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b556ca40f395999f250e9a3d69ca85db0329308d | # Dataset Card for "disc_cla_quinta-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_quinta-2 | [
"region:us"
]
| 2023-03-22T13:29:37+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "comision", "dtype": "string"}, {"name": "fecha_gaceta", "dtype": "string"}, {"name": "gaceta_numero", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 20629809, "num_examples": 7507}], "download_size": 10652869, "dataset_size": 20629809}} | 2023-03-22T13:29:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_quinta-2"
More Information needed | [
"# Dataset Card for \"disc_cla_quinta-2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_quinta-2\"\n\nMore Information needed"
]
| [
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18
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"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_quinta-2\"\n\nMore Information needed"
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|
2ee74199b698ebd72d2959642e6e70c0c2355fb5 | # Dataset Card for "disc_cla_sexta-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_sexta-2 | [
"region:us"
]
| 2023-03-22T13:29:59+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "comision", "dtype": "string"}, {"name": "fecha_gaceta", "dtype": "string"}, {"name": "gaceta_numero", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 15176429, "num_examples": 7591}], "download_size": 7564523, "dataset_size": 15176429}} | 2023-03-22T13:30:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_sexta-2"
More Information needed | [
"# Dataset Card for \"disc_cla_sexta-2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_sexta-2\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_sexta-2\"\n\nMore Information needed"
]
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|
1e8620869df7451874edd56d70393bea4c648b6f | # Dataset Card for "disc_cla_septima-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Sleoruiz/disc_cla_septima-2 | [
"region:us"
]
| 2023-03-22T13:30:23+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "comision", "dtype": "string"}, {"name": "fecha_gaceta", "dtype": "string"}, {"name": "gaceta_numero", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "sequence": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 21725404, "num_examples": 9432}], "download_size": 10861388, "dataset_size": 21725404}} | 2023-03-22T13:30:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "disc_cla_septima-2"
More Information needed | [
"# Dataset Card for \"disc_cla_septima-2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"disc_cla_septima-2\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"disc_cla_septima-2\"\n\nMore Information needed"
]
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|
577cd1ae3d4de3c3115c68f047319b39933792a0 |
# Dataset Card for AIDA CoNLL-YAGO Wikidata
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
## Dataset Description
- **Repository:** [AIDA CoNLL-YAGO Wikidata repository](https://github.com/cyanic-selkie/aida-conll-yago-wikidata)
### Dataset Summary
The AIDA CoNLL-YAGO Wikidata dataset is the same as the original [AIDA CoNLL-YAGO](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ambiverse-nlu/aida/downloads) dataset, but with Wikidata QIDs instead of Wikipedia titles as entity identifiers. They are automatically generated (with a few manual corrections) from Wikidata and Wikipedia dumps (March 1, 2023).
The code for generating the dataset can be found [here](https://github.com/cyanic-selkie/aida-conll-yago-wikidata).
### Supported Tasks
- `named-entity-recognition`: The dataset can be used to train a model for Named Entity Recognition.
- `named-entity-linking`: The dataset can be used to train a model for Named Entity Linking.
### Languages
The text in the dataset is in English. The associated BCP-47 code is `en`.
## Dataset Structure
### Data Instances
A typical data point represents a document (news article).
The `text` field contains the original text in an NFC normalized, UTF-8 encoded string.
The `entities` field contains a list of entities, each represented by a struct with the inclusive starting byte `start` field, exclusive ending byte `end` field, a nullable `qid` field, and a nullable `pageid` field.
Additionally, each document has a unique `document_id` field.
An example from the AIDA CoNLL-YAGO Wikidata test set looks as follows:
```
{
"document_id": 1214,
"text": "RADIO ROMANIA AFTERNOON HEALINES AT 4 PM . BUCHAREST 1996-12-06 Radio Romania news headlines : * The Democratic Convention signed an agreement on government and parliamentary support with its coalition partners the Social Democratic Union and the Hungarian Democratic Union ( UDMR ) . The ceremony was attended by President Emil Constantinescu . * The three parties in the government coalition have committed themselves to a real reform of Romania 's economy , Constantinescu said after the ceremony . * The UDMR wants to contribute to social reform and economic revival in Romania , union leader Marko Bela said . * The international airport in Timisoara and the domestic airports in Arad , Oradea and Sibiu were closed due to fog . -- Bucharest Newsroom 40-1 3120264",
"entities": [
{
"start": 0,
"end": 13,
"tag": "ORG",
"pageid": null,
"qid": null,
"title": null
},
{
"start": 43,
"end": 52,
"tag": "LOC",
"pageid": 36877,
"qid": 19660,
"title": "Bucharest"
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"title": "Democratic_Union_of_Hungarians_in_Romania"
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"title": "Emil_Constantinescu"
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"title": "Romania"
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},
{
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"title": "Béla_Markó"
},
{
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"title": "Timişoara"
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"title": "Arad,_Romania"
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"title": "Oradea_International_Airport"
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}
```
### Data Fields
- `document_id`: an integer that uniquely identifies the document this sentence belongs to
- `sentence_index`: an integer that uniquely identifies the position of the sentence in its original document
- `text`: an NFC normalized, UTF-8 encoded string representing the sentence
- `entities`: a list of structs representing entities, each entity has:
- `start`: an integer representing the inclusive starting UTF-8 code point of the entity
- `end`: an integer representing the exclusive ending UTF-8 code point of the entity
- `tag`: a string representing the entity type (PER, LOC, ORG or MISC)
- `qid`: an integer representing the Wikidata QID this entity refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset
- `pageid`: an integer representing the English Wikipedia's pageID this entity refers to; it can be null if the entity didn't exist in Wikipedia at the time of the creation of the original dataset
- `title`: an NFC normalized, UTF-8 encoded string representing the English Wikipedia's title this entity refers to; it can be null if the entity didn't exist in Wikipedia at the time of the creation of the original dataset
### Data Splits
The data is split into training, validation and test sets; all of the sentences belonging to an article are in the same split. The final split sizes are as follows:
| | Train | Validation | Test |
| :----- | :------: | :-----: | :----: |
| AIDA CoNLL-YAGO Wikidata - documents | 946 | 216 | 231 |
| AIDA CoNLL-YAGO Wikidata - entities | 23,374 | 5,912 | 5,608 |
| AIDA CoNLL-YAGO Wikidata - entities with QIDs | 18,540 | 4,791 | 4,481 |
## Additional Information
### Licensing Information
The licensing status of the dataset is the same as the licensing status of the original [AIDA CoNLL-YAGO](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ambiverse-nlu/aida/downloads) dataset which is under a [Creative Commons Attribution-ShareAlike 3.0 Unported License](http://creativecommons.org/licenses/by-sa/3.0/deed.en_US). | cyanic-selkie/aida-conll-yago-wikidata | [
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-sa-3.0",
"wikidata",
"wikipedia",
"named-entity-recognition",
"named-entity-linking",
"region:us"
]
| 2023-03-22T13:30:44+00:00 | {"language": ["en"], "license": "cc-by-sa-3.0", "size_categories": ["10K<n<100K"], "task_categories": ["token-classification"], "pretty_name": "AIDA CoNLL-YAGO Wikidata", "tags": ["wikidata", "wikipedia", "named-entity-recognition", "named-entity-linking"]} | 2023-06-28T18:01:17+00:00 | []
| [
"en"
]
| TAGS
#task_categories-token-classification #size_categories-10K<n<100K #language-English #license-cc-by-sa-3.0 #wikidata #wikipedia #named-entity-recognition #named-entity-linking #region-us
| Dataset Card for AIDA CoNLL-YAGO Wikidata
=========================================
Table of Contents
-----------------
* Dataset Description
+ Dataset Summary
+ Supported Tasks
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Additional Information
+ Licensing Information
Dataset Description
-------------------
* Repository: AIDA CoNLL-YAGO Wikidata repository
### Dataset Summary
The AIDA CoNLL-YAGO Wikidata dataset is the same as the original AIDA CoNLL-YAGO dataset, but with Wikidata QIDs instead of Wikipedia titles as entity identifiers. They are automatically generated (with a few manual corrections) from Wikidata and Wikipedia dumps (March 1, 2023).
The code for generating the dataset can be found here.
### Supported Tasks
* 'named-entity-recognition': The dataset can be used to train a model for Named Entity Recognition.
* 'named-entity-linking': The dataset can be used to train a model for Named Entity Linking.
### Languages
The text in the dataset is in English. The associated BCP-47 code is 'en'.
Dataset Structure
-----------------
### Data Instances
A typical data point represents a document (news article).
The 'text' field contains the original text in an NFC normalized, UTF-8 encoded string.
The 'entities' field contains a list of entities, each represented by a struct with the inclusive starting byte 'start' field, exclusive ending byte 'end' field, a nullable 'qid' field, and a nullable 'pageid' field.
Additionally, each document has a unique 'document\_id' field.
An example from the AIDA CoNLL-YAGO Wikidata test set looks as follows:
### Data Fields
* 'document\_id': an integer that uniquely identifies the document this sentence belongs to
* 'sentence\_index': an integer that uniquely identifies the position of the sentence in its original document
* 'text': an NFC normalized, UTF-8 encoded string representing the sentence
* 'entities': a list of structs representing entities, each entity has:
+ 'start': an integer representing the inclusive starting UTF-8 code point of the entity
+ 'end': an integer representing the exclusive ending UTF-8 code point of the entity
+ 'tag': a string representing the entity type (PER, LOC, ORG or MISC)
+ 'qid': an integer representing the Wikidata QID this entity refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset
+ 'pageid': an integer representing the English Wikipedia's pageID this entity refers to; it can be null if the entity didn't exist in Wikipedia at the time of the creation of the original dataset
+ 'title': an NFC normalized, UTF-8 encoded string representing the English Wikipedia's title this entity refers to; it can be null if the entity didn't exist in Wikipedia at the time of the creation of the original dataset
### Data Splits
The data is split into training, validation and test sets; all of the sentences belonging to an article are in the same split. The final split sizes are as follows:
Additional Information
----------------------
### Licensing Information
The licensing status of the dataset is the same as the licensing status of the original AIDA CoNLL-YAGO dataset which is under a Creative Commons Attribution-ShareAlike 3.0 Unported License.
| [
"### Dataset Summary\n\n\nThe AIDA CoNLL-YAGO Wikidata dataset is the same as the original AIDA CoNLL-YAGO dataset, but with Wikidata QIDs instead of Wikipedia titles as entity identifiers. They are automatically generated (with a few manual corrections) from Wikidata and Wikipedia dumps (March 1, 2023).\n\n\nThe code for generating the dataset can be found here.",
"### Supported Tasks\n\n\n* 'named-entity-recognition': The dataset can be used to train a model for Named Entity Recognition.\n* 'named-entity-linking': The dataset can be used to train a model for Named Entity Linking.",
"### Languages\n\n\nThe text in the dataset is in English. The associated BCP-47 code is 'en'.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA typical data point represents a document (news article).\n\n\nThe 'text' field contains the original text in an NFC normalized, UTF-8 encoded string.\n\n\nThe 'entities' field contains a list of entities, each represented by a struct with the inclusive starting byte 'start' field, exclusive ending byte 'end' field, a nullable 'qid' field, and a nullable 'pageid' field.\n\n\nAdditionally, each document has a unique 'document\\_id' field.\n\n\nAn example from the AIDA CoNLL-YAGO Wikidata test set looks as follows:",
"### Data Fields\n\n\n* 'document\\_id': an integer that uniquely identifies the document this sentence belongs to\n* 'sentence\\_index': an integer that uniquely identifies the position of the sentence in its original document\n* 'text': an NFC normalized, UTF-8 encoded string representing the sentence\n* 'entities': a list of structs representing entities, each entity has:\n\t+ 'start': an integer representing the inclusive starting UTF-8 code point of the entity\n\t+ 'end': an integer representing the exclusive ending UTF-8 code point of the entity\n\t+ 'tag': a string representing the entity type (PER, LOC, ORG or MISC)\n\t+ 'qid': an integer representing the Wikidata QID this entity refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n\t+ 'pageid': an integer representing the English Wikipedia's pageID this entity refers to; it can be null if the entity didn't exist in Wikipedia at the time of the creation of the original dataset\n\t+ 'title': an NFC normalized, UTF-8 encoded string representing the English Wikipedia's title this entity refers to; it can be null if the entity didn't exist in Wikipedia at the time of the creation of the original dataset",
"### Data Splits\n\n\nThe data is split into training, validation and test sets; all of the sentences belonging to an article are in the same split. The final split sizes are as follows:\n\n\n\nAdditional Information\n----------------------",
"### Licensing Information\n\n\nThe licensing status of the dataset is the same as the licensing status of the original AIDA CoNLL-YAGO dataset which is under a Creative Commons Attribution-ShareAlike 3.0 Unported License."
]
| [
"TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #language-English #license-cc-by-sa-3.0 #wikidata #wikipedia #named-entity-recognition #named-entity-linking #region-us \n",
"### Dataset Summary\n\n\nThe AIDA CoNLL-YAGO Wikidata dataset is the same as the original AIDA CoNLL-YAGO dataset, but with Wikidata QIDs instead of Wikipedia titles as entity identifiers. They are automatically generated (with a few manual corrections) from Wikidata and Wikipedia dumps (March 1, 2023).\n\n\nThe code for generating the dataset can be found here.",
"### Supported Tasks\n\n\n* 'named-entity-recognition': The dataset can be used to train a model for Named Entity Recognition.\n* 'named-entity-linking': The dataset can be used to train a model for Named Entity Linking.",
"### Languages\n\n\nThe text in the dataset is in English. The associated BCP-47 code is 'en'.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA typical data point represents a document (news article).\n\n\nThe 'text' field contains the original text in an NFC normalized, UTF-8 encoded string.\n\n\nThe 'entities' field contains a list of entities, each represented by a struct with the inclusive starting byte 'start' field, exclusive ending byte 'end' field, a nullable 'qid' field, and a nullable 'pageid' field.\n\n\nAdditionally, each document has a unique 'document\\_id' field.\n\n\nAn example from the AIDA CoNLL-YAGO Wikidata test set looks as follows:",
"### Data Fields\n\n\n* 'document\\_id': an integer that uniquely identifies the document this sentence belongs to\n* 'sentence\\_index': an integer that uniquely identifies the position of the sentence in its original document\n* 'text': an NFC normalized, UTF-8 encoded string representing the sentence\n* 'entities': a list of structs representing entities, each entity has:\n\t+ 'start': an integer representing the inclusive starting UTF-8 code point of the entity\n\t+ 'end': an integer representing the exclusive ending UTF-8 code point of the entity\n\t+ 'tag': a string representing the entity type (PER, LOC, ORG or MISC)\n\t+ 'qid': an integer representing the Wikidata QID this entity refers to; it can be null if the entity didn't exist in Wikidata at the time of the creation of the original dataset\n\t+ 'pageid': an integer representing the English Wikipedia's pageID this entity refers to; it can be null if the entity didn't exist in Wikipedia at the time of the creation of the original dataset\n\t+ 'title': an NFC normalized, UTF-8 encoded string representing the English Wikipedia's title this entity refers to; it can be null if the entity didn't exist in Wikipedia at the time of the creation of the original dataset",
"### Data Splits\n\n\nThe data is split into training, validation and test sets; all of the sentences belonging to an article are in the same split. The final split sizes are as follows:\n\n\n\nAdditional Information\n----------------------",
"### Licensing Information\n\n\nThe licensing status of the dataset is the same as the licensing status of the original AIDA CoNLL-YAGO dataset which is under a Creative Commons Attribution-ShareAlike 3.0 Unported License."
]
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| [
"passage: TAGS\n#task_categories-token-classification #size_categories-10K<n<100K #language-English #license-cc-by-sa-3.0 #wikidata #wikipedia #named-entity-recognition #named-entity-linking #region-us \n### Dataset Summary\n\n\nThe AIDA CoNLL-YAGO Wikidata dataset is the same as the original AIDA CoNLL-YAGO dataset, but with Wikidata QIDs instead of Wikipedia titles as entity identifiers. They are automatically generated (with a few manual corrections) from Wikidata and Wikipedia dumps (March 1, 2023).\n\n\nThe code for generating the dataset can be found here.### Supported Tasks\n\n\n* 'named-entity-recognition': The dataset can be used to train a model for Named Entity Recognition.\n* 'named-entity-linking': The dataset can be used to train a model for Named Entity Linking.### Languages\n\n\nThe text in the dataset is in English. The associated BCP-47 code is 'en'.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA typical data point represents a document (news article).\n\n\nThe 'text' field contains the original text in an NFC normalized, UTF-8 encoded string.\n\n\nThe 'entities' field contains a list of entities, each represented by a struct with the inclusive starting byte 'start' field, exclusive ending byte 'end' field, a nullable 'qid' field, and a nullable 'pageid' field.\n\n\nAdditionally, each document has a unique 'document\\_id' field.\n\n\nAn example from the AIDA CoNLL-YAGO Wikidata test set looks as follows:"
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|
6e1a0783042fc8a08f6c600c4eb33350d3efa143 | Generated from https://huggingface.co/datasets/michaelthwan/oa_wiki_qa_bart_10000row
Code: [open-assistant git](https://github.com/LAION-AI/Open-Assistant/tree/main/data/datasets)
-> data/datasets/bart_searchgpt_wiki_nlp_augment
---
license: mit
---
| michaelthwan/oa_wiki_qa_bart_10000row | [
"region:us"
]
| 2023-03-22T13:47:06+00:00 | {} | 2023-03-22T13:50:20+00:00 | []
| []
| TAGS
#region-us
| Generated from URL
Code: open-assistant git
-> data/datasets/bart_searchgpt_wiki_nlp_augment
---
license: mit
---
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
8bc340720aa66759fc2545ba1aee0e43e2517b6a |
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:** [https://satinbenchmark.github.io](https://satinbenchmark.github.io)
- **Repository:**
- **Paper:** [SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models](https://arxiv.org/pdf/2304.11619.pdf)
- **Leaderboard:** [SATIN Leaderboard](https://satinbenchmark.github.io/leaderboard.md)
### Dataset Summary
SATIN (SATellite ImageNet) is a metadataset containing 27 constituent satellite and aerial image datasets spanning 6 distinct tasks: Land Cover, Land Use,
Hierarchical Land Use, Complex Scenes, Rare Scenes, and False Colour Scenes. The imagery is globally distributed, comprised of resolutions spanning 5 orders
of magnitude, multiple fields of view sizes, and over 250 distinct class labels. Presented at ICCV '23 TNGCV Workshop.
## Dataset Structure
The SATIN benchmark is comprised of the following datasets:
#### Task 1: Land Cover
- SAT-4
- SAT-6
- NASC-TG2
#### Task 2: Land Use
- WHU-RS19
- RSSCN7
- RS_C11
- SIRI-WHU
- EuroSAT
- NWPU-RESISC45
- PatternNet
- RSD46-WHU
- GID
- CLRS
- Optimal-31
#### Task 3: Hierarchical Land Use
- Million-AID
- RSI-CB256
#### Task 4: Complex Scenes
- UC_Merced_LandUse_MultiLabel
- MLRSNet
- MultiScene
- AID_MultiLabel
#### Task 5: Rare Scenes
- Airbus-Wind-Turbines-Patches
- USTC_SmokeRS
- Canadian_Cropland
- Ships-In-Satellite-Imagery
- Satellite-Images-of-Hurricane-Damage
#### Task 6: False Colour Scenes
- Brazilian_Coffee_Scenes
- Brazilian_Cerrado-Savanna_Scenes
For ease of use and to avoid having to download the entire benchmark for each use, in this dataset repository, each of the 27 datasets is included as a separate
'config'.
### Example Usage
```python
from datasets import load_dataset
hf_dataset = load_dataset('jonathan-roberts1/SATIN', DATASET_NAME, split='train') # for DATASET_NAME use one of the configs listed above (e.g., EuroSAT)
features = hf_dataset.features
class_labels = features['label'].names
#class_labels = features['label'].feature.names # for the Complex Scenes datasets
#class_labels_1 = features['label_1'].names # for the Hierarchical Land Use datasets, the label field is replaced with label_1, label_2, ...
random_index = 5
example = hf_dataset[random_index]
image, label = example['image'], example['label']
```
### Data Splits
For each config, there is just the single, default 'train' split.
### Source Data
More information regarding the source data can be found in our paper. Additionally, each of the constituent datasets have been uploaded to HuggingFace datasets.
They can be accessed at: huggingface.co/datasets/jonathan-roberts1/DATASET_NAME.
### Dataset Curators
This dataset was curated by Jonathan Roberts, Kai Han, and Samuel Albanie
### Licensing Information
As SATIN is comprised of existing datasets with differing licenses, there is not a single license for SATIN. All of the datasets in SATIN can be used
for research purposes; usage information of specific constituent datasets can be found in the Appendix of our paper.
### Citation Information
```
@article{roberts2023satin,
title = {SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models},
author = {Jonathan Roberts, Kai Han, and Samuel Albanie},
year = {2023},
eprint = {2304.11619},
archivePrefix= {arXiv},
primaryClass = {cs.CV}
}
``` | jonathan-roberts1/SATIN | [
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"size_categories:100K<n<1M",
"language:en",
"license:other",
"arxiv:2304.11619",
"region:us"
]
| 2023-03-22T15:10:38+00:00 | {"language": ["en"], "license": "other", "size_categories": ["100K<n<1M"], "task_categories": ["image-classification", "zero-shot-image-classification"], "pretty_name": "SATellite ImageNet", "configs": [{"config_name": "SAT-4"}, {"config_name": "SAT-6"}, {"config_name": "NASC-TG2"}, {"config_name": "WHU-RS19"}, {"config_name": "RSSCN7"}, {"config_name": "RS_C11"}, {"config_name": "SIRI-WHU"}, {"config_name": "EuroSAT"}, {"config_name": "NWPU-RESISC45"}, {"config_name": "PatternNet"}, {"config_name": "RSD46-WHU"}, {"config_name": "GID"}, {"config_name": "CLRS"}, {"config_name": "Optimal-31"}, {"config_name": "Airbus-Wind-Turbines-Patches"}, {"config_name": "USTC_SmokeRS"}, {"config_name": "Canadian_Cropland"}, {"config_name": "Ships-In-Satellite-Imagery"}, {"config_name": "Satellite-Images-of-Hurricane-Damage"}, {"config_name": "Brazilian_Coffee_Scenes"}, {"config_name": "Brazilian_Cerrado-Savanna_Scenes"}, {"config_name": "Million-AID"}, {"config_name": "UC_Merced_LandUse_MultiLabel"}, {"config_name": "MLRSNet"}, {"config_name": "MultiScene"}, {"config_name": "RSI-CB256"}, {"config_name": "AID_MultiLabel"}]} | 2023-11-06T10:57:50+00:00 | [
"2304.11619"
]
| [
"en"
]
| TAGS
#task_categories-image-classification #task_categories-zero-shot-image-classification #size_categories-100K<n<1M #language-English #license-other #arxiv-2304.11619 #region-us
|
# Dataset Card for Dataset Name
## Dataset Description
- Homepage: URL
- Repository:
- Paper: SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models
- Leaderboard: SATIN Leaderboard
### Dataset Summary
SATIN (SATellite ImageNet) is a metadataset containing 27 constituent satellite and aerial image datasets spanning 6 distinct tasks: Land Cover, Land Use,
Hierarchical Land Use, Complex Scenes, Rare Scenes, and False Colour Scenes. The imagery is globally distributed, comprised of resolutions spanning 5 orders
of magnitude, multiple fields of view sizes, and over 250 distinct class labels. Presented at ICCV '23 TNGCV Workshop.
## Dataset Structure
The SATIN benchmark is comprised of the following datasets:
#### Task 1: Land Cover
- SAT-4
- SAT-6
- NASC-TG2
#### Task 2: Land Use
- WHU-RS19
- RSSCN7
- RS_C11
- SIRI-WHU
- EuroSAT
- NWPU-RESISC45
- PatternNet
- RSD46-WHU
- GID
- CLRS
- Optimal-31
#### Task 3: Hierarchical Land Use
- Million-AID
- RSI-CB256
#### Task 4: Complex Scenes
- UC_Merced_LandUse_MultiLabel
- MLRSNet
- MultiScene
- AID_MultiLabel
#### Task 5: Rare Scenes
- Airbus-Wind-Turbines-Patches
- USTC_SmokeRS
- Canadian_Cropland
- Ships-In-Satellite-Imagery
- Satellite-Images-of-Hurricane-Damage
#### Task 6: False Colour Scenes
- Brazilian_Coffee_Scenes
- Brazilian_Cerrado-Savanna_Scenes
For ease of use and to avoid having to download the entire benchmark for each use, in this dataset repository, each of the 27 datasets is included as a separate
'config'.
### Example Usage
### Data Splits
For each config, there is just the single, default 'train' split.
### Source Data
More information regarding the source data can be found in our paper. Additionally, each of the constituent datasets have been uploaded to HuggingFace datasets.
They can be accessed at: URL
### Dataset Curators
This dataset was curated by Jonathan Roberts, Kai Han, and Samuel Albanie
### Licensing Information
As SATIN is comprised of existing datasets with differing licenses, there is not a single license for SATIN. All of the datasets in SATIN can be used
for research purposes; usage information of specific constituent datasets can be found in the Appendix of our paper.
| [
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models\n- Leaderboard: SATIN Leaderboard",
"### Dataset Summary\n\nSATIN (SATellite ImageNet) is a metadataset containing 27 constituent satellite and aerial image datasets spanning 6 distinct tasks: Land Cover, Land Use, \nHierarchical Land Use, Complex Scenes, Rare Scenes, and False Colour Scenes. The imagery is globally distributed, comprised of resolutions spanning 5 orders \nof magnitude, multiple fields of view sizes, and over 250 distinct class labels. Presented at ICCV '23 TNGCV Workshop.",
"## Dataset Structure\n\nThe SATIN benchmark is comprised of the following datasets:",
"#### Task 1: Land Cover\n- SAT-4\n- SAT-6\n- NASC-TG2",
"#### Task 2: Land Use\n- WHU-RS19\n- RSSCN7\n- RS_C11\n- SIRI-WHU\n- EuroSAT\n- NWPU-RESISC45\n- PatternNet\n- RSD46-WHU\n- GID\n- CLRS\n- Optimal-31",
"#### Task 3: Hierarchical Land Use\n- Million-AID\n- RSI-CB256",
"#### Task 4: Complex Scenes\n- UC_Merced_LandUse_MultiLabel\n- MLRSNet\n- MultiScene\n- AID_MultiLabel",
"#### Task 5: Rare Scenes\n- Airbus-Wind-Turbines-Patches\n- USTC_SmokeRS\n- Canadian_Cropland\n- Ships-In-Satellite-Imagery\n- Satellite-Images-of-Hurricane-Damage",
"#### Task 6: False Colour Scenes\n- Brazilian_Coffee_Scenes\n- Brazilian_Cerrado-Savanna_Scenes\n\nFor ease of use and to avoid having to download the entire benchmark for each use, in this dataset repository, each of the 27 datasets is included as a separate \n'config'.",
"### Example Usage",
"### Data Splits\n\nFor each config, there is just the single, default 'train' split.",
"### Source Data\n\nMore information regarding the source data can be found in our paper. Additionally, each of the constituent datasets have been uploaded to HuggingFace datasets.\nThey can be accessed at: URL",
"### Dataset Curators\n\nThis dataset was curated by Jonathan Roberts, Kai Han, and Samuel Albanie",
"### Licensing Information\n\nAs SATIN is comprised of existing datasets with differing licenses, there is not a single license for SATIN. All of the datasets in SATIN can be used \nfor research purposes; usage information of specific constituent datasets can be found in the Appendix of our paper."
]
| [
"TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #size_categories-100K<n<1M #language-English #license-other #arxiv-2304.11619 #region-us \n",
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models\n- Leaderboard: SATIN Leaderboard",
"### Dataset Summary\n\nSATIN (SATellite ImageNet) is a metadataset containing 27 constituent satellite and aerial image datasets spanning 6 distinct tasks: Land Cover, Land Use, \nHierarchical Land Use, Complex Scenes, Rare Scenes, and False Colour Scenes. The imagery is globally distributed, comprised of resolutions spanning 5 orders \nof magnitude, multiple fields of view sizes, and over 250 distinct class labels. Presented at ICCV '23 TNGCV Workshop.",
"## Dataset Structure\n\nThe SATIN benchmark is comprised of the following datasets:",
"#### Task 1: Land Cover\n- SAT-4\n- SAT-6\n- NASC-TG2",
"#### Task 2: Land Use\n- WHU-RS19\n- RSSCN7\n- RS_C11\n- SIRI-WHU\n- EuroSAT\n- NWPU-RESISC45\n- PatternNet\n- RSD46-WHU\n- GID\n- CLRS\n- Optimal-31",
"#### Task 3: Hierarchical Land Use\n- Million-AID\n- RSI-CB256",
"#### Task 4: Complex Scenes\n- UC_Merced_LandUse_MultiLabel\n- MLRSNet\n- MultiScene\n- AID_MultiLabel",
"#### Task 5: Rare Scenes\n- Airbus-Wind-Turbines-Patches\n- USTC_SmokeRS\n- Canadian_Cropland\n- Ships-In-Satellite-Imagery\n- Satellite-Images-of-Hurricane-Damage",
"#### Task 6: False Colour Scenes\n- Brazilian_Coffee_Scenes\n- Brazilian_Cerrado-Savanna_Scenes\n\nFor ease of use and to avoid having to download the entire benchmark for each use, in this dataset repository, each of the 27 datasets is included as a separate \n'config'.",
"### Example Usage",
"### Data Splits\n\nFor each config, there is just the single, default 'train' split.",
"### Source Data\n\nMore information regarding the source data can be found in our paper. Additionally, each of the constituent datasets have been uploaded to HuggingFace datasets.\nThey can be accessed at: URL",
"### Dataset Curators\n\nThis dataset was curated by Jonathan Roberts, Kai Han, and Samuel Albanie",
"### Licensing Information\n\nAs SATIN is comprised of existing datasets with differing licenses, there is not a single license for SATIN. All of the datasets in SATIN can be used \nfor research purposes; usage information of specific constituent datasets can be found in the Appendix of our paper."
]
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"passage: TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #size_categories-100K<n<1M #language-English #license-other #arxiv-2304.11619 #region-us \n# Dataset Card for Dataset Name## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models\n- Leaderboard: SATIN Leaderboard### Dataset Summary\n\nSATIN (SATellite ImageNet) is a metadataset containing 27 constituent satellite and aerial image datasets spanning 6 distinct tasks: Land Cover, Land Use, \nHierarchical Land Use, Complex Scenes, Rare Scenes, and False Colour Scenes. The imagery is globally distributed, comprised of resolutions spanning 5 orders \nof magnitude, multiple fields of view sizes, and over 250 distinct class labels. Presented at ICCV '23 TNGCV Workshop.## Dataset Structure\n\nThe SATIN benchmark is comprised of the following datasets:#### Task 1: Land Cover\n- SAT-4\n- SAT-6\n- NASC-TG2#### Task 2: Land Use\n- WHU-RS19\n- RSSCN7\n- RS_C11\n- SIRI-WHU\n- EuroSAT\n- NWPU-RESISC45\n- PatternNet\n- RSD46-WHU\n- GID\n- CLRS\n- Optimal-31#### Task 3: Hierarchical Land Use\n- Million-AID\n- RSI-CB256#### Task 4: Complex Scenes\n- UC_Merced_LandUse_MultiLabel\n- MLRSNet\n- MultiScene\n- AID_MultiLabel#### Task 5: Rare Scenes\n- Airbus-Wind-Turbines-Patches\n- USTC_SmokeRS\n- Canadian_Cropland\n- Ships-In-Satellite-Imagery\n- Satellite-Images-of-Hurricane-Damage"
]
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|
50e4e8f8de089bf40950ee043ebff51ad33b3ecb | # Dataset Card for Banc Trawsgrifiadau Bangor
This dataset is a bank of 20 hours 6 minutes and 49 seconds of segments of natural speech from over 50 contributors in mp3 file format, together with corresponding 'verbatim' transcripts of the speech in .tsv file format. The majority of the speech is spontaneous, natural speech. The dataset was distributed by Canolfan Bedwyr under a CC0 open license. The original dataset can be found here: [link](https://git.techiaith.bangor.ac.uk/data-porth-technolegau-iaith/banc-trawsgrifiadau-bangor).
## Data Fields
`audio_filename` (`string`): The name of the audio file within the 'clips' folder
`audio_filesize` (`int64`): The size of the file
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`transcript` (`string`): The transcript of the audio clip
`duration` (`duration[ms]`): Duration of the clip in milliseconds
## Licensing Information
The dataset was created by Canolfan Bedwyr, partly funded by the Welsh Government, and released under
[Creative Commons Zero v.1.0 Universal](https://git.techiaith.bangor.ac.uk/data-porth-technolegau-iaith/banc-trawsgrifiadau-bangor/-/blob/master/LICENSE) | prvInSpace/banc-trawsgrifiadau-bangor | [
"task_categories:automatic-speech-recognition",
"size_categories:10K<n<100K",
"language:cy",
"license:cc0-1.0",
"region:us"
]
| 2023-03-22T15:11:54+00:00 | {"language": ["cy"], "license": "cc0-1.0", "size_categories": ["10K<n<100K"], "task_categories": ["automatic-speech-recognition"]} | 2023-03-22T21:21:53+00:00 | []
| [
"cy"
]
| TAGS
#task_categories-automatic-speech-recognition #size_categories-10K<n<100K #language-Welsh #license-cc0-1.0 #region-us
| # Dataset Card for Banc Trawsgrifiadau Bangor
This dataset is a bank of 20 hours 6 minutes and 49 seconds of segments of natural speech from over 50 contributors in mp3 file format, together with corresponding 'verbatim' transcripts of the speech in .tsv file format. The majority of the speech is spontaneous, natural speech. The dataset was distributed by Canolfan Bedwyr under a CC0 open license. The original dataset can be found here: link.
## Data Fields
'audio_filename' ('string'): The name of the audio file within the 'clips' folder
'audio_filesize' ('int64'): The size of the file
'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0]["audio"]' the audio file is automatically decoded and resampled to 'dataset.features["audio"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '"audio"' column, *i.e.* 'dataset[0]["audio"]' should always be preferred over 'dataset["audio"][0]'.
'transcript' ('string'): The transcript of the audio clip
'duration' ('duration[ms]'): Duration of the clip in milliseconds
## Licensing Information
The dataset was created by Canolfan Bedwyr, partly funded by the Welsh Government, and released under
Creative Commons Zero v.1.0 Universal | [
"# Dataset Card for Banc Trawsgrifiadau Bangor\n\nThis dataset is a bank of 20 hours 6 minutes and 49 seconds of segments of natural speech from over 50 contributors in mp3 file format, together with corresponding 'verbatim' transcripts of the speech in .tsv file format. The majority of the speech is spontaneous, natural speech. The dataset was distributed by Canolfan Bedwyr under a CC0 open license. The original dataset can be found here: link.",
"## Data Fields\n\n'audio_filename' ('string'): The name of the audio file within the 'clips' folder\n\n'audio_filesize' ('int64'): The size of the file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'transcript' ('string'): The transcript of the audio clip\n\n'duration' ('duration[ms]'): Duration of the clip in milliseconds",
"## Licensing Information\n\nThe dataset was created by Canolfan Bedwyr, partly funded by the Welsh Government, and released under\nCreative Commons Zero v.1.0 Universal"
]
| [
"TAGS\n#task_categories-automatic-speech-recognition #size_categories-10K<n<100K #language-Welsh #license-cc0-1.0 #region-us \n",
"# Dataset Card for Banc Trawsgrifiadau Bangor\n\nThis dataset is a bank of 20 hours 6 minutes and 49 seconds of segments of natural speech from over 50 contributors in mp3 file format, together with corresponding 'verbatim' transcripts of the speech in .tsv file format. The majority of the speech is spontaneous, natural speech. The dataset was distributed by Canolfan Bedwyr under a CC0 open license. The original dataset can be found here: link.",
"## Data Fields\n\n'audio_filename' ('string'): The name of the audio file within the 'clips' folder\n\n'audio_filesize' ('int64'): The size of the file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'transcript' ('string'): The transcript of the audio clip\n\n'duration' ('duration[ms]'): Duration of the clip in milliseconds",
"## Licensing Information\n\nThe dataset was created by Canolfan Bedwyr, partly funded by the Welsh Government, and released under\nCreative Commons Zero v.1.0 Universal"
]
| [
48,
105,
262,
35
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| [
"passage: TAGS\n#task_categories-automatic-speech-recognition #size_categories-10K<n<100K #language-Welsh #license-cc0-1.0 #region-us \n# Dataset Card for Banc Trawsgrifiadau Bangor\n\nThis dataset is a bank of 20 hours 6 minutes and 49 seconds of segments of natural speech from over 50 contributors in mp3 file format, together with corresponding 'verbatim' transcripts of the speech in .tsv file format. The majority of the speech is spontaneous, natural speech. The dataset was distributed by Canolfan Bedwyr under a CC0 open license. The original dataset can be found here: link.## Data Fields\n\n'audio_filename' ('string'): The name of the audio file within the 'clips' folder\n\n'audio_filesize' ('int64'): The size of the file\n\n'audio' ('dict'): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0][\"audio\"]' the audio file is automatically decoded and resampled to 'dataset.features[\"audio\"].sampling_rate'. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the '\"audio\"' column, *i.e.* 'dataset[0][\"audio\"]' should always be preferred over 'dataset[\"audio\"][0]'.\n\n'transcript' ('string'): The transcript of the audio clip\n\n'duration' ('duration[ms]'): Duration of the clip in milliseconds## Licensing Information\n\nThe dataset was created by Canolfan Bedwyr, partly funded by the Welsh Government, and released under\nCreative Commons Zero v.1.0 Universal"
]
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|
c34331b5c52bce660d9e5e4227cb96669c9122c4 |
# Dataset Card for Alpaca MT
## Dataset Description
- **Homepage:** https://crfm.stanford.edu/2023/03/13/alpaca.html
- **Repository:** https://github.com/juletx/alpaca-lora-mt
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** Rohan Taori
### Dataset Summary
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. This dataset also includes machine-translated data for 6 Iberian languages: Portuguese, Spanish, Catalan, Basque, Galician and Asturian. Translation was done using NLLB-200 3.3B model.
The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications:
- The `text-davinci-003` engine to generate the instruction data instead of `davinci`.
- A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`.
- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.
This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).
In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl).
### Supported Tasks and Leaderboards
The Alpaca dataset designed for instruction training pretrained language models.
### Languages
The original data in Alpaca is in English (BCP-47 en). We also provide machine-translated data for 6 Iberian languages: Portuguese (BCP-47 pt), Spanish (BCP-47 es), Catalan (BCP-47 ca), Basque (BCP-47 eu), Galician (BCP-47 gl) and Asturian (BCP-47 at).
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```json
{
"instruction": "Create a classification task by clustering the given list of items.",
"input": "Apples, oranges, bananas, strawberries, pineapples",
"output": "Class 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
"text": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\nCreate a classification task by clustering the given list of items.\n\n### Input:\nApples, oranges, bananas, strawberries, pineapples\n\n### Response:\nClass 1: Apples, Oranges\nClass 2: Bananas, Strawberries\nClass 3: Pineapples",
}
```
### Data Fields
The data fields are as follows:
* `instruction`: describes the task the model should perform. Each of the 52K instructions is unique.
* `input`: optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input.
* `output`: the answer to the instruction as generated by `text-davinci-003`.
* `text`: the `instruction`, `input` and `output` formatted with the [prompt template](https://github.com/tatsu-lab/stanford_alpaca#data-release) used by the authors for fine-tuning their models.
### Data Splits
| | train |
|---------------|------:|
| en | 52002 |
| pt | 52002 |
| es | 52002 |
| ca | 52002 |
| eu | 52002 |
| gl | 52002 |
| at | 52002 |
## 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
Excerpt the [blog post](https://crfm.stanford.edu/2023/03/13/alpaca.html) accompanying the release of this dataset:
> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
The `alpaca` data is generated by a language model (`text-davinci-003`) and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The dataset is available under the [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).
### Citation Information
```
@misc{alpaca,
author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
title = {Stanford Alpaca: An Instruction-following LLaMA model},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
}
```
### Contributions
[More Information Needed] | HiTZ/alpaca_mt | [
"task_categories:text-generation",
"task_ids:dialogue-modeling",
"annotations_creators:no-annotation",
"language_creators:machine-generated",
"multilinguality:multilingual",
"multilinguality:translation",
"size_categories:10K<n<100K",
"source_datasets:tatsu-lab/alpaca",
"language:en",
"language:pt",
"language:es",
"language:ca",
"language:eu",
"language:gl",
"language:at",
"license:cc-by-nc-4.0",
"instruction-finetuning",
"region:us"
]
| 2023-03-22T15:27:30+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["machine-generated"], "language": ["en", "pt", "es", "ca", "eu", "gl", "at"], "license": "cc-by-nc-4.0", "multilinguality": ["multilingual", "translation"], "size_categories": ["10K<n<100K"], "source_datasets": ["tatsu-lab/alpaca"], "task_categories": ["text-generation"], "task_ids": ["dialogue-modeling"], "pretty_name": "Alpaca MT", "tags": ["instruction-finetuning"], "dataset_info": [{"config_name": "en", "features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32088854, "num_examples": 51942}], "download_size": 22764890, "dataset_size": 32088854}, {"config_name": "pt", "features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 33600380, "num_examples": 51942}], "download_size": 23513483, "dataset_size": 33600380}, {"config_name": "es", "features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35893136, "num_examples": 51942}], "download_size": 24483751, "dataset_size": 35893136}, {"config_name": "ca", "features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 33938638, "num_examples": 51942}], "download_size": 23096222, "dataset_size": 33938638}, {"config_name": "eu", "features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 29977672, "num_examples": 51942}], "download_size": 20469814, "dataset_size": 29977672}, {"config_name": "gl", "features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 32736710, "num_examples": 51942}], "download_size": 22356802, "dataset_size": 32736710}, {"config_name": "at", "features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 31487842, "num_examples": 51942}], "download_size": 20688305, "dataset_size": 31487842}]} | 2023-04-07T14:15:55+00:00 | []
| [
"en",
"pt",
"es",
"ca",
"eu",
"gl",
"at"
]
| TAGS
#task_categories-text-generation #task_ids-dialogue-modeling #annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-multilingual #multilinguality-translation #size_categories-10K<n<100K #source_datasets-tatsu-lab/alpaca #language-English #language-Portuguese #language-Spanish #language-Catalan #language-Basque #language-Galician #language-at #license-cc-by-nc-4.0 #instruction-finetuning #region-us
| Dataset Card for Alpaca MT
==========================
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper:
* Leaderboard:
* Point of Contact: Rohan Taori
### Dataset Summary
Alpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. This dataset also includes machine-translated data for 6 Iberian languages: Portuguese, Spanish, Catalan, Basque, Galician and Asturian. Translation was done using NLLB-200 3.3B model.
The authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:
* The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.
* A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.
* Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
* The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
* Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.
This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).
In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct.
### Supported Tasks and Leaderboards
The Alpaca dataset designed for instruction training pretrained language models.
### Languages
The original data in Alpaca is in English (BCP-47 en). We also provide machine-translated data for 6 Iberian languages: Portuguese (BCP-47 pt), Spanish (BCP-47 es), Catalan (BCP-47 ca), Basque (BCP-47 eu), Galician (BCP-47 gl) and Asturian (BCP-47 at).
Dataset Structure
-----------------
### Data Instances
An example of "train" looks as follows:
### Data Fields
The data fields are as follows:
* 'instruction': describes the task the model should perform. Each of the 52K instructions is unique.
* 'input': optional context or input for the task. For example, when the instruction is "Summarize the following article", the input is the article. Around 40% of the examples have an input.
* 'output': the answer to the instruction as generated by 'text-davinci-003'.
* 'text': the 'instruction', 'input' and 'output' formatted with the prompt template used by the authors for fine-tuning their models.
### 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
Excerpt the blog post accompanying the release of this dataset:
>
> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.
>
>
>
### Discussion of Biases
### Other Known Limitations
The 'alpaca' data is generated by a language model ('text-davinci-003') and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.
Additional Information
----------------------
### Dataset Curators
### Licensing Information
The dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).
