pair Classification | JSTS | 12,451 | 1,457 | 1,589 |
| | JNLI | 20,073 | 2,434 | 2,508 |
| Question Answering | JSQuAD | 62,859 | 4,442 | 4,420 |
| | JCommonsenseQA | 8,939 | 1,119 | 1,118 |
> JCoLA: † in domain. ‡ out of domain.
## Dataset Creation
### Curation Rationale
From [JGLUE's paper](https://aclanthology.org/2022.lrec-1.317/):
> JGLUE is designed to cover a wide range of GLUE and SuperGLUE tasks and consists of three kinds of tasks: text classification, sentence pair classification, and question answering.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
- The source language producers are users of Amazon (MARC-ja), crowd-workers of [Yahoo! Crowdsourcing](https://crowdsourcing.yahoo.co.jp/) (JSTS, JNLI and JCommonsenseQA), writers of the Japanese Wikipedia (JSQuAD), crowd-workers of [Lancers](https://www.lancers.jp/).
### Annotations
#### Annotation process
##### MARC-ja
From [JGLUE's paper](https://aclanthology.org/2022.lrec-1.317/):
> As one of the text classification datasets, we build a dataset based on the Multilingual Amazon Reviews Corpus (MARC) (Keung et al., 2020). MARC is a multilingual corpus of product reviews with 5-level star ratings (1-5) on the Amazon shopping site. This corpus covers six languages, including English and Japanese. For JGLUE, we use the Japanese part of MARC and to make it easy for both humans and computers to judge a class label, we cast the text classification task as a binary classification task, where 1- and 2-star ratings are converted to “negative”, and 4 and 5 are converted to “positive”. We do not use reviews with a 3-star rating.
> One of the problems with MARC is that it sometimes contains data where the rating diverges from the review text. This happens, for example, when a review with positive content is given a rating of 1 or 2. These data degrade the quality of our dataset. To improve the quality of the dev/test instances used for evaluation, we crowdsource a positive/negative judgment task for approximately 12,000 reviews. We adopt only reviews with the same votes from 7 or more out of 10 workers and assign a label of the maximum votes to these reviews. We divide the resulting reviews into dev/test data.
> We obtained 5,654 and 5,639 instances for the dev and test data, respectively, through the above procedure. For the training data, we extracted 187,528 instances directly from MARC without performing the cleaning procedure because of the large number of training instances. The statistics of MARC-ja are listed in Table 2. For the evaluation metric for MARC-ja, we use accuracy because it is a binary classification task of texts.
##### JCoLA
From [JCoLA's paper](https://arxiv.org/abs/2309.12676):
> ### 3 JCoLA
> In this study, we introduce JCoLA (Japanese Corpus of Linguistic Acceptability), which will be the first large-scale acceptability judgment task dataset focusing on Japanese. JCoLA consists of sentences from textbooks and handbooks on Japanese syntax, as well as from journal articles on Japanese syntax that are published in JEAL (Journal of East Asian Linguistics), one of the prestigious journals in theoretical linguistics.
> #### 3.1 Data Collection
> Sentences in JCoLA were collected from prominent textbooks and handbooks focusing on Japanese syntax. In addition to the main text, example sentences included in the footnotes were also considered for collection. We also collected acceptability judgments from journal articles on Japanese syntax published in JEAL (Journal of East Asian Linguistics): one of the prestigious journals in the-oretical linguistics. Specifically, we examined all the articles published in JEAL between 2006 and 2015 (133 papers in total), and extracted 2,252 acceptability judgments from 26 papers on Japanese syntax (Table 2). Acceptability judgments include sentences in appendices and footnotes, but not sentences presented for analyses of syntactic structures (e.g. sentences with brackets to show their syntactic structures). As a result, a total of 11,984 example. sentences were collected. Using this as a basis, JCoLA was constructed through the methodology explained in the following sections.
##### JSTS and JNLI
From [JGLUE's paper](https://aclanthology.org/2022.lrec-1.317/):
> For the sentence pair classification datasets, we construct a semantic textual similarity (STS) dataset, JSTS, and a natural language inference (NLI) dataset, JNLI.
> ### Overview
> STS is a task of estimating the semantic similarity of a sentence pair. Gold similarity is usually assigned as an average of the integer values 0 (completely different meaning) to 5 (equivalent meaning) assigned by multiple workers through crowdsourcing.
> NLI is a task of recognizing the inference relation that a premise sentence has to a hypothesis sentence. Inference relations are generally defined by three labels: “entailment”, “contradiction”, and “neutral”. Gold inference relations are often assigned by majority voting after collecting answers from multiple workers through crowdsourcing.
> For the STS and NLI tasks, STS-B (Cer et al., 2017) and MultiNLI (Williams et al., 2018) are included in GLUE, respectively. As Japanese datasets, JSNLI (Yoshikoshi et al., 2020) is a machine translated dataset of the NLI dataset SNLI (Stanford NLI), and JSICK (Yanaka and Mineshima, 2021) is a human translated dataset of the STS/NLI dataset SICK (Marelli et al., 2014). As mentioned in Section 1, these have problems originating from automatic/manual translations. To solve this problem, we construct STS/NLI datasets in Japanese from scratch. We basically extract sentence pairs in JSTS and JNLI from the Japanese version of the MS COCO Caption Dataset (Chen et al., 2015), the YJ Captions Dataset (Miyazaki and Shimizu, 2016). Most of the sentence pairs in JSTS and JNLI overlap, allowing us to analyze the relationship between similarities and inference relations for the same sentence pairs like SICK and JSICK.
> The similarity value in JSTS is assigned a real number from 0 to 5 as in STS-B. The inference relation in JNLI is assigned from the above three labels as in SNLI and MultiNLI. The definitions of the inference relations are also based on SNLI.
> ### Method of Construction
> Our construction flow for JSTS and JNLI is shown in Figure 1. Basically, two captions for the same image of YJ Captions are used as sentence pairs. For these sentence pairs, similarities and NLI relations of entailment and neutral are obtained by crowdsourcing. However, it is difficult to collect sentence pairs with low similarity and contradiction relations from captions for the same image. To solve this problem, we collect sentence pairs with low similarity from captions for different images. We collect contradiction relations by asking workers to write contradictory sentences for a given caption.
> The detailed construction procedure for JSTS and JNLI is described below.
> 1. We crowdsource an STS task using two captions for the same image from YJ Captions. We ask five workers to answer the similarity between two captions and take the mean value as the gold similarity. We delete sentence pairs with a large variance in the answers because such pairs have poor answer quality. We performed this task on 16,000 sentence pairs and deleted sentence pairs with a similarity variance of 1.0 or higher, resulting in the collection of 10,236 sentence pairs with gold similarity. We refer to this collected data as JSTS-A.
> 2. To collect sentence pairs with low similarity, we crowdsource the same STS task as Step 1 using sentence pairs of captions for different images. We conducted this task on 4,000 sentence pairs and collected 2,970 sentence pairs with gold similarity. We refer to this collected data as JSTS-B.
> 3. For JSTS-A, we crowdsource an NLI task. Since inference relations are directional, we obtain inference relations in both directions for sentence pairs. As mentioned earlier,it is difficult to collect instances of contradiction from JSTS-A, which was collected from the captions of the same images,and thus we collect instances of entailment and neutral in this step. We collect inference relation answers from 10 workers. If six or more people give the same answer, we adopt it as the gold label if it is entailment or neutral. To obtain inference relations in both directions for JSTS-A, we performed this task on 20,472 sentence pairs, twice as many as JSTS-A. As a result, we collected inference relations for 17,501 sentence pairs. We refer to this collected data as JNLI-A. We do not use JSTS-B for the NLI task because it is difficult to define and determine the inference relations between captions of different images.
> 4. To collect NLI instances of contradiction, we crowdsource a task of writing four contradictory sentences for each caption in YJCaptions. From the written sentences, we remove sentence pairs with an edit distance of 0.75 or higher to remove low-quality sentences, such as short sentences and sentences with low relevance to the original sentence. Furthermore, we perform a one-way NLI task with 10 workers to verify whether the created sentence pairs are contradictory. Only the sentence pairs answered as contradiction by at least six workers are adopted. Finally,since the contradiction relation has no direction, we automatically assign contradiction in the opposite direction of the adopted sentence pairs. Using 1,800 captions, we acquired 7,200 sentence pairs, from which we collected 3,779 sentence pairs to which we assigned the one-way contradiction relation.By automatically assigning the contradiction relation in the opposite direction, we doubled the number of instances to 7,558. We refer to this collected data as JNLI-C.
> 5. For the 3,779 sentence pairs collected in Step 4, we crowdsource an STS task, assigning similarity and filtering in the same way as in Steps1 and 2. In this way, we collected 2,303 sentence pairs with gold similarity from 3,779 pairs. We refer to this collected data as JSTS-C.
##### JSQuAD
From [JGLUE's paper](https://aclanthology.org/2022.lrec-1.317/):
> As QA datasets, we build a Japanese version of SQuAD (Rajpurkar et al., 2016), one of the datasets of reading comprehension, and a Japanese version ofCommonsenseQA, which is explained in the next section.
> Reading comprehension is the task of reading a document and answering questions about it. Many reading comprehension evaluation sets have been built in English, followed by those in other languages or multilingual ones.
> In Japanese, reading comprehension datasets for quizzes (Suzukietal.,2018) and those in the drivingdomain (Takahashi et al., 2019) have been built, but none are in the general domain. We use Wikipedia to build a dataset for the general domain. The construction process is basically based on SQuAD 1.1 (Rajpurkar et al., 2016).
> First, to extract high-quality articles from Wikipedia, we use Nayuki, which estimates the quality of articles on the basis of hyperlinks in Wikipedia. We randomly chose 822 articles from the top-ranked 10,000 articles. For example, the articles include “熊本県 (Kumamoto Prefecture)” and “フランス料理 (French cuisine)”. Next, we divide an article into paragraphs, present each paragraph to crowdworkers, and ask them to write questions and answers that can be answered if one understands the paragraph. Figure 2 shows an example of JSQuAD. We ask workers to write two additional answers for the dev and test sets to make the system evaluation robust.
##### JCommonsenseQA
From [JGLUE's paper](https://aclanthology.org/2022.lrec-1.317/):
> ### Overview
> JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor et al., 2019), which consists of five choice QA to evaluate commonsense reasoning ability. Figure 3 shows examples of JCommonsenseQA. In the same way as CommonsenseQA, JCommonsenseQA is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet (Speer et al., 2017). ConceptNet is a multilingual knowledge base that consists of triplets of two concepts and their relation. The triplets are directional and represented as (source concept, relation, target concept), for example (bullet train, AtLocation, station).
> ### Method of Construction
> The construction flow for JCommonsenseQA is shown in Figure 4. First, we collect question sets (QSs) from ConceptNet, each of which consists of a source concept and three target concepts that have the same relation to the source concept. Next, for each QS, we crowdAtLocation 2961source a task of writing a question with only one target concept as the answer and a task of adding two distractors. We describe the detailed construction procedure for JCommonsenseQA below, showing how it differs from CommonsenseQA.
> 1. We collect Japanese QSs from ConceptNet. CommonsenseQA uses only forward relations (source concept, relation, target concept) excluding general ones such as “RelatedTo” and “IsA”. JCommonsenseQA similarly uses a set of 22 relations5, excluding general ones, but the direction of the relations is bidirectional to make the questions more diverse. In other words, we also use relations in the opposite direction (source concept, relation−1, target concept).6 With this setup, we extracted 43,566 QSs with Japanese source/target concepts and randomly selected 7,500 from them.
> 2. Some low-quality questions in CommonsenseQA contain distractors that can be considered to be an answer. To improve the quality of distractors, we add the following two processes that are not adopted in CommonsenseQA. First, if three target concepts of a QS include a spelling variation or a synonym of one another, this QS is removed. To identify spelling variations, we use the word ID of the morphological dictionary Juman Dic7. Second, we crowdsource a task of judging whether target concepts contain a synonym. As a result, we adopted 5,920 QSs from 7,500.
> 3. For each QS, we crowdsource a task of writing a question sentence in which only one from the three target concepts is an answer. In the example shown in Figure 4, “駅 (station)” is an answer, and the others are distractors. To remove low quality question sentences, we remove the following question sentences.
> - Question sentences that contain a choice word(this is because such a question is easily solved).
> - Question sentences that contain the expression “XX characters”.8 (XX is a number).
> - Improperly formatted question sentences that do not end with “?”.
> - As a result, 5,920 × 3 = 17,760question sentences were created, from which we adopted 15,310 by removing inappropriate question sentences.
> 4. In CommonsenseQA, when adding distractors, one is selected from ConceptNet, and the other is created by crowdsourcing. In JCommonsenseQA, to have a wider variety of distractors, two distractors are created by crowdsourcing instead of selecting from ConceptNet. To improve the quality of the questions9, we remove questions whose added distractors fall into one of the following categories:
> - Distractors are included in a question sentence.
> - Distractors overlap with one of existing choices.
> - As a result, distractors were added to the 15,310 questions, of which we adopted 13,906.
> 5. We asked three crowdworkers to answer each question and adopt only those answered correctly by at least two workers. As a result, we adopted 11,263 out of the 13,906 questions.
#### Who are the annotators?
From [JGLUE's README.md](https://github.com/yahoojapan/JGLUE/blob/main/README.md#tasksdatasets):
> We use Yahoo! Crowdsourcing for all crowdsourcing tasks in constructing the datasets.
From [JCoLA's paper](https://arxiv.org/abs/2309.12676):
> As a reference for the upper limit of accuracy in JCoLA, human acceptability judgment experiments were conducted on Lancers2 with a subset of the JCoLA data.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
From [JGLUE's paper](https://aclanthology.org/2022.lrec-1.317/):
> We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
From [JCoLA's paper](https://arxiv.org/abs/2309.12676):
> All the sentences included in JCoLA have been extracted from textbooks, handbooks and journal articles on theoretical syntax. Therefore, those sentences are guaranteed to be theoretically meaningful, making JCoLA a challenging dataset. However, the distribution of linguistic phenomena directly reflects that of the source literature and thus turns out to be extremely skewed. Indeed, as can be seen in Table 3, while the number of sentences exceeds 100 for most linguistic phenomena, there are several linguistic phenomena for which there are only about 10 sentences. In addition, since it is difficult to force language models to interpret sentences given specific contexts, those sentences whose unacceptability depends on contexts were inevitably removed from JCoLA. This removal process resulted in the deletion of unacceptable sentences from some linguistic phenomena (such as ellipsis), consequently skewing the balance between acceptable and unacceptable sentences (with a higher proportion of acceptable sentences).
## Additional Information
- 日本語言語理解ベンチマーク JGLUE の構築 〜 自然言語処理モデルの評価用データセットを公開しました - Yahoo! JAPAN Tech Blog https://techblog.yahoo.co.jp/entry/2022122030379907/
### Dataset Curators
#### MARC-ja
- Keung, Phillip, et al. "The Multilingual Amazon Reviews Corpus." Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.
#### JCoLA
- Someya, Sugimoto, and Oseki. "JCoLA: Japanese Corpus of Linguistic Acceptability." arxiv preprint arXiv:2309.12676 (2023).
#### JSTS and JNLI
- Miyazaki, Takashi, and Nobuyuki Shimizu. "Cross-lingual image caption generation." Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.
#### JSQuAD
The JGLUE's 'authors curated the original data for JSQuAD from the Japanese wikipedia dump.
#### JCommonsenseQA
In the same way as CommonsenseQA, JCommonsenseQA is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet
### Licensing Information
#### JGLUE
From [JGLUE's README.md'](https://github.com/yahoojapan/JGLUE#license):
> This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
#### JCoLA
From [JCoLA's README.md'](https://github.com/osekilab/JCoLA#license):
> The text in this corpus is excerpted from the published works, and copyright (where applicable) remains with the original authors or publishers. We expect that research use within Japan is legal under fair use, but make no guarantee of this.
### Citation Information
#### JGLUE
```bibtex
@inproceedings{kurihara-lrec-2022-jglue,
title={JGLUE: Japanese general language understanding evaluation},
author={Kurihara, Kentaro and Kawahara, Daisuke and Shibata, Tomohide},
booktitle={Proceedings of the Thirteenth Language Resources and Evaluation Conference},
pages={2957--2966},
year={2022},
url={https://aclanthology.org/2022.lrec-1.317/}
}
```
```bibtex
@inproceedings{kurihara-nlp-2022-jglue,
title={JGLUE: 日本語言語理解ベンチマーク},
author={栗原健太郎 and 河原大輔 and 柴田知秀},
booktitle={言語処理学会第 28 回年次大会},
pages={2023--2028},
year={2022},
url={https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E8-4.pdf},
note={in Japanese}
}
```
#### MARC-ja
```bibtex
@inproceedings{marc_reviews,
title={The Multilingual Amazon Reviews Corpus},
author={Keung, Phillip and Lu, Yichao and Szarvas, György and Smith, Noah A.},
booktitle={Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
year={2020}
}
```
#### JCoLA
```bibtex
@article{someya-arxiv-2023-jcola,
title={JCoLA: Japanese Corpus of Linguistic Acceptability},
author={Taiga Someya and Yushi Sugimoto and Yohei Oseki},
year={2023},
eprint={2309.12676},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@inproceedings{someya-nlp-2022-jcola,
title={日本語版 CoLA の構築},
author={染谷 大河 and 大関 洋平},
booktitle={言語処理学会第 28 回年次大会},
pages={1872--1877},
year={2022},
url={https://www.anlp.jp/proceedings/annual_meeting/2022/pdf_dir/E7-1.pdf},
note={in Japanese}
}
```
#### JSTS and JNLI
```bibtex
@inproceedings{miyazaki2016cross,
title={Cross-lingual image caption generation},
author={Miyazaki, Takashi and Shimizu, Nobuyuki},
booktitle={Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
pages={1780--1790},
year={2016}
}
```
### Contributions
Thanks to [Kentaro Kurihara](https://twitter.com/kkurihara_cs), [Daisuke Kawahara](https://twitter.com/daisukekawahar1), and [Tomohide Shibata](https://twitter.com/stomohide) for creating JGLUE dataset.
Thanks to [Taiga Someya](https://twitter.com/T0a8i0g9a) for creating JCoLA dataset.
| shunk031/JGLUE | [
"task_categories:multiple-choice",
"task_categories:question-answering",
"task_categories:sentence-similarity",
"task_categories:text-classification",
"task_ids:multiple-choice-qa",
"task_ids:open-domain-qa",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:ja",
"license:cc-by-4.0",
"MARC",
"CoLA",
"STS",
"NLI",
"SQuAD",
"CommonsenseQA",
"arxiv:2309.12676",
"region:us"
]
| 2023-02-27T08:31:09+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["crowdsourced", "found"], "language": ["ja"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": [], "source_datasets": ["original"], "task_categories": ["multiple-choice", "question-answering", "sentence-similarity", "text-classification"], "task_ids": ["multiple-choice-qa", "open-domain-qa", "multi-class-classification", "sentiment-classification"], "pretty_name": "JGLUE", "tags": ["MARC", "CoLA", "STS", "NLI", "SQuAD", "CommonsenseQA"]} | 2023-09-26T11:41:51+00:00 | [
"2309.12676"
]
| [
"ja"
]
| TAGS
#task_categories-multiple-choice #task_categories-question-answering #task_categories-sentence-similarity #task_categories-text-classification #task_ids-multiple-choice-qa #task_ids-open-domain-qa #task_ids-multi-class-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #source_datasets-original #language-Japanese #license-cc-by-4.0 #MARC #CoLA #STS #NLI #SQuAD #CommonsenseQA #arxiv-2309.12676 #region-us
| Dataset Card for JGLUE
======================
 (Keung+, 2020).
