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4040189d19e7f9dac5b2e4134afc968d765d500a | # Dataset Card for "sq-babi_nli_basic-deduction"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | niv-al/sq-babi_nli_basic-deduction | [
"language:sq",
"region:us"
]
| 2023-02-13T13:58:07+00:00 | {"language": ["sq"], "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "not-entailed", "1": "entailed"}}}}], "splits": [{"name": "train", "num_bytes": 259042, "num_examples": 1000}, {"name": "validation", "num_bytes": 36917, "num_examples": 144}, {"name": "test", "num_bytes": 37063, "num_examples": 144}], "download_size": 29535, "dataset_size": 333022}} | 2023-02-18T19:59:29+00:00 | []
| [
"sq"
]
| TAGS
#language-Albanian #region-us
| # Dataset Card for "sq-babi_nli_basic-deduction"
More Information needed | [
"# Dataset Card for \"sq-babi_nli_basic-deduction\"\n\nMore Information needed"
]
| [
"TAGS\n#language-Albanian #region-us \n",
"# Dataset Card for \"sq-babi_nli_basic-deduction\"\n\nMore Information needed"
]
| [
11,
24
]
| [
"passage: TAGS\n#language-Albanian #region-us \n# Dataset Card for \"sq-babi_nli_basic-deduction\"\n\nMore Information needed"
]
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|
af6d481296045ae333a6baca271e30742d2013aa |
# Dataset Card for MNBVC
## Table of Contents
- [Dataset Card for MNBVC](#dataset-card-for-mnbvc)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [数据集介绍](#数据集介绍)
- [数据子集](#数据子集)
- [数据格式](#数据格式)
- [文本数据](#文本数据)
- [问答数据](#问答数据)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** http://mnbvc.253874.net/
- **Repository:** https://github.com/esbatmop/MNBVC
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** N/A
### 数据集介绍
中文互联网上最古老最神秘(没有之一)的里屋社区于2023.1.1庄重宣布:
在英明神武的里屋管子带领下，决心发挥社区所长(哪都长)，帮助开源社区长期更新一份最大的中文互联网语料集。
Huggingface上的MNBVC数据集在逐渐更新中，请到[https://github.com/esbatmop/MNBVC](https://github.com/esbatmop/MNBVC) 获取未完成清洗的更多数据。
可以使用如下脚本加载：
```python
from datasets import load_dataset
dataset = load_dataset("liwu/MNBVC", 'law_judgement', split='train', streaming=True)
next(iter(dataset)) # get the first line
```
## 数据子集
MNBVC数据集包含数个子集：
- `law_judgement`: 来自法律文书的文本。
- `gov_xuexiqiangguo`: 来自学习强国的文本。
- `gov_report`: 来自政府工作报告的文本。
- `co_ann_report`: 企业年报文本。
- `code_metadata`: 代码元数据。
- `qa_zhihu`: 来自知乎的问答数据。
- `qa_wikihow`: 来自wikihow的问答数据。
- `qa_mfa`: 外交部问答数据。
- `news_peoples_daily`: 来自人民日报的文本数据。
- `wikipedia`: 来自维基百科的文本数据。
- `qa_stackexchange`: 来自StackExchange的问答数据。
- `qa_chatgpt`: 使用ChatGPT构造的问答语料，感谢[genggui001](https://github.com/genggui001)贡献语料。
- `math_qa`: 和数学领域有关的问答数据。
- `math_chat`: 和数学领域有关的对话数据数据，可以提升模型Chain of Thought的能力。
- `crawler_oscar`: 从CommonCrawl中清洗出来的通用文本数据。
## 数据格式
目前MNBVC数据集包含如下几类数据：
- 通用文本
- 问答语料
- 代码语料
- 多轮对话
- 论坛语料
- 平行语料
可以在[MNBVC的wiki页面](https://wiki.mnbvc.org/doku.php/%E7%8E%B0%E6%9C%89%E8%AF%AD%E6%96%99%E6%A0%BC%E5%BC%8F)上查看这几类数据的具体格式。
项目早期所上传的数据使用如下格式，以后这一格式会被废弃，相应数据也会重新上传：
```json
{
"text": datasets.Value("string"),
"meta": datasets.Value("string")
}
```
### Contributions
Thanks to the [Liwu community](http://mnbvc.253874.net/) for constructing this dataset.
Thanks to [silver](https://github.com/silverriver) and [jiaming](https://huggingface.co/Yjiaming) for adding and uploading this dataset to Huggingface. | liwu/MNBVC | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:original",
"language:zh",
"license:mit",
"region:us"
]
| 2023-02-13T14:00:47+00:00 | {"annotations_creators": ["other"], "language_creators": ["other"], "language": ["zh"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "MNBVC"} | 2024-01-13T02:47:24+00:00 | []
| [
"zh"
]
| TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-Chinese #license-mit #region-us
|
# Dataset Card for MNBVC
## Table of Contents
- Dataset Card for MNBVC
- Table of Contents
- Dataset Description
- 数据集介绍
- 数据子集
- 数据格式
- 文本数据
- 问答数据
- Contributions
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: N/A
- Leaderboard: N/A
- Point of Contact: N/A
### 数据集介绍
中文互联网上最古老最神秘(没有之一)的里屋社区于2023.1.1庄重宣布:
在英明神武的里屋管子带领下，决心发挥社区所长(哪都长)，帮助开源社区长期更新一份最大的中文互联网语料集。
Huggingface上的MNBVC数据集在逐渐更新中，请到https://URL 获取未完成清洗的更多数据。
可以使用如下脚本加载：
## 数据子集
MNBVC数据集包含数个子集：
- 'law_judgement': 来自法律文书的文本。
- 'gov_xuexiqiangguo': 来自学习强国的文本。
- 'gov_report': 来自政府工作报告的文本。
- 'co_ann_report': 企业年报文本。
- 'code_metadata': 代码元数据。
- 'qa_zhihu': 来自知乎的问答数据。
- 'qa_wikihow': 来自wikihow的问答数据。
- 'qa_mfa': 外交部问答数据。
- 'news_peoples_daily': 来自人民日报的文本数据。
- 'wikipedia': 来自维基百科的文本数据。
- 'qa_stackexchange': 来自StackExchange的问答数据。
- 'qa_chatgpt': 使用ChatGPT构造的问答语料，感谢genggui001贡献语料。
- 'math_qa': 和数学领域有关的问答数据。
- 'math_chat': 和数学领域有关的对话数据数据，可以提升模型Chain of Thought的能力。
- 'crawler_oscar': 从CommonCrawl中清洗出来的通用文本数据。
## 数据格式
目前MNBVC数据集包含如下几类数据：
- 通用文本
- 问答语料
- 代码语料
- 多轮对话
- 论坛语料
- 平行语料
可以在MNBVC的wiki页面上查看这几类数据的具体格式。
项目早期所上传的数据使用如下格式，以后这一格式会被废弃，相应数据也会重新上传：
### Contributions
Thanks to the Liwu community for constructing this dataset.
Thanks to silver and jiaming for adding and uploading this dataset to Huggingface. | [
"# Dataset Card for MNBVC",
"## Table of Contents\n- Dataset Card for MNBVC\n - Table of Contents\n - Dataset Description\n - 数据集介绍\n - 数据子集\n - 数据格式\n - 文本数据\n - 问答数据\n - Contributions",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: N/A\n- Leaderboard: N/A\n- Point of Contact: N/A",
"### 数据集介绍\n\n中文互联网上最古老最神秘(没有之一)的里屋社区于2023.1.1庄重宣布:\n\n在英明神武的里屋管子带领下，决心发挥社区所长(哪都长)，帮助开源社区长期更新一份最大的中文互联网语料集。\n\nHuggingface上的MNBVC数据集在逐渐更新中，请到https://URL 获取未完成清洗的更多数据。\n\n可以使用如下脚本加载：",
"## 数据子集\n\nMNBVC数据集包含数个子集：\n\n- 'law_judgement': 来自法律文书的文本。\n- 'gov_xuexiqiangguo': 来自学习强国的文本。\n- 'gov_report': 来自政府工作报告的文本。\n- 'co_ann_report': 企业年报文本。\n- 'code_metadata': 代码元数据。\n- 'qa_zhihu': 来自知乎的问答数据。\n- 'qa_wikihow': 来自wikihow的问答数据。\n- 'qa_mfa': 外交部问答数据。\n- 'news_peoples_daily': 来自人民日报的文本数据。\n- 'wikipedia': 来自维基百科的文本数据。\n- 'qa_stackexchange': 来自StackExchange的问答数据。\n- 'qa_chatgpt': 使用ChatGPT构造的问答语料，感谢genggui001贡献语料。\n- 'math_qa': 和数学领域有关的问答数据。\n- 'math_chat': 和数学领域有关的对话数据数据，可以提升模型Chain of Thought的能力。\n- 'crawler_oscar': 从CommonCrawl中清洗出来的通用文本数据。",
"## 数据格式\n\n目前MNBVC数据集包含如下几类数据：\n\n- 通用文本\n- 问答语料\n- 代码语料\n- 多轮对话\n- 论坛语料\n- 平行语料\n\n可以在MNBVC的wiki页面上查看这几类数据的具体格式。\n\n\n项目早期所上传的数据使用如下格式，以后这一格式会被废弃，相应数据也会重新上传：",
"### Contributions\n\nThanks to the Liwu community for constructing this dataset.\nThanks to silver and jiaming for adding and uploading this dataset to Huggingface."
]
| [
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-other #language_creators-other #multilinguality-monolingual #size_categories-unknown #source_datasets-original #language-Chinese #license-mit #region-us \n",
"# Dataset Card for MNBVC",
"## Table of Contents\n- Dataset Card for MNBVC\n - Table of Contents\n - Dataset Description\n - 数据集介绍\n - 数据子集\n - 数据格式\n - 文本数据\n - 问答数据\n - Contributions",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: N/A\n- Leaderboard: N/A\n- Point of Contact: N/A",
"### 数据集介绍\n\n中文互联网上最古老最神秘(没有之一)的里屋社区于2023.1.1庄重宣布:\n\n在英明神武的里屋管子带领下，决心发挥社区所长(哪都长)，帮助开源社区长期更新一份最大的中文互联网语料集。\n\nHuggingface上的MNBVC数据集在逐渐更新中，请到https://URL 获取未完成清洗的更多数据。\n\n可以使用如下脚本加载：",
"## 数据子集\n\nMNBVC数据集包含数个子集：\n\n- 'law_judgement': 来自法律文书的文本。\n- 'gov_xuexiqiangguo': 来自学习强国的文本。\n- 'gov_report': 来自政府工作报告的文本。\n- 'co_ann_report': 企业年报文本。\n- 'code_metadata': 代码元数据。\n- 'qa_zhihu': 来自知乎的问答数据。\n- 'qa_wikihow': 来自wikihow的问答数据。\n- 'qa_mfa': 外交部问答数据。\n- 'news_peoples_daily': 来自人民日报的文本数据。\n- 'wikipedia': 来自维基百科的文本数据。\n- 'qa_stackexchange': 来自StackExchange的问答数据。\n- 'qa_chatgpt': 使用ChatGPT构造的问答语料，感谢genggui001贡献语料。\n- 'math_qa': 和数学领域有关的问答数据。\n- 'math_chat': 和数学领域有关的对话数据数据，可以提升模型Chain of Thought的能力。\n- 'crawler_oscar': 从CommonCrawl中清洗出来的通用文本数据。",
"## 数据格式\n\n目前MNBVC数据集包含如下几类数据：\n\n- 通用文本\n- 问答语料\n- 代码语料\n- 多轮对话\n- 论坛语料\n- 平行语料\n\n可以在MNBVC的wiki页面上查看这几类数据的具体格式。\n\n\n项目早期所上传的数据使用如下格式，以后这一格式会被废弃，相应数据也会重新上传：",
"### Contributions\n\nThanks to the Liwu community for constructing this dataset.\nThanks to silver and jiaming for adding and uploading this dataset to Huggingface."
]
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]
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|
1cb3072ca2e972f46a1f63d1ff2c981c5fb5c75e | # Dataset Card for "contract"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | skang187/contract | [
"region:us"
]
| 2023-02-13T14:25:27+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "clean_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2618442, "num_examples": 7982}, {"name": "val", "num_bytes": 323947, "num_examples": 994}], "download_size": 985710, "dataset_size": 2942389}} | 2023-02-13T14:25:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "contract"
More Information needed | [
"# Dataset Card for \"contract\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"contract\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"contract\"\n\nMore Information needed"
]
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|
3bfd79e598eaab49b8b5ec7397d5c12b0f22416a |
# Russian StackOverflow dataset
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Description](#description)
- [Usage](#usage)
- [Data Instances](#data-instances)
- [Source Data](#source-data)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Licensing Information](#licensing-information)
## Description
**Summary:** Dataset of questions, answers, and comments from [ru.stackoverflow.com](https://ru.stackoverflow.com/).
**Script:** [create_stackoverflow.py](https://github.com/IlyaGusev/rulm/blob/hf/data_processing/create_stackoverflow.py)
**Point of Contact:** [Ilya Gusev]([email protected])
**Languages:** The dataset is in Russian with some programming code.
## Usage
Prerequisites:
```bash
pip install datasets zstandard jsonlines pysimdjson
```
Loading:
```python
from datasets import load_dataset
dataset = load_dataset('IlyaGusev/ru_stackoverflow', split="train")
for example in dataset:
print(example["text_markdown"])
print()
```
## Data Instances
```
{
"question_id": 11235,
"answer_count": 1,
"url": "https://ru.stackoverflow.com/questions/11235",
"score": 2,
"tags": ["c++", "сериализация"],
"title": "Извлечение из файла, запись в файл",
"views": 1309,
"author": "...",
"timestamp": 1303205289,
"text_html": "...",
"text_markdown": "...",
"comments": {
"text": ["...", "...",
"author": ["...", "..."],
"comment_id": [11236, 11237],
"score": [0, 0],
"timestamp": [1303205411, 1303205678]
},
"answers": {
"answer_id": [11243, 11245],
"timestamp": [1303207791, 1303207792],
"is_accepted": [1, 0],
"text_html": ["...", "..."],
"text_markdown": ["...", "..."],
"score": [3, 0],
"author": ["...", "..."],
"comments": {
"text": ["...", "..."],
"author": ["...", "..."],
"comment_id": [11246, 11249],
"score": [0, 0],
"timestamp": [1303207961, 1303207800]
}
}
}
```
You can use this little helper to unflatten sequences:
```python
def revert_flattening(records):
fixed_records = []
for key, values in records.items():
if not fixed_records:
fixed_records = [{} for _ in range(len(values))]
for i, value in enumerate(values):
fixed_records[i][key] = value
return fixed_records
```
The original JSONL is already unflattened.
## Source Data
* The data source is the [Russian StackOverflow](https://ru.stackoverflow.com/) website.
* Original XMLs: [ru.stackoverflow.com.7z](https://ia600107.us.archive.org/27/items/stackexchange/ru.stackoverflow.com.7z).
* Processing script is [here](https://github.com/IlyaGusev/rulm/blob/hf/data_processing/create_stackoverflow.py).
## Personal and Sensitive Information
The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible.
## Licensing Information
According to the license of original data, this dataset is distributed under [CC BY-SA 2.5](https://creativecommons.org/licenses/by-sa/2.5/). | IlyaGusev/ru_stackoverflow | [
"task_categories:text-generation",
"task_categories:question-answering",
"size_categories:100K<n<1M",
"language:ru",
"license:other",
"region:us"
]
| 2023-02-13T14:32:35+00:00 | {"language": ["ru"], "license": "other", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation", "question-answering"], "dataset_info": {"features": [{"name": "question_id", "dtype": "uint32"}, {"name": "url", "dtype": "string"}, {"name": "answer_count", "dtype": "uint32"}, {"name": "text_html", "dtype": "string"}, {"name": "text_markdown", "dtype": "string"}, {"name": "score", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "tags", "sequence": "string"}, {"name": "views", "dtype": "uint64"}, {"name": "author", "dtype": "string"}, {"name": "timestamp", "dtype": "uint64"}, {"name": "comments", "sequence": [{"name": "text", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "comment_id", "dtype": "uint32"}, {"name": "score", "dtype": "int32"}, {"name": "timestamp", "dtype": "uint64"}]}, {"name": "answers", "sequence": [{"name": "answer_id", "dtype": "uint32"}, {"name": "is_accepted", "dtype": "uint8"}, {"name": "text_html", "dtype": "string"}, {"name": "text_markdown", "dtype": "string"}, {"name": "score", "dtype": "int32"}, {"name": "author", "dtype": "string"}, {"name": "timestamp", "dtype": "uint64"}, {"name": "comments", "sequence": [{"name": "text", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "comment_id", "dtype": "uint32"}, {"name": "score", "dtype": "int32"}, {"name": "timestamp", "dtype": "uint64"}]}]}], "splits": [{"name": "train", "num_bytes": 3013377174, "num_examples": 437604}], "download_size": 670468664, "dataset_size": 3013377174}} | 2023-03-09T23:48:16+00:00 | []
| [
"ru"
]
| TAGS
#task_categories-text-generation #task_categories-question-answering #size_categories-100K<n<1M #language-Russian #license-other #region-us
|
# Russian StackOverflow dataset
## Table of Contents
- Table of Contents
- Description
- Usage
- Data Instances
- Source Data
- Personal and Sensitive Information
- Licensing Information
## Description
Summary: Dataset of questions, answers, and comments from URL.
Script: create_stackoverflow.py
Point of Contact: Ilya Gusev
Languages: The dataset is in Russian with some programming code.
## Usage
Prerequisites:
Loading:
## Data Instances
You can use this little helper to unflatten sequences:
The original JSONL is already unflattened.
## Source Data
* The data source is the Russian StackOverflow website.
* Original XMLs: URL.7z.
* Processing script is here.
## Personal and Sensitive Information
The dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible.
## Licensing Information
According to the license of original data, this dataset is distributed under CC BY-SA 2.5. | [
"# Russian StackOverflow dataset",
"## Table of Contents\n- Table of Contents\n- Description\n- Usage\n- Data Instances\n- Source Data\n- Personal and Sensitive Information\n- Licensing Information",
"## Description\n\nSummary: Dataset of questions, answers, and comments from URL.\n\nScript: create_stackoverflow.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: The dataset is in Russian with some programming code.",
"## Usage\n\nPrerequisites:\n\n\nLoading:",
"## Data Instances\n\n\n\nYou can use this little helper to unflatten sequences:\n\n\nThe original JSONL is already unflattened.",
"## Source Data\n\n* The data source is the Russian StackOverflow website.\n* Original XMLs: URL.7z.\n* Processing script is here.",
"## Personal and Sensitive Information\n\nThe dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible.",
"## Licensing Information\n\nAccording to the license of original data, this dataset is distributed under CC BY-SA 2.5."
]
| [
"TAGS\n#task_categories-text-generation #task_categories-question-answering #size_categories-100K<n<1M #language-Russian #license-other #region-us \n",
"# Russian StackOverflow dataset",
"## Table of Contents\n- Table of Contents\n- Description\n- Usage\n- Data Instances\n- Source Data\n- Personal and Sensitive Information\n- Licensing Information",
"## Description\n\nSummary: Dataset of questions, answers, and comments from URL.\n\nScript: create_stackoverflow.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: The dataset is in Russian with some programming code.",
"## Usage\n\nPrerequisites:\n\n\nLoading:",
"## Data Instances\n\n\n\nYou can use this little helper to unflatten sequences:\n\n\nThe original JSONL is already unflattened.",
"## Source Data\n\n* The data source is the Russian StackOverflow website.\n* Original XMLs: URL.7z.\n* Processing script is here.",
"## Personal and Sensitive Information\n\nThe dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible.",
"## Licensing Information\n\nAccording to the license of original data, this dataset is distributed under CC BY-SA 2.5."
]
| [
51,
8,
35,
51,
10,
32,
33,
42,
26
]
| [
"passage: TAGS\n#task_categories-text-generation #task_categories-question-answering #size_categories-100K<n<1M #language-Russian #license-other #region-us \n# Russian StackOverflow dataset## Table of Contents\n- Table of Contents\n- Description\n- Usage\n- Data Instances\n- Source Data\n- Personal and Sensitive Information\n- Licensing Information## Description\n\nSummary: Dataset of questions, answers, and comments from URL.\n\nScript: create_stackoverflow.py\n\nPoint of Contact: Ilya Gusev\n\nLanguages: The dataset is in Russian with some programming code.## Usage\n\nPrerequisites:\n\n\nLoading:## Data Instances\n\n\n\nYou can use this little helper to unflatten sequences:\n\n\nThe original JSONL is already unflattened.## Source Data\n\n* The data source is the Russian StackOverflow website.\n* Original XMLs: URL.7z.\n* Processing script is here.## Personal and Sensitive Information\n\nThe dataset is not anonymized, so individuals' names can be found in the dataset. Information about the original authors is included in the dataset where possible.## Licensing Information\n\nAccording to the license of original data, this dataset is distributed under CC BY-SA 2.5."
]
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|
644407bfeb0ae4f3068b69d16b96fbfabb1784cb | # Dataset Card for "sq-babi_nli_lists-sets"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | niv-al/sq-babi_nli_lists-sets | [
"language:sq",
"region:us"
]
| 2023-02-13T14:34:55+00:00 | {"language": ["sq"], "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "not-entailed", "1": "entailed"}}}}], "splits": [{"name": "train", "num_bytes": 242554, "num_examples": 1000}, {"name": "validation", "num_bytes": 34848, "num_examples": 144}, {"name": "test", "num_bytes": 34315, "num_examples": 144}], "download_size": 62573, "dataset_size": 311717}} | 2023-02-18T19:59:36+00:00 | []
| [
"sq"
]
| TAGS
#language-Albanian #region-us
| # Dataset Card for "sq-babi_nli_lists-sets"
More Information needed | [
"# Dataset Card for \"sq-babi_nli_lists-sets\"\n\nMore Information needed"
]
| [
"TAGS\n#language-Albanian #region-us \n",
"# Dataset Card for \"sq-babi_nli_lists-sets\"\n\nMore Information needed"
]
| [
11,
24
]
| [
"passage: TAGS\n#language-Albanian #region-us \n# Dataset Card for \"sq-babi_nli_lists-sets\"\n\nMore Information needed"
]
| [
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|
0f95276a5e81beb4f4d958d50b7ba0bedb18cd43 |
# Lora - Asbestos_Ceiling
## Dataset Description
- **원본** [수상할 정도로 익숙한 석면 천장 로라 공유 및 사용법](https://arca.live/b/aiart/69669397)
석면 **천장** 로라 파일
## !!사용법!!
그냥 T2I에서 로라 넣고 돌리면 벽까지 침범을 당해서 타율이 매우 떨어짐
천장 쪽만 인페인트해서 돌려야 타율이 좋음
**디노이즈 강도 : 0.5**
**<lora:Asbestos Ceiling:2.0>**
[다운로드](https://huggingface.co/datasets/AIARTCHAN/lora-Asbestos_Ceiling/resolve/main/Asbestos%20Ceiling.safetensors) | AIARTCHAN/lora-Asbestos_Ceiling | [
"license:creativeml-openrail-m",
"lora",
"aiartchan",
"stable-diffusion",
"region:us"
]
| 2023-02-13T14:40:01+00:00 | {"license": "creativeml-openrail-m", "tags": ["lora", "aiartchan", "stable-diffusion"]} | 2023-02-13T14:43:24+00:00 | []
| []
| TAGS
#license-creativeml-openrail-m #lora #aiartchan #stable-diffusion #region-us
|
# Lora - Asbestos_Ceiling
## Dataset Description
- 원본 수상할 정도로 익숙한 석면 천장 로라 공유 및 사용법
석면 천장 로라 파일
## !!사용법!!
그냥 T2I에서 로라 넣고 돌리면 벽까지 침범을 당해서 타율이 매우 떨어짐
천장 쪽만 인페인트해서 돌려야 타율이 좋음
디노이즈 강도 : 0.5
<lora:Asbestos Ceiling:2.0>
다운로드 | [
"# Lora - Asbestos_Ceiling",
"## Dataset Description\n\n- 원본 수상할 정도로 익숙한 석면 천장 로라 공유 및 사용법\n\n석면 천장 로라 파일",
"## !!사용법!!\n\n그냥 T2I에서 로라 넣고 돌리면 벽까지 침범을 당해서 타율이 매우 떨어짐\n\n천장 쪽만 인페인트해서 돌려야 타율이 좋음\n\n\n디노이즈 강도 : 0.5\n\n<lora:Asbestos Ceiling:2.0>\n\n다운로드"
]
| [
"TAGS\n#license-creativeml-openrail-m #lora #aiartchan #stable-diffusion #region-us \n",
"# Lora - Asbestos_Ceiling",
"## Dataset Description\n\n- 원본 수상할 정도로 익숙한 석면 천장 로라 공유 및 사용법\n\n석면 천장 로라 파일",
"## !!사용법!!\n\n그냥 T2I에서 로라 넣고 돌리면 벽까지 침범을 당해서 타율이 매우 떨어짐\n\n천장 쪽만 인페인트해서 돌려야 타율이 좋음\n\n\n디노이즈 강도 : 0.5\n\n<lora:Asbestos Ceiling:2.0>\n\n다운로드"
]
| [
32,
10,
31,
72
]
| [
"passage: TAGS\n#license-creativeml-openrail-m #lora #aiartchan #stable-diffusion #region-us \n# Lora - Asbestos_Ceiling## Dataset Description\n\n- 원본 수상할 정도로 익숙한 석면 천장 로라 공유 및 사용법\n\n석면 천장 로라 파일## !!사용법!!\n\n그냥 T2I에서 로라 넣고 돌리면 벽까지 침범을 당해서 타율이 매우 떨어짐\n\n천장 쪽만 인페인트해서 돌려야 타율이 좋음\n\n\n디노이즈 강도 : 0.5\n\n<lora:Asbestos Ceiling:2.0>\n\n다운로드"
]
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|
87dd5a011e7fb7c02b0213f76e21b0c9fe83f23f | # Dataset Card for "sq-babi_nli_single-supporting-fact"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | niv-al/sq-babi_nli_single-supporting-fact | [
"language:sq",
"region:us"
]
| 2023-02-13T15:05:38+00:00 | {"language": ["sq"], "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "not-entailed", "1": "entailed"}}}}], "splits": [{"name": "train", "num_bytes": 214663, "num_examples": 1000}, {"name": "validation", "num_bytes": 31319, "num_examples": 144}, {"name": "test", "num_bytes": 30966, "num_examples": 144}], "download_size": 50131, "dataset_size": 276948}} | 2023-02-18T19:59:43+00:00 | []
| [
"sq"
]
| TAGS
#language-Albanian #region-us
| # Dataset Card for "sq-babi_nli_single-supporting-fact"
More Information needed | [
"# Dataset Card for \"sq-babi_nli_single-supporting-fact\"\n\nMore Information needed"
]
| [
"TAGS\n#language-Albanian #region-us \n",
"# Dataset Card for \"sq-babi_nli_single-supporting-fact\"\n\nMore Information needed"
]
| [
11,
26
]
| [
"passage: TAGS\n#language-Albanian #region-us \n# Dataset Card for \"sq-babi_nli_single-supporting-fact\"\n\nMore Information needed"
]
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|
6046fc3e81cd0225fb9eb9ca4b51701f58910bf2 | # Dataset Card for "test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Axel578/test | [
"region:us"
]
| 2023-02-13T15:21:29+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1147232.0, "num_examples": 8}], "download_size": 1148603, "dataset_size": 1147232.0}} | 2023-02-13T15:21:44+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test"
More Information needed | [
"# Dataset Card for \"test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test\"\n\nMore Information needed"
]
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|
aa5e7b9770ea24a619e3956c27fba7f709435e7e | # Dataset Card for "sq-babi_nli_simple-negation"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | niv-al/sq-babi_nli_simple-negation | [
"language:sq",
"region:us"
]
| 2023-02-13T15:35:51+00:00 | {"language": ["sq"], "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "not-entailed", "1": "entailed"}}}}], "splits": [{"name": "train", "num_bytes": 215572, "num_examples": 1000}, {"name": "validation", "num_bytes": 32872, "num_examples": 144}, {"name": "test", "num_bytes": 32290, "num_examples": 144}], "download_size": 51452, "dataset_size": 280734}} | 2023-02-18T19:59:51+00:00 | []
| [
"sq"
]
| TAGS
#language-Albanian #region-us
| # Dataset Card for "sq-babi_nli_simple-negation"
More Information needed | [
"# Dataset Card for \"sq-babi_nli_simple-negation\"\n\nMore Information needed"
]
| [
"TAGS\n#language-Albanian #region-us \n",
"# Dataset Card for \"sq-babi_nli_simple-negation\"\n\nMore Information needed"
]
| [
11,
23
]
| [
"passage: TAGS\n#language-Albanian #region-us \n# Dataset Card for \"sq-babi_nli_simple-negation\"\n\nMore Information needed"
]
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|
8e9fc42d666467702a3fd4874d5c580709dce55f | # Dataset Card for "wikipedia.reorder.SOV"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.SOV | [
"region:us"
]
| 2023-02-13T15:49:48+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4084815753, "num_examples": 2312333}], "download_size": 2013973114, "dataset_size": 4084815753}} | 2023-02-13T15:52:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikipedia.reorder.SOV"
More Information needed | [
"# Dataset Card for \"wikipedia.reorder.SOV\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikipedia.reorder.SOV\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia.reorder.SOV\"\n\nMore Information needed"
]
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1c3a1e5200f8577d485bdb6ba7f17b17ce5d0d79 | ERROR: type should be string, got "\nhttps://huggingface.co/spaces/huggingface/datasets-tagging\n\n\n# Dataset Card for Swiss Doc2doc Information Retrieval\n\n## Table of Contents\n- [Table of Contents](#table-of-contents)\n- [Dataset Description](#dataset-description)\n - [Dataset Summary](#dataset-summary)\n - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)\n - [Languages](#languages)\n- [Dataset Structure](#dataset-structure)\n - [Data Instances](#data-instances)\n - [Data Fields](#data-fields)\n - [Data Splits](#data-splits)\n- [Dataset Creation](#dataset-creation)\n - [Curation Rationale](#curation-rationale)\n - [Source Data](#source-data)\n - [Annotations](#annotations)\n - [Personal and Sensitive Information](#personal-and-sensitive-information)\n- [Considerations for Using the Data](#considerations-for-using-the-data)\n - [Social Impact of Dataset](#social-impact-of-dataset)\n - [Discussion of Biases](#discussion-of-biases)\n - [Other Known Limitations](#other-known-limitations)\n- [Additional Information](#additional-information)\n - [Dataset Curators](#dataset-curators)\n - [Licensing Information](#licensing-information)\n - [Citation Information](#citation-information)\n - [Contributions](#contributions)\n\n## Dataset Description\n\n- **Homepage:**\n- **Repository:**\n- **Paper:**\n- **Leaderboard:**\n- **Point of Contact:**\n\n### Dataset Summary\n\nSwiss Doc2doc Information Retrieval is a multilingual, diachronic dataset of 131K Swiss Federal Supreme Court (FSCS) cases annotated with law citations and ruling citations, posing a challenging text classification task. As unique label we are using decision_id of cited rulings and uuid of cited law articles, which can be found in the SwissCourtRulingCorpus. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.\n\n### Supported Tasks and Leaderboards\n\nSwiss Doc2Doc IR can be used as information retrieval task using documents in Swiss Legislation (https://huggingface.co/datasets/rcds/swiss_legislation) and Swiss Leading desicions (https://huggingface.co/datasets/rcds/swiss_leading_decisions).\n\n### Languages\n\nSwitzerland has four official languages with three languages (German 86K, French 30k and Italian 10k) being represented. The decisions are written by the judges and clerks in the language of the proceedings.\n\n## Dataset Structure\n\n### Data Instances\n\n```\n{\n \"decision_id\": \"000127ef-17d2-4ded-8621-c0c962c18fd5\",\n \"language\": de,\n \"year\": 2018,\n \"chamber\": \"CH_BGer_008\",\n \"region\": \"Federation\",\n \"origin_chamber\": 47,\n \"origin_court\": 8,\n \"origin_canton\": 151,\n \"law_area\": \"social_law\",\n \"law_sub_area\": ,\n \"laws\": \"['75488867-c001-4eb9-93b9-04264ea91f55', 'e6b06567-1236-4210-adb3-e11c26e497d5', '04bf6369-99cb-41fa-8aff-413679bc8c18', ...],\n \"cited_rulings\": \"['fe8a76b3-8b0f-4f27-a277-2d887140e7ab', '16fef75e-e8d5-4a51-8230-a9ca3676c8a9', '6d21b282-3b23-41dd-9350-6ba5386df9b1', '302fd9f3-e78a-4a9f-9f8d-cde51fcbdfe7']\",\n \"facts\": \"Sachverhalt: A. A._, geboren 1954, war ab November 2002 als Pflegehilfe im Altersheim C._ angestellt. Am 23. Dezember 2002 meldete sie sich erstmals unter Hinweis auf Depressionen ...\",\n \"considerations\": \"Erwägungen: 1. 1.1. Die Beschwerde kann wegen Rechtsverletzung gemäss Art. 95 und Art. 96 BGG erhoben werden. Das Bundesgericht wendet das ...\",\n \"rulings\": \"Demnach erkennt das Bundesgericht: 1. Die Beschwerde wird abgewiesen. 2. Die Gerichtskosten von Fr. 800.- werden der Beschwerdeführerin ...\",\n}\n```\n\n### Data Fields\n\n```\ndecision_id: (str) a unique identifier of the for the document\nlanguage: (str) one of (de, fr, it)\nyear: (int) the publication year\nchamber: (str) the chamber of the case\nregion: (str) the region of the case\norigin_chamber: (str) the chamber of the origin case\norigin_court: (str) the court of the origin case\norigin_canton: (str) the canton of the origin case\nlaw_area: (str) the law area of the case\nlaw_sub_area:(str) the law sub area of the case\nlaws: (str) a list of law ids\ncited rulings: (str) a list of cited rulings ids\nfacts: (str) the facts of the case\nconsiderations: (str) the considerations of the case\nrulings: (str) the rulings of the case\n```\n\n### Data Splits\n\nThe dataset was split date-stratisfied\n- Train: 2002-2015\n- Validation: 2016-2017\n- Test: 2018-2022\n\n| Language | Subset | Number of Documents (Training/Validation/Test) | \n|------------|------------|------------------------------------------------| \n| German | **de** | 86'832 (59'170 / 19'002 / 8'660) |\n| French | **fr** | 46'203 (30'513 / 10'816 / 4'874) |\n| Italian | **it** | 8'306 (5'673 / 1'855 / 778) |\n\n\n## Dataset Creation\n\n### Curation Rationale\n\nThe dataset was created by Stern et al. (2023).\n\n### Source Data\n\n#### Initial Data Collection and Normalization\n\nThe original data are available at the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. \n\n#### Who are the source language producers?\n\nThe original data are published from the Swiss Federal Supreme Court (https://www.bger.ch) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (https://entscheidsuche.ch) in HTML. \n\n### Annotations\n\n#### Annotation process\n\nThe decisions have been annotated with the citation ids using html tags and parsers.\nFor more details on laws (rcds/swiss_legislation) and rulings (rcds/swiss_rulings).\n\n#### Who are the annotators?\n\nStern annotated the citations.\nMetadata is published by the Swiss Federal Supreme Court (https://www.bger.ch).\n\n### Personal and Sensitive Information\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: https://www.bger.ch/home/juridiction/anonymisierungsregeln.html.\n\n## Considerations for Using the Data\n\n### Social Impact of Dataset\n\n[More Information Needed]\n\n### Discussion of Biases\n\n[More Information Needed]\n\n### Other Known Limitations\n\n[More Information Needed]\n\n## Additional Information\n\n### Dataset Curators\n\n[More Information Needed]\n\n### Licensing Information\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf)\n© Swiss Federal Supreme Court, 2002-2022\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\nSource: https://www.bger.ch/files/live/sites/bger/files/pdf/de/urteilsveroeffentlichung_d.pdf\n\n### Citation Information\n\nPlease cite our [ArXiv-Preprint](https://arxiv.org/abs/2306.09237)\n```\n@misc{rasiah2023scale,\n title={SCALE: Scaling up the Complexity for Advanced Language Model Evaluation}, \n author={Vishvaksenan Rasiah and Ronja Stern and Veton Matoshi and Matthias Stürmer and Ilias Chalkidis and Daniel E. Ho and Joel Niklaus},\n year={2023},\n eprint={2306.09237},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n```\n\n### Contributions\n\nThanks to [@Stern5497](https://github.com/stern5497) for adding this dataset." | rcds/swiss_doc2doc_ir | [
"task_categories:text-classification",
"task_ids:entity-linking-classification",
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"language:de",
"language:fr",
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"license:cc-by-sa-4.0",
"arxiv:2306.09237",
"doi:10.57967/hf/0773",
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| 2023-02-13T15:51:17+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["expert-generated"], "language": ["de", "fr", "it"], "license": ["cc-by-sa-4.0"], "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["entity-linking-classification"], "pretty_name": "Swiss Doc2doc Information Retrieval", "tags": []} | 2023-07-20T06:33:37+00:00 | [
"2306.09237"
]
| [
"de",
"fr",
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| TAGS
#task_categories-text-classification #task_ids-entity-linking-classification #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-German #language-French #language-Italian #license-cc-by-sa-4.0 #arxiv-2306.09237 #doi-10.57967/hf/0773 #region-us
| URL
Dataset Card for Swiss Doc2doc Information Retrieval
====================================================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ 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
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage:
* Repository:
* Paper:
* Leaderboard:
* Point of Contact:
### Dataset Summary
Swiss Doc2doc Information Retrieval is a multilingual, diachronic dataset of 131K Swiss Federal Supreme Court (FSCS) cases annotated with law citations and ruling citations, posing a challenging text classification task. As unique label we are using decision\_id of cited rulings and uuid of cited law articles, which can be found in the SwissCourtRulingCorpus. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.