### Contributions
| [
"### Dataset Summary\n\n\nAlpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. This dataset also includes machine-translated data for 6 Iberian languages: Portuguese, Spanish, Catalan, Basque, Galician and Asturian. Translation was done using NLLB-200 3.3B model.\n\n\nThe authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:\n\n\n* The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.\n* A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.\n* Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.\n* The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.\n* Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.\n\n\nThis produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).\nIn a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct.",
"### Supported Tasks and Leaderboards\n\n\nThe Alpaca dataset designed for instruction training pretrained language models.",
"### Languages\n\n\nThe original data in Alpaca is in English (BCP-47 en). We also provide machine-translated data for 6 Iberian languages: Portuguese (BCP-47 pt), Spanish (BCP-47 es), Catalan (BCP-47 ca), Basque (BCP-47 eu), Galician (BCP-47 gl) and Asturian (BCP-47 at).\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example of \"train\" looks as follows:",
"### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'instruction': describes the task the model should perform. Each of the 52K instructions is unique.\n* 'input': optional context or input for the task. For example, when the instruction is \"Summarize the following article\", the input is the article. Around 40% of the examples have an input.\n* 'output': the answer to the instruction as generated by 'text-davinci-003'.\n* 'text': the 'instruction', 'input' and 'output' formatted with the prompt template used by the authors for fine-tuning their models.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### 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\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nExcerpt the blog post accompanying the release of this dataset:\n\n\n\n> \n> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.\n> \n> \n>",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nThe 'alpaca' data is generated by a language model ('text-davinci-003') and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nThe dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).",
"### Contributions"
]
| [
"TAGS\n#task_categories-text-generation #task_ids-dialogue-modeling #annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-multilingual #multilinguality-translation #size_categories-10K<n<100K #source_datasets-tatsu-lab/alpaca #language-English #language-Portuguese #language-Spanish #language-Catalan #language-Basque #language-Galician #language-at #license-cc-by-nc-4.0 #instruction-finetuning #region-us \n",
"### Dataset Summary\n\n\nAlpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. This dataset also includes machine-translated data for 6 Iberian languages: Portuguese, Spanish, Catalan, Basque, Galician and Asturian. Translation was done using NLLB-200 3.3B model.\n\n\nThe authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:\n\n\n* The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.\n* A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.\n* Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.\n* The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.\n* Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.\n\n\nThis produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).\nIn a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct.",
"### Supported Tasks and Leaderboards\n\n\nThe Alpaca dataset designed for instruction training pretrained language models.",
"### Languages\n\n\nThe original data in Alpaca is in English (BCP-47 en). We also provide machine-translated data for 6 Iberian languages: Portuguese (BCP-47 pt), Spanish (BCP-47 es), Catalan (BCP-47 ca), Basque (BCP-47 eu), Galician (BCP-47 gl) and Asturian (BCP-47 at).\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nAn example of \"train\" looks as follows:",
"### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'instruction': describes the task the model should perform. Each of the 52K instructions is unique.\n* 'input': optional context or input for the task. For example, when the instruction is \"Summarize the following article\", the input is the article. Around 40% of the examples have an input.\n* 'output': the answer to the instruction as generated by 'text-davinci-003'.\n* 'text': the 'instruction', 'input' and 'output' formatted with the prompt template used by the authors for fine-tuning their models.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### 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\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nExcerpt the blog post accompanying the release of this dataset:\n\n\n\n> \n> We believe that releasing the above assets will enable the academic community to perform controlled scientific studies on instruction-following language models, resulting in better science and ultimately new techniques to address the existing deficiencies with these models. At the same time, any release carries some risk. First, we recognize that releasing our training recipe reveals the feasibility of certain capabilities. On one hand, this enables more people (including bad actors) to create models that could cause harm (either intentionally or not). On the other hand, this awareness might incentivize swift defensive action, especially from the academic community, now empowered by the means to perform deeper safety research on such models. Overall, we believe that the benefits for the research community outweigh the risks of this particular release. Given that we are releasing the training recipe, we believe that releasing the data, model weights, and training code incur minimal further risk, given the simplicity of the recipe. At the same time, releasing these assets has enormous benefits for reproducible science, so that the academic community can use standard datasets, models, and code to perform controlled comparisons and to explore extensions. Deploying an interactive demo for Alpaca also poses potential risks, such as more widely disseminating harmful content and lowering the barrier for spam, fraud, or disinformation. We have put into place two risk mitigation strategies. First, we have implemented a content filter using OpenAI’s content moderation API, which filters out harmful content as defined by OpenAI’s usage policies. Second, we watermark all the model outputs using the method described in Kirchenbauer et al. 2023, so that others can detect (with some probability) whether an output comes from Alpaca 7B. Finally, we have strict terms and conditions for using the demo; it is restricted to non-commercial uses and to uses that follow LLaMA’s license agreement. We understand that these mitigation measures can be circumvented once we release the model weights or if users train their own instruction-following models. However, by installing these mitigations, we hope to advance the best practices and ultimately develop community norms for the responsible deployment of foundation models.\n> \n> \n>",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nThe 'alpaca' data is generated by a language model ('text-davinci-003') and inevitably contains some errors or biases. We encourage users to use this data with caution and propose new methods to filter or improve the imperfections.\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nThe dataset is available under the Creative Commons NonCommercial (CC BY-NC 4.0).",
"### Contributions"
]
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335,
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"passage: TAGS\n#task_categories-text-generation #task_ids-dialogue-modeling #annotations_creators-no-annotation #language_creators-machine-generated #multilinguality-multilingual #multilinguality-translation #size_categories-10K<n<100K #source_datasets-tatsu-lab/alpaca #language-English #language-Portuguese #language-Spanish #language-Catalan #language-Basque #language-Galician #language-at #license-cc-by-nc-4.0 #instruction-finetuning #region-us \n### Dataset Summary\n\n\nAlpaca is a dataset of 52,000 instructions and demonstrations generated by OpenAI's 'text-davinci-003' engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better. This dataset also includes machine-translated data for 6 Iberian languages: Portuguese, Spanish, Catalan, Basque, Galician and Asturian. Translation was done using NLLB-200 3.3B model.\n\n\nThe authors built on the data generation pipeline from Self-Instruct framework and made the following modifications:\n\n\n* The 'text-davinci-003' engine to generate the instruction data instead of 'davinci'.\n* A new prompt was written that explicitly gave the requirement of instruction generation to 'text-davinci-003'.\n* Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.\n* The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.\n* Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.\n\n\nThis produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500).\nIn a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by Self-Instruct.### Supported Tasks and Leaderboards\n\n\nThe Alpaca dataset designed for instruction training pretrained language models.",
"passage: ### Languages\n\n\nThe original data in Alpaca is in English (BCP-47 en). We also provide machine-translated data for 6 Iberian languages: Portuguese (BCP-47 pt), Spanish (BCP-47 es), Catalan (BCP-47 ca), Basque (BCP-47 eu), Galician (BCP-47 gl) and Asturian (BCP-47 at).\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nAn example of \"train\" looks as follows:### Data Fields\n\n\nThe data fields are as follows:\n\n\n* 'instruction': describes the task the model should perform. Each of the 52K instructions is unique.\n* 'input': optional context or input for the task. For example, when the instruction is \"Summarize the following article\", the input is the article. Around 40% of the examples have an input.\n* 'output': the answer to the instruction as generated by 'text-davinci-003'.\n* 'text': the 'instruction', 'input' and 'output' formatted with the prompt template used by the authors for fine-tuning their models.### Data Splits\n\n\n\nDataset Creation\n----------------### 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\n\n\nConsiderations for Using the Data\n---------------------------------"
]
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|
d803052c924476522f88e44b8ce1e59d5bd74200 | # Dataset Card for "robotcombinedroboset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | homangab/robotcombinedroboset | [
"region:us"
]
| 2023-03-22T15:30:52+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 83858508.0, "num_examples": 190}], "download_size": 10230491, "dataset_size": 83858508.0}} | 2023-03-22T15:33:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "robotcombinedroboset"
More Information needed | [
"# Dataset Card for \"robotcombinedroboset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"robotcombinedroboset\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"robotcombinedroboset\"\n\nMore Information needed"
]
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|
b3b496e00a52fd99733f378494ef3f6e8a1e8b3c | # Dataset Card for "instructions-ar"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-ar | [
"region:us"
]
| 2023-03-22T15:40:47+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1335708.4343484773, "num_examples": 1802}, {"name": "test", "num_bytes": 74864.90114827758, "num_examples": 101}, {"name": "validation", "num_bytes": 74123.66450324513, "num_examples": 100}], "download_size": 0, "dataset_size": 1484697.0}} | 2023-03-22T15:42:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-ar"
More Information needed | [
"# Dataset Card for \"instructions-ar\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-ar\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-ar\"\n\nMore Information needed"
]
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|
46a61522c7cc8423e7be085a91cca76294e8225d | # Dataset Card for "instructions-bg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-bg | [
"region:us"
]
| 2023-03-22T15:41:55+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1656280.9056415376, "num_examples": 1802}, {"name": "test", "num_bytes": 92832.6145781328, "num_examples": 101}, {"name": "validation", "num_bytes": 91913.47978032951, "num_examples": 100}], "download_size": 862720, "dataset_size": 1841027.0}} | 2023-03-22T15:42:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-bg"
More Information needed | [
"# Dataset Card for \"instructions-bg\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-bg\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-bg\"\n\nMore Information needed"
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|
d5d4f5c0fbea37caeb8b7a29dc521caab6cae15a | # Dataset Card for "instructions-bn"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-bn | [
"region:us"
]
| 2023-03-22T15:42:58+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2244818.7, "num_examples": 1791}, {"name": "test", "num_bytes": 125338.84422110552, "num_examples": 100}, {"name": "validation", "num_bytes": 124085.45577889447, "num_examples": 99}], "download_size": 930748, "dataset_size": 2494243.0}} | 2023-03-22T15:43:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-bn"
More Information needed | [
"# Dataset Card for \"instructions-bn\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-bn\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-bn\"\n\nMore Information needed"
]
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|
410dc405ee624cd0f1f6368dc59505451981ac7b | # Dataset Card for "instructions-ca"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-ca | [
"region:us"
]
| 2023-03-22T15:43:18+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 978054.1677483774, "num_examples": 1802}, {"name": "test", "num_bytes": 54818.796305541684, "num_examples": 101}, {"name": "validation", "num_bytes": 54276.035946080876, "num_examples": 100}], "download_size": 641125, "dataset_size": 1087149.0}} | 2023-03-22T15:43:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-ca"
More Information needed | [
"# Dataset Card for \"instructions-ca\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-ca\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-ca\"\n\nMore Information needed"
]
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|
d4da63a894ff15bc597e26fab2c4590d4d44a2fb | # Dataset Card for "instructions-el"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-el | [
"region:us"
]
| 2023-03-22T15:43:40+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1798844.0259610584, "num_examples": 1802}, {"name": "test", "num_bytes": 100823.1113330005, "num_examples": 101}, {"name": "validation", "num_bytes": 99824.8627059411, "num_examples": 100}], "download_size": 945906, "dataset_size": 1999492.0}} | 2023-03-22T15:43:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-el"
More Information needed | [
"# Dataset Card for \"instructions-el\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-el\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-el\"\n\nMore Information needed"
]
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|
5d225d37c358f1488440d0d2e0c236149299a7a2 | # Dataset Card for "instructions-et"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-et | [
"region:us"
]
| 2023-03-22T15:43:59+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 880362.0, "num_examples": 1800}, {"name": "test", "num_bytes": 48909.0, "num_examples": 100}, {"name": "validation", "num_bytes": 48909.0, "num_examples": 100}], "download_size": 622841, "dataset_size": 978180.0}} | 2023-03-22T15:44:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-et"
More Information needed | [
"# Dataset Card for \"instructions-et\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-et\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-et\"\n\nMore Information needed"
]
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|
d6fdc74817064effba1a10d76b8c00dcb33b6e59 | # Dataset Card for "instructions-fi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-fi | [
"region:us"
]
| 2023-03-22T15:44:21+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 936328.3665338645, "num_examples": 1807}, {"name": "test", "num_bytes": 52334.900398406375, "num_examples": 101}, {"name": "validation", "num_bytes": 51816.73306772908, "num_examples": 100}], "download_size": 640961, "dataset_size": 1040480.0}} | 2023-03-22T15:44:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-fi"
More Information needed | [
"# Dataset Card for \"instructions-fi\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-fi\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-fi\"\n\nMore Information needed"
]
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|
86943d963bac8c6b3be54c3edfa4f7c7d07eb5d5 | # Dataset Card for "instructions-ht"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-ht | [
"region:us"
]
| 2023-03-22T15:44:40+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 849311.1, "num_examples": 1800}, {"name": "test", "num_bytes": 47183.95, "num_examples": 100}, {"name": "validation", "num_bytes": 47183.95, "num_examples": 100}], "download_size": 551605, "dataset_size": 943678.9999999999}} | 2023-03-22T15:44:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-ht"
More Information needed | [
"# Dataset Card for \"instructions-ht\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-ht\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-ht\"\n\nMore Information needed"
]
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|
34e7d70744194a78afc60e7b080dffeea52a799e | # Dataset Card for "instructions-it"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-it | [
"region:us"
]
| 2023-03-22T15:44:58+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 978621.3237195425, "num_examples": 1809}, {"name": "test", "num_bytes": 54638.33814022874, "num_examples": 101}, {"name": "validation", "num_bytes": 54638.33814022874, "num_examples": 101}], "download_size": 648736, "dataset_size": 1087898.0}} | 2023-03-22T15:45:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-it"
More Information needed | [
"# Dataset Card for \"instructions-it\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-it\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-it\"\n\nMore Information needed"
]
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|
1f0406d8bede5d4fb8ae46f6d1381e694b822ea1 | # Dataset Card for "instructions-ko"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-ko | [
"region:us"
]
| 2023-03-22T15:45:16+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1016857.3512963904, "num_examples": 1770}, {"name": "test", "num_bytes": 56875.07219115404, "num_examples": 99}, {"name": "validation", "num_bytes": 56300.57651245552, "num_examples": 98}], "download_size": 631602, "dataset_size": 1130033.0}} | 2023-03-22T15:45:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-ko"
More Information needed | [
"# Dataset Card for \"instructions-ko\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-ko\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-ko\"\n\nMore Information needed"
]
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|
a5d60ae36e0c478f652ae4158482b3b0088ceadc | # Dataset Card for "instructions-sw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-sw | [
"region:us"
]
| 2023-03-22T15:45:33+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 917585.3073463269, "num_examples": 1800}, {"name": "test", "num_bytes": 51486.73113443278, "num_examples": 101}, {"name": "validation", "num_bytes": 50976.96151924038, "num_examples": 100}], "download_size": 581487, "dataset_size": 1020049.0000000001}} | 2023-03-22T15:45:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-sw"
More Information needed | [
"# Dataset Card for \"instructions-sw\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-sw\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-sw\"\n\nMore Information needed"
]
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|
cdb1c6073c5a7f9eb03b0a91cd965a097f72bfd9 | # Dataset Card for "instructions-ta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-ta | [
"region:us"
]
| 2023-03-22T15:45:51+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2612517.3091275846, "num_examples": 1784}, {"name": "test", "num_bytes": 146441.55320221887, "num_examples": 100}, {"name": "validation", "num_bytes": 144977.13767019668, "num_examples": 99}], "download_size": 1024957, "dataset_size": 2903936.0}} | 2023-03-22T15:46:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-ta"
More Information needed | [
"# Dataset Card for \"instructions-ta\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-ta\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-ta\"\n\nMore Information needed"
]
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|
5c841a7db791e37f22c463c17ed47a380dfeb6e2 | # Dataset Card for "instructions-tr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-tr | [
"region:us"
]
| 2023-03-22T15:46:11+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 972793.8509566969, "num_examples": 1787}, {"name": "test", "num_bytes": 54437.26082578046, "num_examples": 100}, {"name": "validation", "num_bytes": 53892.88821752266, "num_examples": 99}], "download_size": 631831, "dataset_size": 1081124.0}} | 2023-03-22T15:46:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-tr"
More Information needed | [
"# Dataset Card for \"instructions-tr\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-tr\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-tr\"\n\nMore Information needed"
]
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|
176e9d18a5e29ecb14b7236c99919e87061ad659 | # Dataset Card for "instructions-ur"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | cahya/instructions-ur | [
"region:us"
]
| 2023-03-22T15:46:30+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1506793.1752371443, "num_examples": 1802}, {"name": "test", "num_bytes": 84454.00149775337, "num_examples": 101}, {"name": "validation", "num_bytes": 83617.82326510234, "num_examples": 100}], "download_size": 791422, "dataset_size": 1674865.0}} | 2023-03-22T15:46:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instructions-ur"
More Information needed | [
"# Dataset Card for \"instructions-ur\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instructions-ur\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instructions-ur\"\n\nMore Information needed"
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|
891b08ba95df9d2b590d2c876b680755771a48b3 | # Dataset Card for "thainer-corpus-v2"
Thai Named Entity Recognition Corpus
Home Page: [https://pythainlp.github.io/Thai-NER/version/2](https://pythainlp.github.io/Thai-NER/version/2)
Training script and split data: [https://zenodo.org/record/7761354](https://zenodo.org/record/7761354)
**You can download .conll to train named entity model in [https://zenodo.org/record/7761354](https://zenodo.org/record/7761354).**
**Size**
- Train: 3,938 docs
- Validation: 1,313 docs
- Test: 1,313 Docs
Some data come from crowdsourcing between Dec 2018 - Nov 2019. [https://github.com/wannaphong/thai-ner](https://github.com/wannaphong/thai-ner)
**Domain**
- News (It, politics, economy, social)
- PR (KKU news)
- general
**Source**
- I use sone data from Nutcha’s theses (http://pioneer.chula.ac.th/~awirote/Data-Nutcha.zip) and improve data by rechecking and adding more tagging.
- Twitter
- Blognone.com - It news
- thaigov.go.th
- kku.ac.th
And more (the lists are lost.)
**Tag**
- DATA - date
- TIME - time
- EMAIL - email
- LEN - length
- LOCATION - Location
- ORGANIZATION - Company / Organization
- PERSON - Person name
- PHONE - phone number
- TEMPERATURE - temperature
- URL - URL
- ZIP - Zip code
- MONEY - the amount
- LAW - legislation
- PERCENT - PERCENT
Download: [HuggingFace Hub](https://huggingface.co/datasets/pythainlp/thainer-corpus-v2)
## Cite
> Wannaphong Phatthiyaphaibun. (2022). Thai NER 2.0 (2.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7761354
or BibTeX
```
@dataset{wannaphong_phatthiyaphaibun_2022_7761354,
author = {Wannaphong Phatthiyaphaibun},
title = {Thai NER 2.0},
month = sep,
year = 2022,
publisher = {Zenodo},
version = {2.0},
doi = {10.5281/zenodo.7761354},
url = {https://doi.org/10.5281/zenodo.7761354}
}
``` | pythainlp/thainer-corpus-v2 | [
"task_categories:token-classification",
"language:th",
"license:cc-by-3.0",
"region:us"
]
| 2023-03-22T16:12:10+00:00 | {"language": ["th"], "license": "cc-by-3.0", "task_categories": ["token-classification"], "dataset_info": {"features": [{"name": "words", "sequence": "string"}, {"name": "ner", "sequence": {"class_label": {"names": {"0": "B-PERSON", "1": "I-PERSON", "2": "O", "3": "B-ORGANIZATION", "4": "B-LOCATION", "5": "I-ORGANIZATION", "6": "I-LOCATION", "7": "B-DATE", "8": "I-DATE", "9": "B-TIME", "10": "I-TIME", "11": "B-MONEY", "12": "I-MONEY", "13": "B-FACILITY", "14": "I-FACILITY", "15": "B-URL", "16": "I-URL", "17": "B-PERCENT", "18": "I-PERCENT", "19": "B-LEN", "20": "I-LEN", "21": "B-AGO", "22": "I-AGO", "23": "B-LAW", "24": "I-LAW", "25": "B-PHONE", "26": "I-PHONE", "27": "B-EMAIL", "28": "I-EMAIL", "29": "B-ZIP", "30": "B-TEMPERATURE", "31": "I-TEMPERATURE", "32": "B-DTAE", "33": "I-DTAE", "34": "B-DATA", "35": "I-DATA"}}}}], "splits": [{"name": "train", "num_bytes": 3736419, "num_examples": 3938}, {"name": "validation", "num_bytes": 1214580, "num_examples": 1313}, {"name": "test", "num_bytes": 1242609, "num_examples": 1313}], "download_size": 974230, "dataset_size": 6193608}} | 2023-03-23T05:23:46+00:00 | []
| [
"th"
]
| TAGS
#task_categories-token-classification #language-Thai #license-cc-by-3.0 #region-us
| # Dataset Card for "thainer-corpus-v2"
Thai Named Entity Recognition Corpus
Home Page: URL
Training script and split data: URL
You can download .conll to train named entity model in URL
Size
- Train: 3,938 docs
- Validation: 1,313 docs
- Test: 1,313 Docs
Some data come from crowdsourcing between Dec 2018 - Nov 2019. URL
Domain
- News (It, politics, economy, social)
- PR (KKU news)
- general
Source
- I use sone data from Nutcha’s theses (URL and improve data by rechecking and adding more tagging.
- Twitter
- URL - It news
- URL
- URL
And more (the lists are lost.)
Tag
- DATA - date
- TIME - time
- EMAIL - email
- LEN - length
- LOCATION - Location
- ORGANIZATION - Company / Organization
- PERSON - Person name
- PHONE - phone number
- TEMPERATURE - temperature
- URL - URL
- ZIP - Zip code
- MONEY - the amount
- LAW - legislation
- PERCENT - PERCENT
Download: HuggingFace Hub
## Cite
> Wannaphong Phatthiyaphaibun. (2022). Thai NER 2.0 (2.0) [Data set]. Zenodo. URL
or BibTeX
| [
"# Dataset Card for \"thainer-corpus-v2\"\n\nThai Named Entity Recognition Corpus\n\nHome Page: URL\n\nTraining script and split data: URL\n\nYou can download .conll to train named entity model in URL\n\nSize\n\n- Train: 3,938 docs\n- Validation: 1,313 docs\n- Test: 1,313 Docs\n\nSome data come from crowdsourcing between Dec 2018 - Nov 2019. URL\n\nDomain\n- News (It, politics, economy, social)\n- PR (KKU news)\n- general\n\nSource\n- I use sone data from Nutcha’s theses (URL and improve data by rechecking and adding more tagging.\n- Twitter\n- URL - It news\n- URL\n- URL\n\nAnd more (the lists are lost.)\n\nTag\n\n- DATA - date\n- TIME - time\n- EMAIL - email\n- LEN - length\n- LOCATION - Location\n- ORGANIZATION - Company / Organization\n- PERSON - Person name\n- PHONE - phone number\n- TEMPERATURE - temperature\n- URL - URL\n- ZIP - Zip code\n- MONEY - the amount\n- LAW - legislation\n- PERCENT - PERCENT\n\nDownload: HuggingFace Hub",
"## Cite\n\n> Wannaphong Phatthiyaphaibun. (2022). Thai NER 2.0 (2.0) [Data set]. Zenodo. URL\n\nor BibTeX"
]
| [
"TAGS\n#task_categories-token-classification #language-Thai #license-cc-by-3.0 #region-us \n",
"# Dataset Card for \"thainer-corpus-v2\"\n\nThai Named Entity Recognition Corpus\n\nHome Page: URL\n\nTraining script and split data: URL\n\nYou can download .conll to train named entity model in URL\n\nSize\n\n- Train: 3,938 docs\n- Validation: 1,313 docs\n- Test: 1,313 Docs\n\nSome data come from crowdsourcing between Dec 2018 - Nov 2019. URL\n\nDomain\n- News (It, politics, economy, social)\n- PR (KKU news)\n- general\n\nSource\n- I use sone data from Nutcha’s theses (URL and improve data by rechecking and adding more tagging.\n- Twitter\n- URL - It news\n- URL\n- URL\n\nAnd more (the lists are lost.)\n\nTag\n\n- DATA - date\n- TIME - time\n- EMAIL - email\n- LEN - length\n- LOCATION - Location\n- ORGANIZATION - Company / Organization\n- PERSON - Person name\n- PHONE - phone number\n- TEMPERATURE - temperature\n- URL - URL\n- ZIP - Zip code\n- MONEY - the amount\n- LAW - legislation\n- PERCENT - PERCENT\n\nDownload: HuggingFace Hub",
"## Cite\n\n> Wannaphong Phatthiyaphaibun. (2022). Thai NER 2.0 (2.0) [Data set]. Zenodo. URL\n\nor BibTeX"
]
| [
32,
250,
38
]
| [
"passage: TAGS\n#task_categories-token-classification #language-Thai #license-cc-by-3.0 #region-us \n# Dataset Card for \"thainer-corpus-v2\"\n\nThai Named Entity Recognition Corpus\n\nHome Page: URL\n\nTraining script and split data: URL\n\nYou can download .conll to train named entity model in URL\n\nSize\n\n- Train: 3,938 docs\n- Validation: 1,313 docs\n- Test: 1,313 Docs\n\nSome data come from crowdsourcing between Dec 2018 - Nov 2019. URL\n\nDomain\n- News (It, politics, economy, social)\n- PR (KKU news)\n- general\n\nSource\n- I use sone data from Nutcha’s theses (URL and improve data by rechecking and adding more tagging.\n- Twitter\n- URL - It news\n- URL\n- URL\n\nAnd more (the lists are lost.)\n\nTag\n\n- DATA - date\n- TIME - time\n- EMAIL - email\n- LEN - length\n- LOCATION - Location\n- ORGANIZATION - Company / Organization\n- PERSON - Person name\n- PHONE - phone number\n- TEMPERATURE - temperature\n- URL - URL\n- ZIP - Zip code\n- MONEY - the amount\n- LAW - legislation\n- PERCENT - PERCENT\n\nDownload: HuggingFace Hub## Cite\n\n> Wannaphong Phatthiyaphaibun. (2022). Thai NER 2.0 (2.0) [Data set]. Zenodo. URL\n\nor BibTeX"
]
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|
4a9d5eb7947786aafc0b10b7fc3964ec6ae3b1a1 |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-text/go in Semeru
# CodeXGLUE -- Code-To-Text
## Task Definition
The task is to generate natural language comments for a code, and evaluted by [smoothed bleu-4](https://www.aclweb.org/anthology/C04-1072.pdf) score.
## Dataset
The dataset we use comes from [CodeSearchNet](https://arxiv.org/pdf/1909.09436.pdf) and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove examples that documents are not English.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- **repo:** the owner/repo
- **path:** the full path to the original file
- **func_name:** the function or method name
- **original_string:** the raw string before tokenization or parsing
- **language:** the programming language
- **code/function:** the part of the `original_string` that is code
- **code_tokens/function_tokens:** tokenized version of `code`
- **docstring:** the top-level comment or docstring, if it exists in the original string
- **docstring_tokens:** tokenized version of `docstring`
### Data Statistic
| Programming Language | Training | Dev | Test |
| :------------------- | :------: | :----: | :----: |
| Go | 167,288 | 7,325 | 8,122 |
## Reference
<pre><code>@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}</code></pre>
| semeru/code-text-go | [
"license:mit",
"arxiv:1909.09436",
"region:us"
]
| 2023-03-22T16:24:12+00:00 | {"license": "mit", "Programminglanguage": "go", "version": "N/A", "Date": "Codesearchnet(Jun 2020 - paper release date)", "Contaminated": "Very Likely", "Size": "Standard Tokenizer (TreeSitter)"} | 2023-03-24T14:01:26+00:00 | [
"1909.09436"
]
| []
| TAGS
#license-mit #arxiv-1909.09436 #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-text/go in Semeru
CodeXGLUE -- Code-To-Text
=========================
Task Definition
---------------
The task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.
Dataset
-------
The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
* Remove examples that codes cannot be parsed into an abstract syntax tree.
* Remove examples that #tokens of documents is < 3 or >256
* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
* Remove examples that documents are not English.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
* repo: the owner/repo
* path: the full path to the original file
* func\_name: the function or method name
* original\_string: the raw string before tokenization or parsing
* language: the programming language
* code/function: the part of the 'original\_string' that is code
* code\_tokens/function\_tokens: tokenized version of 'code'
* docstring: the top-level comment or docstring, if it exists in the original string
* docstring\_tokens: tokenized version of 'docstring'
### Data Statistic
Reference
---------
```
@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/go in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'",
"### Data Statistic\n\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
"TAGS\n#license-mit #arxiv-1909.09436 #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/go in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'",
"### Data Statistic\n\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
19,
208,
187,
104
]
| [
"passage: TAGS\n#license-mit #arxiv-1909.09436 #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/go in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'"
]
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|
b2053328fafdd018ff56cf1dfa9643caaa4e69b8 |
# VSR: Visual Spatial Reasoning
This is the **random set** of **VSR**: *Visual Spatial Reasoning* (TACL 2023) [[paper]](https://arxiv.org/abs/2205.00363).
### Usage
```python
from datasets import load_dataset
data_files = {"train": "train.jsonl", "dev": "dev.jsonl", "test": "test.jsonl"}
dataset = load_dataset("cambridgeltl/vsr_random", data_files=data_files)
```
Note that the image files still need to be downloaded separately. See [`data/`](https://github.com/cambridgeltl/visual-spatial-reasoning/tree/master/data) for details.
Go to our [github repo](https://github.com/cambridgeltl/visual-spatial-reasoning) for more introductions.
### Citation
If you find VSR useful:
```bibtex
@article{Liu2022VisualSR,
title={Visual Spatial Reasoning},
author={Fangyu Liu and Guy Edward Toh Emerson and Nigel Collier},
journal={Transactions of the Association for Computational Linguistics},
year={2023},
}
``` | cambridgeltl/vsr_random | [
"task_categories:text-classification",
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"multimodality",
"vision-and-language",
"arxiv:2205.00363",
"region:us"
]
| 2023-03-22T16:27:00+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification", "question-answering"], "pretty_name": "VSR (random split)", "tags": ["multimodality", "vision-and-language"]} | 2023-03-22T17:28:37+00:00 | [
"2205.00363"
]
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #multimodality #vision-and-language #arxiv-2205.00363 #region-us
|
# VSR: Visual Spatial Reasoning
This is the random set of VSR: *Visual Spatial Reasoning* (TACL 2023) [[paper]](URL
### Usage
Note that the image files still need to be downloaded separately. See 'data/' for details.
Go to our github repo for more introductions.
If you find VSR useful:
| [
"# VSR: Visual Spatial Reasoning\n\nThis is the random set of VSR: *Visual Spatial Reasoning* (TACL 2023) [[paper]](URL",
"### Usage\n\n\n\nNote that the image files still need to be downloaded separately. See 'data/' for details.\n\nGo to our github repo for more introductions.\n\n\nIf you find VSR useful:"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #multimodality #vision-and-language #arxiv-2205.00363 #region-us \n",
"# VSR: Visual Spatial Reasoning\n\nThis is the random set of VSR: *Visual Spatial Reasoning* (TACL 2023) [[paper]](URL",
"### Usage\n\n\n\nNote that the image files still need to be downloaded separately. See 'data/' for details.\n\nGo to our github repo for more introductions.\n\n\nIf you find VSR useful:"
]
| [
72,
38,
44
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-question-answering #size_categories-10K<n<100K #language-English #license-cc-by-4.0 #multimodality #vision-and-language #arxiv-2205.00363 #region-us \n# VSR: Visual Spatial Reasoning\n\nThis is the random set of VSR: *Visual Spatial Reasoning* (TACL 2023) [[paper]](URL### Usage\n\n\n\nNote that the image files still need to be downloaded separately. See 'data/' for details.\n\nGo to our github repo for more introductions.\n\n\nIf you find VSR useful:"
]
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|
f73aa95d9ea6bfd39fd016e493a5591b5050acff |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-text/java in Semeru
# CodeXGLUE -- Code-To-Text
## Task Definition
The task is to generate natural language comments for a code, and evaluted by [smoothed bleu-4](https://www.aclweb.org/anthology/C04-1072.pdf) score.
## Dataset
The dataset we use comes from [CodeSearchNet](https://arxiv.org/pdf/1909.09436.pdf) and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove examples that documents are not English.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- **repo:** the owner/repo
- **path:** the full path to the original file
- **func_name:** the function or method name
- **original_string:** the raw string before tokenization or parsing
- **language:** the programming language
- **code/function:** the part of the `original_string` that is code
- **code_tokens/function_tokens:** tokenized version of `code`
- **docstring:** the top-level comment or docstring, if it exists in the original string
- **docstring_tokens:** tokenized version of `docstring`
### Data Statistic
| Programming Language | Training | Dev | Test |
| :------------------- | :------: | :----: | :----: |
| Java | 164,923 | 5,183 | 10,955 |
## Reference
<pre><code>@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}</code></pre>
| semeru/code-text-java | [
"license:mit",
"arxiv:1909.09436",
"region:us"
]
| 2023-03-22T16:28:40+00:00 | {"license": "mit", "Programminglanguage": "Java", "version": "N/A", "Date": "Codesearchnet(Jun 2020 - paper release date)", "Contaminated": "Very Likely", "Size": "Standard Tokenizer (TreeSitter)"} | 2023-03-23T20:10:55+00:00 | [
"1909.09436"
]
| []
| TAGS
#license-mit #arxiv-1909.09436 #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-text/java in Semeru
CodeXGLUE -- Code-To-Text
=========================
Task Definition
---------------
The task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.
Dataset
-------
The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
* Remove examples that codes cannot be parsed into an abstract syntax tree.
* Remove examples that #tokens of documents is < 3 or >256
* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
* Remove examples that documents are not English.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
* repo: the owner/repo
* path: the full path to the original file
* func\_name: the function or method name
* original\_string: the raw string before tokenization or parsing
* language: the programming language
* code/function: the part of the 'original\_string' that is code
* code\_tokens/function\_tokens: tokenized version of 'code'
* docstring: the top-level comment or docstring, if it exists in the original string
* docstring\_tokens: tokenized version of 'docstring'
### Data Statistic
Reference
---------
```
@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/java in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'",
"### Data Statistic\n\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
"TAGS\n#license-mit #arxiv-1909.09436 #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/java in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'",
"### Data Statistic\n\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
19,
208,
187,
104
]
| [
"passage: TAGS\n#license-mit #arxiv-1909.09436 #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/java in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'"
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|
e63b00e6cedd664bb28f057f9518cd26604b105f |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-text/javascript in Semeru
# CodeXGLUE -- Code-To-Text
## Task Definition
The task is to generate natural language comments for a code, and evaluted by [smoothed bleu-4](https://www.aclweb.org/anthology/C04-1072.pdf) score.
## Dataset
The dataset we use comes from [CodeSearchNet](https://arxiv.org/pdf/1909.09436.pdf) and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove examples that documents are not English.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- **repo:** the owner/repo
- **path:** the full path to the original file
- **func_name:** the function or method name
- **original_string:** the raw string before tokenization or parsing
- **language:** the programming language
- **code/function:** the part of the `original_string` that is code
- **code_tokens/function_tokens:** tokenized version of `code`
- **docstring:** the top-level comment or docstring, if it exists in the original string
- **docstring_tokens:** tokenized version of `docstring`
### Data Statistic
| Programming Language | Training | Dev | Test |
| :------------------- | :------: | :----: | :----: |
| JavaScript | 58,025 | 3,885 | 3,291 |
## Reference
<pre><code>@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}</code></pre>
| semeru/code-text-javascript | [
"license:mit",
"arxiv:1909.09436",
"region:us"
]
| 2023-03-22T16:31:18+00:00 | {"license": "mit", "Programminglanguage": "JavaScript", "version": "N/A", "Date": "Codesearchnet(Jun 2020 - paper release date)", "Contaminated": "Very Likely", "Size": "Standar Tokenizer (TreeSitter)"} | 2023-03-23T20:05:02+00:00 | [
"1909.09436"
]
| []
| TAGS
#license-mit #arxiv-1909.09436 #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-text/javascript in Semeru
CodeXGLUE -- Code-To-Text
=========================
Task Definition
---------------
The task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.
Dataset
-------
The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
* Remove examples that codes cannot be parsed into an abstract syntax tree.
* Remove examples that #tokens of documents is < 3 or >256
* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
* Remove examples that documents are not English.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
* repo: the owner/repo
* path: the full path to the original file
* func\_name: the function or method name
* original\_string: the raw string before tokenization or parsing
* language: the programming language
* code/function: the part of the 'original\_string' that is code
* code\_tokens/function\_tokens: tokenized version of 'code'
* docstring: the top-level comment or docstring, if it exists in the original string
* docstring\_tokens: tokenized version of 'docstring'
### Data Statistic
Reference
---------
```
@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/javascript in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'",
"### Data Statistic\n\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
"TAGS\n#license-mit #arxiv-1909.09436 #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/javascript in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'",
"### Data Statistic\n\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
19,
209,
187,
104
]
| [
"passage: TAGS\n#license-mit #arxiv-1909.09436 #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/javascript in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'"
]
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|
64544fa118dee5926543b1444f0e2ed4db1a403c |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-text/php in Semeru
# CodeXGLUE -- Code-To-Text
## Task Definition
The task is to generate natural language comments for a code, and evaluted by [smoothed bleu-4](https://www.aclweb.org/anthology/C04-1072.pdf) score.
## Dataset
The dataset we use comes from [CodeSearchNet](https://arxiv.org/pdf/1909.09436.pdf) and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove examples that documents are not English.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- **repo:** the owner/repo
- **path:** the full path to the original file
- **func_name:** the function or method name
- **original_string:** the raw string before tokenization or parsing
- **language:** the programming language
- **code/function:** the part of the `original_string` that is code
- **code_tokens/function_tokens:** tokenized version of `code`
- **docstring:** the top-level comment or docstring, if it exists in the original string
- **docstring_tokens:** tokenized version of `docstring`
### Data Statistic
| Programming Language | Training | Dev | Test |
| :------------------- | :------: | :----: | :----: |
| PHP | 241,241 | 12,982 | 14,014 |
## Reference
<pre><code>@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}</code></pre>
| semeru/code-text-php | [
"license:mit",
"arxiv:1909.09436",
"region:us"
]
| 2023-03-22T16:32:44+00:00 | {"license": "mit", "Programminglanguage": "php", "version": "7 or later", "Date": "Codesearchnet(Jun 2020 - paper release date)", "Contaminated": "Very Likely", "Size": "Standar Tokenizer (TreeSitter)"} | 2023-03-23T20:01:10+00:00 | [
"1909.09436"
]
| []
| TAGS
#license-mit #arxiv-1909.09436 #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-text/php in Semeru
CodeXGLUE -- Code-To-Text
=========================
Task Definition
---------------
The task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.
Dataset
-------
The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
* Remove examples that codes cannot be parsed into an abstract syntax tree.
* Remove examples that #tokens of documents is < 3 or >256
* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
* Remove examples that documents are not English.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
* repo: the owner/repo
* path: the full path to the original file
* func\_name: the function or method name
* original\_string: the raw string before tokenization or parsing
* language: the programming language
* code/function: the part of the 'original\_string' that is code
* code\_tokens/function\_tokens: tokenized version of 'code'
* docstring: the top-level comment or docstring, if it exists in the original string
* docstring\_tokens: tokenized version of 'docstring'
### Data Statistic
Reference
---------
```
@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/php in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'",
"### Data Statistic\n\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
"TAGS\n#license-mit #arxiv-1909.09436 #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/php in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'",
"### Data Statistic\n\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
19,
208,
187,
104
]
| [
"passage: TAGS\n#license-mit #arxiv-1909.09436 #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/php in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'"
]
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|
08eba3cbe72e6e738916678467f4daae0fbf6fcc |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-text/python in Semeru
# CodeXGLUE -- Code-To-Text
## Task Definition
The task is to generate natural language comments for a code, and evaluted by [smoothed bleu-4](https://www.aclweb.org/anthology/C04-1072.pdf) score.
## Dataset
The dataset we use comes from [CodeSearchNet](https://arxiv.org/pdf/1909.09436.pdf) and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove examples that documents are not English.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- **repo:** the owner/repo
- **path:** the full path to the original file
- **func_name:** the function or method name
- **original_string:** the raw string before tokenization or parsing
- **language:** the programming language
- **code/function:** the part of the `original_string` that is code
- **code_tokens/function_tokens:** tokenized version of `code`
- **docstring:** the top-level comment or docstring, if it exists in the original string
- **docstring_tokens:** tokenized version of `docstring`
### Data Statistic
| Programming Language | Training | Dev | Test |
| :------------------- | :------: | :----: | :----: |
| Python | 251,820 | 13,914 | 14,918 |
## Reference
<pre><code>@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}</code></pre>
| semeru/code-text-python | [
"license:mit",
"arxiv:1909.09436",
"region:us"
]
| 2023-03-22T16:35:30+00:00 | {"license": "mit", "Programminglanguage": "python", "version": "2.7", "Date": "Codesearchnet(Jun 2020 - paper release date)", "Contaminated": "Very Likely", "Size": "Standar Tokenizer (TreeSitter)"} | 2023-03-23T18:46:18+00:00 | [
"1909.09436"
]
| []
| TAGS
#license-mit #arxiv-1909.09436 #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-text/python in Semeru
CodeXGLUE -- Code-To-Text
=========================
Task Definition
---------------
The task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.
Dataset
-------
The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
* Remove examples that codes cannot be parsed into an abstract syntax tree.
* Remove examples that #tokens of documents is < 3 or >256
* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
* Remove examples that documents are not English.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
* repo: the owner/repo
* path: the full path to the original file
* func\_name: the function or method name
* original\_string: the raw string before tokenization or parsing
* language: the programming language
* code/function: the part of the 'original\_string' that is code
* code\_tokens/function\_tokens: tokenized version of 'code'
* docstring: the top-level comment or docstring, if it exists in the original string
* docstring\_tokens: tokenized version of 'docstring'
### Data Statistic
Reference
---------
```
@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/python in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'",
"### Data Statistic\n\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
"TAGS\n#license-mit #arxiv-1909.09436 #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/python in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'",
"### Data Statistic\n\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
19,
209,
187,
104
]
| [
"passage: TAGS\n#license-mit #arxiv-1909.09436 #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/python in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'"
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|
287537613957da89a958acca990d1bff72ab7812 |
### Dataset is imported from CodeXGLUE and pre-processed using their script.
# Where to find in Semeru:
The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-text/ruby in Semeru
# CodeXGLUE -- Code-To-Text
## Task Definition
The task is to generate natural language comments for a code, and evaluted by [smoothed bleu-4](https://www.aclweb.org/anthology/C04-1072.pdf) score.
## Dataset
The dataset we use comes from [CodeSearchNet](https://arxiv.org/pdf/1909.09436.pdf) and we filter the dataset as the following:
- Remove examples that codes cannot be parsed into an abstract syntax tree.
- Remove examples that #tokens of documents is < 3 or >256
- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
- Remove examples that documents are not English.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. train.jsonl, valid.jsonl, test.jsonl
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
- **repo:** the owner/repo
- **path:** the full path to the original file
- **func_name:** the function or method name
- **original_string:** the raw string before tokenization or parsing
- **language:** the programming language
- **code/function:** the part of the `original_string` that is code
- **code_tokens/function_tokens:** tokenized version of `code`
- **docstring:** the top-level comment or docstring, if it exists in the original string
- **docstring_tokens:** tokenized version of `docstring`
### Data Statistic
| Programming Language | Training | Dev | Test |
| :------------------- | :------: | :----: | :----: |
| Ruby | 24,927 | 1,400 | 1,261 |
## Reference
<pre><code>@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}</code></pre>
| semeru/code-text-ruby | [
"license:mit",
"arxiv:1909.09436",
"region:us"
]
| 2023-03-22T16:37:57+00:00 | {"license": "mit", "Programminglanguage": "ruby", "version": "N/A", "Date": "Codesearchnet(Jun 2020 - paper release date)", "Contaminated": "Very Likely", "Size": "Standar Tokenizer (TreeSitter)"} | 2023-03-23T20:02:18+00:00 | [
"1909.09436"
]
| []
| TAGS
#license-mit #arxiv-1909.09436 #region-us
| ### Dataset is imported from CodeXGLUE and pre-processed using their script.