>
>
>
##### JCoLA
From JCoLA's URL
>
> JCoLA (Japanese Corpus of Linguistic Accept010 ability) is a novel dataset for targeted syntactic evaluations of language models in Japanese, which consists of 10,020 sentences with acceptability judgments by linguists. The sentences are manually extracted from linguistics journals, handbooks and textbooks. JCoLA is included in JGLUE benchmark (Kurihara et al., 2022).
>
>
>
##### JSTS
From JGLUE's URL:
>
> JSTS is a Japanese version of the STS (Semantic Textual Similarity) dataset. STS is a task to estimate the semantic similarity of a sentence pair. The sentences in JSTS and JNLI (described below) are extracted from the Japanese version of the MS COCO Caption Dataset, the YJ Captions Dataset (Miyazaki and Shimizu, 2016).
>
>
>
##### JNLI
From JGLUE's URL:
>
> JNLI is a Japanese version of the NLI (Natural Language Inference) dataset. NLI is a task to recognize the inference relation that a premise sentence has to a hypothesis sentence. The inference relations are entailment, contradiction, and neutral.
>
>
>
##### JSQuAD
From JGLUE's URL:
>
> JSQuAD is a Japanese version of SQuAD (Rajpurkar+, 2018), one of the datasets of reading comprehension. Each instance in the dataset consists of a question regarding a given context (Wikipedia article) and its answer. JSQuAD is based on SQuAD 1.1 (there are no unanswerable questions). We used the Japanese Wikipedia dump as of 20211101.
>
>
>
##### JCommonsenseQA
From JGLUE's URL:
>
> JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor+, 2019), which is a multiple-choice question answering dataset that requires commonsense reasoning ability. It is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet.
>
>
>
#### Leaderboard
From JGLUE's URL:
>
> A leaderboard will be made public soon. The test set will be released at that time.
>
>
>
### Languages
The language data in JGLUE is in Japanese (BCP-47 ja-JP).
Dataset Structure
-----------------
### Data Instances
When loading a specific configuration, users has to append a version dependent suffix:
#### MARC-ja
#### JCoLA
An example of the JCoLA dataset (validation - out of domain annotated) looks as follows:
#### JSTS
An example of the JSTS dataset looks as follows:
#### JNLI
An example of the JNLI dataset looks as follows:
#### JSQuAD
An example of the JSQuAD looks as follows:
#### JCommonsenseQA
An example of the JCommonsenseQA looks as follows:
### Data Fields
#### MARC-ja
* 'sentence\_pair\_id': ID of the sentence pair
* 'yjcaptions\_id': sentence ids in yjcaptions (explained below)
* 'sentence1': first sentence
* 'sentence2': second sentence
* 'label': sentence similarity: 5 (equivalent meaning) - 0 (completely different meaning)
##### Explanation for 'yjcaptions\_id'
From JGLUE's URL, there are the following two cases:
1. sentence pairs in one image: '(image id)-(sentence1 id)-(sentence2 id)'
* e.g., 723-844-847
* a sentence id starting with "g" means a sentence generated by a crowdworker (e.g., 69501-75698-g103): only for JNLI
2. sentence pairs in two images: '(image id of sentence1)\_(image id of sentence2)-(sentence1 id)-(sentence2 id)'
* e.g., 91337\_217583-96105-91680
#### JCoLA
From JCoLA's URL and JCoLA's paper
* 'uid': unique id of the sentence
* 'source': author and the year of publication of the source article
* 'label': acceptability judgement label (0 for unacceptable, 1 for acceptable)
* 'diacritic': acceptability judgement as originally notated in the source article
* 'sentence': sentence (modified by the author if needed)
* 'original': original sentence as presented in the source article
* 'translation': English translation of the sentence as presentend in the source article (if any)
* 'gloss': gloss of the sentence as presented in the source article (if any)
* 'linguistic\_phenomenon'
+ 'argument\_structure': acceptability judgements based on the order of arguments and case marking
+ 'binding': acceptability judgements based on the binding of noun phrases
+ 'control\_raising': acceptability judgements based on predicates that are categorized as control or raising
+ 'ellipsis': acceptability judgements based on the possibility of omitting elements in the sentences
+ 'filler\_gap': acceptability judgements based on the dependency between the moved element and the gap
+ 'island effects': acceptability judgements based on the restrictions on filler-gap dependencies such as wh-movements
+ 'morphology': acceptability judgements based on the morphology
+ 'nominal\_structure': acceptability judgements based on the internal structure of noun phrases
+ 'negative\_polarity\_concord\_items': acceptability judgements based on the restrictions on where negative polarity/concord items (NPIs/NCIs) can appear
+ 'quantifiers': acceptability judgements based on the distribution of quantifiers such as floating quantifiers
+ 'verbal\_agreement': acceptability judgements based on the dependency between subjects and verbs
+ 'simple': acceptability judgements that do not have marked syntactic structures
#### JNLI
* 'sentence\_pair\_id': ID of the sentence pair
* 'yjcaptions\_id': sentence ids in the yjcaptions
* 'sentence1': premise sentence
* 'sentence2': hypothesis sentence
* 'label': inference relation
#### JSQuAD
* 'title': title of a Wikipedia article
* 'paragraphs': a set of paragraphs
* 'qas': a set of pairs of a question and its answer
* 'question': question
* 'id': id of a question
* 'answers': a set of answers
* 'text': answer text
* 'answer\_start': start position (character index)
* 'is\_impossible': all the values are false
* 'context': a concatenation of the title and paragraph
#### JCommonsenseQA
* 'q\_id': ID of the question
* 'question': question
* 'choice{0..4}': choice
* 'label': correct choice id
### Data Splits
From JGLUE's URL:
>
> Only train/dev sets are available now, and the test set will be available after the leaderboard is made public.
>
>
>
From JCoLA's paper:
>
> The in-domain data is split into training data (6,919 instances), development data (865 instances), and test data (865 instances). On the other hand, the out-of-domain data is only used for evaluation, and divided into development data (685 instances) and test data (686 instances).
>
>
>
>
> JCoLA: † in domain. ‡ out of domain.
>
>
>
Dataset Creation
----------------
### Curation Rationale
From JGLUE's paper:
>
> JGLUE is designed to cover a wide range of GLUE and SuperGLUE tasks and consists of three kinds of tasks: text classification, sentence pair classification, and question answering.
>
>
>
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
* The source language producers are users of Amazon (MARC-ja), crowd-workers of Yahoo! Crowdsourcing (JSTS, JNLI and JCommonsenseQA), writers of the Japanese Wikipedia (JSQuAD), crowd-workers of Lancers.
### Annotations
#### Annotation process
##### MARC-ja
From JGLUE's paper:
>
> As one of the text classification datasets, we build a dataset based on the Multilingual Amazon Reviews Corpus (MARC) (Keung et al., 2020). MARC is a multilingual corpus of product reviews with 5-level star ratings (1-5) on the Amazon shopping site. This corpus covers six languages, including English and Japanese. For JGLUE, we use the Japanese part of MARC and to make it easy for both humans and computers to judge a class label, we cast the text classification task as a binary classification task, where 1- and 2-star ratings are converted to “negative”, and 4 and 5 are converted to “positive”. We do not use reviews with a 3-star rating.
>
>
>
>
> One of the problems with MARC is that it sometimes contains data where the rating diverges from the review text. This happens, for example, when a review with positive content is given a rating of 1 or 2. These data degrade the quality of our dataset. To improve the quality of the dev/test instances used for evaluation, we crowdsource a positive/negative judgment task for approximately 12,000 reviews. We adopt only reviews with the same votes from 7 or more out of 10 workers and assign a label of the maximum votes to these reviews. We divide the resulting reviews into dev/test data.
>
>
>
>
> We obtained 5,654 and 5,639 instances for the dev and test data, respectively, through the above procedure. For the training data, we extracted 187,528 instances directly from MARC without performing the cleaning procedure because of the large number of training instances. The statistics of MARC-ja are listed in Table 2. For the evaluation metric for MARC-ja, we use accuracy because it is a binary classification task of texts.
>
>
>
##### JCoLA
From JCoLA's paper:
>
> ### 3 JCoLA
>
>
> In this study, we introduce JCoLA (Japanese Corpus of Linguistic Acceptability), which will be the first large-scale acceptability judgment task dataset focusing on Japanese. JCoLA consists of sentences from textbooks and handbooks on Japanese syntax, as well as from journal articles on Japanese syntax that are published in JEAL (Journal of East Asian Linguistics), one of the prestigious journals in theoretical linguistics.
>
>
>
>
> #### 3.1 Data Collection
>
>
> Sentences in JCoLA were collected from prominent textbooks and handbooks focusing on Japanese syntax. In addition to the main text, example sentences included in the footnotes were also considered for collection. We also collected acceptability judgments from journal articles on Japanese syntax published in JEAL (Journal of East Asian Linguistics): one of the prestigious journals in the-oretical linguistics. Specifically, we examined all the articles published in JEAL between 2006 and 2015 (133 papers in total), and extracted 2,252 acceptability judgments from 26 papers on Japanese syntax (Table 2). Acceptability judgments include sentences in appendices and footnotes, but not sentences presented for analyses of syntactic structures (e.g. sentences with brackets to show their syntactic structures). As a result, a total of 11,984 example. sentences were collected. Using this as a basis, JCoLA was constructed through the methodology explained in the following sections.
>
>
>
##### JSTS and JNLI
From JGLUE's paper:
>
> For the sentence pair classification datasets, we construct a semantic textual similarity (STS) dataset, JSTS, and a natural language inference (NLI) dataset, JNLI.
>
>
>
>
> ### Overview
>
>
> STS is a task of estimating the semantic similarity of a sentence pair. Gold similarity is usually assigned as an average of the integer values 0 (completely different meaning) to 5 (equivalent meaning) assigned by multiple workers through crowdsourcing.
>
>
>
>
> NLI is a task of recognizing the inference relation that a premise sentence has to a hypothesis sentence. Inference relations are generally defined by three labels: “entailment”, “contradiction”, and “neutral”. Gold inference relations are often assigned by majority voting after collecting answers from multiple workers through crowdsourcing.
>
>
>
>
> For the STS and NLI tasks, STS-B (Cer et al., 2017) and MultiNLI (Williams et al., 2018) are included in GLUE, respectively. As Japanese datasets, JSNLI (Yoshikoshi et al., 2020) is a machine translated dataset of the NLI dataset SNLI (Stanford NLI), and JSICK (Yanaka and Mineshima, 2021) is a human translated dataset of the STS/NLI dataset SICK (Marelli et al., 2014). As mentioned in Section 1, these have problems originating from automatic/manual translations. To solve this problem, we construct STS/NLI datasets in Japanese from scratch. We basically extract sentence pairs in JSTS and JNLI from the Japanese version of the MS COCO Caption Dataset (Chen et al., 2015), the YJ Captions Dataset (Miyazaki and Shimizu, 2016). Most of the sentence pairs in JSTS and JNLI overlap, allowing us to analyze the relationship between similarities and inference relations for the same sentence pairs like SICK and JSICK.
>
>
>
>
> The similarity value in JSTS is assigned a real number from 0 to 5 as in STS-B. The inference relation in JNLI is assigned from the above three labels as in SNLI and MultiNLI. The definitions of the inference relations are also based on SNLI.
>
>
>
>
> ### Method of Construction
>
>
> Our construction flow for JSTS and JNLI is shown in Figure 1. Basically, two captions for the same image of YJ Captions are used as sentence pairs. For these sentence pairs, similarities and NLI relations of entailment and neutral are obtained by crowdsourcing. However, it is difficult to collect sentence pairs with low similarity and contradiction relations from captions for the same image. To solve this problem, we collect sentence pairs with low similarity from captions for different images. We collect contradiction relations by asking workers to write contradictory sentences for a given caption.
>
>
>
>
> The detailed construction procedure for JSTS and JNLI is described below.
>
>
> 1. We crowdsource an STS task using two captions for the same image from YJ Captions. We ask five workers to answer the similarity between two captions and take the mean value as the gold similarity. We delete sentence pairs with a large variance in the answers because such pairs have poor answer quality. We performed this task on 16,000 sentence pairs and deleted sentence pairs with a similarity variance of 1.0 or higher, resulting in the collection of 10,236 sentence pairs with gold similarity. We refer to this collected data as JSTS-A.
> 2. To collect sentence pairs with low similarity, we crowdsource the same STS task as Step 1 using sentence pairs of captions for different images. We conducted this task on 4,000 sentence pairs and collected 2,970 sentence pairs with gold similarity. We refer to this collected data as JSTS-B.
> 3. For JSTS-A, we crowdsource an NLI task. Since inference relations are directional, we obtain inference relations in both directions for sentence pairs. As mentioned earlier,it is difficult to collect instances of contradiction from JSTS-A, which was collected from the captions of the same images,and thus we collect instances of entailment and neutral in this step. We collect inference relation answers from 10 workers. If six or more people give the same answer, we adopt it as the gold label if it is entailment or neutral. To obtain inference relations in both directions for JSTS-A, we performed this task on 20,472 sentence pairs, twice as many as JSTS-A. As a result, we collected inference relations for 17,501 sentence pairs. We refer to this collected data as JNLI-A. We do not use JSTS-B for the NLI task because it is difficult to define and determine the inference relations between captions of different images.
> 4. To collect NLI instances of contradiction, we crowdsource a task of writing four contradictory sentences for each caption in YJCaptions. From the written sentences, we remove sentence pairs with an edit distance of 0.75 or higher to remove low-quality sentences, such as short sentences and sentences with low relevance to the original sentence. Furthermore, we perform a one-way NLI task with 10 workers to verify whether the created sentence pairs are contradictory. Only the sentence pairs answered as contradiction by at least six workers are adopted. Finally,since the contradiction relation has no direction, we automatically assign contradiction in the opposite direction of the adopted sentence pairs. Using 1,800 captions, we acquired 7,200 sentence pairs, from which we collected 3,779 sentence pairs to which we assigned the one-way contradiction relation.By automatically assigning the contradiction relation in the opposite direction, we doubled the number of instances to 7,558. We refer to this collected data as JNLI-C.
> 5. For the 3,779 sentence pairs collected in Step 4, we crowdsource an STS task, assigning similarity and filtering in the same way as in Steps1 and 2. In this way, we collected 2,303 sentence pairs with gold similarity from 3,779 pairs. We refer to this collected data as JSTS-C.
>
>
>
##### JSQuAD
From JGLUE's paper:
>
> As QA datasets, we build a Japanese version of SQuAD (Rajpurkar et al., 2016), one of the datasets of reading comprehension, and a Japanese version ofCommonsenseQA, which is explained in the next section.
>
>
>
>
> Reading comprehension is the task of reading a document and answering questions about it. Many reading comprehension evaluation sets have been built in English, followed by those in other languages or multilingual ones.
>
>
>
>
> In Japanese, reading comprehension datasets for quizzes (Suzukietal.,2018) and those in the drivingdomain (Takahashi et al., 2019) have been built, but none are in the general domain. We use Wikipedia to build a dataset for the general domain. The construction process is basically based on SQuAD 1.1 (Rajpurkar et al., 2016).
>
>
>
>
> First, to extract high-quality articles from Wikipedia, we use Nayuki, which estimates the quality of articles on the basis of hyperlinks in Wikipedia. We randomly chose 822 articles from the top-ranked 10,000 articles. For example, the articles include “熊本県 (Kumamoto Prefecture)” and “フランス料理 (French cuisine)”. Next, we divide an article into paragraphs, present each paragraph to crowdworkers, and ask them to write questions and answers that can be answered if one understands the paragraph. Figure 2 shows an example of JSQuAD. We ask workers to write two additional answers for the dev and test sets to make the system evaluation robust.
>
>
>
##### JCommonsenseQA
From JGLUE's paper:
>
> ### Overview
>
>
> JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor et al., 2019), which consists of five choice QA to evaluate commonsense reasoning ability. Figure 3 shows examples of JCommonsenseQA. In the same way as CommonsenseQA, JCommonsenseQA is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet (Speer et al., 2017). ConceptNet is a multilingual knowledge base that consists of triplets of two concepts and their relation. The triplets are directional and represented as (source concept, relation, target concept), for example (bullet train, AtLocation, station).
>
>
>
>
> ### Method of Construction
>
>
> The construction flow for JCommonsenseQA is shown in Figure 4. First, we collect question sets (QSs) from ConceptNet, each of which consists of a source concept and three target concepts that have the same relation to the source concept. Next, for each QS, we crowdAtLocation 2961source a task of writing a question with only one target concept as the answer and a task of adding two distractors. We describe the detailed construction procedure for JCommonsenseQA below, showing how it differs from CommonsenseQA.
>
>
>
>
> 1. We collect Japanese QSs from ConceptNet. CommonsenseQA uses only forward relations (source concept, relation, target concept) excluding general ones such as “RelatedTo” and “IsA”. JCommonsenseQA similarly uses a set of 22 relations5, excluding general ones, but the direction of the relations is bidirectional to make the questions more diverse. In other words, we also use relations in the opposite direction (source concept, relation−1, target concept).6 With this setup, we extracted 43,566 QSs with Japanese source/target concepts and randomly selected 7,500 from them.
> 2. Some low-quality questions in CommonsenseQA contain distractors that can be considered to be an answer. To improve the quality of distractors, we add the following two processes that are not adopted in CommonsenseQA. First, if three target concepts of a QS include a spelling variation or a synonym of one another, this QS is removed. To identify spelling variations, we use the word ID of the morphological dictionary Juman Dic7. Second, we crowdsource a task of judging whether target concepts contain a synonym. As a result, we adopted 5,920 QSs from 7,500.
> 3. For each QS, we crowdsource a task of writing a question sentence in which only one from the three target concepts is an answer. In the example shown in Figure 4, “駅 (station)” is an answer, and the others are distractors. To remove low quality question sentences, we remove the following question sentences.
> * Question sentences that contain a choice word(this is because such a question is easily solved).
> * Question sentences that contain the expression “XX characters”.8 (XX is a number).
> * Improperly formatted question sentences that do not end with “?”.
> * As a result, 5,920 × 3 = 17,760question sentences were created, from which we adopted 15,310 by removing inappropriate question sentences.
> 4. In CommonsenseQA, when adding distractors, one is selected from ConceptNet, and the other is created by crowdsourcing. In JCommonsenseQA, to have a wider variety of distractors, two distractors are created by crowdsourcing instead of selecting from ConceptNet. To improve the quality of the questions9, we remove questions whose added distractors fall into one of the following categories:
> * Distractors are included in a question sentence.
> * Distractors overlap with one of existing choices.
> * As a result, distractors were added to the 15,310 questions, of which we adopted 13,906.
> 5. We asked three crowdworkers to answer each question and adopt only those answered correctly by at least two workers. As a result, we adopted 11,263 out of the 13,906 questions.
>
>
>
#### Who are the annotators?
From JGLUE's URL:
>
> We use Yahoo! Crowdsourcing for all crowdsourcing tasks in constructing the datasets.