### Supported Tasks and Leaderboards
Swiss Doc2Doc IR can be used as information retrieval task using documents in Swiss Legislation (URL and Swiss Leading desicions (URL
### Languages
Switzerland has four official languages with three languages (German 86K, French 30k and Italian 10k) being represented. The decisions are written by the judges and clerks in the language of the proceedings.
Dataset Structure
-----------------
### Data Instances
### Data Fields
### Data Splits
The dataset was split date-stratisfied
* Train: 2002-2015
* Validation: 2016-2017
* Test: 2018-2022
Language: German, Subset: de, Number of Documents (Training/Validation/Test): 86'832 (59'170 / 19'002 / 8'660)
Language: French, Subset: fr, Number of Documents (Training/Validation/Test): 46'203 (30'513 / 10'816 / 4'874)
Language: Italian, Subset: it, Number of Documents (Training/Validation/Test): 8'306 (5'673 / 1'855 / 778)
Dataset Creation
----------------
### Curation Rationale
The dataset was created by Stern et al. (2023).
### Source Data
#### Initial Data Collection and Normalization
The original data are available at the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.
#### Who are the source language producers?
The original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.
### Annotations
#### Annotation process
The decisions have been annotated with the citation ids using html tags and parsers.
For more details on laws (rcds/swiss\_legislation) and rulings (rcds/swiss\_rulings).
#### Who are the annotators?
Stern annotated the citations.
Metadata is published by the Swiss Federal Supreme Court (URL).
### Personal and Sensitive Information
The dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
We release the data under CC-BY-4.0 which complies with the court licensing (URL
© Swiss Federal Supreme Court, 2002-2022
The copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.
Source: URL
Please cite our ArXiv-Preprint
### Contributions
Thanks to @Stern5497 for adding this dataset.
| [
"### Dataset Summary\n\n\nSwiss Doc2doc Information Retrieval is a multilingual, diachronic dataset of 131K Swiss Federal Supreme Court (FSCS) cases annotated with law citations and ruling citations, posing a challenging text classification task. As unique label we are using decision\\_id of cited rulings and uuid of cited law articles, which can be found in the SwissCourtRulingCorpus. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.",
"### Supported Tasks and Leaderboards\n\n\nSwiss Doc2Doc IR can be used as information retrieval task using documents in Swiss Legislation (URL and Swiss Leading desicions (URL",
"### Languages\n\n\nSwitzerland has four official languages with three languages (German 86K, French 30k and Italian 10k) being represented. The decisions are written by the judges and clerks in the language of the proceedings.\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\nThe dataset was split date-stratisfied\n\n\n* Train: 2002-2015\n* Validation: 2016-2017\n* Test: 2018-2022\n\n\nLanguage: German, Subset: de, Number of Documents (Training/Validation/Test): 86'832 (59'170 / 19'002 / 8'660)\nLanguage: French, Subset: fr, Number of Documents (Training/Validation/Test): 46'203 (30'513 / 10'816 / 4'874)\nLanguage: Italian, Subset: it, Number of Documents (Training/Validation/Test): 8'306 (5'673 / 1'855 / 778)\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThe dataset was created by Stern et al. (2023).",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe original data are available at the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.",
"#### Who are the source language producers?\n\n\nThe original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.",
"### Annotations",
"#### Annotation process\n\n\nThe decisions have been annotated with the citation ids using html tags and parsers.\nFor more details on laws (rcds/swiss\\_legislation) and rulings (rcds/swiss\\_rulings).",
"#### Who are the annotators?\n\n\nStern annotated the citations.\nMetadata is published by the Swiss Federal Supreme Court (URL).",
"### Personal and Sensitive Information\n\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (URL\n© Swiss Federal Supreme Court, 2002-2022\n\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\nSource: URL\n\n\nPlease cite our ArXiv-Preprint",
"### Contributions\n\n\nThanks to @Stern5497 for adding this dataset."
]
| [
"TAGS\n#task_categories-text-classification #task_ids-entity-linking-classification #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-German #language-French #language-Italian #license-cc-by-sa-4.0 #arxiv-2306.09237 #doi-10.57967/hf/0773 #region-us \n",
"### Dataset Summary\n\n\nSwiss Doc2doc Information Retrieval is a multilingual, diachronic dataset of 131K Swiss Federal Supreme Court (FSCS) cases annotated with law citations and ruling citations, posing a challenging text classification task. As unique label we are using decision\\_id of cited rulings and uuid of cited law articles, which can be found in the SwissCourtRulingCorpus. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.",
"### Supported Tasks and Leaderboards\n\n\nSwiss Doc2Doc IR can be used as information retrieval task using documents in Swiss Legislation (URL and Swiss Leading desicions (URL",
"### Languages\n\n\nSwitzerland has four official languages with three languages (German 86K, French 30k and Italian 10k) being represented. The decisions are written by the judges and clerks in the language of the proceedings.\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields",
"### Data Splits\n\n\nThe dataset was split date-stratisfied\n\n\n* Train: 2002-2015\n* Validation: 2016-2017\n* Test: 2018-2022\n\n\nLanguage: German, Subset: de, Number of Documents (Training/Validation/Test): 86'832 (59'170 / 19'002 / 8'660)\nLanguage: French, Subset: fr, Number of Documents (Training/Validation/Test): 46'203 (30'513 / 10'816 / 4'874)\nLanguage: Italian, Subset: it, Number of Documents (Training/Validation/Test): 8'306 (5'673 / 1'855 / 778)\n\n\nDataset Creation\n----------------",
"### Curation Rationale\n\n\nThe dataset was created by Stern et al. (2023).",
"### Source Data",
"#### Initial Data Collection and Normalization\n\n\nThe original data are available at the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.",
"#### Who are the source language producers?\n\n\nThe original data are published from the Swiss Federal Supreme Court (URL) in unprocessed formats (HTML). The documents were downloaded from the Entscheidsuche portal (URL) in HTML.",
"### Annotations",
"#### Annotation process\n\n\nThe decisions have been annotated with the citation ids using html tags and parsers.\nFor more details on laws (rcds/swiss\\_legislation) and rulings (rcds/swiss\\_rulings).",
"#### Who are the annotators?\n\n\nStern annotated the citations.\nMetadata is published by the Swiss Federal Supreme Court (URL).",
"### Personal and Sensitive Information\n\n\nThe dataset contains publicly available court decisions from the Swiss Federal Supreme Court. Personal or sensitive information has been anonymized by the court before publication according to the following guidelines: URL\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nWe release the data under CC-BY-4.0 which complies with the court licensing (URL\n© Swiss Federal Supreme Court, 2002-2022\n\n\nThe copyright for the editorial content of this website and the consolidated texts, which is owned by the Swiss Federal Supreme Court, is licensed under the Creative Commons Attribution 4.0 International licence. This means that you can re-use the content provided you acknowledge the source and indicate any changes you have made.\nSource: URL\n\n\nPlease cite our ArXiv-Preprint",
"### Contributions\n\n\nThanks to @Stern5497 for adding this dataset."
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"passage: TAGS\n#task_categories-text-classification #task_ids-entity-linking-classification #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-multilingual #size_categories-100K<n<1M #source_datasets-original #language-German #language-French #language-Italian #license-cc-by-sa-4.0 #arxiv-2306.09237 #doi-10.57967/hf/0773 #region-us \n### Dataset Summary\n\n\nSwiss Doc2doc Information Retrieval is a multilingual, diachronic dataset of 131K Swiss Federal Supreme Court (FSCS) cases annotated with law citations and ruling citations, posing a challenging text classification task. As unique label we are using decision\\_id of cited rulings and uuid of cited law articles, which can be found in the SwissCourtRulingCorpus. We also provide additional metadata, i.e., the publication year, the legal area and the canton of origin per case, to promote robustness and fairness studies on the critical area of legal NLP.### Supported Tasks and Leaderboards\n\n\nSwiss Doc2Doc IR can be used as information retrieval task using documents in Swiss Legislation (URL and Swiss Leading desicions (URL### Languages\n\n\nSwitzerland has four official languages with three languages (German 86K, French 30k and Italian 10k) being represented. The decisions are written by the judges and clerks in the language of the proceedings.\n\n\nDataset Structure\n-----------------### Data Instances### Data Fields"
]
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|
169a56f6c6f3e729077495162fd232fd1fe34064 | # Dataset Card for "sq-babi_nli_basic-coreference"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | niv-al/sq-babi_nli_basic-coreference | [
"language:sq",
"region:us"
]
| 2023-02-13T16:03:24+00:00 | {"language": ["sq"], "dataset_info": {"features": [{"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "labels", "dtype": {"class_label": {"names": {"0": "not-entailed", "1": "entailed"}}}}], "splits": [{"name": "train", "num_bytes": 209225, "num_examples": 1000}, {"name": "validation", "num_bytes": 29532, "num_examples": 144}, {"name": "test", "num_bytes": 30008, "num_examples": 144}], "download_size": 48437, "dataset_size": 268765}} | 2023-02-18T19:59:59+00:00 | []
| [
"sq"
]
| TAGS
#language-Albanian #region-us
| # Dataset Card for "sq-babi_nli_basic-coreference"
More Information needed | [
"# Dataset Card for \"sq-babi_nli_basic-coreference\"\n\nMore Information needed"
]
| [
"TAGS\n#language-Albanian #region-us \n",
"# Dataset Card for \"sq-babi_nli_basic-coreference\"\n\nMore Information needed"
]
| [
11,
24
]
| [
"passage: TAGS\n#language-Albanian #region-us \n# Dataset Card for \"sq-babi_nli_basic-coreference\"\n\nMore Information needed"
]
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|
cb77b7f808e325c98765cd71ac56a5e3b39e62e1 | # Dataset Card for "SynthDog-RU_EN-prepared"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Nyaaneet/SynthDog-RU_EN-prepared | [
"region:us"
]
| 2023-02-13T16:13:49+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 678666738.82, "num_examples": 2430}, {"name": "train", "num_bytes": 2677611433.18, "num_examples": 9570}], "download_size": 3352860165, "dataset_size": 3356278172.0}} | 2023-02-13T16:18:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "SynthDog-RU_EN-prepared"
More Information needed | [
"# Dataset Card for \"SynthDog-RU_EN-prepared\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"SynthDog-RU_EN-prepared\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"SynthDog-RU_EN-prepared\"\n\nMore Information needed"
]
| [
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|
3823deb623b66d25ba52bd1c8d7ea9fd5dfe411c | # Dataset Card for "wikipedia.reorder.VSO"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.VSO | [
"region:us"
]
| 2023-02-13T16:31:17+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4084815753, "num_examples": 2312333}], "download_size": 0, "dataset_size": 4084815753}} | 2023-02-13T17:25:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikipedia.reorder.VSO"
More Information needed | [
"# Dataset Card for \"wikipedia.reorder.VSO\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikipedia.reorder.VSO\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia.reorder.VSO\"\n\nMore Information needed"
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|
6daa334ecbe500e6102d7867928b501f8e3dfab2 |
The full dataset card is visible in the JSON file named "original_cacapo_for_e2e_models-02_13_2023_19_30_07", which has been made with GEMs second datacard creation GUI. | GEM/CACAPO_E2E | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:nl",
"language:en",
"license:cc-by-4.0",
"Reverse Engineered",
"Dutch",
"English",
"RDF to sentence",
"For End To End",
"region:us"
]
| 2023-02-13T16:53:11+00:00 | {"language": ["nl", "en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "Cacapo_E2E", "tags": ["Reverse Engineered", "Dutch", "English", "RDF to sentence", "For End To End"]} | 2023-02-26T13:54:49+00:00 | []
| [
"nl",
"en"
]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-Dutch #language-English #license-cc-by-4.0 #Reverse Engineered #Dutch #English #RDF to sentence #For End To End #region-us
|
The full dataset card is visible in the JSON file named "original_cacapo_for_e2e_models-02_13_2023_19_30_07", which has been made with GEMs second datacard creation GUI. | []
| [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Dutch #language-English #license-cc-by-4.0 #Reverse Engineered #Dutch #English #RDF to sentence #For End To End #region-us \n"
]
| [
70
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Dutch #language-English #license-cc-by-4.0 #Reverse Engineered #Dutch #English #RDF to sentence #For End To End #region-us \n"
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|
aced69a1c172a4d808be355944e47f76296e314d | # Token Impersonation Dataset
This dataset contains 375 erc-like token impersonation contracts used for phishing scams and 85,716 legitimate Etherscan verified contracts.
The dataset includes the following data attributes:
* contract_address: smart contract address on Ethereum
* contract_creation_tx: smart contract deployment tx
* malicious: boolean flag whether a contract is a token impersonation contract or not
* creation_bytecode: smart contract bytecode that includes both contract initialization and execution code
* contract_creator_etherscan_label: contract creator's Etherscan label
* decompiled_opcodes: bytecode decompiled into EVM opcodes
* contract_tag: contract's Etherscan wallet tag
* contract_creator_tag: contract creator's Etherscan wallet tag
| forta/token-impersonation-dataset | [
"license:mit",
"region:us"
]
| 2023-02-13T17:10:56+00:00 | {"license": "mit"} | 2023-02-14T17:48:17+00:00 | []
| []
| TAGS
#license-mit #region-us
| # Token Impersonation Dataset
This dataset contains 375 erc-like token impersonation contracts used for phishing scams and 85,716 legitimate Etherscan verified contracts.
The dataset includes the following data attributes:
* contract_address: smart contract address on Ethereum
* contract_creation_tx: smart contract deployment tx
* malicious: boolean flag whether a contract is a token impersonation contract or not
* creation_bytecode: smart contract bytecode that includes both contract initialization and execution code
* contract_creator_etherscan_label: contract creator's Etherscan label
* decompiled_opcodes: bytecode decompiled into EVM opcodes
* contract_tag: contract's Etherscan wallet tag
* contract_creator_tag: contract creator's Etherscan wallet tag
| [
"# Token Impersonation Dataset\n\nThis dataset contains 375 erc-like token impersonation contracts used for phishing scams and 85,716 legitimate Etherscan verified contracts.\n\nThe dataset includes the following data attributes:\n\n* contract_address: smart contract address on Ethereum\n* contract_creation_tx: smart contract deployment tx\n* malicious: boolean flag whether a contract is a token impersonation contract or not\n* creation_bytecode: smart contract bytecode that includes both contract initialization and execution code\n* contract_creator_etherscan_label: contract creator's Etherscan label\n* decompiled_opcodes: bytecode decompiled into EVM opcodes\n* contract_tag: contract's Etherscan wallet tag\n* contract_creator_tag: contract creator's Etherscan wallet tag"
]
| [
"TAGS\n#license-mit #region-us \n",
"# Token Impersonation Dataset\n\nThis dataset contains 375 erc-like token impersonation contracts used for phishing scams and 85,716 legitimate Etherscan verified contracts.\n\nThe dataset includes the following data attributes:\n\n* contract_address: smart contract address on Ethereum\n* contract_creation_tx: smart contract deployment tx\n* malicious: boolean flag whether a contract is a token impersonation contract or not\n* creation_bytecode: smart contract bytecode that includes both contract initialization and execution code\n* contract_creator_etherscan_label: contract creator's Etherscan label\n* decompiled_opcodes: bytecode decompiled into EVM opcodes\n* contract_tag: contract's Etherscan wallet tag\n* contract_creator_tag: contract creator's Etherscan wallet tag"
]
| [
11,
204
]
| [
"passage: TAGS\n#license-mit #region-us \n# Token Impersonation Dataset\n\nThis dataset contains 375 erc-like token impersonation contracts used for phishing scams and 85,716 legitimate Etherscan verified contracts.\n\nThe dataset includes the following data attributes:\n\n* contract_address: smart contract address on Ethereum\n* contract_creation_tx: smart contract deployment tx\n* malicious: boolean flag whether a contract is a token impersonation contract or not\n* creation_bytecode: smart contract bytecode that includes both contract initialization and execution code\n* contract_creator_etherscan_label: contract creator's Etherscan label\n* decompiled_opcodes: bytecode decompiled into EVM opcodes\n* contract_tag: contract's Etherscan wallet tag\n* contract_creator_tag: contract creator's Etherscan wallet tag"
]
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|
5fd275a7af72831b7479439ed3745a1d6275c599 | # Dataset Card for "wikipedia.reorder.OVS"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.OVS | [
"region:us"
]
| 2023-02-13T17:27:01+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4084815753, "num_examples": 2312333}], "download_size": 2006658115, "dataset_size": 4084815753}} | 2023-02-13T17:29:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikipedia.reorder.OVS"
More Information needed | [
"# Dataset Card for \"wikipedia.reorder.OVS\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikipedia.reorder.OVS\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikipedia.reorder.OVS\"\n\nMore Information needed"
]
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|
5bc64bb14c9e88f4eb8efe7483a131ef5969bd7f | # Urdu_DW-BBC-512
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper: https://doi.org/10.48550/arXiv.2310.02790**
- **Point of Contact: [email protected]**
### Dataset Summary
- Urdu Summarization Dataset containining 76,637 records of Article + Summary pairs scrapped from BBC Urdu and DW Urdu News Websites.
- Preprocessed Version: upto 512 tokens (~words); removed URLs, Pic Captions etc
### Supported Tasks and Leaderboards
Summarization: Extractive and Abstractive
- urT5 adapted from mT5 having monolingual vocabulary only; 40k tokens of Urdu.
- Fine-tuned version @ https://huggingface.co/mbshr/urt5-base-finetuned, ref to https://doi.org/10.48550/arXiv.2310.02790 for details.
- ROUGE-1 F Score: 40.03 combined, 46.35 BBC Urdu datapoints only and 36.91 DW Urdu datapoints only)
- BERTScore: 75.1 combined, 77.0 BBC Urdu datapoints only and 74.16 DW Urdu datapoints only
### Languages
Urdu.
### Data Fields
- url: URL of the article from where it was scrapped (BBC Urdu URLs in english topic text with number & DW Urdu with Urdu topic text)
dtype: {string}
- Summary: Short Summary of article written by author of article like highlights.
dtype: {string}
- Text: Complete Text of article which are intelligently trucated to 512 tokens.
dtype: {string}
### Citation Information
https://doi.org/10.48550/arXiv.2310.02790
| mbshr/XSUMUrdu-DW_BBC | [
"task_categories:summarization",
"size_categories:10K<n<100K",
"language:ur",
"license:cc-by-4.0",
"Urdu",
"Summarization",
"doi:10.57967/hf/1218",
"region:us"
]
| 2023-02-13T18:13:37+00:00 | {"language": ["ur"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["summarization"], "pretty_name": "Urdu Summarization (BBC and DW Urdu News)", "tags": ["Urdu", "Summarization"]} | 2023-10-05T16:48:41+00:00 | []
| [
"ur"
]
| TAGS
#task_categories-summarization #size_categories-10K<n<100K #language-Urdu #license-cc-by-4.0 #Urdu #Summarization #doi-10.57967/hf/1218 #region-us
| # Urdu_DW-BBC-512
## Dataset Description
- Homepage:
- Repository:
- Paper: URL
- Point of Contact: URL@URL
### Dataset Summary
- Urdu Summarization Dataset containining 76,637 records of Article + Summary pairs scrapped from BBC Urdu and DW Urdu News Websites.
- Preprocessed Version: upto 512 tokens (~words); removed URLs, Pic Captions etc
### Supported Tasks and Leaderboards
Summarization: Extractive and Abstractive
- urT5 adapted from mT5 having monolingual vocabulary only; 40k tokens of Urdu.
- Fine-tuned version @ URL ref to URL for details.
- ROUGE-1 F Score: 40.03 combined, 46.35 BBC Urdu datapoints only and 36.91 DW Urdu datapoints only)
- BERTScore: 75.1 combined, 77.0 BBC Urdu datapoints only and 74.16 DW Urdu datapoints only
### Languages
Urdu.
### Data Fields
- url: URL of the article from where it was scrapped (BBC Urdu URLs in english topic text with number & DW Urdu with Urdu topic text)
dtype: {string}
- Summary: Short Summary of article written by author of article like highlights.
dtype: {string}
- Text: Complete Text of article which are intelligently trucated to 512 tokens.
dtype: {string}
URL
| [
"# Urdu_DW-BBC-512",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: URL \n- Point of Contact: URL@URL",
"### Dataset Summary\n\n- Urdu Summarization Dataset containining 76,637 records of Article + Summary pairs scrapped from BBC Urdu and DW Urdu News Websites.\n- Preprocessed Version: upto 512 tokens (~words); removed URLs, Pic Captions etc",
"### Supported Tasks and Leaderboards\nSummarization: Extractive and Abstractive\n- urT5 adapted from mT5 having monolingual vocabulary only; 40k tokens of Urdu.\n - Fine-tuned version @ URL ref to URL for details.\n- ROUGE-1 F Score: 40.03 combined, 46.35 BBC Urdu datapoints only and 36.91 DW Urdu datapoints only)\n- BERTScore: 75.1 combined, 77.0 BBC Urdu datapoints only and 74.16 DW Urdu datapoints only",
"### Languages\n\nUrdu.",
"### Data Fields\n\n - url: URL of the article from where it was scrapped (BBC Urdu URLs in english topic text with number & DW Urdu with Urdu topic text)\n dtype: {string}\n - Summary: Short Summary of article written by author of article like highlights.\n dtype: {string}\n - Text: Complete Text of article which are intelligently trucated to 512 tokens.\n dtype: {string}\n\n\n\nURL"
]
| [
"TAGS\n#task_categories-summarization #size_categories-10K<n<100K #language-Urdu #license-cc-by-4.0 #Urdu #Summarization #doi-10.57967/hf/1218 #region-us \n",
"# Urdu_DW-BBC-512",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: URL \n- Point of Contact: URL@URL",
"### Dataset Summary\n\n- Urdu Summarization Dataset containining 76,637 records of Article + Summary pairs scrapped from BBC Urdu and DW Urdu News Websites.\n- Preprocessed Version: upto 512 tokens (~words); removed URLs, Pic Captions etc",
"### Supported Tasks and Leaderboards\nSummarization: Extractive and Abstractive\n- urT5 adapted from mT5 having monolingual vocabulary only; 40k tokens of Urdu.\n - Fine-tuned version @ URL ref to URL for details.\n- ROUGE-1 F Score: 40.03 combined, 46.35 BBC Urdu datapoints only and 36.91 DW Urdu datapoints only)\n- BERTScore: 75.1 combined, 77.0 BBC Urdu datapoints only and 74.16 DW Urdu datapoints only",
"### Languages\n\nUrdu.",
"### Data Fields\n\n - url: URL of the article from where it was scrapped (BBC Urdu URLs in english topic text with number & DW Urdu with Urdu topic text)\n dtype: {string}\n - Summary: Short Summary of article written by author of article like highlights.\n dtype: {string}\n - Text: Complete Text of article which are intelligently trucated to 512 tokens.\n dtype: {string}\n\n\n\nURL"
]
| [
62,
9,
24,
62,
120,
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93
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| [
"passage: TAGS\n#task_categories-summarization #size_categories-10K<n<100K #language-Urdu #license-cc-by-4.0 #Urdu #Summarization #doi-10.57967/hf/1218 #region-us \n# Urdu_DW-BBC-512## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: URL \n- Point of Contact: URL@URL### Dataset Summary\n\n- Urdu Summarization Dataset containining 76,637 records of Article + Summary pairs scrapped from BBC Urdu and DW Urdu News Websites.\n- Preprocessed Version: upto 512 tokens (~words); removed URLs, Pic Captions etc### Supported Tasks and Leaderboards\nSummarization: Extractive and Abstractive\n- urT5 adapted from mT5 having monolingual vocabulary only; 40k tokens of Urdu.\n - Fine-tuned version @ URL ref to URL for details.\n- ROUGE-1 F Score: 40.03 combined, 46.35 BBC Urdu datapoints only and 36.91 DW Urdu datapoints only)\n- BERTScore: 75.1 combined, 77.0 BBC Urdu datapoints only and 74.16 DW Urdu datapoints only### Languages\n\nUrdu.### Data Fields\n\n - url: URL of the article from where it was scrapped (BBC Urdu URLs in english topic text with number & DW Urdu with Urdu topic text)\n dtype: {string}\n - Summary: Short Summary of article written by author of article like highlights.\n dtype: {string}\n - Text: Complete Text of article which are intelligently trucated to 512 tokens.\n dtype: {string}\n\n\n\nURL"
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|
ff7612239fb22117d1262654636423c4d1957f43 | # Dataset Card for "wikipedia.reorder.sov.fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.sov.fr | [
"region:us"
]
| 2023-02-13T18:34:17+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 886603410, "num_examples": 490371}], "download_size": 402994481, "dataset_size": 886603410}} | 2023-02-13T18:34:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
6839dba8e4ef8c6e803d241260a3f5f0fc733b28 | # Dataset Card for "wikipedia.reorder.vso.fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.vso.fr | [
"region:us"
]
| 2023-02-13T18:35:09+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 886603410, "num_examples": 490371}], "download_size": 404136391, "dataset_size": 886603410}} | 2023-02-13T18:35:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
ad53c52340aa23d871f1c33b1299c81cc4b225f2 | # Dataset Card for "wikipedia.reorder.ovs.fr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lshowway/wikipedia.reorder.ovs.fr | [
"region:us"
]
| 2023-02-13T18:36:01+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 886603410, "num_examples": 490371}], "download_size": 403430121, "dataset_size": 886603410}} | 2023-02-13T18:36:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "URL"
More Information needed | [
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"URL\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"URL\"\n\nMore Information needed"
]
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|
6b37ead34d1c8a5e2fc3dbc395e18bba25be1627 |
# Dataset Card for EusCrawl
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://ixa.ehu.eus/euscrawl/
- **Repository:**
- **Paper:** https://arxiv.org/abs/2203.08111
- **Leaderboard:**
- **Point of Contact:** [email protected]
### Dataset Summary
EusCrawl (http://www.ixa.eus/euscrawl/) is a high-quality corpus for
Basque comprising 12.5 million documents and 423 million tokens,
totalling 2.1 GiB of uncompressed text. EusCrawl was built using
ad-hoc scrapers to extract text from 33 Basque websites with
high-quality content, resulting in cleaner text compared to general
purpose approaches.
### Supported Tasks and Leaderboards
EusCrawl is intended for pretraining models for language modeling or masked language modeling.
### Languages
Basque (eu)
## Dataset Structure
### Data Instances
```json
{
"id": 6,
"title": "Herriko enpresa handien eta txikien arteko topaketak egingo dituzte",
"text": "09:30ean hasiko da bilera eta aurkezpena egingo dute Tubacex, JEZ, Envases, Guardian eta Vidrala enpresek. Eskualdeko lantegi motorrekin beste enpresa txikiak eta ertainak egongo dira. Erakunde publikoaren helburua da euren artean ezagutzea eta elkarlana sustatzea.",
"source": "aiaraldea",
"license": "cc-by-sa 3.0",
"url": "https://aiaraldea.eus/laudio/1494603159768-herriko-enpresa-handien-eta-txikien-arteko-topaketak-egingo-dituzte",
}
```
### Data Fields
- "id": example id
- "title": article title
- "text": article text
- "source": article source
- "license": article license
- "url": article url
### Data Splits
The dataset only has one training split because it is intended for pretraining language models.
## 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
We do not claim ownership of any document in the corpus. All documents
we collected were published under a Creative Commons license in their
original website, and the specific variant can be found in the
"license" field of each document. Should you consider
that our data contains material that is owned by you and you would not
like to be reproduced here, please contact Aitor Soroa at
[email protected].
### Citation Information
If you use our corpus or models for academic research, please cite the paper in question:
```bibtex
@misc{artetxe2022euscrawl,
title={Does corpus quality really matter for low-resource languages?},
author={Mikel Artetxe, Itziar Aldabe, Rodrigo Agerri,
Olatz Perez-de-Viñaspre, Aitor Soroa},
year={2022},
eprint={2203.08111},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@juletx](https://github.com/juletx) for adding this dataset. | HiTZ/euscrawl | [
"task_categories:text-generation",
"task_categories:fill-mask",
"task_ids:language-modeling",
"task_ids:masked-language-modeling",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:eu",
"license:cc",
"high-quality",
"scraping",
"arxiv:2203.08111",
"region:us"
]
| 2023-02-13T20:13:26+00:00 | {"annotations_creators": ["no-annotation"], "language_creators": ["found"], "language": ["eu"], "license": ["cc"], "multilinguality": ["monolingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["text-generation", "fill-mask"], "task_ids": ["language-modeling", "masked-language-modeling"], "pretty_name": "EusCrawl", "tags": ["high-quality", "scraping"], "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2314407002, "num_examples": 1724544}], "download_size": 728281801, "dataset_size": 2314407002}} | 2023-02-14T19:00:22+00:00 | [
"2203.08111"
]
| [
"eu"
]
| TAGS
#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-Basque #license-cc #high-quality #scraping #arxiv-2203.08111 #region-us
|
# Dataset Card for EusCrawl
## Table of Contents
- Table of Contents
- Dataset Description
- Dataset Summary
- Supported Tasks and Leaderboards
- Languages
- Dataset Structure
- Data Instances
- Data Fields
- Data Splits
- Dataset Creation
- Curation Rationale
- Source Data
- Annotations
- 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
- Citation Information
- Contributions
## Dataset Description
- Homepage: URL
- Repository:
- Paper: URL
- Leaderboard:
- Point of Contact: a.soroa@URL
### Dataset Summary
EusCrawl (URL is a high-quality corpus for
Basque comprising 12.5 million documents and 423 million tokens,
totalling 2.1 GiB of uncompressed text. EusCrawl was built using
ad-hoc scrapers to extract text from 33 Basque websites with
high-quality content, resulting in cleaner text compared to general
purpose approaches.
### Supported Tasks and Leaderboards
EusCrawl is intended for pretraining models for language modeling or masked language modeling.
### Languages
Basque (eu)
## Dataset Structure
### Data Instances
### Data Fields
- "id": example id
- "title": article title
- "text": article text
- "source": article source
- "license": article license
- "url": article url
### Data Splits
The dataset only has one training split because it is intended for pretraining language models.
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
We do not claim ownership of any document in the corpus. All documents
we collected were published under a Creative Commons license in their
original website, and the specific variant can be found in the
"license" field of each document. Should you consider
that our data contains material that is owned by you and you would not
like to be reproduced here, please contact Aitor Soroa at
a.soroa@URL.
If you use our corpus or models for academic research, please cite the paper in question:
### Contributions
Thanks to @juletx for adding this dataset. | [
"# Dataset Card for EusCrawl",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: URL\n- Leaderboard:\n- Point of Contact: a.soroa@URL",
"### Dataset Summary\n\nEusCrawl (URL is a high-quality corpus for\nBasque comprising 12.5 million documents and 423 million tokens,\ntotalling 2.1 GiB of uncompressed text. EusCrawl was built using\nad-hoc scrapers to extract text from 33 Basque websites with\nhigh-quality content, resulting in cleaner text compared to general\npurpose approaches.",
"### Supported Tasks and Leaderboards\n\nEusCrawl is intended for pretraining models for language modeling or masked language modeling.",
"### Languages\n\nBasque (eu)",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n- \"id\": example id\n- \"title\": article title\n- \"text\": article text\n- \"source\": article source\n- \"license\": article license\n- \"url\": article url",
"### Data Splits\n\nThe dataset only has one training split because it is intended for pretraining language models.",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\nWe do not claim ownership of any document in the corpus. All documents\nwe collected were published under a Creative Commons license in their\noriginal website, and the specific variant can be found in the\n\"license\" field of each document. Should you consider\nthat our data contains material that is owned by you and you would not\nlike to be reproduced here, please contact Aitor Soroa at\na.soroa@URL.\n\n\n\nIf you use our corpus or models for academic research, please cite the paper in question:",
"### Contributions\n\nThanks to @juletx for adding this dataset."
]
| [
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-Basque #license-cc #high-quality #scraping #arxiv-2203.08111 #region-us \n",
"# Dataset Card for EusCrawl",
"## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions",
"## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: URL\n- Leaderboard:\n- Point of Contact: a.soroa@URL",
"### Dataset Summary\n\nEusCrawl (URL is a high-quality corpus for\nBasque comprising 12.5 million documents and 423 million tokens,\ntotalling 2.1 GiB of uncompressed text. EusCrawl was built using\nad-hoc scrapers to extract text from 33 Basque websites with\nhigh-quality content, resulting in cleaner text compared to general\npurpose approaches.",
"### Supported Tasks and Leaderboards\n\nEusCrawl is intended for pretraining models for language modeling or masked language modeling.",
"### Languages\n\nBasque (eu)",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\n- \"id\": example id\n- \"title\": article title\n- \"text\": article text\n- \"source\": article source\n- \"license\": article license\n- \"url\": article url",
"### Data Splits\n\nThe dataset only has one training split because it is intended for pretraining language models.",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information\n\nWe do not claim ownership of any document in the corpus. All documents\nwe collected were published under a Creative Commons license in their\noriginal website, and the specific variant can be found in the\n\"license\" field of each document. Should you consider\nthat our data contains material that is owned by you and you would not\nlike to be reproduced here, please contact Aitor Soroa at\na.soroa@URL.\n\n\n\nIf you use our corpus or models for academic research, please cite the paper in question:",
"### Contributions\n\nThanks to @juletx for adding this dataset."
]
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"passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #task_ids-language-modeling #task_ids-masked-language-modeling #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-10M<n<100M #source_datasets-original #language-Basque #license-cc #high-quality #scraping #arxiv-2203.08111 #region-us \n# Dataset Card for EusCrawl## Table of Contents\n- Table of Contents\n- Dataset Description\n - Dataset Summary\n - Supported Tasks and Leaderboards\n - Languages\n- Dataset Structure\n - Data Instances\n - Data Fields\n - Data Splits\n- Dataset Creation\n - Curation Rationale\n - Source Data\n - Annotations\n - Personal and Sensitive Information\n- Considerations for Using the Data\n - Social Impact of Dataset\n - Discussion of Biases\n - Other Known Limitations\n- Additional Information\n - Dataset Curators\n - Licensing Information\n - Citation Information\n - Contributions## Dataset Description\n\n- Homepage: URL\n- Repository: \n- Paper: URL\n- Leaderboard:\n- Point of Contact: a.soroa@URL### Dataset Summary\n\nEusCrawl (URL is a high-quality corpus for\nBasque comprising 12.5 million documents and 423 million tokens,\ntotalling 2.1 GiB of uncompressed text. EusCrawl was built using\nad-hoc scrapers to extract text from 33 Basque websites with\nhigh-quality content, resulting in cleaner text compared to general\npurpose approaches.### Supported Tasks and Leaderboards\n\nEusCrawl is intended for pretraining models for language modeling or masked language modeling.### Languages\n\nBasque (eu)## Dataset Structure### Data Instances### Data Fields\n\n- \"id\": example id\n- \"title\": article title\n- \"text\": article text\n- \"source\": article source\n- \"license\": article license\n- \"url\": article url### Data Splits\n\nThe dataset only has one training split because it is intended for pretraining language models."