Where to find in Semeru:
========================
The dataset can be found at /nfs/semeru/semeru\_datasets/code\_xglue/code-to-text/ruby in Semeru
CodeXGLUE -- Code-To-Text
=========================
Task Definition
---------------
The task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.
Dataset
-------
The dataset we use comes from CodeSearchNet and we filter the dataset as the following:
* Remove examples that codes cannot be parsed into an abstract syntax tree.
* Remove examples that #tokens of documents is < 3 or >256
* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
* Remove examples that documents are not English.
### Data Format
After preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL
For each file, each line in the uncompressed file represents one function. One row is illustrated below.
* repo: the owner/repo
* path: the full path to the original file
* func\_name: the function or method name
* original\_string: the raw string before tokenization or parsing
* language: the programming language
* code/function: the part of the 'original\_string' that is code
* code\_tokens/function\_tokens: tokenized version of 'code'
* docstring: the top-level comment or docstring, if it exists in the original string
* docstring\_tokens: tokenized version of 'docstring'
### Data Statistic
Reference
---------
```
@article{husain2019codesearchnet,
title={Codesearchnet challenge: Evaluating the state of semantic code search},
author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},
journal={arXiv preprint arXiv:1909.09436},
year={2019}
}
```
| [
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/ruby in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'",
"### Data Statistic\n\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
"TAGS\n#license-mit #arxiv-1909.09436 #region-us \n",
"### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/ruby in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.",
"### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'",
"### Data Statistic\n\n\n\nReference\n---------\n\n\n\n```\n@article{husain2019codesearchnet,\n title={Codesearchnet challenge: Evaluating the state of semantic code search},\n author={Husain, Hamel and Wu, Ho-Hsiang and Gazit, Tiferet and Allamanis, Miltiadis and Brockschmidt, Marc},\n journal={arXiv preprint arXiv:1909.09436},\n year={2019}\n}\n```"
]
| [
19,
209,
187,
104
]
| [
"passage: TAGS\n#license-mit #arxiv-1909.09436 #region-us \n### Dataset is imported from CodeXGLUE and pre-processed using their script.\n\n\nWhere to find in Semeru:\n========================\n\n\nThe dataset can be found at /nfs/semeru/semeru\\_datasets/code\\_xglue/code-to-text/ruby in Semeru\n\n\nCodeXGLUE -- Code-To-Text\n=========================\n\n\nTask Definition\n---------------\n\n\nThe task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.\n\n\nDataset\n-------\n\n\nThe dataset we use comes from CodeSearchNet and we filter the dataset as the following:\n\n\n* Remove examples that codes cannot be parsed into an abstract syntax tree.\n* Remove examples that #tokens of documents is < 3 or >256\n* Remove examples that documents contain special tokens (e.g. <img ...> or https:...)\n* Remove examples that documents are not English.### Data Format\n\n\nAfter preprocessing dataset, you can obtain three .jsonl files, i.e. URL, URL, URL\n\n\nFor each file, each line in the uncompressed file represents one function. One row is illustrated below.\n\n\n* repo: the owner/repo\n* path: the full path to the original file\n* func\\_name: the function or method name\n* original\\_string: the raw string before tokenization or parsing\n* language: the programming language\n* code/function: the part of the 'original\\_string' that is code\n* code\\_tokens/function\\_tokens: tokenized version of 'code'\n* docstring: the top-level comment or docstring, if it exists in the original string\n* docstring\\_tokens: tokenized version of 'docstring'"
]
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|
148b3777ceef4a1bfe46377614980836db7d12f2 |
# VSR: Visual Spatial Reasoning
This is the **zero-shot set** of **VSR**: *Visual Spatial Reasoning* (TACL 2023) [[paper]](https://arxiv.org/abs/2205.00363).
### Usage
```python
from datasets import load_dataset
data_files = {"train": "train.jsonl", "dev": "dev.jsonl", "test": "test.jsonl"}
dataset = load_dataset("cambridgeltl/vsr_zeroshot", data_files=data_files)
```
Note that the image files still need to be downloaded separately. See [`data/`](https://github.com/cambridgeltl/visual-spatial-reasoning/tree/master/data) for details.
Go to our [github repo](https://github.com/cambridgeltl/visual-spatial-reasoning) for more introductions.
### Citation
If you find VSR useful:
```bibtex
@article{Liu2022VisualSR,
title={Visual Spatial Reasoning},
author={Fangyu Liu and Guy Edward Toh Emerson and Nigel Collier},
journal={Transactions of the Association for Computational Linguistics},
year={2023},
}
```
| cambridgeltl/vsr_zeroshot | [
"task_categories:text-classification",
"task_categories:question-answering",
"size_categories:1K<n<10K",
"language:en",
"license:cc-by-4.0",
"multimodal",
"vision-and-language",
"arxiv:2205.00363",
"region:us"
]
| 2023-03-22T16:42:17+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification", "question-answering"], "pretty_name": "VSR (zeroshot)", "tags": ["multimodal", "vision-and-language"]} | 2023-03-22T17:27:58+00:00 | [
"2205.00363"
]
| [
"en"
]
| TAGS
#task_categories-text-classification #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #multimodal #vision-and-language #arxiv-2205.00363 #region-us
|
# VSR: Visual Spatial Reasoning
This is the zero-shot set of VSR: *Visual Spatial Reasoning* (TACL 2023) [[paper]](URL
### Usage
Note that the image files still need to be downloaded separately. See 'data/' for details.
Go to our github repo for more introductions.
If you find VSR useful:
| [
"# VSR: Visual Spatial Reasoning\n\nThis is the zero-shot set of VSR: *Visual Spatial Reasoning* (TACL 2023) [[paper]](URL",
"### Usage\n\n\n\nNote that the image files still need to be downloaded separately. See 'data/' for details.\n\nGo to our github repo for more introductions.\n\n\nIf you find VSR useful:"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #multimodal #vision-and-language #arxiv-2205.00363 #region-us \n",
"# VSR: Visual Spatial Reasoning\n\nThis is the zero-shot set of VSR: *Visual Spatial Reasoning* (TACL 2023) [[paper]](URL",
"### Usage\n\n\n\nNote that the image files still need to be downloaded separately. See 'data/' for details.\n\nGo to our github repo for more introductions.\n\n\nIf you find VSR useful:"
]
| [
72,
40,
44
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-question-answering #size_categories-1K<n<10K #language-English #license-cc-by-4.0 #multimodal #vision-and-language #arxiv-2205.00363 #region-us \n# VSR: Visual Spatial Reasoning\n\nThis is the zero-shot set of VSR: *Visual Spatial Reasoning* (TACL 2023) [[paper]](URL### Usage\n\n\n\nNote that the image files still need to be downloaded separately. See 'data/' for details.\n\nGo to our github repo for more introductions.\n\n\nIf you find VSR useful:"
]
| [
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|
43f137fd58814dc0e4f633b11a614de75875f349 | # Disclaimer
This was inspired from https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions
# Dataset Card for A subset of Vivian Maier's photographs BLIP captions
The captions are generated with the [pre-trained BLIP model](https://github.com/salesforce/BLIP).
For each row the dataset contains `image` and `caption` keys. `image` is a varying size PIL jpeg, and `caption` is the accompanying text caption. Only a train split is provided.
## Examples

> A group of people

> person floating in the water

> a person standing next to a refrigerator
## Citation
If you use this dataset, please cite it as:
```
@misc{cqueenccc2023vivian,
author = {cQueenccc},
title = {Vivian Maier's photograph split BLIP captions},
year={2023},
howpublished= {\url{https://huggingface.co/datasets/cQueenccc/Vivian-Blip-Captions/}}
}
``` | cQueenccc/Vivian-Blip-Captions | [
"task_categories:text-to-image",
"annotations_creators:machine-generated",
"size_categories:n<1K",
"language:en",
"region:us"
]
| 2023-03-22T17:11:32+00:00 | {"annotations_creators": ["machine-generated"], "language": ["en"], "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 171055893.125, "num_examples": 1087}], "download_size": 170841790, "dataset_size": 171055893.125}} | 2023-03-22T17:45:08+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-to-image #annotations_creators-machine-generated #size_categories-n<1K #language-English #region-us
| # Disclaimer
This was inspired from URL
# Dataset Card for A subset of Vivian Maier's photographs BLIP captions
The captions are generated with the pre-trained BLIP model.
For each row the dataset contains 'image' and 'caption' keys. 'image' is a varying size PIL jpeg, and 'caption' is the accompanying text caption. Only a train split is provided.
## Examples
!URL
> A group of people
!URL
> person floating in the water
!URL
> a person standing next to a refrigerator
If you use this dataset, please cite it as:
| [
"# Disclaimer\nThis was inspired from URL",
"# Dataset Card for A subset of Vivian Maier's photographs BLIP captions\nThe captions are generated with the pre-trained BLIP model.\n\nFor each row the dataset contains 'image' and 'caption' keys. 'image' is a varying size PIL jpeg, and 'caption' is the accompanying text caption. Only a train split is provided.",
"## Examples\n\n\n!URL\n> A group of people\n\n!URL\n> person floating in the water\n\n!URL\n> a person standing next to a refrigerator\n\nIf you use this dataset, please cite it as:"
]
| [
"TAGS\n#task_categories-text-to-image #annotations_creators-machine-generated #size_categories-n<1K #language-English #region-us \n",
"# Disclaimer\nThis was inspired from URL",
"# Dataset Card for A subset of Vivian Maier's photographs BLIP captions\nThe captions are generated with the pre-trained BLIP model.\n\nFor each row the dataset contains 'image' and 'caption' keys. 'image' is a varying size PIL jpeg, and 'caption' is the accompanying text caption. Only a train split is provided.",
"## Examples\n\n\n!URL\n> A group of people\n\n!URL\n> person floating in the water\n\n!URL\n> a person standing next to a refrigerator\n\nIf you use this dataset, please cite it as:"
]
| [
45,
7,
90,
42
]
| [
"passage: TAGS\n#task_categories-text-to-image #annotations_creators-machine-generated #size_categories-n<1K #language-English #region-us \n# Disclaimer\nThis was inspired from URL# Dataset Card for A subset of Vivian Maier's photographs BLIP captions\nThe captions are generated with the pre-trained BLIP model.\n\nFor each row the dataset contains 'image' and 'caption' keys. 'image' is a varying size PIL jpeg, and 'caption' is the accompanying text caption. Only a train split is provided.## Examples\n\n\n!URL\n> A group of people\n\n!URL\n> person floating in the water\n\n!URL\n> a person standing next to a refrigerator\n\nIf you use this dataset, please cite it as:"
]
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|
15914cde080b084e682696ac63a19a12048a3ea4 |
# Indonesian Alpaca Dataset
It's the Indonesian translation of [Alpaca Dataset](https://huggingface.co/datasets/tatsu-lab/alpaca)
| cahya/alpaca-id | [
"license:mit",
"region:us"
]
| 2023-03-22T17:24:51+00:00 | {"license": "mit"} | 2023-03-22T17:28:51+00:00 | []
| []
| TAGS
#license-mit #region-us
|
# Indonesian Alpaca Dataset
It's the Indonesian translation of Alpaca Dataset
| [
"# Indonesian Alpaca Dataset\n\nIt's the Indonesian translation of Alpaca Dataset"
]
| [
"TAGS\n#license-mit #region-us \n",
"# Indonesian Alpaca Dataset\n\nIt's the Indonesian translation of Alpaca Dataset"
]
| [
11,
19
]
| [
"passage: TAGS\n#license-mit #region-us \n# Indonesian Alpaca Dataset\n\nIt's the Indonesian translation of Alpaca Dataset"
]
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|
484c0a38825f28bc3421a2428c3982fead29877e |
In today's data-driven world, metrics are essential tools for measuring performance, tracking progress, and making informed decisions. With the ever-increasing volume of data generated by businesses, organizations, and individuals, it is becoming increasingly important to understand the different metrics available and how to use them effectively.
This book aims to provide a comprehensive overview of the most commonly used metrics in various fields, including business, finance, healthcare, and more. Each metric is defined and explained in detail, along with its applications and limitations.
In addition to the definitions and explanations of metrics, this book also provides guidance on the preferred direction of each metric. Understanding the preferred direction of a metric is critical to interpreting its results accurately and making informed decisions based on those results.
Whether you are a business owner, data analyst, or simply someone interested in understanding the world around you, this book is an essential resource for anyone looking to gain a deeper understanding of metrics and how they can be used to drive success.
| alamshoaib134/Metrics-Dataset | [
"size_categories:1K<n<10K",
"region:us"
]
| 2023-03-22T17:26:52+00:00 | {"size_categories": ["1K<n<10K"], "pretty_name": "Metrics "} | 2023-03-22T17:31:26+00:00 | []
| []
| TAGS
#size_categories-1K<n<10K #region-us
|
In today's data-driven world, metrics are essential tools for measuring performance, tracking progress, and making informed decisions. With the ever-increasing volume of data generated by businesses, organizations, and individuals, it is becoming increasingly important to understand the different metrics available and how to use them effectively.
This book aims to provide a comprehensive overview of the most commonly used metrics in various fields, including business, finance, healthcare, and more. Each metric is defined and explained in detail, along with its applications and limitations.
In addition to the definitions and explanations of metrics, this book also provides guidance on the preferred direction of each metric. Understanding the preferred direction of a metric is critical to interpreting its results accurately and making informed decisions based on those results.
Whether you are a business owner, data analyst, or simply someone interested in understanding the world around you, this book is an essential resource for anyone looking to gain a deeper understanding of metrics and how they can be used to drive success.
| []
| [
"TAGS\n#size_categories-1K<n<10K #region-us \n"
]
| [
18
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|
5a0574ea122038ed5991a4178fe5e039dda6c07b | # AutoTrain Dataset for project: meme-classification
## Dataset Description
This dataset has been automatically processed by AutoTrain for project meme-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": "<657x657 RGB PIL image>",
"target": 1
},
{
"image": "<1124x700 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=['meme', 'not_meme'], 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 | 263 |
| valid | 67 |
| Hrishikesh332/autotrain-data-meme-classification | [
"task_categories:image-classification",
"region:us"
]
| 2023-03-22T17:32:32+00:00 | {"task_categories": ["image-classification"]} | 2023-03-22T17:43:16+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: meme-classification
==================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project meme-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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|
01146c0e3b0dfab1f912e26a96e4becc90f58971 | QVHighlights | genjib/qvh | [
"region:us"
]
| 2023-03-22T18:22:19+00:00 | {} | 2023-03-30T06:53:33+00:00 | []
| []
| TAGS
#region-us
| QVHighlights | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
cdf4f7162db8a9f29ea1ff122604760dd8aa8224 | # Dataset Card for "Names2chinese"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | marcus2000/Names2chinese | [
"region:us"
]
| 2023-03-22T18:29:32+00:00 | {"dataset_info": {"features": [{"name": "English_name", "dtype": "string"}, {"name": "Chinese_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 15768, "num_examples": 723}, {"name": "test", "num_bytes": 1409, "num_examples": 63}], "download_size": 15737, "dataset_size": 17177}} | 2023-03-22T18:36:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Names2chinese"
More Information needed | [
"# Dataset Card for \"Names2chinese\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Names2chinese\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Names2chinese\"\n\nMore Information needed"
]
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|
f79c070ef32015a7233a48d7a849a4476f3c8f32 |
# MORFITT
## Data ([Zenodo](https://zenodo.org/record/7893841#.ZFLFDnZBybg)) | Publication ([HAL](https://hal.science/hal-04131591/))
[Yanis LABRAK](https://www.linkedin.com/in/yanis-labrak-8a7412145/), [Richard DUFOUR](https://cv.hal.science/richard-dufour), [Mickaël ROUVIER](https://cv.hal.science/mickael-rouvier)
[](https://colab.research.google.com/drive/115EixHBcjf-se6xQeaTwZWE1i4idTNbm?usp=sharing) or [](https://github.com/qanastek/MORFITT/blob/main/TrainTransformers.py)
We introduce MORFITT, the first multi-label corpus for the classification of specialties in the medical field, in French. MORFITT is composed of 3,624 summaries of scientific articles from PubMed, annotated in 12 specialties. The article details the corpus, the experiments and the preliminary results obtained using a classifier based on the pre-trained language model CamemBERT.
For more details, please refer to our paper:
**MORFITT: A multi-label topic classification for French Biomedical literature** ([HAL](https://hal.science/hal-04131591/))
# Key Features
## Documents distribution
| Train | Dev | Test |
|-------|-------|-------|
| 1,514 | 1,022 | 1,088 |
## Multi-label distribution
| | Train | Dev | Test | Total |
|:----------------------:|:--------------:|:--------------:|:--------------:|:--------------:|
| Vétérinaire | 320 | 250 | 254 | 824 |
| Étiologie | 317 | 202 | 222 | 741 |
| Psychologie | 255 | 175 | 179 | 609 |
| Chirurgie | 223 | 169 | 157 | 549 |
| Génétique | 207 | 139 | 159 | 505 |
| Physiologie | 217 | 125 | 148 | 490 |
| Pharmacologie | 112 | 84 | 103 | 299 |
| Microbiologie | 115 | 72 | 86 | 273 |
| Immunologie | 106 | 86 | 70 | 262 |
| Chimie | 94 | 53 | 65 | 212 |
| Virologie | 76 | 57 | 67 | 200 |
| Parasitologie | 68 | 34 | 50 | 152 |
| Total | 2,110 | 1,446 | 1,560 | 5,116 |
## Number of labels per document distribution
<p align="left">
<img src="https://github.com/qanastek/MORFITT/raw/main/images/distributions_nbr_elements_colors.png" alt="drawing" width="400"/>
</p>
## Co-occurences distribution
<p align="left">
<img src="https://github.com/qanastek/MORFITT/raw/main/images/distributions_co-references-fixed.png" alt="drawing" width="400"/>
</p>
# If you use HuggingFace Transformers
```python
from datasets import load_dataset
dataset = load_dataset("qanastek/MORFITT")
print(dataset)
```
or
```python
from datasets import load_dataset
dataset_base = load_dataset(
'csv',
data_files={
'train': f"./train.tsv",
'validation': f"./dev.tsv",
'test': f"./test.tsv",
},
delimiter="\t",
)
```
# License and Citation
The code is under [Apache-2.0 License](./LICENSE).
The MORFITT dataset is licensed under *Attribution-ShareAlike 4.0 International* ([CC BY-SA 4.0](https://creativecommons.org/licenses/by/4.0/)).
If you find this project useful in your research, please cite the following papers:
```plain
Labrak, Y., Rouvier, M., & Dufour, R. (2023). MORFITT : Un corpus multi-labels d’articles scientifiques français dans le domaine biomédical. In F. Boudin, B. Daille, R. Dufour, O. Khettari, M. Houbre, L. Jourdan, & N. Kooli (Eds.), 18e Conférence en Recherche d’Information et Applications – 16e Rencontres Jeunes Chercheurs en RI – 30e Conférence sur le Traitement Automatique des Langues Naturelles – 25e Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (pp. 66–70). ATALA. https://hal.science/hal-04131591
```
or using the bibtex:
```bibtex
@inproceedings{labrak:hal-04131591,
TITLE = {{MORFITT : Un corpus multi-labels d'articles scientifiques fran{\c c}ais dans le domaine biom{\'e}dical}},
AUTHOR = {Labrak, Yanis and Rouvier, Mickael and Dufour, Richard},
URL = {https://hal.science/hal-04131591},
BOOKTITLE = {{18e Conf{\'e}rence en Recherche d'Information et Applications -- 16e Rencontres Jeunes Chercheurs en RI -- 30e Conf{\'e}rence sur le Traitement Automatique des Langues Naturelles -- 25e Rencontre des {\'E}tudiants Chercheurs en Informatique pour le Traitement Automatique des Langues}},
ADDRESS = {Paris, France},
EDITOR = {Boudin, Florian and Daille, B{\'e}atrice and Dufour, Richard and Khettari, Oumaima and Houbre, Ma{\"e}l and Jourdan, L{\'e}ane and Kooli, Nihel},
PUBLISHER = {{ATALA}},
PAGES = {66-70},
YEAR = {2023},
KEYWORDS = {Analyse de documents scientifiques ; Jeux de donn{\'e}es compos{\'e}s des textes scientifiques},
PDF = {https://hal.science/hal-04131591/file/1465546.pdf},
HAL_ID = {hal-04131591},
HAL_VERSION = {v1},
}
``` | qanastek/MORFITT | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:fr",
"license:apache-2.0",
"medical",
"biology",
"region:us"
]
| 2023-03-22T18:42:06+00:00 | {"language": ["fr"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "MORFITT", "tags": ["medical", "biology"]} | 2023-08-25T09:41:05+00:00 | []
| [
"fr"
]
| TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-French #license-apache-2.0 #medical #biology #region-us
| MORFITT
=======
Data (Zenodo) | Publication (HAL)
---------------------------------
Yanis LABRAK, Richard DUFOUR, Mickaël ROUVIER

Key Features
============
Documents distribution
----------------------
Train: 1,514, Dev: 1,022, Test: 1,088
Multi-label distribution
------------------------
Number of labels per document distribution
------------------------------------------

Co-occurences distribution
--------------------------

If you use HuggingFace Transformers
===================================
or
License and Citation
====================
The code is under Apache-2.0 License.
The MORFITT dataset is licensed under *Attribution-ShareAlike 4.0 International* (CC BY-SA 4.0).
If you find this project useful in your research, please cite the following papers:
or using the bibtex:
| []
| [
"TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-French #license-apache-2.0 #medical #biology #region-us \n"
]
| [
49
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-French #license-apache-2.0 #medical #biology #region-us \n"
]
| [
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|
4158ed13c095a48d355ffc652e05c403b3d01fbf | # Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lab156/github-issues | [
"region:us"
]
| 2023-03-22T19:23:40+00:00 | {"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "comments", "sequence": "string"}, {"name": "pull_request", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "diff_url", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "merged_at", "dtype": "timestamp[s]"}]}, {"name": "number", "dtype": "int64"}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 13797091, "num_examples": 5599}], "download_size": 5345250, "dataset_size": 13797091}} | 2023-03-23T16:38:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "github-issues"
More Information needed | [
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
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|
edb8bb825827ee7291ba2cd1dc71a0ace42a8070 | Hey there welcome to my image repo, Today I decided I wanted to compare different cfg and denoise values between models to see how each model is influenced
differently by different values. I used the same settings for each generation aside from cfg, denoise, model, and potentially Vae if a model has one baked in
(will be noted on image) I will be updating this repo as time goes on and as I process more models, if there is a model you want me to process please just ask!
Note: because of standardized testing methods not all models are going to be utilized to their full potential. I may add seperate images for optimized use of certain models.
These models will have the tag OPT.
Prompt:(highres:1.2), beautiful lighting, masterpiece, best quality, intricate, (high detail:1.2), high sharpness, 8k, (1girl), volumetric lighting, vibrant colors, ultra detailed, full body, Sakuya izayoi, Sakuya Izayoi from Touhou Project, apron, blue dress, blue eyes, blush, bowtie, braid, colored shirt, dress, frills, green bow, green bowtie, grey_colored_hair, hair bow, puffy sleeves, puffy short sleeves, shirt, short hair, short sleeves, standing, twin braids, white apron, white shirt, blue_maid_outfit, maid, maid headdress, white_maid_headdress
Negative: bad_prompt_version2, bad-artist, (low resolution:1.2), low quality, bad anatomy, bad hands, missing face, missing head, cut off head
Sampler: Euler A
Steps: 25
Dimensions 640x640 (upscaled to 1280x1280)
Seed: -1
Upscaler: 4xAnimeSharp
| Shreddertheman/Model_CFG_and_Denoise_Comaprisons | [
"region:us"
]
| 2023-03-22T19:47:39+00:00 | {} | 2023-03-22T21:50:06+00:00 | []
| []
| TAGS
#region-us
| Hey there welcome to my image repo, Today I decided I wanted to compare different cfg and denoise values between models to see how each model is influenced
differently by different values. I used the same settings for each generation aside from cfg, denoise, model, and potentially Vae if a model has one baked in
(will be noted on image) I will be updating this repo as time goes on and as I process more models, if there is a model you want me to process please just ask!
Note: because of standardized testing methods not all models are going to be utilized to their full potential. I may add seperate images for optimized use of certain models.
These models will have the tag OPT.
Prompt:(highres:1.2), beautiful lighting, masterpiece, best quality, intricate, (high detail:1.2), high sharpness, 8k, (1girl), volumetric lighting, vibrant colors, ultra detailed, full body, Sakuya izayoi, Sakuya Izayoi from Touhou Project, apron, blue dress, blue eyes, blush, bowtie, braid, colored shirt, dress, frills, green bow, green bowtie, grey_colored_hair, hair bow, puffy sleeves, puffy short sleeves, shirt, short hair, short sleeves, standing, twin braids, white apron, white shirt, blue_maid_outfit, maid, maid headdress, white_maid_headdress
Negative: bad_prompt_version2, bad-artist, (low resolution:1.2), low quality, bad anatomy, bad hands, missing face, missing head, cut off head
Sampler: Euler A
Steps: 25
Dimensions 640x640 (upscaled to 1280x1280)
Seed: -1
Upscaler: 4xAnimeSharp
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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| chinese.stackexchange.com.jsonl | 140 |
| christianity.stackexchange.com.jsonl | 365 |
| civicrm.stackexchange.com.jsonl | 37 |
| codegolf.stackexchange.com.jsonl | 15 |
| codereview.stackexchange.com.jsonl | 101 |
| coffee.stackexchange.com.jsonl | 21 |
| cogsci.stackexchange.com.jsonl | 135 |
| computergraphics.stackexchange.com.jsonl | 51 |
| conlang.stackexchange.com.jsonl | 9 |
| cooking.stackexchange.com.jsonl | 320 |
| craftcms.stackexchange.com.jsonl | 79 |
| crafts.stackexchange.com.jsonl | 33 |
| crypto.stackexchange.com.jsonl | 345 |
| cs.stackexchange.com.jsonl | 491 |
| cseducators.stackexchange.com.jsonl | 16 |
| cstheory.stackexchange.com.jsonl | 107 |
| datascience.stackexchange.com.jsonl | 271 |
| dba.stackexchange.com.jsonl | 859 |
| devops.stackexchange.com.jsonl | 60 |
| diy.stackexchange.com.jsonl | 743 |
| drones.stackexchange.com.jsonl | 6 |
| drupal.stackexchange.com.jsonl | 534 |
| dsp.stackexchange.com.jsonl | 261 |
| earthscience.stackexchange.com.jsonl | 105 |
| ebooks.stackexchange.com.jsonl | 10 |
| economics.stackexchange.com.jsonl | 176 |
| electronics.stackexchange.com.jsonl | 1854 |
| elementaryos.stackexchange.com.jsonl | 4 |
| ell.stackexchange.com.jsonl | 1104 |
| emacs.stackexchange.com.jsonl | 208 |
| engineering.stackexchange.com.jsonl | 182 |
| english.stackexchange.com.jsonl | 1219 |
| eosio.stackexchange.com.jsonl | 9 |
| es.stackoverflow.com.jsonl | 1014 |
| esperanto.stackexchange.com.jsonl | 12 |
| ethereum.stackexchange.com.jsonl | 286 |
| expatriates.stackexchange.com.jsonl | 62 |
| expressionengine.stackexchange.com.jsonl | 54 |
| fitness.stackexchange.com.jsonl | 135 |
| freelancing.stackexchange.com.jsonl | 33 |
| french.stackexchange.com.jsonl | 130 |
| gamedev.stackexchange.com.jsonl | 677 |
| gaming.stackexchange.com.jsonl | 1294 |
| gardening.stackexchange.com.jsonl | 220 |
| genealogy.stackexchange.com.jsonl | 56 |
| german.stackexchange.com.jsonl | 169 |
| gis.stackexchange.com.jsonl | 980 |
| graphicdesign.stackexchange.com.jsonl | 350 |
| ham.stackexchange.com.jsonl | 69 |
| hardwarerecs.stackexchange.com.jsonl | 25 |
| health.stackexchange.com.jsonl | 85 |
| hermeneutics.stackexchange.com.jsonl | 349 |
| hinduism.stackexchange.com.jsonl | 130 |
| history.stackexchange.com.jsonl | 506 |
| homebrew.stackexchange.com.jsonl | 44 |
| hsm.stackexchange.com.jsonl | 78 |
| interpersonal.stackexchange.com.jsonl | 74 |
| iot.stackexchange.com.jsonl | 21 |
| iota.stackexchange.com.jsonl | 6 |
| islam.stackexchange.com.jsonl | 103 |
| italian.stackexchange.com.jsonl | 55 |
| ja.stackoverflow.com.jsonl | 5 |
| japanese.stackexchange.com.jsonl | 374 |
| joomla.stackexchange.com.jsonl | 40 |
| judaism.stackexchange.com.jsonl | 223 |
| korean.stackexchange.com.jsonl | 23 |
| languagelearning.stackexchange.com.jsonl | 11 |
| latin.stackexchange.com.jsonl | 120 |
| law.stackexchange.com.jsonl | 579 |
| lifehacks.stackexchange.com.jsonl | 30 |
| linguistics.stackexchange.com.jsonl | 196 |
| literature.stackexchange.com.jsonl | 106 |
| magento.stackexchange.com.jsonl | 315 |
| martialarts.stackexchange.com.jsonl | 40 |
| materials.stackexchange.com.jsonl | 40 |
| matheducators.stackexchange.com.jsonl | 44 |
| mechanics.stackexchange.com.jsonl | 217 |
| moderators.stackexchange.com.jsonl | 9 |
| monero.stackexchange.com.jsonl | 29 |
| money.stackexchange.com.jsonl | 705 |
| movies.stackexchange.com.jsonl | 483 |
| music.stackexchange.com.jsonl | 364 |
| musicfans.stackexchange.com.jsonl | 22 |
| mythology.stackexchange.com.jsonl | 45 |
| networkengineering.stackexchange.com.jsonl | 178 |
| opendata.stackexchange.com.jsonl | 9 |
| opensource.stackexchange.com.jsonl | 72 |
| or.stackexchange.com.jsonl | 16 |
| outdoors.stackexchange.com.jsonl | 102 |
| parenting.stackexchange.com.jsonl | 103 |
| patents.stackexchange.com.jsonl | 40 |
| pets.stackexchange.com.jsonl | 93 |
| philosophy.stackexchange.com.jsonl | 294 |
| photo.stackexchange.com.jsonl | 483 |
| pm.stackexchange.com.jsonl | 77 |
| poker.stackexchange.com.jsonl | 13 |
| politics.stackexchange.com.jsonl | 565 |
| portuguese.stackexchange.com.jsonl | 27 |
| proofassistants.stackexchange.com.jsonl | 11 |
| puzzling.stackexchange.com.jsonl | 185 |
| quant.stackexchange.com.jsonl | 152 |
| quantumcomputing.stackexchange.com.jsonl | 164 |
| raspberrypi.stackexchange.com.jsonl | 119 |
| retrocomputing.stackexchange.com.jsonl | 189 |
| reverseengineering.stackexchange.com.jsonl | 76 |
| robotics.stackexchange.com.jsonl | 58 |
| rpg.stackexchange.com.jsonl | 1402 |
| ru.stackoverflow.com.jsonl | 1922 |
| rus.stackexchange.com.jsonl | 67 |
| russian.stackexchange.com.jsonl | 62 |
| salesforce.stackexchange.com.jsonl | 687 |
| scicomp.stackexchange.com.jsonl | 86 |
| scifi.stackexchange.com.jsonl | 1322 |
| security.stackexchange.com.jsonl | 911 |
| serverfault.com.jsonl | 1905 |
| sharepoint.stackexchange.com.jsonl | 275 |
| sitecore.stackexchange.com.jsonl | 49 |
| skeptics.stackexchange.com.jsonl | 398 |
| softwareengineering.stackexchange.com.jsonl | 1200 |
| softwarerecs.stackexchange.com.jsonl | 48 |
| solana.stackexchange.com.jsonl | 10 |
| sound.stackexchange.com.jsonl | 63 |
| space.stackexchange.com.jsonl | 470 |
| spanish.stackexchange.com.jsonl | 114 |
| sports.stackexchange.com.jsonl | 116 |
| sqa.stackexchange.com.jsonl | 96 |
| stackapps.com.jsonl | 8 |
| stats.stackexchange.com.jsonl | 1650 |
| stellar.stackexchange.com.jsonl | 14 |
| substrate.stackexchange.com.jsonl | 22 |
| superuser.com.jsonl | 2793 |
| sustainability.stackexchange.com.jsonl | 34 |
| tex.stackexchange.com.jsonl | 1962 |
| tezos.stackexchange.com.jsonl | 11 |
| tor.stackexchange.com.jsonl | 30 |
| travel.stackexchange.com.jsonl | 663 |
| tridion.stackexchange.com.jsonl | 29 |
| ukrainian.stackexchange.com.jsonl | 40 |
| unix.stackexchange.com.jsonl | 1779 |
| ux.stackexchange.com.jsonl | 526 |
| vegetarianism.stackexchange.com.jsonl | 10 |
| vi.stackexchange.com.jsonl | 147 |
| webapps.stackexchange.com.jsonl | 131 |
| webmasters.stackexchange.com.jsonl | 298 |
| windowsphone.stackexchange.com.jsonl | 14 |
| woodworking.stackexchange.com.jsonl | 45 |
| wordpress.stackexchange.com.jsonl | 666 |
| workplace.stackexchange.com.jsonl | 624 |
| worldbuilding.stackexchange.com.jsonl | 809 |
| writers.stackexchange.com.jsonl | 210 |
| Total | 55001 |
## Principles
* **StackExchange**: for each site, find questions that: 1) score to the question is top 15% 2) contains accepted answer 3) score to accepted answer is top 15% 4) context length (i.e., body to the question) is longer than 384 characters 5) answer length (i.e., body to the accepted answer) is longer than 384 characters 6) subjectivity of the answer is less than 0.5 (determined by textblob). | ds3lab/instructions | [
"language:en",
"region:us"
]
| 2023-03-22T20:15:51+00:00 | {"language": ["en"], "pretty_name": "Open Instructions"} | 2023-03-22T23:53:27+00:00 | []
| [
"en"
]
| TAGS
#language-English #region-us
| Data Sources
------------
### StackExchange
Principles
----------
* StackExchange: for each site, find questions that: 1) score to the question is top 15% 2) contains accepted answer 3) score to accepted answer is top 15% 4) context length (i.e., body to the question) is longer than 384 characters 5) answer length (i.e., body to the accepted answer) is longer than 384 characters 6) subjectivity of the answer is less than 0.5 (determined by textblob).
| [
"### StackExchange\n\n\n\nPrinciples\n----------\n\n\n* StackExchange: for each site, find questions that: 1) score to the question is top 15% 2) contains accepted answer 3) score to accepted answer is top 15% 4) context length (i.e., body to the question) is longer than 384 characters 5) answer length (i.e., body to the accepted answer) is longer than 384 characters 6) subjectivity of the answer is less than 0.5 (determined by textblob)."
]
| [
"TAGS\n#language-English #region-us \n",
"### StackExchange\n\n\n\nPrinciples\n----------\n\n\n* StackExchange: for each site, find questions that: 1) score to the question is top 15% 2) contains accepted answer 3) score to accepted answer is top 15% 4) context length (i.e., body to the question) is longer than 384 characters 5) answer length (i.e., body to the accepted answer) is longer than 384 characters 6) subjectivity of the answer is less than 0.5 (determined by textblob)."
]
| [
10,
109
]
| [
"passage: TAGS\n#language-English #region-us \n### StackExchange\n\n\n\nPrinciples\n----------\n\n\n* StackExchange: for each site, find questions that: 1) score to the question is top 15% 2) contains accepted answer 3) score to accepted answer is top 15% 4) context length (i.e., body to the question) is longer than 384 characters 5) answer length (i.e., body to the accepted answer) is longer than 384 characters 6) subjectivity of the answer is less than 0.5 (determined by textblob)."
]
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|
58c6a2d74aff228b17f61e2d5d3ab8c0ceba344e |
NOTE:
This Datasets generated using Openai chatgpt same as alpaca dataset
please read openai terms or alpaca terms about license how can be used | acul3/instruct_id_chatgpt | [
"license:cc-by-4.0",
"region:us"
]
| 2023-03-22T20:51:05+00:00 | {"license": "cc-by-4.0"} | 2023-03-22T21:01:01+00:00 | []
| []
| TAGS
#license-cc-by-4.0 #region-us
|
NOTE:
This Datasets generated using Openai chatgpt same as alpaca dataset
please read openai terms or alpaca terms about license how can be used | []
| [
"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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|
13310aeed49a2b5beb815877cf1283b140f031cd | # Dataset Card for "ia-loaded"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | davanstrien/ia-loaded | [
"region:us"
]
| 2023-03-22T20:56:21+00:00 | {"dataset_info": {"features": [{"name": "crawl_date", "dtype": "int64"}, {"name": "last_modified_date", "dtype": "float64"}, {"name": "url", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "extension", "dtype": "string"}, {"name": "mime_type_web_server", "dtype": "string"}, {"name": "mime_type_tika", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "md5", "dtype": "string"}, {"name": "sha1", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1406041380.6716, "num_examples": 4582}], "download_size": 1503042941, "dataset_size": 1406041380.6716}} | 2023-03-22T23:05:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ia-loaded"
More Information needed | [
"# Dataset Card for \"ia-loaded\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ia-loaded\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ia-loaded\"\n\nMore Information needed"
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|
d8116eb4c5c5fb78329213e767b7579b434ab184 |
# SVO-Probes
This dataset comes from https://github.com/deepmind/svo_probes.
## Usage
```python
from datasets import load_dataset
# Note that the following line says "train" split, but there are actually no splits in this dataset.
dataset = load_dataset("MichiganNLP/svo_probes", split="train")
# To see an example, access the first element of the dataset with `dataset[0]`.
```
| MichiganNLP/svo_probes | [
"size_categories:10K<n<100K",
"language:en",
"license:cc-by-4.0",
"region:us"
]
| 2023-03-22T20:57:44+00:00 | {"language": ["en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "pretty_name": "SVO-Probes"} | 2023-06-18T04:28:20+00:00 | []
| [
"en"
]
| TAGS
#size_categories-10K<n<100K #language-English #license-cc-by-4.0 #region-us
|
# SVO-Probes
This dataset comes from URL
## Usage
| [
"# SVO-Probes\n\nThis dataset comes from URL",
"## Usage"
]
| [
"TAGS\n#size_categories-10K<n<100K #language-English #license-cc-by-4.0 #region-us \n",
"# SVO-Probes\n\nThis dataset comes from URL",
"## Usage"
]
| [
31,
12,
3
]
| [
"passage: TAGS\n#size_categories-10K<n<100K #language-English #license-cc-by-4.0 #region-us \n# SVO-Probes\n\nThis dataset comes from URL## Usage"
]
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|
3480355eae6553f91b5a89add1d0348510b616bf | # Dataset Card for Translating Python Programming Puzzles (TP3)
## Dataset Description
- **Repository:** [GitHub Repository](https://github.com/google-research/babelcode)
- **Paper:** [Measuring The Impact Of Programming Language Distribution](https://arxiv.org/abs/2302.01973)
### How To Use This Dataset
To use this dataset, you can either use the original [BabelCode Repo](https://github.com/google-research/babelcode), or you can use the [`bc_eval` Metric](https://huggingface.co/spaces/gabeorlanski/bc_eval).
### Dataset Summary
The Translating Python Programming Puzzles (TP3) dataset is created from the verification functions in the [Python Programming Puzzles dataset (Schuster et al., 2021)](https://github.com/microsoft/PythonProgrammingPuzzles) to create this dataset. These functions are hand-crafted by the
authors and are used to check if an answer satisfies the constraints of the puzzle. These puzzles range in difficulty from basic character checking to competitive programming problems.
### Supported Tasks and Leaderboards
### Languages
BC-TP3 supports:
* C++
* C#
* Dart
* Go
* Haskell
* Java
* Javascript
* Julia
* Kotlin
* Lua
* PHP
* R
* Rust
* Scala
* TypeScript
## Dataset Structure
```python
>>> from datasets import load_dataset
>>> load_dataset("gabeorlanski/tp3")
DatasetDict({
test: Dataset({
features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'source', 'question_info'],
num_rows: 5920
})
})
```
### Data Fields
- `qid`: The question ID used for running tests.
- `title`: The title of the question.
- `language`: The programming language of the example.
- `text`: The description of the problem.
- `signature`: The signature for the problem.
- `signature_with_docstring`: The signature with the adequately formatted docstring for the given problem.
- `arguments`: The arguments of the problem.
- `source`: The source solution in Python.
- `question_info`: The dict of information used for executing predictions. It has the keys:
- `test_code`: The raw testing script used in the language. If you want to use this, replace `PLACEHOLDER_FN_NAME` (and `PLACEHOLDER_CLS_NAME` if needed) with the corresponding entry points. Next, replace `PLACEHOLDER_CODE_BODY` with the postprocessed prediction.
- `test_list`: The raw json line of the list of tests for the problem. To load them, use `json.loads`
- `test_case_ids`: The list of test case ids for the problem. These are used to determine if a prediction passes or not.
- `entry_fn_name`: The function's name to use an entry point.
- `entry_cls_name`: The class name to use an entry point.
- `commands`: The commands used to execute the prediction. Includes a `__FILENAME__` hole that is replaced with the filename.
- `timeouts`: The default timeouts for each command.
- `extension`: The extension for the prediction file.
**NOTE:** If you want to use a different function name (or class name for languages that require class names) for the prediction, you must update the `entry_fn_name` and `entry_cls_name` accordingly. For example, if you have the original question with `entry_fn_name` of `add`, but want to change it to `f`, you must update `ds["question_info"]["entry_fn_name"]` to `f`:
```python
>>> from datasets import load_dataset
>>> ds = load_dataset("gabeorlanski/bc-mbpp")['test']
>>> # The original entry_fn_name
>>> ds[0]['question_info']['entry_fn_name']
removeOcc
>>> # You MUST update the corresponding entry_fn_name
>>> ds[0]['question_info']['entry_fn_name'] = 'f'
>>> ds[0]['question_info']['entry_fn_name']
f
```
## Dataset Creation
See section 2 and section 4.4 of the [BabelCode Paper](https://arxiv.org/abs/2302.01973) to learn more about how the datasets are translated.
For information on how the original P3 dataset was collected, please see [Programming Puzzles paper](https://arxiv.org/abs/2106.05784).
### Dataset Curators
Google Research
### Licensing Information
CC-BY-4.0
### Citation Information
```
@article{orlanski2023measuring,
title={Measuring The Impact Of Programming Language Distribution},
author={Orlanski, Gabriel and Xiao, Kefan and Garcia, Xavier and Hui, Jeffrey and Howland, Joshua and Malmaud, Jonathan and Austin, Jacob and Singh, Rishah and Catasta, Michele},
journal={arXiv preprint arXiv:2302.01973},
year={2023}
}
@inproceedings{
schuster2021programming,
title={Programming Puzzles},
author={Tal Schuster and Ashwin Kalyan and Alex Polozov and Adam Tauman Kalai},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2021},
url={https://arxiv.org/abs/2106.05784}
}
``` | gabeorlanski/tp3 | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_categories:translation",
"size_categories:1K<n<10K",
"source_datasets:original",
"source_datasets:extended|p3",
"language:en",
"license:apache-2.0",
"code",
"arxiv:2302.01973",
"arxiv:2106.05784",
"region:us"
]
| 2023-03-22T21:00:47+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "source_datasets": ["original", "extended|p3"], "task_categories": ["text-generation", "text2text-generation", "translation"], "pretty_name": "BabelCode TP3", "tags": ["code"]} | 2023-07-18T15:22:25+00:00 | [
"2302.01973",
"2106.05784"
]
| [
"en"
]
| TAGS
#task_categories-text-generation #task_categories-text2text-generation #task_categories-translation #size_categories-1K<n<10K #source_datasets-original #source_datasets-extended|p3 #language-English #license-apache-2.0 #code #arxiv-2302.01973 #arxiv-2106.05784 #region-us
| # Dataset Card for Translating Python Programming Puzzles (TP3)
## Dataset Description
- Repository: GitHub Repository
- Paper: Measuring The Impact Of Programming Language Distribution
### How To Use This Dataset
To use this dataset, you can either use the original BabelCode Repo, or you can use the 'bc_eval' Metric.