>
>
>
From JCoLA's paper:
>
> As a reference for the upper limit of accuracy in JCoLA, human acceptability judgment experiments were conducted on Lancers2 with a subset of the JCoLA data.
>
>
>
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
From JGLUE's paper:
>
> We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.
>
>
>
### Discussion of Biases
### Other Known Limitations
From JCoLA's paper:
>
> All the sentences included in JCoLA have been extracted from textbooks, handbooks and journal articles on theoretical syntax. Therefore, those sentences are guaranteed to be theoretically meaningful, making JCoLA a challenging dataset. However, the distribution of linguistic phenomena directly reflects that of the source literature and thus turns out to be extremely skewed. Indeed, as can be seen in Table 3, while the number of sentences exceeds 100 for most linguistic phenomena, there are several linguistic phenomena for which there are only about 10 sentences. In addition, since it is difficult to force language models to interpret sentences given specific contexts, those sentences whose unacceptability depends on contexts were inevitably removed from JCoLA. This removal process resulted in the deletion of unacceptable sentences from some linguistic phenomena (such as ellipsis), consequently skewing the balance between acceptable and unacceptable sentences (with a higher proportion of acceptable sentences).
>
>
>
Additional Information
----------------------
* 日本語言語理解ベンチマーク JGLUE の構築 〜 自然言語処理モデルの評価用データセットを公開しました - Yahoo! JAPAN Tech Blog URL
### Dataset Curators
#### MARC-ja
* Keung, Phillip, et al. "The Multilingual Amazon Reviews Corpus." Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.
#### JCoLA
* Someya, Sugimoto, and Oseki. "JCoLA: Japanese Corpus of Linguistic Acceptability." arxiv preprint arXiv:2309.12676 (2023).
#### JSTS and JNLI
* Miyazaki, Takashi, and Nobuyuki Shimizu. "Cross-lingual image caption generation." Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.
#### JSQuAD
The JGLUE's 'authors curated the original data for JSQuAD from the Japanese wikipedia dump.
#### JCommonsenseQA
In the same way as CommonsenseQA, JCommonsenseQA is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet
### Licensing Information
#### JGLUE
From JGLUE's URL':
>
> This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
>
>
>
#### JCoLA
From JCoLA's URL':
>
> The text in this corpus is excerpted from the published works, and copyright (where applicable) remains with the original authors or publishers. We expect that research use within Japan is legal under fair use, but make no guarantee of this.
>
>
>
#### JGLUE
#### MARC-ja
#### JCoLA
#### JSTS and JNLI
### Contributions
Thanks to Kentaro Kurihara, Daisuke Kawahara, and Tomohide Shibata for creating JGLUE dataset.
Thanks to Taiga Someya for creating JCoLA dataset.
| [
"### Dataset Summary\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JGLUE, Japanese General Language Understanding Evaluation, is built to measure the general NLU ability in Japanese. JGLUE has been constructed from scratch without translation. We hope that JGLUE will facilitate NLU research in Japanese.\n> \n> \n> \n\n\n\n> \n> JGLUE has been constructed by a joint research project of Yahoo Japan Corporation and Kawahara Lab at Waseda University.\n> \n> \n>",
"### Supported Tasks and Leaderboards\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JGLUE consists of the tasks of text classification, sentence pair classification, and QA. Each task consists of multiple datasets.\n> \n> \n>",
"#### Supported Tasks",
"##### MARC-ja\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> MARC-ja is a dataset of the text classification task. This dataset is based on the Japanese portion of Multilingual Amazon Reviews Corpus (MARC) (Keung+, 2020).\n> \n> \n>",
"##### JCoLA\n\n\nFrom JCoLA's URL\n\n\n\n> \n> JCoLA (Japanese Corpus of Linguistic Accept010 ability) is a novel dataset for targeted syntactic evaluations of language models in Japanese, which consists of 10,020 sentences with acceptability judgments by linguists. The sentences are manually extracted from linguistics journals, handbooks and textbooks. JCoLA is included in JGLUE benchmark (Kurihara et al., 2022).\n> \n> \n>",
"##### JSTS\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JSTS is a Japanese version of the STS (Semantic Textual Similarity) dataset. STS is a task to estimate the semantic similarity of a sentence pair. The sentences in JSTS and JNLI (described below) are extracted from the Japanese version of the MS COCO Caption Dataset, the YJ Captions Dataset (Miyazaki and Shimizu, 2016).\n> \n> \n>",
"##### JNLI\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JNLI is a Japanese version of the NLI (Natural Language Inference) dataset. NLI is a task to recognize the inference relation that a premise sentence has to a hypothesis sentence. The inference relations are entailment, contradiction, and neutral.\n> \n> \n>",
"##### JSQuAD\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JSQuAD is a Japanese version of SQuAD (Rajpurkar+, 2018), one of the datasets of reading comprehension. Each instance in the dataset consists of a question regarding a given context (Wikipedia article) and its answer. JSQuAD is based on SQuAD 1.1 (there are no unanswerable questions). We used the Japanese Wikipedia dump as of 20211101.\n> \n> \n>",
"##### JCommonsenseQA\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor+, 2019), which is a multiple-choice question answering dataset that requires commonsense reasoning ability. It is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet.\n> \n> \n>",
"#### Leaderboard\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> A leaderboard will be made public soon. The test set will be released at that time.\n> \n> \n>",
"### Languages\n\n\nThe language data in JGLUE is in Japanese (BCP-47 ja-JP).\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nWhen loading a specific configuration, users has to append a version dependent suffix:",
"#### MARC-ja",
"#### JCoLA\n\n\nAn example of the JCoLA dataset (validation - out of domain annotated) looks as follows:",
"#### JSTS\n\n\nAn example of the JSTS dataset looks as follows:",
"#### JNLI\n\n\nAn example of the JNLI dataset looks as follows:",
"#### JSQuAD\n\n\nAn example of the JSQuAD looks as follows:",
"#### JCommonsenseQA\n\n\nAn example of the JCommonsenseQA looks as follows:",
"### Data Fields",
"#### MARC-ja\n\n\n* 'sentence\\_pair\\_id': ID of the sentence pair\n* 'yjcaptions\\_id': sentence ids in yjcaptions (explained below)\n* 'sentence1': first sentence\n* 'sentence2': second sentence\n* 'label': sentence similarity: 5 (equivalent meaning) - 0 (completely different meaning)",
"##### Explanation for 'yjcaptions\\_id'\n\n\nFrom JGLUE's URL, there are the following two cases:\n\n\n1. sentence pairs in one image: '(image id)-(sentence1 id)-(sentence2 id)'\n\t* e.g., 723-844-847\n\t* a sentence id starting with \"g\" means a sentence generated by a crowdworker (e.g., 69501-75698-g103): only for JNLI\n2. sentence pairs in two images: '(image id of sentence1)\\_(image id of sentence2)-(sentence1 id)-(sentence2 id)'\n\t* e.g., 91337\\_217583-96105-91680",
"#### JCoLA\n\n\nFrom JCoLA's URL and JCoLA's paper\n\n\n* 'uid': unique id of the sentence\n* 'source': author and the year of publication of the source article\n* 'label': acceptability judgement label (0 for unacceptable, 1 for acceptable)\n* 'diacritic': acceptability judgement as originally notated in the source article\n* 'sentence': sentence (modified by the author if needed)\n* 'original': original sentence as presented in the source article\n* 'translation': English translation of the sentence as presentend in the source article (if any)\n* 'gloss': gloss of the sentence as presented in the source article (if any)\n* 'linguistic\\_phenomenon'\n\t+ 'argument\\_structure': acceptability judgements based on the order of arguments and case marking\n\t+ 'binding': acceptability judgements based on the binding of noun phrases\n\t+ 'control\\_raising': acceptability judgements based on predicates that are categorized as control or raising\n\t+ 'ellipsis': acceptability judgements based on the possibility of omitting elements in the sentences\n\t+ 'filler\\_gap': acceptability judgements based on the dependency between the moved element and the gap\n\t+ 'island effects': acceptability judgements based on the restrictions on filler-gap dependencies such as wh-movements\n\t+ 'morphology': acceptability judgements based on the morphology\n\t+ 'nominal\\_structure': acceptability judgements based on the internal structure of noun phrases\n\t+ 'negative\\_polarity\\_concord\\_items': acceptability judgements based on the restrictions on where negative polarity/concord items (NPIs/NCIs) can appear\n\t+ 'quantifiers': acceptability judgements based on the distribution of quantifiers such as floating quantifiers\n\t+ 'verbal\\_agreement': acceptability judgements based on the dependency between subjects and verbs\n\t+ 'simple': acceptability judgements that do not have marked syntactic structures",
"#### JNLI\n\n\n* 'sentence\\_pair\\_id': ID of the sentence pair\n* 'yjcaptions\\_id': sentence ids in the yjcaptions\n* 'sentence1': premise sentence\n* 'sentence2': hypothesis sentence\n* 'label': inference relation",
"#### JSQuAD\n\n\n* 'title': title of a Wikipedia article\n* 'paragraphs': a set of paragraphs\n* 'qas': a set of pairs of a question and its answer\n* 'question': question\n* 'id': id of a question\n* 'answers': a set of answers\n* 'text': answer text\n* 'answer\\_start': start position (character index)\n* 'is\\_impossible': all the values are false\n* 'context': a concatenation of the title and paragraph",
"#### JCommonsenseQA\n\n\n* 'q\\_id': ID of the question\n* 'question': question\n* 'choice{0..4}': choice\n* 'label': correct choice id",
"### Data Splits\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> Only train/dev sets are available now, and the test set will be available after the leaderboard is made public.\n> \n> \n> \n\n\nFrom JCoLA's paper:\n\n\n\n> \n> The in-domain data is split into training data (6,919 instances), development data (865 instances), and test data (865 instances). On the other hand, the out-of-domain data is only used for evaluation, and divided into development data (685 instances) and test data (686 instances).\n> \n> \n> \n\n\n\n\n> \n> JCoLA: † in domain. ‡ out of domain.\n> \n> \n> \n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> JGLUE is designed to cover a wide range of GLUE and SuperGLUE tasks and consists of three kinds of tasks: text classification, sentence pair classification, and question answering.\n> \n> \n>",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?\n\n\n* The source language producers are users of Amazon (MARC-ja), crowd-workers of Yahoo! Crowdsourcing (JSTS, JNLI and JCommonsenseQA), writers of the Japanese Wikipedia (JSQuAD), crowd-workers of Lancers.",
"### Annotations",
"#### Annotation process",
"##### MARC-ja\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> As one of the text classification datasets, we build a dataset based on the Multilingual Amazon Reviews Corpus (MARC) (Keung et al., 2020). MARC is a multilingual corpus of product reviews with 5-level star ratings (1-5) on the Amazon shopping site. This corpus covers six languages, including English and Japanese. For JGLUE, we use the Japanese part of MARC and to make it easy for both humans and computers to judge a class label, we cast the text classification task as a binary classification task, where 1- and 2-star ratings are converted to “negative”, and 4 and 5 are converted to “positive”. We do not use reviews with a 3-star rating.\n> \n> \n> \n\n\n\n> \n> One of the problems with MARC is that it sometimes contains data where the rating diverges from the review text. This happens, for example, when a review with positive content is given a rating of 1 or 2. These data degrade the quality of our dataset. To improve the quality of the dev/test instances used for evaluation, we crowdsource a positive/negative judgment task for approximately 12,000 reviews. We adopt only reviews with the same votes from 7 or more out of 10 workers and assign a label of the maximum votes to these reviews. We divide the resulting reviews into dev/test data.\n> \n> \n> \n\n\n\n> \n> We obtained 5,654 and 5,639 instances for the dev and test data, respectively, through the above procedure. For the training data, we extracted 187,528 instances directly from MARC without performing the cleaning procedure because of the large number of training instances. The statistics of MARC-ja are listed in Table 2. For the evaluation metric for MARC-ja, we use accuracy because it is a binary classification task of texts.\n> \n> \n>",
"##### JCoLA\n\n\nFrom JCoLA's paper:\n\n\n\n> \n> ### 3 JCoLA\n> \n> \n> In this study, we introduce JCoLA (Japanese Corpus of Linguistic Acceptability), which will be the first large-scale acceptability judgment task dataset focusing on Japanese. JCoLA consists of sentences from textbooks and handbooks on Japanese syntax, as well as from journal articles on Japanese syntax that are published in JEAL (Journal of East Asian Linguistics), one of the prestigious journals in theoretical linguistics.\n> \n> \n> \n\n\n\n> \n> #### 3.1 Data Collection\n> \n> \n> Sentences in JCoLA were collected from prominent textbooks and handbooks focusing on Japanese syntax. In addition to the main text, example sentences included in the footnotes were also considered for collection. We also collected acceptability judgments from journal articles on Japanese syntax published in JEAL (Journal of East Asian Linguistics): one of the prestigious journals in the-oretical linguistics. Specifically, we examined all the articles published in JEAL between 2006 and 2015 (133 papers in total), and extracted 2,252 acceptability judgments from 26 papers on Japanese syntax (Table 2). Acceptability judgments include sentences in appendices and footnotes, but not sentences presented for analyses of syntactic structures (e.g. sentences with brackets to show their syntactic structures). As a result, a total of 11,984 example. sentences were collected. Using this as a basis, JCoLA was constructed through the methodology explained in the following sections.\n> \n> \n>",
"##### JSTS and JNLI\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> For the sentence pair classification datasets, we construct a semantic textual similarity (STS) dataset, JSTS, and a natural language inference (NLI) dataset, JNLI.\n> \n> \n> \n\n\n\n> \n> ### Overview\n> \n> \n> STS is a task of estimating the semantic similarity of a sentence pair. Gold similarity is usually assigned as an average of the integer values 0 (completely different meaning) to 5 (equivalent meaning) assigned by multiple workers through crowdsourcing.\n> \n> \n> \n\n\n\n> \n> NLI is a task of recognizing the inference relation that a premise sentence has to a hypothesis sentence. Inference relations are generally defined by three labels: “entailment”, “contradiction”, and “neutral”. Gold inference relations are often assigned by majority voting after collecting answers from multiple workers through crowdsourcing.\n> \n> \n> \n\n\n\n> \n> For the STS and NLI tasks, STS-B (Cer et al., 2017) and MultiNLI (Williams et al., 2018) are included in GLUE, respectively. As Japanese datasets, JSNLI (Yoshikoshi et al., 2020) is a machine translated dataset of the NLI dataset SNLI (Stanford NLI), and JSICK (Yanaka and Mineshima, 2021) is a human translated dataset of the STS/NLI dataset SICK (Marelli et al., 2014). As mentioned in Section 1, these have problems originating from automatic/manual translations. To solve this problem, we construct STS/NLI datasets in Japanese from scratch. We basically extract sentence pairs in JSTS and JNLI from the Japanese version of the MS COCO Caption Dataset (Chen et al., 2015), the YJ Captions Dataset (Miyazaki and Shimizu, 2016). Most of the sentence pairs in JSTS and JNLI overlap, allowing us to analyze the relationship between similarities and inference relations for the same sentence pairs like SICK and JSICK.\n> \n> \n> \n\n\n\n> \n> The similarity value in JSTS is assigned a real number from 0 to 5 as in STS-B. The inference relation in JNLI is assigned from the above three labels as in SNLI and MultiNLI. The definitions of the inference relations are also based on SNLI.\n> \n> \n> \n\n\n\n> \n> ### Method of Construction\n> \n> \n> Our construction flow for JSTS and JNLI is shown in Figure 1. Basically, two captions for the same image of YJ Captions are used as sentence pairs. For these sentence pairs, similarities and NLI relations of entailment and neutral are obtained by crowdsourcing. However, it is difficult to collect sentence pairs with low similarity and contradiction relations from captions for the same image. To solve this problem, we collect sentence pairs with low similarity from captions for different images. We collect contradiction relations by asking workers to write contradictory sentences for a given caption.\n> \n> \n> \n\n\n\n> \n> The detailed construction procedure for JSTS and JNLI is described below.\n> \n> \n> 1. We crowdsource an STS task using two captions for the same image from YJ Captions. We ask five workers to answer the similarity between two captions and take the mean value as the gold similarity. We delete sentence pairs with a large variance in the answers because such pairs have poor answer quality. We performed this task on 16,000 sentence pairs and deleted sentence pairs with a similarity variance of 1.0 or higher, resulting in the collection of 10,236 sentence pairs with gold similarity. We refer to this collected data as JSTS-A.\n> 2. To collect sentence pairs with low similarity, we crowdsource the same STS task as Step 1 using sentence pairs of captions for different images. We conducted this task on 4,000 sentence pairs and collected 2,970 sentence pairs with gold similarity. We refer to this collected data as JSTS-B.\n> 3. For JSTS-A, we crowdsource an NLI task. Since inference relations are directional, we obtain inference relations in both directions for sentence pairs. As mentioned earlier,it is difficult to collect instances of contradiction from JSTS-A, which was collected from the captions of the same images,and thus we collect instances of entailment and neutral in this step. We collect inference relation answers from 10 workers. If six or more people give the same answer, we adopt it as the gold label if it is entailment or neutral. To obtain inference relations in both directions for JSTS-A, we performed this task on 20,472 sentence pairs, twice as many as JSTS-A. As a result, we collected inference relations for 17,501 sentence pairs. We refer to this collected data as JNLI-A. We do not use JSTS-B for the NLI task because it is difficult to define and determine the inference relations between captions of different images.\n> 4. To collect NLI instances of contradiction, we crowdsource a task of writing four contradictory sentences for each caption in YJCaptions. From the written sentences, we remove sentence pairs with an edit distance of 0.75 or higher to remove low-quality sentences, such as short sentences and sentences with low relevance to the original sentence. Furthermore, we perform a one-way NLI task with 10 workers to verify whether the created sentence pairs are contradictory. Only the sentence pairs answered as contradiction by at least six workers are adopted. Finally,since the contradiction relation has no direction, we automatically assign contradiction in the opposite direction of the adopted sentence pairs. Using 1,800 captions, we acquired 7,200 sentence pairs, from which we collected 3,779 sentence pairs to which we assigned the one-way contradiction relation.By automatically assigning the contradiction relation in the opposite direction, we doubled the number of instances to 7,558. We refer to this collected data as JNLI-C.\n> 5. For the 3,779 sentence pairs collected in Step 4, we crowdsource an STS task, assigning similarity and filtering in the same way as in Steps1 and 2. In this way, we collected 2,303 sentence pairs with gold similarity from 3,779 pairs. We refer to this collected data as JSTS-C.\n> \n> \n>",
"##### JSQuAD\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> As QA datasets, we build a Japanese version of SQuAD (Rajpurkar et al., 2016), one of the datasets of reading comprehension, and a Japanese version ofCommonsenseQA, which is explained in the next section.\n> \n> \n> \n\n\n\n> \n> Reading comprehension is the task of reading a document and answering questions about it. Many reading comprehension evaluation sets have been built in English, followed by those in other languages or multilingual ones.\n> \n> \n> \n\n\n\n> \n> In Japanese, reading comprehension datasets for quizzes (Suzukietal.,2018) and those in the drivingdomain (Takahashi et al., 2019) have been built, but none are in the general domain. We use Wikipedia to build a dataset for the general domain. The construction process is basically based on SQuAD 1.1 (Rajpurkar et al., 2016).\n> \n> \n> \n\n\n\n> \n> First, to extract high-quality articles from Wikipedia, we use Nayuki, which estimates the quality of articles on the basis of hyperlinks in Wikipedia. We randomly chose 822 articles from the top-ranked 10,000 articles. For example, the articles include “熊本県 (Kumamoto Prefecture)” and “フランス料理 (French cuisine)”. Next, we divide an article into paragraphs, present each paragraph to crowdworkers, and ask them to write questions and answers that can be answered if one understands the paragraph. Figure 2 shows an example of JSQuAD. We ask workers to write two additional answers for the dev and test sets to make the system evaluation robust.\n> \n> \n>",