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|
a0f5cdbba5e38624ab9cfe9c37580f35623830e9 | # Dataset Card for "large_spanish_corpus_ds_tokenized_and_gropuped"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mrm8488/large_spanish_corpus_ds_tokenized_and_gropuped | [
"region:us"
]
| 2023-02-13T20:19:48+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 16824296700, "num_examples": 4103487}, {"name": "test", "num_bytes": 885489300, "num_examples": 215973}], "download_size": 8311975924, "dataset_size": 17709786000}} | 2023-02-13T20:28:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "large_spanish_corpus_ds_tokenized_and_gropuped"
More Information needed | [
"# Dataset Card for \"large_spanish_corpus_ds_tokenized_and_gropuped\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"large_spanish_corpus_ds_tokenized_and_gropuped\"\n\nMore Information needed"
]
| [
6,
30
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"large_spanish_corpus_ds_tokenized_and_gropuped\"\n\nMore Information needed"
]
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|
f3d098572a200c0c0cde473e6fab82bd6433b41e |
The full dataset information can be found in the JSON file named "augmented_cacapo_for_e2e-02_13_2023_22_17_09", which was created with the interactive dataset creator provided by Huggingface. | GEM/Augmented_CACAPO_for_E2E | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:nl",
"language:en",
"license:cc-by-4.0",
"Dutch",
"English",
"Reverse Engineered",
"RDF To Sentence",
"Augmented Training set",
"region:us"
]
| 2023-02-13T20:57:22+00:00 | {"language": ["nl", "en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "CACAPO_with_augmented_train", "tags": ["Dutch", "English", "Reverse Engineered", "RDF To Sentence", "Augmented Training set"]} | 2023-02-26T13:58:32+00:00 | []
| [
"nl",
"en"
]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-Dutch #language-English #license-cc-by-4.0 #Dutch #English #Reverse Engineered #RDF To Sentence #Augmented Training set #region-us
|
The full dataset information can be found in the JSON file named "augmented_cacapo_for_e2e-02_13_2023_22_17_09", which was created with the interactive dataset creator provided by Huggingface. | []
| [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Dutch #language-English #license-cc-by-4.0 #Dutch #English #Reverse Engineered #RDF To Sentence #Augmented Training set #region-us \n"
]
| [
72
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Dutch #language-English #license-cc-by-4.0 #Dutch #English #Reverse Engineered #RDF To Sentence #Augmented Training set #region-us \n"
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|
216a271d3873169ad7975b88db9706f9943fc3f3 | # Dataset Card for "VQAv2_train"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_train | [
"region:us"
]
| 2023-02-13T20:57:35+00:00 | {"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_clip_ViT_L_14_blip_caption", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "caption", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_wo_openai", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_wo_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_bigG_14_2B_with_openai", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_bigG_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 71983155971.0, "num_examples": 443757}], "download_size": 13852702465, "dataset_size": 71983155971.0}} | 2023-04-26T00:37:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_train"
More Information needed | [
"# Dataset Card for \"VQAv2_train\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_train\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_train\"\n\nMore Information needed"
]
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|
5fc2db512ac193372421081e4ef7a59a944c929d |
Dataset information can be found in the JSON file named "elongated_training_cacapo_updated-02_22_2023_23_23_20.json", which was created with the interactive dataset creator provided by Huggingface. | GEM/Elongated_CACAPO_for_E2E | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:nl",
"language:en",
"license:cc-by-4.0",
"E2E",
"Dutch",
"English",
"Reverse Engineered",
"RDF to Sentence",
"region:us"
]
| 2023-02-13T21:40:35+00:00 | {"language": ["nl", "en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "CACAPO_elongated_training", "tags": ["E2E", "Dutch", "English", "Reverse Engineered", "RDF to Sentence"]} | 2023-02-26T13:29:43+00:00 | []
| [
"nl",
"en"
]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-Dutch #language-English #license-cc-by-4.0 #E2E #Dutch #English #Reverse Engineered #RDF to Sentence #region-us
|
Dataset information can be found in the JSON file named "elongated_training_cacapo_updated-02_22_2023_23_23_20.json", which was created with the interactive dataset creator provided by Huggingface. | []
| [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Dutch #language-English #license-cc-by-4.0 #E2E #Dutch #English #Reverse Engineered #RDF to Sentence #region-us \n"
]
| [
70
]
| [
"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-Dutch #language-English #license-cc-by-4.0 #E2E #Dutch #English #Reverse Engineered #RDF to Sentence #region-us \n"
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|
5fbf0291e59ce23c406e8025f2fbc7876e112ba2 |
# AI2THOR-Hab
AI2THOR scene datasets include iTHOR, RoboTHOR, ProcTHOR-10K, ArchitecTHOR. Many of the assets of the interactable objects are shared across these datasets.
* **iTHOR**: includes 120 single room scenes, 30 scenes for each bedroom, bathroom, kitchen, and living room. In our extracted dataset, there are additional 30 foyers scenes.
* **RoboTHOR**: includes 89 apartments in maze style, where the rooms are subdivided by wall panels. Same of the scenes share the same room layout, but with different objects and object placements.
* **ArchitecTHOR**: includes 10 multiple-room sized houses, used for evaluation of the ProcTHOR.
* **ProcTHOR** : include 12000 procedurally generated multiple-room sized houses. The rooms in the house have 4 room types same as the iTHOR, and the objects assets are all form the iTHOR assets.
<table style="table-layout: fixed;">
<tr>
<td style="text-align: center; vertical-align: middle; width: 25%"> <img src="https://i.imgur.com/Y3MWIRO.png" alt="ithor" width="70%" style="display: block; margin-left: auto; margin-right: auto;"></td>
<td style="text-align: center; vertical-align: middle; width: 25%"> <img src="https://i.imgur.com/e8NKzc2.png" alt="robothor" width="100%" style="display: block; margin-left: auto; margin-right: auto;"></td>
<td style="text-align: center; vertical-align: middle; width: 25%"> <img src="https://i.imgur.com/yiYofx3.png" alt="architecthor" width="100%" style="display: block; margin-left: auto; margin-right: auto;"></td>
<td style="text-align: center; vertical-align: middle; width: 25%"> <img src="https://i.imgur.com/X6loIpw.png" alt="procthor" width="80%" style="display: block; margin-left: auto; margin-right: auto;"></td>
</tr>
<tr>
<td style="text-align: center; vertical-align: middle; width: 25%">iTHOR</td>
<td style="text-align: center; vertical-align: middle; width: 25%">RoboTHOR</td>
<td style="text-align: center; vertical-align: middle; width: 25%">ArchitecTHOR</td>
<td style="text-align: center; vertical-align: middle; width: 25%">ProcTHOR</td>
</td>
</tr>
</table>
## Dataset Structure
Following is the dataset structure for the AI2Thor Habitat Scene Dataset:
```bash
ai2thorhab
├── assets
│ ├── objects
│ └── stages
│ ├── ArchitecTHOR
│ ├── iTHOR
│ ├── ProcTHOR
│ └── RoboTHOR
└── configs
├── objects
├── scenes
│ ├── ArchitecTHOR
│ ├── iTHOR
│ ├── ProcTHOR
│ └── RoboTHOR
└── stages
├── ArchitecTHOR
├── iTHOR
├── ProcTHOR
└── RoboTHOR
```
Data documentation:
1. [ai2thor.scene_dataset_config.json ](https://aihabitat.org/docs/habitat-sim/attributesJSON.html#scenedatasetattributes): This SceneDataset config file enumerates and aggregates the various assets and metadata necessary to fully describe a set of stages, objects, and/or scenes. It hold relative filepaths to all linked assets and additional configs.
2. [\<objectname\>.object_config.json](https://aihabitat.org/docs/habitat-sim/attributesJSON.html#objectattributes): Object config files with descriptive information for instancing rigid objects into Habitat.
3. [\<scenename\>.scene_instance.json](https://aihabitat.org/docs/habitat-sim/attributesJSON.html#sceneinstanceattributes): A scene is a single 3D world composed of a static stage and a variable number of objects. This folder includes the config files for each scene that pulls together other assets registered in the SceneDataset to form a cohesive 3D world for simulation.
4. `object_semantic_id_mapping.json`: Semantic Scene Descriptor (SSD) with a mapping from object names to unique IDs.
5. `objects/*.glb`: Movable object assets.
6. `stages/*.glb`: Static stage scene asset.
## Load AI2Thor Habitat Scene Dataset in Habitat-Sim
Load the AI2Thor Habitat Scene Dataset into [Habitat-Sim](https://github.com/facebookresearch/habitat-sim).
Run the following command to load the AI2Thor scene into Habitat-Sim:
```bash
habitat-viewer --dataset /path/to/ai2thorhab/ai2thor.scene_dataset_config.json -- name-of-the-scene
# for example:
# habitat-viewer --dataset /path/to/ai2thorhab/ai2thor.scene_dataset_config.json -- FloorPlan1_physics
```
| hssd/ai2thor-hab | [
"language:en",
"3D scenes",
"Embodied AI",
"region:us"
]
| 2023-02-13T21:44:18+00:00 | {"language": ["en"], "pretty_name": "AI2THOR-Hab", "tags": ["3D scenes", "Embodied AI"]} | 2024-01-31T07:54:37+00:00 | []
| [
"en"
]
| TAGS
#language-English #3D scenes #Embodied AI #region-us
|
# AI2THOR-Hab
AI2THOR scene datasets include iTHOR, RoboTHOR, ProcTHOR-10K, ArchitecTHOR. Many of the assets of the interactable objects are shared across these datasets.
* iTHOR: includes 120 single room scenes, 30 scenes for each bedroom, bathroom, kitchen, and living room. In our extracted dataset, there are additional 30 foyers scenes.
* RoboTHOR: includes 89 apartments in maze style, where the rooms are subdivided by wall panels. Same of the scenes share the same room layout, but with different objects and object placements.
* ArchitecTHOR: includes 10 multiple-room sized houses, used for evaluation of the ProcTHOR.
* ProcTHOR : include 12000 procedurally generated multiple-room sized houses. The rooms in the house have 4 room types same as the iTHOR, and the objects assets are all form the iTHOR assets.
<table style="table-layout: fixed;">
<tr>
<td style="text-align: center; vertical-align: middle; width: 25%"> <img src="https://i.URL alt="ithor" width="70%" style="display: block; margin-left: auto; margin-right: auto;"></td>
<td style="text-align: center; vertical-align: middle; width: 25%"> <img src="https://i.URL alt="robothor" width="100%" style="display: block; margin-left: auto; margin-right: auto;"></td>
<td style="text-align: center; vertical-align: middle; width: 25%"> <img src="https://i.URL alt="architecthor" width="100%" style="display: block; margin-left: auto; margin-right: auto;"></td>
<td style="text-align: center; vertical-align: middle; width: 25%"> <img src="https://i.URL alt="procthor" width="80%" style="display: block; margin-left: auto; margin-right: auto;"></td>
</tr>
<tr>
<td style="text-align: center; vertical-align: middle; width: 25%">iTHOR</td>
<td style="text-align: center; vertical-align: middle; width: 25%">RoboTHOR</td>
<td style="text-align: center; vertical-align: middle; width: 25%">ArchitecTHOR</td>
<td style="text-align: center; vertical-align: middle; width: 25%">ProcTHOR</td>
</td>
</tr>
</table>
## Dataset Structure
Following is the dataset structure for the AI2Thor Habitat Scene Dataset:
Data documentation:
1. ai2thor.scene_dataset_config.json : This SceneDataset config file enumerates and aggregates the various assets and metadata necessary to fully describe a set of stages, objects, and/or scenes. It hold relative filepaths to all linked assets and additional configs.
2. \<objectname\>.object_config.json: Object config files with descriptive information for instancing rigid objects into Habitat.
3. \<scenename\>.scene_instance.json: A scene is a single 3D world composed of a static stage and a variable number of objects. This folder includes the config files for each scene that pulls together other assets registered in the SceneDataset to form a cohesive 3D world for simulation.
4. 'object_semantic_id_mapping.json': Semantic Scene Descriptor (SSD) with a mapping from object names to unique IDs.
5. 'objects/*.glb': Movable object assets.
6. 'stages/*.glb': Static stage scene asset.
## Load AI2Thor Habitat Scene Dataset in Habitat-Sim
Load the AI2Thor Habitat Scene Dataset into Habitat-Sim.
Run the following command to load the AI2Thor scene into Habitat-Sim:
| [
"# AI2THOR-Hab\n\nAI2THOR scene datasets include iTHOR, RoboTHOR, ProcTHOR-10K, ArchitecTHOR. Many of the assets of the interactable objects are shared across these datasets.\n\n* iTHOR: includes 120 single room scenes, 30 scenes for each bedroom, bathroom, kitchen, and living room. In our extracted dataset, there are additional 30 foyers scenes.\n* RoboTHOR: includes 89 apartments in maze style, where the rooms are subdivided by wall panels. Same of the scenes share the same room layout, but with different objects and object placements.\n* ArchitecTHOR: includes 10 multiple-room sized houses, used for evaluation of the ProcTHOR.\n* ProcTHOR : include 12000 procedurally generated multiple-room sized houses. The rooms in the house have 4 room types same as the iTHOR, and the objects assets are all form the iTHOR assets.\n\n<table style=\"table-layout: fixed;\">\n <tr>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\"> <img src=\"https://i.URL alt=\"ithor\" width=\"70%\" style=\"display: block; margin-left: auto; margin-right: auto;\"></td>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\"> <img src=\"https://i.URL alt=\"robothor\" width=\"100%\" style=\"display: block; margin-left: auto; margin-right: auto;\"></td>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\"> <img src=\"https://i.URL alt=\"architecthor\" width=\"100%\" style=\"display: block; margin-left: auto; margin-right: auto;\"></td>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\"> <img src=\"https://i.URL alt=\"procthor\" width=\"80%\" style=\"display: block; margin-left: auto; margin-right: auto;\"></td>\n </tr> \n <tr>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\">iTHOR</td>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\">RoboTHOR</td>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\">ArchitecTHOR</td>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\">ProcTHOR</td>\n </td>\n </tr>\n</table>",
"## Dataset Structure\nFollowing is the dataset structure for the AI2Thor Habitat Scene Dataset:\n\n\nData documentation:\n1. ai2thor.scene_dataset_config.json : This SceneDataset config file enumerates and aggregates the various assets and metadata necessary to fully describe a set of stages, objects, and/or scenes. It hold relative filepaths to all linked assets and additional configs.\n2. \\<objectname\\>.object_config.json: Object config files with descriptive information for instancing rigid objects into Habitat.\n3. \\<scenename\\>.scene_instance.json: A scene is a single 3D world composed of a static stage and a variable number of objects. This folder includes the config files for each scene that pulls together other assets registered in the SceneDataset to form a cohesive 3D world for simulation.\n4. 'object_semantic_id_mapping.json': Semantic Scene Descriptor (SSD) with a mapping from object names to unique IDs.\n5. 'objects/*.glb': Movable object assets.\n6. 'stages/*.glb': Static stage scene asset.",
"## Load AI2Thor Habitat Scene Dataset in Habitat-Sim\n\nLoad the AI2Thor Habitat Scene Dataset into Habitat-Sim.\n\nRun the following command to load the AI2Thor scene into Habitat-Sim:"
]
| [
"TAGS\n#language-English #3D scenes #Embodied AI #region-us \n",
"# AI2THOR-Hab\n\nAI2THOR scene datasets include iTHOR, RoboTHOR, ProcTHOR-10K, ArchitecTHOR. Many of the assets of the interactable objects are shared across these datasets.\n\n* iTHOR: includes 120 single room scenes, 30 scenes for each bedroom, bathroom, kitchen, and living room. In our extracted dataset, there are additional 30 foyers scenes.\n* RoboTHOR: includes 89 apartments in maze style, where the rooms are subdivided by wall panels. Same of the scenes share the same room layout, but with different objects and object placements.\n* ArchitecTHOR: includes 10 multiple-room sized houses, used for evaluation of the ProcTHOR.\n* ProcTHOR : include 12000 procedurally generated multiple-room sized houses. The rooms in the house have 4 room types same as the iTHOR, and the objects assets are all form the iTHOR assets.\n\n<table style=\"table-layout: fixed;\">\n <tr>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\"> <img src=\"https://i.URL alt=\"ithor\" width=\"70%\" style=\"display: block; margin-left: auto; margin-right: auto;\"></td>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\"> <img src=\"https://i.URL alt=\"robothor\" width=\"100%\" style=\"display: block; margin-left: auto; margin-right: auto;\"></td>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\"> <img src=\"https://i.URL alt=\"architecthor\" width=\"100%\" style=\"display: block; margin-left: auto; margin-right: auto;\"></td>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\"> <img src=\"https://i.URL alt=\"procthor\" width=\"80%\" style=\"display: block; margin-left: auto; margin-right: auto;\"></td>\n </tr> \n <tr>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\">iTHOR</td>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\">RoboTHOR</td>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\">ArchitecTHOR</td>\n <td style=\"text-align: center; vertical-align: middle; width: 25%\">ProcTHOR</td>\n </td>\n </tr>\n</table>",
"## Dataset Structure\nFollowing is the dataset structure for the AI2Thor Habitat Scene Dataset:\n\n\nData documentation:\n1. ai2thor.scene_dataset_config.json : This SceneDataset config file enumerates and aggregates the various assets and metadata necessary to fully describe a set of stages, objects, and/or scenes. It hold relative filepaths to all linked assets and additional configs.\n2. \\<objectname\\>.object_config.json: Object config files with descriptive information for instancing rigid objects into Habitat.\n3. \\<scenename\\>.scene_instance.json: A scene is a single 3D world composed of a static stage and a variable number of objects. This folder includes the config files for each scene that pulls together other assets registered in the SceneDataset to form a cohesive 3D world for simulation.\n4. 'object_semantic_id_mapping.json': Semantic Scene Descriptor (SSD) with a mapping from object names to unique IDs.\n5. 'objects/*.glb': Movable object assets.\n6. 'stages/*.glb': Static stage scene asset.",
"## Load AI2Thor Habitat Scene Dataset in Habitat-Sim\n\nLoad the AI2Thor Habitat Scene Dataset into Habitat-Sim.\n\nRun the following command to load the AI2Thor scene into Habitat-Sim:"
]
| [
20,
648,
284,
53
]
| [
"passage: TAGS\n#language-English #3D scenes #Embodied AI #region-us \n"
]
| [
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|
d400ee8cd114eaa09b1dbf3e44c2f248b2b1b5ec | # DreamBank - Dreams
The dataset is a collection of ~20 k textual reports of dreams, originally scraped from the [DreamBank](https://www.dreambank.net/) databased by
[`mattbierner`](https://github.com/mattbierner/DreamScrape). The DreamBank reports are divided into `series`,
which are collections of individuals or research projects/groups that have gathered the dreams.
## Content
The dataset revolves around three main features:
- `dreams`: the content of each dream report.
- `series`: the series to which a report belongs
- `description`: a brief description of the `series`
- `gender`: the gender of the individual(s) in the `series`
- `year`: the time window of the recordings
## Series distribution
The following is a summary of (alphabetically ordered) DreamBank's series together with their total amount of dream reports.
- alta: 422
- angie: 48
- arlie: 212
- b: 3114
- b-baseline: 250
- b2: 1138
- bay_area_girls_456: 234
- bay_area_girls_789: 154
- bea1: 223
- bea2: 63
- blind-f: 238
- blind-m: 143
- bosnak: 53
- chris: 100
- chuck: 75
- dahlia: 24
- david: 166
- dorothea: 899
- ed: 143
- edna: 19
- elizabeth: 1707
- emma: 1221
- emmas_husband: 72
- esther: 110
- hall_female: 681
- jasmine1: 39
- jasmine2: 269
- jasmine3: 259
- jasmine4: 94
- jeff: 87
- joan: 42
- kenneth: 2021
- lawrence: 206
- mack: 38
- madeline1-hs: 98
- madeline2-dorms: 186
- madeline3-offcampus: 348
- madeline4-postgrad: 294
- mark: 23
- melissa: 89
- melora: 211
- melvin: 128
- merri: 315
- miami-home: 171
- miami-lab: 274
- midwest_teens-f: 111
- midwest_teens-m: 83
- nancy: 44
- natural_scientist: 234
- norman: 1235
- norms-f: 490
- norms-m: 491
- pegasus: 1093
- peru-f: 381
- peru-m: 384
- phil1: 106
- phil2: 220
- phil3: 180
- physiologist: 86
- ringo: 16
- samantha: 63
- seventh_graders: 69
- toby: 33
- tom: 27
- ucsc_women: 81
- vickie: 35
- vietnam_vet: 98
- wedding: 65
- west_coast_teens: 89
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | DReAMy-lib/DreamBank-dreams-en | [
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"region:us"
]
| 2023-02-13T22:20:25+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "dataset_info": {"features": [{"name": "series", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "dreams", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "year", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21526822, "num_examples": 22415}], "download_size": 11984242, "dataset_size": 21526822}} | 2023-02-13T22:51:35+00:00 | []
| [
"en"
]
| TAGS
#size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us
| # DreamBank - Dreams
The dataset is a collection of ~20 k textual reports of dreams, originally scraped from the DreamBank databased by
'mattbierner'. The DreamBank reports are divided into 'series',
which are collections of individuals or research projects/groups that have gathered the dreams.
## Content
The dataset revolves around three main features:
- 'dreams': the content of each dream report.
- 'series': the series to which a report belongs
- 'description': a brief description of the 'series'
- 'gender': the gender of the individual(s) in the 'series'
- 'year': the time window of the recordings
## Series distribution
The following is a summary of (alphabetically ordered) DreamBank's series together with their total amount of dream reports.
- alta: 422
- angie: 48
- arlie: 212
- b: 3114
- b-baseline: 250
- b2: 1138
- bay_area_girls_456: 234
- bay_area_girls_789: 154
- bea1: 223
- bea2: 63
- blind-f: 238
- blind-m: 143
- bosnak: 53
- chris: 100
- chuck: 75
- dahlia: 24
- david: 166
- dorothea: 899
- ed: 143
- edna: 19
- elizabeth: 1707
- emma: 1221
- emmas_husband: 72
- esther: 110
- hall_female: 681
- jasmine1: 39
- jasmine2: 269
- jasmine3: 259
- jasmine4: 94
- jeff: 87
- joan: 42
- kenneth: 2021
- lawrence: 206
- mack: 38
- madeline1-hs: 98
- madeline2-dorms: 186
- madeline3-offcampus: 348
- madeline4-postgrad: 294
- mark: 23
- melissa: 89
- melora: 211
- melvin: 128
- merri: 315
- miami-home: 171
- miami-lab: 274
- midwest_teens-f: 111
- midwest_teens-m: 83
- nancy: 44
- natural_scientist: 234
- norman: 1235
- norms-f: 490
- norms-m: 491
- pegasus: 1093
- peru-f: 381
- peru-m: 384
- phil1: 106
- phil2: 220
- phil3: 180
- physiologist: 86
- ringo: 16
- samantha: 63
- seventh_graders: 69
- toby: 33
- tom: 27
- ucsc_women: 81
- vickie: 35
- vietnam_vet: 98
- wedding: 65
- west_coast_teens: 89
More Information needed | [
"# DreamBank - Dreams\n\nThe dataset is a collection of ~20 k textual reports of dreams, originally scraped from the DreamBank databased by \n'mattbierner'. The DreamBank reports are divided into 'series', \nwhich are collections of individuals or research projects/groups that have gathered the dreams.",
"## Content\nThe dataset revolves around three main features:\n- 'dreams': the content of each dream report.\n- 'series': the series to which a report belongs\n- 'description': a brief description of the 'series'\n- 'gender': the gender of the individual(s) in the 'series'\n- 'year': the time window of the recordings",
"## Series distribution\nThe following is a summary of (alphabetically ordered) DreamBank's series together with their total amount of dream reports. \n\n- alta: 422\n- angie: 48\n- arlie: 212\n- b: 3114\n- b-baseline: 250\n- b2: 1138\n- bay_area_girls_456: 234\n- bay_area_girls_789: 154\n- bea1: 223\n- bea2: 63\n- blind-f: 238\n- blind-m: 143\n- bosnak: 53\n- chris: 100\n- chuck: 75\n- dahlia: 24\n- david: 166\n- dorothea: 899\n- ed: 143\n- edna: 19\n- elizabeth: 1707\n- emma: 1221\n- emmas_husband: 72\n- esther: 110\n- hall_female: 681\n- jasmine1: 39\n- jasmine2: 269\n- jasmine3: 259\n- jasmine4: 94\n- jeff: 87\n- joan: 42\n- kenneth: 2021\n- lawrence: 206\n- mack: 38\n- madeline1-hs: 98\n- madeline2-dorms: 186\n- madeline3-offcampus: 348\n- madeline4-postgrad: 294\n- mark: 23\n- melissa: 89\n- melora: 211\n- melvin: 128\n- merri: 315\n- miami-home: 171\n- miami-lab: 274\n- midwest_teens-f: 111\n- midwest_teens-m: 83\n- nancy: 44\n- natural_scientist: 234\n- norman: 1235\n- norms-f: 490\n- norms-m: 491\n- pegasus: 1093\n- peru-f: 381\n- peru-m: 384\n- phil1: 106\n- phil2: 220\n- phil3: 180\n- physiologist: 86\n- ringo: 16\n- samantha: 63\n- seventh_graders: 69\n- toby: 33\n- tom: 27\n- ucsc_women: 81\n- vickie: 35\n- vietnam_vet: 98\n- wedding: 65\n- west_coast_teens: 89\nMore Information needed"
]
| [
"TAGS\n#size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n",
"# DreamBank - Dreams\n\nThe dataset is a collection of ~20 k textual reports of dreams, originally scraped from the DreamBank databased by \n'mattbierner'. The DreamBank reports are divided into 'series', \nwhich are collections of individuals or research projects/groups that have gathered the dreams.",
"## Content\nThe dataset revolves around three main features:\n- 'dreams': the content of each dream report.\n- 'series': the series to which a report belongs\n- 'description': a brief description of the 'series'\n- 'gender': the gender of the individual(s) in the 'series'\n- 'year': the time window of the recordings",
"## Series distribution\nThe following is a summary of (alphabetically ordered) DreamBank's series together with their total amount of dream reports. \n\n- alta: 422\n- angie: 48\n- arlie: 212\n- b: 3114\n- b-baseline: 250\n- b2: 1138\n- bay_area_girls_456: 234\n- bay_area_girls_789: 154\n- bea1: 223\n- bea2: 63\n- blind-f: 238\n- blind-m: 143\n- bosnak: 53\n- chris: 100\n- chuck: 75\n- dahlia: 24\n- david: 166\n- dorothea: 899\n- ed: 143\n- edna: 19\n- elizabeth: 1707\n- emma: 1221\n- emmas_husband: 72\n- esther: 110\n- hall_female: 681\n- jasmine1: 39\n- jasmine2: 269\n- jasmine3: 259\n- jasmine4: 94\n- jeff: 87\n- joan: 42\n- kenneth: 2021\n- lawrence: 206\n- mack: 38\n- madeline1-hs: 98\n- madeline2-dorms: 186\n- madeline3-offcampus: 348\n- madeline4-postgrad: 294\n- mark: 23\n- melissa: 89\n- melora: 211\n- melvin: 128\n- merri: 315\n- miami-home: 171\n- miami-lab: 274\n- midwest_teens-f: 111\n- midwest_teens-m: 83\n- nancy: 44\n- natural_scientist: 234\n- norman: 1235\n- norms-f: 490\n- norms-m: 491\n- pegasus: 1093\n- peru-f: 381\n- peru-m: 384\n- phil1: 106\n- phil2: 220\n- phil3: 180\n- physiologist: 86\n- ringo: 16\n- samantha: 63\n- seventh_graders: 69\n- toby: 33\n- tom: 27\n- ucsc_women: 81\n- vickie: 35\n- vietnam_vet: 98\n- wedding: 65\n- west_coast_teens: 89\nMore Information needed"
]
| [
30,
75,
87,
475
]
| [
"passage: TAGS\n#size_categories-10K<n<100K #language-English #license-apache-2.0 #region-us \n# DreamBank - Dreams\n\nThe dataset is a collection of ~20 k textual reports of dreams, originally scraped from the DreamBank databased by \n'mattbierner'. The DreamBank reports are divided into 'series', \nwhich are collections of individuals or research projects/groups that have gathered the dreams.## Content\nThe dataset revolves around three main features:\n- 'dreams': the content of each dream report.\n- 'series': the series to which a report belongs\n- 'description': a brief description of the 'series'\n- 'gender': the gender of the individual(s) in the 'series'\n- 'year': the time window of the recordings"
]
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|
677b8bc487983cec6310a26ca1a26e271f184b8a |
# `coco_superpixels_edge_wt_only_coord_10`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| COCO-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
``` | LRGB/coco_superpixels_edge_wt_only_coord_10 | [
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"lrgb",
"region:us"
]
| 2023-02-13T22:25:08+00:00 | {"license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"], "dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "edge_index", "dtype": "int64"}, {"name": "edge_attr", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3625184, "num_examples": 113287}, {"name": "val", "num_bytes": 160032, "num_examples": 5001}, {"name": "test", "num_bytes": 160032, "num_examples": 5001}], "download_size": 3250471, "dataset_size": 3945248}} | 2023-04-14T12:27:36+00:00 | []
| []
| TAGS
#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us
| 'coco\_superpixels\_edge\_wt\_only\_coord\_10'
==============================================
### Dataset Summary
Additional Information
----------------------
### Dataset Curators
* Vijay Prakash Dwivedi (vijaydwivedi75)
| [
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
"TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n",
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
41,
13,
17
]
| [
"passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
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|
efaf67c09d87488a0483747396d3486ce5a51620 | # Dataset Card for Flan V2
## Dataset Description
- **Homepage:** https://ai.googleblog.com/2023/02/the-flan-collection-advancing-open.html
- **Repository:** https://github.com/google-research/FLAN/tree/main/flan/v2
- **Paper:** https://arxiv.org/abs/2301.13688
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This is a processed version of the Flan V2 dataset.
I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.
The authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.
## Setup Instructions
Here are the steps I followed to get everything working:
### Build AESLC and WinoGrande datasets manually
The repos for these datasets were updated recently and checksums need to be recomputed in TFDS
- `tfds build --dataset aeslc --register_checksums`
- `tfds build --dataset winogrande --register_checksums`
### Fix dataset versions
I've opened a PR [here](https://github.com/google-research/FLAN/pull/20) to get these updated in the upstream FLAN repo, until that gets merged in run these locally to fix any dataset version errors.
- `sed -i 's/glue\/cola:1.0.0/glue\/cola:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/common_gen:1.0.0/gem\/common_gen:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/dart:1.0.0/gem\/dart:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/e2e_nlg:1.0.0/gem\/e2e_nlg:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/web_nlg_en:1.0.0/gem\/web_nlg_en:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/gem\/common_gen:1.0.0/gem\/common_gen:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/paws_wiki:1.0.0/paws_wiki:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/mrpc:1.0.0/glue\/mrpc:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/qqp:1.0.0/glue\/qqp:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/sst2:1.0.0/glue\/sst2:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/mnli:1.0.0/glue\/mnli:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/qnli:1.0.0/glue\/qnli:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/wnli:1.0.0/glue\/wnli:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/glue\/stsb:1.0.0/glue\/stsb:2.0.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/hellaswag:0.0.1/hellaswag:1.1.0/g' flan/v2/task_configs_v1.py`
- `sed -i 's/xsum:1.0.0/huggingface:xsum/g' flan/v2/task_configs_v1.py`
### Download and install manual steps
Save these to `~/tensorflow_datasets/downloads/manual`.
- [CzEng (deduped ignoring sections)](https://ufal.mff.cuni.cz/czeng/czeng16pre)
- [Newsroom (extract)](https://lil.nlp.cornell.edu/newsroom/download/index.html)
- [Yandex 1M Corpus](https://translate.yandex.ru/corpus?lang=en)
- [Story Cloze (extract and rename to cloze_test_test__spring2016.csv and cloze_test_val__spring2016.csv)](https://cs.rochester.edu/nlp/)
### Finally, export tasks
```python
import tensorflow as tf
tf.config.set_visible_devices([], 'GPU')
from flan.v2 import constants
from flan.v2 import constants_t0
from flan.v2 import mixtures_utils
from flan.v2 import mixtures
from flan.v2 import tasks
import json
import t5
import seqio
import itertools
from multiprocessing import Pool
seqio.add_global_cache_dirs(constants.CACHE_DIRS)
seqio.set_global_cache_dirs(constants.CACHE_DIRS)
vocab = t5.data.get_default_vocabulary()
def prepare_task(split, shots, opt, task):
dataset = seqio.get_mixture_or_task(f'palmflan_{task}_{shots}_{opt}').get_dataset(
split=split,
num_epochs=1,
sequence_length={'inputs':4096,'targets':4096}
)
print("starting", task, shots, opt, split)
with open(f'./data/{task}_{shots}_{opt}_{split}.jsonl', 'w') as f:
for ex in dataset.as_numpy_iterator():
f.write(
json.dumps({
"inputs": vocab.decode(ex["inputs"]),
"targets": vocab.decode(ex["targets"]),
"task": task,
}))
f.write("\n")
print("done with", task, shots, opt, split)
# prepare_task("train", "zs", "noopt", "dialog") # use this to export a single task
tasks = itertools.product(["train"], ["zs", "fs"], ["opt", "noopt"], ["dialog", "t0", "niv2", "flan", "cot"])
with Pool(5) as p:
p.starmap(prepare_task, [(task[0], task[1], task[2], task[3]) for task in tasks])
```
## Dataset Structure
### Data Instances
Flan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog)
### Data Fields
Instruction data comes in a few formats:
- Few Shot (fs)
- Zero Shot (zs)
- Options Provided in context (i.e. multiple choice pick one) (opt)
- No Options Provided (noopt)
Each combination of the above tasks + formats are saved as a JSONL with following schema `{"input": ..., "target": ..., "task": ...}`
### Data Splits
Everything is saved as a train split
Note: FLAN-fs-opt-train is too big to be uploaded even when gzipped, so its split into 45gb chunks. To combine and recover, run `cat flan_fs_opt_train_*.gz | gunzip -c > flan_fs_opt_train.jsonl`
| SirNeural/flan_v2 | [
"license:apache-2.0",
"flan",
"flan 2022",
"flan v2",
"arxiv:2301.13688",
"region:us"
]
| 2023-02-13T23:02:33+00:00 | {"license": "apache-2.0", "pretty_name": "Flan v2", "tags": ["flan", "flan 2022", "flan v2"]} | 2023-02-24T19:05:00+00:00 | [
"2301.13688"
]
| []
| TAGS
#license-apache-2.0 #flan #flan 2022 #flan v2 #arxiv-2301.13688 #region-us
| # Dataset Card for Flan V2
## Dataset Description
- Homepage: URL
- Repository: URL
- Paper: URL
- Leaderboard:
- Point of Contact:
### Dataset Summary
This is a processed version of the Flan V2 dataset.
I'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.
The authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.
## Setup Instructions
Here are the steps I followed to get everything working:
### Build AESLC and WinoGrande datasets manually
The repos for these datasets were updated recently and checksums need to be recomputed in TFDS
- 'tfds build --dataset aeslc --register_checksums'
- 'tfds build --dataset winogrande --register_checksums'
### Fix dataset versions
I've opened a PR here to get these updated in the upstream FLAN repo, until that gets merged in run these locally to fix any dataset version errors.