### Dataset Summary
The Translating Python Programming Puzzles (TP3) dataset is created from the verification functions in the Python Programming Puzzles dataset (Schuster et al., 2021) to create this dataset. These functions are hand-crafted by the
authors and are used to check if an answer satisfies the constraints of the puzzle. These puzzles range in difficulty from basic character checking to competitive programming problems.
### Supported Tasks and Leaderboards
### Languages
BC-TP3 supports:
* C++
* C#
* Dart
* Go
* Haskell
* Java
* Javascript
* Julia
* Kotlin
* Lua
* PHP
* R
* Rust
* Scala
* TypeScript
## Dataset Structure
### Data Fields
- 'qid': The question ID used for running tests.
- 'title': The title of the question.
- 'language': The programming language of the example.
- 'text': The description of the problem.
- 'signature': The signature for the problem.
- 'signature_with_docstring': The signature with the adequately formatted docstring for the given problem.
- 'arguments': The arguments of the problem.
- 'source': The source solution in Python.
- 'question_info': The dict of information used for executing predictions. It has the keys:
- 'test_code': The raw testing script used in the language. If you want to use this, replace 'PLACEHOLDER_FN_NAME' (and 'PLACEHOLDER_CLS_NAME' if needed) with the corresponding entry points. Next, replace 'PLACEHOLDER_CODE_BODY' with the postprocessed prediction.
- 'test_list': The raw json line of the list of tests for the problem. To load them, use 'URL'
- 'test_case_ids': The list of test case ids for the problem. These are used to determine if a prediction passes or not.
- 'entry_fn_name': The function's name to use an entry point.
- 'entry_cls_name': The class name to use an entry point.
- 'commands': The commands used to execute the prediction. Includes a '__FILENAME__' hole that is replaced with the filename.
- 'timeouts': The default timeouts for each command.
- 'extension': The extension for the prediction file.
NOTE: If you want to use a different function name (or class name for languages that require class names) for the prediction, you must update the 'entry_fn_name' and 'entry_cls_name' accordingly. For example, if you have the original question with 'entry_fn_name' of 'add', but want to change it to 'f', you must update 'ds["question_info"]["entry_fn_name"]' to 'f':
## Dataset Creation
See section 2 and section 4.4 of the BabelCode Paper to learn more about how the datasets are translated.
For information on how the original P3 dataset was collected, please see Programming Puzzles paper.
### Dataset Curators
Google Research
### Licensing Information
CC-BY-4.0
| [
"# Dataset Card for Translating Python Programming Puzzles (TP3)",
"## Dataset Description\n\n- Repository: GitHub Repository\n- Paper: Measuring The Impact Of Programming Language Distribution",
"### How To Use This Dataset\n\nTo use this dataset, you can either use the original BabelCode Repo, or you can use the 'bc_eval' Metric.",
"### Dataset Summary\n\nThe Translating Python Programming Puzzles (TP3) dataset is created from the verification functions in the Python Programming Puzzles dataset (Schuster et al., 2021) to create this dataset. These functions are hand-crafted by the\nauthors and are used to check if an answer satisfies the constraints of the puzzle. These puzzles range in difficulty from basic character checking to competitive programming problems.",
"### Supported Tasks and Leaderboards",
"### Languages\nBC-TP3 supports:\n* C++\n* C#\n* Dart\n* Go\n* Haskell\n* Java\n* Javascript\n* Julia\n* Kotlin\n* Lua\n* PHP\n* R\n* Rust\n* Scala\n* TypeScript",
"## Dataset Structure",
"### Data Fields\n\n- 'qid': The question ID used for running tests.\n- 'title': The title of the question.\n- 'language': The programming language of the example.\n- 'text': The description of the problem.\n- 'signature': The signature for the problem.\n- 'signature_with_docstring': The signature with the adequately formatted docstring for the given problem.\n- 'arguments': The arguments of the problem.\n- 'source': The source solution in Python.\n- 'question_info': The dict of information used for executing predictions. It has the keys:\n - 'test_code': The raw testing script used in the language. If you want to use this, replace 'PLACEHOLDER_FN_NAME' (and 'PLACEHOLDER_CLS_NAME' if needed) with the corresponding entry points. Next, replace 'PLACEHOLDER_CODE_BODY' with the postprocessed prediction. \n - 'test_list': The raw json line of the list of tests for the problem. To load them, use 'URL'\n - 'test_case_ids': The list of test case ids for the problem. These are used to determine if a prediction passes or not.\n - 'entry_fn_name': The function's name to use an entry point.\n - 'entry_cls_name': The class name to use an entry point. \n - 'commands': The commands used to execute the prediction. Includes a '__FILENAME__' hole that is replaced with the filename.\n - 'timeouts': The default timeouts for each command.\n - 'extension': The extension for the prediction file.\n\nNOTE: If you want to use a different function name (or class name for languages that require class names) for the prediction, you must update the 'entry_fn_name' and 'entry_cls_name' accordingly. For example, if you have the original question with 'entry_fn_name' of 'add', but want to change it to 'f', you must update 'ds[\"question_info\"][\"entry_fn_name\"]' to 'f':",
"## Dataset Creation\nSee section 2 and section 4.4 of the BabelCode Paper to learn more about how the datasets are translated. \n\nFor information on how the original P3 dataset was collected, please see Programming Puzzles paper.",
"### Dataset Curators\nGoogle Research",
"### Licensing Information\nCC-BY-4.0"
]
| [
"TAGS\n#task_categories-text-generation #task_categories-text2text-generation #task_categories-translation #size_categories-1K<n<10K #source_datasets-original #source_datasets-extended|p3 #language-English #license-apache-2.0 #code #arxiv-2302.01973 #arxiv-2106.05784 #region-us \n",
"# Dataset Card for Translating Python Programming Puzzles (TP3)",
"## Dataset Description\n\n- Repository: GitHub Repository\n- Paper: Measuring The Impact Of Programming Language Distribution",
"### How To Use This Dataset\n\nTo use this dataset, you can either use the original BabelCode Repo, or you can use the 'bc_eval' Metric.",
"### Dataset Summary\n\nThe Translating Python Programming Puzzles (TP3) dataset is created from the verification functions in the Python Programming Puzzles dataset (Schuster et al., 2021) to create this dataset. These functions are hand-crafted by the\nauthors and are used to check if an answer satisfies the constraints of the puzzle. These puzzles range in difficulty from basic character checking to competitive programming problems.",
"### Supported Tasks and Leaderboards",
"### Languages\nBC-TP3 supports:\n* C++\n* C#\n* Dart\n* Go\n* Haskell\n* Java\n* Javascript\n* Julia\n* Kotlin\n* Lua\n* PHP\n* R\n* Rust\n* Scala\n* TypeScript",
"## Dataset Structure",
"### Data Fields\n\n- 'qid': The question ID used for running tests.\n- 'title': The title of the question.\n- 'language': The programming language of the example.\n- 'text': The description of the problem.\n- 'signature': The signature for the problem.\n- 'signature_with_docstring': The signature with the adequately formatted docstring for the given problem.\n- 'arguments': The arguments of the problem.\n- 'source': The source solution in Python.\n- 'question_info': The dict of information used for executing predictions. It has the keys:\n - 'test_code': The raw testing script used in the language. If you want to use this, replace 'PLACEHOLDER_FN_NAME' (and 'PLACEHOLDER_CLS_NAME' if needed) with the corresponding entry points. Next, replace 'PLACEHOLDER_CODE_BODY' with the postprocessed prediction. \n - 'test_list': The raw json line of the list of tests for the problem. To load them, use 'URL'\n - 'test_case_ids': The list of test case ids for the problem. These are used to determine if a prediction passes or not.\n - 'entry_fn_name': The function's name to use an entry point.\n - 'entry_cls_name': The class name to use an entry point. \n - 'commands': The commands used to execute the prediction. Includes a '__FILENAME__' hole that is replaced with the filename.\n - 'timeouts': The default timeouts for each command.\n - 'extension': The extension for the prediction file.\n\nNOTE: If you want to use a different function name (or class name for languages that require class names) for the prediction, you must update the 'entry_fn_name' and 'entry_cls_name' accordingly. For example, if you have the original question with 'entry_fn_name' of 'add', but want to change it to 'f', you must update 'ds[\"question_info\"][\"entry_fn_name\"]' to 'f':",
"## Dataset Creation\nSee section 2 and section 4.4 of the BabelCode Paper to learn more about how the datasets are translated. \n\nFor information on how the original P3 dataset was collected, please see Programming Puzzles paper.",
"### Dataset Curators\nGoogle Research",
"### Licensing Information\nCC-BY-4.0"
]
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"passage: TAGS\n#task_categories-text-generation #task_categories-text2text-generation #task_categories-translation #size_categories-1K<n<10K #source_datasets-original #source_datasets-extended|p3 #language-English #license-apache-2.0 #code #arxiv-2302.01973 #arxiv-2106.05784 #region-us \n# Dataset Card for Translating Python Programming Puzzles (TP3)## Dataset Description\n\n- Repository: GitHub Repository\n- Paper: Measuring The Impact Of Programming Language Distribution### How To Use This Dataset\n\nTo use this dataset, you can either use the original BabelCode Repo, or you can use the 'bc_eval' Metric.### Dataset Summary\n\nThe Translating Python Programming Puzzles (TP3) dataset is created from the verification functions in the Python Programming Puzzles dataset (Schuster et al., 2021) to create this dataset. These functions are hand-crafted by the\nauthors and are used to check if an answer satisfies the constraints of the puzzle. These puzzles range in difficulty from basic character checking to competitive programming problems.### Supported Tasks and Leaderboards### Languages\nBC-TP3 supports:\n* C++\n* C#\n* Dart\n* Go\n* Haskell\n* Java\n* Javascript\n* Julia\n* Kotlin\n* Lua\n* PHP\n* R\n* Rust\n* Scala\n* TypeScript## Dataset Structure"
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|
fa83864c1d7966f4ead1eb1e64c91632020a1932 | # Dataset Card for "somos-alpaca-es"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dvilasuero/somos-alpaca-es | [
"region:us"
]
| 2023-03-22T21:16:08+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "dtype": "null"}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "null"}, {"name": "annotation_agent", "dtype": "null"}, {"name": "vectors", "dtype": "null"}, {"name": "multi_label", "dtype": "null"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "null"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "null"}, {"name": "event_timestamp", "dtype": "null"}, {"name": "metrics", "dtype": "null"}], "splits": [{"name": "train"}], "download_size": 0, "dataset_size": 0}} | 2023-03-23T23:41:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "somos-alpaca-es"
More Information needed | [
"# Dataset Card for \"somos-alpaca-es\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"somos-alpaca-es\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"somos-alpaca-es\"\n\nMore Information needed"
]
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|
26b2647d2649b6c9806ebbe0e2eb0e08cd07f151 | # Dataset Card for "somos-alpaca-es-rg"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dvilasuero/somos-alpaca-es-rg | [
"region:us"
]
| 2023-03-22T21:27:35+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "null"}, {"name": "annotation_agent", "dtype": "null"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 984065676, "num_examples": 52002}], "download_size": 652741327, "dataset_size": 984065676}} | 2023-03-22T21:30:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "somos-alpaca-es-rg"
More Information needed | [
"# Dataset Card for \"somos-alpaca-es-rg\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"somos-alpaca-es-rg\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"somos-alpaca-es-rg\"\n\nMore Information needed"
]
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|
635341e2952912dbefbd92b0603149898cee2d38 | # Dataset Card de "somos-alpaca-es"
Este conjunto de datos es una versión traducida del dataset Alpaca en Español.
Este conjunto de datos sirve como referencia para el esfuerzo colaborativo de limpieza y mejora del dataset durante el hackathon SomosNLP 2023.
Cuantas más personas y equipos participen mayor calidad final se podrá obtener.
---
➡️ **ACTUALIZACION**: Contribuye al etiquetado de la traducción de la versión limpia: [somos-clean-alpaca-es](https://huggingface.co/datasets/somosnlp/somos-clean-alpaca-es)
---
## El reto
A continuación se describen los pasos y normas para participar:
1. Se debe utilizar este conjunto de datos como punto de partida y mantener tanto los `ids` como la estructura. Esto es así para poder realizar tareas posteriores de validación cruzada y mejoras programáticas del dataset final.
2. Se trata de un dataset en formato compatible con Argilla. Cada equipo o persona que quiera participar, puede trabajar con su propia instancia de Argilla. Una forma fácil de desplegar Argilla es desplegar [Argilla en Spaces](https://huggingface.co/new-space?template=argilla/argilla-template-space).
3. Si se usa Argilla en Spaces, es muy recomendable realizar copias periódicas del dataset utilizando `rg.load("nombre_del_dataset").to_datasets().push_to_hub()` dado que si se reinicia el Space se pierden los datos. También se puede hacer un upgrade del Space a CPU-Upgrade.
4. Argilla se puede utilizar para validar y etiquetar manualmente y usando búsquedas y similitud semántica desde la UI. Para ello se pondrán ejemplos de uso del lenguaje de búsqueda en esta página, pero se recomienda consultar [la guía de uso](https://docs.argilla.io/en/latest/guides/query_datasets.html).
5. La validación humana es necesaria para garantizar la calidad final pero se pueden también limpiezas programáticas para aquellos casos en los que sea más eficiente. En cualquier caso, para el éxito del experimento se deberán utilizar las etiquetas propuestas, aunque se modifique programáticamente el dataset.
6. No se deben borrar registros del dataset, si un registro es inválido se deberá indicar en la etiqueta (por ejemplo `BAD INPUT`) o con el status `discard`.
El resultado del reto será un dataset por persona o equipo que contenga el dataset original etiquetado parcialmente, y opcionalmente otras versiones/subconjuntos del dataset con los datos corregidos, mejorados o aumentados. En estos casos es conveniente mantener un dataset a parte con los ids originales.
## Como empezar a etiquetar
Para etiquetar el dataset tienes que:
1. Lanzar tu Argilla Space siguiendo [este link](https://huggingface.co/spaces/somosnlp/somos-alpaca-es?duplicate=true). Esto te guiará para crear una instancia de Argilla en el Hub que cargará automaticamente el dataset (ver captura de pantalla abajo). **IMPORTANTE**: que el Space sea Public para poder leer los datos etiquetados desde Python. El proceso de carga puede tardar hasta 10 minutos, puedes consultar los logs para comprobar que se están cargando los datos.
2. **IMPORTANTE:** Si se quiere sincronizar los datos validados con el Hub para no perder las anotaciones si se reinicia el Space, hay que configurar dos secrets (en Settings del Space): `HF_TOKEN` que es [vuestro token de escritura](https://huggingface.co/settings/tokens), y `HF_DATASET_NAME` que es el dataset donde queréis guardarlo, importante incluir la organizacion o persona seguido de un / y el nombre del dataset. Por ejemplo `juanmartinez/somos-alpaca-es-validations` o `miempresa/somos-alpaca-es-validations`.
3. Mientras se carga tu Argilla Space con el dataset puedes aprovechar para leer las guías de anotación.
4. Aunque en principio se va sincronizar el dataset anotado, recomendamos que abras Colab o un notebook en local y que guardes el dataset periodicamente en un dataset del Hub (puede ser en tu espacio personal o tu organización). Para ello recomendamos leer el apartado como guardar el dataset en el Hub.
Se recomienda mirar el log del Space para ver si hay errores a la hora de configurar los Secret `HF_TOKEN` y `HF_DATASET_NAME`.

## Desplegar Argilla localmente o en un servidor cloud
Para equipos que tengan el tiempo y quieran desplegar una versión con más capacidad de computación y estabilidad que Spaces, [aquí hay una guía explicativa](https://docs.argilla.io/en/latest/getting_started/installation/deployments/deployments.html).
Una vez instalada, se deben subir los datos con [este notebook](https://colab.research.google.com/drive/1KyikSFeJe6_lQNs-9cHveIOGM99ENha9#scrollTo=jbfdRoRVXTW6).
## Guías de anotación
Antes de empezar a anotar, es necesario leer la [guía de anotación](guia-de-anotacion.md) al completo.
## IMPORTANTE: Guardar el dataset en el Hub periodicamente
Aunque se ha configurado el Space para que se sincronice con un dataset del Hub a vuestra elección, para tener más seguridad se recomienda guardar una copia del dataset en el Hub ejecutando el siguiente código. Es necesario hacer login con Python usando `from huggingface_hub import notebook_login` o añadir el token directamente al hacer el push_to_hub:
```python
import argilla as rg
# usar rg.init() para definir la API_URL (la direct URL de tu Space de Argilla) y API_KEY
rg.init(
api_url="https://tu-space-de-argilla.hf.space",
api_key="team.apikey"
)
# el primer nombre es el dataset en argilla, el segundo es el dataset en el Hub.
rg.load("somos-alpaca-es-team").to_datasets().push_to_hub("somos-alpaca-es", token="TU TOKEN WRITE EN SETTINGS HUB. NO NECESARIO SI HAS HECHO LOGIN")
```
Una vez hecho esto se puede recuperar el dataset y volver a cargar en Argilla con el notebook de "Como cargar el dataset en Argilla"
## Ejemplos de consultas y trucos para etiquetar
Se recomienda comenzar explorando y etiquetando el dataset de manera secuencial para entender la estructura e ir identificando patrones.
Una vez hecho esto se recomienda combinarlo con:
### Utilizar el buscador
Tanto con palabras clave, como con expresiones regulares, y wildcards y expresiones booleanas, ver [la guía de uso](https://docs.argilla.io/en/latest/guides/query_datasets.html).
Un aspecto interesante es la capacidad de buscar solo en determinados campos. Para ello, hay que utilizar la siguiente sintaxis `inputs.nombre_del_campo:"consulta"`:
Por ejemplo: `inputs.1-instruction:"Crear una página"` encontraría todos aquellos registros con este texto en la instrucción
Además esto se puede combinar con expresiones booleanas para buscar en varios campos: `inputs.1-instruction:"Crear una página" AND inputs.3-output:"html"`
### Find similar
Cuando encontramos patrones interesantes o erroneos en un registro y campo, podemos usar el botón find similar para encontrar ejemplos similares gracias al uso de similarity search usando embeddings.
### Etiquetado en lote (bulk)
Si encontramos un patrón muy claro, podemos revisar los ejemplos más rápido y anotarlos en bloque usando la barra superior, debajo del buscador. Si hay mucho ejemplos se puede aumentar el número de registros por página. Se recomienda en cualquier caso revisar los ejemplos.
| somosnlp/somos-alpaca-es | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:es",
"region:us"
]
| 2023-03-22T21:35:52+00:00 | {"language": ["es"], "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "text", "dtype": "null"}, {"name": "inputs", "struct": [{"name": "1-instruction", "dtype": "string"}, {"name": "2-input", "dtype": "string"}, {"name": "3-output", "dtype": "string"}]}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "dtype": "null"}, {"name": "annotation_agent", "dtype": "null"}, {"name": "vectors", "struct": [{"name": "input", "sequence": "float64"}, {"name": "instruction", "sequence": "float64"}, {"name": "output", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "dtype": "null"}], "splits": [{"name": "train", "num_bytes": 984065676, "num_examples": 52002}], "download_size": 652741327, "dataset_size": 984065676}} | 2023-03-24T16:41:21+00:00 | []
| [
"es"
]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-Spanish #region-us
| # Dataset Card de "somos-alpaca-es"
Este conjunto de datos es una versión traducida del dataset Alpaca en Español.
Este conjunto de datos sirve como referencia para el esfuerzo colaborativo de limpieza y mejora del dataset durante el hackathon SomosNLP 2023.
Cuantas más personas y equipos participen mayor calidad final se podrá obtener.
---
️ ACTUALIZACION: Contribuye al etiquetado de la traducción de la versión limpia: somos-clean-alpaca-es
---
## El reto
A continuación se describen los pasos y normas para participar:
1. Se debe utilizar este conjunto de datos como punto de partida y mantener tanto los 'ids' como la estructura. Esto es así para poder realizar tareas posteriores de validación cruzada y mejoras programáticas del dataset final.
2. Se trata de un dataset en formato compatible con Argilla. Cada equipo o persona que quiera participar, puede trabajar con su propia instancia de Argilla. Una forma fácil de desplegar Argilla es desplegar Argilla en Spaces.
3. Si se usa Argilla en Spaces, es muy recomendable realizar copias periódicas del dataset utilizando 'URL("nombre_del_dataset").to_datasets().push_to_hub()' dado que si se reinicia el Space se pierden los datos. También se puede hacer un upgrade del Space a CPU-Upgrade.
4. Argilla se puede utilizar para validar y etiquetar manualmente y usando búsquedas y similitud semántica desde la UI. Para ello se pondrán ejemplos de uso del lenguaje de búsqueda en esta página, pero se recomienda consultar la guía de uso.
5. La validación humana es necesaria para garantizar la calidad final pero se pueden también limpiezas programáticas para aquellos casos en los que sea más eficiente. En cualquier caso, para el éxito del experimento se deberán utilizar las etiquetas propuestas, aunque se modifique programáticamente el dataset.
6. No se deben borrar registros del dataset, si un registro es inválido se deberá indicar en la etiqueta (por ejemplo 'BAD INPUT') o con el status 'discard'.
El resultado del reto será un dataset por persona o equipo que contenga el dataset original etiquetado parcialmente, y opcionalmente otras versiones/subconjuntos del dataset con los datos corregidos, mejorados o aumentados. En estos casos es conveniente mantener un dataset a parte con los ids originales.
## Como empezar a etiquetar
Para etiquetar el dataset tienes que:
1. Lanzar tu Argilla Space siguiendo este link. Esto te guiará para crear una instancia de Argilla en el Hub que cargará automaticamente el dataset (ver captura de pantalla abajo). IMPORTANTE: que el Space sea Public para poder leer los datos etiquetados desde Python. El proceso de carga puede tardar hasta 10 minutos, puedes consultar los logs para comprobar que se están cargando los datos.
2. IMPORTANTE: Si se quiere sincronizar los datos validados con el Hub para no perder las anotaciones si se reinicia el Space, hay que configurar dos secrets (en Settings del Space): 'HF_TOKEN' que es vuestro token de escritura, y 'HF_DATASET_NAME' que es el dataset donde queréis guardarlo, importante incluir la organizacion o persona seguido de un / y el nombre del dataset. Por ejemplo 'juanmartinez/somos-alpaca-es-validations' o 'miempresa/somos-alpaca-es-validations'.
3. Mientras se carga tu Argilla Space con el dataset puedes aprovechar para leer las guías de anotación.
4. Aunque en principio se va sincronizar el dataset anotado, recomendamos que abras Colab o un notebook en local y que guardes el dataset periodicamente en un dataset del Hub (puede ser en tu espacio personal o tu organización). Para ello recomendamos leer el apartado como guardar el dataset en el Hub.
Se recomienda mirar el log del Space para ver si hay errores a la hora de configurar los Secret 'HF_TOKEN' y 'HF_DATASET_NAME'.
!Duplicar Space
## Desplegar Argilla localmente o en un servidor cloud
Para equipos que tengan el tiempo y quieran desplegar una versión con más capacidad de computación y estabilidad que Spaces, aquí hay una guía explicativa.
Una vez instalada, se deben subir los datos con este notebook.
## Guías de anotación
Antes de empezar a anotar, es necesario leer la guía de anotación al completo.
## IMPORTANTE: Guardar el dataset en el Hub periodicamente
Aunque se ha configurado el Space para que se sincronice con un dataset del Hub a vuestra elección, para tener más seguridad se recomienda guardar una copia del dataset en el Hub ejecutando el siguiente código. Es necesario hacer login con Python usando 'from huggingface_hub import notebook_login' o añadir el token directamente al hacer el push_to_hub:
Una vez hecho esto se puede recuperar el dataset y volver a cargar en Argilla con el notebook de "Como cargar el dataset en Argilla"
## Ejemplos de consultas y trucos para etiquetar
Se recomienda comenzar explorando y etiquetando el dataset de manera secuencial para entender la estructura e ir identificando patrones.
Una vez hecho esto se recomienda combinarlo con:
### Utilizar el buscador
Tanto con palabras clave, como con expresiones regulares, y wildcards y expresiones booleanas, ver la guía de uso.
Un aspecto interesante es la capacidad de buscar solo en determinados campos. Para ello, hay que utilizar la siguiente sintaxis 'inputs.nombre_del_campo:"consulta"':
Por ejemplo: 'inputs.1-instruction:"Crear una página"' encontraría todos aquellos registros con este texto en la instrucción
Además esto se puede combinar con expresiones booleanas para buscar en varios campos: 'inputs.1-instruction:"Crear una página" AND inputs.3-output:"html"'
### Find similar
Cuando encontramos patrones interesantes o erroneos en un registro y campo, podemos usar el botón find similar para encontrar ejemplos similares gracias al uso de similarity search usando embeddings.
### Etiquetado en lote (bulk)
Si encontramos un patrón muy claro, podemos revisar los ejemplos más rápido y anotarlos en bloque usando la barra superior, debajo del buscador. Si hay mucho ejemplos se puede aumentar el número de registros por página. Se recomienda en cualquier caso revisar los ejemplos.
| [
"# Dataset Card de \"somos-alpaca-es\"\n\nEste conjunto de datos es una versión traducida del dataset Alpaca en Español. \n\nEste conjunto de datos sirve como referencia para el esfuerzo colaborativo de limpieza y mejora del dataset durante el hackathon SomosNLP 2023.\n\nCuantas más personas y equipos participen mayor calidad final se podrá obtener. \n\n---\n️ ACTUALIZACION: Contribuye al etiquetado de la traducción de la versión limpia: somos-clean-alpaca-es\n\n---",
"## El reto\n\nA continuación se describen los pasos y normas para participar:\n\n1. Se debe utilizar este conjunto de datos como punto de partida y mantener tanto los 'ids' como la estructura. Esto es así para poder realizar tareas posteriores de validación cruzada y mejoras programáticas del dataset final.\n2. Se trata de un dataset en formato compatible con Argilla. Cada equipo o persona que quiera participar, puede trabajar con su propia instancia de Argilla. Una forma fácil de desplegar Argilla es desplegar Argilla en Spaces.\n3. Si se usa Argilla en Spaces, es muy recomendable realizar copias periódicas del dataset utilizando 'URL(\"nombre_del_dataset\").to_datasets().push_to_hub()' dado que si se reinicia el Space se pierden los datos. También se puede hacer un upgrade del Space a CPU-Upgrade.\n4. Argilla se puede utilizar para validar y etiquetar manualmente y usando búsquedas y similitud semántica desde la UI. Para ello se pondrán ejemplos de uso del lenguaje de búsqueda en esta página, pero se recomienda consultar la guía de uso.\n5. La validación humana es necesaria para garantizar la calidad final pero se pueden también limpiezas programáticas para aquellos casos en los que sea más eficiente. En cualquier caso, para el éxito del experimento se deberán utilizar las etiquetas propuestas, aunque se modifique programáticamente el dataset.\n6. No se deben borrar registros del dataset, si un registro es inválido se deberá indicar en la etiqueta (por ejemplo 'BAD INPUT') o con el status 'discard'.\n\nEl resultado del reto será un dataset por persona o equipo que contenga el dataset original etiquetado parcialmente, y opcionalmente otras versiones/subconjuntos del dataset con los datos corregidos, mejorados o aumentados. En estos casos es conveniente mantener un dataset a parte con los ids originales.",
"## Como empezar a etiquetar\n\nPara etiquetar el dataset tienes que:\n\n1. Lanzar tu Argilla Space siguiendo este link. Esto te guiará para crear una instancia de Argilla en el Hub que cargará automaticamente el dataset (ver captura de pantalla abajo). IMPORTANTE: que el Space sea Public para poder leer los datos etiquetados desde Python. El proceso de carga puede tardar hasta 10 minutos, puedes consultar los logs para comprobar que se están cargando los datos.\n2. IMPORTANTE: Si se quiere sincronizar los datos validados con el Hub para no perder las anotaciones si se reinicia el Space, hay que configurar dos secrets (en Settings del Space): 'HF_TOKEN' que es vuestro token de escritura, y 'HF_DATASET_NAME' que es el dataset donde queréis guardarlo, importante incluir la organizacion o persona seguido de un / y el nombre del dataset. Por ejemplo 'juanmartinez/somos-alpaca-es-validations' o 'miempresa/somos-alpaca-es-validations'.\n3. Mientras se carga tu Argilla Space con el dataset puedes aprovechar para leer las guías de anotación.\n4. Aunque en principio se va sincronizar el dataset anotado, recomendamos que abras Colab o un notebook en local y que guardes el dataset periodicamente en un dataset del Hub (puede ser en tu espacio personal o tu organización). Para ello recomendamos leer el apartado como guardar el dataset en el Hub.\n\nSe recomienda mirar el log del Space para ver si hay errores a la hora de configurar los Secret 'HF_TOKEN' y 'HF_DATASET_NAME'.\n!Duplicar Space",
"## Desplegar Argilla localmente o en un servidor cloud\nPara equipos que tengan el tiempo y quieran desplegar una versión con más capacidad de computación y estabilidad que Spaces, aquí hay una guía explicativa.\nUna vez instalada, se deben subir los datos con este notebook.",
"## Guías de anotación\n\nAntes de empezar a anotar, es necesario leer la guía de anotación al completo.",
"## IMPORTANTE: Guardar el dataset en el Hub periodicamente\n\nAunque se ha configurado el Space para que se sincronice con un dataset del Hub a vuestra elección, para tener más seguridad se recomienda guardar una copia del dataset en el Hub ejecutando el siguiente código. Es necesario hacer login con Python usando 'from huggingface_hub import notebook_login' o añadir el token directamente al hacer el push_to_hub:\n\n\n\nUna vez hecho esto se puede recuperar el dataset y volver a cargar en Argilla con el notebook de \"Como cargar el dataset en Argilla\"",
"## Ejemplos de consultas y trucos para etiquetar\n\nSe recomienda comenzar explorando y etiquetando el dataset de manera secuencial para entender la estructura e ir identificando patrones.\n\nUna vez hecho esto se recomienda combinarlo con:",
"### Utilizar el buscador\n\nTanto con palabras clave, como con expresiones regulares, y wildcards y expresiones booleanas, ver la guía de uso.\n\nUn aspecto interesante es la capacidad de buscar solo en determinados campos. Para ello, hay que utilizar la siguiente sintaxis 'inputs.nombre_del_campo:\"consulta\"':\n\nPor ejemplo: 'inputs.1-instruction:\"Crear una página\"' encontraría todos aquellos registros con este texto en la instrucción\n\nAdemás esto se puede combinar con expresiones booleanas para buscar en varios campos: 'inputs.1-instruction:\"Crear una página\" AND inputs.3-output:\"html\"'",
"### Find similar\nCuando encontramos patrones interesantes o erroneos en un registro y campo, podemos usar el botón find similar para encontrar ejemplos similares gracias al uso de similarity search usando embeddings.",
"### Etiquetado en lote (bulk)\nSi encontramos un patrón muy claro, podemos revisar los ejemplos más rápido y anotarlos en bloque usando la barra superior, debajo del buscador. Si hay mucho ejemplos se puede aumentar el número de registros por página. Se recomienda en cualquier caso revisar los ejemplos."
]
| [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Spanish #region-us \n",
"# Dataset Card de \"somos-alpaca-es\"\n\nEste conjunto de datos es una versión traducida del dataset Alpaca en Español. \n\nEste conjunto de datos sirve como referencia para el esfuerzo colaborativo de limpieza y mejora del dataset durante el hackathon SomosNLP 2023.\n\nCuantas más personas y equipos participen mayor calidad final se podrá obtener. \n\n---\n️ ACTUALIZACION: Contribuye al etiquetado de la traducción de la versión limpia: somos-clean-alpaca-es\n\n---",
"## El reto\n\nA continuación se describen los pasos y normas para participar:\n\n1. Se debe utilizar este conjunto de datos como punto de partida y mantener tanto los 'ids' como la estructura. Esto es así para poder realizar tareas posteriores de validación cruzada y mejoras programáticas del dataset final.\n2. Se trata de un dataset en formato compatible con Argilla. Cada equipo o persona que quiera participar, puede trabajar con su propia instancia de Argilla. Una forma fácil de desplegar Argilla es desplegar Argilla en Spaces.\n3. Si se usa Argilla en Spaces, es muy recomendable realizar copias periódicas del dataset utilizando 'URL(\"nombre_del_dataset\").to_datasets().push_to_hub()' dado que si se reinicia el Space se pierden los datos. También se puede hacer un upgrade del Space a CPU-Upgrade.\n4. Argilla se puede utilizar para validar y etiquetar manualmente y usando búsquedas y similitud semántica desde la UI. Para ello se pondrán ejemplos de uso del lenguaje de búsqueda en esta página, pero se recomienda consultar la guía de uso.\n5. La validación humana es necesaria para garantizar la calidad final pero se pueden también limpiezas programáticas para aquellos casos en los que sea más eficiente. En cualquier caso, para el éxito del experimento se deberán utilizar las etiquetas propuestas, aunque se modifique programáticamente el dataset.\n6. No se deben borrar registros del dataset, si un registro es inválido se deberá indicar en la etiqueta (por ejemplo 'BAD INPUT') o con el status 'discard'.\n\nEl resultado del reto será un dataset por persona o equipo que contenga el dataset original etiquetado parcialmente, y opcionalmente otras versiones/subconjuntos del dataset con los datos corregidos, mejorados o aumentados. En estos casos es conveniente mantener un dataset a parte con los ids originales.",
"## Como empezar a etiquetar\n\nPara etiquetar el dataset tienes que:\n\n1. Lanzar tu Argilla Space siguiendo este link. Esto te guiará para crear una instancia de Argilla en el Hub que cargará automaticamente el dataset (ver captura de pantalla abajo). IMPORTANTE: que el Space sea Public para poder leer los datos etiquetados desde Python. El proceso de carga puede tardar hasta 10 minutos, puedes consultar los logs para comprobar que se están cargando los datos.\n2. IMPORTANTE: Si se quiere sincronizar los datos validados con el Hub para no perder las anotaciones si se reinicia el Space, hay que configurar dos secrets (en Settings del Space): 'HF_TOKEN' que es vuestro token de escritura, y 'HF_DATASET_NAME' que es el dataset donde queréis guardarlo, importante incluir la organizacion o persona seguido de un / y el nombre del dataset. Por ejemplo 'juanmartinez/somos-alpaca-es-validations' o 'miempresa/somos-alpaca-es-validations'.\n3. Mientras se carga tu Argilla Space con el dataset puedes aprovechar para leer las guías de anotación.\n4. Aunque en principio se va sincronizar el dataset anotado, recomendamos que abras Colab o un notebook en local y que guardes el dataset periodicamente en un dataset del Hub (puede ser en tu espacio personal o tu organización). Para ello recomendamos leer el apartado como guardar el dataset en el Hub.\n\nSe recomienda mirar el log del Space para ver si hay errores a la hora de configurar los Secret 'HF_TOKEN' y 'HF_DATASET_NAME'.\n!Duplicar Space",
"## Desplegar Argilla localmente o en un servidor cloud\nPara equipos que tengan el tiempo y quieran desplegar una versión con más capacidad de computación y estabilidad que Spaces, aquí hay una guía explicativa.\nUna vez instalada, se deben subir los datos con este notebook.",
"## Guías de anotación\n\nAntes de empezar a anotar, es necesario leer la guía de anotación al completo.",
"## IMPORTANTE: Guardar el dataset en el Hub periodicamente\n\nAunque se ha configurado el Space para que se sincronice con un dataset del Hub a vuestra elección, para tener más seguridad se recomienda guardar una copia del dataset en el Hub ejecutando el siguiente código. Es necesario hacer login con Python usando 'from huggingface_hub import notebook_login' o añadir el token directamente al hacer el push_to_hub:\n\n\n\nUna vez hecho esto se puede recuperar el dataset y volver a cargar en Argilla con el notebook de \"Como cargar el dataset en Argilla\"",
"## Ejemplos de consultas y trucos para etiquetar\n\nSe recomienda comenzar explorando y etiquetando el dataset de manera secuencial para entender la estructura e ir identificando patrones.\n\nUna vez hecho esto se recomienda combinarlo con:",
"### Utilizar el buscador\n\nTanto con palabras clave, como con expresiones regulares, y wildcards y expresiones booleanas, ver la guía de uso.\n\nUn aspecto interesante es la capacidad de buscar solo en determinados campos. Para ello, hay que utilizar la siguiente sintaxis 'inputs.nombre_del_campo:\"consulta\"':\n\nPor ejemplo: 'inputs.1-instruction:\"Crear una página\"' encontraría todos aquellos registros con este texto en la instrucción\n\nAdemás esto se puede combinar con expresiones booleanas para buscar en varios campos: 'inputs.1-instruction:\"Crear una página\" AND inputs.3-output:\"html\"'",
"### Find similar\nCuando encontramos patrones interesantes o erroneos en un registro y campo, podemos usar el botón find similar para encontrar ejemplos similares gracias al uso de similarity search usando embeddings.",
"### Etiquetado en lote (bulk)\nSi encontramos un patrón muy claro, podemos revisar los ejemplos más rápido y anotarlos en bloque usando la barra superior, debajo del buscador. Si hay mucho ejemplos se puede aumentar el número de registros por página. Se recomienda en cualquier caso revisar los ejemplos."
]
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370,
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"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Spanish #region-us \n# Dataset Card de \"somos-alpaca-es\"\n\nEste conjunto de datos es una versión traducida del dataset Alpaca en Español. \n\nEste conjunto de datos sirve como referencia para el esfuerzo colaborativo de limpieza y mejora del dataset durante el hackathon SomosNLP 2023.\n\nCuantas más personas y equipos participen mayor calidad final se podrá obtener. \n\n---\n️ ACTUALIZACION: Contribuye al etiquetado de la traducción de la versión limpia: somos-clean-alpaca-es\n\n---",
"passage: ## El reto\n\nA continuación se describen los pasos y normas para participar:\n\n1. Se debe utilizar este conjunto de datos como punto de partida y mantener tanto los 'ids' como la estructura. Esto es así para poder realizar tareas posteriores de validación cruzada y mejoras programáticas del dataset final.\n2. Se trata de un dataset en formato compatible con Argilla. Cada equipo o persona que quiera participar, puede trabajar con su propia instancia de Argilla. Una forma fácil de desplegar Argilla es desplegar Argilla en Spaces.\n3. Si se usa Argilla en Spaces, es muy recomendable realizar copias periódicas del dataset utilizando 'URL(\"nombre_del_dataset\").to_datasets().push_to_hub()' dado que si se reinicia el Space se pierden los datos. También se puede hacer un upgrade del Space a CPU-Upgrade.\n4. Argilla se puede utilizar para validar y etiquetar manualmente y usando búsquedas y similitud semántica desde la UI. Para ello se pondrán ejemplos de uso del lenguaje de búsqueda en esta página, pero se recomienda consultar la guía de uso.\n5. La validación humana es necesaria para garantizar la calidad final pero se pueden también limpiezas programáticas para aquellos casos en los que sea más eficiente. En cualquier caso, para el éxito del experimento se deberán utilizar las etiquetas propuestas, aunque se modifique programáticamente el dataset.\n6. No se deben borrar registros del dataset, si un registro es inválido se deberá indicar en la etiqueta (por ejemplo 'BAD INPUT') o con el status 'discard'.\n\nEl resultado del reto será un dataset por persona o equipo que contenga el dataset original etiquetado parcialmente, y opcionalmente otras versiones/subconjuntos del dataset con los datos corregidos, mejorados o aumentados. En estos casos es conveniente mantener un dataset a parte con los ids originales.## Como empezar a etiquetar\n\nPara etiquetar el dataset tienes que:\n\n1. Lanzar tu Argilla Space siguiendo este link. Esto te guiará para crear una instancia de Argilla en el Hub que cargará automaticamente el dataset (ver captura de pantalla abajo). IMPORTANTE: que el Space sea Public para poder leer los datos etiquetados desde Python. El proceso de carga puede tardar hasta 10 minutos, puedes consultar los logs para comprobar que se están cargando los datos.\n2. IMPORTANTE: Si se quiere sincronizar los datos validados con el Hub para no perder las anotaciones si se reinicia el Space, hay que configurar dos secrets (en Settings del Space): 'HF_TOKEN' que es vuestro token de escritura, y 'HF_DATASET_NAME' que es el dataset donde queréis guardarlo, importante incluir la organizacion o persona seguido de un / y el nombre del dataset. Por ejemplo 'juanmartinez/somos-alpaca-es-validations' o 'miempresa/somos-alpaca-es-validations'.\n3. Mientras se carga tu Argilla Space con el dataset puedes aprovechar para leer las guías de anotación.\n4. Aunque en principio se va sincronizar el dataset anotado, recomendamos que abras Colab o un notebook en local y que guardes el dataset periodicamente en un dataset del Hub (puede ser en tu espacio personal o tu organización). Para ello recomendamos leer el apartado como guardar el dataset en el Hub.\n\nSe recomienda mirar el log del Space para ver si hay errores a la hora de configurar los Secret 'HF_TOKEN' y 'HF_DATASET_NAME'.\n!Duplicar Space## Desplegar Argilla localmente o en un servidor cloud\nPara equipos que tengan el tiempo y quieran desplegar una versión con más capacidad de computación y estabilidad que Spaces, aquí hay una guía explicativa.\nUna vez instalada, se deben subir los datos con este notebook.## Guías de anotación\n\nAntes de empezar a anotar, es necesario leer la guía de anotación al completo."
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|
c0cf38e4f91fa5226b0864000d4ead573fddf638 | # Dataset Card for "ia-loaded2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | davanstrien/ia-loaded2 | [
"region:us"
]
| 2023-03-22T21:47:42+00:00 | {"dataset_info": {"features": [{"name": "crawl_date", "dtype": "int64"}, {"name": "last_modified_date", "dtype": "float64"}, {"name": "url", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "extension", "dtype": "string"}, {"name": "mime_type_web_server", "dtype": "string"}, {"name": "mime_type_tika", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "md5", "dtype": "string"}, {"name": "sha1", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 214200379.736, "num_examples": 658}], "download_size": 0, "dataset_size": 214200379.736}} | 2023-03-23T13:56:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ia-loaded2"
More Information needed | [
"# Dataset Card for \"ia-loaded2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ia-loaded2\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ia-loaded2\"\n\nMore Information needed"
]
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|
f33e78c242b0a83b343ac1eb13618fd7680e7e5d |
# Dataset Card for IMDB 3000 Sphere
- **Homepage:** [http://ai.stanford.edu/~amaas/data/sentiment/](http://ai.stanford.edu/~amaas/data/sentiment/)
## Dataset Summary
Large Movie Review Dataset.
This is a 3000 item selection from the `imdb` dataset for binary sentiment classification for use in the Sphere course on AutoTrain.
## Dataset Structure
An example of 'train' looks as follows.
```
{
"label": 0,
"text": "Goodbye world2\n"
}
``` | enoreyes/imdb_3000_sphere | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
]
| 2023-03-22T22:00:57+00:00 | {"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "imdb_3000"} | 2023-03-22T22:10:44+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-mit #region-us
|
# Dataset Card for IMDB 3000 Sphere
- Homepage: URL
## Dataset Summary
Large Movie Review Dataset.
This is a 3000 item selection from the 'imdb' dataset for binary sentiment classification for use in the Sphere course on AutoTrain.
## Dataset Structure
An example of 'train' looks as follows.
| [
"# Dataset Card for IMDB 3000 Sphere\n\n- Homepage: URL",
"## Dataset Summary\n\nLarge Movie Review Dataset.\nThis is a 3000 item selection from the 'imdb' dataset for binary sentiment classification for use in the Sphere course on AutoTrain.",
"## Dataset Structure\n\nAn example of 'train' looks as follows."
]
| [
"TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-mit #region-us \n",
"# Dataset Card for IMDB 3000 Sphere\n\n- Homepage: URL",
"## Dataset Summary\n\nLarge Movie Review Dataset.\nThis is a 3000 item selection from the 'imdb' dataset for binary sentiment classification for use in the Sphere course on AutoTrain.",
"## Dataset Structure\n\nAn example of 'train' looks as follows."
]
| [
38,
14,
43,
18
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-English #license-mit #region-us \n# Dataset Card for IMDB 3000 Sphere\n\n- Homepage: URL## Dataset Summary\n\nLarge Movie Review Dataset.\nThis is a 3000 item selection from the 'imdb' dataset for binary sentiment classification for use in the Sphere course on AutoTrain.## Dataset Structure\n\nAn example of 'train' looks as follows."