"##### JCommonsenseQA\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> ### Overview\n> \n> \n> JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor et al., 2019), which consists of five choice QA to evaluate commonsense reasoning ability. Figure 3 shows examples of JCommonsenseQA. In the same way as CommonsenseQA, JCommonsenseQA is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet (Speer et al., 2017). ConceptNet is a multilingual knowledge base that consists of triplets of two concepts and their relation. The triplets are directional and represented as (source concept, relation, target concept), for example (bullet train, AtLocation, station).\n> \n> \n> \n\n\n\n> \n> ### Method of Construction\n> \n> \n> The construction flow for JCommonsenseQA is shown in Figure 4. First, we collect question sets (QSs) from ConceptNet, each of which consists of a source concept and three target concepts that have the same relation to the source concept. Next, for each QS, we crowdAtLocation 2961source a task of writing a question with only one target concept as the answer and a task of adding two distractors. We describe the detailed construction procedure for JCommonsenseQA below, showing how it differs from CommonsenseQA.\n> \n> \n> \n\n\n\n> \n> 1. We collect Japanese QSs from ConceptNet. CommonsenseQA uses only forward relations (source concept, relation, target concept) excluding general ones such as “RelatedTo” and “IsA”. JCommonsenseQA similarly uses a set of 22 relations5, excluding general ones, but the direction of the relations is bidirectional to make the questions more diverse. In other words, we also use relations in the opposite direction (source concept, relation−1, target concept).6 With this setup, we extracted 43,566 QSs with Japanese source/target concepts and randomly selected 7,500 from them.\n> 2. Some low-quality questions in CommonsenseQA contain distractors that can be considered to be an answer. To improve the quality of distractors, we add the following two processes that are not adopted in CommonsenseQA. First, if three target concepts of a QS include a spelling variation or a synonym of one another, this QS is removed. To identify spelling variations, we use the word ID of the morphological dictionary Juman Dic7. Second, we crowdsource a task of judging whether target concepts contain a synonym. As a result, we adopted 5,920 QSs from 7,500.\n> 3. For each QS, we crowdsource a task of writing a question sentence in which only one from the three target concepts is an answer. In the example shown in Figure 4, “駅 (station)” is an answer, and the others are distractors. To remove low quality question sentences, we remove the following question sentences.\n> \t* Question sentences that contain a choice word(this is because such a question is easily solved).\n> \t* Question sentences that contain the expression “XX characters”.8 (XX is a number).\n> \t* Improperly formatted question sentences that do not end with “?”.\n> \t* As a result, 5,920 × 3 = 17,760question sentences were created, from which we adopted 15,310 by removing inappropriate question sentences.\n> 4. In CommonsenseQA, when adding distractors, one is selected from ConceptNet, and the other is created by crowdsourcing. In JCommonsenseQA, to have a wider variety of distractors, two distractors are created by crowdsourcing instead of selecting from ConceptNet. To improve the quality of the questions9, we remove questions whose added distractors fall into one of the following categories:\n> \t* Distractors are included in a question sentence.\n> \t* Distractors overlap with one of existing choices.\n> \t* As a result, distractors were added to the 15,310 questions, of which we adopted 13,906.\n> 5. We asked three crowdworkers to answer each question and adopt only those answered correctly by at least two workers. As a result, we adopted 11,263 out of the 13,906 questions.\n> \n> \n>",
"#### Who are the annotators?\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> We use Yahoo! Crowdsourcing for all crowdsourcing tasks in constructing the datasets.\n> \n> \n> \n\n\nFrom JCoLA's paper:\n\n\n\n> \n> As a reference for the upper limit of accuracy in JCoLA, human acceptability judgment experiments were conducted on Lancers2 with a subset of the JCoLA data.\n> \n> \n>",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.\n> \n> \n>",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nFrom JCoLA's paper:\n\n\n\n> \n> All the sentences included in JCoLA have been extracted from textbooks, handbooks and journal articles on theoretical syntax. Therefore, those sentences are guaranteed to be theoretically meaningful, making JCoLA a challenging dataset. However, the distribution of linguistic phenomena directly reflects that of the source literature and thus turns out to be extremely skewed. Indeed, as can be seen in Table 3, while the number of sentences exceeds 100 for most linguistic phenomena, there are several linguistic phenomena for which there are only about 10 sentences. In addition, since it is difficult to force language models to interpret sentences given specific contexts, those sentences whose unacceptability depends on contexts were inevitably removed from JCoLA. This removal process resulted in the deletion of unacceptable sentences from some linguistic phenomena (such as ellipsis), consequently skewing the balance between acceptable and unacceptable sentences (with a higher proportion of acceptable sentences).\n> \n> \n> \n\n\nAdditional Information\n----------------------\n\n\n* 日本語言語理解ベンチマーク JGLUE の構築 〜 自然言語処理モデルの評価用データセットを公開しました - Yahoo! JAPAN Tech Blog URL",
"### Dataset Curators",
"#### MARC-ja\n\n\n* Keung, Phillip, et al. \"The Multilingual Amazon Reviews Corpus.\" Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.",
"#### JCoLA\n\n\n* Someya, Sugimoto, and Oseki. \"JCoLA: Japanese Corpus of Linguistic Acceptability.\" arxiv preprint arXiv:2309.12676 (2023).",
"#### JSTS and JNLI\n\n\n* Miyazaki, Takashi, and Nobuyuki Shimizu. \"Cross-lingual image caption generation.\" Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.",
"#### JSQuAD\n\n\nThe JGLUE's 'authors curated the original data for JSQuAD from the Japanese wikipedia dump.",
"#### JCommonsenseQA\n\n\nIn the same way as CommonsenseQA, JCommonsenseQA is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet",
"### Licensing Information",
"#### JGLUE\n\n\nFrom JGLUE's URL':\n\n\n\n> \n> This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.\n> \n> \n>",
"#### JCoLA\n\n\nFrom JCoLA's URL':\n\n\n\n> \n> The text in this corpus is excerpted from the published works, and copyright (where applicable) remains with the original authors or publishers. We expect that research use within Japan is legal under fair use, but make no guarantee of this.\n> \n> \n>",
"#### JGLUE",
"#### MARC-ja",
"#### JCoLA",
"#### JSTS and JNLI",
"### Contributions\n\n\nThanks to Kentaro Kurihara, Daisuke Kawahara, and Tomohide Shibata for creating JGLUE dataset.\nThanks to Taiga Someya for creating JCoLA dataset."
]
| [
"TAGS\n#task_categories-multiple-choice #task_categories-question-answering #task_categories-sentence-similarity #task_categories-text-classification #task_ids-multiple-choice-qa #task_ids-open-domain-qa #task_ids-multi-class-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #source_datasets-original #language-Japanese #license-cc-by-4.0 #MARC #CoLA #STS #NLI #SQuAD #CommonsenseQA #arxiv-2309.12676 #region-us \n",
"### Dataset Summary\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JGLUE, Japanese General Language Understanding Evaluation, is built to measure the general NLU ability in Japanese. JGLUE has been constructed from scratch without translation. We hope that JGLUE will facilitate NLU research in Japanese.\n> \n> \n> \n\n\n\n> \n> JGLUE has been constructed by a joint research project of Yahoo Japan Corporation and Kawahara Lab at Waseda University.\n> \n> \n>",
"### Supported Tasks and Leaderboards\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JGLUE consists of the tasks of text classification, sentence pair classification, and QA. Each task consists of multiple datasets.\n> \n> \n>",
"#### Supported Tasks",
"##### MARC-ja\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> MARC-ja is a dataset of the text classification task. This dataset is based on the Japanese portion of Multilingual Amazon Reviews Corpus (MARC) (Keung+, 2020).\n> \n> \n>",
"##### JCoLA\n\n\nFrom JCoLA's URL\n\n\n\n> \n> JCoLA (Japanese Corpus of Linguistic Accept010 ability) is a novel dataset for targeted syntactic evaluations of language models in Japanese, which consists of 10,020 sentences with acceptability judgments by linguists. The sentences are manually extracted from linguistics journals, handbooks and textbooks. JCoLA is included in JGLUE benchmark (Kurihara et al., 2022).\n> \n> \n>",
"##### JSTS\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JSTS is a Japanese version of the STS (Semantic Textual Similarity) dataset. STS is a task to estimate the semantic similarity of a sentence pair. The sentences in JSTS and JNLI (described below) are extracted from the Japanese version of the MS COCO Caption Dataset, the YJ Captions Dataset (Miyazaki and Shimizu, 2016).\n> \n> \n>",
"##### JNLI\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JNLI is a Japanese version of the NLI (Natural Language Inference) dataset. NLI is a task to recognize the inference relation that a premise sentence has to a hypothesis sentence. The inference relations are entailment, contradiction, and neutral.\n> \n> \n>",
"##### JSQuAD\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JSQuAD is a Japanese version of SQuAD (Rajpurkar+, 2018), one of the datasets of reading comprehension. Each instance in the dataset consists of a question regarding a given context (Wikipedia article) and its answer. JSQuAD is based on SQuAD 1.1 (there are no unanswerable questions). We used the Japanese Wikipedia dump as of 20211101.\n> \n> \n>",
"##### JCommonsenseQA\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor+, 2019), which is a multiple-choice question answering dataset that requires commonsense reasoning ability. It is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet.\n> \n> \n>",
"#### Leaderboard\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> A leaderboard will be made public soon. The test set will be released at that time.\n> \n> \n>",
"### Languages\n\n\nThe language data in JGLUE is in Japanese (BCP-47 ja-JP).\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nWhen loading a specific configuration, users has to append a version dependent suffix:",
"#### MARC-ja",
"#### JCoLA\n\n\nAn example of the JCoLA dataset (validation - out of domain annotated) looks as follows:",
"#### JSTS\n\n\nAn example of the JSTS dataset looks as follows:",
"#### JNLI\n\n\nAn example of the JNLI dataset looks as follows:",
"#### JSQuAD\n\n\nAn example of the JSQuAD looks as follows:",
"#### JCommonsenseQA\n\n\nAn example of the JCommonsenseQA looks as follows:",
"### Data Fields",
"#### MARC-ja\n\n\n* 'sentence\\_pair\\_id': ID of the sentence pair\n* 'yjcaptions\\_id': sentence ids in yjcaptions (explained below)\n* 'sentence1': first sentence\n* 'sentence2': second sentence\n* 'label': sentence similarity: 5 (equivalent meaning) - 0 (completely different meaning)",
"##### Explanation for 'yjcaptions\\_id'\n\n\nFrom JGLUE's URL, there are the following two cases:\n\n\n1. sentence pairs in one image: '(image id)-(sentence1 id)-(sentence2 id)'\n\t* e.g., 723-844-847\n\t* a sentence id starting with \"g\" means a sentence generated by a crowdworker (e.g., 69501-75698-g103): only for JNLI\n2. sentence pairs in two images: '(image id of sentence1)\\_(image id of sentence2)-(sentence1 id)-(sentence2 id)'\n\t* e.g., 91337\\_217583-96105-91680",
"#### JCoLA\n\n\nFrom JCoLA's URL and JCoLA's paper\n\n\n* 'uid': unique id of the sentence\n* 'source': author and the year of publication of the source article\n* 'label': acceptability judgement label (0 for unacceptable, 1 for acceptable)\n* 'diacritic': acceptability judgement as originally notated in the source article\n* 'sentence': sentence (modified by the author if needed)\n* 'original': original sentence as presented in the source article\n* 'translation': English translation of the sentence as presentend in the source article (if any)\n* 'gloss': gloss of the sentence as presented in the source article (if any)\n* 'linguistic\\_phenomenon'\n\t+ 'argument\\_structure': acceptability judgements based on the order of arguments and case marking\n\t+ 'binding': acceptability judgements based on the binding of noun phrases\n\t+ 'control\\_raising': acceptability judgements based on predicates that are categorized as control or raising\n\t+ 'ellipsis': acceptability judgements based on the possibility of omitting elements in the sentences\n\t+ 'filler\\_gap': acceptability judgements based on the dependency between the moved element and the gap\n\t+ 'island effects': acceptability judgements based on the restrictions on filler-gap dependencies such as wh-movements\n\t+ 'morphology': acceptability judgements based on the morphology\n\t+ 'nominal\\_structure': acceptability judgements based on the internal structure of noun phrases\n\t+ 'negative\\_polarity\\_concord\\_items': acceptability judgements based on the restrictions on where negative polarity/concord items (NPIs/NCIs) can appear\n\t+ 'quantifiers': acceptability judgements based on the distribution of quantifiers such as floating quantifiers\n\t+ 'verbal\\_agreement': acceptability judgements based on the dependency between subjects and verbs\n\t+ 'simple': acceptability judgements that do not have marked syntactic structures",
"#### JNLI\n\n\n* 'sentence\\_pair\\_id': ID of the sentence pair\n* 'yjcaptions\\_id': sentence ids in the yjcaptions\n* 'sentence1': premise sentence\n* 'sentence2': hypothesis sentence\n* 'label': inference relation",
"#### JSQuAD\n\n\n* 'title': title of a Wikipedia article\n* 'paragraphs': a set of paragraphs\n* 'qas': a set of pairs of a question and its answer\n* 'question': question\n* 'id': id of a question\n* 'answers': a set of answers\n* 'text': answer text\n* 'answer\\_start': start position (character index)\n* 'is\\_impossible': all the values are false\n* 'context': a concatenation of the title and paragraph",
"#### JCommonsenseQA\n\n\n* 'q\\_id': ID of the question\n* 'question': question\n* 'choice{0..4}': choice\n* 'label': correct choice id",
"### Data Splits\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> Only train/dev sets are available now, and the test set will be available after the leaderboard is made public.\n> \n> \n> \n\n\nFrom JCoLA's paper:\n\n\n\n> \n> The in-domain data is split into training data (6,919 instances), development data (865 instances), and test data (865 instances). On the other hand, the out-of-domain data is only used for evaluation, and divided into development data (685 instances) and test data (686 instances).\n> \n> \n> \n\n\n\n\n> \n> JCoLA: † in domain. ‡ out of domain.\n> \n> \n> \n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> JGLUE is designed to cover a wide range of GLUE and SuperGLUE tasks and consists of three kinds of tasks: text classification, sentence pair classification, and question answering.\n> \n> \n>",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?\n\n\n* The source language producers are users of Amazon (MARC-ja), crowd-workers of Yahoo! Crowdsourcing (JSTS, JNLI and JCommonsenseQA), writers of the Japanese Wikipedia (JSQuAD), crowd-workers of Lancers.",
"### Annotations",
"#### Annotation process",
"##### MARC-ja\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> As one of the text classification datasets, we build a dataset based on the Multilingual Amazon Reviews Corpus (MARC) (Keung et al., 2020). MARC is a multilingual corpus of product reviews with 5-level star ratings (1-5) on the Amazon shopping site. This corpus covers six languages, including English and Japanese. For JGLUE, we use the Japanese part of MARC and to make it easy for both humans and computers to judge a class label, we cast the text classification task as a binary classification task, where 1- and 2-star ratings are converted to “negative”, and 4 and 5 are converted to “positive”. We do not use reviews with a 3-star rating.\n> \n> \n> \n\n\n\n> \n> One of the problems with MARC is that it sometimes contains data where the rating diverges from the review text. This happens, for example, when a review with positive content is given a rating of 1 or 2. These data degrade the quality of our dataset. To improve the quality of the dev/test instances used for evaluation, we crowdsource a positive/negative judgment task for approximately 12,000 reviews. We adopt only reviews with the same votes from 7 or more out of 10 workers and assign a label of the maximum votes to these reviews. We divide the resulting reviews into dev/test data.\n> \n> \n> \n\n\n\n> \n> We obtained 5,654 and 5,639 instances for the dev and test data, respectively, through the above procedure. For the training data, we extracted 187,528 instances directly from MARC without performing the cleaning procedure because of the large number of training instances. The statistics of MARC-ja are listed in Table 2. For the evaluation metric for MARC-ja, we use accuracy because it is a binary classification task of texts.\n> \n> \n>",
"##### JCoLA\n\n\nFrom JCoLA's paper:\n\n\n\n> \n> ### 3 JCoLA\n> \n> \n> In this study, we introduce JCoLA (Japanese Corpus of Linguistic Acceptability), which will be the first large-scale acceptability judgment task dataset focusing on Japanese. JCoLA consists of sentences from textbooks and handbooks on Japanese syntax, as well as from journal articles on Japanese syntax that are published in JEAL (Journal of East Asian Linguistics), one of the prestigious journals in theoretical linguistics.\n> \n> \n> \n\n\n\n> \n> #### 3.1 Data Collection\n> \n> \n> Sentences in JCoLA were collected from prominent textbooks and handbooks focusing on Japanese syntax. In addition to the main text, example sentences included in the footnotes were also considered for collection. We also collected acceptability judgments from journal articles on Japanese syntax published in JEAL (Journal of East Asian Linguistics): one of the prestigious journals in the-oretical linguistics. Specifically, we examined all the articles published in JEAL between 2006 and 2015 (133 papers in total), and extracted 2,252 acceptability judgments from 26 papers on Japanese syntax (Table 2). Acceptability judgments include sentences in appendices and footnotes, but not sentences presented for analyses of syntactic structures (e.g. sentences with brackets to show their syntactic structures). As a result, a total of 11,984 example. sentences were collected. Using this as a basis, JCoLA was constructed through the methodology explained in the following sections.\n> \n> \n>",
"##### JSTS and JNLI\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> For the sentence pair classification datasets, we construct a semantic textual similarity (STS) dataset, JSTS, and a natural language inference (NLI) dataset, JNLI.\n> \n> \n> \n\n\n\n> \n> ### Overview\n> \n> \n> STS is a task of estimating the semantic similarity of a sentence pair. Gold similarity is usually assigned as an average of the integer values 0 (completely different meaning) to 5 (equivalent meaning) assigned by multiple workers through crowdsourcing.\n> \n> \n> \n\n\n\n> \n> NLI is a task of recognizing the inference relation that a premise sentence has to a hypothesis sentence. Inference relations are generally defined by three labels: “entailment”, “contradiction”, and “neutral”. Gold inference relations are often assigned by majority voting after collecting answers from multiple workers through crowdsourcing.\n> \n> \n> \n\n\n\n> \n> For the STS and NLI tasks, STS-B (Cer et al., 2017) and MultiNLI (Williams et al., 2018) are included in GLUE, respectively. As Japanese datasets, JSNLI (Yoshikoshi et al., 2020) is a machine translated dataset of the NLI dataset SNLI (Stanford NLI), and JSICK (Yanaka and Mineshima, 2021) is a human translated dataset of the STS/NLI dataset SICK (Marelli et al., 2014). As mentioned in Section 1, these have problems originating from automatic/manual translations. To solve this problem, we construct STS/NLI datasets in Japanese from scratch. We basically extract sentence pairs in JSTS and JNLI from the Japanese version of the MS COCO Caption Dataset (Chen et al., 2015), the YJ Captions Dataset (Miyazaki and Shimizu, 2016). Most of the sentence pairs in JSTS and JNLI overlap, allowing us to analyze the relationship between similarities and inference relations for the same sentence pairs like SICK and JSICK.\n> \n> \n> \n\n\n\n> \n> The similarity value in JSTS is assigned a real number from 0 to 5 as in STS-B. The inference relation in JNLI is assigned from the above three labels as in SNLI and MultiNLI. The definitions of the inference relations are also based on SNLI.\n> \n> \n> \n\n\n\n> \n> ### Method of Construction\n> \n> \n> Our construction flow for JSTS and JNLI is shown in Figure 1. Basically, two captions for the same image of YJ Captions are used as sentence pairs. For these sentence pairs, similarities and NLI relations of entailment and neutral are obtained by crowdsourcing. However, it is difficult to collect sentence pairs with low similarity and contradiction relations from captions for the same image. To solve this problem, we collect sentence pairs with low similarity from captions for different images. We collect contradiction relations by asking workers to write contradictory sentences for a given caption.\n> \n> \n> \n\n\n\n> \n> The detailed construction procedure for JSTS and JNLI is described below.\n> \n> \n> 1. We crowdsource an STS task using two captions for the same image from YJ Captions. We ask five workers to answer the similarity between two captions and take the mean value as the gold similarity. We delete sentence pairs with a large variance in the answers because such pairs have poor answer quality. We performed this task on 16,000 sentence pairs and deleted sentence pairs with a similarity variance of 1.0 or higher, resulting in the collection of 10,236 sentence pairs with gold similarity. We refer to this collected data as JSTS-A.\n> 2. To collect sentence pairs with low similarity, we crowdsource the same STS task as Step 1 using sentence pairs of captions for different images. We conducted this task on 4,000 sentence pairs and collected 2,970 sentence pairs with gold similarity. We refer to this collected data as JSTS-B.\n> 3. For JSTS-A, we crowdsource an NLI task. Since inference relations are directional, we obtain inference relations in both directions for sentence pairs. As mentioned earlier,it is difficult to collect instances of contradiction from JSTS-A, which was collected from the captions of the same images,and thus we collect instances of entailment and neutral in this step. We collect inference relation answers from 10 workers. If six or more people give the same answer, we adopt it as the gold label if it is entailment or neutral. To obtain inference relations in both directions for JSTS-A, we performed this task on 20,472 sentence pairs, twice as many as JSTS-A. As a result, we collected inference relations for 17,501 sentence pairs. We refer to this collected data as JNLI-A. We do not use JSTS-B for the NLI task because it is difficult to define and determine the inference relations between captions of different images.\n> 4. To collect NLI instances of contradiction, we crowdsource a task of writing four contradictory sentences for each caption in YJCaptions. From the written sentences, we remove sentence pairs with an edit distance of 0.75 or higher to remove low-quality sentences, such as short sentences and sentences with low relevance to the original sentence. Furthermore, we perform a one-way NLI task with 10 workers to verify whether the created sentence pairs are contradictory. Only the sentence pairs answered as contradiction by at least six workers are adopted. Finally,since the contradiction relation has no direction, we automatically assign contradiction in the opposite direction of the adopted sentence pairs. Using 1,800 captions, we acquired 7,200 sentence pairs, from which we collected 3,779 sentence pairs to which we assigned the one-way contradiction relation.By automatically assigning the contradiction relation in the opposite direction, we doubled the number of instances to 7,558. We refer to this collected data as JNLI-C.\n> 5. For the 3,779 sentence pairs collected in Step 4, we crowdsource an STS task, assigning similarity and filtering in the same way as in Steps1 and 2. In this way, we collected 2,303 sentence pairs with gold similarity from 3,779 pairs. We refer to this collected data as JSTS-C.\n> \n> \n>",