- 'sed -i 's/glue\/cola:1.0.0/glue\/cola:2.0.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/gem\/common_gen:1.0.0/gem\/common_gen:1.1.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/gem\/dart:1.0.0/gem\/dart:1.1.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/gem\/e2e_nlg:1.0.0/gem\/e2e_nlg:1.1.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/gem\/web_nlg_en:1.0.0/gem\/web_nlg_en:1.1.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/gem\/common_gen:1.0.0/gem\/common_gen:1.1.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/paws_wiki:1.0.0/paws_wiki:1.1.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/glue\/mrpc:1.0.0/glue\/mrpc:2.0.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/glue\/qqp:1.0.0/glue\/qqp:2.0.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/glue\/sst2:1.0.0/glue\/sst2:2.0.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/glue\/mnli:1.0.0/glue\/mnli:2.0.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/glue\/qnli:1.0.0/glue\/qnli:2.0.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/glue\/wnli:1.0.0/glue\/wnli:2.0.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/glue\/stsb:1.0.0/glue\/stsb:2.0.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/hellaswag:0.0.1/hellaswag:1.1.0/g' flan/v2/task_configs_v1.py'
- 'sed -i 's/xsum:1.0.0/huggingface:xsum/g' flan/v2/task_configs_v1.py'
### Download and install manual steps
Save these to '~/tensorflow_datasets/downloads/manual'.
- CzEng (deduped ignoring sections)
- Newsroom (extract)
- Yandex 1M Corpus
- Story Cloze (extract and rename to cloze_test_test__spring2016.csv and cloze_test_val__spring2016.csv)
### Finally, export tasks
## Dataset Structure
### Data Instances
Flan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog)
### Data Fields
Instruction data comes in a few formats:
- Few Shot (fs)
- Zero Shot (zs)
- Options Provided in context (i.e. multiple choice pick one) (opt)
- No Options Provided (noopt)
Each combination of the above tasks + formats are saved as a JSONL with following schema '{"input": ..., "target": ..., "task": ...}'
### Data Splits
Everything is saved as a train split
Note: FLAN-fs-opt-train is too big to be uploaded even when gzipped, so its split into 45gb chunks. To combine and recover, run 'cat flan_fs_opt_train_*.gz | gunzip -c > flan_fs_opt_train.jsonl'
| [
"# Dataset Card for Flan V2",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis is a processed version of the Flan V2 dataset.\n\nI'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.\n\nThe authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.",
"## Setup Instructions\n\nHere are the steps I followed to get everything working:",
"### Build AESLC and WinoGrande datasets manually\n\nThe repos for these datasets were updated recently and checksums need to be recomputed in TFDS\n\n- 'tfds build --dataset aeslc --register_checksums'\n- 'tfds build --dataset winogrande --register_checksums'",
"### Fix dataset versions\n\nI've opened a PR here to get these updated in the upstream FLAN repo, until that gets merged in run these locally to fix any dataset version errors.\n\n- 'sed -i 's/glue\\/cola:1.0.0/glue\\/cola:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/gem\\/common_gen:1.0.0/gem\\/common_gen:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/gem\\/dart:1.0.0/gem\\/dart:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/gem\\/e2e_nlg:1.0.0/gem\\/e2e_nlg:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/gem\\/web_nlg_en:1.0.0/gem\\/web_nlg_en:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/gem\\/common_gen:1.0.0/gem\\/common_gen:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/paws_wiki:1.0.0/paws_wiki:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/mrpc:1.0.0/glue\\/mrpc:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/qqp:1.0.0/glue\\/qqp:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/sst2:1.0.0/glue\\/sst2:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/mnli:1.0.0/glue\\/mnli:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/qnli:1.0.0/glue\\/qnli:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/wnli:1.0.0/glue\\/wnli:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/stsb:1.0.0/glue\\/stsb:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/hellaswag:0.0.1/hellaswag:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/xsum:1.0.0/huggingface:xsum/g' flan/v2/task_configs_v1.py'",
"### Download and install manual steps\n\nSave these to '~/tensorflow_datasets/downloads/manual'. \n\n- CzEng (deduped ignoring sections)\n- Newsroom (extract)\n- Yandex 1M Corpus\n- Story Cloze (extract and rename to cloze_test_test__spring2016.csv and cloze_test_val__spring2016.csv)",
"### Finally, export tasks",
"## Dataset Structure",
"### Data Instances\n\nFlan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog)",
"### Data Fields\n\nInstruction data comes in a few formats:\n- Few Shot (fs)\n- Zero Shot (zs)\n- Options Provided in context (i.e. multiple choice pick one) (opt)\n- No Options Provided (noopt)\n\nEach combination of the above tasks + formats are saved as a JSONL with following schema '{\"input\": ..., \"target\": ..., \"task\": ...}'",
"### Data Splits\n\nEverything is saved as a train split\nNote: FLAN-fs-opt-train is too big to be uploaded even when gzipped, so its split into 45gb chunks. To combine and recover, run 'cat flan_fs_opt_train_*.gz | gunzip -c > flan_fs_opt_train.jsonl'"
]
| [
"TAGS\n#license-apache-2.0 #flan #flan 2022 #flan v2 #arxiv-2301.13688 #region-us \n",
"# Dataset Card for Flan V2",
"## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nThis is a processed version of the Flan V2 dataset.\n\nI'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.\n\nThe authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.",
"## Setup Instructions\n\nHere are the steps I followed to get everything working:",
"### Build AESLC and WinoGrande datasets manually\n\nThe repos for these datasets were updated recently and checksums need to be recomputed in TFDS\n\n- 'tfds build --dataset aeslc --register_checksums'\n- 'tfds build --dataset winogrande --register_checksums'",
"### Fix dataset versions\n\nI've opened a PR here to get these updated in the upstream FLAN repo, until that gets merged in run these locally to fix any dataset version errors.\n\n- 'sed -i 's/glue\\/cola:1.0.0/glue\\/cola:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/gem\\/common_gen:1.0.0/gem\\/common_gen:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/gem\\/dart:1.0.0/gem\\/dart:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/gem\\/e2e_nlg:1.0.0/gem\\/e2e_nlg:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/gem\\/web_nlg_en:1.0.0/gem\\/web_nlg_en:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/gem\\/common_gen:1.0.0/gem\\/common_gen:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/paws_wiki:1.0.0/paws_wiki:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/mrpc:1.0.0/glue\\/mrpc:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/qqp:1.0.0/glue\\/qqp:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/sst2:1.0.0/glue\\/sst2:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/mnli:1.0.0/glue\\/mnli:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/qnli:1.0.0/glue\\/qnli:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/wnli:1.0.0/glue\\/wnli:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/glue\\/stsb:1.0.0/glue\\/stsb:2.0.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/hellaswag:0.0.1/hellaswag:1.1.0/g' flan/v2/task_configs_v1.py'\n- 'sed -i 's/xsum:1.0.0/huggingface:xsum/g' flan/v2/task_configs_v1.py'",
"### Download and install manual steps\n\nSave these to '~/tensorflow_datasets/downloads/manual'. \n\n- CzEng (deduped ignoring sections)\n- Newsroom (extract)\n- Yandex 1M Corpus\n- Story Cloze (extract and rename to cloze_test_test__spring2016.csv and cloze_test_val__spring2016.csv)",
"### Finally, export tasks",
"## Dataset Structure",
"### Data Instances\n\nFlan 2021 (flan), P3 (t0), Super-Natural Instructions (niv2), Chain-of-thought (cot), and Dialog (dialog)",
"### Data Fields\n\nInstruction data comes in a few formats:\n- Few Shot (fs)\n- Zero Shot (zs)\n- Options Provided in context (i.e. multiple choice pick one) (opt)\n- No Options Provided (noopt)\n\nEach combination of the above tasks + formats are saved as a JSONL with following schema '{\"input\": ..., \"target\": ..., \"task\": ...}'",
"### Data Splits\n\nEverything is saved as a train split\nNote: FLAN-fs-opt-train is too big to be uploaded even when gzipped, so its split into 45gb chunks. To combine and recover, run 'cat flan_fs_opt_train_*.gz | gunzip -c > flan_fs_opt_train.jsonl'"
]
| [
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| [
"passage: TAGS\n#license-apache-2.0 #flan #flan 2022 #flan v2 #arxiv-2301.13688 #region-us \n# Dataset Card for Flan V2## Dataset Description\n\n- Homepage: URL\n- Repository: URL\n- Paper: URL\n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\nThis is a processed version of the Flan V2 dataset.\n\nI'm not affiliated with the creators, I'm just releasing the files in an easier-to-access format after processing.\n\nThe authors of the Flan Collection recommend experimenting with different mixing ratio's of tasks to get optimal results downstream.## Setup Instructions\n\nHere are the steps I followed to get everything working:### Build AESLC and WinoGrande datasets manually\n\nThe repos for these datasets were updated recently and checksums need to be recomputed in TFDS\n\n- 'tfds build --dataset aeslc --register_checksums'\n- 'tfds build --dataset winogrande --register_checksums'"
]
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|
864c12bdadf998b062fad4c4eae7e1bc0e85549a |
# `coco_superpixels_edge_wt_only_coord_30`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| COCO-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
``` | LRGB/coco_superpixels_edge_wt_only_coord_30 | [
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"lrgb",
"region:us"
]
| 2023-02-13T23:02:49+00:00 | {"license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"], "dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "edge_index", "dtype": "int64"}, {"name": "edge_attr", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3625184, "num_examples": 113287}, {"name": "val", "num_bytes": 160032, "num_examples": 5001}, {"name": "test", "num_bytes": 160032, "num_examples": 5001}], "download_size": 3256545, "dataset_size": 3945248}} | 2023-04-14T15:42:12+00:00 | []
| []
| TAGS
#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us
| 'coco\_superpixels\_edge\_wt\_only\_coord\_30'
==============================================
### Dataset Summary
Additional Information
----------------------
### Dataset Curators
* Vijay Prakash Dwivedi (vijaydwivedi75)
| [
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
"TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n",
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
41,
13,
17
]
| [
"passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
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|
d11c6084dd2bb5575f9ce224cbcc435a687e67bf | More info: https://github.com/songlab-cal/gpn | songlab/genomes-brassicales-balanced-v1 | [
"license:mit",
"dna",
"biology",
"genomics",
"region:us"
]
| 2023-02-14T02:48:37+00:00 | {"license": "mit", "tags": ["dna", "biology", "genomics"]} | 2024-01-27T18:18:26+00:00 | []
| []
| TAGS
#license-mit #dna #biology #genomics #region-us
| More info: URL | []
| [
"TAGS\n#license-mit #dna #biology #genomics #region-us \n"
]
| [
19
]
| [
"passage: TAGS\n#license-mit #dna #biology #genomics #region-us \n"
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|
a5e5f362b9a3cf50b5f08bf49f2c39cf05ad050b | # Dataset Card for "poetry-detailed-analysis"
This dataset contains scraped per-stanza analyses. Poems in this dataset also appear in [isaacrehg/poetry-summary](https://huggingface.co/datasets/isaacrehg/poetry-summary).
Each row contains the following data:
- _id: ID of the poem (for reference in [isaacrehg/poetry-summary](https://huggingface.co/datasets/isaacrehg/poetry-summary))
- title: The title of the poem
- author: The poem's author
- url: URL scraped from analysis content where the full poem can be found (may be missing or incorrect)
- stanza_index: index for the section of the poem that this record pertains to
- stanza_header: natural language description of the pertinant stanza (ie. "Stanza One" or "Lines 10-16")
- content: poem content for this stanza (may be missing or partially ommited, ie. "Curling its coral feet, (…) Men long dead.")
- analysis: analysis of this stanza | isaacrehg/poetry-detailed-analysis | [
"region:us"
]
| 2023-02-14T04:14:12+00:00 | {"dataset_info": {"features": [{"name": "_id", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "stanza_index", "dtype": "int64"}, {"name": "stanza_header", "dtype": "string"}, {"name": "content", "dtype": "string"}, {"name": "analysis", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18347594, "num_examples": 14507}], "download_size": 9751592, "dataset_size": 18347594}} | 2023-02-22T05:17:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "poetry-detailed-analysis"
This dataset contains scraped per-stanza analyses. Poems in this dataset also appear in isaacrehg/poetry-summary.
Each row contains the following data:
- _id: ID of the poem (for reference in isaacrehg/poetry-summary)
- title: The title of the poem
- author: The poem's author
- url: URL scraped from analysis content where the full poem can be found (may be missing or incorrect)
- stanza_index: index for the section of the poem that this record pertains to
- stanza_header: natural language description of the pertinant stanza (ie. "Stanza One" or "Lines 10-16")
- content: poem content for this stanza (may be missing or partially ommited, ie. "Curling its coral feet, (…) Men long dead.")
- analysis: analysis of this stanza | [
"# Dataset Card for \"poetry-detailed-analysis\"\n\nThis dataset contains scraped per-stanza analyses. Poems in this dataset also appear in isaacrehg/poetry-summary.\n\nEach row contains the following data:\n\n- _id: ID of the poem (for reference in isaacrehg/poetry-summary)\n- title: The title of the poem\n- author: The poem's author\n- url: URL scraped from analysis content where the full poem can be found (may be missing or incorrect)\n- stanza_index: index for the section of the poem that this record pertains to\n- stanza_header: natural language description of the pertinant stanza (ie. \"Stanza One\" or \"Lines 10-16\")\n- content: poem content for this stanza (may be missing or partially ommited, ie. \"Curling its coral feet, (…) Men long dead.\")\n- analysis: analysis of this stanza"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"poetry-detailed-analysis\"\n\nThis dataset contains scraped per-stanza analyses. Poems in this dataset also appear in isaacrehg/poetry-summary.\n\nEach row contains the following data:\n\n- _id: ID of the poem (for reference in isaacrehg/poetry-summary)\n- title: The title of the poem\n- author: The poem's author\n- url: URL scraped from analysis content where the full poem can be found (may be missing or incorrect)\n- stanza_index: index for the section of the poem that this record pertains to\n- stanza_header: natural language description of the pertinant stanza (ie. \"Stanza One\" or \"Lines 10-16\")\n- content: poem content for this stanza (may be missing or partially ommited, ie. \"Curling its coral feet, (…) Men long dead.\")\n- analysis: analysis of this stanza"
]
| [
6,
219
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"poetry-detailed-analysis\"\n\nThis dataset contains scraped per-stanza analyses. Poems in this dataset also appear in isaacrehg/poetry-summary.\n\nEach row contains the following data:\n\n- _id: ID of the poem (for reference in isaacrehg/poetry-summary)\n- title: The title of the poem\n- author: The poem's author\n- url: URL scraped from analysis content where the full poem can be found (may be missing or incorrect)\n- stanza_index: index for the section of the poem that this record pertains to\n- stanza_header: natural language description of the pertinant stanza (ie. \"Stanza One\" or \"Lines 10-16\")\n- content: poem content for this stanza (may be missing or partially ommited, ie. \"Curling its coral feet, (…) Men long dead.\")\n- analysis: analysis of this stanza"
]
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|
385dde338599c65c773edbe19740abe3ba266750 | # Dataset Card for "poetry-summary"
This dataset contains scraped poem summarizations. Poems in this dataset also appear in [isaacrehg/poetry-detailed-analysis](https://huggingface.co/datasets/isaacrehg/poetry-detailed-analysis).
Each row contains the following data:
- _id: ID of this poem (for reference in [isaacrehg/poetry-detailed-analysis](https://huggingface.co/datasets/isaacrehg/poetry-detailed-analysis))
- title: The title of the poem
- author: The poem's author
- summary: The crawled summarization for this poem
| isaacrehg/poetry-summary | [
"region:us"
]
| 2023-02-14T04:14:31+00:00 | {"dataset_info": {"features": [{"name": "_id", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1881481, "num_examples": 3420}], "download_size": 1080975, "dataset_size": 1881481}} | 2023-02-22T05:07:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "poetry-summary"
This dataset contains scraped poem summarizations. Poems in this dataset also appear in isaacrehg/poetry-detailed-analysis.
Each row contains the following data:
- _id: ID of this poem (for reference in isaacrehg/poetry-detailed-analysis)
- title: The title of the poem
- author: The poem's author
- summary: The crawled summarization for this poem
| [
"# Dataset Card for \"poetry-summary\"\n\nThis dataset contains scraped poem summarizations. Poems in this dataset also appear in isaacrehg/poetry-detailed-analysis.\n\nEach row contains the following data:\n\n- _id: ID of this poem (for reference in isaacrehg/poetry-detailed-analysis)\n- title: The title of the poem\n- author: The poem's author\n- summary: The crawled summarization for this poem"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"poetry-summary\"\n\nThis dataset contains scraped poem summarizations. Poems in this dataset also appear in isaacrehg/poetry-detailed-analysis.\n\nEach row contains the following data:\n\n- _id: ID of this poem (for reference in isaacrehg/poetry-detailed-analysis)\n- title: The title of the poem\n- author: The poem's author\n- summary: The crawled summarization for this poem"
]
| [
6,
116
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"poetry-summary\"\n\nThis dataset contains scraped poem summarizations. Poems in this dataset also appear in isaacrehg/poetry-detailed-analysis.\n\nEach row contains the following data:\n\n- _id: ID of this poem (for reference in isaacrehg/poetry-detailed-analysis)\n- title: The title of the poem\n- author: The poem's author\n- summary: The crawled summarization for this poem"
]
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|
14270bcfcf2b7854cddce4395c0b38a199edef05 | # Dataset Card for "simplewiki2023-all-minilm-l6-v2-embedding"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lsb/simplewiki2023-all-minilm-l6-v2-embedding | [
"region:us"
]
| 2023-02-14T06:06:33+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "embedding", "sequence": "float32"}, {"name": "id_int", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 624653676, "num_examples": 225332}], "download_size": 636699593, "dataset_size": 624653676}} | 2023-02-14T06:25:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "simplewiki2023-all-minilm-l6-v2-embedding"
More Information needed | [
"# Dataset Card for \"simplewiki2023-all-minilm-l6-v2-embedding\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"simplewiki2023-all-minilm-l6-v2-embedding\"\n\nMore Information needed"
]
| [
6,
27
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"simplewiki2023-all-minilm-l6-v2-embedding\"\n\nMore Information needed"
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|
4238d44b57034e8bf5e46b310ea099d3a1eb451a | # Dataset Card for "openwebtext-all-minilm-l6-v2-embedding"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lsb/openwebtext-all-minilm-l6-v2-embedding | [
"region:us"
]
| 2023-02-14T06:32:39+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "embedding", "sequence": "float32"}], "splits": [{"name": "train", "num_bytes": 52110695948, "num_examples": 8013769}], "download_size": 41532022145, "dataset_size": 52110695948}} | 2023-02-14T07:28:40+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "openwebtext-all-minilm-l6-v2-embedding"
More Information needed | [
"# Dataset Card for \"openwebtext-all-minilm-l6-v2-embedding\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"openwebtext-all-minilm-l6-v2-embedding\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"openwebtext-all-minilm-l6-v2-embedding\"\n\nMore Information needed"
]
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|
ec12424c73d1b924a1fbba1713abdaeceb6934d6 | # Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
KG dataset created by using spaCy PoS and Dependency parser.
### Supported Tasks and Leaderboards
Can be leveraged for token classification for detection of knowledge graph entities and relations.
### Languages
English
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
Important fields for the token classification task are
* tokens - tokenized text
* tags - Tags for each token
{'SRC' - Source, 'REL' - Relation, 'TGT' - Target, 'O' - Others}
### Data Splits
One data file for around 15k records
## 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] | vishnun/NLP-KnowledgeGraph | [
"task_categories:token-classification",
"size_categories:10K<n<100K",
"language:en",
"license:cc0-1.0",
"ML",
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| 2023-02-14T06:45:57+00:00 | {"language": ["en"], "license": "cc0-1.0", "size_categories": ["10K<n<100K"], "task_categories": ["token-classification"], "tags": ["ML", "NLP"]} | 2023-02-15T04:24:58+00:00 | []
| [
"en"
]
| TAGS
#task_categories-token-classification #size_categories-10K<n<100K #language-English #license-cc0-1.0 #ML #NLP #region-us
| # Dataset Card for Dataset Name
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
KG dataset created by using spaCy PoS and Dependency parser.
### Supported Tasks and Leaderboards
Can be leveraged for token classification for detection of knowledge graph entities and relations.
### Languages
English
## Dataset Structure
### Data Instances
### Data Fields
Important fields for the token classification task are
* tokens - tokenized text
* tags - Tags for each token
{'SRC' - Source, 'REL' - Relation, 'TGT' - Target, 'O' - Others}
### Data Splits
One data file for around 15k records
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nKG dataset created by using spaCy PoS and Dependency parser.",
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"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances",
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"### Social Impact of Dataset",
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"### Dataset Curators",
"### Licensing Information",
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"# Dataset Card for Dataset Name",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\nKG dataset created by using spaCy PoS and Dependency parser.",
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"### Languages\n\nEnglish",
"## Dataset Structure",
"### Data Instances",
"### Data Fields\n\nImportant fields for the token classification task are\n\n* tokens - tokenized text\n* tags - Tags for each token\n\n{'SRC' - Source, 'REL' - Relation, 'TGT' - Target, 'O' - Others}",
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|
810b10f9ed25a0c828807f74ede579c772decd0e | # Dataset Card for "adv-ele"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | joheras/adv-ele | [
"region:us"
]
| 2023-02-14T08:08:34+00:00 | {"dataset_info": {"features": [{"name": "ADV", "dtype": "string"}, {"name": "ELE", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 430918.56140350876, "num_examples": 1732}, {"name": "test", "num_bytes": 107978.43859649122, "num_examples": 434}], "download_size": 299002, "dataset_size": 538897.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]} | 2024-02-15T15:51:29+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "adv-ele"
More Information needed | [
"# Dataset Card for \"adv-ele\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"adv-ele\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"adv-ele\"\n\nMore Information needed"
]
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a28c0e4bea268c4d07391140d81c271b1f4e2511 | # Dataset Card for "patched_test_p_40_m1_predictions_v3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | roa7n/patched_test_p_40_m1_predictions_v3 | [
"region:us"
]
| 2023-02-14T08:15:13+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sequence_str", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "m1_preds", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 1471779018, "num_examples": 2637494}], "download_size": 129165768, "dataset_size": 1471779018}} | 2023-02-14T08:15:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "patched_test_p_40_m1_predictions_v3"
More Information needed | [
"# Dataset Card for \"patched_test_p_40_m1_predictions_v3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"patched_test_p_40_m1_predictions_v3\"\n\nMore Information needed"
]
| [
6,
28
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"passage: TAGS\n#region-us \n# Dataset Card for \"patched_test_p_40_m1_predictions_v3\"\n\nMore Information needed"
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|
1ce9d90a764aec1219db960cbae1d76948864d0b |
This dataset is curated by [GIZ Data Service Center](https://www.giz.de/expertise/html/63018.html) in the form of Sqaud dataset with features 'question', 'answers', 'answers_start' and 'context'. The source dataset for this
comes from [Climatewatchdata](https://www.climatewatchdata.org/data-explorer/historical-emissions?historical-emissions-data-sources=climate-watch&historical-emissions-gases=all-ghg&historical-emissions-regions=All%20Selected&historical-emissions-sectors=total-including-lucf%2Ctotal-including-lucf&page=1),
where Climatewatch has analysed Intended nationally determined contribution (INDC), NDC and Revised/Updated NDC of the countries to answer some important questions related to Climate change.
Specifications
- Dataset size: 31382
- Average Context length : 50 words
- Language: English
The list of Sectors covered include: Agriculture', 'Coastal Zone', 'Cross-Cutting Area', 'Education', 'Energy', 'Environment', 'Water', 'Buildings', 'Economy-wide', 'Industries', 'Transport', 'Waste', 'Health', 'LULUCF/Forestry', 'Social Development', 'Disaster Risk Management (DRM)', 'Urban','Tourism'.
Some of the important question categories pertaining to climate change(adapted from climatewatchdata) include
- Sectoral Policies
- Sectoral Unconditional Actions
- Building on existing downstream actions
- Sectoral plans
- Sectoral targets
- Action and priority
- Adapt Now sector
- Emission reduction potential
- Capacity Building Needs for Sectoral Implementation
- Sectoral Conditional Actions
- Technology Transfer Needs for Sectoral Implementation
- Conditional part of mitigation target
- Capacity building needs
- Technology needs
- Unconditional part of mitigation target
- Time frame
- Emission reduction potential
No answer category like 'Squad2' is not part of dataset but can be easily curated from existing examples. | GIZ/policy_qa_v0 | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"climate",
"region:us"
]
| 2023-02-14T08:39:09+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "tags": ["climate"]} | 2023-05-31T07:59:44+00:00 | []
| [
"en"
]
| TAGS
#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-apache-2.0 #climate #region-us
|
This dataset is curated by GIZ Data Service Center in the form of Sqaud dataset with features 'question', 'answers', 'answers_start' and 'context'. The source dataset for this
comes from Climatewatchdata,
where Climatewatch has analysed Intended nationally determined contribution (INDC), NDC and Revised/Updated NDC of the countries to answer some important questions related to Climate change.
Specifications
- Dataset size: 31382
- Average Context length : 50 words
- Language: English
The list of Sectors covered include: Agriculture', 'Coastal Zone', 'Cross-Cutting Area', 'Education', 'Energy', 'Environment', 'Water', 'Buildings', 'Economy-wide', 'Industries', 'Transport', 'Waste', 'Health', 'LULUCF/Forestry', 'Social Development', 'Disaster Risk Management (DRM)', 'Urban','Tourism'.
Some of the important question categories pertaining to climate change(adapted from climatewatchdata) include
- Sectoral Policies
- Sectoral Unconditional Actions
- Building on existing downstream actions
- Sectoral plans
- Sectoral targets
- Action and priority
- Adapt Now sector
- Emission reduction potential
- Capacity Building Needs for Sectoral Implementation
- Sectoral Conditional Actions
- Technology Transfer Needs for Sectoral Implementation
- Conditional part of mitigation target
- Capacity building needs
- Technology needs
- Unconditional part of mitigation target
- Time frame
- Emission reduction potential
No answer category like 'Squad2' is not part of dataset but can be easily curated from existing examples. | []
| [
"TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-apache-2.0 #climate #region-us \n"
]
| [
46
]
| [
"passage: TAGS\n#task_categories-question-answering #size_categories-10K<n<100K #language-English #license-apache-2.0 #climate #region-us \n"
]
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|
612d0cb8a15ec1930a1f49179acd526431c13560 | # Dataset Card for "python_comment_code_ratio_08"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lz88/python_comment_code_ratio_08 | [
"region:us"
]
| 2023-02-14T11:13:42+00:00 | {"dataset_info": {"features": [{"name": "content", "dtype": "string"}, {"name": "avg_line_length", "dtype": "float64"}, {"name": "max_line_length", "dtype": "int64"}, {"name": "alphanum_fraction", "dtype": "float64"}, {"name": "licenses", "sequence": "string"}, {"name": "repository_name", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "size", "dtype": "int64"}, {"name": "lang", "dtype": "string"}, {"name": "nl_text", "dtype": "string"}, {"name": "nl_size", "dtype": "int64"}, {"name": "nl_ratio", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 1271905.6847, "num_examples": 131}], "download_size": 324517, "dataset_size": 1271905.6847}} | 2023-02-14T11:13:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "python_comment_code_ratio_08"
More Information needed | [
"# Dataset Card for \"python_comment_code_ratio_08\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"python_comment_code_ratio_08\"\n\nMore Information needed"
]
| [
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22
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"python_comment_code_ratio_08\"\n\nMore Information needed"
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|
0f63fd5fb0ae6d60a5334c5a796e2a7aeef395c3 | # Dataset Card for "Million-AID"
## Dataset Description
- **Paper** [On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid](https://ieeexplore.ieee.org/iel7/4609443/9314330/09393553.pdf)
- **Split** Train
## Split Information
This HuggingFace dataset repository contains just the Train split.
### Licensing Information
[CC BY-NC-ND 4.0](https://competitions.codalab.org/competitions/35974#learn_the_details-terms-and-conditions)
## Citation Information
[On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid](https://ieeexplore.ieee.org/iel7/4609443/9314330/09393553.pdf)
```
@article{long2021creating,
title = {On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid},
author = {Long, Yang and Xia, Gui-Song and Li, Shengyang and Yang, Wen and Yang, Michael Ying and Zhu, Xiao Xiang and Zhang, Liangpei and Li, Deren},
year = 2021,
journal = {IEEE Journal of selected topics in applied earth observations and remote sensing},
publisher = {IEEE},
volume = 14,
pages = {4205--4230}
}
``` | jonathan-roberts1/Million-AID | [
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
]
| 2023-02-14T12:49:18+00:00 | {"license": "other", "task_categories": ["image-classification", "zero-shot-image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label_1", "dtype": {"class_label": {"names": {"0": "unutilized land", "1": "commercial land", "2": "public service land", "3": "transportation land", "4": "industrial land", "5": "water area", "6": "residential land", "7": "agriculture land"}}}}, {"name": "label_2", "dtype": {"class_label": {"names": {"0": "dam", "1": "religious land", "2": "rock land", "3": "sparse shrub land", "4": "arable land", "5": "factory area", "6": "detached house", "7": "desert", "8": "lake", "9": "power station", "10": "beach", "11": "ice land", "12": "bare land", "13": "island", "14": "woodland", "15": "mobile home park", "16": "railway area", "17": "river", "18": "grassland", "19": "apartment", "20": "special land", "21": "port area", "22": "commercial area", "23": "highway area", "24": "mining area", "25": "sports land", "26": "airport area", "27": "leisure land"}}}}, {"name": "label_3", "dtype": {"class_label": {"names": {"0": "dam", "1": "parking lot", "2": "greenhouse", "3": "pier", "4": "bridge", "5": "mine", "6": "rock land", "7": "baseball field", "8": "apron", "9": "tennis court", "10": "sparse shrub land", "11": "works", "12": "oil field", "13": "meadow", "14": "ground track field", "15": "detached house", "16": "golf course", "17": "forest", "18": "desert", "19": "lake", "20": "beach", "21": "paddy field", "22": "ice land", "23": "bare land", "24": "storage tank", "25": "basketball court", "26": "island", "27": "substation", "28": "mobile home park", "29": "cemetery", "30": "quarry", "31": "solar power plant", "32": "helipad", "33": "roundabout", "34": "runway", "35": "wastewater plant", "36": "river", "37": "apartment", "38": "dry field", "39": "intersection", "40": "swimming pool", "41": "commercial area", "42": "church", "43": "road", "44": "orchard", "45": "terraced field", "46": "stadium", "47": "train station", "48": "railway", "49": "viaduct", "50": "wind turbine"}}}}], "splits": [{"name": "train", "num_bytes": 871962498, "num_examples": 10000}], "download_size": 871644115, "dataset_size": 871962498}} | 2023-03-31T14:46:07+00:00 | []
| []
| TAGS
#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us
| # Dataset Card for "Million-AID"
## Dataset Description
- Paper On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid
- Split Train
## Split Information
This HuggingFace dataset repository contains just the Train split.
### Licensing Information
CC BY-NC-ND 4.0
On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid
| [
"# Dataset Card for \"Million-AID\"",
"## Dataset Description\n\n- Paper On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid\n- Split Train",
"## Split Information\n\nThis HuggingFace dataset repository contains just the Train split.",
"### Licensing Information\n\nCC BY-NC-ND 4.0\n\n\n\nOn creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid"
]
| [
"TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n",
"# Dataset Card for \"Million-AID\"",
"## Dataset Description\n\n- Paper On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid\n- Split Train",
"## Split Information\n\nThis HuggingFace dataset repository contains just the Train split.",
"### Licensing Information\n\nCC BY-NC-ND 4.0\n\n\n\nOn creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid"
]
| [
37,
12,
31,
20,
35
]
| [
"passage: TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n# Dataset Card for \"Million-AID\"## Dataset Description\n\n- Paper On creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid\n- Split Train## Split Information\n\nThis HuggingFace dataset repository contains just the Train split.### Licensing Information\n\nCC BY-NC-ND 4.0\n\n\n\nOn creating benchmark dataset for aerial image interpretation: Reviews, guidances, and million-aid"
]
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|
f5e50880bfc2c650b52430e9f6f2542dcb545a00 |
# `coco_superpixels_edge_wt_only_feat_10`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| COCO-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
``` | LRGB/coco_superpixels_edge_wt_coord_feat_10 | [
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"lrgb",
"region:us"
]
| 2023-02-14T13:23:21+00:00 | {"license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"], "dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "edge_index", "dtype": "int64"}, {"name": "edge_attr", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3625184, "num_examples": 113287}, {"name": "val", "num_bytes": 160032, "num_examples": 5001}, {"name": "test", "num_bytes": 160032, "num_examples": 5001}], "download_size": 3250471, "dataset_size": 3945248}} | 2023-04-14T14:58:21+00:00 | []
| []
| TAGS
#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us
| 'coco\_superpixels\_edge\_wt\_only\_feat\_10'
=============================================
### Dataset Summary
Additional Information
----------------------
### Dataset Curators
* Vijay Prakash Dwivedi (vijaydwivedi75)
| [
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
"TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n",
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
41,
13,
17
]
| [
"passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
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|
b99789ed3c749b70aba4f886a557e7163cd1b15d |
# Dataset card for personSegSmall
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset description](#dataset-description)
- [Dataset categories](#dataset-categories)
## Dataset description
- **Homepage:** https://segments.ai/shahardekel/personSegSmall
This dataset was created using [Segments.ai](https://segments.ai). It can be found [here](https://segments.ai/shahardekel/personSegSmall).
## Dataset categories
| Id | Name | Description |
| --- | ---- | ----------- |
| 1 | person | - |
| shahardekel/personSegSmall | [
"task_categories:image-segmentation",
"region:us"
]
| 2023-02-14T14:39:02+00:00 | {"task_categories": ["image-segmentation"]} | 2023-02-14T14:39:05+00:00 | []
| []
| TAGS
#task_categories-image-segmentation #region-us
| Dataset card for personSegSmall
===============================
Table of Contents
-----------------
* Table of Contents
* Dataset description
* Dataset categories
Dataset description
-------------------
* Homepage: URL
This dataset was created using URL. It can be found here.
Dataset categories
------------------
Id: 1, Name: person, Description: -
| []
| [
"TAGS\n#task_categories-image-segmentation #region-us \n"
]
| [
18
]
| [
"passage: TAGS\n#task_categories-image-segmentation #region-us \n"
]
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|
7393ac3c3b949077c5a289a7b7e8635010d6c8aa |
# `coco_superpixels_edge_wt_only_feat_30`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| COCO-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
``` | LRGB/coco_superpixels_edge_wt_only_feat_30 | [
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"lrgb",
"region:us"
]
| 2023-02-14T14:43:03+00:00 | {"license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"], "dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "edge_index", "dtype": "int64"}, {"name": "edge_attr", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3625184, "num_examples": 113287}, {"name": "val", "num_bytes": 160032, "num_examples": 5001}, {"name": "test", "num_bytes": 160032, "num_examples": 5001}], "download_size": 3256545, "dataset_size": 3945248}} | 2023-04-14T14:33:49+00:00 | []
| []
| TAGS
#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us
| 'coco\_superpixels\_edge\_wt\_only\_feat\_30'
=============================================
### Dataset Summary
Additional Information
----------------------
### Dataset Curators
* Vijay Prakash Dwivedi (vijaydwivedi75)
| [
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
"TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n",
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
41,
13,
17
]
| [
"passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
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|
66c6d5190d373c4df6f52fdcb9c58e5883b7aa48 |
# Dataset card for personSegSmall
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset description](#dataset-description)
- [Dataset categories](#dataset-categories)
## Dataset description
- **Homepage:** https://segments.ai/shahardekel/personSegSmall
This dataset was created using [Segments.ai](https://segments.ai). It can be found [here](https://segments.ai/shahardekel/personSegSmall).
## Dataset categories
| Id | Name | Description |
| --- | ---- | ----------- |
| 1 | person | - |
| shahardekel/personSegSmall1 | [
"task_categories:image-segmentation",
"region:us"
]
| 2023-02-14T14:45:23+00:00 | {"task_categories": ["image-segmentation"]} | 2023-02-15T12:18:29+00:00 | []
| []
| TAGS
#task_categories-image-segmentation #region-us
| Dataset card for personSegSmall
===============================
Table of Contents
-----------------
* Table of Contents
* Dataset description
* Dataset categories
Dataset description
-------------------
* Homepage: URL
This dataset was created using URL. It can be found here.