]
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|
35eb02ca7b378c4e03c60ee4e6ed01f76e96475e | # Dataset Card for "CIS6930_DAAGR_Empathetic_Dialogues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | aegrif/CIS6930_DAAGR_Empathetic_Dialogues | [
"doi:10.57967/hf/0550",
"region:us"
]
| 2023-03-22T22:20:20+00:00 | {"dataset_info": {"features": [{"name": "conv_id", "dtype": "string"}, {"name": "utterance_idx", "dtype": "int64"}, {"name": "context", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "utterance", "dtype": "string"}, {"name": "new_context", "dtype": "string"}, {"name": "previous_utterance", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 23146751, "num_examples": 84167}, {"name": "validation", "num_bytes": 3522545, "num_examples": 12077}, {"name": "test", "num_bytes": 3490587, "num_examples": 10973}], "download_size": 11165291, "dataset_size": 30159883}} | 2023-03-27T22:10:22+00:00 | []
| []
| TAGS
#doi-10.57967/hf/0550 #region-us
| # Dataset Card for "CIS6930_DAAGR_Empathetic_Dialogues"
More Information needed | [
"# Dataset Card for \"CIS6930_DAAGR_Empathetic_Dialogues\"\n\nMore Information needed"
]
| [
"TAGS\n#doi-10.57967/hf/0550 #region-us \n",
"# Dataset Card for \"CIS6930_DAAGR_Empathetic_Dialogues\"\n\nMore Information needed"
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"passage: TAGS\n#doi-10.57967/hf/0550 #region-us \n# Dataset Card for \"CIS6930_DAAGR_Empathetic_Dialogues\"\n\nMore Information needed"
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|
ff603244295d216263e46bc7aa8acf5d1179f06e | # Dataset Card for "fungifutures2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pimentooliver/fungifutures2 | [
"region:us"
]
| 2023-03-22T22:34:22+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3145618642.0, "num_examples": 998}], "download_size": 2160387541, "dataset_size": 3145618642.0}} | 2023-03-22T22:37:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fungifutures2"
More Information needed | [
"# Dataset Card for \"fungifutures2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fungifutures2\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"fungifutures2\"\n\nMore Information needed"
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|
96f22ed9c72a2ce3b29d5ea50efae881b782b390 | # Dataset Card for "fungi"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | pimentooliver/fungi | [
"region:us"
]
| 2023-03-22T22:37:45+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 41590978.0, "num_examples": 841}], "download_size": 40501239, "dataset_size": 41590978.0}} | 2023-03-22T23:35:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fungi"
More Information needed | [
"# Dataset Card for \"fungi\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fungi\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"fungi\"\n\nMore Information needed"
]
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|
3fcc21604145332e103014fa9b545d114a144ea4 | # Dataset Card for "ia-loaded-embedded-gpu"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | davanstrien/ia-loaded-embedded-gpu | [
"region:us"
]
| 2023-03-22T23:14:56+00:00 | {"dataset_info": {"features": [{"name": "crawl_date", "dtype": "int64"}, {"name": "last_modified_date", "dtype": "float64"}, {"name": "url", "dtype": "string"}, {"name": "filename", "dtype": "string"}, {"name": "extension", "dtype": "string"}, {"name": "mime_type_web_server", "dtype": "string"}, {"name": "mime_type_tika", "dtype": "string"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "md5", "dtype": "string"}, {"name": "sha1", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "embedding", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 1542994483.25, "num_examples": 4582}], "download_size": 1515373521, "dataset_size": 1542994483.25}} | 2023-03-23T11:46:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ia-loaded-embedded-gpu"
More Information needed | [
"# Dataset Card for \"ia-loaded-embedded-gpu\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ia-loaded-embedded-gpu\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ia-loaded-embedded-gpu\"\n\nMore Information needed"
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|
b3b224330b2ba715c46ddfdd1778c47bbd9adc30 |
# Dataset Card
- **Homepage:** https://bit.ly/ischool-berkeley-capstone
- **Repository:** https://github.com/daniel-furman/Capstone
- **Point of Contact:** [email protected]
## Dataset Summary
This is the dataset for **Polyglot or Not?: Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models**.
## Test Description
Given a factual association such as *The capital of France is **Paris***, we determine whether a model adequately "knows" this information with the following test:
* Step **1**: prompt the model to predict the likelihood of the token **Paris** following *The Capital of France is*
* Step **2**: prompt the model to predict the average likelihood of a set of false, counterfactual tokens following the same stem.
If the value from **1** is greater than the value from **2** we conclude that model adequately recalls that fact. Formally, this is an application of the Contrastive Knowledge Assessment proposed in [[1][bib]].
For every foundation model of interest (like [LLaMA](https://arxiv.org/abs/2302.13971)), we perform this assessment on a set of facts translated into 20 languages. All told, we score foundation models on 303k fact-completions ([results](https://github.com/daniel-furman/capstone#multilingual-fact-completion-results)).
We also score monolingual models (like [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)) on English-only fact-completion ([results](https://github.com/daniel-furman/capstone#english-fact-completion-results)).
## Languages
The dataset covers 20 languages, which use either the Latin or Cyrillic scripts: bg, ca, cs, da, de, en, es, fr, hr, hu, it,
nl, pl, pt, ro, ru, sl, sr, sv, uk.
## Data Splits
The dataset splits correspond to the 20 languages above.
## Source Data
We sourced the English cut of the dataset from [1] and [2] and used the Google Translate API to produce the other 19 language cuts.
## Licensing Information
The dataset is licensed under the Apache 2.0 license and may be used with the corresponding affordances without limit.
## Citation Information
```
@misc{schott2023polyglot,
doi = {10.48550/arXiv.2305.13675},
title={Polyglot or Not? Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models},
author={Tim Schott and Daniel Furman and Shreshta Bhat},
year={2023},
eprint={2305.13675,
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Bibliography
[1] Dong, Qingxiu, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. "Calibrating Factual Knowledge in Pretrained Language Models". In Findings of the Association for Computational Linguistics: EMNLP 2022. [arXiv:2210.03329][cka] (2022).
```
@misc{dong2022calibrating,
doi = {10.48550/arXiv.2210.03329},
title={Calibrating Factual Knowledge in Pretrained Language Models},
author={Qingxiu Dong and Damai Dai and Yifan Song and Jingjing Xu and Zhifang Sui and Lei Li},
year={2022},
eprint={2210.03329},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
[2] Meng, Kevin, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. "Mass Editing Memory in a Transformer." arXiv preprint [arXiv:2210.07229][memit] (2022).
```
@misc{meng2022massediting,
doi = {10.48550/arXiv.2210.07229},
title={Mass-Editing Memory in a Transformer},
author={Kevin Meng and Arnab Sen Sharma and Alex Andonian and Yonatan Belinkov and David Bau},
year={2022},
eprint={2210.07229},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` | Polyglot-or-Not/Fact-Completion | [
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]
| 2023-03-22T23:42:30+00:00 | {"language_creators": ["expert-generated", "machine-generated"], "language": ["en", "fr", "es", "de", "uk", "bg", "ca", "da", "hr", "hu", "it", "nl", "pl", "pt", "ro", "ru", "sl", "sr", "sv", "cs"], "license": "apache-2.0", "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "fill-mask", "text2text-generation"], "pretty_name": "Polyglot or Not? Fact-Completion Benchmark", "tags": ["natural-language-understanding"], "dataset_info": {"features": [{"name": "dataset_id", "dtype": "string"}, {"name": "stem", "dtype": "string"}, {"name": "true", "dtype": "string"}, {"name": "false", "dtype": "string"}, {"name": "relation", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "object", "dtype": "string"}], "splits": [{"name": "English", "num_bytes": 3474255, "num_examples": 26254}, {"name": "Spanish", "num_bytes": 3175733, "num_examples": 18786}, {"name": "French", "num_bytes": 3395566, "num_examples": 18395}, {"name": "Russian", "num_bytes": 659526, "num_examples": 3289}, {"name": "Portuguese", "num_bytes": 4158146, "num_examples": 22974}, {"name": "German", "num_bytes": 2611160, "num_examples": 16287}, {"name": "Italian", "num_bytes": 3709786, "num_examples": 20448}, {"name": "Ukrainian", "num_bytes": 1868358, "num_examples": 7918}, {"name": "Polish", "num_bytes": 1683647, "num_examples": 9484}, {"name": "Romanian", "num_bytes": 2846002, "num_examples": 17568}, {"name": "Czech", "num_bytes": 1631582, "num_examples": 9427}, {"name": "Bulgarian", "num_bytes": 4597410, "num_examples": 20577}, {"name": "Swedish", "num_bytes": 3226502, "num_examples": 21576}, {"name": "Serbian", "num_bytes": 1327674, "num_examples": 5426}, {"name": "Hungarian", "num_bytes": 865409, "num_examples": 4650}, {"name": "Croatian", "num_bytes": 1195097, "num_examples": 7358}, {"name": "Danish", "num_bytes": 3580458, "num_examples": 23365}, {"name": "Slovenian", "num_bytes": 1299653, "num_examples": 7873}, {"name": "Dutch", "num_bytes": 3732795, "num_examples": 22590}, {"name": "Catalan", "num_bytes": 3319466, "num_examples": 18898}], "download_size": 27090207, "dataset_size": 52358225}} | 2023-06-14T02:05:21+00:00 | [
"2302.13971",
"2305.13675",
"2210.03329",
"2210.07229"
]
| [
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]
| TAGS
#task_categories-text-generation #task_categories-fill-mask #task_categories-text2text-generation #language_creators-expert-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-100K<n<1M #language-English #language-French #language-Spanish #language-German #language-Ukrainian #language-Bulgarian #language-Catalan #language-Danish #language-Croatian #language-Hungarian #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Russian #language-Slovenian #language-Serbian #language-Swedish #language-Czech #license-apache-2.0 #natural-language-understanding #arxiv-2302.13971 #arxiv-2305.13675 #arxiv-2210.03329 #arxiv-2210.07229 #region-us
|
# Dataset Card
- Homepage: URL
- Repository: URL
- Point of Contact: daniel_furman@URL
## Dataset Summary
This is the dataset for Polyglot or Not?: Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models.
## Test Description
Given a factual association such as *The capital of France is Paris*, we determine whether a model adequately "knows" this information with the following test:
* Step 1: prompt the model to predict the likelihood of the token Paris following *The Capital of France is*
* Step 2: prompt the model to predict the average likelihood of a set of false, counterfactual tokens following the same stem.
If the value from 1 is greater than the value from 2 we conclude that model adequately recalls that fact. Formally, this is an application of the Contrastive Knowledge Assessment proposed in [[1][bib]].
For every foundation model of interest (like LLaMA), we perform this assessment on a set of facts translated into 20 languages. All told, we score foundation models on 303k fact-completions (results).
We also score monolingual models (like GPT-2) on English-only fact-completion (results).
## Languages
The dataset covers 20 languages, which use either the Latin or Cyrillic scripts: bg, ca, cs, da, de, en, es, fr, hr, hu, it,
nl, pl, pt, ro, ru, sl, sr, sv, uk.
## Data Splits
The dataset splits correspond to the 20 languages above.
## Source Data
We sourced the English cut of the dataset from [1] and [2] and used the Google Translate API to produce the other 19 language cuts.
## Licensing Information
The dataset is licensed under the Apache 2.0 license and may be used with the corresponding affordances without limit.
## Bibliography
[1] Dong, Qingxiu, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. "Calibrating Factual Knowledge in Pretrained Language Models". In Findings of the Association for Computational Linguistics: EMNLP 2022. [arXiv:2210.03329][cka] (2022).
[2] Meng, Kevin, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. "Mass Editing Memory in a Transformer." arXiv preprint [arXiv:2210.07229][memit] (2022).
| [
"# Dataset Card\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: daniel_furman@URL",
"## Dataset Summary\n\nThis is the dataset for Polyglot or Not?: Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models.",
"## Test Description\n\n Given a factual association such as *The capital of France is Paris*, we determine whether a model adequately \"knows\" this information with the following test:\n \n * Step 1: prompt the model to predict the likelihood of the token Paris following *The Capital of France is*\n \n * Step 2: prompt the model to predict the average likelihood of a set of false, counterfactual tokens following the same stem.\n \n If the value from 1 is greater than the value from 2 we conclude that model adequately recalls that fact. Formally, this is an application of the Contrastive Knowledge Assessment proposed in [[1][bib]]. \n \n For every foundation model of interest (like LLaMA), we perform this assessment on a set of facts translated into 20 languages. All told, we score foundation models on 303k fact-completions (results). \n \n We also score monolingual models (like GPT-2) on English-only fact-completion (results).",
"## Languages\n\nThe dataset covers 20 languages, which use either the Latin or Cyrillic scripts: bg, ca, cs, da, de, en, es, fr, hr, hu, it,\nnl, pl, pt, ro, ru, sl, sr, sv, uk.",
"## Data Splits\n\nThe dataset splits correspond to the 20 languages above.",
"## Source Data\n\nWe sourced the English cut of the dataset from [1] and [2] and used the Google Translate API to produce the other 19 language cuts.",
"## Licensing Information\n\nThe dataset is licensed under the Apache 2.0 license and may be used with the corresponding affordances without limit.",
"## Bibliography\n\n[1] Dong, Qingxiu, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. \"Calibrating Factual Knowledge in Pretrained Language Models\". In Findings of the Association for Computational Linguistics: EMNLP 2022. [arXiv:2210.03329][cka] (2022).\n\n\n\n[2] Meng, Kevin, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. \"Mass Editing Memory in a Transformer.\" arXiv preprint [arXiv:2210.07229][memit] (2022)."
]
| [
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_categories-text2text-generation #language_creators-expert-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-100K<n<1M #language-English #language-French #language-Spanish #language-German #language-Ukrainian #language-Bulgarian #language-Catalan #language-Danish #language-Croatian #language-Hungarian #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Russian #language-Slovenian #language-Serbian #language-Swedish #language-Czech #license-apache-2.0 #natural-language-understanding #arxiv-2302.13971 #arxiv-2305.13675 #arxiv-2210.03329 #arxiv-2210.07229 #region-us \n",
"# Dataset Card\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: daniel_furman@URL",
"## Dataset Summary\n\nThis is the dataset for Polyglot or Not?: Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models.",
"## Test Description\n\n Given a factual association such as *The capital of France is Paris*, we determine whether a model adequately \"knows\" this information with the following test:\n \n * Step 1: prompt the model to predict the likelihood of the token Paris following *The Capital of France is*\n \n * Step 2: prompt the model to predict the average likelihood of a set of false, counterfactual tokens following the same stem.\n \n If the value from 1 is greater than the value from 2 we conclude that model adequately recalls that fact. Formally, this is an application of the Contrastive Knowledge Assessment proposed in [[1][bib]]. \n \n For every foundation model of interest (like LLaMA), we perform this assessment on a set of facts translated into 20 languages. All told, we score foundation models on 303k fact-completions (results). \n \n We also score monolingual models (like GPT-2) on English-only fact-completion (results).",
"## Languages\n\nThe dataset covers 20 languages, which use either the Latin or Cyrillic scripts: bg, ca, cs, da, de, en, es, fr, hr, hu, it,\nnl, pl, pt, ro, ru, sl, sr, sv, uk.",
"## Data Splits\n\nThe dataset splits correspond to the 20 languages above.",
"## Source Data\n\nWe sourced the English cut of the dataset from [1] and [2] and used the Google Translate API to produce the other 19 language cuts.",
"## Licensing Information\n\nThe dataset is licensed under the Apache 2.0 license and may be used with the corresponding affordances without limit.",
"## Bibliography\n\n[1] Dong, Qingxiu, Damai Dai, Yifan Song, Jingjing Xu, Zhifang Sui, and Lei Li. \"Calibrating Factual Knowledge in Pretrained Language Models\". In Findings of the Association for Computational Linguistics: EMNLP 2022. [arXiv:2210.03329][cka] (2022).\n\n\n\n[2] Meng, Kevin, Arnab Sen Sharma, Alex Andonian, Yonatan Belinkov, and David Bau. \"Mass Editing Memory in a Transformer.\" arXiv preprint [arXiv:2210.07229][memit] (2022)."
]
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"passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_categories-text2text-generation #language_creators-expert-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-100K<n<1M #language-English #language-French #language-Spanish #language-German #language-Ukrainian #language-Bulgarian #language-Catalan #language-Danish #language-Croatian #language-Hungarian #language-Italian #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Russian #language-Slovenian #language-Serbian #language-Swedish #language-Czech #license-apache-2.0 #natural-language-understanding #arxiv-2302.13971 #arxiv-2305.13675 #arxiv-2210.03329 #arxiv-2210.07229 #region-us \n# Dataset Card\n\n- Homepage: URL\n- Repository: URL\n- Point of Contact: daniel_furman@URL## Dataset Summary\n\nThis is the dataset for Polyglot or Not?: Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models."
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|
488fdaf0aaa39c99ba7dd4b5bb6098b7fe6ae2d8 |
**==========================================**
**_IN PROGRESS - NOT READY FOR LOADING OR USE_**
**==========================================**
---
# Dataset Card for climate-fever-nli-stsb
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
The CLIMATE-FEVER dataset modified to supply NLI-style (**cf-nli**) features or STSb-style (**cf-stsb**) features that SentenceBERT training scripts can use as drop-in replacements for AllNLI and/or STSb datasets.
There are two **cf-nli** datasets: one derived from only SUPPORTS and REFUTES evidence (**cf-nli**), and one that also derived data from NOT_ENOUGH_INFO evidence based on the annotator votes (**cf-nli-nei**).
The feature style is specified as a named configuration when loading the dataset: cf-nli, cf-nli-nei, or cf-stsb. See usage notes below for `load_dataset` examples.
### Usage
Load the **cf-nli** dataset
```python
# if datasets not already in your environment
!pip install datasets
from datasets import load_dataset
# all splits...
dd = load_dataset('climate-fever-nli-stsb', 'cf-nli')
# ... or specific split (only 'train' is available)
ds_train = load_dataset('climate-fever-nli-stsb', 'cf-nli', split='train')
## ds_train can now be injected into SentenceBERT training scripts at the point
## where individual sentence pairs are aggregated into
## {'claim': {'entailment': set(), 'contradiction': set(), 'neutral': set()}} dicts
## for further processing into training samples
```
Load the **cf-nli-nei** dataset
```python
# if datasets not already in your environment
!pip install datasets
from datasets import load_dataset
# all splits...
dd = load_dataset('climate-fever-nli-stsb', 'cf-nli-nei')
# ... or specific split (only 'train' is available)
ds_train = load_dataset('climate-fever-nli-stsb', 'cf-nli-nei', split='train')
## ds_train can now be injected into SentenceBERT training scripts at the point
## where individual sentence pairs are aggregated into
## {'claim': {'entailment': set(), 'contradiction': set(), 'neutral': set()}} dicts
## for further processing into training samples
```
Load the **cf-stsb** dataset
```python
# if datasets not already in your environment
!pip install datasets
from datasets import load_dataset
# all splits...
dd = load_dataset('climate-fever-nli-stsb', 'cf-stsb')
# ... or specific split ('train', 'dev', 'test' available)
ds_dev = load_dataset('climate-fever-nli-stsb', 'cf-stsb', split='dev')
## ds_dev (or test) can now be injected into SentenceBERT training scripts at the point
## where individual sentence pairs are aggregated into
## a list of dev (or test) samples
```
<!--
### 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
SentenceBERT models are designed for 'Domain Adaptation' and/or 'Fine-tuning' using labeled data in the downstream task domain. As a bi-encoder, the primary objective function is real-valued similarity scoring. Typical training datasets use NLI-style features as input, and STSb-style features as model evaluation during training, and to measure post-hoc, _intrinsic_ STSb performance. Classification tasks typically use a classifier network that accepts SentenceBERT encodings as input, and is trained on class-labeled datasets.
So, to fine-tune a SentenceBERT model in a climate-change domain, a labeled climate change dataset would be ideal. Much like the authors of the CLIMATE-FEVER dataset, we know of no other _labeled_ datasets specific to climate change. And while CLIMATE-FEVER is suitably labeled for classification tasks, it is not ready for similarity tuning in the style of SentenceBERT.
This modified CLIMATE-FEVER dataset attempts to fill that gap by deriving NLI-style features typically used in pre-training and fine-tuning a SentenceBERT model. SentenceBERT also uses STSb-style features to evaluate model performance both during training and after training to gauge _intrinsic_ model performance on STSb.
### Source Data
#### Initial Data Collection and Normalization
see CLIMATE-FEVER
#### Who are the source language producers?
see CLIMATE-FEVER
<!--
### Annotations
-->
### Annotation process
#### **cf-nli**
For each Claim that has both SUPPORTS evidence and REFUTES evidence, create labeled pairs in the style of NLI dataset:
| split | dataset | sentence1 | sentence2 | label |
|---|---|---|---|---|
| {'train', 'test'} | 'climate-fever' | claim | evidence | evidence_label SUPPORTS -> 'entailment', REFUTES -> 'contradiction' |
> Note that by defintion, only claims classified as DISPUTED include both SUPPORTS and REFUTES evidence, so this dataset is limited to a small subset of CLIMATE-FEVER.
### **cf-nli-nei**
This dataset uses the list of annotator 'votes' to cast a NOT_ENOUGH_INFO (NEI) evidence to a SUPPORTS or REFUTES evidence. By doing so, Claims in the SUPPORTS, REFUTES, and NEI classes can be used to generate additional sentence pairs.
| votes | effective evidence_label |
|---|---|
| SUPPORTS > REFUTES | _SUPPORTS_ |
| SUPPORTS < REFUTES | _REFUTES_ |
In addition to all the claims in **cf-nli**, any Claims that have,
* **_at least one_** SUPPORTS or REFUTES evidence, AND
* NEI evidences that can be cast to effective _SUPPORTS_ or _REFUTES_
are included in the datasset.
### **cf-stsb**
For each Claim <-> Evidence pair, create labeled pairs in the style of STSb dataset:
| split | dataset | score | sentence1 | sentence2 |
|---|---|---|---|---|
| {'train', 'dev', 'test'} | 'climate-fever' | cos_sim score | claim | evidence |
This dataset uses 'evidence_label', vote 'entropy', and the list of annotator 'votes' to derive a similarity score for each claim <-> evidence pairing. Similarity score conversion:
> `mean(entropy)` refers to the average entropy within the defined group of evidence
| evidence_label | votes | similarity score |
|---|---|---|
| SUPPORTS | SUPPORTS > 0, REFUTES == 0, NOT_ENOUGH_INFO (NEI) == 0 | 1 |
| | SUPPORTS > 0, REFUTES == 0 | mean(entropy) |
| | SUPPORTS > 0, REFUTES > 0 | 1 - mean(entropy) |
| NEI | SUPPORTS > REFUTES | (1 - mean(entropy)) / 2|
| | SUPPORTS == REFUTES | 0 |
| | SUPPORTS < REFUTES | -(1 - mean(entropy)) / 2 |
| REFUTES | SUPPORTS > 0, REFUTES > 0 | -(1 - mean(entropy)) |
| | SUPPORTS == 0, REFUTES > 0 | -mean(entropy) |
| | SUPPORTS == 0, REFUTES > 0, NEI == 0 | -1 |
The above derivation roughly maps the strength of evidence annotation (REFUTES..NEI..SUPPORTS) to cosine similarity (-1..0..1).
<!--
#### 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
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[More Information Needed]
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--> | slhenty/climate-fever-nli-stsb | [
"license:unknown",
"region:us"
]
| 2023-03-23T00:10:11+00:00 | {"license": "unknown", "viewer": false} | 2023-03-24T21:08:44+00:00 | []
| []
| TAGS
#license-unknown #region-us
| ==========================================
*IN PROGRESS - NOT READY FOR LOADING OR USE*
==========================================
---
Dataset Card for climate-fever-nli-stsb
=======================================
Dataset Description
-------------------
* Homepage:
* Repository:
* Paper:
* Leaderboard:
* Point of Contact:
### Dataset Summary
The CLIMATE-FEVER dataset modified to supply NLI-style (cf-nli) features or STSb-style (cf-stsb) features that SentenceBERT training scripts can use as drop-in replacements for AllNLI and/or STSb datasets.
There are two cf-nli datasets: one derived from only SUPPORTS and REFUTES evidence (cf-nli), and one that also derived data from NOT\_ENOUGH\_INFO evidence based on the annotator votes (cf-nli-nei).
The feature style is specified as a named configuration when loading the dataset: cf-nli, cf-nli-nei, or cf-stsb. See usage notes below for 'load\_dataset' examples.
### Usage
Load the cf-nli dataset
Load the cf-nli-nei dataset
Load the cf-stsb dataset
Dataset Structure
-----------------
### Data Instances
### Data Fields
### Data Splits
Dataset Creation
----------------
### Curation Rationale
SentenceBERT models are designed for 'Domain Adaptation' and/or 'Fine-tuning' using labeled data in the downstream task domain. As a bi-encoder, the primary objective function is real-valued similarity scoring. Typical training datasets use NLI-style features as input, and STSb-style features as model evaluation during training, and to measure post-hoc, *intrinsic* STSb performance. Classification tasks typically use a classifier network that accepts SentenceBERT encodings as input, and is trained on class-labeled datasets.
So, to fine-tune a SentenceBERT model in a climate-change domain, a labeled climate change dataset would be ideal. Much like the authors of the CLIMATE-FEVER dataset, we know of no other *labeled* datasets specific to climate change. And while CLIMATE-FEVER is suitably labeled for classification tasks, it is not ready for similarity tuning in the style of SentenceBERT.
This modified CLIMATE-FEVER dataset attempts to fill that gap by deriving NLI-style features typically used in pre-training and fine-tuning a SentenceBERT model. SentenceBERT also uses STSb-style features to evaluate model performance both during training and after training to gauge *intrinsic* model performance on STSb.
### Source Data
#### Initial Data Collection and Normalization
see CLIMATE-FEVER
#### Who are the source language producers?
see CLIMATE-FEVER
### Annotation process
#### cf-nli
For each Claim that has both SUPPORTS evidence and REFUTES evidence, create labeled pairs in the style of NLI dataset:
>
> Note that by defintion, only claims classified as DISPUTED include both SUPPORTS and REFUTES evidence, so this dataset is limited to a small subset of CLIMATE-FEVER.
>
>
>
### cf-nli-nei
This dataset uses the list of annotator 'votes' to cast a NOT\_ENOUGH\_INFO (NEI) evidence to a SUPPORTS or REFUTES evidence. By doing so, Claims in the SUPPORTS, REFUTES, and NEI classes can be used to generate additional sentence pairs.
In addition to all the claims in cf-nli, any Claims that have,
* *at least one* SUPPORTS or REFUTES evidence, AND
* NEI evidences that can be cast to effective *SUPPORTS* or *REFUTES*
are included in the datasset.
### cf-stsb
For each Claim <-> Evidence pair, create labeled pairs in the style of STSb dataset:
This dataset uses 'evidence\_label', vote 'entropy', and the list of annotator 'votes' to derive a similarity score for each claim <-> evidence pairing. Similarity score conversion:
>
> 'mean(entropy)' refers to the average entropy within the defined group of evidence
>
>
>
evidence\_label: SUPPORTS, votes: SUPPORTS > 0, REFUTES == 0, NOT\_ENOUGH\_INFO (NEI) == 0, similarity score: 1
evidence\_label: , votes: SUPPORTS > 0, REFUTES == 0, similarity score: mean(entropy)
evidence\_label: , votes: SUPPORTS > 0, REFUTES > 0, similarity score: 1 - mean(entropy)
evidence\_label: NEI, votes: SUPPORTS > REFUTES, similarity score: (1 - mean(entropy)) / 2
evidence\_label: , votes: SUPPORTS == REFUTES, similarity score: 0
evidence\_label: , votes: SUPPORTS < REFUTES, similarity score: -(1 - mean(entropy)) / 2
evidence\_label: REFUTES, votes: SUPPORTS > 0, REFUTES > 0, similarity score: -(1 - mean(entropy))
evidence\_label: , votes: SUPPORTS == 0, REFUTES > 0, similarity score: -mean(entropy)
evidence\_label: , votes: SUPPORTS == 0, REFUTES > 0, NEI == 0, similarity score: -1
The above derivation roughly maps the strength of evidence annotation (REFUTES..NEI..SUPPORTS) to cosine similarity (-1..0..1).
| [
"### Dataset Summary\n\n\nThe CLIMATE-FEVER dataset modified to supply NLI-style (cf-nli) features or STSb-style (cf-stsb) features that SentenceBERT training scripts can use as drop-in replacements for AllNLI and/or STSb datasets.\n\n\nThere are two cf-nli datasets: one derived from only SUPPORTS and REFUTES evidence (cf-nli), and one that also derived data from NOT\\_ENOUGH\\_INFO evidence based on the annotator votes (cf-nli-nei).\n\n\nThe feature style is specified as a named configuration when loading the dataset: cf-nli, cf-nli-nei, or cf-stsb. See usage notes below for 'load\\_dataset' examples.",
"### Usage\n\n\nLoad the cf-nli dataset\n\n\nLoad the cf-nli-nei dataset\n\n\nLoad the cf-stsb dataset\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nSentenceBERT models are designed for 'Domain Adaptation' and/or 'Fine-tuning' using labeled data in the downstream task domain. As a bi-encoder, the primary objective function is real-valued similarity scoring. Typical training datasets use NLI-style features as input, and STSb-style features as model evaluation during training, and to measure post-hoc, *intrinsic* STSb performance. Classification tasks typically use a classifier network that accepts SentenceBERT encodings as input, and is trained on class-labeled datasets.\n\n\nSo, to fine-tune a SentenceBERT model in a climate-change domain, a labeled climate change dataset would be ideal. Much like the authors of the CLIMATE-FEVER dataset, we know of no other *labeled* datasets specific to climate change. And while CLIMATE-FEVER is suitably labeled for classification tasks, it is not ready for similarity tuning in the style of SentenceBERT.\n\n\nThis modified CLIMATE-FEVER dataset attempts to fill that gap by deriving NLI-style features typically used in pre-training and fine-tuning a SentenceBERT model. SentenceBERT also uses STSb-style features to evaluate model performance both during training and after training to gauge *intrinsic* model performance on STSb.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nsee CLIMATE-FEVER",
"#### Who are the source language producers?\n\n\nsee CLIMATE-FEVER",
"### Annotation process",
"#### cf-nli\n\n\nFor each Claim that has both SUPPORTS evidence and REFUTES evidence, create labeled pairs in the style of NLI dataset:\n\n\n\n\n> \n> Note that by defintion, only claims classified as DISPUTED include both SUPPORTS and REFUTES evidence, so this dataset is limited to a small subset of CLIMATE-FEVER.\n> \n> \n>",
"### cf-nli-nei\n\n\nThis dataset uses the list of annotator 'votes' to cast a NOT\\_ENOUGH\\_INFO (NEI) evidence to a SUPPORTS or REFUTES evidence. By doing so, Claims in the SUPPORTS, REFUTES, and NEI classes can be used to generate additional sentence pairs.\n\n\n\nIn addition to all the claims in cf-nli, any Claims that have,\n\n\n* *at least one* SUPPORTS or REFUTES evidence, AND\n* NEI evidences that can be cast to effective *SUPPORTS* or *REFUTES*\n\n\nare included in the datasset.",
"### cf-stsb\n\n\nFor each Claim <-> Evidence pair, create labeled pairs in the style of STSb dataset:\n\n\n\nThis dataset uses 'evidence\\_label', vote 'entropy', and the list of annotator 'votes' to derive a similarity score for each claim <-> evidence pairing. Similarity score conversion:\n\n\n\n> \n> 'mean(entropy)' refers to the average entropy within the defined group of evidence\n> \n> \n> \n\n\nevidence\\_label: SUPPORTS, votes: SUPPORTS > 0, REFUTES == 0, NOT\\_ENOUGH\\_INFO (NEI) == 0, similarity score: 1\nevidence\\_label: , votes: SUPPORTS > 0, REFUTES == 0, similarity score: mean(entropy)\nevidence\\_label: , votes: SUPPORTS > 0, REFUTES > 0, similarity score: 1 - mean(entropy)\nevidence\\_label: NEI, votes: SUPPORTS > REFUTES, similarity score: (1 - mean(entropy)) / 2\nevidence\\_label: , votes: SUPPORTS == REFUTES, similarity score: 0\nevidence\\_label: , votes: SUPPORTS < REFUTES, similarity score: -(1 - mean(entropy)) / 2\nevidence\\_label: REFUTES, votes: SUPPORTS > 0, REFUTES > 0, similarity score: -(1 - mean(entropy))\nevidence\\_label: , votes: SUPPORTS == 0, REFUTES > 0, similarity score: -mean(entropy)\nevidence\\_label: , votes: SUPPORTS == 0, REFUTES > 0, NEI == 0, similarity score: -1\n\n\nThe above derivation roughly maps the strength of evidence annotation (REFUTES..NEI..SUPPORTS) to cosine similarity (-1..0..1)."
]
| [
"TAGS\n#license-unknown #region-us \n",
"### Dataset Summary\n\n\nThe CLIMATE-FEVER dataset modified to supply NLI-style (cf-nli) features or STSb-style (cf-stsb) features that SentenceBERT training scripts can use as drop-in replacements for AllNLI and/or STSb datasets.\n\n\nThere are two cf-nli datasets: one derived from only SUPPORTS and REFUTES evidence (cf-nli), and one that also derived data from NOT\\_ENOUGH\\_INFO evidence based on the annotator votes (cf-nli-nei).\n\n\nThe feature style is specified as a named configuration when loading the dataset: cf-nli, cf-nli-nei, or cf-stsb. See usage notes below for 'load\\_dataset' examples.",
"### Usage\n\n\nLoad the cf-nli dataset\n\n\nLoad the cf-nli-nei dataset\n\n\nLoad the cf-stsb dataset\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nSentenceBERT models are designed for 'Domain Adaptation' and/or 'Fine-tuning' using labeled data in the downstream task domain. As a bi-encoder, the primary objective function is real-valued similarity scoring. Typical training datasets use NLI-style features as input, and STSb-style features as model evaluation during training, and to measure post-hoc, *intrinsic* STSb performance. Classification tasks typically use a classifier network that accepts SentenceBERT encodings as input, and is trained on class-labeled datasets.\n\n\nSo, to fine-tune a SentenceBERT model in a climate-change domain, a labeled climate change dataset would be ideal. Much like the authors of the CLIMATE-FEVER dataset, we know of no other *labeled* datasets specific to climate change. And while CLIMATE-FEVER is suitably labeled for classification tasks, it is not ready for similarity tuning in the style of SentenceBERT.\n\n\nThis modified CLIMATE-FEVER dataset attempts to fill that gap by deriving NLI-style features typically used in pre-training and fine-tuning a SentenceBERT model. SentenceBERT also uses STSb-style features to evaluate model performance both during training and after training to gauge *intrinsic* model performance on STSb.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nsee CLIMATE-FEVER",
"#### Who are the source language producers?\n\n\nsee CLIMATE-FEVER",
"### Annotation process",
"#### cf-nli\n\n\nFor each Claim that has both SUPPORTS evidence and REFUTES evidence, create labeled pairs in the style of NLI dataset:\n\n\n\n\n> \n> Note that by defintion, only claims classified as DISPUTED include both SUPPORTS and REFUTES evidence, so this dataset is limited to a small subset of CLIMATE-FEVER.\n> \n> \n>",
"### cf-nli-nei\n\n\nThis dataset uses the list of annotator 'votes' to cast a NOT\\_ENOUGH\\_INFO (NEI) evidence to a SUPPORTS or REFUTES evidence. By doing so, Claims in the SUPPORTS, REFUTES, and NEI classes can be used to generate additional sentence pairs.\n\n\n\nIn addition to all the claims in cf-nli, any Claims that have,\n\n\n* *at least one* SUPPORTS or REFUTES evidence, AND\n* NEI evidences that can be cast to effective *SUPPORTS* or *REFUTES*\n\n\nare included in the datasset.",
"### cf-stsb\n\n\nFor each Claim <-> Evidence pair, create labeled pairs in the style of STSb dataset:\n\n\n\nThis dataset uses 'evidence\\_label', vote 'entropy', and the list of annotator 'votes' to derive a similarity score for each claim <-> evidence pairing. Similarity score conversion:\n\n\n\n> \n> 'mean(entropy)' refers to the average entropy within the defined group of evidence\n> \n> \n> \n\n\nevidence\\_label: SUPPORTS, votes: SUPPORTS > 0, REFUTES == 0, NOT\\_ENOUGH\\_INFO (NEI) == 0, similarity score: 1\nevidence\\_label: , votes: SUPPORTS > 0, REFUTES == 0, similarity score: mean(entropy)\nevidence\\_label: , votes: SUPPORTS > 0, REFUTES > 0, similarity score: 1 - mean(entropy)\nevidence\\_label: NEI, votes: SUPPORTS > REFUTES, similarity score: (1 - mean(entropy)) / 2\nevidence\\_label: , votes: SUPPORTS == REFUTES, similarity score: 0\nevidence\\_label: , votes: SUPPORTS < REFUTES, similarity score: -(1 - mean(entropy)) / 2\nevidence\\_label: REFUTES, votes: SUPPORTS > 0, REFUTES > 0, similarity score: -(1 - mean(entropy))\nevidence\\_label: , votes: SUPPORTS == 0, REFUTES > 0, similarity score: -mean(entropy)\nevidence\\_label: , votes: SUPPORTS == 0, REFUTES > 0, NEI == 0, similarity score: -1\n\n\nThe above derivation roughly maps the strength of evidence annotation (REFUTES..NEI..SUPPORTS) to cosine similarity (-1..0..1)."
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"passage: TAGS\n#license-unknown #region-us \n### Dataset Summary\n\n\nThe CLIMATE-FEVER dataset modified to supply NLI-style (cf-nli) features or STSb-style (cf-stsb) features that SentenceBERT training scripts can use as drop-in replacements for AllNLI and/or STSb datasets.\n\n\nThere are two cf-nli datasets: one derived from only SUPPORTS and REFUTES evidence (cf-nli), and one that also derived data from NOT\\_ENOUGH\\_INFO evidence based on the annotator votes (cf-nli-nei).\n\n\nThe feature style is specified as a named configuration when loading the dataset: cf-nli, cf-nli-nei, or cf-stsb. See usage notes below for 'load\\_dataset' examples.### Usage\n\n\nLoad the cf-nli dataset\n\n\nLoad the cf-nli-nei dataset\n\n\nLoad the cf-stsb dataset\n\n\nDataset Structure\n-----------------### Data Instances### Data Fields### Data Splits\n\n\nDataset Creation\n----------------",
"passage: ### Curation Rationale\n\n\nSentenceBERT models are designed for 'Domain Adaptation' and/or 'Fine-tuning' using labeled data in the downstream task domain. As a bi-encoder, the primary objective function is real-valued similarity scoring. Typical training datasets use NLI-style features as input, and STSb-style features as model evaluation during training, and to measure post-hoc, *intrinsic* STSb performance. Classification tasks typically use a classifier network that accepts SentenceBERT encodings as input, and is trained on class-labeled datasets.\n\n\nSo, to fine-tune a SentenceBERT model in a climate-change domain, a labeled climate change dataset would be ideal. Much like the authors of the CLIMATE-FEVER dataset, we know of no other *labeled* datasets specific to climate change. And while CLIMATE-FEVER is suitably labeled for classification tasks, it is not ready for similarity tuning in the style of SentenceBERT.\n\n\nThis modified CLIMATE-FEVER dataset attempts to fill that gap by deriving NLI-style features typically used in pre-training and fine-tuning a SentenceBERT model. SentenceBERT also uses STSb-style features to evaluate model performance both during training and after training to gauge *intrinsic* model performance on STSb.### Source Data#### Initial Data Collection and Normalization\n\n\nsee CLIMATE-FEVER#### Who are the source language producers?\n\n\nsee CLIMATE-FEVER### Annotation process#### cf-nli\n\n\nFor each Claim that has both SUPPORTS evidence and REFUTES evidence, create labeled pairs in the style of NLI dataset:\n\n\n\n\n> \n> Note that by defintion, only claims classified as DISPUTED include both SUPPORTS and REFUTES evidence, so this dataset is limited to a small subset of CLIMATE-FEVER.\n> \n> \n>### cf-nli-nei\n\n\nThis dataset uses the list of annotator 'votes' to cast a NOT\\_ENOUGH\\_INFO (NEI) evidence to a SUPPORTS or REFUTES evidence. By doing so, Claims in the SUPPORTS, REFUTES, and NEI classes can be used to generate additional sentence pairs.\n\n\n\nIn addition to all the claims in cf-nli, any Claims that have,\n\n\n* *at least one* SUPPORTS or REFUTES evidence, AND\n* NEI evidences that can be cast to effective *SUPPORTS* or *REFUTES*\n\n\nare included in the datasset."
]
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|
70cde1a942c6bc687fa298acee7bf3c3af875172 | # Dataset Card for "boolq-ko"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nayohan/boolq-ko | [
"region:us"
]
| 2023-03-23T00:40:44+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "answer", "dtype": "bool"}, {"name": "passage", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6349601, "num_examples": 9427}, {"name": "valid", "num_bytes": 2176494, "num_examples": 3270}], "download_size": 5393710, "dataset_size": 8526095}} | 2023-03-23T00:41:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "boolq-ko"
More Information needed | [
"# Dataset Card for \"boolq-ko\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"boolq-ko\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"boolq-ko\"\n\nMore Information needed"
]
| [
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|
849c9351310920cbf8462f2bca00f73287526b8a | # Dataset Card for "commonsense_qa-ko"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | nayohan/commonsense_qa-ko | [
"region:us"
]
| 2023-03-23T00:44:42+00:00 | {"dataset_info": {"features": [{"name": "stem", "dtype": "string"}, {"name": "label_A", "dtype": "string"}, {"name": "label_B", "dtype": "string"}, {"name": "label_C", "dtype": "string"}, {"name": "label_D", "dtype": "string"}, {"name": "label_E", "dtype": "string"}, {"name": "answerKey", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1662060, "num_examples": 9741}, {"name": "valid", "num_bytes": 206056, "num_examples": 1221}], "download_size": 1169959, "dataset_size": 1868116}} | 2023-03-23T00:45:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "commonsense_qa-ko"
More Information needed | [
"# Dataset Card for \"commonsense_qa-ko\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"commonsense_qa-ko\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"commonsense_qa-ko\"\n\nMore Information needed"
]
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|
3b550ae20bf5c1d6123a0e093e51311e055498e5 | # Dataset Card for "mrc_aihub_books"
[도서자료 기계독해](https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=92) | wisenut-nlp-team/aihub_mrc_books | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"task_ids:open-domain-qa",
"annotations_creators:crowdsourced",
"language_creators:found",
"size_categories:10M<n<100M",
"source_datasets:original",
"license:cc-by-4.0",
"mrc",
"region:us"
]
| 2023-03-23T00:45:16+00:00 | {"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": [], "license": ["cc-by-4.0"], "multilinguality": [], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["extractive-qa", "open-domain-qa"], "pretty_name": "wisenut-nlp-team/aihub_mrc_books", "dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "agency", "dtype": "string"}, {"name": "year", "dtype": "string"}, {"name": "content_id", "dtype": "string"}, {"name": "kdc", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "is_impossible", "dtype": "bool"}, {"name": "answers", "struct": [{"name": "answer_start", "dtype": "int64"}, {"name": "text", "dtype": "string"}]}], "splits": [{"name": "train", "num_bytes": 1344986838, "num_examples": 900000}, {"name": "validation", "num_bytes": 74048965, "num_examples": 50000}], "download_size": 186230823, "dataset_size": 1419035803}, "tags": ["mrc"]} | 2023-05-23T23:31:29+00:00 | []
| []
| TAGS
#task_categories-question-answering #task_ids-extractive-qa #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-found #size_categories-10M<n<100M #source_datasets-original #license-cc-by-4.0 #mrc #region-us
| # Dataset Card for "mrc_aihub_books"
도서자료 기계독해 | [
"# Dataset Card for \"mrc_aihub_books\"\n\n도서자료 기계독해"
]
| [
"TAGS\n#task_categories-question-answering #task_ids-extractive-qa #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-found #size_categories-10M<n<100M #source_datasets-original #license-cc-by-4.0 #mrc #region-us \n",
"# Dataset Card for \"mrc_aihub_books\"\n\n도서자료 기계독해"
]
| [
94,
20
]
| [
"passage: TAGS\n#task_categories-question-answering #task_ids-extractive-qa #task_ids-open-domain-qa #annotations_creators-crowdsourced #language_creators-found #size_categories-10M<n<100M #source_datasets-original #license-cc-by-4.0 #mrc #region-us \n# Dataset Card for \"mrc_aihub_books\"\n\n도서자료 기계독해"
]
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|
92fde701612b85562e56347b1f89f4b15543e619 | # Bank
The [Bank dataset](https://archive.ics.uci.edu/ml/datasets/bank+marketing) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Potential clients are contacted by a bank during a second advertisement campaign.