"##### JSQuAD\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> As QA datasets, we build a Japanese version of SQuAD (Rajpurkar et al., 2016), one of the datasets of reading comprehension, and a Japanese version ofCommonsenseQA, which is explained in the next section.\n> \n> \n> \n\n\n\n> \n> Reading comprehension is the task of reading a document and answering questions about it. Many reading comprehension evaluation sets have been built in English, followed by those in other languages or multilingual ones.\n> \n> \n> \n\n\n\n> \n> In Japanese, reading comprehension datasets for quizzes (Suzukietal.,2018) and those in the drivingdomain (Takahashi et al., 2019) have been built, but none are in the general domain. We use Wikipedia to build a dataset for the general domain. The construction process is basically based on SQuAD 1.1 (Rajpurkar et al., 2016).\n> \n> \n> \n\n\n\n> \n> First, to extract high-quality articles from Wikipedia, we use Nayuki, which estimates the quality of articles on the basis of hyperlinks in Wikipedia. We randomly chose 822 articles from the top-ranked 10,000 articles. For example, the articles include “熊本県 (Kumamoto Prefecture)” and “フランス料理 (French cuisine)”. Next, we divide an article into paragraphs, present each paragraph to crowdworkers, and ask them to write questions and answers that can be answered if one understands the paragraph. Figure 2 shows an example of JSQuAD. We ask workers to write two additional answers for the dev and test sets to make the system evaluation robust.\n> \n> \n>",
"##### JCommonsenseQA\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> ### Overview\n> \n> \n> JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor et al., 2019), which consists of five choice QA to evaluate commonsense reasoning ability. Figure 3 shows examples of JCommonsenseQA. In the same way as CommonsenseQA, JCommonsenseQA is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet (Speer et al., 2017). ConceptNet is a multilingual knowledge base that consists of triplets of two concepts and their relation. The triplets are directional and represented as (source concept, relation, target concept), for example (bullet train, AtLocation, station).\n> \n> \n> \n\n\n\n> \n> ### Method of Construction\n> \n> \n> The construction flow for JCommonsenseQA is shown in Figure 4. First, we collect question sets (QSs) from ConceptNet, each of which consists of a source concept and three target concepts that have the same relation to the source concept. Next, for each QS, we crowdAtLocation 2961source a task of writing a question with only one target concept as the answer and a task of adding two distractors. We describe the detailed construction procedure for JCommonsenseQA below, showing how it differs from CommonsenseQA.\n> \n> \n> \n\n\n\n> \n> 1. We collect Japanese QSs from ConceptNet. CommonsenseQA uses only forward relations (source concept, relation, target concept) excluding general ones such as “RelatedTo” and “IsA”. JCommonsenseQA similarly uses a set of 22 relations5, excluding general ones, but the direction of the relations is bidirectional to make the questions more diverse. In other words, we also use relations in the opposite direction (source concept, relation−1, target concept).6 With this setup, we extracted 43,566 QSs with Japanese source/target concepts and randomly selected 7,500 from them.\n> 2. Some low-quality questions in CommonsenseQA contain distractors that can be considered to be an answer. To improve the quality of distractors, we add the following two processes that are not adopted in CommonsenseQA. First, if three target concepts of a QS include a spelling variation or a synonym of one another, this QS is removed. To identify spelling variations, we use the word ID of the morphological dictionary Juman Dic7. Second, we crowdsource a task of judging whether target concepts contain a synonym. As a result, we adopted 5,920 QSs from 7,500.\n> 3. For each QS, we crowdsource a task of writing a question sentence in which only one from the three target concepts is an answer. In the example shown in Figure 4, “駅 (station)” is an answer, and the others are distractors. To remove low quality question sentences, we remove the following question sentences.\n> \t* Question sentences that contain a choice word(this is because such a question is easily solved).\n> \t* Question sentences that contain the expression “XX characters”.8 (XX is a number).\n> \t* Improperly formatted question sentences that do not end with “?”.\n> \t* As a result, 5,920 × 3 = 17,760question sentences were created, from which we adopted 15,310 by removing inappropriate question sentences.\n> 4. In CommonsenseQA, when adding distractors, one is selected from ConceptNet, and the other is created by crowdsourcing. In JCommonsenseQA, to have a wider variety of distractors, two distractors are created by crowdsourcing instead of selecting from ConceptNet. To improve the quality of the questions9, we remove questions whose added distractors fall into one of the following categories:\n> \t* Distractors are included in a question sentence.\n> \t* Distractors overlap with one of existing choices.\n> \t* As a result, distractors were added to the 15,310 questions, of which we adopted 13,906.\n> 5. We asked three crowdworkers to answer each question and adopt only those answered correctly by at least two workers. As a result, we adopted 11,263 out of the 13,906 questions.\n> \n> \n>",
"#### Who are the annotators?\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> We use Yahoo! Crowdsourcing for all crowdsourcing tasks in constructing the datasets.\n> \n> \n> \n\n\nFrom JCoLA's paper:\n\n\n\n> \n> As a reference for the upper limit of accuracy in JCoLA, human acceptability judgment experiments were conducted on Lancers2 with a subset of the JCoLA data.\n> \n> \n>",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.\n> \n> \n>",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nFrom JCoLA's paper:\n\n\n\n> \n> All the sentences included in JCoLA have been extracted from textbooks, handbooks and journal articles on theoretical syntax. Therefore, those sentences are guaranteed to be theoretically meaningful, making JCoLA a challenging dataset. However, the distribution of linguistic phenomena directly reflects that of the source literature and thus turns out to be extremely skewed. Indeed, as can be seen in Table 3, while the number of sentences exceeds 100 for most linguistic phenomena, there are several linguistic phenomena for which there are only about 10 sentences. In addition, since it is difficult to force language models to interpret sentences given specific contexts, those sentences whose unacceptability depends on contexts were inevitably removed from JCoLA. This removal process resulted in the deletion of unacceptable sentences from some linguistic phenomena (such as ellipsis), consequently skewing the balance between acceptable and unacceptable sentences (with a higher proportion of acceptable sentences).\n> \n> \n> \n\n\nAdditional Information\n----------------------\n\n\n* 日本語言語理解ベンチマーク JGLUE の構築 〜 自然言語処理モデルの評価用データセットを公開しました - Yahoo! JAPAN Tech Blog URL",
"### Dataset Curators",
"#### MARC-ja\n\n\n* Keung, Phillip, et al. \"The Multilingual Amazon Reviews Corpus.\" Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.",
"#### JCoLA\n\n\n* Someya, Sugimoto, and Oseki. \"JCoLA: Japanese Corpus of Linguistic Acceptability.\" arxiv preprint arXiv:2309.12676 (2023).",
"#### JSTS and JNLI\n\n\n* Miyazaki, Takashi, and Nobuyuki Shimizu. \"Cross-lingual image caption generation.\" Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.",
"#### JSQuAD\n\n\nThe JGLUE's 'authors curated the original data for JSQuAD from the Japanese wikipedia dump.",
"#### JCommonsenseQA\n\n\nIn the same way as CommonsenseQA, JCommonsenseQA is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet",
"### Licensing Information",
"#### JGLUE\n\n\nFrom JGLUE's URL':\n\n\n\n> \n> This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.\n> \n> \n>",
"#### JCoLA\n\n\nFrom JCoLA's URL':\n\n\n\n> \n> The text in this corpus is excerpted from the published works, and copyright (where applicable) remains with the original authors or publishers. We expect that research use within Japan is legal under fair use, but make no guarantee of this.\n> \n> \n>",
"#### JGLUE",
"#### MARC-ja",
"#### JCoLA",
"#### JSTS and JNLI",
"### Contributions\n\n\nThanks to Kentaro Kurihara, Daisuke Kawahara, and Tomohide Shibata for creating JGLUE dataset.\nThanks to Taiga Someya for creating JCoLA dataset."
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"passage: TAGS\n#task_categories-multiple-choice #task_categories-question-answering #task_categories-sentence-similarity #task_categories-text-classification #task_ids-multiple-choice-qa #task_ids-open-domain-qa #task_ids-multi-class-classification #task_ids-sentiment-classification #annotations_creators-crowdsourced #language_creators-crowdsourced #language_creators-found #multilinguality-monolingual #source_datasets-original #language-Japanese #license-cc-by-4.0 #MARC #CoLA #STS #NLI #SQuAD #CommonsenseQA #arxiv-2309.12676 #region-us \n### Dataset Summary\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JGLUE, Japanese General Language Understanding Evaluation, is built to measure the general NLU ability in Japanese. JGLUE has been constructed from scratch without translation. We hope that JGLUE will facilitate NLU research in Japanese.\n> \n> \n> \n\n\n\n> \n> JGLUE has been constructed by a joint research project of Yahoo Japan Corporation and Kawahara Lab at Waseda University.\n> \n> \n>### Supported Tasks and Leaderboards\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JGLUE consists of the tasks of text classification, sentence pair classification, and QA. Each task consists of multiple datasets.\n> \n> \n>#### Supported Tasks##### MARC-ja\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> MARC-ja is a dataset of the text classification task. This dataset is based on the Japanese portion of Multilingual Amazon Reviews Corpus (MARC) (Keung+, 2020).\n> \n> \n>",
"passage: ##### JCoLA\n\n\nFrom JCoLA's URL\n\n\n\n> \n> JCoLA (Japanese Corpus of Linguistic Accept010 ability) is a novel dataset for targeted syntactic evaluations of language models in Japanese, which consists of 10,020 sentences with acceptability judgments by linguists. The sentences are manually extracted from linguistics journals, handbooks and textbooks. JCoLA is included in JGLUE benchmark (Kurihara et al., 2022).\n> \n> \n>##### JSTS\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JSTS is a Japanese version of the STS (Semantic Textual Similarity) dataset. STS is a task to estimate the semantic similarity of a sentence pair. The sentences in JSTS and JNLI (described below) are extracted from the Japanese version of the MS COCO Caption Dataset, the YJ Captions Dataset (Miyazaki and Shimizu, 2016).\n> \n> \n>##### JNLI\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JNLI is a Japanese version of the NLI (Natural Language Inference) dataset. NLI is a task to recognize the inference relation that a premise sentence has to a hypothesis sentence. The inference relations are entailment, contradiction, and neutral.\n> \n> \n>##### JSQuAD\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JSQuAD is a Japanese version of SQuAD (Rajpurkar+, 2018), one of the datasets of reading comprehension. Each instance in the dataset consists of a question regarding a given context (Wikipedia article) and its answer. JSQuAD is based on SQuAD 1.1 (there are no unanswerable questions). We used the Japanese Wikipedia dump as of 20211101.\n> \n> \n>##### JCommonsenseQA\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor+, 2019), which is a multiple-choice question answering dataset that requires commonsense reasoning ability. It is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet.\n> \n> \n>#### Leaderboard\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> A leaderboard will be made public soon. The test set will be released at that time.\n> \n> \n>### Languages\n\n\nThe language data in JGLUE is in Japanese (BCP-47 ja-JP).\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nWhen loading a specific configuration, users has to append a version dependent suffix:#### MARC-ja#### JCoLA\n\n\nAn example of the JCoLA dataset (validation - out of domain annotated) looks as follows:",
"passage: #### JSTS\n\n\nAn example of the JSTS dataset looks as follows:#### JNLI\n\n\nAn example of the JNLI dataset looks as follows:#### JSQuAD\n\n\nAn example of the JSQuAD looks as follows:#### JCommonsenseQA\n\n\nAn example of the JCommonsenseQA looks as follows:### Data Fields#### MARC-ja\n\n\n* 'sentence\\_pair\\_id': ID of the sentence pair\n* 'yjcaptions\\_id': sentence ids in yjcaptions (explained below)\n* 'sentence1': first sentence\n* 'sentence2': second sentence\n* 'label': sentence similarity: 5 (equivalent meaning) - 0 (completely different meaning)##### Explanation for 'yjcaptions\\_id'\n\n\nFrom JGLUE's URL, there are the following two cases:\n\n\n1. sentence pairs in one image: '(image id)-(sentence1 id)-(sentence2 id)'\n\t* e.g., 723-844-847\n\t* a sentence id starting with \"g\" means a sentence generated by a crowdworker (e.g., 69501-75698-g103): only for JNLI\n2. sentence pairs in two images: '(image id of sentence1)\\_(image id of sentence2)-(sentence1 id)-(sentence2 id)'\n\t* e.g., 91337\\_217583-96105-91680",
"passage: #### JCoLA\n\n\nFrom JCoLA's URL and JCoLA's paper\n\n\n* 'uid': unique id of the sentence\n* 'source': author and the year of publication of the source article\n* 'label': acceptability judgement label (0 for unacceptable, 1 for acceptable)\n* 'diacritic': acceptability judgement as originally notated in the source article\n* 'sentence': sentence (modified by the author if needed)\n* 'original': original sentence as presented in the source article\n* 'translation': English translation of the sentence as presentend in the source article (if any)\n* 'gloss': gloss of the sentence as presented in the source article (if any)\n* 'linguistic\\_phenomenon'\n\t+ 'argument\\_structure': acceptability judgements based on the order of arguments and case marking\n\t+ 'binding': acceptability judgements based on the binding of noun phrases\n\t+ 'control\\_raising': acceptability judgements based on predicates that are categorized as control or raising\n\t+ 'ellipsis': acceptability judgements based on the possibility of omitting elements in the sentences\n\t+ 'filler\\_gap': acceptability judgements based on the dependency between the moved element and the gap\n\t+ 'island effects': acceptability judgements based on the restrictions on filler-gap dependencies such as wh-movements\n\t+ 'morphology': acceptability judgements based on the morphology\n\t+ 'nominal\\_structure': acceptability judgements based on the internal structure of noun phrases\n\t+ 'negative\\_polarity\\_concord\\_items': acceptability judgements based on the restrictions on where negative polarity/concord items (NPIs/NCIs) can appear\n\t+ 'quantifiers': acceptability judgements based on the distribution of quantifiers such as floating quantifiers\n\t+ 'verbal\\_agreement': acceptability judgements based on the dependency between subjects and verbs\n\t+ 'simple': acceptability judgements that do not have marked syntactic structures#### JNLI\n\n\n* 'sentence\\_pair\\_id': ID of the sentence pair\n* 'yjcaptions\\_id': sentence ids in the yjcaptions\n* 'sentence1': premise sentence\n* 'sentence2': hypothesis sentence\n* 'label': inference relation#### JSQuAD\n\n\n* 'title': title of a Wikipedia article\n* 'paragraphs': a set of paragraphs\n* 'qas': a set of pairs of a question and its answer\n* 'question': question\n* 'id': id of a question\n* 'answers': a set of answers\n* 'text': answer text\n* 'answer\\_start': start position (character index)\n* 'is\\_impossible': all the values are false\n* 'context': a concatenation of the title and paragraph#### JCommonsenseQA\n\n\n* 'q\\_id': ID of the question\n* 'question': question\n* 'choice{0..4}': choice\n* 'label': correct choice id### Data Splits\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> Only train/dev sets are available now, and the test set will be available after the leaderboard is made public.\n> \n> \n> \n\n\nFrom JCoLA's paper:\n\n\n\n> \n> The in-domain data is split into training data (6,919 instances), development data (865 instances), and test data (865 instances). On the other hand, the out-of-domain data is only used for evaluation, and divided into development data (685 instances) and test data (686 instances).\n> \n> \n> \n\n\n\n\n> \n> JCoLA: † in domain. ‡ out of domain.\n> \n> \n> \n\n\nDataset Creation\n----------------### Curation Rationale\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> JGLUE is designed to cover a wide range of GLUE and SuperGLUE tasks and consists of three kinds of tasks: text classification, sentence pair classification, and question answering.\n> \n> \n>### Source Data#### Initial Data Collection and Normalization",
"passage: #### Who are the source language producers?\n\n\n* The source language producers are users of Amazon (MARC-ja), crowd-workers of Yahoo! Crowdsourcing (JSTS, JNLI and JCommonsenseQA), writers of the Japanese Wikipedia (JSQuAD), crowd-workers of Lancers.### Annotations#### Annotation process##### MARC-ja\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> As one of the text classification datasets, we build a dataset based on the Multilingual Amazon Reviews Corpus (MARC) (Keung et al., 2020). MARC is a multilingual corpus of product reviews with 5-level star ratings (1-5) on the Amazon shopping site. This corpus covers six languages, including English and Japanese. For JGLUE, we use the Japanese part of MARC and to make it easy for both humans and computers to judge a class label, we cast the text classification task as a binary classification task, where 1- and 2-star ratings are converted to “negative”, and 4 and 5 are converted to “positive”. We do not use reviews with a 3-star rating.\n> \n> \n> \n\n\n\n> \n> One of the problems with MARC is that it sometimes contains data where the rating diverges from the review text. This happens, for example, when a review with positive content is given a rating of 1 or 2. These data degrade the quality of our dataset. To improve the quality of the dev/test instances used for evaluation, we crowdsource a positive/negative judgment task for approximately 12,000 reviews. We adopt only reviews with the same votes from 7 or more out of 10 workers and assign a label of the maximum votes to these reviews. We divide the resulting reviews into dev/test data.\n> \n> \n> \n\n\n\n> \n> We obtained 5,654 and 5,639 instances for the dev and test data, respectively, through the above procedure. For the training data, we extracted 187,528 instances directly from MARC without performing the cleaning procedure because of the large number of training instances. The statistics of MARC-ja are listed in Table 2. For the evaluation metric for MARC-ja, we use accuracy because it is a binary classification task of texts.\n> \n> \n>",
"passage: ##### JCoLA\n\n\nFrom JCoLA's paper:\n\n\n\n> \n> ### 3 JCoLA\n> \n> \n> In this study, we introduce JCoLA (Japanese Corpus of Linguistic Acceptability), which will be the first large-scale acceptability judgment task dataset focusing on Japanese. JCoLA consists of sentences from textbooks and handbooks on Japanese syntax, as well as from journal articles on Japanese syntax that are published in JEAL (Journal of East Asian Linguistics), one of the prestigious journals in theoretical linguistics.\n> \n> \n> \n\n\n\n> \n> #### 3.1 Data Collection\n> \n> \n> Sentences in JCoLA were collected from prominent textbooks and handbooks focusing on Japanese syntax. In addition to the main text, example sentences included in the footnotes were also considered for collection. We also collected acceptability judgments from journal articles on Japanese syntax published in JEAL (Journal of East Asian Linguistics): one of the prestigious journals in the-oretical linguistics. Specifically, we examined all the articles published in JEAL between 2006 and 2015 (133 papers in total), and extracted 2,252 acceptability judgments from 26 papers on Japanese syntax (Table 2). Acceptability judgments include sentences in appendices and footnotes, but not sentences presented for analyses of syntactic structures (e.g. sentences with brackets to show their syntactic structures). As a result, a total of 11,984 example. sentences were collected. Using this as a basis, JCoLA was constructed through the methodology explained in the following sections.\n> \n> \n>",