Dataset categories
------------------
Id: 1, Name: person, Description: -
| []
| [
"TAGS\n#task_categories-image-segmentation #region-us \n"
]
| [
18
]
| [
"passage: TAGS\n#task_categories-image-segmentation #region-us \n"
]
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|
900350daf1710799cafe22c159c5d4a8acd24c9e | # Dataset Card for "instruct-foo"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | lewtun/instruct-foo | [
"region:us"
]
| 2023-02-14T14:48:42+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "source", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "outputs", "list": [{"name": "model", "dtype": "string"}, {"name": "outputs", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 3591, "num_examples": 1}], "download_size": 6053, "dataset_size": 3591}} | 2023-02-14T16:15:00+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "instruct-foo"
More Information needed | [
"# Dataset Card for \"instruct-foo\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"instruct-foo\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"instruct-foo\"\n\nMore Information needed"
]
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|
a034c4991a4875cd67fe1611dff685967092cec2 |
# Dataset Card for SF Nexus Extracted Features: Chapters and Chunks
## Dataset Description
- **Homepage: https://sfnexus.io/**
- **Repository: https://github.com/SF-Nexus/extracted-features-notebooks**
- **Point of Contact: Alex Wermer-Colan**
### Dataset Summary
The SF Nexus Extracted Features Chapters and Chunks dataset contains text and metadata from 403 mid-twentieth century science fiction books, originally digitized from Temple University Libraries' Paskow Science Fiction Collection.
After digitization, the books were cleaned using Abbyy FineReader.
Because this is a collection of copyrighted fiction, the books have been disaggregated.
To improve performance of topic modeling and other nlp tasks, each book has also been split into chapters and then into chunks of approx. 1000 words.
Each row of this dataset contains one "chunk" of text as well as metadata about that text's title, author and publication.
### About the SF Nexus Corpus
The Paskow Science Fiction collection contains primarily materials from post-WWII, especially mass-market works of the New Wave era (often dated to 1964-1980).
The digitized texts have also been ingested into HathiTrust's repository for preservation and data curation; they are now viewable on HathiTrust's [Temple page](https://babel.hathitrust.org/cgi/ls?field1=ocr;q1=%2A;a=srchls;facet=htsource%3A%22Temple%20University%22;pn=4) for non-consumptive research.
For more information on the project to digitize and curate a corpus of "New Wave" science fiction, see Alex Wermer-Colan's post on the Temple University Scholars Studio blog, ["Building a New Wave Science Fiction Corpus."](https://sites.temple.edu/tudsc/2017/12/20/building-new-wave-science-fiction-corpus/).
### Languages
English
## Dataset Structure
This dataset contains disaggregated "chunks" of text from mid-twentieth century science fiction books and associated metadata. For example:
```
{'Unnamed': 7299,
'Title': 'MILLENNIUM,
'Author': 'VARLEY',
'Pub Year': '1983',
'Chapter': 'None',
'Chunk': '105',
'Text': '. . . . . . / 1958 1976 249 A Ambler, And As Baker Ben Berkley Bogart Bova Bova, By Casablanca DEMON Eric Eugene, Eugene, Goes HOTLINE, Herman Humphrey Hupfeld It John John MILLENNIUM MacQuitty, Millennium, Mister Night OF OPHIUCHI One Oregon Oregon...” Organisation, PERSISTENCE Rank Remember Roy Sam THE THE TITAN, The The Time Titanic, VISION, Varley Varley WIZARD, William a about acknowledgement: also an and and and asked author be began bestselling by by by by by by completed continued course, directed do excellent film final had in in in is is is is, name nothing novel novel novel, octopus of of of of of of pet play produced published rain-shrouded s screenplay sinking song soon that the the the the the the the this time title title to to to to travel trilogy was was with with with with written written ’ “ ”'
'Clean Text': 'a ambler and as baker ben berkley bogart bova bova by casablanca demon eric eugene eugene goes hotline herman humphrey hupfeld it john john millennium macquitty millennium mister night of ophiuchi one oregon oregon organisation persistence rank remember roy sam the the titan the the time titanic vision varley varley wizard william a about acknowledgement also an and and and asked author be began bestselling by by by by by by completed continued course directed do excellent film final had in in in is is is is name nothing novel novel novel octopus of of of of of of pet play produced published rain shrouded s screenplay sinking song soon that the the the the the the the this time title title to to to to travel trilogy was was with with with with written written ''
'Chunk Word Count': '948',
}
```
### Data Fields
- **Unnamed: int** A unique id for the text
- **Title: str** The title of the book from which the text has been extracted
- **Author: str** The author of the book from which the text has been extracted
- **Pub Year: str** The date on which the book was published (first printing)
- **Chapter: int** The chapter in the book from which the text has been extracted
- **Chunk: int** Number indicating which "chunk" of text has been extracted (chunks are numbered per book; each book was split by chapter and then into n chunks of approx. 1000 words)
- **Text: str** The chunk of text extracted from the book
- **Clean Text: str** The chunk of text extracted from the book with lowercasing performed and punctuation, numbers and extra spaces removed
- **Chunk Word Count: int** The number of words the chunk of text contains
To Be Added:
- **summary: str** A brief summary of the book, if extracted from library records
- **pub_date: int** The date on which the book was published (first printing)
- **pub_city: int** The city in which the book was published (first printing)
- **lcgft_category: str** Information from the Library of Congress Genre/Form Terms for Library and Archival Materials, if known
### Loading the Dataset
Use the following code to load the dataset in a Python environment (note: does not work with repo set to private)
```
from datasets import load_dataset
# If the dataset is gated/private, make sure you have run huggingface-cli login
dataset = load_dataset("SF-Corpus/EF_Chapters_and_Chunks")
```
Or just clone the dataset repo
```
git lfs install
git clone https://huggingface.co/datasets/SF-Corpus/EF_Chapters_and_Chunks
# if you want to clone without large files – just their pointers
# prepend your git clone with the following env var:
GIT_LFS_SKIP_SMUDGE=1
```
## Dataset Creation
### Curation Rationale
For an overview of our approach to data curation of literary texts, see Alex Wermer-Colan’s and James Kopaczewski’s article, “The New Wave of Digital Collections: Speculating on the Future of Library Curation”(2022)
### Source Data
The Loretta C. Duckworth Scholars Studio has partnered with Temple University Libraries’ Special Collections Research Center (SCRC) and Digital Library Initiatives (DLI) to build a digitized corpus of copyrighted science fiction literature. Besides its voluminous Urban Archives, the SCRC also houses a significant collection of science-fiction literature. The Paskow Science Fiction Collection was originally established in 1972, when Temple acquired 5,000 science fiction paperbacks from a Temple alumnus, the late David C. Paskow. Subsequent donations, including troves of fanzines and the papers of such sci-fi writers as John Varley and Stanley G. Weinbaum, expanded the collection over the last few decades, both in size and in the range of genres. SCRC staff and undergraduate student workers recently performed the usual comparison of gift titles against cataloged books, removing science fiction items that were exact duplicates of existing holdings. A refocusing of the SCRC’s collection development policy for science fiction de-emphasized fantasy and horror titles, so some titles in those genres were removed as well.
## Considerations for Using the Data
This data card only exhibits extracted features for copyrighted fiction; no copyrighted work is being made available for consumption. These digitized files are made accessible for purposes of education and research. Temple University Libraries have given attribution to rights holders when possible. If you hold the rights to materials in our digitized collections that are unattributed, please let us know so that we may maintain accurate information about these materials.
If you are a rights holder and are concerned that you have found material on this website for which you have not granted permission (or is not covered by a copyright exception under US copyright laws), you may request the removal of the material from our site by writing to [email protected].
For more information on non-consumptive research, check out HathiTrust Research Center’s Non-Consumptive Use Research Policy.
## Additional Information
### Dataset Curators
For a full list of conributors to the SF Nexus project, visit [https://sfnexus.io/people/](https://sfnexus.io/people/). | SF-Corpus/EF_Chapters_and_Chunks | [
"language:en",
"region:us"
]
| 2023-02-14T14:50:01+00:00 | {"language": ["en"], "pretty_name": "sf-nexus-ef-chapters-and-chunks"} | 2023-05-24T13:39:05+00:00 | []
| [
"en"
]
| TAGS
#language-English #region-us
|
# Dataset Card for SF Nexus Extracted Features: Chapters and Chunks
## Dataset Description
- Homepage: URL
- Repository: URL
- Point of Contact: Alex Wermer-Colan
### Dataset Summary
The SF Nexus Extracted Features Chapters and Chunks dataset contains text and metadata from 403 mid-twentieth century science fiction books, originally digitized from Temple University Libraries' Paskow Science Fiction Collection.
After digitization, the books were cleaned using Abbyy FineReader.
Because this is a collection of copyrighted fiction, the books have been disaggregated.
To improve performance of topic modeling and other nlp tasks, each book has also been split into chapters and then into chunks of approx. 1000 words.
Each row of this dataset contains one "chunk" of text as well as metadata about that text's title, author and publication.
### About the SF Nexus Corpus
The Paskow Science Fiction collection contains primarily materials from post-WWII, especially mass-market works of the New Wave era (often dated to 1964-1980).
The digitized texts have also been ingested into HathiTrust's repository for preservation and data curation; they are now viewable on HathiTrust's Temple page for non-consumptive research.
For more information on the project to digitize and curate a corpus of "New Wave" science fiction, see Alex Wermer-Colan's post on the Temple University Scholars Studio blog, "Building a New Wave Science Fiction Corpus.".
### Languages
English
## Dataset Structure
This dataset contains disaggregated "chunks" of text from mid-twentieth century science fiction books and associated metadata. For example:
### Data Fields
- Unnamed: int A unique id for the text
- Title: str The title of the book from which the text has been extracted
- Author: str The author of the book from which the text has been extracted
- Pub Year: str The date on which the book was published (first printing)
- Chapter: int The chapter in the book from which the text has been extracted
- Chunk: int Number indicating which "chunk" of text has been extracted (chunks are numbered per book; each book was split by chapter and then into n chunks of approx. 1000 words)
- Text: str The chunk of text extracted from the book
- Clean Text: str The chunk of text extracted from the book with lowercasing performed and punctuation, numbers and extra spaces removed
- Chunk Word Count: int The number of words the chunk of text contains
To Be Added:
- summary: str A brief summary of the book, if extracted from library records
- pub_date: int The date on which the book was published (first printing)
- pub_city: int The city in which the book was published (first printing)
- lcgft_category: str Information from the Library of Congress Genre/Form Terms for Library and Archival Materials, if known
### Loading the Dataset
Use the following code to load the dataset in a Python environment (note: does not work with repo set to private)
Or just clone the dataset repo
## Dataset Creation
### Curation Rationale
For an overview of our approach to data curation of literary texts, see Alex Wermer-Colan’s and James Kopaczewski’s article, “The New Wave of Digital Collections: Speculating on the Future of Library Curation”(2022)
### Source Data
The Loretta C. Duckworth Scholars Studio has partnered with Temple University Libraries’ Special Collections Research Center (SCRC) and Digital Library Initiatives (DLI) to build a digitized corpus of copyrighted science fiction literature. Besides its voluminous Urban Archives, the SCRC also houses a significant collection of science-fiction literature. The Paskow Science Fiction Collection was originally established in 1972, when Temple acquired 5,000 science fiction paperbacks from a Temple alumnus, the late David C. Paskow. Subsequent donations, including troves of fanzines and the papers of such sci-fi writers as John Varley and Stanley G. Weinbaum, expanded the collection over the last few decades, both in size and in the range of genres. SCRC staff and undergraduate student workers recently performed the usual comparison of gift titles against cataloged books, removing science fiction items that were exact duplicates of existing holdings. A refocusing of the SCRC’s collection development policy for science fiction de-emphasized fantasy and horror titles, so some titles in those genres were removed as well.
## Considerations for Using the Data
This data card only exhibits extracted features for copyrighted fiction; no copyrighted work is being made available for consumption. These digitized files are made accessible for purposes of education and research. Temple University Libraries have given attribution to rights holders when possible. If you hold the rights to materials in our digitized collections that are unattributed, please let us know so that we may maintain accurate information about these materials.
If you are a rights holder and are concerned that you have found material on this website for which you have not granted permission (or is not covered by a copyright exception under US copyright laws), you may request the removal of the material from our site by writing to digitalscholarship@URL.
For more information on non-consumptive research, check out HathiTrust Research Center’s Non-Consumptive Use Research Policy.
## Additional Information
### Dataset Curators
For a full list of conributors to the SF Nexus project, visit URL | [
"# Dataset Card for SF Nexus Extracted Features: Chapters and Chunks",
"## Dataset Description\n\n- Homepage: URL \n- Repository: URL \n- Point of Contact: Alex Wermer-Colan",
"### Dataset Summary\n\nThe SF Nexus Extracted Features Chapters and Chunks dataset contains text and metadata from 403 mid-twentieth century science fiction books, originally digitized from Temple University Libraries' Paskow Science Fiction Collection.\nAfter digitization, the books were cleaned using Abbyy FineReader.\nBecause this is a collection of copyrighted fiction, the books have been disaggregated. \nTo improve performance of topic modeling and other nlp tasks, each book has also been split into chapters and then into chunks of approx. 1000 words. \nEach row of this dataset contains one \"chunk\" of text as well as metadata about that text's title, author and publication.",
"### About the SF Nexus Corpus\nThe Paskow Science Fiction collection contains primarily materials from post-WWII, especially mass-market works of the New Wave era (often dated to 1964-1980).\nThe digitized texts have also been ingested into HathiTrust's repository for preservation and data curation; they are now viewable on HathiTrust's Temple page for non-consumptive research.\nFor more information on the project to digitize and curate a corpus of \"New Wave\" science fiction, see Alex Wermer-Colan's post on the Temple University Scholars Studio blog, \"Building a New Wave Science Fiction Corpus.\".",
"### Languages\n\nEnglish",
"## Dataset Structure\n\nThis dataset contains disaggregated \"chunks\" of text from mid-twentieth century science fiction books and associated metadata. For example:",
"### Data Fields\n\n- Unnamed: int A unique id for the text\n- Title: str The title of the book from which the text has been extracted\n- Author: str The author of the book from which the text has been extracted\n- Pub Year: str The date on which the book was published (first printing)\n- Chapter: int The chapter in the book from which the text has been extracted\n- Chunk: int Number indicating which \"chunk\" of text has been extracted (chunks are numbered per book; each book was split by chapter and then into n chunks of approx. 1000 words)\n- Text: str The chunk of text extracted from the book\n- Clean Text: str The chunk of text extracted from the book with lowercasing performed and punctuation, numbers and extra spaces removed\n- Chunk Word Count: int The number of words the chunk of text contains\n\nTo Be Added: \n- summary: str A brief summary of the book, if extracted from library records\n- pub_date: int The date on which the book was published (first printing)\n- pub_city: int The city in which the book was published (first printing)\n- lcgft_category: str Information from the Library of Congress Genre/Form Terms for Library and Archival Materials, if known",
"### Loading the Dataset\n\nUse the following code to load the dataset in a Python environment (note: does not work with repo set to private)\n\nOr just clone the dataset repo",
"## Dataset Creation",
"### Curation Rationale\n\nFor an overview of our approach to data curation of literary texts, see Alex Wermer-Colan’s and James Kopaczewski’s article, “The New Wave of Digital Collections: Speculating on the Future of Library Curation”(2022)",
"### Source Data\n\nThe Loretta C. Duckworth Scholars Studio has partnered with Temple University Libraries’ Special Collections Research Center (SCRC) and Digital Library Initiatives (DLI) to build a digitized corpus of copyrighted science fiction literature. Besides its voluminous Urban Archives, the SCRC also houses a significant collection of science-fiction literature. The Paskow Science Fiction Collection was originally established in 1972, when Temple acquired 5,000 science fiction paperbacks from a Temple alumnus, the late David C. Paskow. Subsequent donations, including troves of fanzines and the papers of such sci-fi writers as John Varley and Stanley G. Weinbaum, expanded the collection over the last few decades, both in size and in the range of genres. SCRC staff and undergraduate student workers recently performed the usual comparison of gift titles against cataloged books, removing science fiction items that were exact duplicates of existing holdings. A refocusing of the SCRC’s collection development policy for science fiction de-emphasized fantasy and horror titles, so some titles in those genres were removed as well.",
"## Considerations for Using the Data\n\nThis data card only exhibits extracted features for copyrighted fiction; no copyrighted work is being made available for consumption. These digitized files are made accessible for purposes of education and research. Temple University Libraries have given attribution to rights holders when possible. If you hold the rights to materials in our digitized collections that are unattributed, please let us know so that we may maintain accurate information about these materials.\n\nIf you are a rights holder and are concerned that you have found material on this website for which you have not granted permission (or is not covered by a copyright exception under US copyright laws), you may request the removal of the material from our site by writing to digitalscholarship@URL.\n\nFor more information on non-consumptive research, check out HathiTrust Research Center’s Non-Consumptive Use Research Policy.",
"## Additional Information",
"### Dataset Curators\nFor a full list of conributors to the SF Nexus project, visit URL"
]
| [
"TAGS\n#language-English #region-us \n",
"# Dataset Card for SF Nexus Extracted Features: Chapters and Chunks",
"## Dataset Description\n\n- Homepage: URL \n- Repository: URL \n- Point of Contact: Alex Wermer-Colan",
"### Dataset Summary\n\nThe SF Nexus Extracted Features Chapters and Chunks dataset contains text and metadata from 403 mid-twentieth century science fiction books, originally digitized from Temple University Libraries' Paskow Science Fiction Collection.\nAfter digitization, the books were cleaned using Abbyy FineReader.\nBecause this is a collection of copyrighted fiction, the books have been disaggregated. \nTo improve performance of topic modeling and other nlp tasks, each book has also been split into chapters and then into chunks of approx. 1000 words. \nEach row of this dataset contains one \"chunk\" of text as well as metadata about that text's title, author and publication.",
"### About the SF Nexus Corpus\nThe Paskow Science Fiction collection contains primarily materials from post-WWII, especially mass-market works of the New Wave era (often dated to 1964-1980).\nThe digitized texts have also been ingested into HathiTrust's repository for preservation and data curation; they are now viewable on HathiTrust's Temple page for non-consumptive research.\nFor more information on the project to digitize and curate a corpus of \"New Wave\" science fiction, see Alex Wermer-Colan's post on the Temple University Scholars Studio blog, \"Building a New Wave Science Fiction Corpus.\".",
"### Languages\n\nEnglish",
"## Dataset Structure\n\nThis dataset contains disaggregated \"chunks\" of text from mid-twentieth century science fiction books and associated metadata. For example:",
"### Data Fields\n\n- Unnamed: int A unique id for the text\n- Title: str The title of the book from which the text has been extracted\n- Author: str The author of the book from which the text has been extracted\n- Pub Year: str The date on which the book was published (first printing)\n- Chapter: int The chapter in the book from which the text has been extracted\n- Chunk: int Number indicating which \"chunk\" of text has been extracted (chunks are numbered per book; each book was split by chapter and then into n chunks of approx. 1000 words)\n- Text: str The chunk of text extracted from the book\n- Clean Text: str The chunk of text extracted from the book with lowercasing performed and punctuation, numbers and extra spaces removed\n- Chunk Word Count: int The number of words the chunk of text contains\n\nTo Be Added: \n- summary: str A brief summary of the book, if extracted from library records\n- pub_date: int The date on which the book was published (first printing)\n- pub_city: int The city in which the book was published (first printing)\n- lcgft_category: str Information from the Library of Congress Genre/Form Terms for Library and Archival Materials, if known",
"### Loading the Dataset\n\nUse the following code to load the dataset in a Python environment (note: does not work with repo set to private)\n\nOr just clone the dataset repo",
"## Dataset Creation",
"### Curation Rationale\n\nFor an overview of our approach to data curation of literary texts, see Alex Wermer-Colan’s and James Kopaczewski’s article, “The New Wave of Digital Collections: Speculating on the Future of Library Curation”(2022)",
"### Source Data\n\nThe Loretta C. Duckworth Scholars Studio has partnered with Temple University Libraries’ Special Collections Research Center (SCRC) and Digital Library Initiatives (DLI) to build a digitized corpus of copyrighted science fiction literature. Besides its voluminous Urban Archives, the SCRC also houses a significant collection of science-fiction literature. The Paskow Science Fiction Collection was originally established in 1972, when Temple acquired 5,000 science fiction paperbacks from a Temple alumnus, the late David C. Paskow. Subsequent donations, including troves of fanzines and the papers of such sci-fi writers as John Varley and Stanley G. Weinbaum, expanded the collection over the last few decades, both in size and in the range of genres. SCRC staff and undergraduate student workers recently performed the usual comparison of gift titles against cataloged books, removing science fiction items that were exact duplicates of existing holdings. A refocusing of the SCRC’s collection development policy for science fiction de-emphasized fantasy and horror titles, so some titles in those genres were removed as well.",
"## Considerations for Using the Data\n\nThis data card only exhibits extracted features for copyrighted fiction; no copyrighted work is being made available for consumption. These digitized files are made accessible for purposes of education and research. Temple University Libraries have given attribution to rights holders when possible. If you hold the rights to materials in our digitized collections that are unattributed, please let us know so that we may maintain accurate information about these materials.\n\nIf you are a rights holder and are concerned that you have found material on this website for which you have not granted permission (or is not covered by a copyright exception under US copyright laws), you may request the removal of the material from our site by writing to digitalscholarship@URL.\n\nFor more information on non-consumptive research, check out HathiTrust Research Center’s Non-Consumptive Use Research Policy.",
"## Additional Information",
"### Dataset Curators\nFor a full list of conributors to the SF Nexus project, visit URL"
]
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"passage: TAGS\n#language-English #region-us \n# Dataset Card for SF Nexus Extracted Features: Chapters and Chunks## Dataset Description\n\n- Homepage: URL \n- Repository: URL \n- Point of Contact: Alex Wermer-Colan### Dataset Summary\n\nThe SF Nexus Extracted Features Chapters and Chunks dataset contains text and metadata from 403 mid-twentieth century science fiction books, originally digitized from Temple University Libraries' Paskow Science Fiction Collection.\nAfter digitization, the books were cleaned using Abbyy FineReader.\nBecause this is a collection of copyrighted fiction, the books have been disaggregated. \nTo improve performance of topic modeling and other nlp tasks, each book has also been split into chapters and then into chunks of approx. 1000 words. \nEach row of this dataset contains one \"chunk\" of text as well as metadata about that text's title, author and publication.### About the SF Nexus Corpus\nThe Paskow Science Fiction collection contains primarily materials from post-WWII, especially mass-market works of the New Wave era (often dated to 1964-1980).\nThe digitized texts have also been ingested into HathiTrust's repository for preservation and data curation; they are now viewable on HathiTrust's Temple page for non-consumptive research.\nFor more information on the project to digitize and curate a corpus of \"New Wave\" science fiction, see Alex Wermer-Colan's post on the Temple University Scholars Studio blog, \"Building a New Wave Science Fiction Corpus.\".### Languages\n\nEnglish## Dataset Structure\n\nThis dataset contains disaggregated \"chunks\" of text from mid-twentieth century science fiction books and associated metadata. For example:",
"passage: ### Data Fields\n\n- Unnamed: int A unique id for the text\n- Title: str The title of the book from which the text has been extracted\n- Author: str The author of the book from which the text has been extracted\n- Pub Year: str The date on which the book was published (first printing)\n- Chapter: int The chapter in the book from which the text has been extracted\n- Chunk: int Number indicating which \"chunk\" of text has been extracted (chunks are numbered per book; each book was split by chapter and then into n chunks of approx. 1000 words)\n- Text: str The chunk of text extracted from the book\n- Clean Text: str The chunk of text extracted from the book with lowercasing performed and punctuation, numbers and extra spaces removed\n- Chunk Word Count: int The number of words the chunk of text contains\n\nTo Be Added: \n- summary: str A brief summary of the book, if extracted from library records\n- pub_date: int The date on which the book was published (first printing)\n- pub_city: int The city in which the book was published (first printing)\n- lcgft_category: str Information from the Library of Congress Genre/Form Terms for Library and Archival Materials, if known### Loading the Dataset\n\nUse the following code to load the dataset in a Python environment (note: does not work with repo set to private)\n\nOr just clone the dataset repo## Dataset Creation### Curation Rationale\n\nFor an overview of our approach to data curation of literary texts, see Alex Wermer-Colan’s and James Kopaczewski’s article, “The New Wave of Digital Collections: Speculating on the Future of Library Curation”(2022)### Source Data\n\nThe Loretta C. Duckworth Scholars Studio has partnered with Temple University Libraries’ Special Collections Research Center (SCRC) and Digital Library Initiatives (DLI) to build a digitized corpus of copyrighted science fiction literature. Besides its voluminous Urban Archives, the SCRC also houses a significant collection of science-fiction literature. The Paskow Science Fiction Collection was originally established in 1972, when Temple acquired 5,000 science fiction paperbacks from a Temple alumnus, the late David C. Paskow. Subsequent donations, including troves of fanzines and the papers of such sci-fi writers as John Varley and Stanley G. Weinbaum, expanded the collection over the last few decades, both in size and in the range of genres. SCRC staff and undergraduate student workers recently performed the usual comparison of gift titles against cataloged books, removing science fiction items that were exact duplicates of existing holdings. A refocusing of the SCRC’s collection development policy for science fiction de-emphasized fantasy and horror titles, so some titles in those genres were removed as well."
]
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# 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] | yogeshdai/quora_dataset | [
"region:us"
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| 2023-02-14T15:39:37+00:00 | {} | 2023-02-14T15:51:51+00:00 | []
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| TAGS
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# 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.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
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### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
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|
1280870a0d229b53f2f61456f675b8d5edf36199 | Upload my data | Matilde/Homo_ita | [
"region:us"
]
| 2023-02-14T15:57:44+00:00 | {} | 2023-06-08T13:59:35+00:00 | []
| []
| TAGS
#region-us
| Upload my data | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
a3a439f64b98e97eab2e4c1de6699a0814856a10 | # Dataset Card for "mscoco_50k_10k_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | JotDe/mscoco_50k_10k_test | [
"region:us"
]
| 2023-02-14T16:03:43+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 889160555.425, "num_examples": 9999}], "download_size": 173395947, "dataset_size": 889160555.425}} | 2023-02-14T16:05:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mscoco_50k_10k_test"
More Information needed | [
"# Dataset Card for \"mscoco_50k_10k_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mscoco_50k_10k_test\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mscoco_50k_10k_test\"\n\nMore Information needed"
]
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|
c4b9d19ca71eced688ba6943a1b3f521ea9aeaa9 | # Dataset Card for "mscoco_50k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | JotDe/mscoco_50k | [
"region:us"
]
| 2023-02-14T16:05:20+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 889160555.425, "num_examples": 9999}], "download_size": 173395947, "dataset_size": 889160555.425}} | 2023-02-14T16:06:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mscoco_50k"
More Information needed | [
"# Dataset Card for \"mscoco_50k\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mscoco_50k\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mscoco_50k\"\n\nMore Information needed"
]
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|
c0565beb3ba83a3d2c199337aed44c35ac5b9310 | # Dataset Card for "skillspan_job_ner"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Maiia/skillspan_job_ner | [
"region:us"
]
| 2023-02-14T16:06:10+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int64"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "B-Skill I-Knowledge", "1": "I-Skill B-Knowledge", "2": "B-Knowledge", "3": "I-Skill I-Knowledge", "4": "I-Skill", "5": "B-Skill", "6": "I-Knowledge", "7": "O", "8": -100}}}}], "splits": [{"name": "train", "num_bytes": 3144024, "num_examples": 8005}, {"name": "test", "num_bytes": 1004456, "num_examples": 3565}], "download_size": 548417, "dataset_size": 4148480}} | 2023-02-14T17:33:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "skillspan_job_ner"
More Information needed | [
"# Dataset Card for \"skillspan_job_ner\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"skillspan_job_ner\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"skillspan_job_ner\"\n\nMore Information needed"
]
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|
a179e91c8c02cb9f9cf869ae2e4cfb4c52d48386 |
# `coco_superpixels_edge_wt_region_boundary_10`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| COCO-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
``` | LRGB/coco_superpixels_edge_wt_region_boundary_10 | [
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"lrgb",
"region:us"
]
| 2023-02-14T16:16:14+00:00 | {"license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"], "dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "edge_index", "dtype": "int64"}, {"name": "edge_attr", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3625184, "num_examples": 113287}, {"name": "val", "num_bytes": 160032, "num_examples": 5001}, {"name": "test", "num_bytes": 160032, "num_examples": 5001}], "download_size": 3252505, "dataset_size": 3945248}} | 2023-04-14T14:33:03+00:00 | []
| []
| TAGS
#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us
| 'coco\_superpixels\_edge\_wt\_region\_boundary\_10'
===================================================
### Dataset Summary
Additional Information
----------------------
### Dataset Curators
* Vijay Prakash Dwivedi (vijaydwivedi75)
| [
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
"TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n",
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
41,
13,
17
]
| [
"passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
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|
369b968f59c01b9310c56139c36df54be1755e01 | # Dataset Card for "skillspan_job_ner_without_capitalization"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Maiia/skillspan_job_ner_without_capitalization | [
"region:us"
]
| 2023-02-14T16:19:17+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int64"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "B-Skill I-Knowledge", "1": "I-Skill B-Knowledge", "2": "B-Knowledge", "3": "I-Skill I-Knowledge", "4": "I-Skill", "5": "B-Skill", "6": "I-Knowledge", "7": "O", "8": -100}}}}], "splits": [{"name": "train", "num_bytes": 7211448, "num_examples": 8005}, {"name": "test", "num_bytes": 2374184, "num_examples": 3565}], "download_size": 0, "dataset_size": 9585632}} | 2023-02-14T16:42:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "skillspan_job_ner_without_capitalization"
More Information needed | [
"# Dataset Card for \"skillspan_job_ner_without_capitalization\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"skillspan_job_ner_without_capitalization\"\n\nMore Information needed"
]
| [
6,
23
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"skillspan_job_ner_without_capitalization\"\n\nMore Information needed"
]
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2bd3445338f577d03b47e17ee0564069c3127634 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_8 | [
"region:us"
]
| 2023-02-14T16:31:58+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 202371, "num_examples": 8}], "download_size": 0, "dataset_size": 202371}} | 2023-02-14T20:17:24+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_8"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_8\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_8\"\n\nMore Information needed"
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_8\"\n\nMore Information needed"
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|
0b1971da6b1598b2f52094c87bd91bff0f311b83 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_10 | [
"region:us"
]
| 2023-02-14T16:37:14+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 252916, "num_examples": 10}, {"name": "fewshot_0_bs_128", "num_bytes": 255042, "num_examples": 10}, {"name": "fewshot_0_bs_16", "num_bytes": 255039, "num_examples": 10}], "download_size": 164794, "dataset_size": 762997}} | 2023-02-17T22:51:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_10"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_10\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_10\"\n\nMore Information needed"
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_10\"\n\nMore Information needed"
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|
8dfb78595b10c16ffd337366a7cb28f61cc915d5 | # Dataset Card for "skillspan_job_ner_without_cap"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Maiia/skillspan_job_ner_without_cap | [
"region:us"
]
| 2023-02-14T16:43:21+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int64"}, {"name": "labels", "sequence": {"class_label": {"names": {"0": "B-Skill I-Knowledge", "1": "I-Skill B-Knowledge", "2": "B-Knowledge", "3": "I-Skill I-Knowledge", "4": "I-Skill", "5": "B-Skill", "6": "I-Knowledge", "7": "O", "8": -100}}}}], "splits": [{"name": "train", "num_bytes": 2952664, "num_examples": 8005}, {"name": "test", "num_bytes": 1001832, "num_examples": 3565}], "download_size": 523562, "dataset_size": 3954496}} | 2023-02-14T17:27:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "skillspan_job_ner_without_cap"
More Information needed | [
"# Dataset Card for \"skillspan_job_ner_without_cap\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"skillspan_job_ner_without_cap\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"skillspan_job_ner_without_cap\"\n\nMore Information needed"
]
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|
fef6947c79868134ae4d2203dd6a593a6427687b | # Dataset Card for "jupyter-code-text-pairs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bigcode/jupyter-code-text-pairs | [
"region:us"
]
| 2023-02-14T16:46:34+00:00 | {"dataset_info": {"features": [{"name": "markdown", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "license", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "repo_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 13985979285, "num_examples": 9305991}], "download_size": 6176464336, "dataset_size": 13985979285}} | 2023-02-21T20:05:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "jupyter-code-text-pairs"
More Information needed | [
"# Dataset Card for \"jupyter-code-text-pairs\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"jupyter-code-text-pairs\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"jupyter-code-text-pairs\"\n\nMore Information needed"
]
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2ebc84f6111371ce411100023db2a507d94248b4 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_8 | [
"region:us"
]
| 2023-02-14T16:51:27+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 202362, "num_examples": 8}], "download_size": 45171, "dataset_size": 202362}} | 2023-02-18T00:58:20+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_8"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_8\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_8\"\n\nMore Information needed"
]
| [
6,
43
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_8\"\n\nMore Information needed"
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|
7c7e2c679a9bfc6ecb90e5593e7619939fe5daca |
# `coco_superpixels_edge_wt_region_boundary_30`
### Dataset Summary
| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| COCO-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter
|---|---:|---:|---:|:---:|---:|---:|---:|---:|
| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
## Additional Information
### Dataset Curators
* Vijay Prakash Dwivedi ([vijaydwivedi75](https://github.com/vijaydwivedi75))
### Citation Information
```
@article{dwivedi2022LRGB,
title={Long Range Graph Benchmark},
author={Dwivedi, Vijay Prakash and Rampášek, Ladislav and Galkin, Mikhail and Parviz, Ali and Wolf, Guy and Luu, Anh Tuan and Beaini, Dominique},
journal={arXiv:2206.08164},
year={2022}
}
``` | LRGB/coco_superpixels_edge_wt_region_boundary_30 | [
"task_categories:graph-ml",
"size_categories:1M<n<10M",
"license:cc-by-4.0",
"lrgb",
"region:us"
]
| 2023-02-14T17:10:42+00:00 | {"license": "cc-by-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["graph-ml"], "tags": ["lrgb"], "dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "edge_index", "dtype": "int64"}, {"name": "edge_attr", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3625184, "num_examples": 113287}, {"name": "val", "num_bytes": 160032, "num_examples": 5001}, {"name": "test", "num_bytes": 160032, "num_examples": 5001}], "download_size": 3257505, "dataset_size": 3945248}} | 2023-04-14T14:33:24+00:00 | []
| []
| TAGS
#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us
| 'coco\_superpixels\_edge\_wt\_region\_boundary\_30'
===================================================
### Dataset Summary
Additional Information
----------------------
### Dataset Curators
* Vijay Prakash Dwivedi (vijaydwivedi75)
| [
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
"TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n",
"### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------",
"### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
| [
41,
13,
17
]
| [
"passage: TAGS\n#task_categories-graph-ml #size_categories-1M<n<10M #license-cc-by-4.0 #lrgb #region-us \n### Dataset Summary\n\n\n\n\nAdditional Information\n----------------------### Dataset Curators\n\n\n* Vijay Prakash Dwivedi (vijaydwivedi75)"
]
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|
85118a51212a42a13e9bf0ad751e7c7b81a4c177 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_8 | [
"region:us"
]
| 2023-02-14T17:25:32+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 202345, "num_examples": 8}], "download_size": 45104, "dataset_size": 202345}} | 2023-02-14T17:25:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_8"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_8\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_8\"\n\nMore Information needed"
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_8\"\n\nMore Information needed"
]
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|
633d90f68759ea6f5fccd9c2ed872aab82cecc58 | # Dataset Card for "mini-algae-rgb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | samitizerxu/mini-algae-rgb | [
"region:us"
]
| 2023-02-14T17:36:22+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 15787445.414, "num_examples": 4039}, {"name": "test", "num_bytes": 6040387.721, "num_examples": 1521}], "download_size": 21439845, "dataset_size": 21827833.135}} | 2023-02-14T17:36:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mini-algae-rgb"
More Information needed | [
"# Dataset Card for \"mini-algae-rgb\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mini-algae-rgb\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mini-algae-rgb\"\n\nMore Information needed"
]
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|
f91dc8ad1331908abb2a6c69d0831ddcee841134 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_8 | [
"region:us"
]
| 2023-02-14T17:39:15+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 202345, "num_examples": 8}], "download_size": 0, "dataset_size": 202345}} | 2023-02-14T20:26:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_8"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_8\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_8\"\n\nMore Information needed"
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_8\"\n\nMore Information needed"
]
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|
eb9680f23234a97f261e2b1cd84d1d965c4d6c7c | # Dataset Card for "NaSC-TG2"
## Dataset Description
- **Paper:** [NaSC-TG2: Natural scene classification with Tiangong-2 remotely sensed imagery](https://ieeexplore.ieee.org/iel7/4609443/9314330/09366968.pdf)
### 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
Public Domain
## Citation Information
[https://ieeexplore.ieee.org/iel7/4609443/9314330/09366968.pdf](https://ieeexplore.ieee.org/iel7/4609443/9314330/09366968.pdf)
```
@article{Zhou2021NaSCTG2,
title = {NaSC-TG2: Natural Scene Classification With Tiangong-2 Remotely Sensed Imagery},
author = {Zhuang Zhou and Shengyang Li and Wei Wu and Weilong Guo and Xuan Li and Guisong Xiaand Zifei Zhao},
year = 2021,
journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
volume = 14,
pages = {3228--3242}
}
```
### Contributions
[More Information Needed] | jonathan-roberts1/NaSC-TG2 | [
"license:cc-by-nc-sa-4.0",
"region:us"
]
| 2023-02-14T17:46:25+00:00 | {"license": "cc-by-nc-sa-4.0", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "beach", "1": "circular farmland", "2": "cloud", "3": "desert", "4": "forest", "5": "mountain", "6": "rectangular farmland", "7": "residential", "8": "river", "9": "snowberg"}}}}], "splits": [{"name": "train", "num_bytes": 53937080, "num_examples": 20000}], "download_size": 75374352, "dataset_size": 53937080}} | 2023-03-26T09:59:31+00:00 | []
| []
| TAGS
#license-cc-by-nc-sa-4.0 #region-us
| # Dataset Card for "NaSC-TG2"
## Dataset Description
- Paper: NaSC-TG2: Natural scene classification with Tiangong-2 remotely sensed imagery
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
Public Domain
URL
### Contributions
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"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
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0.11895950883626938,
0.10463889688253403,
0.012238243594765663,
-0.0474841371178627,
-0.07797931879758835,
-0.01306329108774662,
0.08978249132633209,
-0.09108473360538483,
0.004115915857255459
]
|
0b22f730140a3494f7c714ca6035aa54e9d3d1b1 |
# Dataset Card for MC4_Legal: A Corpus Covering the Legal Part of MC4 for European Languages
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [GitHub](https://github.com/JoelNiklaus/LegalDatasets/tree/main/pretrain/mc4_legal)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Joel Niklaus](mailto:[email protected])
### Dataset Summary
This dataset contains large text resources (~106GB in total) from mc4 filtered for legal data that can be used for pretraining language models.