This datasets records the customer, the interaction with the AD campaign, and if they subscribed to a proposed bank plan or not.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|-----------------------------------------------------------------|
| encoding | | Encoding dictionary showing original values of encoded features.|
| subscription | Binary classification | Has the customer subscribed to a bank plan? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/bank", "subscription")["train"]
```
# Features
| **Name** |**Type** |
|-----------------------------------------------|-----------|
|`age` |`int64` |
|`job` |`string` |
|`marital_status` |`string` |
|`education` |`int8` |
|`has_defaulted` |`int8` |
|`account_balance` |`int64` |
|`has_housing_loan` |`int8` |
|`has_personal_loan` |`int8` |
|`month_of_last_contact` |`string` |
|`number_of_calls_in_ad_campaign` |`string` |
|`days_since_last_contact_of_previous_campaign` |`int16` |
|`number_of_calls_before_this_campaign` |`int16` |
|`successfull_subscription` |`int8` | | mstz/bank | [
"task_categories:tabular-classification",
"size_categories:1K<n<10K",
"language:en",
"compas",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
]
| 2023-03-23T00:56:08+00:00 | {"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["tabular-classification"], "pretty_name": "Bank", "tags": ["compas", "tabular_classification", "binary_classification", "UCI"], "configs": ["encoding", "subscription"]} | 2023-04-15T10:16:43+00:00 | []
| [
"en"
]
| TAGS
#task_categories-tabular-classification #size_categories-1K<n<10K #language-English #compas #tabular_classification #binary_classification #UCI #region-us
| Bank
====
The Bank dataset from the UCI ML repository.
Potential clients are contacted by a bank during a second advertisement campaign.
This datasets records the customer, the interaction with the AD campaign, and if they subscribed to a proposed bank plan or not.
Configurations and tasks
========================
Configuration: encoding, Task: , Description: Encoding dictionary showing original values of encoded features.
Configuration: subscription, Task: Binary classification, Description: Has the customer subscribed to a bank plan?
Usage
=====
Features
========
| []
| [
"TAGS\n#task_categories-tabular-classification #size_categories-1K<n<10K #language-English #compas #tabular_classification #binary_classification #UCI #region-us \n"
]
| [
51
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-1K<n<10K #language-English #compas #tabular_classification #binary_classification #UCI #region-us \n"
]
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|
b306f5f77df7dc0dffce22451144f1e3801d519f | # Positive Psychology Frames
_Inducing Positive Perspectives with Text Reframing_
[[Read the Paper]](https://faculty.cc.gatech.edu/~dyang888/docs/acl22_reframing.pdf) | [[Download the Data]](https://www.dropbox.com/sh/pnoczmv0uyn51e6/AAAGek6yX12Yc4PA2RwtZeZKa?dl=0) | [[Demo]](https://huggingface.co/spaces/Ella2323/Positive-Reframing)
<img src="frontpage.png" alt="frontpage" width="650"/>
## *Why Positive Frames?*
This work was inspired by the need to escape the negative patterns of thinking that began to overwhelm the authors during the COVID-19 pandemic. We realized that what we needed was not some naive belief that everything would be okay if we ignored our problems. Instead, we needed _reframing_, or a shift in focus, with less weight on the negative things we can't control, and more weight on the positive things about ourselves and our situation which we can control.
_Positive reframing_ induces a complementary positive viewpoint (e.g. glass-half-full), which nevertheless supports the underlying content of the original sentence (see diagram above). The reframe implicates rather than contradicts the source, and the transformation is motivated by theoretically justified strategies from positive psychology (see _What's 'in the box?'_).
Our work shows how NLP can help lead the way by automatically reframing overly negative text using strategies from positive psychology.
## *What's 'in the box?'*
The `Positive Psychology Frames` dataset contains **8,349** reframed sentence pairs, where the original sentence is drawn from a negative tweet (\#stressed), and a reframed copy is provided by a crowdworker who was trained in the methods of positive psychology. Our positive psychology frames taxonomy is defined below (with the distribution of labels shown on the left).
*  **Growth Mindset:** Viewing a challenging event as an opportunity for the author specifically to grow or improve themselves.
*  **Impermanence:** Saying bad things don't last forever, will get better soon, and/or that others have experienced similar struggles.
*  **Neutralizing:** Replacing a negative word with a neutral word.
*  **Optimism:** Focusing on things about the situation itself, in that moment, that are good (not just forecasting a better future).
*  **Self-Affirmation:** Talking about what strengths the author already has, or the values they admire, like love, courage, perseverance, etc.
*  **Thankfulness:** Expressing thankfulness or gratitude with key words like appreciate, glad that, thankful for, good thing, etc.
## *What can I do with this data?*
State-of-the-art neural models can learn from our data how to (1) shift a negatively distorted text into a more positive perspective using a combination of strategies from positive psychology; and (2) recognize or classify the psychological strategies that are used to reframe a given source.
As our paper baselines show, neural models still have a long ways to go before they can reliably generate positive perspectives. We see particular errors from _insubstantial changes, contradictions to the premise, self-contradictions, and hallucinations_. Overall, our suggests that our dataset can serve as a useful benchmark for building natural language generation systems with positive perspectives. For more information, please [read the paper](https://faculty.cc.gatech.edu/~dyang888/docs/acl22_reframing.pdf).
## *How do I run the baseline models?*
**1. Set Up Environment**
* CUDA, cudnn
* anaconda
```
conda create --name reframe python=3.7
conda activate reframe
pip install -r requirements.txt
```
**2. Dataset Preparation**
The datasets are under the data/ folder.
-Random, SBERT, T5, BART: wholetrain.csv, wholetest.csv
The datasets contain fields: original_text, reframed_text, strategy, original_with_label
-GPT, GPT2: wholetrain_gpt.txt, wholetest.csv
The train data contains <startoftext> token and <endoftext> token for each sentence pair. Also, ‘reframed: ‘ token indicates the position where the reframed sentence begins for each sentence pair. Each sentence pair starts on a new line.
-Seq2SeqLSTM: for_train.txt, for_test.txt
The datasets contain paired texts separated by tab in each line: original_text \t reframed_text
-CopyNMT: train-original.txt, train-reframed.txt, validation-original.txt, validation-reframed.txt, test-original.txt, test-reframed.txt
Each file contains the original/reframed sentences separated by \n.
**3 . Run the Baseline Models**
Random, Sbert, T5, BART, GPT, GPT2:
```python3 run.py —-arguments```
Arguments:
--model: choose from random, sbert, t5, BART
--setting: default is unconstrained, controlled/predict setting is supported for t5 and BART
--train: path to train data file
--dev: path to dev data file
--test: path to test data file
CopyNMT: Execute copynmt_train.sh and copynmt_eval.sh
```
bash copynmt_train.sh
bash copynmt_eval.sh
```
Seq2Seq-lstm: git clone https://github.com/bond005/seq2seq.git, replace the data files in the data/ folder and follow the instructions to train the seq2seq-lstm model
## *How do I cite this work?*
**Citation:**
> Ziems, C., Li, M., Zhang, A., & Yang, D. (2022). Inducing Positive Perspectives with Text Reframing. In _Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)_.
**BibTeX:**
```tex
@inproceedings{ziems-etal-2022-positive-frames,
title = "Inducing Positive Perspectives with Text Reframing",
author = "Ziems, Caleb and
Li, Minzhi and
Zhang, Anthony and
Yang, Diyi",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics",
month = may,
year = "2022",
address = "Online and Dublin, Ireland",
publisher = "Association for Computational Linguistics"
}
``` | SALT-NLP/positive_reframing | [
"region:us"
]
| 2023-03-23T01:33:32+00:00 | {} | 2023-03-23T01:36:18+00:00 | []
| []
| TAGS
#region-us
| # Positive Psychology Frames
_Inducing Positive Perspectives with Text Reframing_
[[Read the Paper]](URL | [[Download the Data]](URL | [[Demo]](URL
<img src="URL" alt="frontpage" width="650"/>
## *Why Positive Frames?*
This work was inspired by the need to escape the negative patterns of thinking that began to overwhelm the authors during the COVID-19 pandemic. We realized that what we needed was not some naive belief that everything would be okay if we ignored our problems. Instead, we needed _reframing_, or a shift in focus, with less weight on the negative things we can't control, and more weight on the positive things about ourselves and our situation which we can control.
_Positive reframing_ induces a complementary positive viewpoint (e.g. glass-half-full), which nevertheless supports the underlying content of the original sentence (see diagram above). The reframe implicates rather than contradicts the source, and the transformation is motivated by theoretically justified strategies from positive psychology (see _What's 'in the box?'_).
Our work shows how NLP can help lead the way by automatically reframing overly negative text using strategies from positive psychology.
## *What's 'in the box?'*
The 'Positive Psychology Frames' dataset contains 8,349 reframed sentence pairs, where the original sentence is drawn from a negative tweet (\#stressed), and a reframed copy is provided by a crowdworker who was trained in the methods of positive psychology. Our positive psychology frames taxonomy is defined below (with the distribution of labels shown on the left).
* !25.4% Growth Mindset: Viewing a challenging event as an opportunity for the author specifically to grow or improve themselves.
* !19.5% Impermanence: Saying bad things don't last forever, will get better soon, and/or that others have experienced similar struggles.
* !36.1% Neutralizing: Replacing a negative word with a neutral word.
* !48.7% Optimism: Focusing on things about the situation itself, in that moment, that are good (not just forecasting a better future).
* !10.1% Self-Affirmation: Talking about what strengths the author already has, or the values they admire, like love, courage, perseverance, etc.
* !13.0% Thankfulness: Expressing thankfulness or gratitude with key words like appreciate, glad that, thankful for, good thing, etc.
## *What can I do with this data?*
State-of-the-art neural models can learn from our data how to (1) shift a negatively distorted text into a more positive perspective using a combination of strategies from positive psychology; and (2) recognize or classify the psychological strategies that are used to reframe a given source.
As our paper baselines show, neural models still have a long ways to go before they can reliably generate positive perspectives. We see particular errors from _insubstantial changes, contradictions to the premise, self-contradictions, and hallucinations_. Overall, our suggests that our dataset can serve as a useful benchmark for building natural language generation systems with positive perspectives. For more information, please read the paper.
## *How do I run the baseline models?*
1. Set Up Environment
* CUDA, cudnn
* anaconda
2. Dataset Preparation
The datasets are under the data/ folder.
-Random, SBERT, T5, BART: URL, URL
The datasets contain fields: original_text, reframed_text, strategy, original_with_label
-GPT, GPT2: wholetrain_gpt.txt, URL
The train data contains <startoftext> token and <endoftext> token for each sentence pair. Also, ‘reframed: ‘ token indicates the position where the reframed sentence begins for each sentence pair. Each sentence pair starts on a new line.
-Seq2SeqLSTM: for_train.txt, for_test.txt
The datasets contain paired texts separated by tab in each line: original_text \t reframed_text
-CopyNMT: URL, URL, URL, URL, URL, URL
Each file contains the original/reframed sentences separated by \n.
3 . Run the Baseline Models
Random, Sbert, T5, BART, GPT, GPT2:
Arguments:
--model: choose from random, sbert, t5, BART
--setting: default is unconstrained, controlled/predict setting is supported for t5 and BART
--train: path to train data file
--dev: path to dev data file
--test: path to test data file
CopyNMT: Execute copynmt_train.sh and copynmt_eval.sh
Seq2Seq-lstm: git clone URL replace the data files in the data/ folder and follow the instructions to train the seq2seq-lstm model
## *How do I cite this work?*
Citation:
> Ziems, C., Li, M., Zhang, A., & Yang, D. (2022). Inducing Positive Perspectives with Text Reframing. In _Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)_.
BibTeX:
| [
"# Positive Psychology Frames\n\n_Inducing Positive Perspectives with Text Reframing_\n\n[[Read the Paper]](URL | [[Download the Data]](URL | [[Demo]](URL\n\n<img src=\"URL\" alt=\"frontpage\" width=\"650\"/>",
"## *Why Positive Frames?* \n\nThis work was inspired by the need to escape the negative patterns of thinking that began to overwhelm the authors during the COVID-19 pandemic. We realized that what we needed was not some naive belief that everything would be okay if we ignored our problems. Instead, we needed _reframing_, or a shift in focus, with less weight on the negative things we can't control, and more weight on the positive things about ourselves and our situation which we can control.\n\n_Positive reframing_ induces a complementary positive viewpoint (e.g. glass-half-full), which nevertheless supports the underlying content of the original sentence (see diagram above). The reframe implicates rather than contradicts the source, and the transformation is motivated by theoretically justified strategies from positive psychology (see _What's 'in the box?'_).\n\nOur work shows how NLP can help lead the way by automatically reframing overly negative text using strategies from positive psychology.",
"## *What's 'in the box?'* \n\nThe 'Positive Psychology Frames' dataset contains 8,349 reframed sentence pairs, where the original sentence is drawn from a negative tweet (\\#stressed), and a reframed copy is provided by a crowdworker who was trained in the methods of positive psychology. Our positive psychology frames taxonomy is defined below (with the distribution of labels shown on the left).\n\n* !25.4% Growth Mindset: Viewing a challenging event as an opportunity for the author specifically to grow or improve themselves.\n* !19.5% Impermanence: Saying bad things don't last forever, will get better soon, and/or that others have experienced similar struggles.\n* !36.1% Neutralizing: Replacing a negative word with a neutral word.\n* !48.7% Optimism: Focusing on things about the situation itself, in that moment, that are good (not just forecasting a better future).\n* !10.1% Self-Affirmation: Talking about what strengths the author already has, or the values they admire, like love, courage, perseverance, etc.\n* !13.0% Thankfulness: Expressing thankfulness or gratitude with key words like appreciate, glad that, thankful for, good thing, etc.",
"## *What can I do with this data?* \n\nState-of-the-art neural models can learn from our data how to (1) shift a negatively distorted text into a more positive perspective using a combination of strategies from positive psychology; and (2) recognize or classify the psychological strategies that are used to reframe a given source. \n\nAs our paper baselines show, neural models still have a long ways to go before they can reliably generate positive perspectives. We see particular errors from _insubstantial changes, contradictions to the premise, self-contradictions, and hallucinations_. Overall, our suggests that our dataset can serve as a useful benchmark for building natural language generation systems with positive perspectives. For more information, please read the paper.",
"## *How do I run the baseline models?* \n1. Set Up Environment\n* CUDA, cudnn\n* anaconda\n\n2. Dataset Preparation\nThe datasets are under the data/ folder.\n\n-Random, SBERT, T5, BART: URL, URL\nThe datasets contain fields: original_text, reframed_text, strategy, original_with_label\n\n-GPT, GPT2: wholetrain_gpt.txt, URL\nThe train data contains <startoftext> token and <endoftext> token for each sentence pair. Also, ‘reframed: ‘ token indicates the position where the reframed sentence begins for each sentence pair. Each sentence pair starts on a new line.\n\n-Seq2SeqLSTM: for_train.txt, for_test.txt\nThe datasets contain paired texts separated by tab in each line: original_text \\t reframed_text\n\n-CopyNMT: URL, URL, URL, URL, URL, URL\nEach file contains the original/reframed sentences separated by \\n.\n\n\n3 . Run the Baseline Models\n\nRandom, Sbert, T5, BART, GPT, GPT2: \n\nArguments:\n--model: choose from random, sbert, t5, BART\n--setting: default is unconstrained, controlled/predict setting is supported for t5 and BART\n--train: path to train data file\n--dev: path to dev data file\n--test: path to test data file\n\nCopyNMT: Execute copynmt_train.sh and copynmt_eval.sh\n\n\nSeq2Seq-lstm: git clone URL replace the data files in the data/ folder and follow the instructions to train the seq2seq-lstm model",
"## *How do I cite this work?* \n\nCitation:\n\n> Ziems, C., Li, M., Zhang, A., & Yang, D. (2022). Inducing Positive Perspectives with Text Reframing. In _Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)_.\n\nBibTeX:"
]
| [
"TAGS\n#region-us \n",
"# Positive Psychology Frames\n\n_Inducing Positive Perspectives with Text Reframing_\n\n[[Read the Paper]](URL | [[Download the Data]](URL | [[Demo]](URL\n\n<img src=\"URL\" alt=\"frontpage\" width=\"650\"/>",
"## *Why Positive Frames?* \n\nThis work was inspired by the need to escape the negative patterns of thinking that began to overwhelm the authors during the COVID-19 pandemic. We realized that what we needed was not some naive belief that everything would be okay if we ignored our problems. Instead, we needed _reframing_, or a shift in focus, with less weight on the negative things we can't control, and more weight on the positive things about ourselves and our situation which we can control.\n\n_Positive reframing_ induces a complementary positive viewpoint (e.g. glass-half-full), which nevertheless supports the underlying content of the original sentence (see diagram above). The reframe implicates rather than contradicts the source, and the transformation is motivated by theoretically justified strategies from positive psychology (see _What's 'in the box?'_).\n\nOur work shows how NLP can help lead the way by automatically reframing overly negative text using strategies from positive psychology.",
"## *What's 'in the box?'* \n\nThe 'Positive Psychology Frames' dataset contains 8,349 reframed sentence pairs, where the original sentence is drawn from a negative tweet (\\#stressed), and a reframed copy is provided by a crowdworker who was trained in the methods of positive psychology. Our positive psychology frames taxonomy is defined below (with the distribution of labels shown on the left).\n\n* !25.4% Growth Mindset: Viewing a challenging event as an opportunity for the author specifically to grow or improve themselves.\n* !19.5% Impermanence: Saying bad things don't last forever, will get better soon, and/or that others have experienced similar struggles.\n* !36.1% Neutralizing: Replacing a negative word with a neutral word.\n* !48.7% Optimism: Focusing on things about the situation itself, in that moment, that are good (not just forecasting a better future).\n* !10.1% Self-Affirmation: Talking about what strengths the author already has, or the values they admire, like love, courage, perseverance, etc.\n* !13.0% Thankfulness: Expressing thankfulness or gratitude with key words like appreciate, glad that, thankful for, good thing, etc.",
"## *What can I do with this data?* \n\nState-of-the-art neural models can learn from our data how to (1) shift a negatively distorted text into a more positive perspective using a combination of strategies from positive psychology; and (2) recognize or classify the psychological strategies that are used to reframe a given source. \n\nAs our paper baselines show, neural models still have a long ways to go before they can reliably generate positive perspectives. We see particular errors from _insubstantial changes, contradictions to the premise, self-contradictions, and hallucinations_. Overall, our suggests that our dataset can serve as a useful benchmark for building natural language generation systems with positive perspectives. For more information, please read the paper.",
"## *How do I run the baseline models?* \n1. Set Up Environment\n* CUDA, cudnn\n* anaconda\n\n2. Dataset Preparation\nThe datasets are under the data/ folder.\n\n-Random, SBERT, T5, BART: URL, URL\nThe datasets contain fields: original_text, reframed_text, strategy, original_with_label\n\n-GPT, GPT2: wholetrain_gpt.txt, URL\nThe train data contains <startoftext> token and <endoftext> token for each sentence pair. Also, ‘reframed: ‘ token indicates the position where the reframed sentence begins for each sentence pair. Each sentence pair starts on a new line.\n\n-Seq2SeqLSTM: for_train.txt, for_test.txt\nThe datasets contain paired texts separated by tab in each line: original_text \\t reframed_text\n\n-CopyNMT: URL, URL, URL, URL, URL, URL\nEach file contains the original/reframed sentences separated by \\n.\n\n\n3 . Run the Baseline Models\n\nRandom, Sbert, T5, BART, GPT, GPT2: \n\nArguments:\n--model: choose from random, sbert, t5, BART\n--setting: default is unconstrained, controlled/predict setting is supported for t5 and BART\n--train: path to train data file\n--dev: path to dev data file\n--test: path to test data file\n\nCopyNMT: Execute copynmt_train.sh and copynmt_eval.sh\n\n\nSeq2Seq-lstm: git clone URL replace the data files in the data/ folder and follow the instructions to train the seq2seq-lstm model",
"## *How do I cite this work?* \n\nCitation:\n\n> Ziems, C., Li, M., Zhang, A., & Yang, D. (2022). Inducing Positive Perspectives with Text Reframing. In _Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (ACL)_.\n\nBibTeX:"
]
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"passage: TAGS\n#region-us \n# Positive Psychology Frames\n\n_Inducing Positive Perspectives with Text Reframing_\n\n[[Read the Paper]](URL | [[Download the Data]](URL | [[Demo]](URL\n\n<img src=\"URL\" alt=\"frontpage\" width=\"650\"/>## *Why Positive Frames?* \n\nThis work was inspired by the need to escape the negative patterns of thinking that began to overwhelm the authors during the COVID-19 pandemic. We realized that what we needed was not some naive belief that everything would be okay if we ignored our problems. Instead, we needed _reframing_, or a shift in focus, with less weight on the negative things we can't control, and more weight on the positive things about ourselves and our situation which we can control.\n\n_Positive reframing_ induces a complementary positive viewpoint (e.g. glass-half-full), which nevertheless supports the underlying content of the original sentence (see diagram above). The reframe implicates rather than contradicts the source, and the transformation is motivated by theoretically justified strategies from positive psychology (see _What's 'in the box?'_).\n\nOur work shows how NLP can help lead the way by automatically reframing overly negative text using strategies from positive psychology.",
"passage: ## *What's 'in the box?'* \n\nThe 'Positive Psychology Frames' dataset contains 8,349 reframed sentence pairs, where the original sentence is drawn from a negative tweet (\\#stressed), and a reframed copy is provided by a crowdworker who was trained in the methods of positive psychology. Our positive psychology frames taxonomy is defined below (with the distribution of labels shown on the left).\n\n* !25.4% Growth Mindset: Viewing a challenging event as an opportunity for the author specifically to grow or improve themselves.\n* !19.5% Impermanence: Saying bad things don't last forever, will get better soon, and/or that others have experienced similar struggles.\n* !36.1% Neutralizing: Replacing a negative word with a neutral word.\n* !48.7% Optimism: Focusing on things about the situation itself, in that moment, that are good (not just forecasting a better future).\n* !10.1% Self-Affirmation: Talking about what strengths the author already has, or the values they admire, like love, courage, perseverance, etc.\n* !13.0% Thankfulness: Expressing thankfulness or gratitude with key words like appreciate, glad that, thankful for, good thing, etc.## *What can I do with this data?* \n\nState-of-the-art neural models can learn from our data how to (1) shift a negatively distorted text into a more positive perspective using a combination of strategies from positive psychology; and (2) recognize or classify the psychological strategies that are used to reframe a given source. \n\nAs our paper baselines show, neural models still have a long ways to go before they can reliably generate positive perspectives. We see particular errors from _insubstantial changes, contradictions to the premise, self-contradictions, and hallucinations_. Overall, our suggests that our dataset can serve as a useful benchmark for building natural language generation systems with positive perspectives. For more information, please read the paper."
]
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|
2f9a435a8ebb6869a3205374e414f1bb1c9848d5 |
# Introduction
These are chat logs and speech-to-text conversations obtained from Twitch channels of VTubers. All usernames have been masked with "[CHAT]" and username mentions with "@username".
VOD transcriptions are only added after prior consent of their respective streamer. | Differentiable/processed_chats | [
"language:en",
"license:bigscience-openrail-m",
"twitch",
"chat",
"region:us"
]
| 2023-03-23T03:12:17+00:00 | {"language": ["en"], "license": "bigscience-openrail-m", "pretty_name": "twitch-vtuber-chat", "tags": ["twitch", "chat"]} | 2023-03-23T03:22:44+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-bigscience-openrail-m #twitch #chat #region-us
|
# Introduction
These are chat logs and speech-to-text conversations obtained from Twitch channels of VTubers. All usernames have been masked with "[CHAT]" and username mentions with "@username".
VOD transcriptions are only added after prior consent of their respective streamer. | [
"# Introduction\n\nThese are chat logs and speech-to-text conversations obtained from Twitch channels of VTubers. All usernames have been masked with \"[CHAT]\" and username mentions with \"@username\".\nVOD transcriptions are only added after prior consent of their respective streamer."
]
| [
"TAGS\n#language-English #license-bigscience-openrail-m #twitch #chat #region-us \n",
"# Introduction\n\nThese are chat logs and speech-to-text conversations obtained from Twitch channels of VTubers. All usernames have been masked with \"[CHAT]\" and username mentions with \"@username\".\nVOD transcriptions are only added after prior consent of their respective streamer."
]
| [
26,
71
]
| [
"passage: TAGS\n#language-English #license-bigscience-openrail-m #twitch #chat #region-us \n# Introduction\n\nThese are chat logs and speech-to-text conversations obtained from Twitch channels of VTubers. All usernames have been masked with \"[CHAT]\" and username mentions with \"@username\".\nVOD transcriptions are only added after prior consent of their respective streamer."
]
| [
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|
18b2fcebb36f67771cd383a2deaea6b087b3233b | # Dataset Card for "starthack-supercell-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ogimgio/starthack-supercell-dataset | [
"region:us"
]
| 2023-03-23T04:19:24+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "Negative", "1": "Positive"}}}}], "splits": [{"name": "train", "num_bytes": 34103600.0, "num_examples": 206}, {"name": "validation", "num_bytes": 5764205.0, "num_examples": 35}], "download_size": 35724512, "dataset_size": 39867805.0}} | 2023-03-23T10:34:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "starthack-supercell-dataset"
More Information needed | [
"# Dataset Card for \"starthack-supercell-dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"starthack-supercell-dataset\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"starthack-supercell-dataset\"\n\nMore Information needed"
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|
92faa38f4657aee08a17313599379834ac9e0256 | dataset_info:
features:
- name: url
dtype: string
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dtype: string
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dtype: string
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struct:
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dtype: string
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dtype: string
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dtype: bool
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dtype: int64
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dtype: timestamp[s]
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dtype: timestamp[s]
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dtype: string
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dtype: 'null'
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dtype: string
- name: reactions
struct:
- name: url
dtype: string
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dtype: int64
- name: '+1'
dtype: int64
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dtype: int64
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dtype: int64
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dtype: int64
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dtype: int64
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dtype: int64
- name: timeline_url
dtype: string
- name: performed_via_github_app
dtype: 'null'
- name: state_reason
dtype: string
- name: draft
dtype: bool
- name: pull_request
struct:
- name: url
dtype: string
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dtype: string
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dtype: string
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dtype: timestamp[s]
- name: is_pull_request
dtype: bool
splits:
- name: train
num_bytes: 201451
num_examples: 60
download_size: 0
dataset_size: 201451
---
# Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | yangwooko/github-issues | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"task_ids:sentiment-classification",
"multilinguality:monolingual",
"size_categories:n<1K",
"language:en",
"region:us"
]
| 2023-03-23T04:22:45+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["n<1K"], "source_datasets": [], "task_categories": ["text-classification"], "task_ids": ["semantic-similarity-classification", "sentiment-classification"], "pretty_name": "github issues very small", "tags": []} | 2023-03-23T04:47:27+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_ids-semantic-similarity-classification #task_ids-sentiment-classification #multilinguality-monolingual #size_categories-n<1K #language-English #region-us
| dataset_info:
features:
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dtype: string
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dtype: string
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dtype: string
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dtype: string
- name: html_url
dtype: string
- name: id
dtype: int64
- name: node_id
dtype: string
- name: number
dtype: int64
- name: title
dtype: string
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struct:
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dtype: string
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dtype: string
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dtype: string
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dtype: bool
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list:
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struct:
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dtype: timestamp[s]
- name: author_association
dtype: string
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dtype: 'null'
- name: body
dtype: string
- name: reactions
struct:
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dtype: string
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dtype: int64
- name: '+1'
dtype: int64
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- name: laugh
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- name: heart
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- name: rocket
dtype: int64
- name: eyes
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- name: timeline_url
dtype: string
- name: performed_via_github_app
dtype: 'null'
- name: state_reason
dtype: string
- name: draft
dtype: bool
- name: pull_request
struct:
- name: url
dtype: string
- name: html_url
dtype: string
- name: diff_url
dtype: string
- name: patch_url
dtype: string
- name: merged_at
dtype: timestamp[s]
- name: is_pull_request
dtype: bool
splits:
- name: train
num_bytes: 201451
num_examples: 60
download_size: 0
dataset_size: 201451
---
# Dataset Card for "github-issues"
More Information needed | [
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
| [
"TAGS\n#task_categories-text-classification #task_ids-semantic-similarity-classification #task_ids-sentiment-classification #multilinguality-monolingual #size_categories-n<1K #language-English #region-us \n",
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
| [
65,
15
]
| [
"passage: TAGS\n#task_categories-text-classification #task_ids-semantic-similarity-classification #task_ids-sentiment-classification #multilinguality-monolingual #size_categories-n<1K #language-English #region-us \n# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
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|
8e46706c832692f64a89077026def70d28658984 | # Dataset Card for "es-en"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dandrade/es-en | [
"region:us"
]
| 2023-03-23T05:44:44+00:00 | {"dataset_info": {"features": [{"name": "ES", "dtype": "string"}, {"name": "EN", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1236977.6, "num_examples": 3200}, {"name": "test", "num_bytes": 309244.4, "num_examples": 800}], "download_size": 931996, "dataset_size": 1546222.0}} | 2023-03-23T05:44:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "es-en"
More Information needed | [
"# Dataset Card for \"es-en\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"es-en\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"es-en\"\n\nMore Information needed"
]
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|
c9131d3c9a7e41f476aaeed478b45ee6ac73e2b2 | # Dataset Card for "imagenet-1k_mini_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | theodor1289/imagenet-1k_tiny | [
"region:us"
]
| 2023-03-23T08:14:03+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "tench, Tinca tinca", "1": "goldfish, Carassius auratus", "2": "great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias", "3": "tiger shark, Galeocerdo cuvieri", "4": "hammerhead, hammerhead shark", "5": "electric ray, crampfish, numbfish, torpedo", "6": "stingray", "7": "cock", "8": "hen", "9": "ostrich, Struthio camelus", "10": "brambling, Fringilla montifringilla", "11": "goldfinch, Carduelis carduelis", "12": "house finch, linnet, Carpodacus mexicanus", "13": "junco, snowbird", "14": "indigo bunting, indigo finch, indigo bird, Passerina cyanea", "15": "robin, American robin, Turdus migratorius", "16": "bulbul", "17": "jay", "18": "magpie", "19": "chickadee", "20": "water ouzel, dipper", "21": "kite", "22": "bald eagle, American eagle, Haliaeetus leucocephalus", "23": "vulture", "24": "great grey owl, great gray owl, Strix nebulosa", "25": "European fire salamander, Salamandra salamandra", "26": "common newt, Triturus vulgaris", "27": "eft", "28": "spotted salamander, Ambystoma maculatum", "29": "axolotl, mud puppy, Ambystoma mexicanum", "30": "bullfrog, Rana catesbeiana", "31": "tree frog, tree-frog", "32": "tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui", "33": "loggerhead, loggerhead turtle, Caretta caretta", "34": "leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea", "35": "mud turtle", "36": "terrapin", "37": "box turtle, box tortoise", "38": "banded gecko", "39": "common iguana, iguana, Iguana iguana", "40": "American chameleon, anole, Anolis carolinensis", "41": "whiptail, whiptail lizard", "42": "agama", "43": "frilled lizard, Chlamydosaurus kingi", "44": "alligator lizard", "45": "Gila monster, Heloderma suspectum", "46": "green lizard, Lacerta viridis", "47": "African chameleon, Chamaeleo chamaeleon", "48": "Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis", "49": "African crocodile, Nile crocodile, Crocodylus niloticus", "50": "American alligator, Alligator mississipiensis", "51": "triceratops", "52": "thunder snake, worm snake, Carphophis amoenus", "53": "ringneck snake, ring-necked snake, ring snake", "54": "hognose snake, puff adder, sand viper", "55": "green snake, grass snake", "56": "king snake, kingsnake", "57": "garter snake, grass snake", "58": "water snake", "59": "vine snake", "60": "night snake, Hypsiglena torquata", "61": "boa constrictor, Constrictor constrictor", "62": "rock python, rock snake, Python sebae", "63": "Indian cobra, Naja naja", "64": "green mamba", "65": "sea snake", "66": "horned viper, cerastes, sand viper, horned asp, Cerastes cornutus", "67": "diamondback, diamondback rattlesnake, Crotalus adamanteus", "68": "sidewinder, horned rattlesnake, Crotalus cerastes", "69": "trilobite", "70": "harvestman, daddy longlegs, Phalangium opilio", "71": "scorpion", "72": "black and gold garden spider, Argiope aurantia", "73": "barn spider, Araneus cavaticus", "74": "garden spider, Aranea diademata", "75": "black widow, Latrodectus mactans", "76": "tarantula", "77": "wolf spider, hunting spider", "78": "tick", "79": "centipede", "80": "black grouse", "81": "ptarmigan", "82": "ruffed grouse, partridge, Bonasa umbellus", "83": "prairie chicken, prairie grouse, prairie fowl", "84": "peacock", "85": "quail", "86": "partridge", "87": "African grey, African gray, Psittacus erithacus", "88": "macaw", "89": "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", "90": "lorikeet", "91": "coucal", "92": "bee eater", "93": "hornbill", "94": "hummingbird", "95": "jacamar", "96": "toucan", "97": "drake", "98": "red-breasted merganser, Mergus serrator", "99": "goose", "100": "black swan, Cygnus atratus", "101": "tusker", "102": "echidna, spiny anteater, anteater", "103": "platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus", "104": "wallaby, brush kangaroo", "105": "koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus", "106": "wombat", "107": "jellyfish", "108": "sea anemone, anemone", "109": "brain coral", "110": "flatworm, platyhelminth", "111": "nematode, nematode worm, roundworm", "112": "conch", "113": "snail", "114": "slug", "115": "sea slug, nudibranch", "116": "chiton, coat-of-mail shell, sea cradle, polyplacophore", "117": "chambered nautilus, pearly nautilus, nautilus", "118": "Dungeness crab, Cancer magister", "119": "rock crab, Cancer irroratus", "120": "fiddler crab", "121": "king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica", "122": "American lobster, Northern lobster, Maine lobster, Homarus americanus", "123": "spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish", "124": "crayfish, crawfish, crawdad, crawdaddy", "125": "hermit crab", "126": "isopod", "127": "white stork, Ciconia ciconia", "128": "black stork, Ciconia nigra", "129": "spoonbill", "130": "flamingo", "131": "little blue heron, Egretta caerulea", "132": "American egret, great white heron, Egretta albus", "133": "bittern", "134": "crane", "135": "limpkin, Aramus pictus", "136": "European gallinule, Porphyrio porphyrio", "137": "American coot, marsh hen, mud hen, water hen, Fulica americana", "138": "bustard", "139": "ruddy turnstone, Arenaria interpres", "140": "red-backed sandpiper, dunlin, Erolia alpina", "141": "redshank, Tringa totanus", "142": "dowitcher", "143": "oystercatcher, oyster catcher", "144": "pelican", "145": "king penguin, Aptenodytes patagonica", "146": "albatross, mollymawk", "147": "grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus", "148": "killer whale, killer, orca, grampus, sea wolf, Orcinus orca", "149": "dugong, Dugong dugon", "150": "sea lion", "151": "Chihuahua", "152": "Japanese spaniel", "153": "Maltese dog, Maltese terrier, Maltese", "154": "Pekinese, Pekingese, Peke", "155": "Shih-Tzu", "156": "Blenheim spaniel", "157": "papillon", "158": "toy terrier", "159": "Rhodesian ridgeback", "160": "Afghan hound, Afghan", "161": "basset, basset hound", "162": "beagle", "163": "bloodhound, sleuthhound", "164": "bluetick", "165": "black-and-tan coonhound", "166": "Walker hound, Walker foxhound", "167": "English foxhound", "168": "redbone", "169": "borzoi, Russian wolfhound", "170": "Irish wolfhound", "171": "Italian greyhound", "172": "whippet", "173": "Ibizan hound, Ibizan Podenco", "174": "Norwegian elkhound, elkhound", "175": "otterhound, otter hound", "176": "Saluki, gazelle hound", "177": "Scottish deerhound, deerhound", "178": "Weimaraner", "179": "Staffordshire bullterrier, Staffordshire bull terrier", "180": "American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier", "181": "Bedlington terrier", "182": "Border terrier", "183": "Kerry blue terrier", "184": "Irish terrier", "185": "Norfolk terrier", "186": "Norwich terrier", "187": "Yorkshire terrier", "188": "wire-haired fox terrier", "189": "Lakeland terrier", "190": "Sealyham terrier, Sealyham", "191": "Airedale, Airedale terrier", "192": "cairn, cairn terrier", "193": "Australian terrier", "194": "Dandie Dinmont, Dandie Dinmont terrier", "195": "Boston bull, Boston terrier", "196": "miniature schnauzer", "197": "giant schnauzer", "198": "standard schnauzer", "199": "Scotch terrier, Scottish terrier, Scottie", "200": "Tibetan terrier, chrysanthemum dog", "201": "silky terrier, Sydney silky", "202": "soft-coated wheaten terrier", "203": "West Highland white terrier", "204": "Lhasa, Lhasa apso", "205": "flat-coated retriever", "206": "curly-coated retriever", "207": "golden retriever", "208": "Labrador retriever", "209": "Chesapeake Bay retriever", "210": "German short-haired pointer", "211": "vizsla, Hungarian pointer", "212": "English setter", "213": "Irish setter, red setter", "214": "Gordon setter", "215": "Brittany spaniel", "216": "clumber, clumber spaniel", "217": "English springer, English springer spaniel", "218": "Welsh springer spaniel", "219": "cocker spaniel, English cocker spaniel, cocker", "220": "Sussex spaniel", "221": "Irish water spaniel", "222": "kuvasz", "223": "schipperke", "224": "groenendael", "225": "malinois", "226": "briard", "227": "kelpie", "228": "komondor", "229": "Old English sheepdog, bobtail", "230": "Shetland sheepdog, Shetland sheep dog, Shetland", "231": "collie", "232": "Border collie", "233": "Bouvier des Flandres, Bouviers des Flandres", "234": "Rottweiler", "235": "German shepherd, German shepherd dog, German police dog, alsatian", "236": "Doberman, Doberman pinscher", "237": "miniature pinscher", "238": "Greater Swiss Mountain dog", "239": "Bernese mountain dog", "240": "Appenzeller", "241": "EntleBucher", "242": "boxer", "243": "bull mastiff", "244": "Tibetan mastiff", "245": "French bulldog", "246": "Great Dane", "247": "Saint Bernard, St Bernard", "248": "Eskimo dog, husky", "249": "malamute, malemute, Alaskan malamute", "250": "Siberian husky", "251": "dalmatian, coach dog, carriage dog", "252": "affenpinscher, monkey pinscher, monkey dog", "253": "basenji", "254": "pug, pug-dog", "255": "Leonberg", "256": "Newfoundland, Newfoundland dog", "257": "Great Pyrenees", "258": "Samoyed, Samoyede", "259": "Pomeranian", "260": "chow, chow chow", "261": "keeshond", "262": "Brabancon griffon", "263": "Pembroke, Pembroke Welsh corgi", "264": "Cardigan, Cardigan Welsh corgi", "265": "toy poodle", "266": "miniature poodle", "267": "standard poodle", "268": "Mexican hairless", "269": "timber wolf, grey wolf, gray wolf, Canis lupus", "270": "white wolf, Arctic wolf, Canis lupus tundrarum", "271": "red wolf, maned wolf, Canis rufus, Canis niger", "272": "coyote, prairie wolf, brush wolf, Canis latrans", "273": "dingo, warrigal, warragal, Canis dingo", "274": "dhole, Cuon alpinus", "275": "African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus", "276": "hyena, hyaena", "277": "red fox, Vulpes vulpes", "278": "kit fox, Vulpes macrotis", "279": "Arctic fox, white fox, Alopex lagopus", "280": "grey fox, gray fox, Urocyon cinereoargenteus", "281": "tabby, tabby cat", "282": "tiger cat", "283": "Persian cat", "284": "Siamese cat, Siamese", "285": "Egyptian cat", "286": "cougar, puma, catamount, mountain lion, painter, panther, Felis concolor", "287": "lynx, catamount", "288": "leopard, Panthera pardus", "289": "snow leopard, ounce, Panthera uncia", "290": "jaguar, panther, Panthera onca, Felis onca", "291": "lion, king of beasts, Panthera leo", "292": "tiger, Panthera tigris", "293": "cheetah, chetah, Acinonyx jubatus", "294": "brown bear, bruin, Ursus arctos", "295": "American black bear, black bear, Ursus americanus, Euarctos americanus", "296": "ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus", "297": "sloth bear, Melursus ursinus, Ursus ursinus", "298": "mongoose", "299": "meerkat, mierkat", "300": "tiger beetle", "301": "ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle", "302": "ground beetle, carabid beetle", "303": "long-horned beetle, longicorn, longicorn beetle", "304": "leaf beetle, chrysomelid", "305": "dung beetle", "306": "rhinoceros beetle", "307": "weevil", "308": "fly", "309": "bee", "310": "ant, emmet, pismire", "311": "grasshopper, hopper", "312": "cricket", "313": "walking stick, walkingstick, stick insect", "314": "cockroach, roach", "315": "mantis, mantid", "316": "cicada, cicala", "317": "leafhopper", "318": "lacewing, lacewing fly", "319": "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk", "320": "damselfly", "321": "admiral", "322": "ringlet, ringlet butterfly", "323": "monarch, monarch butterfly, milkweed butterfly, Danaus plexippus", "324": "cabbage butterfly", "325": "sulphur butterfly, sulfur butterfly", "326": "lycaenid, lycaenid butterfly", "327": "starfish, sea star", "328": "sea urchin", "329": "sea cucumber, holothurian", "330": "wood rabbit, cottontail, cottontail rabbit", "331": "hare", "332": "Angora, Angora rabbit", "333": "hamster", "334": "porcupine, hedgehog", "335": "fox squirrel, eastern fox squirrel, Sciurus niger", "336": "marmot", "337": "beaver", "338": "guinea pig, Cavia cobaya", "339": "sorrel", "340": "zebra", "341": "hog, pig, grunter, squealer, Sus scrofa", "342": "wild boar, boar, Sus scrofa", "343": "warthog", "344": "hippopotamus, hippo, river horse, Hippopotamus amphibius", "345": "ox", "346": "water buffalo, water ox, Asiatic buffalo, Bubalus