"passage: ##### JSTS and JNLI\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> For the sentence pair classification datasets, we construct a semantic textual similarity (STS) dataset, JSTS, and a natural language inference (NLI) dataset, JNLI.\n> \n> \n> \n\n\n\n> \n> ### Overview\n> \n> \n> STS is a task of estimating the semantic similarity of a sentence pair. Gold similarity is usually assigned as an average of the integer values 0 (completely different meaning) to 5 (equivalent meaning) assigned by multiple workers through crowdsourcing.\n> \n> \n> \n\n\n\n> \n> NLI is a task of recognizing the inference relation that a premise sentence has to a hypothesis sentence. Inference relations are generally defined by three labels: “entailment”, “contradiction”, and “neutral”. Gold inference relations are often assigned by majority voting after collecting answers from multiple workers through crowdsourcing.\n> \n> \n> \n\n\n\n> \n> For the STS and NLI tasks, STS-B (Cer et al., 2017) and MultiNLI (Williams et al., 2018) are included in GLUE, respectively. As Japanese datasets, JSNLI (Yoshikoshi et al., 2020) is a machine translated dataset of the NLI dataset SNLI (Stanford NLI), and JSICK (Yanaka and Mineshima, 2021) is a human translated dataset of the STS/NLI dataset SICK (Marelli et al., 2014). As mentioned in Section 1, these have problems originating from automatic/manual translations. To solve this problem, we construct STS/NLI datasets in Japanese from scratch. We basically extract sentence pairs in JSTS and JNLI from the Japanese version of the MS COCO Caption Dataset (Chen et al., 2015), the YJ Captions Dataset (Miyazaki and Shimizu, 2016). Most of the sentence pairs in JSTS and JNLI overlap, allowing us to analyze the relationship between similarities and inference relations for the same sentence pairs like SICK and JSICK.\n> \n> \n> \n\n\n\n> \n> The similarity value in JSTS is assigned a real number from 0 to 5 as in STS-B. The inference relation in JNLI is assigned from the above three labels as in SNLI and MultiNLI. The definitions of the inference relations are also based on SNLI.\n> \n> \n> \n\n\n\n> \n> ### Method of Construction\n> \n> \n> Our construction flow for JSTS and JNLI is shown in Figure 1. Basically, two captions for the same image of YJ Captions are used as sentence pairs. For these sentence pairs, similarities and NLI relations of entailment and neutral are obtained by crowdsourcing. However, it is difficult to collect sentence pairs with low similarity and contradiction relations from captions for the same image. To solve this problem, we collect sentence pairs with low similarity from captions for different images. We collect contradiction relations by asking workers to write contradictory sentences for a given caption.\n> \n> \n> \n\n\n\n> \n> The detailed construction procedure for JSTS and JNLI is described below.\n> \n> \n> 1. We crowdsource an STS task using two captions for the same image from YJ Captions. We ask five workers to answer the similarity between two captions and take the mean value as the gold similarity. We delete sentence pairs with a large variance in the answers because such pairs have poor answer quality. We performed this task on 16,000 sentence pairs and deleted sentence pairs with a similarity variance of 1.0 or higher, resulting in the collection of 10,236 sentence pairs with gold similarity. We refer to this collected data as JSTS-A.\n> 2. To collect sentence pairs with low similarity, we crowdsource the same STS task as Step 1 using sentence pairs of captions for different images. We conducted this task on 4,000 sentence pairs and collected 2,970 sentence pairs with gold similarity. We refer to this collected data as JSTS-B.\n> 3. For JSTS-A, we crowdsource an NLI task. Since inference relations are directional, we obtain inference relations in both directions for sentence pairs. As mentioned earlier,it is difficult to collect instances of contradiction from JSTS-A, which was collected from the captions of the same images,and thus we collect instances of entailment and neutral in this step. We collect inference relation answers from 10 workers. If six or more people give the same answer, we adopt it as the gold label if it is entailment or neutral. To obtain inference relations in both directions for JSTS-A, we performed this task on 20,472 sentence pairs, twice as many as JSTS-A. As a result, we collected inference relations for 17,501 sentence pairs. We refer to this collected data as JNLI-A. We do not use JSTS-B for the NLI task because it is difficult to define and determine the inference relations between captions of different images.\n> 4. To collect NLI instances of contradiction, we crowdsource a task of writing four contradictory sentences for each caption in YJCaptions. From the written sentences, we remove sentence pairs with an edit distance of 0.75 or higher to remove low-quality sentences, such as short sentences and sentences with low relevance to the original sentence. Furthermore, we perform a one-way NLI task with 10 workers to verify whether the created sentence pairs are contradictory. Only the sentence pairs answered as contradiction by at least six workers are adopted. Finally,since the contradiction relation has no direction, we automatically assign contradiction in the opposite direction of the adopted sentence pairs. Using 1,800 captions, we acquired 7,200 sentence pairs, from which we collected 3,779 sentence pairs to which we assigned the one-way contradiction relation.By automatically assigning the contradiction relation in the opposite direction, we doubled the number of instances to 7,558. We refer to this collected data as JNLI-C.\n> 5. For the 3,779 sentence pairs collected in Step 4, we crowdsource an STS task, assigning similarity and filtering in the same way as in Steps1 and 2. In this way, we collected 2,303 sentence pairs with gold similarity from 3,779 pairs. We refer to this collected data as JSTS-C.\n> \n> \n>##### JSQuAD\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> As QA datasets, we build a Japanese version of SQuAD (Rajpurkar et al., 2016), one of the datasets of reading comprehension, and a Japanese version ofCommonsenseQA, which is explained in the next section.\n> \n> \n> \n\n\n\n> \n> Reading comprehension is the task of reading a document and answering questions about it. Many reading comprehension evaluation sets have been built in English, followed by those in other languages or multilingual ones.\n> \n> \n> \n\n\n\n> \n> In Japanese, reading comprehension datasets for quizzes (Suzukietal.,2018) and those in the drivingdomain (Takahashi et al., 2019) have been built, but none are in the general domain. We use Wikipedia to build a dataset for the general domain. The construction process is basically based on SQuAD 1.1 (Rajpurkar et al., 2016).\n> \n> \n> \n\n\n\n> \n> First, to extract high-quality articles from Wikipedia, we use Nayuki, which estimates the quality of articles on the basis of hyperlinks in Wikipedia. We randomly chose 822 articles from the top-ranked 10,000 articles. For example, the articles include “熊本県 (Kumamoto Prefecture)” and “フランス料理 (French cuisine)”. Next, we divide an article into paragraphs, present each paragraph to crowdworkers, and ask them to write questions and answers that can be answered if one understands the paragraph. Figure 2 shows an example of JSQuAD. We ask workers to write two additional answers for the dev and test sets to make the system evaluation robust.\n> \n> \n>",
"passage: ##### JCommonsenseQA\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> ### Overview\n> \n> \n> JCommonsenseQA is a Japanese version of CommonsenseQA (Talmor et al., 2019), which consists of five choice QA to evaluate commonsense reasoning ability. Figure 3 shows examples of JCommonsenseQA. In the same way as CommonsenseQA, JCommonsenseQA is built using crowdsourcing with seeds extracted from the knowledge base ConceptNet (Speer et al., 2017). ConceptNet is a multilingual knowledge base that consists of triplets of two concepts and their relation. The triplets are directional and represented as (source concept, relation, target concept), for example (bullet train, AtLocation, station).\n> \n> \n> \n\n\n\n> \n> ### Method of Construction\n> \n> \n> The construction flow for JCommonsenseQA is shown in Figure 4. First, we collect question sets (QSs) from ConceptNet, each of which consists of a source concept and three target concepts that have the same relation to the source concept. Next, for each QS, we crowdAtLocation 2961source a task of writing a question with only one target concept as the answer and a task of adding two distractors. We describe the detailed construction procedure for JCommonsenseQA below, showing how it differs from CommonsenseQA.\n> \n> \n> \n\n\n\n> \n> 1. We collect Japanese QSs from ConceptNet. CommonsenseQA uses only forward relations (source concept, relation, target concept) excluding general ones such as “RelatedTo” and “IsA”. JCommonsenseQA similarly uses a set of 22 relations5, excluding general ones, but the direction of the relations is bidirectional to make the questions more diverse. In other words, we also use relations in the opposite direction (source concept, relation−1, target concept).6 With this setup, we extracted 43,566 QSs with Japanese source/target concepts and randomly selected 7,500 from them.\n> 2. Some low-quality questions in CommonsenseQA contain distractors that can be considered to be an answer. To improve the quality of distractors, we add the following two processes that are not adopted in CommonsenseQA. First, if three target concepts of a QS include a spelling variation or a synonym of one another, this QS is removed. To identify spelling variations, we use the word ID of the morphological dictionary Juman Dic7. Second, we crowdsource a task of judging whether target concepts contain a synonym. As a result, we adopted 5,920 QSs from 7,500.\n> 3. For each QS, we crowdsource a task of writing a question sentence in which only one from the three target concepts is an answer. In the example shown in Figure 4, “駅 (station)” is an answer, and the others are distractors. To remove low quality question sentences, we remove the following question sentences.\n> \t* Question sentences that contain a choice word(this is because such a question is easily solved).\n> \t* Question sentences that contain the expression “XX characters”.8 (XX is a number).\n> \t* Improperly formatted question sentences that do not end with “?”.\n> \t* As a result, 5,920 × 3 = 17,760question sentences were created, from which we adopted 15,310 by removing inappropriate question sentences.\n> 4. In CommonsenseQA, when adding distractors, one is selected from ConceptNet, and the other is created by crowdsourcing. In JCommonsenseQA, to have a wider variety of distractors, two distractors are created by crowdsourcing instead of selecting from ConceptNet. To improve the quality of the questions9, we remove questions whose added distractors fall into one of the following categories:\n> \t* Distractors are included in a question sentence.\n> \t* Distractors overlap with one of existing choices.\n> \t* As a result, distractors were added to the 15,310 questions, of which we adopted 13,906.\n> 5. We asked three crowdworkers to answer each question and adopt only those answered correctly by at least two workers. As a result, we adopted 11,263 out of the 13,906 questions.\n> \n> \n>#### Who are the annotators?\n\n\nFrom JGLUE's URL:\n\n\n\n> \n> We use Yahoo! Crowdsourcing for all crowdsourcing tasks in constructing the datasets.\n> \n> \n> \n\n\nFrom JCoLA's paper:\n\n\n\n> \n> As a reference for the upper limit of accuracy in JCoLA, human acceptability judgment experiments were conducted on Lancers2 with a subset of the JCoLA data.\n> \n> \n>### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset\n\n\nFrom JGLUE's paper:\n\n\n\n> \n> We build a Japanese NLU benchmark, JGLUE, from scratch without translation to measure the general NLU ability in Japanese. We hope that JGLUE will facilitate NLU research in Japanese.\n> \n> \n>### Discussion of Biases### Other Known Limitations\n\n\nFrom JCoLA's paper:\n\n\n\n> \n> All the sentences included in JCoLA have been extracted from textbooks, handbooks and journal articles on theoretical syntax. Therefore, those sentences are guaranteed to be theoretically meaningful, making JCoLA a challenging dataset. However, the distribution of linguistic phenomena directly reflects that of the source literature and thus turns out to be extremely skewed. Indeed, as can be seen in Table 3, while the number of sentences exceeds 100 for most linguistic phenomena, there are several linguistic phenomena for which there are only about 10 sentences. In addition, since it is difficult to force language models to interpret sentences given specific contexts, those sentences whose unacceptability depends on contexts were inevitably removed from JCoLA. This removal process resulted in the deletion of unacceptable sentences from some linguistic phenomena (such as ellipsis), consequently skewing the balance between acceptable and unacceptable sentences (with a higher proportion of acceptable sentences).\n> \n> \n> \n\n\nAdditional Information\n----------------------\n\n\n* 日本語言語理解ベンチマーク JGLUE の構築 〜 自然言語処理モデルの評価用データセットを公開しました - Yahoo! JAPAN Tech Blog URL### Dataset Curators"
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|
e1d0531e474b2c7e22fd5fa29d2f980debb29ab2 | Implicature corpus
```bib
@article{george2020conversational,
title={Conversational implicatures in English dialogue: Annotated dataset},
author={George, Elizabeth Jasmi and Mamidi, Radhika},
journal={Procedia Computer Science},
volume={171},
pages={2316--2323},
year={2020},
publisher={Elsevier}
}
```
Augmented with generated distractors https://colab.research.google.com/drive/1ix0FgwzPAjQkIQA2E3ctlylvcmya7vGy?usp=sharing, for tasksource
```bib
@article{sileo2023tasksource,
title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation},
author={Sileo, Damien},
url= {https://arxiv.org/abs/2301.05948},
journal={arXiv preprint arXiv:2301.05948},
year={2023}
}
``` | tasksource/implicatures | [
"license:gpl",
"arxiv:2301.05948",
"region:us"
]
| 2023-02-27T08:57:17+00:00 | {"license": "gpl"} | 2023-02-27T09:01:42+00:00 | [
"2301.05948"
]
| []
| TAGS
#license-gpl #arxiv-2301.05948 #region-us
| Implicature corpus
Augmented with generated distractors URL for tasksource
| []
| [
"TAGS\n#license-gpl #arxiv-2301.05948 #region-us \n"
]
| [
21
]
| [
"passage: TAGS\n#license-gpl #arxiv-2301.05948 #region-us \n"
]
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|
7fc8ba185af3039a569b4a4083f44d2e4250c956 | # Dataset Card for "donut-docvqa-concert1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | aymanechilah/donut-docvqa-concert1 | [
"region:us"
]
| 2023-02-27T10:36:43+00:00 | {"dataset_info": {"features": [{"name": "file_name", "dtype": "string"}, {"name": "ground_truth", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 246710917.83032492, "num_examples": 249}, {"name": "test", "num_bytes": 27742593.16967509, "num_examples": 28}], "download_size": 73786135, "dataset_size": 274453511.0}} | 2023-02-27T10:36:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "donut-docvqa-concert1"
More Information needed | [
"# Dataset Card for \"donut-docvqa-concert1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"donut-docvqa-concert1\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"donut-docvqa-concert1\"\n\nMore Information needed"
]
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|
63f2d6ef47be7832a6c20a6a238dab7029c5b7c9 | # Dataset Card for "summarize-from-feedback"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | HuggingFaceH4/summarize-from-feedback | [
"region:us"
]
| 2023-02-27T10:50:52+00:00 | {"dataset_info": {"features": [{"name": "meta", "struct": [{"name": "id", "dtype": "string"}, {"name": "post", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "subreddit", "dtype": "string"}, {"name": "site", "dtype": "string"}, {"name": "article", "dtype": "string"}]}, {"name": "responses", "list": [{"name": "text", "dtype": "string"}, {"name": "policy", "dtype": "string"}, {"name": "note", "dtype": "string"}]}, {"name": "label", "dtype": "int32"}, {"name": "worker", "dtype": "string"}, {"name": "batch", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "extra", "struct": [{"name": "confidence", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 172539153, "num_examples": 92858}, {"name": "validation", "num_bytes": 170579710, "num_examples": 86086}], "download_size": 43943406, "dataset_size": 343118863}} | 2023-02-27T11:56:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "summarize-from-feedback"
More Information needed | [
"# Dataset Card for \"summarize-from-feedback\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"summarize-from-feedback\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"summarize-from-feedback\"\n\nMore Information needed"
]
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|
06b74fb30ea77051503b57d8400c800fd497b965 | # Dataset Card for "balanced_augmented_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Jsevisal/balanced_augmented_dataset | [
"region:us"
]
| 2023-02-27T11:21:13+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "tokens", "sequence": "string"}, {"name": "gestures", "sequence": "string"}, {"name": "label", "sequence": {"class_label": {"names": {"0": "B-BUT", "1": "I-BUT", "2": "B-CALM_DOWN", "3": "I-CALM_DOWN", "4": "B-COME_ON", "5": "I-COME_ON", "6": "B-EMPHATIC", "7": "I-EMPHATIC", "8": "B-ENTHUSIASTIC", "9": "I-ENTHUSIASTIC", "10": "B-EXPLAIN", "11": "I-EXPLAIN", "12": "B-FRONT", "13": "I-FRONT", "14": "B-GREET", "15": "I-GREET", "16": "B-ITERATE", "17": "I-ITERATE", "18": "B-NEUTRAL", "19": "I-NEUTRAL", "20": "B-NO", "21": "I-NO", "22": "B-NO_GESTURE", "23": "I-NO_GESTURE", "24": "B-OTHER_PEER", "25": "I-OTHER_PEER", "26": "B-PLEASE", "27": "I-PLEASE", "28": "B-QUESTION", "29": "I-QUESTION", "30": "B-SELF", "31": "I-SELF", "32": "B-SORRY", "33": "I-SORRY", "34": "B-THANKS", "35": "I-THANKS", "36": "B-THINKING", "37": "I-THINKING", "38": "B-THIRD_PERSON", "39": "I-THIRD_PERSON", "40": "B-YES", "41": "I-YES"}}}}], "splits": [{"name": "train", "num_bytes": 165787.0, "num_examples": 504}, {"name": "test", "num_bytes": 53037.0, "num_examples": 121}], "download_size": 40359, "dataset_size": 218824.0}} | 2023-09-14T10:31:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "balanced_augmented_dataset"
More Information needed | [
"# Dataset Card for \"balanced_augmented_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"balanced_augmented_dataset\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"balanced_augmented_dataset\"\n\nMore Information needed"
]
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|
512509ffaed10c913f9dd8975938d290fd55855e | # Dataset Card for "go-emotion-dk-autotranlated-10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | RJuro/go-emotion-dk-autotranlated-10k | [
"region:us"
]
| 2023-02-27T12:59:56+00:00 | {"dataset_info": {"features": [{"name": "text_en", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "admiration", "1": "amusement", "2": "anger", "3": "annoyance", "4": "approval", "5": "caring", "6": "confusion", "7": "curiosity", "8": "desire", "9": "disappointment", "10": "disapproval", "11": "disgust", "12": "embarrassment", "13": "excitement", "14": "fear", "15": "gratitude", "16": "grief", "17": "joy", "18": "love", "19": "nervousness", "20": "neutral", "21": "optimism", "22": "pride", "23": "realization", "24": "relief", "25": "remorse", "26": "sadness", "27": "surprise"}}}}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 2916184, "num_examples": 9000}, {"name": "test", "num_bytes": 159317, "num_examples": 500}, {"name": "valid", "num_bytes": 162379, "num_examples": 500}], "download_size": 1662215, "dataset_size": 3237880}} | 2023-02-27T13:00:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "go-emotion-dk-autotranlated-10k"
More Information needed | [
"# Dataset Card for \"go-emotion-dk-autotranlated-10k\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"go-emotion-dk-autotranlated-10k\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"go-emotion-dk-autotranlated-10k\"\n\nMore Information needed"
]
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|
17d61a19643ca7dafa5a0c2f62d6415962e39c07 | # Dataset Card for "go-emotion-dk-autotranlated-10k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Korsholm22/go-emotion-dk-autotranlated-10k | [
"region:us"
]
| 2023-02-27T13:00:01+00:00 | {"dataset_info": {"features": [{"name": "text_en", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "admiration", "1": "amusement", "2": "anger", "3": "annoyance", "4": "approval", "5": "caring", "6": "confusion", "7": "curiosity", "8": "desire", "9": "disappointment", "10": "disapproval", "11": "disgust", "12": "embarrassment", "13": "excitement", "14": "fear", "15": "gratitude", "16": "grief", "17": "joy", "18": "love", "19": "nervousness", "20": "neutral", "21": "optimism", "22": "pride", "23": "realization", "24": "relief", "25": "remorse", "26": "sadness", "27": "surprise"}}}}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 2905258, "num_examples": 9000}, {"name": "test", "num_bytes": 163076, "num_examples": 500}, {"name": "valid", "num_bytes": 169546, "num_examples": 500}], "download_size": 1662396, "dataset_size": 3237880}} | 2023-02-27T13:00:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "go-emotion-dk-autotranlated-10k"
More Information needed | [
"# Dataset Card for \"go-emotion-dk-autotranlated-10k\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"go-emotion-dk-autotranlated-10k\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"go-emotion-dk-autotranlated-10k\"\n\nMore Information needed"
]
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221003b6900b8eaaa9a6c3e3aaa0bf78812dc51d | Comming soon...!