This dataset uses a different filtering method compared to [mc4_legal](https://huggingface.co/datasets/joelito/mc4_legal) and uses the smaller filtered [c4](https://huggingface.co/datasets/c4) dataset for the English split to speed up the filtering.
Use the dataset like this:
```python
from datasets import load_dataset
dataset = load_dataset("joelito/mc4_legal", "de", split='train', streaming=True)
```
### Supported Tasks and Leaderboards
The dataset supports the task of masked language modeling.
### Languages
The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
## Dataset Structure
### Data Instances
The file format is jsonl.xz and there is a validation and train split available.
| Source | Size (MB) | Words | Documents | Words/Document |
|:---------|------------:|------------:|------------:|-----------------:|
| all | 448980 | 28599300521 | 9873288 | 2896 |
| bg | 57 | 2390349 | 379 | 6306 |
| cs | 31005 | 1840827375 | 677796 | 2715 |
| da | 162 | 10466716 | 3231 | 3239 |
| de | 105739 | 6184578784 | 3164461 | 1954 |
| el | 30 | 1155977 | 307 | 3765 |
| en | 13734 | 966539309 | 359283 | 2690 |
| es | 132053 | 9058939804 | 2281888 | 3969 |
| et | 2059 | 110198368 | 49987 | 2204 |
| fi | 1270 | 62799074 | 44875 | 1399 |
| fr | 30878 | 2117306229 | 598983 | 3534 |
| ga | 1 | 32772 | 8 | 4096 |
| hu | 4677 | 244911748 | 58857 | 4161 |
| it | 46957 | 3053920779 | 990823 | 3082 |
| lt | 156 | 9142223 | 1529 | 5979 |
| lv | 1 | 58702 | 16 | 3668 |
| mt | 65 | 3479869 | 731 | 4760 |
| nl | 326 | 21962633 | 6875 | 3194 |
| pl | 37950 | 2235839721 | 827641 | 2701 |
| pt | 20120 | 1338147828 | 382173 | 3501 |
| ro | 8816 | 551372510 | 136513 | 4038 |
| sk | 5850 | 349265172 | 130701 | 2672 |
| sl | 1742 | 107493024 | 32574 | 3299 |
| sv | 5332 | 328471555 | 123657 | 2656 |
### Data Fields
[More Information Needed]
### Data Splits
#### Data Size
```bash
$ xz --list data/*.xz
Strms Blocks Compressed Uncompressed Ratio Check Filename
1 1 2,080.7 KiB 33.4 MiB 0.061 CRC64 data/bg.train.0.jsonl.xz
1 1 22.8 KiB 315.9 KiB 0.072 CRC64 data/bg.validation.0.jsonl.xz
1 1 608.0 MiB 3,881.0 MiB 0.157 CRC64 data/cs.train.0.jsonl.xz
1 1 608.0 MiB 3,902.6 MiB 0.156 CRC64 data/cs.train.1.jsonl.xz
1 1 256.1 MiB 1,644.5 MiB 0.156 CRC64 data/cs.train.2.jsonl.xz
1 1 1,450.6 KiB 8,690.7 KiB 0.167 CRC64 data/cs.validation.0.jsonl.xz
1 1 7,578.6 KiB 38.3 MiB 0.193 CRC64 data/da.train.0.jsonl.xz
1 1 19.7 KiB 82.3 KiB 0.240 CRC64 data/da.validation.0.jsonl.xz
1 1 608.0 MiB 3,026.9 MiB 0.201 CRC64 data/de.train.0.jsonl.xz
1 1 608.0 MiB 3,038.7 MiB 0.200 CRC64 data/de.train.1.jsonl.xz
1 1 608.0 MiB 3,036.1 MiB 0.200 CRC64 data/de.train.2.jsonl.xz
1 1 608.0 MiB 3,040.3 MiB 0.200 CRC64 data/de.train.3.jsonl.xz
1 1 608.0 MiB 3,038.6 MiB 0.200 CRC64 data/de.train.4.jsonl.xz
1 1 608.0 MiB 3,044.2 MiB 0.200 CRC64 data/de.train.5.jsonl.xz
1 1 608.0 MiB 3,043.8 MiB 0.200 CRC64 data/de.train.6.jsonl.xz
1 1 608.0 MiB 3,038.2 MiB 0.200 CRC64 data/de.train.7.jsonl.xz
1 1 55.1 MiB 274.7 MiB 0.201 CRC64 data/de.train.8.jsonl.xz
1 1 5,033.5 KiB 24.5 MiB 0.201 CRC64 data/de.validation.0.jsonl.xz
1 1 1,280.9 KiB 17.0 MiB 0.073 CRC64 data/el.train.0.jsonl.xz
1 1 5,552 B 15.7 KiB 0.346 CRC64 data/el.validation.0.jsonl.xz
1 1 608.0 MiB 2,602.1 MiB 0.234 CRC64 data/en.train.0.jsonl.xz
1 1 90.0 MiB 386.5 MiB 0.233 CRC64 data/en.train.1.jsonl.xz
1 1 826.6 KiB 3,298.8 KiB 0.251 CRC64 data/en.validation.0.jsonl.xz
1 1 608.0 MiB 3,106.5 MiB 0.196 CRC64 data/es.train.0.jsonl.xz
1 1 608.0 MiB 3,118.1 MiB 0.195 CRC64 data/es.train.1.jsonl.xz
1 1 608.0 MiB 3,113.6 MiB 0.195 CRC64 data/es.train.2.jsonl.xz
1 1 608.0 MiB 3,122.5 MiB 0.195 CRC64 data/es.train.3.jsonl.xz
1 1 608.0 MiB 3,121.5 MiB 0.195 CRC64 data/es.train.4.jsonl.xz
1 1 608.0 MiB 3,122.9 MiB 0.195 CRC64 data/es.train.5.jsonl.xz
1 1 608.0 MiB 3,128.4 MiB 0.194 CRC64 data/es.train.6.jsonl.xz
1 1 608.0 MiB 3,129.5 MiB 0.194 CRC64 data/es.train.7.jsonl.xz
1 1 608.0 MiB 3,132.2 MiB 0.194 CRC64 data/es.train.8.jsonl.xz
1 1 528.5 MiB 2,722.5 MiB 0.194 CRC64 data/es.train.9.jsonl.xz
1 1 6,159.9 KiB 30.7 MiB 0.196 CRC64 data/es.validation.0.jsonl.xz
1 1 93.5 MiB 506.2 MiB 0.185 CRC64 data/et.train.0.jsonl.xz
1 1 136.2 KiB 571.3 KiB 0.238 CRC64 data/et.validation.0.jsonl.xz
1 1 60.6 MiB 312.6 MiB 0.194 CRC64 data/fi.train.0.jsonl.xz
1 1 63.2 KiB 262.4 KiB 0.241 CRC64 data/fi.validation.0.jsonl.xz
1 1 608.0 MiB 3,400.7 MiB 0.179 CRC64 data/fr.train.0.jsonl.xz
1 1 608.0 MiB 3,405.5 MiB 0.179 CRC64 data/fr.train.1.jsonl.xz
1 1 135.9 MiB 763.7 MiB 0.178 CRC64 data/fr.train.2.jsonl.xz
1 1 1,414.3 KiB 7,626.1 KiB 0.185 CRC64 data/fr.validation.0.jsonl.xz
1 1 31.2 KiB 146.4 KiB 0.213 CRC64 data/ga.train.0.jsonl.xz
1 0 32 B 0 B --- CRC64 data/ga.validation.0.jsonl.xz
1 1 211.5 MiB 1,407.3 MiB 0.150 CRC64 data/hu.train.0.jsonl.xz
1 1 212.9 KiB 1,287.6 KiB 0.165 CRC64 data/hu.validation.0.jsonl.xz
1 1 608.0 MiB 2,963.4 MiB 0.205 CRC64 data/it.train.0.jsonl.xz
1 1 608.0 MiB 2,970.0 MiB 0.205 CRC64 data/it.train.1.jsonl.xz
1 1 608.0 MiB 2,973.7 MiB 0.204 CRC64 data/it.train.2.jsonl.xz
1 1 315.2 MiB 1,541.6 MiB 0.204 CRC64 data/it.train.3.jsonl.xz
1 1 2,419.3 KiB 11.2 MiB 0.211 CRC64 data/it.validation.0.jsonl.xz
1 1 9,966.7 KiB 38.2 MiB 0.255 CRC64 data/lt.train.0.jsonl.xz
1 1 17.2 KiB 84.7 KiB 0.203 CRC64 data/lt.validation.0.jsonl.xz
1 1 66.4 KiB 326.7 KiB 0.203 CRC64 data/lv.train.0.jsonl.xz
1 0 32 B 0 B --- CRC64 data/lv.validation.0.jsonl.xz
1 1 2,851.6 KiB 16.7 MiB 0.167 CRC64 data/mt.train.0.jsonl.xz
1 1 2,092 B 5,079 B 0.412 CRC64 data/mt.validation.0.jsonl.xz
1 1 14.6 MiB 71.6 MiB 0.203 CRC64 data/nl.train.0.jsonl.xz
1 1 23.5 KiB 79.2 KiB 0.296 CRC64 data/nl.validation.0.jsonl.xz
1 1 608.0 MiB 3,635.5 MiB 0.167 CRC64 data/pl.train.0.jsonl.xz
1 1 608.0 MiB 3,646.0 MiB 0.167 CRC64 data/pl.train.1.jsonl.xz
1 1 401.9 MiB 2,409.0 MiB 0.167 CRC64 data/pl.train.2.jsonl.xz
1 1 1,870.5 KiB 10.5 MiB 0.173 CRC64 data/pl.validation.0.jsonl.xz
1 1 608.0 MiB 3,173.1 MiB 0.192 CRC64 data/pt.train.0.jsonl.xz
1 1 329.1 MiB 1,721.6 MiB 0.191 CRC64 data/pt.train.1.jsonl.xz
1 1 989.0 KiB 4,841.2 KiB 0.204 CRC64 data/pt.validation.0.jsonl.xz
1 1 365.2 MiB 2,237.9 MiB 0.163 CRC64 data/ro.train.0.jsonl.xz
1 1 419.2 KiB 2,320.4 KiB 0.181 CRC64 data/ro.validation.0.jsonl.xz
1 1 266.1 MiB 1,668.1 MiB 0.160 CRC64 data/sk.train.0.jsonl.xz
1 1 304.1 KiB 1,618.2 KiB 0.188 CRC64 data/sk.validation.0.jsonl.xz
1 1 81.6 MiB 416.1 MiB 0.196 CRC64 data/sl.train.0.jsonl.xz
1 1 101.0 KiB 416.6 KiB 0.242 CRC64 data/sl.validation.0.jsonl.xz
1 1 252.0 MiB 1,423.2 MiB 0.177 CRC64 data/sv.train.0.jsonl.xz
1 1 210.8 KiB 1,091.2 KiB 0.193 CRC64 data/sv.validation.0.jsonl.xz
-------------------------------------------------------------------------------
74 72 20.0 GiB 106.2 GiB 0.189 CRC64 74 files
```
## Dataset Creation
The dataset was created by filtering mc4 for legal data.
We used terms indicating legal citations to get the texts.
Note that this dataset can be quite noisy, and the quality is not known.
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@JoelNiklaus](https://github.com/joelniklaus) for adding this dataset.
| joelniklaus/legal-mc4 | [
"task_categories:fill-mask",
"annotations_creators:other",
"language_creators:found",
"multilinguality:multilingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:bg",
"language:cs",
"language:da",
"language:de",
"language:el",
"language:en",
"language:es",
"language:et",
"language:fi",
"language:fr",
"language:ga",
"language:hu",
"language:it",
"language:lt",
"language:lv",
"language:mt",
"language:nl",
"language:pl",
"language:pt",
"language:ro",
"language:sk",
"language:sl",
"language:sv",
"license:cc-by-4.0",
"region:us"
]
| 2023-02-14T17:48:58+00:00 | {"annotations_creators": ["other"], "language_creators": ["found"], "language": ["bg", "cs", "da", "de", "el", "en", "es", "et", "fi", "fr", "ga", "hu", "it", "lt", "lv", "mt", "nl", "pl", "pt", "ro", "sk", "sl", "sv"], "license": ["cc-by-4.0"], "multilinguality": ["multilingual"], "size_categories": ["10M<n<100M"], "source_datasets": ["original"], "task_categories": ["fill-mask"], "pretty_name": "MC4_Legal: A Corpus Covering the Legal Part of MC4 for European Languages"} | 2023-08-06T21:54:20+00:00 | []
| [
"bg",
"cs",
"da",
"de",
"el",
"en",
"es",
"et",
"fi",
"fr",
"ga",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"pl",
"pt",
"ro",
"sk",
"sl",
"sv"
]
| TAGS
#task_categories-fill-mask #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-cc-by-4.0 #region-us
| Dataset Card for MC4\_Legal: A Corpus Covering the Legal Part of MC4 for European Languages
===========================================================================================
Table of Contents
-----------------
* Table of Contents
* Dataset Description
+ Dataset Summary
+ Supported Tasks and Leaderboards
+ Languages
* Dataset Structure
+ Data Instances
+ Data Fields
+ Data Splits
* Dataset Creation
+ Curation Rationale
+ Source Data
+ Annotations
+ 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
+ Citation Information
+ Contributions
Dataset Description
-------------------
* Homepage:
* Repository: GitHub
* Paper:
* Leaderboard:
* Point of Contact: Joel Niklaus
### Dataset Summary
This dataset contains large text resources (~106GB in total) from mc4 filtered for legal data that can be used for pretraining language models.
This dataset uses a different filtering method compared to mc4\_legal and uses the smaller filtered c4 dataset for the English split to speed up the filtering.
Use the dataset like this:
### Supported Tasks and Leaderboards
The dataset supports the task of masked language modeling.
### Languages
The following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv
Dataset Structure
-----------------
### Data Instances
The file format is URL and there is a validation and train split available.
### Data Fields
### Data Splits
#### Data Size
Dataset Creation
----------------
The dataset was created by filtering mc4 for legal data.
We used terms indicating legal citations to get the texts.
Note that this dataset can be quite noisy, and the quality is not known.
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
Additional Information
----------------------
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @JoelNiklaus for adding this dataset.
| [
"### Dataset Summary\n\n\nThis dataset contains large text resources (~106GB in total) from mc4 filtered for legal data that can be used for pretraining language models.\n\n\nThis dataset uses a different filtering method compared to mc4\\_legal and uses the smaller filtered c4 dataset for the English split to speed up the filtering.\n\n\nUse the dataset like this:",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports the task of masked language modeling.",
"### Languages\n\n\nThe following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nThe file format is URL and there is a validation and train split available.",
"### Data Fields",
"### Data Splits",
"#### Data Size\n\n\nDataset Creation\n----------------\n\n\nThe dataset was created by filtering mc4 for legal data.\nWe used terms indicating legal citations to get the texts.\nNote that this dataset can be quite noisy, and the quality is not known.",
"### 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",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @JoelNiklaus for adding this dataset."
]
| [
"TAGS\n#task_categories-fill-mask #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-cc-by-4.0 #region-us \n",
"### Dataset Summary\n\n\nThis dataset contains large text resources (~106GB in total) from mc4 filtered for legal data that can be used for pretraining language models.\n\n\nThis dataset uses a different filtering method compared to mc4\\_legal and uses the smaller filtered c4 dataset for the English split to speed up the filtering.\n\n\nUse the dataset like this:",
"### Supported Tasks and Leaderboards\n\n\nThe dataset supports the task of masked language modeling.",
"### Languages\n\n\nThe following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nThe file format is URL and there is a validation and train split available.",
"### Data Fields",
"### Data Splits",
"#### Data Size\n\n\nDataset Creation\n----------------\n\n\nThe dataset was created by filtering mc4 for legal data.\nWe used terms indicating legal citations to get the texts.\nNote that this dataset can be quite noisy, and the quality is not known.",
"### 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",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\n\nThanks to @JoelNiklaus for adding this dataset."
]
| [
202,
87,
24,
69,
22,
5,
5,
57,
7,
4,
10,
10,
5,
5,
9,
18,
7,
8,
14,
6,
6,
18
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| [
"passage: TAGS\n#task_categories-fill-mask #annotations_creators-other #language_creators-found #multilinguality-multilingual #size_categories-10M<n<100M #source_datasets-original #language-Bulgarian #language-Czech #language-Danish #language-German #language-Modern Greek (1453-) #language-English #language-Spanish #language-Estonian #language-Finnish #language-French #language-Irish #language-Hungarian #language-Italian #language-Lithuanian #language-Latvian #language-Maltese #language-Dutch #language-Polish #language-Portuguese #language-Romanian #language-Slovak #language-Slovenian #language-Swedish #license-cc-by-4.0 #region-us \n### Dataset Summary\n\n\nThis dataset contains large text resources (~106GB in total) from mc4 filtered for legal data that can be used for pretraining language models.\n\n\nThis dataset uses a different filtering method compared to mc4\\_legal and uses the smaller filtered c4 dataset for the English split to speed up the filtering.\n\n\nUse the dataset like this:### Supported Tasks and Leaderboards\n\n\nThe dataset supports the task of masked language modeling.### Languages\n\n\nThe following languages are supported: bg, cs, da, de, el, en, es, et, fi, fr, ga, hu, it, lt, lv, mt, nl, pl, pt, ro, sk, sl, sv\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nThe file format is URL and there is a validation and train split available.### Data Fields### Data Splits#### Data Size\n\n\nDataset Creation\n----------------\n\n\nThe dataset was created by filtering mc4 for legal data.\nWe used terms indicating legal citations to get the texts.\nNote that this dataset can be quite noisy, and the quality is not known.### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations"
]
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|
9534565516f1d94713635b720f2d8a3c9b9aba57 | # Dataset Card for "mini-algae-wirs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | samitizerxu/mini-algae-wirs | [
"region:us"
]
| 2023-02-14T17:56:57+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "1", "1": "2", "2": "3", "3": "4", "4": "5", "5": "test"}}}}], "splits": [{"name": "train", "num_bytes": 12520132.715, "num_examples": 4039}, {"name": "test", "num_bytes": 2971288.064, "num_examples": 1521}], "download_size": 15414584, "dataset_size": 15491420.779}} | 2023-02-14T17:58:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mini-algae-wirs"
More Information needed | [
"# Dataset Card for \"mini-algae-wirs\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mini-algae-wirs\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mini-algae-wirs\"\n\nMore Information needed"
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|
5511e606d24ee9d655813e31263e2ea31ce6aeb0 | # Dataset Card for "RS_C11"
## Dataset Description
- **Paper** [Feature significance-based multibag-of-visual-words model for remote sensing image scene classification](https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-10/issue-3/035004/Feature-significance-based-multibag-of-visual-words-model-for-remote/10.1117/1.JRS.10.035004.pdf)
### Licensing Information
Free usage without license.
## Citation Information
[Feature significance-based multibag-of-visual-words model for remote sensing image scene classification](https://www.spiedigitallibrary.org/journals/journal-of-applied-remote-sensing/volume-10/issue-3/035004/Feature-significance-based-multibag-of-visual-words-model-for-remote/10.1117/1.JRS.10.035004.pdf)
```
@article{zhao2016feature,
title = {Feature significance-based multibag-of-visual-words model for remote sensing image scene classification},
author = {Zhao, Lijun and Tang, Ping and Huo, Lianzhi},
year = 2016,
journal = {Journal of Applied Remote Sensing},
publisher = {Society of Photo-Optical Instrumentation Engineers},
volume = 10,
number = 3,
pages = {035004--035004}
}
``` | jonathan-roberts1/RS_C11 | [
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
]
| 2023-02-14T18:12:02+00:00 | {"license": "other", "task_categories": ["image-classification", "zero-shot-image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "dense forest", "1": "grassland", "2": "harbor", "3": "high buildings", "4": "low buildings", "5": "overpass", "6": "railway", "7": "residential area", "8": "roads", "9": "sparse forest", "10": "storage tanks"}}}}], "splits": [{"name": "train", "num_bytes": 969136595.28, "num_examples": 1232}], "download_size": 916398984, "dataset_size": 969136595.28}} | 2023-03-31T16:07:50+00:00 | []
| []
| TAGS
#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us
| # Dataset Card for "RS_C11"
## Dataset Description
- Paper Feature significance-based multibag-of-visual-words model for remote sensing image scene classification
### Licensing Information
Free usage without license.
Feature significance-based multibag-of-visual-words model for remote sensing image scene classification
| [
"# Dataset Card for \"RS_C11\"",
"## Dataset Description\n\n- Paper Feature significance-based multibag-of-visual-words model for remote sensing image scene classification",
"### Licensing Information\n\nFree usage without license.\n\n\n\nFeature significance-based multibag-of-visual-words model for remote sensing image scene classification"
]
| [
"TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n",
"# Dataset Card for \"RS_C11\"",
"## Dataset Description\n\n- Paper Feature significance-based multibag-of-visual-words model for remote sensing image scene classification",
"### Licensing Information\n\nFree usage without license.\n\n\n\nFeature significance-based multibag-of-visual-words model for remote sensing image scene classification"
]
| [
37,
11,
30,
35
]
| [
"passage: TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n# Dataset Card for \"RS_C11\"## Dataset Description\n\n- Paper Feature significance-based multibag-of-visual-words model for remote sensing image scene classification### Licensing Information\n\nFree usage without license.\n\n\n\nFeature significance-based multibag-of-visual-words model for remote sensing image scene classification"
]
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|
3976c543c8b99c310eef4b863503e2041558ceb5 | # Dataset Card for "Brazilian_Coffee_Scenes"
## Dataset Description
- **Paper** [Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W13/papers/Penatti_Do_Deep_Features_2015_CVPR_paper.pdf)
### Licensing Information
[CC BY-NC]
## Citation Information
[Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?](https://www.cv-foundation.org/openaccess/content_cvpr_workshops_2015/W13/papers/Penatti_Do_Deep_Features_2015_CVPR_paper.pdf)
```
@inproceedings{penatti2015deep,
title = {Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?},
author = {Penatti, Ot{\'a}vio AB and Nogueira, Keiller and Dos Santos, Jefersson A},
year = 2015,
booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition workshops},
pages = {44--51}
}
``` | jonathan-roberts1/Brazilian_Coffee_Scenes | [
"task_categories:image-classification",
"license:other",
"region:us"
]
| 2023-02-14T18:27:36+00:00 | {"license": "other", "task_categories": ["image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "coffee", "1": "no coffee"}}}}], "splits": [{"name": "train", "num_bytes": 4256968.464, "num_examples": 2876}], "download_size": 2830232, "dataset_size": 4256968.464}} | 2023-03-31T14:27:06+00:00 | []
| []
| TAGS
#task_categories-image-classification #license-other #region-us
| # Dataset Card for "Brazilian_Coffee_Scenes"
## Dataset Description
- Paper Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?
### Licensing Information
[CC BY-NC]
Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?
| [
"# Dataset Card for \"Brazilian_Coffee_Scenes\"",
"## Dataset Description\n\n- Paper Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?",
"### Licensing Information\n\n[CC BY-NC]\n\n\n\nDo deep features generalize from everyday objects to remote sensing and aerial scenes domains?"
]
| [
"TAGS\n#task_categories-image-classification #license-other #region-us \n",
"# Dataset Card for \"Brazilian_Coffee_Scenes\"",
"## Dataset Description\n\n- Paper Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?",
"### Licensing Information\n\n[CC BY-NC]\n\n\n\nDo deep features generalize from everyday objects to remote sensing and aerial scenes domains?"
]
| [
22,
17,
28,
34
]
| [
"passage: TAGS\n#task_categories-image-classification #license-other #region-us \n# Dataset Card for \"Brazilian_Coffee_Scenes\"## Dataset Description\n\n- Paper Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?### Licensing Information\n\n[CC BY-NC]\n\n\n\nDo deep features generalize from everyday objects to remote sensing and aerial scenes domains?"
]
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|
7b9d36c4cf0cf8d2f8a26ed401986eebee0baf4b | # Dataset Card for "Brazilian_Cerrado-Savanna_Scenes"
## Dataset Description
- **Paper** [Towards vegetation species discrimination by using data-driven descriptors](https://vision.unipv.it/CV/materiale2016-17/3rd%20Choice/0022.pdf)
-
### Licensing Information
[CC BY-NC]
## Citation Information
[Towards vegetation species discrimination by using data-driven descriptors](https://vision.unipv.it/CV/materiale2016-17/3rd%20Choice/0022.pdf)
```
@inproceedings{nogueira2016towards,
title = {Towards vegetation species discrimination by using data-driven descriptors},
author = {Nogueira, Keiller and Dos Santos, Jefersson A and Fornazari, Tamires and Silva, Thiago Sanna Freire and Morellato, Leonor Patricia and Torres, Ricardo da S},
year = 2016,
booktitle = {2016 9th IAPR Workshop on Pattern Recogniton in Remote Sensing (PRRS)},
pages = {1--6},
organization = {Ieee}
}
``` | jonathan-roberts1/Brazilian_Cerrado-Savanna_Scenes | [
"task_categories:zero-shot-image-classification",
"task_categories:image-classification",
"license:other",
"region:us"
]
| 2023-02-14T18:28:02+00:00 | {"license": "other", "task_categories": ["zero-shot-image-classification", "image-classification"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "agriculture", "1": "arboreal vegetation", "2": "herbaceous vegetation", "3": "shrubby vegetation"}}}}], "splits": [{"name": "train", "num_bytes": 16933385.557, "num_examples": 1311}], "download_size": 14574976, "dataset_size": 16933385.557}} | 2023-03-31T14:28:58+00:00 | []
| []
| TAGS
#task_categories-zero-shot-image-classification #task_categories-image-classification #license-other #region-us
| # Dataset Card for "Brazilian_Cerrado-Savanna_Scenes"
## Dataset Description
- Paper Towards vegetation species discrimination by using data-driven descriptors
-
### Licensing Information
[CC BY-NC]
Towards vegetation species discrimination by using data-driven descriptors
| [
"# Dataset Card for \"Brazilian_Cerrado-Savanna_Scenes\"",
"## Dataset Description\n\n- Paper Towards vegetation species discrimination by using data-driven descriptors\n-",
"### Licensing Information\n\n[CC BY-NC]\n\n\n\nTowards vegetation species discrimination by using data-driven descriptors"
]
| [
"TAGS\n#task_categories-zero-shot-image-classification #task_categories-image-classification #license-other #region-us \n",
"# Dataset Card for \"Brazilian_Cerrado-Savanna_Scenes\"",
"## Dataset Description\n\n- Paper Towards vegetation species discrimination by using data-driven descriptors\n-",
"### Licensing Information\n\n[CC BY-NC]\n\n\n\nTowards vegetation species discrimination by using data-driven descriptors"
]
| [
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| [
"passage: TAGS\n#task_categories-zero-shot-image-classification #task_categories-image-classification #license-other #region-us \n# Dataset Card for \"Brazilian_Cerrado-Savanna_Scenes\"## Dataset Description\n\n- Paper Towards vegetation species discrimination by using data-driven descriptors\n-### Licensing Information\n\n[CC BY-NC]\n\n\n\nTowards vegetation species discrimination by using data-driven descriptors"
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|
b1aed3941349646e032b1b1abd6452b923307cd6 | # Dataset Card for "algae-wirs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | samitizerxu/algae-wirs | [
"region:us"
]
| 2023-02-14T18:56:40+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "1", "1": "2", "2": "3", "3": "4", "4": "5", "5": "test"}}}}], "splits": [{"name": "train", "num_bytes": 33936156.629999995, "num_examples": 17035}, {"name": "test", "num_bytes": 12474396.284, "num_examples": 6494}], "download_size": 45458394, "dataset_size": 46410552.914}} | 2023-02-14T19:24:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "algae-wirs"
More Information needed | [
"# Dataset Card for \"algae-wirs\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"algae-wirs\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"algae-wirs\"\n\nMore Information needed"
]
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|
bb0d4012c364f989325cb205b07c18626604015b | # Dataset Card for "age_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | bstds/age_dataset | [
"region:us"
]
| 2023-02-14T19:03:47+00:00 | {"dataset_info": {"features": [{"name": "entity_id", "dtype": "int32"}, {"name": "name", "dtype": "string"}, {"name": "short_description", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "country", "dtype": "string"}, {"name": "occupation", "dtype": "string"}, {"name": "birth_year", "dtype": "string"}, {"name": "manner_of_death", "dtype": "string"}, {"name": "age_of_death", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 135675467, "num_examples": 1223009}], "download_size": 54907598, "dataset_size": 135675467}} | 2023-02-14T19:11:38+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "age_dataset"
More Information needed | [
"# Dataset Card for \"age_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"age_dataset\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"age_dataset\"\n\nMore Information needed"
]
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|
2a102f1877fc51f5af21891a3c5210b25fd56a43 | # Dataset Card for "algae-rgb"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | samitizerxu/algae-rgb | [
"region:us"
]
| 2023-02-14T19:04:53+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "1", "1": "2", "2": "3", "3": "4", "4": "5", "5": "test"}}}}], "splits": [{"name": "train", "num_bytes": 44920154.28, "num_examples": 17035}, {"name": "test", "num_bytes": 17356455.604, "num_examples": 6494}], "download_size": 61006757, "dataset_size": 62276609.884}} | 2023-02-14T19:23:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "algae-rgb"
More Information needed | [
"# Dataset Card for \"algae-rgb\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"algae-rgb\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"algae-rgb\"\n\nMore Information needed"
]
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|
1092e5b41e75f9063026c6196617f86f6306cd12 | # Dataset Card for "RSI-CB256"
## Dataset Description
- **Paper** [Exploring Models and Data for Remote Sensing Image Caption Generation](https://ieeexplore.ieee.org/iel7/36/4358825/08240966.pdf)
-
### Licensing Information
For academic purposes.
## Citation Information
[Exploring Models and Data for Remote Sensing Image Caption Generation](https://ieeexplore.ieee.org/iel7/36/4358825/08240966.pdf)
```
@article{lu2017exploring,
title = {Exploring Models and Data for Remote Sensing Image Caption Generation},
author = {Lu, Xiaoqiang and Wang, Binqiang and Zheng, Xiangtao and Li, Xuelong},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
volume = 56,
number = 4,
pages = {2183--2195},
doi = {10.1109/TGRS.2017.2776321},
year={2018}
}
``` | jonathan-roberts1/RSI-CB256 | [
"task_categories:image-classification",
"task_categories:zero-shot-image-classification",
"license:other",
"region:us"
]
| 2023-02-14T19:09:45+00:00 | {"license": "other", "task_categories": ["image-classification", "zero-shot-image-classification"], "dataset_info": {"features": [{"name": "label_1", "dtype": {"class_label": {"names": {"0": "transportation", "1": "other objects", "2": "woodland", "3": "water area", "4": "other land", "5": "cultivated land", "6": "construction land"}}}}, {"name": "label_2", "dtype": {"class_label": {"names": {"0": "parking lot", "1": "avenue", "2": "highway", "3": "bridge", "4": "marina", "5": "crossroads", "6": "airport runway", "7": "pipeline", "8": "town", "9": "airplane", "10": "forest", "11": "mangrove", "12": "artificial grassland", "13": "river protection forest", "14": "shrubwood", "15": "sapling", "16": "sparse forest", "17": "lakeshore", "18": "river", "19": "stream", "20": "coastline", "21": "hirst", "22": "dam", "23": "sea", "24": "snow mountain", "25": "sandbeach", "26": "mountain", "27": "desert", "28": "dry farm", "29": "green farmland", "30": "bare land", "31": "city building", "32": "residents", "33": "container", "34": "storage room"}}}}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 4901667781.625, "num_examples": 24747}], "download_size": 4198991130, "dataset_size": 4901667781.625}} | 2023-03-31T16:11:50+00:00 | []
| []
| TAGS
#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us
| # Dataset Card for "RSI-CB256"
## Dataset Description
- Paper Exploring Models and Data for Remote Sensing Image Caption Generation
-
### Licensing Information
For academic purposes.