bubalis", "347": "bison", "348": "ram, tup", "349": "bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis", "350": "ibex, Capra ibex", "351": "hartebeest", "352": "impala, Aepyceros melampus", "353": "gazelle", "354": "Arabian camel, dromedary, Camelus dromedarius", "355": "llama", "356": "weasel", "357": "mink", "358": "polecat, fitch, foulmart, foumart, Mustela putorius", "359": "black-footed ferret, ferret, Mustela nigripes", "360": "otter", "361": "skunk, polecat, wood pussy", "362": "badger", "363": "armadillo", "364": "three-toed sloth, ai, Bradypus tridactylus", "365": "orangutan, orang, orangutang, Pongo pygmaeus", "366": "gorilla, Gorilla gorilla", "367": "chimpanzee, chimp, Pan troglodytes", "368": "gibbon, Hylobates lar", "369": "siamang, Hylobates syndactylus, Symphalangus syndactylus", "370": "guenon, guenon monkey", "371": "patas, hussar monkey, Erythrocebus patas", "372": "baboon", "373": "macaque", "374": "langur", "375": "colobus, colobus monkey", "376": "proboscis monkey, Nasalis larvatus", "377": "marmoset", "378": "capuchin, ringtail, Cebus capucinus", "379": "howler monkey, howler", "380": "titi, titi monkey", "381": "spider monkey, Ateles geoffroyi", "382": "squirrel monkey, Saimiri sciureus", "383": "Madagascar cat, ring-tailed lemur, Lemur catta", "384": "indri, indris, Indri indri, Indri brevicaudatus", "385": "Indian elephant, Elephas maximus", "386": "African elephant, Loxodonta africana", "387": "lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens", "388": "giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca", "389": "barracouta, snoek", "390": "eel", "391": "coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch", "392": "rock beauty, Holocanthus tricolor", "393": "anemone fish", "394": "sturgeon", "395": "gar, garfish, garpike, billfish, Lepisosteus osseus", "396": "lionfish", "397": "puffer, pufferfish, blowfish, globefish", "398": "abacus", "399": "abaya", "400": "academic gown, academic robe, judge's robe", "401": "accordion, piano accordion, squeeze box", "402": "acoustic guitar", "403": "aircraft carrier, carrier, flattop, attack aircraft carrier", "404": "airliner", "405": "airship, dirigible", "406": "altar", "407": "ambulance", "408": "amphibian, amphibious vehicle", "409": "analog clock", "410": "apiary, bee house", "411": "apron", "412": "ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin", "413": "assault rifle, assault gun", "414": "backpack, back pack, knapsack, packsack, rucksack, haversack", "415": "bakery, bakeshop, bakehouse", "416": "balance beam, beam", "417": "balloon", "418": "ballpoint, ballpoint pen, ballpen, Biro", "419": "Band Aid", "420": "banjo", "421": "bannister, banister, balustrade, balusters, handrail", "422": "barbell", "423": "barber chair", "424": "barbershop", "425": "barn", "426": "barometer", "427": "barrel, cask", "428": "barrow, garden cart, lawn cart, wheelbarrow", "429": "baseball", "430": "basketball", "431": "bassinet", "432": "bassoon", "433": "bathing cap, swimming cap", "434": "bath towel", "435": "bathtub, bathing tub, bath, tub", "436": "beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon", "437": "beacon, lighthouse, beacon light, pharos", "438": "beaker", "439": "bearskin, busby, shako", "440": "beer bottle", "441": "beer glass", "442": "bell cote, bell cot", "443": "bib", "444": "bicycle-built-for-two, tandem bicycle, tandem", "445": "bikini, two-piece", "446": "binder, ring-binder", "447": "binoculars, field glasses, opera glasses", "448": "birdhouse", "449": "boathouse", "450": "bobsled, bobsleigh, bob", "451": "bolo tie, bolo, bola tie, bola", "452": "bonnet, poke bonnet", "453": "bookcase", "454": "bookshop, bookstore, bookstall", "455": "bottlecap", "456": "bow", "457": "bow tie, bow-tie, bowtie", "458": "brass, memorial tablet, plaque", "459": "brassiere, bra, bandeau", "460": "breakwater, groin, groyne, mole, bulwark, seawall, jetty", "461": "breastplate, aegis, egis", "462": "broom", "463": "bucket, pail", "464": "buckle", "465": "bulletproof vest", "466": "bullet train, bullet", "467": "butcher shop, meat market", "468": "cab, hack, taxi, taxicab", "469": "caldron, cauldron", "470": "candle, taper, wax light", "471": "cannon", "472": "canoe", "473": "can opener, tin opener", "474": "cardigan", "475": "car mirror", "476": "carousel, carrousel, merry-go-round, roundabout, whirligig", "477": "carpenter's kit, tool kit", "478": "carton", "479": "car wheel", "480": "cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM", "481": "cassette", "482": "cassette player", "483": "castle", "484": "catamaran", "485": "CD player", "486": "cello, violoncello", "487": "cellular telephone, cellular phone, cellphone, cell, mobile phone", "488": "chain", "489": "chainlink fence", "490": "chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour", "491": "chain saw, chainsaw", "492": "chest", "493": "chiffonier, commode", "494": "chime, bell, gong", "495": "china cabinet, china closet", "496": "Christmas stocking", "497": "church, church building", "498": "cinema, movie theater, movie theatre, movie house, picture palace", "499": "cleaver, meat cleaver, chopper", "500": "cliff dwelling", "501": "cloak", "502": "clog, geta, patten, sabot", "503": "cocktail shaker", "504": "coffee mug", "505": "coffeepot", "506": "coil, spiral, volute, whorl, helix", "507": "combination lock", "508": "computer keyboard, keypad", "509": "confectionery, confectionary, candy store", "510": "container ship, containership, container vessel", "511": "convertible", "512": "corkscrew, bottle screw", "513": "cornet, horn, trumpet, trump", "514": "cowboy boot", "515": "cowboy hat, ten-gallon hat", "516": "cradle", "517": "crane2", "518": "crash helmet", "519": "crate", "520": "crib, cot", "521": "Crock Pot", "522": "croquet ball", "523": "crutch", "524": "cuirass", "525": "dam, dike, dyke", "526": "desk", "527": "desktop computer", "528": "dial telephone, dial phone", "529": "diaper, nappy, napkin", "530": "digital clock", "531": "digital watch", "532": "dining table, board", "533": "dishrag, dishcloth", "534": "dishwasher, dish washer, dishwashing machine", "535": "disk brake, disc brake", "536": "dock, dockage, docking facility", "537": "dogsled, dog sled, dog sleigh", "538": "dome", "539": "doormat, welcome mat", "540": "drilling platform, offshore rig", "541": "drum, membranophone, tympan", "542": "drumstick", "543": "dumbbell", "544": "Dutch oven", "545": "electric fan, blower", "546": "electric guitar", "547": "electric locomotive", "548": "entertainment center", "549": "envelope", "550": "espresso maker", "551": "face powder", "552": "feather boa, boa", "553": "file, file cabinet, filing cabinet", "554": "fireboat", "555": "fire engine, fire truck", "556": "fire screen, fireguard", "557": "flagpole, flagstaff", "558": "flute, transverse flute", "559": "folding chair", "560": "football helmet", "561": "forklift", "562": "fountain", "563": "fountain pen", "564": "four-poster", "565": "freight car", "566": "French horn, horn", "567": "frying pan, frypan, skillet", "568": "fur coat", "569": "garbage truck, dustcart", "570": "gasmask, respirator, gas helmet", "571": "gas pump, gasoline pump, petrol pump, island dispenser", "572": "goblet", "573": "go-kart", "574": "golf ball", "575": "golfcart, golf cart", "576": "gondola", "577": "gong, tam-tam", "578": "gown", "579": "grand piano, grand", "580": "greenhouse, nursery, glasshouse", "581": "grille, radiator grille", "582": "grocery store, grocery, food market, market", "583": "guillotine", "584": "hair slide", "585": "hair spray", "586": "half track", "587": "hammer", "588": "hamper", "589": "hand blower, blow dryer, blow drier, hair dryer, hair drier", "590": "hand-held computer, hand-held microcomputer", "591": "handkerchief, hankie, hanky, hankey", "592": "hard disc, hard disk, fixed disk", "593": "harmonica, mouth organ, harp, mouth harp", "594": "harp", "595": "harvester, reaper", "596": "hatchet", "597": "holster", "598": "home theater, home theatre", "599": "honeycomb", "600": "hook, claw", "601": "hoopskirt, crinoline", "602": "horizontal bar, high bar", "603": "horse cart, horse-cart", "604": "hourglass", "605": "iPod", "606": "iron, smoothing iron", "607": "jack-o'-lantern", "608": "jean, blue jean, denim", "609": "jeep, landrover", "610": "jersey, T-shirt, tee shirt", "611": "jigsaw puzzle", "612": "jinrikisha, ricksha, rickshaw", "613": "joystick", "614": "kimono", "615": "knee pad", "616": "knot", "617": "lab coat, laboratory coat", "618": "ladle", "619": "lampshade, lamp shade", "620": "laptop, laptop computer", "621": "lawn mower, mower", "622": "lens cap, lens cover", "623": "letter opener, paper knife, paperknife", "624": "library", "625": "lifeboat", "626": "lighter, light, igniter, ignitor", "627": "limousine, limo", "628": "liner, ocean liner", "629": "lipstick, lip rouge", "630": "Loafer", "631": "lotion", "632": "loudspeaker, speaker, speaker unit, loudspeaker system, speaker system", "633": "loupe, jeweler's loupe", "634": "lumbermill, sawmill", "635": "magnetic compass", "636": "mailbag, postbag", "637": "mailbox, letter box", "638": "maillot", "639": "maillot, tank suit", "640": "manhole cover", "641": "maraca", "642": "marimba, xylophone", "643": "mask", "644": "matchstick", "645": "maypole", "646": "maze, labyrinth", "647": "measuring cup", "648": "medicine chest, medicine cabinet", "649": "megalith, megalithic structure", "650": "microphone, mike", "651": "microwave, microwave oven", "652": "military uniform", "653": "milk can", "654": "minibus", "655": "miniskirt, mini", "656": "minivan", "657": "missile", "658": "mitten", "659": "mixing bowl", "660": "mobile home, manufactured home", "661": "Model T", "662": "modem", "663": "monastery", "664": "monitor", "665": "moped", "666": "mortar", "667": "mortarboard", "668": "mosque", "669": "mosquito net", "670": "motor scooter, scooter", "671": "mountain bike, all-terrain bike, off-roader", "672": "mountain tent", "673": "mouse, computer mouse", "674": "mousetrap", "675": "moving van", "676": "muzzle", "677": "nail", "678": "neck brace", "679": "necklace", "680": "nipple", "681": "notebook, notebook computer", "682": "obelisk", "683": "oboe, hautboy, hautbois", "684": "ocarina, sweet potato", "685": "odometer, hodometer, mileometer, milometer", "686": "oil filter", "687": "organ, pipe organ", "688": "oscilloscope, scope, cathode-ray oscilloscope, CRO", "689": "overskirt", "690": "oxcart", "691": "oxygen mask", "692": "packet", "693": "paddle, boat paddle", "694": "paddlewheel, paddle wheel", "695": "padlock", "696": "paintbrush", "697": "pajama, pyjama, pj's, jammies", "698": "palace", "699": "panpipe, pandean pipe, syrinx", "700": "paper towel", "701": "parachute, chute", "702": "parallel bars, bars", "703": "park bench", "704": "parking meter", "705": "passenger car, coach, carriage", "706": "patio, terrace", "707": "pay-phone, pay-station", "708": "pedestal, plinth, footstall", "709": "pencil box, pencil case", "710": "pencil sharpener", "711": "perfume, essence", "712": "Petri dish", "713": "photocopier", "714": "pick, plectrum, plectron", "715": "pickelhaube", "716": "picket fence, paling", "717": "pickup, pickup truck", "718": "pier", "719": "piggy bank, penny bank", "720": "pill bottle", "721": "pillow", "722": "ping-pong ball", "723": "pinwheel", "724": "pirate, pirate ship", "725": "pitcher, ewer", "726": "plane, carpenter's plane, woodworking plane", "727": "planetarium", "728": "plastic bag", "729": "plate rack", "730": "plow, plough", "731": "plunger, plumber's helper", "732": "Polaroid camera, Polaroid Land camera", "733": "pole", "734": "police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria", "735": "poncho", "736": "pool table, billiard table, snooker table", "737": "pop bottle, soda bottle", "738": "pot, flowerpot", "739": "potter's wheel", "740": "power drill", "741": "prayer rug, prayer mat", "742": "printer", "743": "prison, prison house", "744": "projectile, missile", "745": "projector", "746": "puck, hockey puck", "747": "punching bag, punch bag, punching ball, punchball", "748": "purse", "749": "quill, quill pen", "750": "quilt, comforter, comfort, puff", "751": "racer, race car, racing car", "752": "racket, racquet", "753": "radiator", "754": "radio, wireless", "755": "radio telescope, radio reflector", "756": "rain barrel", "757": "recreational vehicle, RV, R.V.", "758": "reel", "759": "reflex camera", "760": "refrigerator, icebox", "761": "remote control, remote", "762": "restaurant, eating house, eating place, eatery", "763": "revolver, six-gun, six-shooter", "764": "rifle", "765": "rocking chair, rocker", "766": "rotisserie", "767": "rubber eraser, rubber, pencil eraser", "768": "rugby ball", "769": "rule, ruler", "770": "running shoe", "771": "safe", "772": "safety pin", "773": "saltshaker, salt shaker", "774": "sandal", "775": "sarong", "776": "sax, saxophone", "777": "scabbard", "778": "scale, weighing machine", "779": "school bus", "780": "schooner", "781": "scoreboard", "782": "screen, CRT screen", "783": "screw", "784": "screwdriver", "785": "seat belt, seatbelt", "786": "sewing machine", "787": "shield, buckler", "788": "shoe shop, shoe-shop, shoe store", "789": "shoji", "790": "shopping basket", "791": "shopping cart", "792": "shovel", "793": "shower cap", "794": "shower curtain", "795": "ski", "796": "ski mask", "797": "sleeping bag", "798": "slide rule, slipstick", "799": "sliding door", "800": "slot, one-armed bandit", "801": "snorkel", "802": "snowmobile", "803": "snowplow, snowplough", "804": "soap dispenser", "805": "soccer ball", "806": "sock", "807": "solar dish, solar collector, solar furnace", "808": "sombrero", "809": "soup bowl", "810": "space bar", "811": "space heater", "812": "space shuttle", "813": "spatula", "814": "speedboat", "815": "spider web, spider's web", "816": "spindle", "817": "sports car, sport car", "818": "spotlight, spot", "819": "stage", "820": "steam locomotive", "821": "steel arch bridge", "822": "steel drum", "823": "stethoscope", "824": "stole", "825": "stone wall", "826": "stopwatch, stop watch", "827": "stove", "828": "strainer", "829": "streetcar, tram, tramcar, trolley, trolley car", "830": "stretcher", "831": "studio couch, day bed", "832": "stupa, tope", "833": "submarine, pigboat, sub, U-boat", "834": "suit, suit of clothes", "835": "sundial", "836": "sunglass", "837": "sunglasses, dark glasses, shades", "838": "sunscreen, sunblock, sun blocker", "839": "suspension bridge", "840": "swab, swob, mop", "841": "sweatshirt", "842": "swimming trunks, bathing trunks", "843": "swing", "844": "switch, electric switch, electrical switch", "845": "syringe", "846": "table lamp", "847": "tank, army tank, armored combat vehicle, armoured combat vehicle", "848": "tape player", "849": "teapot", "850": "teddy, teddy bear", "851": "television, television system", "852": "tennis ball", "853": "thatch, thatched roof", "854": "theater curtain, theatre curtain", "855": "thimble", "856": "thresher, thrasher, threshing machine", "857": "throne", "858": "tile roof", "859": "toaster", "860": "tobacco shop, tobacconist shop, tobacconist", "861": "toilet seat", "862": "torch", "863": "totem pole", "864": "tow truck, tow car, wrecker", "865": "toyshop", "866": "tractor", "867": "trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi", "868": "tray", "869": "trench coat", "870": "tricycle, trike, velocipede", "871": "trimaran", "872": "tripod", "873": "triumphal arch", "874": "trolleybus, trolley coach, trackless trolley", "875": "trombone", "876": "tub, vat", "877": "turnstile", "878": "typewriter keyboard", "879": "umbrella", "880": "unicycle, monocycle", "881": "upright, upright piano", "882": "vacuum, vacuum cleaner", "883": "vase", "884": "vault", "885": "velvet", "886": "vending machine", "887": "vestment", "888": "viaduct", "889": "violin, fiddle", "890": "volleyball", "891": "waffle iron", "892": "wall clock", "893": "wallet, billfold, notecase, pocketbook", "894": "wardrobe, closet, press", "895": "warplane, military plane", "896": "washbasin, handbasin, washbowl, lavabo, wash-hand basin", "897": "washer, automatic washer, washing machine", "898": "water bottle", "899": "water jug", "900": "water tower", "901": "whiskey jug", "902": "whistle", "903": "wig", "904": "window screen", "905": "window shade", "906": "Windsor tie", "907": "wine bottle", "908": "wing", "909": "wok", "910": "wooden spoon", "911": "wool, woolen, woollen", "912": "worm fence, snake fence, snake-rail fence, Virginia fence", "913": "wreck", "914": "yawl", "915": "yurt", "916": "web site, website, internet site, site", "917": "comic book", "918": "crossword puzzle, crossword", "919": "street sign", "920": "traffic light, traffic signal, stoplight", "921": "book jacket, dust cover, dust jacket, dust wrapper", "922": "menu", "923": "plate", "924": "guacamole", "925": "consomme", "926": "hot pot, hotpot", "927": "trifle", "928": "ice cream, icecream", "929": "ice lolly, lolly, lollipop, popsicle", "930": "French loaf", "931": "bagel, beigel", "932": "pretzel", "933": "cheeseburger", "934": "hotdog, hot dog, red hot", "935": "mashed potato", "936": "head cabbage", "937": "broccoli", "938": "cauliflower", "939": "zucchini, courgette", "940": "spaghetti squash", "941": "acorn squash", "942": "butternut squash", "943": "cucumber, cuke", "944": "artichoke, globe artichoke", "945": "bell pepper", "946": "cardoon", "947": "mushroom", "948": "Granny Smith", "949": "strawberry", "950": "orange", "951": "lemon", "952": "fig", "953": "pineapple, ananas", "954": "banana", "955": "jackfruit, jak, jack", "956": "custard apple", "957": "pomegranate", "958": "hay", "959": "carbonara", "960": "chocolate sauce, chocolate syrup", "961": "dough", "962": "meat loaf, meatloaf", "963": "pizza, pizza pie", "964": "potpie", "965": "burrito", "966": "red wine", "967": "espresso", "968": "cup", "969": "eggnog", "970": "alp", "971": "bubble", "972": "cliff, drop, drop-off", "973": "coral reef", "974": "geyser", "975": "lakeside, lakeshore", "976": "promontory, headland, head, foreland", "977": "sandbar, sand bar", "978": "seashore, coast, seacoast, sea-coast", "979": "valley, vale", "980": "volcano", "981": "ballplayer, baseball player", "982": "groom, bridegroom", "983": "scuba diver", "984": "rapeseed", "985": "daisy", "986": "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum", "987": "corn", "988": "acorn", "989": "hip, rose hip, rosehip", "990": "buckeye, horse chestnut, conker", "991": "coral fungus", "992": "agaric", "993": "gyromitra", "994": "stinkhorn, carrion fungus", "995": "earthstar", "996": "hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa", "997": "bolete", "998": "ear, spike, capitulum", "999": "toilet tissue, toilet paper, bathroom tissue"}}}}], "splits": [{"name": "train", "num_bytes": 11957097.0, "num_examples": 100}], "download_size": 11936960, "dataset_size": 11957097.0}} | 2023-03-23T08:14:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "imagenet-1k_mini_100"
More Information needed | [
"# Dataset Card for \"imagenet-1k_mini_100\"\n\nMore Information needed"
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| [
"TAGS\n#region-us \n",
"# Dataset Card for \"imagenet-1k_mini_100\"\n\nMore Information needed"
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"imagenet-1k_mini_100\"\n\nMore Information needed"
]
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|
a77b24f55a1e194c3551e10aee328c94162c74b4 | # AutoTrain Dataset for project: tree-classification
## Dataset Description
This dataset has been automatically processed by AutoTrain for project tree-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": "<194x259 RGB PIL image>",
"target": 0
},
{
"image": "<259x194 RGB PIL image>",
"target": 9
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['Araucaria columnaris', 'Archontophenix alexandrae', 'Bischofia javanica', 'Callistemon viminalis', 'Casuarina equisetifolia', 'Cinnamomum burmannii', 'Dicranopteris pedata', 'Hibiscus tiliaceus', 'Livistona chinensis', 'Machilus chekiangensis', 'Melaleuca cajuputi subsp. cumingiana', 'Psychotria asiatica', 'Terminalia mantaly'], 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 | 68 |
| valid | 24 |
| OttoYu/TreeDemoData | [
"task_categories:image-classification",
"region:us"
]
| 2023-03-23T08:39:39+00:00 | {"task_categories": ["image-classification"]} | 2023-03-23T08:59:09+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: tree-classification
==================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project tree-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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|
18c49d53fcf0c04e36543a434b323bfd5245d526 | # The CoreSearch Dataset
A cleaner version of the CoreSearch dataset</br>
## Cleaning done in this version
Below examples of fixes done in this dataset version
### Fix event mention spans
| Before Span Fix | After Span Fix |
| :------ | :----- |
| Academy Awards 65th ceremony | Academy Awards |
| 65th Annual Academy Awards | Annual Academy Awards |
| 2015 Southland Conference Women's Basketball Tournament | Southland Conference Women's Basketball Tournament |
| 2010 Austin suicide attack | attack |
| an earthquake in 1948 | earthquake |
| tragic plane crash | crash |
| Lokomotiv Yaroslavl plane tragedy | tragedy |
| just been shot | shot |
| wounded by gunfire | wounded |
| Kennedy's assassination in Los Angeles | assassination |
| Arab League summit in Cairo | summit |
| plane crash on 4 July 1943 | crash |
### Resolve cases of two events in a single span
| Before Span Fix | After Span Fix |
| :------ | :----- |
| killing or abduction of several hundred villagers | killing |
| kidnapping and murder of Aldo Moro | kidnapping |
### Remove erroneous event mentions (Entities)
| Mention Removed | Reason (not event) |
| :------ | :----- |
| I Want Your Love | Song name |
| 2006 Screen Actors Guild Award for Best Ensemble | Award name |
| in 1896 | Year |
- **Paper:** [Cross-document Event Coreference Search: Task, Dataset and Modeling](https://arxiv.org/abs/2210.12654)
### Languages
English
## Load Dataset
You can read/download the dataset files following Huggingface Hub instructions.</br>
For example, below code will load CoreSearch DPR folder:
```python
from huggingface_hub import hf_hub_url, cached_download
import json
REPO_ID = "datasets/Intel/CoreSearchV2"
DPR_FILES = "/dpr/"
dpr_files = ["dpr/Dev.json", "dpr/Train.json", "dpr/Test.json"]
dpr_jsons = list()
for _file in dpr_files:
dpr_jsons.append(json.load(open(cached_download(
hf_hub_url(REPO_ID, _file)), "r")))
```
### Data Splits
- **Final version of the CD event coreference search dataset**<br>
| | Train | Valid | Test | Total |
| ----- | ------ | ----- | ---- | ---- |
| WEC-Eng Validated Data | | | | |
| # Clusters | 229 | 48 | 226 | 503 |
| # Passages (with Mentions) | 1,429 | 335 | 1,206 | 2,970 |
| # Added Destructor Passages | 922,736 | 923,376 | 923,746 | 2,769,858 |
| # Total Passages | 924,239 | 923,717 | 925,012 | 2,772,968 |
## Citation
```
@inproceedings{eirew-etal-2022-cross,
title = "Cross-document Event Coreference Search: Task, Dataset and Modeling",
author = "Eirew, Alon and
Caciularu, Avi and
Dagan, Ido",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.58",
pages = "900--913",
abstract = "The task of Cross-document Coreference Resolution has been traditionally formulated as requiring to identify all coreference links across a given set of documents. We propose an appealing, and often more applicable, complementary set up for the task {--} Cross-document Coreference Search, focusing in this paper on event coreference. Concretely, given a mention in context of an event of interest, considered as a query, the task is to find all coreferring mentions for the query event in a large document collection. To support research on this task, we create a corresponding dataset, which is derived from Wikipedia while leveraging annotations in the available Wikipedia Event Coreferecene dataset (WEC-Eng). Observing that the coreference search setup is largely analogous to the setting of Open Domain Question Answering, we adapt the prominent Deep Passage Retrieval (DPR) model to our setting, as an appealing baseline. Finally, we present a novel model that integrates a powerful coreference scoring scheme into the DPR architecture, yielding improved performance.",
}
```
## License
We provide the following data sets under a <a href="https://creativecommons.org/licenses/by-sa/3.0/deed.en_US">Creative Commons Attribution-ShareAlike 3.0 Unported License</a>. It is based on content extracted from Wikipedia that is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License
## Contact
If you have any questions please create a Github issue at <a href="https://github.com/AlonEirew/CoreSearch">https://github.com/AlonEirew/CoreSearch</a>. | biu-nlp/CoreSearchV2 | [
"arxiv:2210.12654",
"region:us"
]
| 2023-03-23T08:44:13+00:00 | {} | 2023-03-23T09:36:41+00:00 | [
"2210.12654"
]
| []
| TAGS
#arxiv-2210.12654 #region-us
| The CoreSearch Dataset
======================
A cleaner version of the CoreSearch dataset
Cleaning done in this version
-----------------------------
Below examples of fixes done in this dataset version
### Fix event mention spans
### Resolve cases of two events in a single span
### Remove erroneous event mentions (Entities)
* Paper: Cross-document Event Coreference Search: Task, Dataset and Modeling
### Languages
English
Load Dataset
------------
You can read/download the dataset files following Huggingface Hub instructions.
For example, below code will load CoreSearch DPR folder:
### Data Splits
* Final version of the CD event coreference search dataset
| | Train | Valid | Test | Total |
| ----- | ------ | ----- | ---- | ---- |
| WEC-Eng Validated Data | | | | |
| # Clusters | 229 | 48 | 226 | 503 |
| # Passages (with Mentions) | 1,429 | 335 | 1,206 | 2,970 |
| # Added Destructor Passages | 922,736 | 923,376 | 923,746 | 2,769,858 |
| # Total Passages | 924,239 | 923,717 | 925,012 | 2,772,968 |
License
-------
We provide the following data sets under a <a href="URL Commons Attribution-ShareAlike 3.0 Unported License. It is based on content extracted from Wikipedia that is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License
Contact
-------
If you have any questions please create a Github issue at <a href="URL/URL
| [
"### Fix event mention spans",
"### Resolve cases of two events in a single span",
"### Remove erroneous event mentions (Entities)\n\n\n\n* Paper: Cross-document Event Coreference Search: Task, Dataset and Modeling",
"### Languages\n\n\nEnglish\n\n\nLoad Dataset\n------------\n\n\nYou can read/download the dataset files following Huggingface Hub instructions.\nFor example, below code will load CoreSearch DPR folder:",
"### Data Splits\n\n\n* Final version of the CD event coreference search dataset \n\n| | Train | Valid | Test | Total |\n| ----- | ------ | ----- | ---- | ---- |\n| WEC-Eng Validated Data | | | | |\n| # Clusters | 229 | 48 | 226 | 503 |\n| # Passages (with Mentions) | 1,429 | 335 | 1,206 | 2,970 |\n| # Added Destructor Passages | 922,736 | 923,376 | 923,746 | 2,769,858 |\n| # Total Passages | 924,239 | 923,717 | 925,012 | 2,772,968 |\n\n\nLicense\n-------\n\n\nWe provide the following data sets under a <a href=\"URL Commons Attribution-ShareAlike 3.0 Unported License. It is based on content extracted from Wikipedia that is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License\n\n\nContact\n-------\n\n\nIf you have any questions please create a Github issue at <a href=\"URL/URL"
]
| [
"TAGS\n#arxiv-2210.12654 #region-us \n",
"### Fix event mention spans",
"### Resolve cases of two events in a single span",
"### Remove erroneous event mentions (Entities)\n\n\n\n* Paper: Cross-document Event Coreference Search: Task, Dataset and Modeling",
"### Languages\n\n\nEnglish\n\n\nLoad Dataset\n------------\n\n\nYou can read/download the dataset files following Huggingface Hub instructions.\nFor example, below code will load CoreSearch DPR folder:",
"### Data Splits\n\n\n* Final version of the CD event coreference search dataset \n\n| | Train | Valid | Test | Total |\n| ----- | ------ | ----- | ---- | ---- |\n| WEC-Eng Validated Data | | | | |\n| # Clusters | 229 | 48 | 226 | 503 |\n| # Passages (with Mentions) | 1,429 | 335 | 1,206 | 2,970 |\n| # Added Destructor Passages | 922,736 | 923,376 | 923,746 | 2,769,858 |\n| # Total Passages | 924,239 | 923,717 | 925,012 | 2,772,968 |\n\n\nLicense\n-------\n\n\nWe provide the following data sets under a <a href=\"URL Commons Attribution-ShareAlike 3.0 Unported License. It is based on content extracted from Wikipedia that is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License\n\n\nContact\n-------\n\n\nIf you have any questions please create a Github issue at <a href=\"URL/URL"
]
| [
15,
7,
12,
33,
39,
271
]
| [
"passage: TAGS\n#arxiv-2210.12654 #region-us \n### Fix event mention spans### Resolve cases of two events in a single span### Remove erroneous event mentions (Entities)\n\n\n\n* Paper: Cross-document Event Coreference Search: Task, Dataset and Modeling### Languages\n\n\nEnglish\n\n\nLoad Dataset\n------------\n\n\nYou can read/download the dataset files following Huggingface Hub instructions.\nFor example, below code will load CoreSearch DPR folder:### Data Splits\n\n\n* Final version of the CD event coreference search dataset \n\n| | Train | Valid | Test | Total |\n| ----- | ------ | ----- | ---- | ---- |\n| WEC-Eng Validated Data | | | | |\n| # Clusters | 229 | 48 | 226 | 503 |\n| # Passages (with Mentions) | 1,429 | 335 | 1,206 | 2,970 |\n| # Added Destructor Passages | 922,736 | 923,376 | 923,746 | 2,769,858 |\n| # Total Passages | 924,239 | 923,717 | 925,012 | 2,772,968 |\n\n\nLicense\n-------\n\n\nWe provide the following data sets under a <a href=\"URL Commons Attribution-ShareAlike 3.0 Unported License. It is based on content extracted from Wikipedia that is licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License\n\n\nContact\n-------\n\n\nIf you have any questions please create a Github issue at <a href=\"URL/URL"
]
| [
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|
920a7d8f9c36ac9cec74a390f01d66ce26c0c770 | # Dataset Card for "emr_instruction_response"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | arfu/emr_instruction_response | [
"region:us"
]
| 2023-03-23T08:55:19+00:00 | {"dataset_info": {"features": [{"name": "instructions", "dtype": "string"}, {"name": "response", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1383248.0, "num_examples": 360}, {"name": "test", "num_bytes": 345812.0, "num_examples": 90}], "download_size": 598465, "dataset_size": 1729060.0}} | 2023-03-23T08:55:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "emr_instruction_response"
More Information needed | [
"# Dataset Card for \"emr_instruction_response\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"emr_instruction_response\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"emr_instruction_response\"\n\nMore Information needed"
]
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|
391dc3cc316882d33473239f1f82ef3ab278a739 | # Dataset Card for "Babelscape-wikineural-joined-small"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dmargutierrez/Babelscape-wikineural-joined-small | [
"region:us"
]
| 2023-03-23T09:08:11+00:00 | {"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}, {"name": "lang", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 101040326.0, "num_examples": 267680}, {"name": "validation", "num_bytes": 12193180.0, "num_examples": 33460}, {"name": "test", "num_bytes": 12258433.0, "num_examples": 33587}], "download_size": 37525170, "dataset_size": 125491939.0}} | 2023-03-23T09:08:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Babelscape-wikineural-joined-small"
More Information needed | [
"# Dataset Card for \"Babelscape-wikineural-joined-small\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Babelscape-wikineural-joined-small\"\n\nMore Information needed"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Babelscape-wikineural-joined-small\"\n\nMore Information needed"
]
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|
3515d71e9bd0120edceda41c78828752cd165d5d | ### Dataset Summary
This dataset is based on [olm/wikipedia](https://huggingface.co/datasets/olm/wikipedia).
The main difference is that we add `Section::::` prefix to each section title to keep the section structure information.
We also use `:` to join the hierarchical section titles.
Following is an example.
```text
Alison Jane Horner (born June 1966) is a British businesswoman, and, until it was sold in 2020, was the CEO of the Asian arm of the Tesco supermarket chain.
Section::::Early life
Alison Jane Horner was born in June 1966. She earned a bachelor's degree in chemistry from the University of Manchester, and an MBA from Manchester Business School.
Section::::Career
Section::::Career:Tesco
Horner joined Tesco as a personnel manager in 1999 and was on Tesco's executive committee from 2011.
In October 2013, Horner became a founding member of The Guardian's Women in Leadership network. in 2015, she became a member of Alliance Manchester Business School's advisory board.
Horner was Tesco' chief people officer (chief human resources officer) of Tesco until May 2018, when she was promoted to be chief executive of Tesco's Asia business in Malaysia and Thailand, until it was sold in late 2020. She was set to step down in February 2021 after 22 years with Tesco.
Section::::Career:Carillion non-executive role
Horner was a non-executive director of Carillion from December 2013, chairing the remuneration committee from June 2014. As of 30 December 2016 her basic compensation was £61,000. After the company went into liquidation in January 2018, Horner was one of the non-executive directors who gave evidence to the House of Commons Business and Work and Pensions select committees on 6 February 2018. In the final report of the Parliamentary Inquiry, published on 16 May 2018, Horner was criticised by MPs; the report concluded:
"... Alison Horner presided over growing salaries and bonuses at the top of the company as its performance faltered. In her evidence to us, she sought to justify her approach by pointing to industry standards, the guidance of advisors, and conversations with shareholders. She failed to demonstrate to us any sense of challenge to the advice she was given, any concern about the views of stakeholders, or any regret at the largesse at the top of Carillion. Ms Horner continues to hold the role of Chief People Officer of Tesco, where she has responsibilities to more than half a million employees. We hope that, in that post, she will reflect on the lessons learned from Carillion and her role in its collapse."
In January 2021, the Insolvency Service said it would seek to ban eight former Carillion directors, including Horner, from holding senior boardroom positions.
Section::::References
Living people
1966 births
British businesspeople in retailing
Tesco people
Alumni of the University of Manchester
Alumni of the Manchester Business School
Carillion people
```
### Data Fields
- `title`: a `string` feature.
- `text`: a `string` feature.
### How to use this dataset
To load this dataset you need to install these first:
```shell
pip install mwparserfromhell==0.6.4 multiprocess==0.70.13
```
Then, you can load any subset of Wikipedia per language and per date this way:
```python
from datasets import load_dataset
dataset = load_dataset("intfloat/wikipedia", language="en", date="20230401")
```
For more information,
please check out [olm/wikipedia](https://huggingface.co/datasets/olm/wikipedia).
## Supported Languages
```
aa
ab
ace
ady
af
ak
als
am
an
ang
ar
arc
arz
as
ast
atj
av
ay
az
azb
ba
bar
bat-smg
bcl
be
be-x-old
bg
bh
bi
bjn
bm
bn
bo
bpy
br
bs
bug
bxr
ca
cbk-zam
cdo
ce
ceb
ch
cho
chr
chy
ckb
co
cr
crh
cs
csb
cu
cv
cy
da
de
din
diq
dsb
dty
dv
dz
ee
el
eml
en
eo
es
et
eu
ext
fa
ff
fi
fiu-vro
fj
fo
fr
frp
frr
fur
fy
ga
gag
gan
gd
gl
glk
gn
gom
gor
got
gu
gv
ha
hak
haw
he
hi
hif
ho
hr
hsb
ht
hu
hy
ia
id
ie
ig
ii
ik
ilo
inh
io
is
it
iu
ja
jam
jbo
jv
ka
kaa
kab
kbd
kbp
kg
ki
kj
kk
kl
km
kn
ko
koi
krc
ks
ksh
ku
kv
kw
ky
la
lad
lb
lbe
lez
lfn
lg
li
lij
lmo
ln
lo
lrc
lt
ltg
lv
mai
map-bms
mdf
mg
mh
mhr
mi
min
mk
ml
mn
mr
mrj
ms
mt
mus
mwl
my
myv
mzn
na
nah
nap
nds
nds-nl
ne
new
ng
nl
nn
no
nov
nrm
nso
nv
ny
oc
olo
om
or
os
pa
pag
pam
pap
pcd
pdc
pfl
pi
pih
pl
pms
pnb
pnt
ps
pt
qu
rm
rmy
rn
ro
roa-rup
roa-tara
ru
rue
rw
sa
sah
sat
sc
scn
sco
sd
se
sg
sh
si
simple
sk
sl
sm
sn
so
sq
sr
srn
ss
st
stq
su
sv
sw
szl
ta
tcy
te
tet
tg
th
ti
tk
tl
tn
to
tpi
tr
ts
tt
tum
tw
ty
tyv
udm
ug
uk
ur
uz
ve
vec
vep
vi
vls
vo
wa
war
wo
wuu
xal
xh
xmf
yi
yo
za
zea
zh
zh-classical
zh-min-nan
zh-yue
zu
``` | intfloat/wikipedia | [
"size_categories:100M<n<1B",
"region:us"
]
| 2023-03-23T09:12:08+00:00 | {"size_categories": ["100M<n<1B"]} | 2023-04-23T07:36:49+00:00 | []
| []
| TAGS
#size_categories-100M<n<1B #region-us
| ### Dataset Summary
This dataset is based on olm/wikipedia.
The main difference is that we add 'Section::::' prefix to each section title to keep the section structure information.
We also use ':' to join the hierarchical section titles.
Following is an example.
### Data Fields
- 'title': a 'string' feature.
- 'text': a 'string' feature.
### How to use this dataset
To load this dataset you need to install these first:
Then, you can load any subset of Wikipedia per language and per date this way:
For more information,
please check out olm/wikipedia.
## Supported Languages
| [
"### Dataset Summary\n\nThis dataset is based on olm/wikipedia.\nThe main difference is that we add 'Section::::' prefix to each section title to keep the section structure information.\nWe also use ':' to join the hierarchical section titles.\n\nFollowing is an example.",
"### Data Fields\n\n- 'title': a 'string' feature.\n- 'text': a 'string' feature.",
"### How to use this dataset\n\nTo load this dataset you need to install these first:\n\n\n\nThen, you can load any subset of Wikipedia per language and per date this way:\n\n\n\nFor more information,\nplease check out olm/wikipedia.",
"## Supported Languages"
]
| [
"TAGS\n#size_categories-100M<n<1B #region-us \n",
"### Dataset Summary\n\nThis dataset is based on olm/wikipedia.\nThe main difference is that we add 'Section::::' prefix to each section title to keep the section structure information.\nWe also use ':' to join the hierarchical section titles.\n\nFollowing is an example.",
"### Data Fields\n\n- 'title': a 'string' feature.\n- 'text': a 'string' feature.",
"### How to use this dataset\n\nTo load this dataset you need to install these first:\n\n\n\nThen, you can load any subset of Wikipedia per language and per date this way:\n\n\n\nFor more information,\nplease check out olm/wikipedia.",
"## Supported Languages"
]
| [
18,
66,
27,
50,
5
]
| [
"passage: TAGS\n#size_categories-100M<n<1B #region-us \n### Dataset Summary\n\nThis dataset is based on olm/wikipedia.\nThe main difference is that we add 'Section::::' prefix to each section title to keep the section structure information.\nWe also use ':' to join the hierarchical section titles.\n\nFollowing is an example.### Data Fields\n\n- 'title': a 'string' feature.\n- 'text': a 'string' feature.### How to use this dataset\n\nTo load this dataset you need to install these first:\n\n\n\nThen, you can load any subset of Wikipedia per language and per date this way:\n\n\n\nFor more information,\nplease check out olm/wikipedia.## Supported Languages"
]
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|
d726613c403302b4e6a28843f8b3eb9fb584274e | # Breast cancer
The [Breast cancer dataset](https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Classify cancerousness of the given cell.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|---------------------------------------------------------------|
| cancer | Binary classification | Is the cell clump cancerous? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/breast", "cancer")["train"]
```
# Features
| **Name** |**Type**|**Description** |
|-------------------------------|--------|----------------------------|
|`clump_thickness` |`int8` |Thickness of the clump |
|`uniformity_of_cell_size` |`int8` |Uniformity of cell size |
|`uniformity_of_cell_shape` |`int8` |Uniformity of cell shape |
|`marginal_adhesion` |`int8` |Marginal adhesion |
|`single_epithelial_cell_size` |`int8` |single_epithelial_cell_size |
|`bare_nuclei` |`int8` |bare_nuclei |
|`bland_chromatin` |`int8` |bland_chromatin |
|`normal_nucleoli` |`int8` |normal_nucleoli |
|`mitoses` |`int8` |mitoses |
|**is_cancer** |`int8` |Is the clump cancer | | mstz/breast | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"breast",
"tabular_classification",
"binary_classification",
"UCI",
"region:us"
]
| 2023-03-23T09:31:30+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "Breast", "tags": ["breast", "tabular_classification", "binary_classification", "UCI"], "configs": ["cancer"]} | 2023-04-16T15:47:59+00:00 | []
| [
"en"
]
| TAGS
#task_categories-tabular-classification #size_categories-n<1K #language-English #license-cc #breast #tabular_classification #binary_classification #UCI #region-us
| Breast cancer
=============
The Breast cancer dataset from the UCI ML repository.
Classify cancerousness of the given cell.
Configurations and tasks
========================
Configuration: cancer, Task: Binary classification, Description: Is the cell clump cancerous?
Usage
=====
Features
========
Name: 'clump\_thickness', Type: 'int8', Description: Thickness of the clump
Name: 'uniformity\_of\_cell\_size', Type: 'int8', Description: Uniformity of cell size
Name: 'uniformity\_of\_cell\_shape', Type: 'int8', Description: Uniformity of cell shape
Name: 'marginal\_adhesion', Type: 'int8', Description: Marginal adhesion
Name: 'single\_epithelial\_cell\_size', Type: 'int8', Description: single\_epithelial\_cell\_size
Name: 'bare\_nuclei', Type: 'int8', Description: bare\_nuclei
Name: 'bland\_chromatin', Type: 'int8', Description: bland\_chromatin
Name: 'normal\_nucleoli', Type: 'int8', Description: normal\_nucleoli
Name: 'mitoses', Type: 'int8', Description: mitoses
Name: is\_cancer, Type: 'int8', Description: Is the clump cancer
| []
| [
"TAGS\n#task_categories-tabular-classification #size_categories-n<1K #language-English #license-cc #breast #tabular_classification #binary_classification #UCI #region-us \n"
]
| [
54
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-n<1K #language-English #license-cc #breast #tabular_classification #binary_classification #UCI #region-us \n"
]
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|
ada2afe72f645094fadc5c6a0471f5abb66153af | # Dataset Card for "Aladdin"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/Aladdin | [
"region:us"
]
| 2023-03-23T09:47:35+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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| []
| TAGS
#region-us
| # Dataset Card for "Aladdin"
More Information needed | [
"# Dataset Card for \"Aladdin\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Aladdin\"\n\nMore Information needed"
]
| [
6,
13
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Aladdin\"\n\nMore Information needed"
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|
1d63652e5f4b8a88c0c18ee294bdf671fe1aed4e | # Dataset Card for "github-issues"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | NickHai/github-issues | [
"region:us"
]
| 2023-03-23T09:54:21+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "labels", "list": [{"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, 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2000}], "download_size": 3007718, "dataset_size": 11113700}} | 2023-03-23T10:34:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "github-issues"
More Information needed | [
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"github-issues\"\n\nMore Information needed"
]
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|
de222d434e26379aa3d4f3dd1b2caf502427a8b2 | The complete dataset is available [here](https://huggingface.co/datasets/DarthReca/california_burned_areas). Please refer to that if you want to use these data. | chabud-team/chabud-ecml-pkdd2023 | [
"region:us"
]
| 2023-03-23T10:19:22+00:00 | {} | 2023-09-13T11:53:50+00:00 | []
| []
| TAGS
#region-us
| The complete dataset is available here. Please refer to that if you want to use these data. | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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223a177720ddb7e2534ef6b1d9182b946ca0a114 | # 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]
### 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] | abhi28577/nennepedia | [
"task_categories:question-answering",
"size_categories:n<1K",
"language:en",
"license:openrail",
"region:us"
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| 2023-03-23T10:55:02+00:00 | {"language": ["en"], "license": "openrail", "size_categories": ["n<1K"], "task_categories": ["question-answering"], "pretty_name": "nennepedia"} | 2023-06-24T07:27:44+00:00 | []
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#task_categories-question-answering #size_categories-n<1K #language-English #license-openrail #region-us
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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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### Curation Rationale
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#### 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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|
d89b2820082181e638eafeb0a562fef2f3feb8b5 | Lazy dataset of Azur Lane ironblood ship rigs with lazily pruned tags and 'Ironblood' as activation tag. | FireZealot/AL_Ironblood_rigging | [
"region:us"
]
| 2023-03-23T10:57:16+00:00 | {} | 2023-03-23T11:00:43+00:00 | []
| []
| TAGS
#region-us
| Lazy dataset of Azur Lane ironblood ship rigs with lazily pruned tags and 'Ironblood' as activation tag. | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
| [
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|
93a7d4088bad372aff5357bfde5a5f0fa00d5ade |
Dataset generated from Cyrillic train set using ScrabbleGAN
======================================================
Number of images: 300000
Sources:
* [Cyrillic dataset](https://www.kaggle.com/datasets/constantinwerner/cyrillic-handwriting-dataset)
* [ScrabbleGAN code](https://github.com/ai-forever/ScrabbleGAN) | nastyboget/gan_cyrillic | [
"task_categories:image-to-text",
"size_categories:100K<n<1M",
"language:ru",
"license:mit",
"region:us"
]
| 2023-03-23T11:08:05+00:00 | {"language": ["ru"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text"]} | 2023-03-23T18:46:01+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us
|
Dataset generated from Cyrillic train set using ScrabbleGAN
======================================================
Number of images: 300000
Sources:
* Cyrillic dataset
* ScrabbleGAN code | []
| [
"TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us \n"
]
| [
40
]
| [
"passage: TAGS\n#task_categories-image-to-text #size_categories-100K<n<1M #language-Russian #license-mit #region-us \n"
]
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|
122baf7f11ad5ec6192038d9351cb8f3f500b7bf | # German
The [German dataset](https://archive.ics.uci.edu/ml/datasets/Statlog+%28German+Credit+Data%29) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).