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
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## Additional Information
### Dataset Curators
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### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | deepsynthbody/conditional-polyp-diffusion | [
"doi:10.57967/hf/0430",
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| 2023-02-27T13:24:47+00:00 | {} | 2023-03-11T00:31:35+00:00 | []
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| TAGS
#doi-10.57967/hf/0430 #region-us
| Comming soon...!
# Dataset Card for Dataset Name
## Dataset Description
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- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
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#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
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### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
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b41a243c5e6c1b8b3a0fd26a2cf20aacfa463620 | # Dataset Card for "test-_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | juancopi81/test-_8 | [
"region:us"
]
| 2023-02-27T15:15:12+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "playlist_title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1052419.0, "num_examples": 3}], "download_size": 1054186, "dataset_size": 1052419.0}} | 2023-02-27T15:15:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test-_8"
More Information needed | [
"# Dataset Card for \"test-_8\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test-_8\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test-_8\"\n\nMore Information needed"
]
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|
a9f95c5cadd648bae49357cb784f510f9a4996e2 | # Dataset Card for "UC_Merced_LandUse_MultiLabel"
## Dataset Description
- **Paper:** [Bag-of-visual-words and spatial extensions for land-use classification](https://dl.acm.org/doi/pdf/10.1145/1869790.1869829)
- **Paper:** [Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method](https://ieeexplore.ieee.org/iel7/36/4358825/08089668.pdf)
### Licensing Information
Public Domain; “Map services and data available from U.S. Geological Survey, National Geospatial Program.”
## Citation Information
Imagery:
[Bag-of-visual-words and spatial extensions for land-use classification](https://dl.acm.org/doi/pdf/10.1145/1869790.1869829)
Multilabels:
[Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method](https://ieeexplore.ieee.org/iel7/36/4358825/08089668.pdf)
```
@inproceedings{yang2010bag,
title = {Bag-of-visual-words and spatial extensions for land-use classification},
author = {Yang, Yi and Newsam, Shawn},
year = 2010,
booktitle = {Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems},
pages = {270--279}
}
@article{8089668,
title = {Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method},
author = {Chaudhuri, Bindita and Demir, Begüm and Chaudhuri, Subhasis and Bruzzone, Lorenzo},
year = 2018,
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = 56,
number = 2,
pages = {1144--1158},
doi = {10.1109/TGRS.2017.2760909}
}
``` | jonathan-roberts1/UC_Merced_LandUse_MultiLabel | [
"license:other",
"region:us"
]
| 2023-02-27T15:54:34+00:00 | {"license": "other", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "sequence": {"class_label": {"names": {"0": "airplane", "1": "bare soil", "2": "buildings", "3": "cars", "4": "chaparral", "5": "court", "6": "dock", "7": "field", "8": "grass", "9": "mobile home", "10": "pavement", "11": "sand", "12": "sea", "13": "ship", "14": "tanks", "15": "trees", "16": "water"}}}}], "splits": [{"name": "train", "num_bytes": 438859816.5, "num_examples": 2100}], "download_size": 416309630, "dataset_size": 438859816.5}} | 2023-04-03T15:33:24+00:00 | []
| []
| TAGS
#license-other #region-us
| # Dataset Card for "UC_Merced_LandUse_MultiLabel"
## Dataset Description
- Paper: Bag-of-visual-words and spatial extensions for land-use classification
- Paper: Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method
### Licensing Information
Public Domain; “Map services and data available from U.S. Geological Survey, National Geospatial Program.”
Imagery:
Bag-of-visual-words and spatial extensions for land-use classification
Multilabels:
Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method
| [
"# Dataset Card for \"UC_Merced_LandUse_MultiLabel\"",
"## Dataset Description\n\n- Paper: Bag-of-visual-words and spatial extensions for land-use classification\n- Paper: Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method",
"### Licensing Information\n\nPublic Domain; “Map services and data available from U.S. Geological Survey, National Geospatial Program.”\n\n\n\nImagery:\n\nBag-of-visual-words and spatial extensions for land-use classification\n\nMultilabels:\n\nMultilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method"
]
| [
"TAGS\n#license-other #region-us \n",
"# Dataset Card for \"UC_Merced_LandUse_MultiLabel\"",
"## Dataset Description\n\n- Paper: Bag-of-visual-words and spatial extensions for land-use classification\n- Paper: Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method",
"### Licensing Information\n\nPublic Domain; “Map services and data available from U.S. Geological Survey, National Geospatial Program.”\n\n\n\nImagery:\n\nBag-of-visual-words and spatial extensions for land-use classification\n\nMultilabels:\n\nMultilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method"
]
| [
11,
20,
54,
82
]
| [
"passage: TAGS\n#license-other #region-us \n# Dataset Card for \"UC_Merced_LandUse_MultiLabel\"## Dataset Description\n\n- Paper: Bag-of-visual-words and spatial extensions for land-use classification\n- Paper: Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method### Licensing Information\n\nPublic Domain; “Map services and data available from U.S. Geological Survey, National Geospatial Program.”\n\n\n\nImagery:\n\nBag-of-visual-words and spatial extensions for land-use classification\n\nMultilabels:\n\nMultilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method"
]
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|
733f3c4f48046646326b688097127fe875db0c07 |
# Dataset Card for funsd-vqa
## Dataset Description
- **Homepage:** https://huggingface.co/datasets/munish0838/funsd_vqa
- **Repository:** https://github.com/munish0838/FUNSD
- **Point of Contact:** [email protected]
### Dataset Summary
This dataset has been processed to be used by Donut model for DocVQA fine tuninf on FUNSD dataset. The final dataset is in `.jsonl` file format.
### Languages
- English
## Dataset Structure
### Data Fields
id -> Name of Image file/json file
file_name -> Path of image file
questions -> array of all questions in corresponding to the image
words -> list of all words present in image
bounding_boxes -> contains bounding box of all words
answers -> array of all answers in corresponding to the image
grount_truth -> has gt_parses in donut required format for processing
## Dataset Creation
Refer this github repo link: https://github.com/munish0838/FUNSD
### Source Data
https://guillaumejaume.github.io/FUNSD/
| munish0838/funsd-vqa | [
"task_categories:document-question-answering",
"size_categories:n<1K",
"language:en",
"license:openrail",
"region:us"
]
| 2023-02-27T16:01:49+00:00 | {"language": ["en"], "license": "openrail", "size_categories": ["n<1K"], "task_categories": ["document-question-answering"]} | 2024-02-15T03:31:07+00:00 | []
| [
"en"
]
| TAGS
#task_categories-document-question-answering #size_categories-n<1K #language-English #license-openrail #region-us
|
# Dataset Card for funsd-vqa
## Dataset Description
- Homepage: URL
- Repository: URL
- Point of Contact: munishkumar19042002@URL
### Dataset Summary
This dataset has been processed to be used by Donut model for DocVQA fine tuninf on FUNSD dataset. The final dataset is in '.jsonl' file format.
### Languages
- English
## Dataset Structure
### Data Fields
id -> Name of Image file/json file
file_name -> Path of image file
questions -> array of all questions in corresponding to the image
words -> list of all words present in image
bounding_boxes -> contains bounding box of all words
answers -> array of all answers in corresponding to the image
grount_truth -> has gt_parses in donut required format for processing
## Dataset Creation
Refer this github repo link: URL
### Source Data
URL
| [
"# Dataset Card for funsd-vqa",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: munishkumar19042002@URL",
"### Dataset Summary\n\nThis dataset has been processed to be used by Donut model for DocVQA fine tuninf on FUNSD dataset. The final dataset is in '.jsonl' file format.",
"### Languages\n\n- English",
"## Dataset Structure",
"### Data Fields\n\nid -> Name of Image file/json file \nfile_name -> Path of image file \nquestions -> array of all questions in corresponding to the image \nwords -> list of all words present in image \nbounding_boxes -> contains bounding box of all words \nanswers -> array of all answers in corresponding to the image \ngrount_truth -> has gt_parses in donut required format for processing",
"## Dataset Creation\n\nRefer this github repo link: URL",
"### Source Data\n\nURL"
]
| [
"TAGS\n#task_categories-document-question-answering #size_categories-n<1K #language-English #license-openrail #region-us \n",
"# Dataset Card for funsd-vqa",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: munishkumar19042002@URL",
"### Dataset Summary\n\nThis dataset has been processed to be used by Donut model for DocVQA fine tuninf on FUNSD dataset. The final dataset is in '.jsonl' file format.",
"### Languages\n\n- English",
"## Dataset Structure",
"### Data Fields\n\nid -> Name of Image file/json file \nfile_name -> Path of image file \nquestions -> array of all questions in corresponding to the image \nwords -> list of all words present in image \nbounding_boxes -> contains bounding box of all words \nanswers -> array of all answers in corresponding to the image \ngrount_truth -> has gt_parses in donut required format for processing",
"## Dataset Creation\n\nRefer this github repo link: URL",
"### Source Data\n\nURL"
]
| [
40,
11,
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| [
"passage: TAGS\n#task_categories-document-question-answering #size_categories-n<1K #language-English #license-openrail #region-us \n# Dataset Card for funsd-vqa## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: munishkumar19042002@URL### Dataset Summary\n\nThis dataset has been processed to be used by Donut model for DocVQA fine tuninf on FUNSD dataset. The final dataset is in '.jsonl' file format.### Languages\n\n- English## Dataset Structure### Data Fields\n\nid -> Name of Image file/json file \nfile_name -> Path of image file \nquestions -> array of all questions in corresponding to the image \nwords -> list of all words present in image \nbounding_boxes -> contains bounding box of all words \nanswers -> array of all answers in corresponding to the image \ngrount_truth -> has gt_parses in donut required format for processing## Dataset Creation\n\nRefer this github repo link: URL### Source Data\n\nURL"
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|
10d052310cbb1b10211b1cb119a1184e1090cdf3 | # Dataset Card for "test-audio"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | juancopi81/test-audio | [
"region:us"
]
| 2023-02-27T16:10:20+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "playlist_title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1052419.0, "num_examples": 3}], "download_size": 0, "dataset_size": 1052419.0}} | 2023-03-03T13:52:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test-audio"
More Information needed | [
"# Dataset Card for \"test-audio\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test-audio\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test-audio\"\n\nMore Information needed"
]
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|
c47490c6f28c70bc01d5d275029e152835e9da40 | # Dataset Card for "VISBank_CleaneParsed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Yamei/VISBank_Final | [
"region:us"
]
| 2023-02-27T16:21:54+00:00 | {"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "paper_id", "dtype": "int64"}, {"name": "abstract", "dtype": "string"}, {"name": "authors", "list": [{"name": "first", "dtype": "string"}, {"name": "middle", "sequence": "string"}, {"name": "last", "dtype": "string"}, {"name": "suffix", "dtype": "string"}]}, {"name": "year", "dtype": "float64"}, {"name": "arxiv_id", "dtype": "string"}, {"name": "acl_id", "dtype": "string"}, {"name": "pmc_id", "dtype": "string"}, {"name": "pubmed_id", "dtype": "string"}, {"name": "doi", "dtype": "string"}, {"name": "venue", "dtype": "string"}, {"name": "journal", "dtype": "string"}, {"name": "mag_id", "dtype": "string"}, {"name": "outbound_citations", "sequence": "string"}, {"name": "inbound_citations", "sequence": "string"}, {"name": "has_outbound_citations", "dtype": "bool"}, {"name": "has_inbound_citations", "dtype": "bool"}, {"name": "has_pdf_parse", "dtype": "bool"}, {"name": "s2_url", "dtype": "string"}, {"name": "has_pdf_body_text", "dtype": "float64"}, {"name": "has_pdf_parsed_abstract", "dtype": "float64"}, {"name": "has_pdf_parsed_body_text", "dtype": "float64"}, {"name": "has_pdf_parsed_bib_entries", "dtype": "float64"}, {"name": "has_pdf_parsed_ref_entries", "dtype": "float64"}, {"name": "entities", "sequence": {"sequence": "string"}}], "splits": [{"name": "train", "num_bytes": 254427395, "num_examples": 125745}], "download_size": 133946624, "dataset_size": 254427395}} | 2023-03-04T20:18:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VISBank_CleaneParsed"
More Information needed | [
"# Dataset Card for \"VISBank_CleaneParsed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VISBank_CleaneParsed\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VISBank_CleaneParsed\"\n\nMore Information needed"
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37fb78be2f9ed923c7ad9543bb3bb19d98057f65 | # Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
### 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
[train]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
[More Information Needed] | michaelpenaariet/PIdemo | [
"size_categories:n<1K",
"language:en",
"region:us"
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| 2023-02-27T17:13:27+00:00 | {"language": ["en"], "size_categories": ["n<1K"]} | 2023-03-15T20:41:48+00:00 | []
| [
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## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
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### Data Fields
### Data Splits
[train]
## 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
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
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|
e6010aa8b32734e2f2dbc11ffdeebb01f2a6858d | # Dataset Card for "MLRSNet"
## Dataset Description
- **Paper:** [MLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding](https://www.sciencedirect.com/science/article/pii/S0924271620302677)
### Licensing Information
CC BY 4.0
## Citation Information
[MLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding](https://www.sciencedirect.com/science/article/pii/S0924271620302677)
```
@article{qi2020mlrsnet,
title = {MLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding},
author = {Qi, Xiaoman and Zhu, Panpan and Wang, Yuebin and Zhang, Liqiang and Peng, Junhuan and Wu, Mengfan and Chen, Jialong and Zhao, Xudong and Zang, Ning and Mathiopoulos, P Takis},
year = 2020,
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
publisher = {Elsevier},
volume = 169,
pages = {337--350}
}
``` | jonathan-roberts1/MLRSNet | [
"license:cc-by-4.0",
"region:us"
]
| 2023-02-27T18:19:58+00:00 | {"license": "cc-by-4.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "sequence": {"class_label": {"names": {"0": "airplane", "1": "airport", "2": "bare soil", "3": "baseball diamond", "4": "basketball court", "5": "beach", "6": "bridge", "7": "buildings", "8": "cars", "9": "chaparral", "10": "cloud", "11": "containers", "12": "crosswalk", "13": "dense residential area", "14": "desert", "15": "dock", "16": "factory", "17": "field", "18": "football field", "19": "forest", "20": "freeway", "21": "golf course", "22": "grass", "23": "greenhouse", "24": "gully", "25": "habor", "26": "intersection", "27": "island", "28": "lake", "29": "mobile home", "30": "mountain", "31": "overpass", "32": "park", "33": "parking lot", "34": "parkway", "35": "pavement", "36": "railway", "37": "railway station", "38": "river", "39": "road", "40": "roundabout", "41": "runway", "42": "sand", "43": "sea", "44": "ships", "45": "snow", "46": "snowberg", "47": "sparse residential area", "48": "stadium", "49": "swimming pool", "50": "tanks", "51": "tennis court", "52": "terrace", "53": "track", "54": "trail", "55": "transmission tower", "56": "trees", "57": "water", "58": "wetland", "59": "wind turbine"}}}}], "splits": [{"name": "train", "num_bytes": 1327782862.875, "num_examples": 109161}], "download_size": 1304951717, "dataset_size": 1327782862.875}} | 2023-04-03T15:34:12+00:00 | []
| []
| TAGS
#license-cc-by-4.0 #region-us
| # Dataset Card for "MLRSNet"
## Dataset Description
- Paper: MLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding
### Licensing Information
CC BY 4.0
MLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding
| [
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"## Dataset Description\n\n- Paper: MLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding",
"### Licensing Information\n\nCC BY 4.0\n\n\n\nMLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding"
]
| [
"TAGS\n#license-cc-by-4.0 #region-us \n",
"# Dataset Card for \"MLRSNet\"",
"## Dataset Description\n\n- Paper: MLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding",
"### Licensing Information\n\nCC BY 4.0\n\n\n\nMLRSNet: A multi-label high spatial resolution remote sensing dataset for semantic scene understanding"
]
| [
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|
5928ac565d22df80e018bee0c5ad1e25b9e70dc9 | # Dataset Card for "captionary-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | eeshclusive/captionary-dataset | [
"region:us"
]
| 2023-02-27T18:46:46+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 55042409.0, "num_examples": 162}, {"name": "test", "num_bytes": 16034324.0, "num_examples": 51}], "download_size": 14804192, "dataset_size": 71076733.0}} | 2023-02-27T19:40:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "captionary-dataset"
More Information needed | [
"# Dataset Card for \"captionary-dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"captionary-dataset\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"captionary-dataset\"\n\nMore Information needed"
]
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|
3abe1044f9048306ce710180d8fefd38bf85b1ec | # Dataset Card for "biomed-fr-pubmed-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rntc/biomed-fr-pubmed-en | [
"region:us"
]
| 2023-02-27T20:24:10+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4562571188, "num_examples": 15561370}, {"name": "validation", "num_bytes": 46015018, "num_examples": 157186}], "download_size": 3088461733, "dataset_size": 4608586206}} | 2023-02-27T20:51:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "biomed-fr-pubmed-en"
More Information needed | [
"# Dataset Card for \"biomed-fr-pubmed-en\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"biomed-fr-pubmed-en\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"biomed-fr-pubmed-en\"\n\nMore Information needed"
]
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|
c6dab4788cb6b2e62c4f462063f4f56c78a95180 | # Adult
The [Adult dataset](https://archive.ics.uci.edu/ml/datasets/Adult) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Census dataset including personal characteristic of a person, and their income threshold.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|-----------------------------------------------------------------|
| encoding | | Encoding dictionary showing original values of encoded features.|
| income | Binary classification | Classify the person's income as over or under the threshold. |
| income-no race | Binary classification | As `income`, but the `race` feature is removed. |
| race | Multiclass classification | Predict the race of the individual. |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/adult", "income")["train"]
```
# Features
Target feature changes according to the selected configuration and is always in last position in the dataset.