Exploring Models and Data for Remote Sensing Image Caption Generation
| [
"# Dataset Card for \"RSI-CB256\"",
"## Dataset Description\n\n- Paper Exploring Models and Data for Remote Sensing Image Caption Generation\n-",
"### Licensing Information\n\nFor academic purposes.\n\n\n\nExploring Models and Data for Remote Sensing Image Caption Generation"
]
| [
"TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n",
"# Dataset Card for \"RSI-CB256\"",
"## Dataset Description\n\n- Paper Exploring Models and Data for Remote Sensing Image Caption Generation\n-",
"### Licensing Information\n\nFor academic purposes.\n\n\n\nExploring Models and Data for Remote Sensing Image Caption Generation"
]
| [
37,
12,
21,
25
]
| [
"passage: TAGS\n#task_categories-image-classification #task_categories-zero-shot-image-classification #license-other #region-us \n# Dataset Card for \"RSI-CB256\"## Dataset Description\n\n- Paper Exploring Models and Data for Remote Sensing Image Caption Generation\n-### Licensing Information\n\nFor academic purposes.\n\n\n\nExploring Models and Data for Remote Sensing Image Caption Generation"
]
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|
6d2f0f751d549f99b669ee3cdbb9b7f746f7078c | # Dataset Card for "geonames"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
[Source](https://download.geonames.org/export/dump/)
```
"geonameid", # integer id of record in geonames database
"name", # name of geographical point (utf8) varchar(200)
"asciiname", # name of geographical point in plain ascii characters, varchar(200)
"alternatenames",
# alternatenames, comma separated, ascii names automatically transliterated, convenience attribute from alternatename table, varchar(10000)
"latitude", # latitude in decimal degrees (wgs84)
"longitude", # longitude in decimal degrees (wgs84)
"feature_class", # see http://www.geonames.org/export/codes.html, char(1)
"feature_code", # see http://www.geonames.org/export/codes.html, varchar(10)
"country_code", # ISO-3166 2-letter country code, 2 characters
"cc2",
# alternate country codes, comma separated, ISO-3166 2-letter country code, 200 characters
"admin1_code",
# fipscode (subject to change to iso code), see exceptions below, see file admin1Codes.txt for display names of this code; varchar(20)
"admin2_code",
# code for the second administrative division, a county in the US, see file admin2Codes.txt; varchar(80)
"admin3_code", # code for third level administrative division, varchar(20)
"admin4_code", # code for fourth level administrative division, varchar(20)
"population", # bigint (8 byte int)
"elevation", # in meters, integer
"dem",
# digital elevation model, srtm3 or gtopo30, average elevation of 3''x3'' (ca 90mx90m) or 30''x30'' (ca 900mx900m) area in meters, integer. srtm processed by cgiar/ciat.
"timezone", # the iana timezone id (see file timeZone.txt) varchar(40)
"modification_date", # date of last modification in yyyy-MM-dd format"
``` | bstds/geonames | [
"region:us"
]
| 2023-02-14T19:12:27+00:00 | {"dataset_info": {"features": [{"name": "geonameid", "dtype": "int64"}, {"name": "name", "dtype": "string"}, {"name": "asciiname", "dtype": "string"}, {"name": "alternatenames", "dtype": "string"}, {"name": "latitude", "dtype": "float64"}, {"name": "longitude", "dtype": "float64"}, {"name": "feature_class", "dtype": "string"}, {"name": "feature_code", "dtype": "string"}, {"name": "country_code", "dtype": "string"}, {"name": "cc2", "dtype": "string"}, {"name": "admin1_code", "dtype": "string"}, {"name": "admin2_code", "dtype": "string"}, {"name": "admin3_code", "dtype": "string"}, {"name": "admin4_code", "dtype": "string"}, {"name": "population", "dtype": "int64"}, {"name": "elevation", "dtype": "float64"}, {"name": "dem", "dtype": "int64"}, {"name": "timezone", "dtype": "string"}, {"name": "modification_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2378719253, "num_examples": 12368001}], "download_size": 824343833, "dataset_size": 2378719253}} | 2023-02-14T19:32:06+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "geonames"
More Information needed
Source
| [
"# Dataset Card for \"geonames\"\n\nMore Information needed\n\nSource"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"geonames\"\n\nMore Information needed\n\nSource"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"geonames\"\n\nMore Information needed\n\nSource"
]
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|
06a42b8eb06bae0c40b7ab2546d45884aa0cae1d | # Dataset Card for "SmallNoisyCommonSpeechEN"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | rohitp1/SmallNoisyCommonSpeechEN | [
"region:us"
]
| 2023-02-14T19:25:23+00:00 | {"dataset_info": {"features": [{"name": "audio", "struct": [{"name": "array", "sequence": "float64"}, {"name": "path", "dtype": "null"}, {"name": "sampling_rate", "dtype": "int64"}]}, {"name": "sentence", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 78272771286, "num_examples": 100000}, {"name": "val", "num_bytes": 3796055210, "num_examples": 5000}, {"name": "test", "num_bytes": 3840811928, "num_examples": 5000}], "download_size": 7663445403, "dataset_size": 85909638424}} | 2023-02-15T09:18:59+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "SmallNoisyCommonSpeechEN"
More Information needed | [
"# Dataset Card for \"SmallNoisyCommonSpeechEN\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"SmallNoisyCommonSpeechEN\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"SmallNoisyCommonSpeechEN\"\n\nMore Information needed"
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|
d98e4caa1b3e890210eb68fe4e039153eaa5cda0 | # Dataset Card for "job_titles"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Normalized dataset of 70k job titles | bstds/job_titles | [
"region:us"
]
| 2023-02-14T19:31:04+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2451067, "num_examples": 73380}], "download_size": 1258591, "dataset_size": 2451067}} | 2023-02-14T19:34:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "job_titles"
More Information needed
Normalized dataset of 70k job titles | [
"# Dataset Card for \"job_titles\"\n\nMore Information needed\n\nNormalized dataset of 70k job titles"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"job_titles\"\n\nMore Information needed\n\nNormalized dataset of 70k job titles"
]
| [
6,
24
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"job_titles\"\n\nMore Information needed\n\nNormalized dataset of 70k job titles"
]
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|
0acc2ccc8982e509b1bc95feedbc6016027891e2 | # Dataset Card for "us_patent"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Dataset of the U.S. Patent Phrase to Phrase Matching - https://www.kaggle.com/competitions/us-patent-phrase-to-phrase-matching | bstds/us_patent | [
"region:us"
]
| 2023-02-14T19:34:50+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "anchor", "dtype": "string"}, {"name": "target", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "score", "dtype": "float32"}], "splits": [{"name": "train", "num_bytes": 2580483, "num_examples": 36473}, {"name": "test", "num_bytes": 2521, "num_examples": 36}], "download_size": 1161327, "dataset_size": 2583004}} | 2023-02-14T19:39:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "us_patent"
More Information needed
Dataset of the U.S. Patent Phrase to Phrase Matching - URL | [
"# Dataset Card for \"us_patent\"\n\nMore Information needed\n\nDataset of the U.S. Patent Phrase to Phrase Matching - URL"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"us_patent\"\n\nMore Information needed\n\nDataset of the U.S. Patent Phrase to Phrase Matching - URL"
]
| [
6,
32
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"us_patent\"\n\nMore Information needed\n\nDataset of the U.S. Patent Phrase to Phrase Matching - URL"
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|
bc93bcae2161570bdbded1c752ad04610b015cf9 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_8"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_8 | [
"region:us"
]
| 2023-02-14T20:02:48+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 202359, "num_examples": 8}], "download_size": 0, "dataset_size": 202359}} | 2023-02-14T20:22:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_8"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_8\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_8\"\n\nMore Information needed"
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_8\"\n\nMore Information needed"
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|
a791f1298435d3c103078043d983f68a5860385e | # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_16 | [
"region:us"
]
| 2023-02-14T20:37:45+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 404649, "num_examples": 16}], "download_size": 81032, "dataset_size": 404649}} | 2023-02-14T20:37:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_16"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_16\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_16\"\n\nMore Information needed"
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_16\"\n\nMore Information needed"
]
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|
617b74132044bd7d52d8f88f91a7966406a660f5 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_16"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_16 | [
"region:us"
]
| 2023-02-14T20:43:51+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 404667, "num_examples": 16}], "download_size": 80966, "dataset_size": 404667}} | 2023-02-14T20:43:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_16"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_16\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_16\"\n\nMore Information needed"
]
| [
6,
43
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_16\"\n\nMore Information needed"
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|
ab28bdcabff9cd5b24c0319998deba86fe6b483e | # Dataset Card for "VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_32"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_32 | [
"region:us"
]
| 2023-02-14T20:47:20+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 809098, "num_examples": 32}], "download_size": 152186, "dataset_size": 809098}} | 2023-02-14T20:47:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_32"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_32\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_32\"\n\nMore Information needed"
]
| [
6,
43
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_32\"\n\nMore Information needed"
]
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|
2863c97d29665a148241081b85db57957d803d5d | # Dataset Card for "VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_64 | [
"region:us"
]
| 2023-02-14T20:50:19+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 1618060, "num_examples": 64}], "download_size": 293591, "dataset_size": 1618060}} | 2023-02-14T20:50:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_64"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_64\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_64\"\n\nMore Information needed"
]
| [
6,
43
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_2.7b_VQAv2_visclues_ns_64\"\n\nMore Information needed"
]
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c03a9cfbfffedb0b5300e593a0b170eb6bc8aefe | # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_64"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_64 | [
"region:us"
]
| 2023-02-14T20:53:53+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 1618019, "num_examples": 64}], "download_size": 318934, "dataset_size": 1618019}} | 2023-02-14T20:53:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_64"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_64\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_64\"\n\nMore Information needed"
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_64\"\n\nMore Information needed"
]
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|
dd4110f7dde72bb5134b43dbd30e416d90ef5992 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_128"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_128 | [
"region:us"
]
| 2023-02-14T21:02:15+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 3262469, "num_examples": 128}, {"name": "fewshot_1_bs_16", "num_bytes": 3301613, "num_examples": 128}], "download_size": 1302280, "dataset_size": 6564082}} | 2023-02-14T23:50:49+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_128"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_128\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_128\"\n\nMore Information needed"
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_6.7b_VQAv2_visclues_ns_128\"\n\nMore Information needed"
]
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|
665b3f7fd78e3099d6949adedfb90eb9213d97e3 | # Dataset Card for "mscoco_100k_30k_test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | JotDe/mscoco_100k_30k_test | [
"region:us"
]
| 2023-02-14T21:08:24+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2740227007.482, "num_examples": 29997}], "download_size": 507305710, "dataset_size": 2740227007.482}} | 2023-02-14T21:13:33+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mscoco_100k_30k_test"
More Information needed | [
"# Dataset Card for \"mscoco_100k_30k_test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mscoco_100k_30k_test\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mscoco_100k_30k_test\"\n\nMore Information needed"
]
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|
b3cfd179ea2a7f078181e7a208a6dc7b219d1d68 | # 岁己SUI的直播音频和大部分字幕
不能预览是因为它不支持aac，也没必要预览
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
岁己每月直播的音频，因为录制直播流网络不稳定、断流，会导致部分文件时间码错误，使用时建议先转码为wav/flac等无损格式
PM结尾的字幕包括当天和次日凌晨的录播，主播的作息懂的都懂
下面是一个简单的aac转wav的powershell脚本
```powershell
$OutPutPath = ".\"
$InputSuffix = "aac"
$OutputSuffix = "wav"
New-Item $OutPutPath -Type Directory
foreach($Files in Get-Item * -Include *$InputSuffix){
$OutputFile = $OutPutPath + $Files.BaseName + "." + $OutputSuffix
ffmpeg.exe -i $Files $OutputFile
#如果同时要转换为单声道：
#ffmpeg.exe -i $Files -ac 1 $OutputFile
}
Pause
```
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Chinese(98%)
English(1%)
Japanese(1%)
## 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] | Miuzarte/SUILiveAudio | [
"language:zh",
"AIvtuber",
"VirtuaReal",
"region:us"
]
| 2023-02-14T21:15:36+00:00 | {"language": ["zh"], "tags": ["AIvtuber", "VirtuaReal"]} | 2023-04-20T03:15:12+00:00 | []
| [
"zh"
]
| TAGS
#language-Chinese #AIvtuber #VirtuaReal #region-us
| # 岁己SUI的直播音频和大部分字幕
不能预览是因为它不支持aac，也没必要预览
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
岁己每月直播的音频，因为录制直播流网络不稳定、断流，会导致部分文件时间码错误，使用时建议先转码为wav/flac等无损格式
PM结尾的字幕包括当天和次日凌晨的录播，主播的作息懂的都懂
下面是一个简单的aac转wav的powershell脚本
### Supported Tasks and Leaderboards
### Languages
Chinese(98%)
English(1%)
Japanese(1%)
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
| [
"# 岁己SUI的直播音频和大部分字幕\n\n不能预览是因为它不支持aac，也没必要预览",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\n岁己每月直播的音频，因为录制直播流网络不稳定、断流，会导致部分文件时间码错误，使用时建议先转码为wav/flac等无损格式\n\nPM结尾的字幕包括当天和次日凌晨的录播，主播的作息懂的都懂\n\n下面是一个简单的aac转wav的powershell脚本",
"### Supported Tasks and Leaderboards",
"### Languages\n\nChinese(98%)\n\nEnglish(1%)\n\nJapanese(1%)",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
"TAGS\n#language-Chinese #AIvtuber #VirtuaReal #region-us \n",
"# 岁己SUI的直播音频和大部分字幕\n\n不能预览是因为它不支持aac，也没必要预览",
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
"### Dataset Summary\n\n岁己每月直播的音频，因为录制直播流网络不稳定、断流，会导致部分文件时间码错误，使用时建议先转码为wav/flac等无损格式\n\nPM结尾的字幕包括当天和次日凌晨的录播，主播的作息懂的都懂\n\n下面是一个简单的aac转wav的powershell脚本",
"### Supported Tasks and Leaderboards",
"### Languages\n\nChinese(98%)\n\nEnglish(1%)\n\nJapanese(1%)",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions"
]
| [
20,
28,
24,
86,
10,
14,
6,
6,
5,
5,
5,
7,
4,
10,
10,
5,
5,
9,
8,
8,
7,
8,
7,
5,
6,
6,
5
]
| [
"passage: TAGS\n#language-Chinese #AIvtuber #VirtuaReal #region-us \n# 岁己SUI的直播音频和大部分字幕\n\n不能预览是因为它不支持aac，也没必要预览## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:### Dataset Summary\n\n岁己每月直播的音频，因为录制直播流网络不稳定、断流，会导致部分文件时间码错误，使用时建议先转码为wav/flac等无损格式\n\nPM结尾的字幕包括当天和次日凌晨的录播，主播的作息懂的都懂\n\n下面是一个简单的aac转wav的powershell脚本### Supported Tasks and Leaderboards### Languages\n\nChinese(98%)\n\nEnglish(1%)\n\nJapanese(1%)## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions"
]
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|
f9c69d9cbc5a36025e04debcf517eda664c64e4c | # Dataset Card for "mscoco_100k"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | JotDe/mscoco_100k | [
"region:us"
]
| 2023-02-14T21:15:55+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8199285732.23, "num_examples": 99990}], "download_size": 2449411067, "dataset_size": 8199285732.23}} | 2023-02-14T23:22:53+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mscoco_100k"
More Information needed | [
"# Dataset Card for \"mscoco_100k\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mscoco_100k\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mscoco_100k\"\n\nMore Information needed"
]
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|
18dd8037d570a2a638eaaf3148c6295b033a4371 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_128"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_128 | [
"region:us"
]
| 2023-02-14T21:33:47+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 3262495, "num_examples": 128}], "download_size": 641392, "dataset_size": 3262495}} | 2023-02-14T21:33:50+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_128"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_128\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_128\"\n\nMore Information needed"
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_13b_VQAv2_visclues_ns_128\"\n\nMore Information needed"
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|
b82a88ac3d097432af1f5dc23dae96c27d1676c8 | # Amazon Berkeley Objects (c) by Amazon.com
## License
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0
International Public License. To obtain a copy of the full license, see
`LICENSE-CC-BY-NC-4.0.txt`, visit
[CreativeCommons.org](https://creativecommons.org/licenses/by-nc/4.0/)
or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
Under the following terms:
* Attribution — You must give appropriate credit, provide a link to the
license, and indicate if changes were made. You may do so in any reasonable
manner, but not in any way that suggests the licensor endorses you or your
use.
* NonCommercial — You may not use the material for commercial purposes.
* No additional restrictions — You may not apply legal terms or technological
measures that legally restrict others from doing anything the license
permits.
## Attribution
Credit for the data, including all images and 3d models, must be given to:
> Amazon.com
Credit for building the dataset, archives and benchmark sets must be given to:
> Matthieu Guillaumin (Amazon.com), Thomas Dideriksen (Amazon.com),
> Kenan Deng (Amazon.com), Himanshu Arora (Amazon.com),
> Jasmine Collins (UC Berkeley) and Jitendra Malik (UC Berkeley)
## Description
Amazon Berkeley Objects is a collection of 147,702 product listings with
multilingual metadata and 398,212 unique catalog images. 8,222 listings come
with turntable photography (also referred as *spin* or *360º-View* images), as
sequences of 24 or 72 images, for a total of 586,584 images in 8,209 unique
sequences. For 7,953 products, the collection also provides high-quality 3d
models, as glTF 2.0 files.
The collection is made of the following files:
* `README.md` - The present file.
* `LICENSE-CC-BY-NC-4.0.txt` - The License file. You must read, agree and
comply to the License before using the Amazon Berkeley Objects data.
* `listings/metadata/listings_<i>.json.gz` - Product description and metadata.
Each of the 16 files is encoded with UTF-8 and gzip-compressed. Each line of
the decompressed files corresponds to one product as a JSON object (see
http://ndjson.org/ or https://jsonlines.org/ ). Each product JSON object
(a.k.a dictionary) has any number of the following keys:
- `brand`
- Content: Brand name
- Format: `[{ "language_tag": <str>, "value": <str> }, ...]`
- `bullet_point`
- Content: Important features of the products
- Format: `[{ "language_tag": <str>, "value": <str> }, ...]`
- `color`
- Content: Color of the product as text
- Format: `[{"language_tag": <str>, "standardized_values": [<str>],
"value": <str>}, ...]`
- `color_code`
- Content: Color of the product as HTML color code
- Format: `[<str>, ...]`
- `country`
- Content: Country of the marketplace, as an
[ISO 3166-1 alpha 2](https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2)
code
- Format: `<str>`
- `domain_name`
- Content: Domain name of the marketplace where the product is found.
A product listing in this collection is uniquely identified by
(`item_id`, `domain_name`)
- Format: `<str>`
- `fabric_type`
- Content: Description of product fabric
- Format: `[{ "language_tag": <str>, "value": <str> }, ...]`
- `finish_type`
- Content: Description of product finish
- Format: `[{ "language_tag": <str>, "value": <str> }, ...]`
- `item_dimensions`
- Content: Dimensions of the product (height, width, length)
- Format: `{"height": {"normalized_value": {"unit": <str>, "value":
<float>}, "unit": <str>, "value": <float>}, "length":
{"normalized_value": {"unit": <str>, "value": <float>}, "unit": <str>,
"value": <float>}, "width": {"normalized_value": {"unit": <str>,
"value": <float>}, "unit": <str>, "value": <float>}}}`
- `item_id`
- Content: The product reference id. A product listing in this
collection is uniquely identified by (`item_id`, `domain_name`).
A corresponding product page may exist at
`https://www.<domain_name>/dp/<item_id>`
- Format: `<str>`
- `item_keywords`
- Content: Keywords for the product
- Format: `[{ "language_tag": <str>, "value": <str> }, ...]`
- `item_name`
- Content: The product name
- Format: `[{ "language_tag": <str>, "value": <str> }, ...]`
- `item_shape`
- Content: Description of the product shape
- Format: `[{ "language_tag": <str>, "value": <str> }, ...]`
- `item_weight`
- Content: The product weight
- Format: `[{"normalized_value": {"unit": <str>, "value": <float>},
"unit": <str>, "value": <float>}, ...]`
- `main_image_id`
- Content: The main product image, provided as an `image_id`. See the
descripton of `images/metadata/images.csv.gz` below
- Format: `<str>`
- `marketplace`
- Content: Retail website name (Amazon, AmazonFresh, AmazonGo, ...)
- Format: `<str>`
- `material`
- Content: Description of the product material
- Format: `[{ "language_tag": <str>, "value": <str> }, ...]`
- `model_name`
- Content: Model name
- Format: `[{ "language_tag": <str>, "value": <str> }, ...]`
- `model_number`
- Content: Model number
- Format: `[{ "language_tag": <str>, "value": <str> }, ...]`
- `model_year`
- Content: Model year
- Format: `[{ "language_tag": <str>, "value": <int> }, ...]`
- `node`
- Content: Location of the product in the category tree. A node page
may exist at `https://www.<domain_name>/b/?node=<node_id>` for
browsing
- Format: `[{ "node_id": <int>, "path": <str>}, ...]`
- `other_image_id`
- Content: Other available images for the product, provided as
`image_id`. See the description of `images/metadata/images.csv.gz`
below
- Format: `[<str>, ...]`
- `pattern`
- Content: Product pattern
- Format: `[{ "language_tag": <str>, "value": <int> }, ...]`
- `product_description`
- Content: Product description as HTML
- Format: `[{ "language_tag": <str>, "value": <int> }, ...]`
- `product_type`
- Content: Product type (category)
- Format: `<str>`
- `spin_id`
- Content: Reference to the 360º View image sequence. See the
description of `spins/metadata/spins.csv.gz` below
- Format: `<str>`
- `style`
- Content: Style of the product
- Format: `[{ "language_tag": <str>, "value": <int> }, ...]`
- `3dmodel_id`
- Content: Reference to the 3d model of the product. See the description
of `3dmodels/metadata/3models.csv.gz`
- Format: `<str>`
* `images/metadata/images.csv.gz` - Image metadata. This file is a
gzip-compressed comma-separated value (CSV) file with the following
columns: `image_id`, `height`, `width`, and `path`.
- `image_id` (string): this id uniquely refers to a product image. This id
can be used to retrieve the image data from Amazon's Content Delivery
Network (CDN) using the template:
`https://m.media-amazon.com/image/I/<image_id>.<extension>` [^1],
where `<extension>` is composed of the characters following the dot in the
`path` field. Any value occurring in the `main_image` and `other_images`
attributes of product metadata is an `image_id` present in this file.
- `height` (int) and `width` (int): respectively, the height and width of
the original image.
- `path`: the location of the image file relative to the `images/original/`
or `images/small/` directories. A path is composed of lowercase hex
characters (`0-9a-f`) that also uniquely identifies images. The first two
characters are used to build a file hierarchy and reduce the number of
images in a single directory. The extension is `jpg` except for few `png`
files.
Below are are first 10 lines of `images/metadata/images.csv.gz`:
```
image_id,height,width,path
010-mllS7JL,106,106,14/14fe8812.jpg
01dkn0Gyx0L,122,122,da/daab0cad.jpg
01sUPg0387L,111,111,d2/d2daaae9.jpg
1168jc-5r1L,186,186,3a/3a4e88e6.jpg
11RUV5Fs65L,30,500,d9/d91ab9cf.jpg
11X4pFHqYOL,35,500,20/20098c4d.jpg
11Y+Xpt1lfL,103,196,99/9987a1c8.jpg
11rL64ZLPYL,64,500,89/89a2ff4d.jpg
11xjmNF5TAL,117,88,ee/ee239f0f.jpg
```
* `images/original/<path>` - Original image data. This directory contains the
original high-resolution version of the images. See
`images/metadata/images.csv.gz` for details of image naming.
* `images/small/<path>` - Downscaled image data. This directory contains the
version of the images, where they have been downscaled such that their
largest axis (height or width) is a maximum of 256 pixels. See
`images/metadata/images.csv.gz` for details of image naming.
* `spins/metadata/spins.csv.gz` - Spin / 360º-View image metadata. This file
is a gzip-compressed comma-separated value (CSV) file with the following
fields: `spin_id`, `azimuth`, `image_id`, `height`, `width`, and `path`.
- `spin_id`: a unique identifier for the image sequence.
- `azimuth`: an integer between 0 and 71, representing the index in the spin
sequence and the azimuth of the camera (in steps of 5º).
- `image_id`: this id uniquely refers to an image. This is can be used
to retrieve the image data using the template:
`https://m.media-amazon.com/image/I/<image_id>.jpg` [^1].
- `height` and `width`: respectively, the height and width of the image.
- `path`: the location of the image file relative to the `spins/original/`
directory. The extension is `jpg` for all the images. The `path` is build
from the `spin_id` and `azimuth` using the template
`<spin_id>_<azimuth:02d>.jpg` and the first two characters of `spin_id`
are used to build a file hierarchy and reduce the number of files in a
single directory.
Below are are first 10 lines of `spins/metadata/spins.csv.gz`:
```
spin_id,azimuth,image_id,height,width,path
61c91265,0,41wqHws7a6L,248,1075,61/61c91265/61c91265_00.jpg
61c91265,1,41++eZZHP9L,248,1075,61/61c91265/61c91265_01.jpg
61c91265,2,41YF86LhGDL,248,1075,61/61c91265/61c91265_02.jpg
61c91265,3,41I5Zz-kbAL,248,1075,61/61c91265/61c91265_03.jpg
61c91265,4,41lAQM2Ys5L,248,1075,61/61c91265/61c91265_04.jpg
61c91265,5,41OJT+p8JgL,248,1075,61/61c91265/61c91265_05.jpg
61c91265,6,412kYqOnqHL,248,1075,61/61c91265/61c91265_06.jpg
61c91265,7,41rgUZ0NuFL,248,1075,61/61c91265/61c91265_07.jpg
61c91265,8,41PJ4ks-cWL,248,1075,61/61c91265/61c91265_08.jpg
```
* `spins/original/<path>` - Spin / 360º-View image files. Each file
corresponds to one row in `spins/metadata/spins.csv.gz`, named by the value
of the `path` column.
* `3dmodels/metadata/3dmodels.csv.gz` - 3d model metadata. This file is a
gzip-compressed comma-separated value (CSV) file with the following fields:
`3dmodel_id`, `path`, `meshes`, `materials`, `textures`, `images`,
`image_height_max`, `image_height_min`, `image_width_max`,
`image_width_min`, `vertices`, `faces`, `extent_x`, `extent_y`, `extent_z`.
- `3dmodel_id`: Reference for the 3d model, as provided in the `3dmodel_id`
field of the listings metadata
- `path`: Location of the 3d model, relative to `3dmodels/original/`
- `meshes`: Number of meshes in the geometry
- `materials`: Number of materials in the 3d model
- `textures`: Number of textures in the 3d model
- `images`: Number of image resources in the 3d model
- `image_{heigh,width}_{min,max}`: Minimal and maximal dimensions of the
image resources in the 3d model
- `vertices`: Number of vertices in the geometry
- `faces`: Number of faces in the geometry
- `extent_{x,y,z}`: Extent of the geometry in each dimension
Below are are first 10 lines of `3dmodels/metadata/3dmodels.csv.gz`:
```
3dmodel_id,path,meshes,materials,textures,images,image_height_max,image_height_min,image_width_max,image_width_min,vertices,faces,extent_x,extent_y,extent_z
B01N2PLWIL,L/B01N2PLWIL.glb,1,1,3,3,4096,4096,4096,4096,10990,14380,0.571499943733216,0.11684001048680606,0.07111982014177629
B075QFCHM9,9/B075QFCHM9.glb,1,1,3,3,2048,2048,2048,2048,11973,19568,1.840071976184845,1.0669103860855103,2.3675915002822876
B07H469871,1/B07H469871.glb,1,1,3,3,4096,4096,4096,4096,1602,1950,1.1113524436950684,1.3880813121795654,0.39794909954071045
B07H8V49M2,2/B07H8V49M2.glb,1,1,3,3,4096,4096,4096,4096,3760,5710,1.4998703368605968,2.11988401412964,0.5897879977087402
B07DBHPK4G,G/B07DBHPK4G.glb,1,1,3,3,4096,4096,4096,4096,13704,22736,0.37921489775180817,1.6228150129318237,0.37921497225761414
B0842LM2DN,N/B0842LM2DN.glb,1,1,3,3,4096,4096,4096,4096,4078,7584,0.22779017686843872,0.2348586767911911,0.22779015451669693
B07HK6B4D7,7/B07HK6B4D7.glb,1,1,4,4,2048,2048,2048,2048,12221,19268,0.1887289583683014,0.6650936603546143,0.421692430973053
B07B4FZN9H,H/B07B4FZN9H.glb,1,1,3,3,2048,2048,2048,2048,13595,22644,3.3838289976119995,0.9963648915290833,2.048073887825012
B07B4Z9BS4,4/B07B4Z9BS4.glb,1,1,4,4,2048,2048,2048,2048,9259,16178,0.2793540060520172,0.2693932056427002,0.2793540358543396
```
* `3dmodels/original/<path>` - 3d model files. The 3d models are provided in
the [glTF-2.0 format](
https://github.com/KhronosGroup/glTF/blob/master/specification/2.0/README.md
) (GLB/binary representation). All models adhere to the following
conventions:
1. Positive `Y` direction is up.
2. Positive `Z` direction is pointing to the *natural* front-side of the
product, wherever applicable.
3. Products that are designed to stand on a surface (i.e. a floor) are
centered on the origin, but translated up (towards positive `Y`) such
that they *stand* on the `Y=0` plane.
4. Products that are designed to hang from a surface (i.e. a ceiling) are
centered on the origin, but translated down (towards negative `Y`) such
that they *hang* from the `Y=0` plane.
5. Products that are designed to hang on a wall are centered on the origin,
but translated forward (towards positive `Z`) such that their backside
aligns with the `Z=0` plane.
* `archives/abo-listings.tar` - Contains all the files in `listings/` as a
tar archive.
* `archives/abo-images-original.tar` - Contains the metadata and original
images from `images/original/` as a tar archive.
* `archives/abo-images-small.tar` - Contains the metadata and downscaled
images from `images/small/` as a tar archive.
* `archives/abo-spins.tar` - Contains the metadata and images from `spins/`
as a tar archive.
* `archives/abo-3dmodels.tar` - Contains the metadata and images from `3dmodels/`
as a tar archive.
## Footnotes
[^1]: Importantly, there is no guarantee that those URLs will remain unchanged
and available on the long term, we thus recommend using the images provided in this
archive instead.
| bstds/abo_listings | [
"region:us"
]
| 2023-02-14T21:48:21+00:00 | {} | 2023-02-14T21:49:49+00:00 | []
| []
| TAGS
#region-us
| # Amazon Berkeley Objects (c) by URL
## License
This work is licensed under the Creative Commons Attribution-NonCommercial 4.0
International Public License. To obtain a copy of the full license, see
'LICENSE-CC-BY-NC-4.0.txt', visit
URL
or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
Under the following terms:
* Attribution — You must give appropriate credit, provide a link to the
license, and indicate if changes were made. You may do so in any reasonable
manner, but not in any way that suggests the licensor endorses you or your
use.
* NonCommercial — You may not use the material for commercial purposes.
* No additional restrictions — You may not apply legal terms or technological
measures that legally restrict others from doing anything the license
permits.
## Attribution
Credit for the data, including all images and 3d models, must be given to:
> URL
Credit for building the dataset, archives and benchmark sets must be given to:
> Matthieu Guillaumin (URL), Thomas Dideriksen (URL),
> Kenan Deng (URL), Himanshu Arora (URL),
> Jasmine Collins (UC Berkeley) and Jitendra Malik (UC Berkeley)
## Description
Amazon Berkeley Objects is a collection of 147,702 product listings with
multilingual metadata and 398,212 unique catalog images. 8,222 listings come
with turntable photography (also referred as *spin* or *360º-View* images), as
sequences of 24 or 72 images, for a total of 586,584 images in 8,209 unique
sequences. For 7,953 products, the collection also provides high-quality 3d
models, as glTF 2.0 files.
The collection is made of the following files:
* 'URL' - The present file.
* 'LICENSE-CC-BY-NC-4.0.txt' - The License file. You must read, agree and
comply to the License before using the Amazon Berkeley Objects data.
* 'listings/metadata/listings_<i>.URL' - Product description and metadata.
Each of the 16 files is encoded with UTF-8 and gzip-compressed. Each line of
the decompressed files corresponds to one product as a JSON object (see
URL or URL ). Each product JSON object
(a.k.a dictionary) has any number of the following keys:
- 'brand'
- Content: Brand name
- Format: '[{ "language_tag": <str>, "value": <str> }, ...]'
- 'bullet_point'
- Content: Important features of the products
- Format: '[{ "language_tag": <str>, "value": <str> }, ...]'
- 'color'
- Content: Color of the product as text
- Format: '[{"language_tag": <str>, "standardized_values": [<str>],
"value": <str>}, ...]'
- 'color_code'
- Content: Color of the product as HTML color code
- Format: '[<str>, ...]'
- 'country'
- Content: Country of the marketplace, as an
ISO 3166-1 alpha 2
code
- Format: '<str>'
- 'domain_name'
- Content: Domain name of the marketplace where the product is found.
A product listing in this collection is uniquely identified by
('item_id', 'domain_name')
- Format: '<str>'
- 'fabric_type'
- Content: Description of product fabric
- Format: '[{ "language_tag": <str>, "value": <str> }, ...]'
- 'finish_type'
- Content: Description of product finish
- Format: '[{ "language_tag": <str>, "value": <str> }, ...]'
- 'item_dimensions'
- Content: Dimensions of the product (height, width, length)
- Format: '{"height": {"normalized_value": {"unit": <str>, "value":
<float>}, "unit": <str>, "value": <float>}, "length":
{"normalized_value": {"unit": <str>, "value": <float>}, "unit": <str>,
"value": <float>}, "width": {"normalized_value": {"unit": <str>,
"value": <float>}, "unit": <str>, "value": <float>}}}'
- 'item_id'
- Content: The product reference id. A product listing in this
collection is uniquely identified by ('item_id', 'domain_name').
A corresponding product page may exist at
'https://www.<domain_name>/dp/<item_id>'
- Format: '<str>'
- 'item_keywords'
- Content: Keywords for the product
- Format: '[{ "language_tag": <str>, "value": <str> }, ...]'
- 'item_name'
- Content: The product name
- Format: '[{ "language_tag": <str>, "value": <str> }, ...]'
- 'item_shape'
- Content: Description of the product shape
- Format: '[{ "language_tag": <str>, "value": <str> }, ...]'
- 'item_weight'
- Content: The product weight
- Format: '[{"normalized_value": {"unit": <str>, "value": <float>},
"unit": <str>, "value": <float>}, ...]'
- 'main_image_id'
- Content: The main product image, provided as an 'image_id'. See the
descripton of 'images/metadata/URL' below
- Format: '<str>'
- 'marketplace'
- Content: Retail website name (Amazon, AmazonFresh, AmazonGo, ...)
- Format: '<str>'
- 'material'
- Content: Description of the product material
- Format: '[{ "language_tag": <str>, "value": <str> }, ...]'
- 'model_name'
- Content: Model name
- Format: '[{ "language_tag": <str>, "value": <str> }, ...]'
- 'model_number'
- Content: Model number
- Format: '[{ "language_tag": <str>, "value": <str> }, ...]'
- 'model_year'
- Content: Model year
- Format: '[{ "language_tag": <str>, "value": <int> }, ...]'
- 'node'
- Content: Location of the product in the category tree. A node page
may exist at 'https://www.<domain_name>/b/?node=<node_id>' for
browsing
- Format: '[{ "node_id": <int>, "path": <str>}, ...]'
- 'other_image_id'
- Content: Other available images for the product, provided as
'image_id'. See the description of 'images/metadata/URL'
below
- Format: '[<str>, ...]'
- 'pattern'
- Content: Product pattern
- Format: '[{ "language_tag": <str>, "value": <int> }, ...]'
- 'product_description'
- Content: Product description as HTML
- Format: '[{ "language_tag": <str>, "value": <int> }, ...]'
- 'product_type'
- Content: Product type (category)
- Format: '<str>'
- 'spin_id'
- Content: Reference to the 360º View image sequence. See the
description of 'spins/metadata/URL' below
- Format: '<str>'
- 'style'
- Content: Style of the product
- Format: '[{ "language_tag": <str>, "value": <int> }, ...]'
- '3dmodel_id'
- Content: Reference to the 3d model of the product. See the description
of '3dmodels/metadata/URL'
- Format: '<str>'
* 'images/metadata/URL' - Image metadata. This file is a
gzip-compressed comma-separated value (CSV) file with the following
columns: 'image_id', 'height', 'width', and 'path'.
- 'image_id' (string): this id uniquely refers to a product image. This id
can be used to retrieve the image data from Amazon's Content Delivery
Network (CDN) using the template:
'https://m.URL [^1],
where '<extension>' is composed of the characters following the dot in the
'path' field. Any value occurring in the 'main_image' and 'other_images'
attributes of product metadata is an 'image_id' present in this file.
- 'height' (int) and 'width' (int): respectively, the height and width of
the original image.
- 'path': the location of the image file relative to the 'images/original/'
or 'images/small/' directories. A path is composed of lowercase hex
characters ('0-9a-f') that also uniquely identifies images. The first two
characters are used to build a file hierarchy and reduce the number of
images in a single directory. The extension is 'jpg' except for few 'png'
files.