Dataset on loan grants to customers.
# Configurations and tasks
| **Configuration** | **Task** | Description |
|-------------------|---------------------------|-----------------------------------------------------------------|
| encoding | | Encoding dictionary showing original values of encoded features.|
| loan | Binary classification | Has the loan request been accepted? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/german", "loan")["train"]
```
# Features
|**Feature** |**Type** |
|------------------------------------|-----------|
|`checking_account_status` | `int8` |
|`account_life_in_months` | `int8` |
|`credit_status` | `int8` |
|`loan_purpose` | `string` |
|`current_credit` | `int32` |
|`current_savings` | `int8` |
|`employed_since` | `int8` |
|`installment_rate_percentage` | `int8` |
|`sex` | `int8` |
|`marital_status` | `string` |
|`guarantors` | `int8` |
|`years_living_in_current_residence` | `int8` |
|`age` | `int8` |
|`installment_plans` | `string` |
|`housing_status` | `int8` |
|`nr_credit_accounts_in_bank` | `int8` |
|`job_status` | `int8` |
|`number_of_people_in_support` | `int8` |
|`has_registered_phone_number` | `int8` |
|`is_foreign` | `int8` | | mstz/german | [
"task_categories:tabular-classification",
"size_categories:n<1K",
"language:en",
"license:cc",
"breast",
"tabular_classification",
"binary_classification",
"region:us"
]
| 2023-03-23T11:32:15+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["n<1K"], "task_categories": ["tabular-classification"], "pretty_name": "German", "tags": ["breast", "tabular_classification", "binary_classification"], "configs": ["encoding", "loan"]} | 2023-04-16T16:28:54+00:00 | []
| [
"en"
]
| TAGS
#task_categories-tabular-classification #size_categories-n<1K #language-English #license-cc #breast #tabular_classification #binary_classification #region-us
| German
======
The German dataset from the UCI ML repository.
Dataset on loan grants to customers.
Configurations and tasks
========================
Configuration: encoding, Task: , Description: Encoding dictionary showing original values of encoded features.
Configuration: loan, Task: Binary classification, Description: Has the loan request been accepted?
Usage
=====
Features
========
| []
| [
"TAGS\n#task_categories-tabular-classification #size_categories-n<1K #language-English #license-cc #breast #tabular_classification #binary_classification #region-us \n"
]
| [
52
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-n<1K #language-English #license-cc #breast #tabular_classification #binary_classification #region-us \n"
]
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|
a97716df74a1598981ab821aef52d08ea75f4e8c | # Dataset Card for "tib-wip-filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | gigant/tib-wip-filtered | [
"region:us"
]
| 2023-03-23T12:28:05+00:00 | {"dataset_info": {"features": [{"name": "doi", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "video_url", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "genre", "dtype": "string"}, {"name": "release_year", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "contributors", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "transcript", "dtype": "string"}, {"name": "transcript_segments", "sequence": [{"name": "id", "dtype": "int32"}, {"name": "seek", "dtype": "int32"}, {"name": "start", "dtype": "float32"}, {"name": "end", "dtype": "float32"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "int32"}, {"name": "temperature", "dtype": "float32"}, {"name": "avg_logprob", "dtype": "float32"}, {"name": "compression_ratio", "dtype": "float32"}, {"name": "no_speech_prob", "dtype": "float32"}]}, {"name": "keyframes", "sequence": [{"name": "slide", "dtype": "string"}, {"name": "frames", "sequence": "int32"}, {"name": "timestamp", "sequence": "float32"}]}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1062896143.5539255, "num_examples": 9294}], "download_size": 511200645, "dataset_size": 1062896143.5539255}} | 2023-03-23T12:28:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tib-wip-filtered"
More Information needed | [
"# Dataset Card for \"tib-wip-filtered\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tib-wip-filtered\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tib-wip-filtered\"\n\nMore Information needed"
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|
3f78a00cf2503a5f190f319d9183cde76515fc8d | # Dataset Card for "stars"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | open-source-metrics/stars | [
"region:us"
]
| 2023-03-23T12:51:59+00:00 | {"dataset_info": {"features": [{"name": "login", "dtype": "string"}, {"name": "dates", "dtype": "string"}], "splits": [{"name": "transformers", "num_bytes": 4484080, "num_examples": 120154}, {"name": "peft", "num_bytes": 465816, "num_examples": 12524}, {"name": "evaluate", "num_bytes": 64197, "num_examples": 1714}, {"name": "huggingface_hub", "num_bytes": 56558, "num_examples": 1503}, {"name": "accelerate", "num_bytes": 242677, "num_examples": 6504}, {"name": "datasets", "num_bytes": 676788, "num_examples": 18024}, {"name": "optimum", "num_bytes": 73568, "num_examples": 1964}, {"name": "pytorch_image_models", "num_bytes": 1069862, "num_examples": 28691}, {"name": "gradio", "num_bytes": 978080, "num_examples": 26282}, {"name": "tokenizers", "num_bytes": 301458, "num_examples": 8126}, {"name": "diffusers", "num_bytes": 779023, "num_examples": 20936}, {"name": "safetensors", "num_bytes": 75658, "num_examples": 2041}, {"name": "candle", "num_bytes": 447690, "num_examples": 12076}, {"name": "text_generation_inference", "num_bytes": 258847, "num_examples": 6951}, {"name": "chat_ui", "num_bytes": 197261, "num_examples": 5301}, {"name": "hub_docs", "num_bytes": 8224, "num_examples": 219}], "download_size": 6188098, "dataset_size": 10179787}, "configs": [{"config_name": "default", "data_files": [{"split": "peft", "path": "data/peft-*"}, {"split": "hub_docs", "path": "data/hub_docs-*"}, {"split": "evaluate", "path": "data/evaluate-*"}, {"split": "huggingface_hub", "path": "data/huggingface_hub-*"}, {"split": "accelerate", "path": "data/accelerate-*"}, {"split": "datasets", "path": "data/datasets-*"}, {"split": "optimum", "path": "data/optimum-*"}, {"split": "pytorch_image_models", "path": "data/pytorch_image_models-*"}, {"split": "gradio", "path": "data/gradio-*"}, {"split": "tokenizers", "path": "data/tokenizers-*"}, {"split": "diffusers", "path": "data/diffusers-*"}, {"split": "transformers", "path": "data/transformers-*"}, {"split": "safetensors", "path": "data/safetensors-*"}]}]} | 2024-02-15T13:20:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "stars"
More Information needed | [
"# Dataset Card for \"stars\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"stars\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"stars\"\n\nMore Information needed"
]
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|
2f7e828658d33a0e8aca4b2f9f4ccb26e27ac32f |
This is a cleaned and splitted version of this dataset (https://www.kaggle.com/datasets/sadikaljarif/fake-news-detection-dataset-english) <br>
Labels:
- Fake News: 0
- Real News: 1
<br>
You can find the cleansing script at: https://github.com/ErfanMoosaviMonazzah/Fake-News-Detection | ErfanMoosaviMonazzah/fake-news-detection-dataset-English | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:openrail",
"fake news",
"text classification",
"region:us"
]
| 2023-03-23T12:55:15+00:00 | {"language": ["en"], "license": "openrail", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": "Fake News Detection Dataset (English)", "tags": ["fake news", "text classification"]} | 2023-03-23T13:05:33+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-openrail #fake news #text classification #region-us
|
This is a cleaned and splitted version of this dataset (URL <br>
Labels:
- Fake News: 0
- Real News: 1
<br>
You can find the cleansing script at: URL | []
| [
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-openrail #fake news #text classification #region-us \n"
]
| [
47
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-openrail #fake news #text classification #region-us \n"
]
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|
dd872875d9b273b48162e9b8a7734ec729457e26 | # Dataset Card for "tib_2303_filtered"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | gigant/tib_2303_filtered | [
"region:us"
]
| 2023-03-23T13:33:55+00:00 | {"dataset_info": {"features": [{"name": "doi", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "video_url", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "subject", "dtype": "string"}, {"name": "genre", "dtype": "string"}, {"name": "release_year", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "contributors", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "transcript", "dtype": "string"}, {"name": "transcript_segments", "sequence": [{"name": "id", "dtype": "int32"}, {"name": "seek", "dtype": "int32"}, {"name": "start", "dtype": "float32"}, {"name": "end", "dtype": "float32"}, {"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "int32"}, {"name": "temperature", "dtype": "float32"}, {"name": "avg_logprob", "dtype": "float32"}, {"name": "compression_ratio", "dtype": "float32"}, {"name": "no_speech_prob", "dtype": "float32"}]}, {"name": "keyframes", "sequence": [{"name": "slide", "dtype": "string"}, {"name": "frames", "sequence": "int32"}, {"name": "timestamp", "sequence": "float32"}]}, {"name": "language", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1032202393.542393, "num_examples": 9103}], "download_size": 500663533, "dataset_size": 1032202393.542393}} | 2023-03-23T13:34:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tib_2303_filtered"
More Information needed | [
"# Dataset Card for \"tib_2303_filtered\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tib_2303_filtered\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tib_2303_filtered\"\n\nMore Information needed"
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|
aa2d5aba694f1ff25fc018e449f9d3f82050734a | # Dataset Card for "chainreaction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | k0ntra/chainreaction | [
"region:us"
]
| 2023-03-23T13:53:44+00:00 | {"dataset_info": {"features": [{"name": "0", "dtype": "float32"}, {"name": "1", "dtype": "float32"}, {"name": "2", "dtype": "float32"}, {"name": "3", "dtype": "float32"}, {"name": "4", "dtype": "float32"}, {"name": "5", "dtype": "float32"}, {"name": "6", "dtype": "float32"}, {"name": "7", "dtype": "float32"}, {"name": "8", "dtype": "float32"}, {"name": "9", "dtype": "float32"}, {"name": "10", "dtype": "float32"}, {"name": "11", "dtype": "float32"}, {"name": "12", "dtype": "float32"}, {"name": "13", "dtype": "float32"}, {"name": "14", "dtype": "float32"}, {"name": "15", "dtype": "float32"}, {"name": "16", "dtype": "float32"}, {"name": "17", "dtype": "float32"}, {"name": "18", "dtype": "float32"}, {"name": "19", "dtype": "float32"}, {"name": "20", "dtype": "float32"}, {"name": "21", "dtype": "float32"}, {"name": "22", "dtype": "float32"}, {"name": "23", "dtype": "float32"}, {"name": "24", "dtype": "float32"}, {"name": "25", "dtype": "float32"}, {"name": 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{"name": "379", "dtype": "float32"}, {"name": "380", "dtype": "float32"}, {"name": "381", "dtype": "float32"}, {"name": "382", "dtype": "float32"}, {"name": "383", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 81408, "num_examples": 53}], "download_size": 258600, "dataset_size": 81408}} | 2023-03-23T13:53:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "chainreaction"
More Information needed | [
"# Dataset Card for \"chainreaction\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"chainreaction\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"chainreaction\"\n\nMore Information needed"
]
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|
83218545470d54d6caea5081510786f54e21862d | # HELOC
The [HELOC dataset](https://community.fico.com/s/explainable-machine-learning-challenge?tabset-158d9=d157e) from FICO.
Each entry in the dataset is a line of credit, typically offered by a bank as a percentage of home equity (the difference between the current market value of a home and its purchase price).
The customers in this dataset have requested a credit line in the range of $5,000 - $150,000.
The fundamental task is to use the information about the applicant in their credit report to predict whether they will repay their HELOC account within 2 years.
# Configurations and tasks
| **Configuration** | **Task** | **Description** |
|-------------------|---------------------------|-----------------------------------------------------------------|
| risk | Binary classification | Will the customer default? |
# Usage
```python
from datasets import load_dataset
dataset = load_dataset("mstz/heloc")["train"]
```
# Features
|**Feature** |**Type**|
|-------------------------------------------|--------|
|`estimate_of_risk` |`int8` |
|`months_since_first_trade` |`int32` |
|`months_since_last_trade` |`int32` |
|`average_duration_of_resolution` |`int32` |
|`number_of_satisfactory_trades` |`int16` |
|`nr_trades_insolvent_for_over_60_days` |`int16` |
|`nr_trades_insolvent_for_over_90_days` |`int16` |
|`percentage_of_legal_trades` |`int16` |
|`months_since_last_illegal_trade` |`int32` |
|`maximum_illegal_trades_over_last_year` |`int8` |
|`maximum_illegal_trades` |`int16` |
|`nr_total_trades` |`int16` |
|`nr_trades_initiated_in_last_year` |`int16` |
|`percentage_of_installment_trades` |`int16` |
|`months_since_last_inquiry_not_recent` |`int16` |
|`nr_inquiries_in_last_6_months` |`int16` |
|`nr_inquiries_in_last_6_months_not_recent` |`int16` |
|`net_fraction_of_revolving_burden` |`int32` |
|`net_fraction_of_installment_burden` |`int32` |
|`nr_revolving_trades_with_balance` |`int16` |
|`nr_installment_trades_with_balance` |`int16` |
|`nr_banks_with_high_ratio` |`int16` |
|`percentage_trades_with_balance` |`int16` | | mstz/heloc | [
"task_categories:tabular-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc",
"heloc",
"fico",
"tabular_classification",
"binary_classification",
"region:us"
]
| 2023-03-23T14:08:41+00:00 | {"language": ["en"], "license": "cc", "size_categories": ["10K<n<100K"], "task_categories": ["tabular-classification"], "pretty_name": "Heloc", "tags": ["heloc", "fico", "tabular_classification", "binary_classification"], "configs": ["risk"]} | 2023-04-07T12:57:28+00:00 | []
| [
"en"
]
| TAGS
#task_categories-tabular-classification #size_categories-10K<n<100K #language-English #license-cc #heloc #fico #tabular_classification #binary_classification #region-us
| HELOC
=====
The HELOC dataset from FICO.
Each entry in the dataset is a line of credit, typically offered by a bank as a percentage of home equity (the difference between the current market value of a home and its purchase price).
The customers in this dataset have requested a credit line in the range of $5,000 - $150,000.
The fundamental task is to use the information about the applicant in their credit report to predict whether they will repay their HELOC account within 2 years.
Configurations and tasks
========================
Configuration: risk, Task: Binary classification, Description: Will the customer default?
Usage
=====
Features
========
| []
| [
"TAGS\n#task_categories-tabular-classification #size_categories-10K<n<100K #language-English #license-cc #heloc #fico #tabular_classification #binary_classification #region-us \n"
]
| [
56
]
| [
"passage: TAGS\n#task_categories-tabular-classification #size_categories-10K<n<100K #language-English #license-cc #heloc #fico #tabular_classification #binary_classification #region-us \n"
]
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|
19d42bc5f6abd604a62eac2884e6f00bc564eb5f | # Dataset Card for "un_corpus_for_sitemap_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ranWang/un_corpus_for_sitemap_test | [
"region:us"
]
| 2023-03-23T14:27:54+00:00 | {"dataset_info": {"features": [{"name": "uuid", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "news_content", "sequence": {"sequence": {"sequence": "string"}}}, {"name": "html_content", "dtype": "string"}], "splits": [{"name": "ar", "num_bytes": 4731475832, "num_examples": 35807}, {"name": "ru", "num_bytes": 4612097472, "num_examples": 34774}, {"name": "zh", "num_bytes": 4853260847, "num_examples": 39850}, {"name": "en", "num_bytes": 8601079816, "num_examples": 67374}, {"name": "fr", "num_bytes": 6032856702, "num_examples": 46756}, {"name": "es", "num_bytes": 5542173377, "num_examples": 44877}], "download_size": 13991912058, "dataset_size": 34372944046}} | 2023-03-24T04:54:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "un_corpus_for_sitemap_test"
More Information needed | [
"# Dataset Card for \"un_corpus_for_sitemap_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"un_corpus_for_sitemap_test\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"un_corpus_for_sitemap_test\"\n\nMore Information needed"
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|
b7b9fe926d86de3af69b03ca3a958cb5cdea694b | # AutoTrain Dataset for project: clasificacion_pisicinas
## Dataset Description
This dataset has been automatically processed by AutoTrain for project clasificacion_pisicinas.
### 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": "<11x10 RGB PIL image>",
"target": 1
},
{
"image": "<12x15 RGB PIL image>",
"target": 1
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['psicina', 'psicinas', 'tierra'], 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 | 255 |
| valid | 108 |
| Eredim/autotrain-data-clasificacion_pisicinas | [
"task_categories:image-classification",
"region:us"
]
| 2023-03-23T14:56:30+00:00 | {"task_categories": ["image-classification"]} | 2023-04-05T21:54:31+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: clasificacion\_pisicinas
=======================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project clasificacion\_pisicinas.
### 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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|
4917d103f0fba3ca908afcd0e71cfd34fdf358a7 | This repository contains the results of all experiments (inlcuding every single hyperparameter run) reported in the following paper:
Orhan AE (2023) [Recognition, recall, and retention of few-shot memories in large language models.](https://arxiv.org/abs/2303.17557) arXiv:2303.17557.
A brief description of the directories included in this repository:
* [`evals`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/evals): contains the results of all recognition experiments
* [`recalls`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/recalls): contains the results of all recall experiments
* [`re-evals`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/re-evals): contains the results of all recognition experiments during the retention phase
* [`re-recalls`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/re-recalls): contains the results of all recall experiments during the retention phase
* [`scratch-evals`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/scratch-evals), [`scratch-recalls`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/scratch-recalls), [`scratch-re-evals`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/scratch-re-evals), [`scratch-re-recalls`](https://huggingface.co/datasets/eminorhan/llm-memory/tree/main/scratch-re-recalls): similar to the above, but the results are for the `gpt-j-6B-st` model trained from scratch on [`wikitext-103-raw-v1`](https://huggingface.co/datasets/wikitext). | eminorhan/llm-memory | [
"license:mit",
"arxiv:2303.17557",
"region:us"
]
| 2023-03-23T16:07:14+00:00 | {"license": "mit"} | 2023-03-30T23:38:46+00:00 | [
"2303.17557"
]
| []
| TAGS
#license-mit #arxiv-2303.17557 #region-us
| This repository contains the results of all experiments (inlcuding every single hyperparameter run) reported in the following paper:
Orhan AE (2023) Recognition, recall, and retention of few-shot memories in large language models. arXiv:2303.17557.
A brief description of the directories included in this repository:
* 'evals': contains the results of all recognition experiments
* 'recalls': contains the results of all recall experiments
* 're-evals': contains the results of all recognition experiments during the retention phase
* 're-recalls': contains the results of all recall experiments during the retention phase
* 'scratch-evals', 'scratch-recalls', 'scratch-re-evals', 'scratch-re-recalls': similar to the above, but the results are for the 'gpt-j-6B-st' model trained from scratch on 'wikitext-103-raw-v1'. | []
| [
"TAGS\n#license-mit #arxiv-2303.17557 #region-us \n"
]
| [
19
]
| [
"passage: TAGS\n#license-mit #arxiv-2303.17557 #region-us \n"
]
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|
0414b39bfc39b2d555b48fa245fc198744c46215 | https://whyu.me/reclor/
```bib
@inproceedings{yu2020reclor,
author = {Yu, Weihao and Jiang, Zihang and Dong, Yanfei and Feng, Jiashi},
title = {ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning},
booktitle = {International Conference on Learning Representations (ICLR)},
month = {April},
year = {2020}
}
``` | metaeval/reclor | [
"language:en",
"license:other",
"region:us"
]
| 2023-03-23T16:31:55+00:00 | {"language": ["en"], "license": "other"} | 2023-05-31T08:59:42+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-other #region-us
| URL
| []
| [
"TAGS\n#language-English #license-other #region-us \n"
]
| [
15
]
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|
3951afb02bff436933c2eb8095d7ccd185ff8f25 | # Dataset Card for "UA_speech_noisereduced_CM04_M04"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | AravindVadlapudi02/UA_speech_noisereduced_CM04_M04 | [
"region:us"
]
| 2023-03-23T16:58:33+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "healthy control", "1": "pathology"}}}}, {"name": "input_features", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 384132800, "num_examples": 400}, {"name": "test", "num_bytes": 4983162748, "num_examples": 5189}], "download_size": 620634515, "dataset_size": 5367295548}} | 2023-03-23T17:02:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "UA_speech_noisereduced_CM04_M04"
More Information needed | [
"# Dataset Card for \"UA_speech_noisereduced_CM04_M04\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"UA_speech_noisereduced_CM04_M04\"\n\nMore Information needed"
]
| [
6,
25
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"UA_speech_noisereduced_CM04_M04\"\n\nMore Information needed"
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|
707102c719ed7f0a45df09497d1ff2e2681c82b2 | # Computer Science Named Entity Recognition in the Open Research Knowledge Graph (CS-NER dataset)
### 1) About
This work proposes a standardized CS-NER task by defining a set of seven _contribution-centric_ scholarly
entities for CS NER viz., _research problem_ , _solution_ , _resource_ , _language_ ,
_tool_ , _method_ , and _dataset_ .
The main contributions are:
1) Merges annotations for contribution-centric named entities from related work as the following datasets:
- The dataset proposed in [Analyzing the Dynamics of Research by Extracting Key Aspects of Scientific Papers](https://aclanthology.org/I11-1001/) (Gupta & Manning, IJCNLP 2011) is the source for [ftd](https://github.com/jd-coderepos/contributions-ner-cs/tree/main/ftd), annotated for both titles and abstracts for the following select entities mapped to our standardized types _focus_ -> _solution_ ; _domain_ -> _research problem_ ; and _technique_ -> _method_
- The dataset proposed in [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://aclanthology.org/D18-1360/) (Luan et al., EMNLP 2018) is the source for [scierc](https://github.com/jd-coderepos/contributions-ner-cs/tree/main/scierc), annotated for abstracts for the following select entities with mappings _task_ -> _research problem_
- The dataset proposed in [SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge Graph](https://aclanthology.org/2021.semeval-1.44/) (D’Souza et al., SemEval 2021) is the source for [ncg](https://github.com/jd-coderepos/contributions-ner-cs/tree/main/ncg), annotated for both titles and abstracts for _research problem_
- https://paperswithcode.com/ as the [pwc](https://github.com/jd-coderepos/contributions-ner-cs/tree/main/pwc) annotated for both titles and abstracts for _task_ -> _research problem_ and _method_ entities.
2) Additionally, supplies a new annotated dataset for the titles in the ACL anthology in the [acl repository](https://github.com/jd-coderepos/contributions-ner-cs/tree/main/acl)
where titles are annotated with all seven entities.
### 2) Dataset Statistics for [full dataset](https://github.com/jd-coderepos/contributions-ner-cs/tree/main/full%20dataset)
Please note the numbers below reflect the total annotated entities. They do not reflect the unique set of annotated entities.
#### Titles
`train.data`
| NER | Count |
| --- | --- |
| solution | 18,924 |
| research problem | 15,646 |
| method | 8,854 |
| resource | 7,346 |
| tool | 1,718 |
| language | 1,141 |
| dataset | 882 |
`dev.data`
| NER | Count |
| --- | --- |
| solution | 1,072 |
| research problem | 989 |
| method | 574 |
| resource | 439 |
| tool | 93 |
| language | 50 |
| dataset | 39 |
`test.data`
| NER | Count |
| --- | --- |
| solution | 8,316 |
| research problem | 4,070 |
| resource | 3,226 |
| method | 2,768 |
| tool | 743 |
| language | 499 |
| dataset | 228 |
#### Abstracts
`train-abs.data`
| NER | Count |
| --- | --- |
| method | 10,992 |
| research problem | 7,485 |
`dev-abs.data`
| NER | Count |
| --- | --- |
| method | 719 |
| research problem | 603 |
`test-abs.data`
| NER | Count |
| --- | --- |
| method | 2,723 |
| research problem | 2,100 |
The remaining repositories have specialized README files with the respective dataset statistics.
### 3) Citation
Accepted for publication in [ICADL 2022](https://icadl.net/icadl2022/) proceedings.
`Citation information forthcoming`
Preprint
```
@article{d2022computer,
title={Computer Science Named Entity Recognition in the Open Research Knowledge Graph},
author={D'Souza, Jennifer and Auer, S{\"o}ren},
journal={arXiv preprint arXiv:2203.14579},
year={2022}
}
```
### 4) Additional resources
#### CS NER Software trained on the dataset in this repository
Codebase: https://gitlab.com/TIBHannover/orkg/nlp/orkg-nlp-experiments/-/tree/master/orkg_cs_ner
Service URL - REST API: https://orkg.org/nlp/api/docs#/annotation/annotates_paper_annotation_csner_post
Service URL - PyPi: https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html#cs-ner-computer-science-named-entity-recognition
| jxhzxn/contributions-ner-cs | [
"region:us"
]
| 2023-03-23T17:35:04+00:00 | {} | 2023-03-23T18:39:33+00:00 | []
| []
| TAGS
#region-us
| Computer Science Named Entity Recognition in the Open Research Knowledge Graph (CS-NER dataset)
===============================================================================================
### 1) About
This work proposes a standardized CS-NER task by defining a set of seven *contribution-centric* scholarly
entities for CS NER viz., *research problem* , *solution* , *resource* , *language* ,
*tool* , *method* , and *dataset* .
The main contributions are:
1. Merges annotations for contribution-centric named entities from related work as the following datasets:
* The dataset proposed in Analyzing the Dynamics of Research by Extracting Key Aspects of Scientific Papers (Gupta & Manning, IJCNLP 2011) is the source for ftd, annotated for both titles and abstracts for the following select entities mapped to our standardized types *focus* -> *solution* ; *domain* -> *research problem* ; and *technique* -> *method*
* The dataset proposed in Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (Luan et al., EMNLP 2018) is the source for scierc, annotated for abstracts for the following select entities with mappings *task* -> *research problem*
* The dataset proposed in SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge Graph (D’Souza et al., SemEval 2021) is the source for ncg, annotated for both titles and abstracts for *research problem*
* URL as the pwc annotated for both titles and abstracts for *task* -> *research problem* and *method* entities.
2. Additionally, supplies a new annotated dataset for the titles in the ACL anthology in the acl repository
where titles are annotated with all seven entities.
### 2) Dataset Statistics for full dataset
Please note the numbers below reflect the total annotated entities. They do not reflect the unique set of annotated entities.
#### Titles
'URL'
'URL'
'URL'
#### Abstracts
'URL'
'URL'
'URL'
The remaining repositories have specialized README files with the respective dataset statistics.
### 3) Citation
Accepted for publication in ICADL 2022 proceedings.
'Citation information forthcoming'
Preprint
### 4) Additional resources
#### CS NER Software trained on the dataset in this repository
Codebase: URL
Service URL - REST API: URL
Service URL - PyPi: URL
| [
"### 1) About\n\n\nThis work proposes a standardized CS-NER task by defining a set of seven *contribution-centric* scholarly\nentities for CS NER viz., *research problem* , *solution* , *resource* , *language* ,\n*tool* , *method* , and *dataset* .\n\n\nThe main contributions are:\n\n\n1. Merges annotations for contribution-centric named entities from related work as the following datasets:\n\n\n\t* The dataset proposed in Analyzing the Dynamics of Research by Extracting Key Aspects of Scientific Papers (Gupta & Manning, IJCNLP 2011) is the source for ftd, annotated for both titles and abstracts for the following select entities mapped to our standardized types *focus* -> *solution* ; *domain* -> *research problem* ; and *technique* -> *method*\n\t* The dataset proposed in Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (Luan et al., EMNLP 2018) is the source for scierc, annotated for abstracts for the following select entities with mappings *task* -> *research problem*\n\t* The dataset proposed in SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge Graph (D’Souza et al., SemEval 2021) is the source for ncg, annotated for both titles and abstracts for *research problem*\n\t* URL as the pwc annotated for both titles and abstracts for *task* -> *research problem* and *method* entities.\n2. Additionally, supplies a new annotated dataset for the titles in the ACL anthology in the acl repository\nwhere titles are annotated with all seven entities.",
"### 2) Dataset Statistics for full dataset\n\n\nPlease note the numbers below reflect the total annotated entities. They do not reflect the unique set of annotated entities.",
"#### Titles\n\n\n'URL'\n\n\n\n'URL'\n\n\n\n'URL'",
"#### Abstracts\n\n\n'URL'\n\n\n\n'URL'\n\n\n\n'URL'\n\n\n\nThe remaining repositories have specialized README files with the respective dataset statistics.",
"### 3) Citation\n\n\nAccepted for publication in ICADL 2022 proceedings.\n\n\n'Citation information forthcoming'\n\n\nPreprint",
"### 4) Additional resources",
"#### CS NER Software trained on the dataset in this repository\n\n\nCodebase: URL\n\n\nService URL - REST API: URL\n\n\nService URL - PyPi: URL"
]
| [
"TAGS\n#region-us \n",
"### 1) About\n\n\nThis work proposes a standardized CS-NER task by defining a set of seven *contribution-centric* scholarly\nentities for CS NER viz., *research problem* , *solution* , *resource* , *language* ,\n*tool* , *method* , and *dataset* .\n\n\nThe main contributions are:\n\n\n1. Merges annotations for contribution-centric named entities from related work as the following datasets:\n\n\n\t* The dataset proposed in Analyzing the Dynamics of Research by Extracting Key Aspects of Scientific Papers (Gupta & Manning, IJCNLP 2011) is the source for ftd, annotated for both titles and abstracts for the following select entities mapped to our standardized types *focus* -> *solution* ; *domain* -> *research problem* ; and *technique* -> *method*\n\t* The dataset proposed in Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (Luan et al., EMNLP 2018) is the source for scierc, annotated for abstracts for the following select entities with mappings *task* -> *research problem*\n\t* The dataset proposed in SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge Graph (D’Souza et al., SemEval 2021) is the source for ncg, annotated for both titles and abstracts for *research problem*\n\t* URL as the pwc annotated for both titles and abstracts for *task* -> *research problem* and *method* entities.\n2. Additionally, supplies a new annotated dataset for the titles in the ACL anthology in the acl repository\nwhere titles are annotated with all seven entities.",
"### 2) Dataset Statistics for full dataset\n\n\nPlease note the numbers below reflect the total annotated entities. They do not reflect the unique set of annotated entities.",
"#### Titles\n\n\n'URL'\n\n\n\n'URL'\n\n\n\n'URL'",
"#### Abstracts\n\n\n'URL'\n\n\n\n'URL'\n\n\n\n'URL'\n\n\n\nThe remaining repositories have specialized README files with the respective dataset statistics.",
"### 3) Citation\n\n\nAccepted for publication in ICADL 2022 proceedings.\n\n\n'Citation information forthcoming'\n\n\nPreprint",
"### 4) Additional resources",
"#### CS NER Software trained on the dataset in this repository\n\n\nCodebase: URL\n\n\nService URL - REST API: URL\n\n\nService URL - PyPi: URL"
]
| [
6,
443,
39,
13,
33,
28,
7,
36
]
| [
"passage: TAGS\n#region-us \n### 1) About\n\n\nThis work proposes a standardized CS-NER task by defining a set of seven *contribution-centric* scholarly\nentities for CS NER viz., *research problem* , *solution* , *resource* , *language* ,\n*tool* , *method* , and *dataset* .\n\n\nThe main contributions are:\n\n\n1. Merges annotations for contribution-centric named entities from related work as the following datasets:\n\n\n\t* The dataset proposed in Analyzing the Dynamics of Research by Extracting Key Aspects of Scientific Papers (Gupta & Manning, IJCNLP 2011) is the source for ftd, annotated for both titles and abstracts for the following select entities mapped to our standardized types *focus* -> *solution* ; *domain* -> *research problem* ; and *technique* -> *method*\n\t* The dataset proposed in Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction (Luan et al., EMNLP 2018) is the source for scierc, annotated for abstracts for the following select entities with mappings *task* -> *research problem*\n\t* The dataset proposed in SemEval-2021 Task 11: NLPContributionGraph - Structuring Scholarly NLP Contributions for a Research Knowledge Graph (D’Souza et al., SemEval 2021) is the source for ncg, annotated for both titles and abstracts for *research problem*\n\t* URL as the pwc annotated for both titles and abstracts for *task* -> *research problem* and *method* entities.\n2. Additionally, supplies a new annotated dataset for the titles in the ACL anthology in the acl repository\nwhere titles are annotated with all seven entities.### 2) Dataset Statistics for full dataset\n\n\nPlease note the numbers below reflect the total annotated entities. They do not reflect the unique set of annotated entities.#### Titles\n\n\n'URL'\n\n\n\n'URL'\n\n\n\n'URL'"
]
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|
e14570067465ee79b6f787f7198b75087489a1d4 | # Dataset Card for "UA_speech_noisereduced_CM12_M11"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | AravindVadlapudi02/UA_speech_noisereduced_CM12_M11 | [
"region:us"
]
| 2023-03-23T17:49:47+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": {"class_label": {"names": {"0": "healthy control", "1": "pathology"}}}}, {"name": "input_features", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 384132800, "num_examples": 400}, {"name": "test", "num_bytes": 4983162748, "num_examples": 5189}], "download_size": 620681042, "dataset_size": 5367295548}} | 2023-03-23T17:51:43+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "UA_speech_noisereduced_CM12_M11"
More Information needed | [
"# Dataset Card for \"UA_speech_noisereduced_CM12_M11\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"UA_speech_noisereduced_CM12_M11\"\n\nMore Information needed"
]
| [
6,
25
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"UA_speech_noisereduced_CM12_M11\"\n\nMore Information needed"
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|
884c0b2380b04235c3c38788c7ac47478d4ab96d | Combination of English Wikipedia and Simple Wikipedia, from the `wikipedia` dataset on HuggingFace.
## License Info
"Most of Wikipedia's text and many of its images are co-licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License (CC BY-SA) and the GNU Free Documentation License (GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts). Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes the text." | andersonbcdefg/combined_en_wikipedia | [
"license:other",
"region:us"
]
| 2023-03-23T18:20:25+00:00 | {"license": "other"} | 2023-03-23T22:38:23+00:00 | []
| []
| TAGS
#license-other #region-us
| Combination of English Wikipedia and Simple Wikipedia, from the 'wikipedia' dataset on HuggingFace.
## License Info
"Most of Wikipedia's text and many of its images are co-licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License (CC BY-SA) and the GNU Free Documentation License (GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts). Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes the text." | [
"## License Info\n\"Most of Wikipedia's text and many of its images are co-licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License (CC BY-SA) and the GNU Free Documentation License (GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts). Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes the text.\""
]
| [
"TAGS\n#license-other #region-us \n",
"## License Info\n\"Most of Wikipedia's text and many of its images are co-licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License (CC BY-SA) and the GNU Free Documentation License (GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts). Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes the text.\""
]
| [
11,
136
]
| [
"passage: TAGS\n#license-other #region-us \n## License Info\n\"Most of Wikipedia's text and many of its images are co-licensed under the Creative Commons Attribution-ShareAlike 3.0 Unported License (CC BY-SA) and the GNU Free Documentation License (GFDL) (unversioned, with no invariant sections, front-cover texts, or back-cover texts). Some text has been imported only under CC BY-SA and CC BY-SA-compatible license and cannot be reused under GFDL; such text will be identified on the page footer, in the page history, or on the discussion page of the article that utilizes the text.\""
]
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|
38c83eb97e5ed5f4375f22c5e50615e8107a9e51 | # Dataset Card for "la_en_aeneid_holdout"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | grosenthal/la_en_aeneid_holdout | [
"region:us"
]
| 2023-03-23T18:45:29+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "la", "dtype": "string"}, {"name": "en", "dtype": "string"}, {"name": "file", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 37702700.38548382, "num_examples": 97437}, {"name": "test", "num_bytes": 388099.6420118343, "num_examples": 991}, {"name": "valid", "num_bytes": 368502.42998027615, "num_examples": 974}, {"name": "aeneid", "num_bytes": 1145100.6051040238, "num_examples": 2960}], "download_size": 24019549, "dataset_size": 39604403.06257996}} | 2023-03-23T18:46:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "la_en_aeneid_holdout"
More Information needed | [
"# Dataset Card for \"la_en_aeneid_holdout\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"la_en_aeneid_holdout\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"la_en_aeneid_holdout\"\n\nMore Information needed"
]
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|
60517de6058c356db813b0969e3e22fe89965356 | # Dataset Card for "ce-aesthetics-a2z"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | previsone/ce-aesthetics-a2z | [
"region:us"
]
| 2023-03-23T19:13:48+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 126654282.663, "num_examples": 3837}], "download_size": 86045928, "dataset_size": 126654282.663}} | 2023-03-23T19:49:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ce-aesthetics-a2z"
More Information needed | [
"# Dataset Card for \"ce-aesthetics-a2z\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ce-aesthetics-a2z\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ce-aesthetics-a2z\"\n\nMore Information needed"
]
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|
50cf8973a52af7b4444ebc5dcbb5522b4dda69e8 | # Dataset Card for "gen-qm-17000"
### Dataset Summary
Dataset for converting request into query and extracting model name.
DEV/VAL/TEST: 90/10/10
SIZE: 17000
### Supported Tasks and Leaderboards
The tasks represented in GEN-QM cover a text2text generation for producing qureries based on request or extracting models.
### Languages
The data in QM are in English.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
```bash
{
'answer': '$count(EventCategory.Children) $neq 1029',
'utterance': 'Instructions: Based on Request and Model Description generate query with represents requests filter. Generaly query statement consists of path to the models column on the left, operator of comparison in the middle started with $ and comparison value on the right. Also query can contain more than one statement combined with $and or $or operator.\nModel Description: CreatedByUserName as created by user name;ModifiedByUserName as modified by user name;CreatedOn as created on;ModifiedOn as modified on;EventCategory.IsApprovalRequired as is approval required of experience category;EventCategory.Name as name of experience category;EventCategory.Code as code of experience category;EventCategory.CreatedByUserName as created by user name of experience category;EventCategory.ModifiedByUserName as modified by user name of experience category;EventCategory.Priority as priority of experience category;EventCategory.CreatedOn as created on of experience category;EventCategory.ModifiedOn as modified on of experience category;EventCategory.EventInCategories as experience in categories of experience category,event in categories of event category;EventCategory.EventCategoryInTypes as event category in types of experience category,experience category in types of event category;EventCategory.Children as children of experience category,children categories of event category;EventCategoryType.Name as name of experience category type;EventCategoryType.CreatedByUserName as created by user name of experience category type;EventCategoryType.ModifiedByUserName as modified by user name of experience category type;EventCategoryType.CreatedOn as created on of experience category type;EventCategoryType.ModifiedOn as modified on of experience category type;EventCategoryType.EventCategoryInTypes as event category in types of experience category type,experience category in types of event category type\nRequest: select event category in type where count of children of experience category != one thousand and twenty-nine\nQuery:'
}
```
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0. | dkuntso/gen-qm-17000 | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-03-23T19:29:27+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "Generate Query/Model from Request 15000/1000/1000", "dataset_info": {"features": [{"name": "utterance", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27449266, "num_examples": 14960}, {"name": "test", "num_bytes": 1929362, "num_examples": 1020}, {"name": "validation", "num_bytes": 1871516, "num_examples": 1020}], "download_size": 3761317, "dataset_size": 31250144}} | 2023-03-24T21:51:38+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us
| # Dataset Card for "gen-qm-17000"
### Dataset Summary
Dataset for converting request into query and extracting model name.
DEV/VAL/TEST: 90/10/10
SIZE: 17000
### Supported Tasks and Leaderboards
The tasks represented in GEN-QM cover a text2text generation for producing qureries based on request or extracting models.
### Languages
The data in QM are in English.
## Dataset Structure
### Data Instances
An example of "train" looks as follows:
## Additional Information
### Licensing Information
The dataset is released under Apache 2.0. | [
"# Dataset Card for \"gen-qm-17000\"",
"### Dataset Summary\n\nDataset for converting request into query and extracting model name.\n\nDEV/VAL/TEST: 90/10/10\n\nSIZE: 17000",
"### Supported Tasks and Leaderboards\n\nThe tasks represented in GEN-QM cover a text2text generation for producing qureries based on request or extracting models.",
"### Languages\n\nThe data in QM are in English.",
"## Dataset Structure",
"### Data Instances\n\nAn example of \"train\" looks as follows:",
"## Additional Information",
"### Licensing Information\nThe dataset is released under Apache 2.0."
]
| [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n",
"# Dataset Card for \"gen-qm-17000\"",
"### Dataset Summary\n\nDataset for converting request into query and extracting model name.\n\nDEV/VAL/TEST: 90/10/10\n\nSIZE: 17000",
"### Supported Tasks and Leaderboards\n\nThe tasks represented in GEN-QM cover a text2text generation for producing qureries based on request or extracting models.",
"### Languages\n\nThe data in QM are in English.",
"## Dataset Structure",
"### Data Instances\n\nAn example of \"train\" looks as follows:",
"## Additional Information",
"### Licensing Information\nThe dataset is released under Apache 2.0."
]
| [
41,
13,
37,
39,
13,
6,
18,
5,
16
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n# Dataset Card for \"gen-qm-17000\"### Dataset Summary\n\nDataset for converting request into query and extracting model name.\n\nDEV/VAL/TEST: 90/10/10\n\nSIZE: 17000### Supported Tasks and Leaderboards\n\nThe tasks represented in GEN-QM cover a text2text generation for producing qureries based on request or extracting models.### Languages\n\nThe data in QM are in English.## Dataset Structure### Data Instances\n\nAn example of \"train\" looks as follows:## Additional Information### Licensing Information\nThe dataset is released under Apache 2.0."
]
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23c07577ae7d98d696806b794289926673929de6 | # Dataset Card for "rvl_cdip_100_examples_per_class"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jordyvl/rvl_cdip_100_examples_per_class | [
"region:us"
]
| 2023-03-23T19:58:02+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "letter", "1": "form", "2": "email", "3": "handwritten", "4": "advertisement", "5": "scientific report", "6": "scientific publication", "7": "specification", "8": "file folder", "9": "news article", "10": "budget", "11": "invoice", "12": "presentation", "13": "questionnaire", "14": "resume", "15": "memo"}}}}], "splits": [{"name": "train", "num_bytes": 97000316.76, "num_examples": 800}, {"name": "test", "num_bytes": 48612840.21, "num_examples": 400}, {"name": "validation", "num_bytes": 48666549.76, "num_examples": 400}], "download_size": 180034173, "dataset_size": 194279706.73}} | 2023-03-23T20:55:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "rvl_cdip_100_examples_per_class"
More Information needed | [
"# Dataset Card for \"rvl_cdip_100_examples_per_class\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"rvl_cdip_100_examples_per_class\"\n\nMore Information needed"
]
| [
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25
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"rvl_cdip_100_examples_per_class\"\n\nMore Information needed"
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|
d427aa9782e70e6a9e5e31610469b5a06d9bab68 | For LXMERT model fine-tuning on airsim imagery. | mperic/lxmert-airsim | [
"region:us"
]
| 2023-03-23T21:44:25+00:00 | {} | 2023-03-23T21:48:42+00:00 | []
| []
| TAGS
#region-us
| For LXMERT model fine-tuning on airsim imagery. | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
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