|**Feature** |**Type** | **Description** |
|-------------------------------|-----------|------------------------------------------------------------|
|`age` |`[int64]` | Age of the person. |
|`capital_gain` |`[float64]`| Capital gained by the person. |
|`capital_loss` |`[float64]`| Capital lost by the person. |
|`education` |`[int8]` | Education level: the higher, the more educated the person. |
|`final_weight` |`[int64]` | |
|`hours_worked_per_week` |`[int64]` | Hours worked per week. |
|`marital_status` |`[string]` | Marital status of the person. |
|`native_country` |`[string]` | Native country of the person. |
|`occupation` |`[string]` | Job of the person. |
|`race` |`[string]` | Race of the person. |
|`relationship` |`[string]` | |
|`is_male` |`[bool]` | Man/Woman. |
|`workclass` |`[string]` | Type of job of the person. |
|**over_threshold** |`int8` | `1` for income `>= 50k$`, `0` otherwise. | | mstz/adult | [
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"adult",
"tabular_classification",
"binary_classification",
"multiclass_classification",
"UCI",
"region:us"
]
| 2023-02-27T21:17:48+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["10K<n<100K"], "task_categories": ["tabular-classification"], "pretty_name": "Adult", "tags": ["adult", "tabular_classification", "binary_classification", "multiclass_classification", "UCI"], "configs": ["encoding", "income", "income-no race", "race"]} | 2023-04-15T10:37:47+00:00 | []
| [
"en"
]
| TAGS
#task_categories-tabular-classification #size_categories-10K<n<100K #language-English #license-cc #adult #tabular_classification #binary_classification #multiclass_classification #UCI #region-us
| Adult
=====
The Adult dataset from the UCI ML repository.
Census dataset including personal characteristic of a person, and their income threshold.
Configurations and tasks
========================
Configuration: encoding, Task: , Description: Encoding dictionary showing original values of encoded features.
Configuration: income, Task: Binary classification, Description: Classify the person's income as over or under the threshold.
Configuration: income-no race, Task: Binary classification, Description: As 'income', but the 'race' feature is removed.
Configuration: race, Task: Multiclass classification, Description: Predict the race of the individual.
Usage
=====
Features
========
Target feature changes according to the selected configuration and is always in last position in the dataset.
Feature: 'age', Type: '[int64]', Description: Age of the person.
Feature: 'capital\_gain', Type: '[float64]', Description: Capital gained by the person.
Feature: 'capital\_loss', Type: '[float64]', Description: Capital lost by the person.
Feature: 'education', Type: '[int8]', Description: Education level: the higher, the more educated the person.
Feature: 'final\_weight', Type: '[int64]', Description:
Feature: 'hours\_worked\_per\_week', Type: '[int64]', Description: Hours worked per week.
Feature: 'marital\_status', Type: '[string]', Description: Marital status of the person.
Feature: 'native\_country', Type: '[string]', Description: Native country of the person.
Feature: 'occupation', Type: '[string]', Description: Job of the person.
Feature: 'race', Type: '[string]', Description: Race of the person.
Feature: 'relationship', Type: '[string]', Description:
Feature: 'is\_male', Type: '[bool]', Description: Man/Woman.
Feature: 'workclass', Type: '[string]', Description: Type of job of the person.
Feature: over\_threshold, Type: 'int8', Description: '1' for income '>= 50k$', '0' otherwise.
| []
| [
"TAGS\n#task_categories-tabular-classification #size_categories-10K<n<100K #language-English #license-cc #adult #tabular_classification #binary_classification #multiclass_classification #UCI #region-us \n"
]
| [
61
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-10K<n<100K #language-English #license-cc #adult #tabular_classification #binary_classification #multiclass_classification #UCI #region-us \n"
]
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|
287c90b7e555d2a8e67baf0f392e8424235eee37 | # Dataset Card for "ImageNet15_animals_unbalanced_augmented1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | CVdatasets/ImageNet15_animals_unbalanced_augmented1 | [
"region:us"
]
| 2023-02-27T23:05:15+00:00 | {"dataset_info": {"features": [{"name": "labels", "dtype": {"class_label": {"names": {"0": "Italian greyhound", "1": "coyote, prairie wolf, brush wolf, Canis latrans", "2": "beagle", "3": "Rottweiler", "4": "hyena, hyaena", "5": "Greater Swiss Mountain dog", "6": "triceratops", "7": "French bulldog", "8": "red wolf, maned wolf, Canis rufus, Canis niger", "9": "Egyptian cat", "10": "Chihuahua", "11": "Irish terrier", "12": "tiger cat", "13": "white wolf, Arctic wolf, Canis lupus tundrarum", "14": "timber wolf, grey wolf, gray wolf, Canis lupus"}}}}, {"name": "img", "dtype": "image"}], "splits": [{"name": "validation", "num_bytes": 60570468.125, "num_examples": 1439}, {"name": "train", "num_bytes": 161485444.02117264, "num_examples": 3681}], "download_size": 222111550, "dataset_size": 222055912.14617264}} | 2023-02-27T23:05:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ImageNet15_animals_unbalanced_augmented1"
More Information needed | [
"# Dataset Card for \"ImageNet15_animals_unbalanced_augmented1\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ImageNet15_animals_unbalanced_augmented1\"\n\nMore Information needed"
]
| [
6,
25
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ImageNet15_animals_unbalanced_augmented1\"\n\nMore Information needed"
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|
93aa4f6e6d06371cb51f76e4414fa91b059905ef | # Dataset Card for "latin_english_parallel"
101k translation pairs between Latin and English, split 99/1/1 as train/test/val. These have been collected roughly 66% from the Loeb Classical Library and 34% from the Vulgate translation.
For those that were gathered from the Loeb Classical Library, alignment was performd manually between Source and Target sequences.
Each sample is annotated with the index and file (and therefore author/work) that the sample is from. If you find errors, please feel free to submit a PR to fix them.
 | grosenthal/latin_english_translation | [
"task_categories:translation",
"size_categories:10K<n<100K",
"language:la",
"language:en",
"license:mit",
"doi:10.57967/hf/0903",
"region:us"
]
| 2023-02-28T00:10:51+00:00 | {"language": ["la", "en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["translation"], "pretty_name": "Latin to English Translation Pairs", "dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "la", "dtype": "string"}, {"name": "en", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 39252644, "num_examples": 99343}, {"name": "test", "num_bytes": 405056, "num_examples": 1014}, {"name": "valid", "num_bytes": 392886, "num_examples": 1014}], "download_size": 25567350, "dataset_size": 40050586}} | 2023-07-17T20:59:06+00:00 | []
| [
"la",
"en"
]
| TAGS
#task_categories-translation #size_categories-10K<n<100K #language-Latin #language-English #license-mit #doi-10.57967/hf/0903 #region-us
| # Dataset Card for "latin_english_parallel"
101k translation pairs between Latin and English, split 99/1/1 as train/test/val. These have been collected roughly 66% from the Loeb Classical Library and 34% from the Vulgate translation.
For those that were gathered from the Loeb Classical Library, alignment was performd manually between Source and Target sequences.
Each sample is annotated with the index and file (and therefore author/work) that the sample is from. If you find errors, please feel free to submit a PR to fix them.
!alt text | [
"# Dataset Card for \"latin_english_parallel\"\n\n101k translation pairs between Latin and English, split 99/1/1 as train/test/val. These have been collected roughly 66% from the Loeb Classical Library and 34% from the Vulgate translation. \n\nFor those that were gathered from the Loeb Classical Library, alignment was performd manually between Source and Target sequences.\n\nEach sample is annotated with the index and file (and therefore author/work) that the sample is from. If you find errors, please feel free to submit a PR to fix them.\n\n!alt text"
]
| [
"TAGS\n#task_categories-translation #size_categories-10K<n<100K #language-Latin #language-English #license-mit #doi-10.57967/hf/0903 #region-us \n",
"# Dataset Card for \"latin_english_parallel\"\n\n101k translation pairs between Latin and English, split 99/1/1 as train/test/val. These have been collected roughly 66% from the Loeb Classical Library and 34% from the Vulgate translation. \n\nFor those that were gathered from the Loeb Classical Library, alignment was performd manually between Source and Target sequences.\n\nEach sample is annotated with the index and file (and therefore author/work) that the sample is from. If you find errors, please feel free to submit a PR to fix them.\n\n!alt text"
]
| [
53,
137
]
| [
"passage: TAGS\n#task_categories-translation #size_categories-10K<n<100K #language-Latin #language-English #license-mit #doi-10.57967/hf/0903 #region-us \n# Dataset Card for \"latin_english_parallel\"\n\n101k translation pairs between Latin and English, split 99/1/1 as train/test/val. These have been collected roughly 66% from the Loeb Classical Library and 34% from the Vulgate translation. \n\nFor those that were gathered from the Loeb Classical Library, alignment was performd manually between Source and Target sequences.\n\nEach sample is annotated with the index and file (and therefore author/work) that the sample is from. If you find errors, please feel free to submit a PR to fix them.\n\n!alt text"
]
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|
8d51ad6c40af4095a750d23e6c684c3e8573bb34 | # Dataset Card for "audio-diffusion-512"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tvergho/audio-diffusion-512 | [
"region:us"
]
| 2023-02-28T00:16:35+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "audio_file", "dtype": "string"}, {"name": "slice", "dtype": "int16"}], "splits": [{"name": "train", "num_bytes": 896860831.5, "num_examples": 6964}], "download_size": 895892605, "dataset_size": 896860831.5}} | 2023-02-28T01:03:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "audio-diffusion-512"
More Information needed | [
"# Dataset Card for \"audio-diffusion-512\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"audio-diffusion-512\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"audio-diffusion-512\"\n\nMore Information needed"
]
| [
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|
442ce64039b5234704f0447107aa80987fccb922 | # Dataset Card for "presidents"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Tuana/presidents | [
"region:us"
]
| 2023-02-28T00:51:03+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "content_type", "dtype": "string"}, {"name": "meta", "struct": [{"name": "url", "dtype": "string"}, {"name": "_split_id", "dtype": "int64"}]}, {"name": "id_hash_keys", "sequence": "string"}, {"name": "score", "dtype": "null"}, {"name": "embedding", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 9366886, "num_examples": 5529}], "download_size": 4997888, "dataset_size": 9366886}} | 2023-02-28T01:06:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "presidents"
More Information needed | [
"# Dataset Card for \"presidents\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"presidents\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"presidents\"\n\nMore Information needed"
]
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|
b72078fb93374c7f338d896ce9410513d8793c37 | # Dataset Card for "guilbert_tok"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mekjr1/guilbert_tok | [
"region:us"
]
| 2023-02-28T02:14:23+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "emotion_name", "dtype": {"class_label": {"names": {"0": "Adoring", "1": "Amused", "2": "Angry", "3": "Annoyed", "4": "Caring", "5": "Confused", "6": "Curious", "7": "Disappointed", "8": "Disgusted", "9": "Embarrassed", "10": "Excited", "11": "Guilty", "12": "Happy", "13": "Heartbroken", "14": "Loving", "15": "Nervous", "16": "Passionate", "17": "Proud", "18": "Relieved", "19": "Sad", "20": "Sorry", "21": "Surprised", "22": "Thoughtful"}}}}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "word_ids", "sequence": "int64"}], "splits": [{"name": "validation", "num_bytes": 41853427, "num_examples": 5561}, {"name": "train", "num_bytes": 984129684, "num_examples": 130680}, {"name": "test", "num_bytes": 20945009, "num_examples": 2781}], "download_size": 48041275, "dataset_size": 1046928120}} | 2023-02-28T02:14:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "guilbert_tok"
More Information needed | [
"# Dataset Card for \"guilbert_tok\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"guilbert_tok\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"guilbert_tok\"\n\nMore Information needed"
]
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|
6965b3e3e0bd23606a5a04b5b9b85957cef7e58c | # Dataset Card for "maestro"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tvergho/maestro | [
"region:us"
]
| 2023-02-28T02:27:28+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "audio_file", "dtype": "string"}, {"name": "slice", "dtype": "int16"}], "splits": [{"name": "train", "num_bytes": 8059364821.5, "num_examples": 59668}], "download_size": 8051660600, "dataset_size": 8059364821.5}} | 2023-02-28T04:05:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "maestro"
More Information needed | [
"# Dataset Card for \"maestro\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"maestro\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"maestro\"\n\nMore Information needed"
]
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b1b50ac0de05f0e0bf1088f1fae918921d4a3102 | # Dataset Card for "VQAv2_minival_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_1000_open_ended"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_minival_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_1000_open_ended | [
"region:us"
]
| 2023-02-28T03:31:57+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 145355, "num_examples": 1000}], "download_size": 55086, "dataset_size": 145355}} | 2023-02-28T03:31:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_minival_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_1000_open_ended"
More Information needed | [
"# Dataset Card for \"VQAv2_minival_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_1000_open_ended\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_minival_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_1000_open_ended\"\n\nMore Information needed"
]
| [
6,
56
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_minival_google_flan_t5_xxl_mode_VQAv2_visclues_detection_caption_module_ns_1000_open_ended\"\n\nMore Information needed"
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|
0770f3084f9d960be398d9be26c4d5b62dd69b6d | # Dataset Card for "LEGIT-2023"
Label key:
- 0 or 1: word 0 or 1 is more legible, other unknown
- 2: both words are equally legible
- 3: neither word is legible | dvsth/LEGIT | [
"region:us"
]
| 2023-02-28T04:10:36+00:00 | {"dataset_info": {"features": [{"name": "choice", "dtype": "int64"}, {"name": "k", "dtype": "int64"}, {"name": "k1", "dtype": "int64"}, {"name": "n", "dtype": "float64"}, {"name": "n1", "dtype": "float64"}, {"name": "word", "dtype": "string"}, {"name": "word0", "dtype": "string"}, {"name": "word1", "dtype": "string"}, {"name": "model0", "dtype": "string"}, {"name": "model1", "dtype": "string"}, {"name": "img0", "dtype": "image"}, {"name": "img1", "dtype": "image"}], "splits": [{"name": "test", "num_bytes": 3686021.0, "num_examples": 3712}, {"name": "train", "num_bytes": 14024307.25, "num_examples": 14283}, {"name": "valid", "num_bytes": 3184961.75, "num_examples": 3237}], "download_size": 17726271, "dataset_size": 20895290.0}} | 2023-02-28T05:19:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "LEGIT-2023"
Label key:
- 0 or 1: word 0 or 1 is more legible, other unknown
- 2: both words are equally legible
- 3: neither word is legible | [
"# Dataset Card for \"LEGIT-2023\"\n\nLabel key:\n - 0 or 1: word 0 or 1 is more legible, other unknown\n - 2: both words are equally legible\n - 3: neither word is legible"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"LEGIT-2023\"\n\nLabel key:\n - 0 or 1: word 0 or 1 is more legible, other unknown\n - 2: both words are equally legible\n - 3: neither word is legible"
]
| [
6,
48
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"LEGIT-2023\"\n\nLabel key:\n - 0 or 1: word 0 or 1 is more legible, other unknown\n - 2: both words are equally legible\n - 3: neither word is legible"
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|
eec02cd0f1c057c8a4ab8377d8c86efe11b702a0 | # Dataset Card for "big-animal-dataset-high-res-embedding"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Isamu136/big-animal-dataset-high-res-embedding | [
"region:us"
]
| 2023-02-28T04:11:45+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "l14_embeddings", "sequence": "float32"}, {"name": "moco_vitb_imagenet_embeddings", "sequence": "float32"}, {"name": "moco_vitb_imagenet_embeddings_without_last_layer", "sequence": "float32"}, {"name": "ibot_b_16_embedding", "sequence": "float32"}, {"name": "ibot_b_16_last_self_attn", "sequence": "float32"}, {"name": "midas_dpt_swin2_large_384", "dtype": "image"}, {"name": "subject_noun", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3744432126.3, "num_examples": 26180}], "download_size": 3795367998, "dataset_size": 3744432126.3}} | 2023-03-12T15:55:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "big-animal-dataset-high-res-embedding"
More Information needed | [
"# Dataset Card for \"big-animal-dataset-high-res-embedding\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"big-animal-dataset-high-res-embedding\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"big-animal-dataset-high-res-embedding\"\n\nMore Information needed"
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|
2069fb706f36c6db8edad904fa2174e62514f4a8 | # Dataset Card for "avatar_captioned-augmented"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/avatar_captioned-augmented | [
"region:us"
]
| 2023-02-28T07:29:17+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "src", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1616992137.25, "num_examples": 6894}], "download_size": 1616179230, "dataset_size": 1616992137.25}} | 2023-03-19T04:48:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "avatar_captioned-augmented"
More Information needed | [
"# Dataset Card for \"avatar_captioned-augmented\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"avatar_captioned-augmented\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"avatar_captioned-augmented\"\n\nMore Information needed"
]
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|
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