Below are are first 10 lines of 'images/metadata/URL':
* 'images/original/<path>' - Original image data. This directory contains the
original high-resolution version of the images. See
'images/metadata/URL' for details of image naming.
* 'images/small/<path>' - Downscaled image data. This directory contains the
version of the images, where they have been downscaled such that their
largest axis (height or width) is a maximum of 256 pixels. See
'images/metadata/URL' for details of image naming.
* 'spins/metadata/URL' - Spin / 360º-View image metadata. This file
is a gzip-compressed comma-separated value (CSV) file with the following
fields: 'spin_id', 'azimuth', 'image_id', 'height', 'width', and 'path'.
- 'spin_id': a unique identifier for the image sequence.
- 'azimuth': an integer between 0 and 71, representing the index in the spin
sequence and the azimuth of the camera (in steps of 5º).
- 'image_id': this id uniquely refers to an image. This is can be used
to retrieve the image data using the template:
'https://m.URL [^1].
- 'height' and 'width': respectively, the height and width of the image.
- 'path': the location of the image file relative to the 'spins/original/'
directory. The extension is 'jpg' for all the images. The 'path' is build
from the 'spin_id' and 'azimuth' using the template
'<spin_id>_<azimuth:02d>.jpg' and the first two characters of 'spin_id'
are used to build a file hierarchy and reduce the number of files in a
single directory.
Below are are first 10 lines of 'spins/metadata/URL':
* 'spins/original/<path>' - Spin / 360º-View image files. Each file
corresponds to one row in 'spins/metadata/URL', named by the value
of the 'path' column.
* '3dmodels/metadata/URL' - 3d model metadata. This file is a
gzip-compressed comma-separated value (CSV) file with the following fields:
'3dmodel_id', 'path', 'meshes', 'materials', 'textures', 'images',
'image_height_max', 'image_height_min', 'image_width_max',
'image_width_min', 'vertices', 'faces', 'extent_x', 'extent_y', 'extent_z'.
- '3dmodel_id': Reference for the 3d model, as provided in the '3dmodel_id'
field of the listings metadata
- 'path': Location of the 3d model, relative to '3dmodels/original/'
- 'meshes': Number of meshes in the geometry
- 'materials': Number of materials in the 3d model
- 'textures': Number of textures in the 3d model
- 'images': Number of image resources in the 3d model
- 'image_{heigh,width}_{min,max}': Minimal and maximal dimensions of the
image resources in the 3d model
- 'vertices': Number of vertices in the geometry
- 'faces': Number of faces in the geometry
- 'extent_{x,y,z}': Extent of the geometry in each dimension
Below are are first 10 lines of '3dmodels/metadata/URL':
* '3dmodels/original/<path>' - 3d model files. The 3d models are provided in
the glTF-2.0 format (GLB/binary representation). All models adhere to the following
conventions:
1. Positive 'Y' direction is up.
2. Positive 'Z' direction is pointing to the *natural* front-side of the
product, wherever applicable.
3. Products that are designed to stand on a surface (i.e. a floor) are
centered on the origin, but translated up (towards positive 'Y') such
that they *stand* on the 'Y=0' plane.
4. Products that are designed to hang from a surface (i.e. a ceiling) are
centered on the origin, but translated down (towards negative 'Y') such
that they *hang* from the 'Y=0' plane.
5. Products that are designed to hang on a wall are centered on the origin,
but translated forward (towards positive 'Z') such that their backside
aligns with the 'Z=0' plane.
* 'archives/URL' - Contains all the files in 'listings/' as a
tar archive.
* 'archives/URL' - Contains the metadata and original
images from 'images/original/' as a tar archive.
* 'archives/URL' - Contains the metadata and downscaled
images from 'images/small/' as a tar archive.
* 'archives/URL' - Contains the metadata and images from 'spins/'
as a tar archive.
* 'archives/URL' - Contains the metadata and images from '3dmodels/'
as a tar archive.
## Footnotes
[^1]: Importantly, there is no guarantee that those URLs will remain unchanged
and available on the long term, we thus recommend using the images provided in this
archive instead.
| [
"# Amazon Berkeley Objects (c) by URL",
"## License\n\nThis work is licensed under the Creative Commons Attribution-NonCommercial 4.0\nInternational Public License. To obtain a copy of the full license, see\n'LICENSE-CC-BY-NC-4.0.txt', visit\nURL\nor send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.\n\nUnder the following terms:\n\n * Attribution — You must give appropriate credit, provide a link to the\n license, and indicate if changes were made. You may do so in any reasonable\n manner, but not in any way that suggests the licensor endorses you or your\n use.\n\n * NonCommercial — You may not use the material for commercial purposes.\n\n * No additional restrictions — You may not apply legal terms or technological\n measures that legally restrict others from doing anything the license\n permits.",
"## Attribution\n\nCredit for the data, including all images and 3d models, must be given to:\n\n> URL\n\nCredit for building the dataset, archives and benchmark sets must be given to:\n\n> Matthieu Guillaumin (URL), Thomas Dideriksen (URL),\n> Kenan Deng (URL), Himanshu Arora (URL),\n> Jasmine Collins (UC Berkeley) and Jitendra Malik (UC Berkeley)",
"## Description\n\nAmazon Berkeley Objects is a collection of 147,702 product listings with\nmultilingual metadata and 398,212 unique catalog images. 8,222 listings come\nwith turntable photography (also referred as *spin* or *360º-View* images), as\nsequences of 24 or 72 images, for a total of 586,584 images in 8,209 unique\nsequences. For 7,953 products, the collection also provides high-quality 3d\nmodels, as glTF 2.0 files.\n\nThe collection is made of the following files:\n\n * 'URL' - The present file.\n\n * 'LICENSE-CC-BY-NC-4.0.txt' - The License file. You must read, agree and\n comply to the License before using the Amazon Berkeley Objects data.\n\n * 'listings/metadata/listings_<i>.URL' - Product description and metadata.\n Each of the 16 files is encoded with UTF-8 and gzip-compressed. Each line of\n the decompressed files corresponds to one product as a JSON object (see\n URL or URL ). Each product JSON object\n (a.k.a dictionary) has any number of the following keys:\n \n - 'brand'\n - Content: Brand name\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'bullet_point'\n - Content: Important features of the products\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'color'\n - Content: Color of the product as text\n - Format: '[{\"language_tag\": <str>, \"standardized_values\": [<str>],\n \"value\": <str>}, ...]'\n - 'color_code'\n - Content: Color of the product as HTML color code\n - Format: '[<str>, ...]'\n - 'country'\n - Content: Country of the marketplace, as an\n ISO 3166-1 alpha 2\n code\n - Format: '<str>'\n - 'domain_name'\n - Content: Domain name of the marketplace where the product is found.\n A product listing in this collection is uniquely identified by\n ('item_id', 'domain_name')\n - Format: '<str>'\n - 'fabric_type'\n - Content: Description of product fabric\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'finish_type'\n - Content: Description of product finish\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'item_dimensions'\n - Content: Dimensions of the product (height, width, length)\n - Format: '{\"height\": {\"normalized_value\": {\"unit\": <str>, \"value\":\n <float>}, \"unit\": <str>, \"value\": <float>}, \"length\":\n {\"normalized_value\": {\"unit\": <str>, \"value\": <float>}, \"unit\": <str>,\n \"value\": <float>}, \"width\": {\"normalized_value\": {\"unit\": <str>,\n \"value\": <float>}, \"unit\": <str>, \"value\": <float>}}}'\n - 'item_id'\n - Content: The product reference id. A product listing in this\n collection is uniquely identified by ('item_id', 'domain_name').\n A corresponding product page may exist at\n 'https://www.<domain_name>/dp/<item_id>'\n - Format: '<str>'\n - 'item_keywords'\n - Content: Keywords for the product\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'item_name'\n - Content: The product name\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'item_shape'\n - Content: Description of the product shape\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'item_weight'\n - Content: The product weight\n - Format: '[{\"normalized_value\": {\"unit\": <str>, \"value\": <float>},\n \"unit\": <str>, \"value\": <float>}, ...]'\n - 'main_image_id'\n - Content: The main product image, provided as an 'image_id'. See the\n descripton of 'images/metadata/URL' below\n - Format: '<str>'\n - 'marketplace'\n - Content: Retail website name (Amazon, AmazonFresh, AmazonGo, ...)\n - Format: '<str>'\n - 'material'\n - Content: Description of the product material\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'model_name'\n - Content: Model name\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'model_number'\n - Content: Model number\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'model_year'\n - Content: Model year\n - Format: '[{ \"language_tag\": <str>, \"value\": <int> }, ...]'\n - 'node'\n - Content: Location of the product in the category tree. A node page\n may exist at 'https://www.<domain_name>/b/?node=<node_id>' for\n browsing\n - Format: '[{ \"node_id\": <int>, \"path\": <str>}, ...]'\n - 'other_image_id'\n - Content: Other available images for the product, provided as\n 'image_id'. See the description of 'images/metadata/URL'\n below\n - Format: '[<str>, ...]'\n - 'pattern'\n - Content: Product pattern\n - Format: '[{ \"language_tag\": <str>, \"value\": <int> }, ...]'\n - 'product_description'\n - Content: Product description as HTML \n - Format: '[{ \"language_tag\": <str>, \"value\": <int> }, ...]'\n - 'product_type'\n - Content: Product type (category)\n - Format: '<str>'\n - 'spin_id'\n - Content: Reference to the 360º View image sequence. See the\n description of 'spins/metadata/URL' below\n - Format: '<str>'\n - 'style'\n - Content: Style of the product\n - Format: '[{ \"language_tag\": <str>, \"value\": <int> }, ...]'\n - '3dmodel_id'\n - Content: Reference to the 3d model of the product. See the description\n of '3dmodels/metadata/URL'\n - Format: '<str>'\n\n * 'images/metadata/URL' - Image metadata. This file is a\n gzip-compressed comma-separated value (CSV) file with the following\n columns: 'image_id', 'height', 'width', and 'path'.\n - 'image_id' (string): this id uniquely refers to a product image. This id\n can be used to retrieve the image data from Amazon's Content Delivery\n Network (CDN) using the template:\n 'https://m.URL [^1],\n where '<extension>' is composed of the characters following the dot in the\n 'path' field. Any value occurring in the 'main_image' and 'other_images'\n attributes of product metadata is an 'image_id' present in this file.\n - 'height' (int) and 'width' (int): respectively, the height and width of\n the original image.\n - 'path': the location of the image file relative to the 'images/original/'\n or 'images/small/' directories. A path is composed of lowercase hex\n characters ('0-9a-f') that also uniquely identifies images. The first two\n characters are used to build a file hierarchy and reduce the number of\n images in a single directory. The extension is 'jpg' except for few 'png'\n files.\n \n Below are are first 10 lines of 'images/metadata/URL':\n\n \n * 'images/original/<path>' - Original image data. This directory contains the\n original high-resolution version of the images. See\n 'images/metadata/URL' for details of image naming.\n\n * 'images/small/<path>' - Downscaled image data. This directory contains the\n version of the images, where they have been downscaled such that their\n largest axis (height or width) is a maximum of 256 pixels. See\n 'images/metadata/URL' for details of image naming.\n\n * 'spins/metadata/URL' - Spin / 360º-View image metadata. This file\n is a gzip-compressed comma-separated value (CSV) file with the following\n fields: 'spin_id', 'azimuth', 'image_id', 'height', 'width', and 'path'.\n - 'spin_id': a unique identifier for the image sequence.\n - 'azimuth': an integer between 0 and 71, representing the index in the spin\n sequence and the azimuth of the camera (in steps of 5º).\n - 'image_id': this id uniquely refers to an image. This is can be used\n to retrieve the image data using the template:\n 'https://m.URL [^1].\n - 'height' and 'width': respectively, the height and width of the image.\n - 'path': the location of the image file relative to the 'spins/original/'\n directory. The extension is 'jpg' for all the images. The 'path' is build\n from the 'spin_id' and 'azimuth' using the template\n '<spin_id>_<azimuth:02d>.jpg' and the first two characters of 'spin_id'\n are used to build a file hierarchy and reduce the number of files in a\n single directory.\n\n Below are are first 10 lines of 'spins/metadata/URL':\n\n\n * 'spins/original/<path>' - Spin / 360º-View image files. Each file\n corresponds to one row in 'spins/metadata/URL', named by the value\n of the 'path' column.\n\n * '3dmodels/metadata/URL' - 3d model metadata. This file is a\n gzip-compressed comma-separated value (CSV) file with the following fields:\n '3dmodel_id', 'path', 'meshes', 'materials', 'textures', 'images', \n 'image_height_max', 'image_height_min', 'image_width_max',\n 'image_width_min', 'vertices', 'faces', 'extent_x', 'extent_y', 'extent_z'.\n\n - '3dmodel_id': Reference for the 3d model, as provided in the '3dmodel_id'\n field of the listings metadata\n - 'path': Location of the 3d model, relative to '3dmodels/original/'\n - 'meshes': Number of meshes in the geometry\n - 'materials': Number of materials in the 3d model\n - 'textures': Number of textures in the 3d model\n - 'images': Number of image resources in the 3d model\n - 'image_{heigh,width}_{min,max}': Minimal and maximal dimensions of the\n image resources in the 3d model\n - 'vertices': Number of vertices in the geometry\n - 'faces': Number of faces in the geometry\n - 'extent_{x,y,z}': Extent of the geometry in each dimension\n \n Below are are first 10 lines of '3dmodels/metadata/URL':\n\n\n * '3dmodels/original/<path>' - 3d model files. The 3d models are provided in\n the glTF-2.0 format (GLB/binary representation). All models adhere to the following\n conventions:\n 1. Positive 'Y' direction is up.\n 2. Positive 'Z' direction is pointing to the *natural* front-side of the\n product, wherever applicable.\n 3. Products that are designed to stand on a surface (i.e. a floor) are\n centered on the origin, but translated up (towards positive 'Y') such\n that they *stand* on the 'Y=0' plane.\n 4. Products that are designed to hang from a surface (i.e. a ceiling) are\n centered on the origin, but translated down (towards negative 'Y') such\n that they *hang* from the 'Y=0' plane.\n 5. Products that are designed to hang on a wall are centered on the origin,\n but translated forward (towards positive 'Z') such that their backside\n aligns with the 'Z=0' plane.\n\n * 'archives/URL' - Contains all the files in 'listings/' as a\n tar archive.\n\n * 'archives/URL' - Contains the metadata and original\n images from 'images/original/' as a tar archive.\n\n * 'archives/URL' - Contains the metadata and downscaled\n images from 'images/small/' as a tar archive.\n\n * 'archives/URL' - Contains the metadata and images from 'spins/'\n as a tar archive.\n \n * 'archives/URL' - Contains the metadata and images from '3dmodels/'\n as a tar archive.",
"## Footnotes\n\n[^1]: Importantly, there is no guarantee that those URLs will remain unchanged\nand available on the long term, we thus recommend using the images provided in this\narchive instead."
]
| [
"TAGS\n#region-us \n",
"# Amazon Berkeley Objects (c) by URL",
"## License\n\nThis work is licensed under the Creative Commons Attribution-NonCommercial 4.0\nInternational Public License. To obtain a copy of the full license, see\n'LICENSE-CC-BY-NC-4.0.txt', visit\nURL\nor send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.\n\nUnder the following terms:\n\n * Attribution — You must give appropriate credit, provide a link to the\n license, and indicate if changes were made. You may do so in any reasonable\n manner, but not in any way that suggests the licensor endorses you or your\n use.\n\n * NonCommercial — You may not use the material for commercial purposes.\n\n * No additional restrictions — You may not apply legal terms or technological\n measures that legally restrict others from doing anything the license\n permits.",
"## Attribution\n\nCredit for the data, including all images and 3d models, must be given to:\n\n> URL\n\nCredit for building the dataset, archives and benchmark sets must be given to:\n\n> Matthieu Guillaumin (URL), Thomas Dideriksen (URL),\n> Kenan Deng (URL), Himanshu Arora (URL),\n> Jasmine Collins (UC Berkeley) and Jitendra Malik (UC Berkeley)",
"## Description\n\nAmazon Berkeley Objects is a collection of 147,702 product listings with\nmultilingual metadata and 398,212 unique catalog images. 8,222 listings come\nwith turntable photography (also referred as *spin* or *360º-View* images), as\nsequences of 24 or 72 images, for a total of 586,584 images in 8,209 unique\nsequences. For 7,953 products, the collection also provides high-quality 3d\nmodels, as glTF 2.0 files.\n\nThe collection is made of the following files:\n\n * 'URL' - The present file.\n\n * 'LICENSE-CC-BY-NC-4.0.txt' - The License file. You must read, agree and\n comply to the License before using the Amazon Berkeley Objects data.\n\n * 'listings/metadata/listings_<i>.URL' - Product description and metadata.\n Each of the 16 files is encoded with UTF-8 and gzip-compressed. Each line of\n the decompressed files corresponds to one product as a JSON object (see\n URL or URL ). Each product JSON object\n (a.k.a dictionary) has any number of the following keys:\n \n - 'brand'\n - Content: Brand name\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'bullet_point'\n - Content: Important features of the products\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'color'\n - Content: Color of the product as text\n - Format: '[{\"language_tag\": <str>, \"standardized_values\": [<str>],\n \"value\": <str>}, ...]'\n - 'color_code'\n - Content: Color of the product as HTML color code\n - Format: '[<str>, ...]'\n - 'country'\n - Content: Country of the marketplace, as an\n ISO 3166-1 alpha 2\n code\n - Format: '<str>'\n - 'domain_name'\n - Content: Domain name of the marketplace where the product is found.\n A product listing in this collection is uniquely identified by\n ('item_id', 'domain_name')\n - Format: '<str>'\n - 'fabric_type'\n - Content: Description of product fabric\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'finish_type'\n - Content: Description of product finish\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'item_dimensions'\n - Content: Dimensions of the product (height, width, length)\n - Format: '{\"height\": {\"normalized_value\": {\"unit\": <str>, \"value\":\n <float>}, \"unit\": <str>, \"value\": <float>}, \"length\":\n {\"normalized_value\": {\"unit\": <str>, \"value\": <float>}, \"unit\": <str>,\n \"value\": <float>}, \"width\": {\"normalized_value\": {\"unit\": <str>,\n \"value\": <float>}, \"unit\": <str>, \"value\": <float>}}}'\n - 'item_id'\n - Content: The product reference id. A product listing in this\n collection is uniquely identified by ('item_id', 'domain_name').\n A corresponding product page may exist at\n 'https://www.<domain_name>/dp/<item_id>'\n - Format: '<str>'\n - 'item_keywords'\n - Content: Keywords for the product\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'item_name'\n - Content: The product name\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'item_shape'\n - Content: Description of the product shape\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'item_weight'\n - Content: The product weight\n - Format: '[{\"normalized_value\": {\"unit\": <str>, \"value\": <float>},\n \"unit\": <str>, \"value\": <float>}, ...]'\n - 'main_image_id'\n - Content: The main product image, provided as an 'image_id'. See the\n descripton of 'images/metadata/URL' below\n - Format: '<str>'\n - 'marketplace'\n - Content: Retail website name (Amazon, AmazonFresh, AmazonGo, ...)\n - Format: '<str>'\n - 'material'\n - Content: Description of the product material\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'model_name'\n - Content: Model name\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'model_number'\n - Content: Model number\n - Format: '[{ \"language_tag\": <str>, \"value\": <str> }, ...]'\n - 'model_year'\n - Content: Model year\n - Format: '[{ \"language_tag\": <str>, \"value\": <int> }, ...]'\n - 'node'\n - Content: Location of the product in the category tree. A node page\n may exist at 'https://www.<domain_name>/b/?node=<node_id>' for\n browsing\n - Format: '[{ \"node_id\": <int>, \"path\": <str>}, ...]'\n - 'other_image_id'\n - Content: Other available images for the product, provided as\n 'image_id'. See the description of 'images/metadata/URL'\n below\n - Format: '[<str>, ...]'\n - 'pattern'\n - Content: Product pattern\n - Format: '[{ \"language_tag\": <str>, \"value\": <int> }, ...]'\n - 'product_description'\n - Content: Product description as HTML \n - Format: '[{ \"language_tag\": <str>, \"value\": <int> }, ...]'\n - 'product_type'\n - Content: Product type (category)\n - Format: '<str>'\n - 'spin_id'\n - Content: Reference to the 360º View image sequence. See the\n description of 'spins/metadata/URL' below\n - Format: '<str>'\n - 'style'\n - Content: Style of the product\n - Format: '[{ \"language_tag\": <str>, \"value\": <int> }, ...]'\n - '3dmodel_id'\n - Content: Reference to the 3d model of the product. See the description\n of '3dmodels/metadata/URL'\n - Format: '<str>'\n\n * 'images/metadata/URL' - Image metadata. This file is a\n gzip-compressed comma-separated value (CSV) file with the following\n columns: 'image_id', 'height', 'width', and 'path'.\n - 'image_id' (string): this id uniquely refers to a product image. This id\n can be used to retrieve the image data from Amazon's Content Delivery\n Network (CDN) using the template:\n 'https://m.URL [^1],\n where '<extension>' is composed of the characters following the dot in the\n 'path' field. Any value occurring in the 'main_image' and 'other_images'\n attributes of product metadata is an 'image_id' present in this file.\n - 'height' (int) and 'width' (int): respectively, the height and width of\n the original image.\n - 'path': the location of the image file relative to the 'images/original/'\n or 'images/small/' directories. A path is composed of lowercase hex\n characters ('0-9a-f') that also uniquely identifies images. The first two\n characters are used to build a file hierarchy and reduce the number of\n images in a single directory. The extension is 'jpg' except for few 'png'\n files.\n \n Below are are first 10 lines of 'images/metadata/URL':\n\n \n * 'images/original/<path>' - Original image data. This directory contains the\n original high-resolution version of the images. See\n 'images/metadata/URL' for details of image naming.\n\n * 'images/small/<path>' - Downscaled image data. This directory contains the\n version of the images, where they have been downscaled such that their\n largest axis (height or width) is a maximum of 256 pixels. See\n 'images/metadata/URL' for details of image naming.\n\n * 'spins/metadata/URL' - Spin / 360º-View image metadata. This file\n is a gzip-compressed comma-separated value (CSV) file with the following\n fields: 'spin_id', 'azimuth', 'image_id', 'height', 'width', and 'path'.\n - 'spin_id': a unique identifier for the image sequence.\n - 'azimuth': an integer between 0 and 71, representing the index in the spin\n sequence and the azimuth of the camera (in steps of 5º).\n - 'image_id': this id uniquely refers to an image. This is can be used\n to retrieve the image data using the template:\n 'https://m.URL [^1].\n - 'height' and 'width': respectively, the height and width of the image.\n - 'path': the location of the image file relative to the 'spins/original/'\n directory. The extension is 'jpg' for all the images. The 'path' is build\n from the 'spin_id' and 'azimuth' using the template\n '<spin_id>_<azimuth:02d>.jpg' and the first two characters of 'spin_id'\n are used to build a file hierarchy and reduce the number of files in a\n single directory.\n\n Below are are first 10 lines of 'spins/metadata/URL':\n\n\n * 'spins/original/<path>' - Spin / 360º-View image files. Each file\n corresponds to one row in 'spins/metadata/URL', named by the value\n of the 'path' column.\n\n * '3dmodels/metadata/URL' - 3d model metadata. This file is a\n gzip-compressed comma-separated value (CSV) file with the following fields:\n '3dmodel_id', 'path', 'meshes', 'materials', 'textures', 'images', \n 'image_height_max', 'image_height_min', 'image_width_max',\n 'image_width_min', 'vertices', 'faces', 'extent_x', 'extent_y', 'extent_z'.\n\n - '3dmodel_id': Reference for the 3d model, as provided in the '3dmodel_id'\n field of the listings metadata\n - 'path': Location of the 3d model, relative to '3dmodels/original/'\n - 'meshes': Number of meshes in the geometry\n - 'materials': Number of materials in the 3d model\n - 'textures': Number of textures in the 3d model\n - 'images': Number of image resources in the 3d model\n - 'image_{heigh,width}_{min,max}': Minimal and maximal dimensions of the\n image resources in the 3d model\n - 'vertices': Number of vertices in the geometry\n - 'faces': Number of faces in the geometry\n - 'extent_{x,y,z}': Extent of the geometry in each dimension\n \n Below are are first 10 lines of '3dmodels/metadata/URL':\n\n\n * '3dmodels/original/<path>' - 3d model files. The 3d models are provided in\n the glTF-2.0 format (GLB/binary representation). All models adhere to the following\n conventions:\n 1. Positive 'Y' direction is up.\n 2. Positive 'Z' direction is pointing to the *natural* front-side of the\n product, wherever applicable.\n 3. Products that are designed to stand on a surface (i.e. a floor) are\n centered on the origin, but translated up (towards positive 'Y') such\n that they *stand* on the 'Y=0' plane.\n 4. Products that are designed to hang from a surface (i.e. a ceiling) are\n centered on the origin, but translated down (towards negative 'Y') such\n that they *hang* from the 'Y=0' plane.\n 5. Products that are designed to hang on a wall are centered on the origin,\n but translated forward (towards positive 'Z') such that their backside\n aligns with the 'Z=0' plane.\n\n * 'archives/URL' - Contains all the files in 'listings/' as a\n tar archive.\n\n * 'archives/URL' - Contains the metadata and original\n images from 'images/original/' as a tar archive.\n\n * 'archives/URL' - Contains the metadata and downscaled\n images from 'images/small/' as a tar archive.\n\n * 'archives/URL' - Contains the metadata and images from 'spins/'\n as a tar archive.\n \n * 'archives/URL' - Contains the metadata and images from '3dmodels/'\n as a tar archive.",
"## Footnotes\n\n[^1]: Importantly, there is no guarantee that those URLs will remain unchanged\nand available on the long term, we thus recommend using the images provided in this\narchive instead."
]
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6,
12,
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94,
3271,
45
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"passage: TAGS\n#region-us \n# Amazon Berkeley Objects (c) by URL## License\n\nThis work is licensed under the Creative Commons Attribution-NonCommercial 4.0\nInternational Public License. To obtain a copy of the full license, see\n'LICENSE-CC-BY-NC-4.0.txt', visit\nURL\nor send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.\n\nUnder the following terms:\n\n * Attribution — You must give appropriate credit, provide a link to the\n license, and indicate if changes were made. You may do so in any reasonable\n manner, but not in any way that suggests the licensor endorses you or your\n use.\n\n * NonCommercial — You may not use the material for commercial purposes.\n\n * No additional restrictions — You may not apply legal terms or technological\n measures that legally restrict others from doing anything the license\n permits.## Attribution\n\nCredit for the data, including all images and 3d models, must be given to:\n\n> URL\n\nCredit for building the dataset, archives and benchmark sets must be given to:\n\n> Matthieu Guillaumin (URL), Thomas Dideriksen (URL),\n> Kenan Deng (URL), Himanshu Arora (URL),\n> Jasmine Collins (UC Berkeley) and Jitendra Malik (UC Berkeley)"
]
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|
75f0bc9fde5a110864cf39a840469c0bb64d1903 | # Dataset Card for "samsum_nor_final"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jkorsvik/samsum_nor_final | [
"region:us"
]
| 2023-02-14T21:50:01+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "dialogue", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6447450, "num_examples": 14732}, {"name": "test", "num_bytes": 358829, "num_examples": 819}, {"name": "validation", "num_bytes": 354580, "num_examples": 818}], "download_size": 4502615, "dataset_size": 7160859}} | 2023-02-14T21:50:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "samsum_nor_final"
More Information needed | [
"# Dataset Card for \"samsum_nor_final\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"samsum_nor_final\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"samsum_nor_final\"\n\nMore Information needed"
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|
6e69ccfa82a536d6a3ab0e6b91df8cfeaf804801 | # Dataset Card for "wikitext2_VALUE"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | liuyanchen1015/wikitext2_VALUE | [
"region:us"
]
| 2023-02-14T22:19:50+00:00 | {"dataset_info": {"features": [{"name": "sentence-glue", "dtype": "string"}, {"name": "sentence-glue-html", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "sentence-ass", "dtype": "int64"}, {"name": "sentence-been_done", "dtype": "int64"}, {"name": "sentence-dey_it", "dtype": "int64"}, {"name": "sentence-drop_aux", "dtype": "int64"}, {"name": "sentence-got", "dtype": "int64"}, {"name": "sentence-lexical", "dtype": "int64"}, {"name": "sentence-negative_concord", "dtype": "int64"}, {"name": "sentence-negative_inversion", "dtype": "int64"}, {"name": "sentence-null_genetive", "dtype": "int64"}, {"name": "sentence-null_relcl", "dtype": "int64"}, {"name": "sentence-total", "dtype": "int64"}, {"name": "sentence-uninflect", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 4493287, "num_examples": 2891}, {"name": "train", "num_bytes": 38101936, "num_examples": 23754}, {"name": "validation", "num_bytes": 3962066, "num_examples": 2411}], "download_size": 24290252, "dataset_size": 46557289}} | 2023-02-14T22:19:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wikitext2_VALUE"
More Information needed | [
"# Dataset Card for \"wikitext2_VALUE\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wikitext2_VALUE\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wikitext2_VALUE\"\n\nMore Information needed"
]
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|
ed95c56542702cfac8cbd172a18400dfb3e00e0d | # Dataset Card for "dilbert-comic-sample-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | akadhim-ai/dilbert-comic-sample-dataset | [
"region:us"
]
| 2023-02-14T23:35:56+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 530433.0, "num_examples": 7}], "download_size": 531593, "dataset_size": 530433.0}} | 2023-02-14T23:36:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dilbert-comic-sample-dataset"
More Information needed | [
"# Dataset Card for \"dilbert-comic-sample-dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dilbert-comic-sample-dataset\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"dilbert-comic-sample-dataset\"\n\nMore Information needed"
]
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|
3937b259bc835478f6a14ba7dd016d8a4f08f808 | # Dataset Card for "iris"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | comet-team/iris | [
"region:us"
]
| 2023-02-15T00:53:32+00:00 | {"dataset_info": {"features": [{"name": "Id", "dtype": "int64"}, {"name": "SepalLengthCm", "dtype": "float64"}, {"name": "SepalWidthCm", "dtype": "float64"}, {"name": "PetalLengthCm", "dtype": "float64"}, {"name": "PetalWidthCm", "dtype": "float64"}, {"name": "Species", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 8600, "num_examples": 150}], "download_size": 4333, "dataset_size": 8600}} | 2023-02-15T00:56:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "iris"
More Information needed | [
"# Dataset Card for \"iris\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"iris\"\n\nMore Information needed"
]
| [
6,
11
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"iris\"\n\nMore Information needed"
]
| [
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|
e1ee838c6f7894c8d695bfd14c1b5f1ce5134c7d | # svg_icons
## Dataset Description
- **Homepage:[text_to_icon.kmoz.dev](https://text_to_icon.kmoz.dev)**
- **Repository: [@KM8Oz/text_to_icon](https://github.com/KM8Oz/text_to_icon)**
### Dataset Summary
This dataset card aims to be classify svgs icons intos images/label set
## Dataset Structure
- dataset_info:
- features:
- name: word
- dtype: string
- name: icon
- dtype: string | niceblueman/icons_dataset | [
"size_categories:100M<n<1B",
"language:en",
"license:apache-2.0",
"icons",
"svgs",
"doi:10.57967/hf/0375",
"region:us"
]
| 2023-02-15T02:07:41+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["100M<n<1B"], "pretty_name": "SQuAD", "dataset_info": {"features": [{"name": "word", "dtype": "string"}, {"name": "icon", "dtype": "string"}], "config_name": "svg_icons", "splits": [{"name": "train", "num_bytes": 240641, "num_examples": 22}], "download_size": 0, "dataset_size": 240641}, "tags": ["icons", "svgs"]} | 2023-02-15T15:01:32+00:00 | []
| [
"en"
]
| TAGS
#size_categories-100M<n<1B #language-English #license-apache-2.0 #icons #svgs #doi-10.57967/hf/0375 #region-us
| # svg_icons
## Dataset Description
- Homepage:text_to_icon.URL
- Repository: @KM8Oz/text_to_icon
### Dataset Summary
This dataset card aims to be classify svgs icons intos images/label set
## Dataset Structure
- dataset_info:
- features:
- name: word
- dtype: string
- name: icon
- dtype: string | [
"# svg_icons",
"## Dataset Description\n\n- Homepage:text_to_icon.URL \n- Repository: @KM8Oz/text_to_icon",
"### Dataset Summary\n\nThis dataset card aims to be classify svgs icons intos images/label set",
"## Dataset Structure\n - dataset_info:\n - features:\n - name: word\n - dtype: string\n - name: icon\n - dtype: string"
]
| [
"TAGS\n#size_categories-100M<n<1B #language-English #license-apache-2.0 #icons #svgs #doi-10.57967/hf/0375 #region-us \n",
"# svg_icons",
"## Dataset Description\n\n- Homepage:text_to_icon.URL \n- Repository: @KM8Oz/text_to_icon",
"### Dataset Summary\n\nThis dataset card aims to be classify svgs icons intos images/label set",
"## Dataset Structure\n - dataset_info:\n - features:\n - name: word\n - dtype: string\n - name: icon\n - dtype: string"
]
| [
48,
6,
32,
25,
33
]
| [
"passage: TAGS\n#size_categories-100M<n<1B #language-English #license-apache-2.0 #icons #svgs #doi-10.57967/hf/0375 #region-us \n# svg_icons## Dataset Description\n\n- Homepage:text_to_icon.URL \n- Repository: @KM8Oz/text_to_icon### Dataset Summary\n\nThis dataset card aims to be classify svgs icons intos images/label set## Dataset Structure\n - dataset_info:\n - features:\n - name: word\n - dtype: string\n - name: icon\n - dtype: string"
]
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|
758ea4b2c7b8ba339ae0af055ee9a40813dc8a95 | # AutoTrain Dataset for project: code-mixed-language-identification
## Dataset Description
This dataset has been automatically processed by AutoTrain for project code-mixed-language-identification.
### 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
[
{
"feat_Unnamed: 0": 1104,
"tokens": [
"@user",
"salah",
"satu",
"dari",
"4",
"anak",
"dr",
"sunardi",
"ada",
"yg",
"berprofesi",
"sbg",
"dokter",
"juga",
",",
"lulusan",
"unair",
",",
"sudah",
"selesai",
"koas",
"dan",
"intern",
"tolong",
"disupport",
"pak",
"anak",
"beliau"
],
"tags": [
6,
1,
1,
1,
6,
1,
6,
6,
1,
1,
1,
1,
1,
1,
6,
1,
6,
6,
1,
1,
1,
1,
0,
1,
3,
1,
1,
1
]
},
{
"feat_Unnamed: 0": 239,
"tokens": [
"@user",
"kamu",
"pake",
"apa",
"toh",
"?",
"aku",
"pake",
"xl",
"banter",
"lho",
"di",
"apartemen",
"pun",
"bisa",
"download",
"yutub"
],
"tags": [
6,
1,
1,
1,
1,
6,
1,
1,
6,
1,
1,
1,
1,
1,
1,
0,
6
]
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"feat_Unnamed: 0": "Value(dtype='int64', id=None)",
"tokens": "Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)",
"tags": "Sequence(feature=ClassLabel(names=['EN', 'ID', 'JV', 'MIX_ID_EN', 'MIX_ID_JV', 'MIX_JV_EN', 'OTH'], id=None), length=-1, 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 | 1105 |
| valid | 438 |
| fathan/autotrain-data-code-mixed-language-identification | [
"task_categories:token-classification",
"region:us"
]
| 2023-02-15T02:54:00+00:00 | {"task_categories": ["token-classification"]} | 2023-02-15T03:19:07+00:00 | []
| []
| TAGS
#task_categories-token-classification #region-us
| AutoTrain Dataset for project: code-mixed-language-identification
=================================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project code-mixed-language-identification.
### 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-token-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:"
]
| [
18,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-token-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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|
611c3b1a210b3b1fc2f487d613ff131c91efda33 | # Dataset Card for "VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_1000 | [
"region:us"
]
| 2023-02-15T02:57:08+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 25491823, "num_examples": 1000}], "download_size": 4914865, "dataset_size": 25491823}} | 2023-02-15T02:57:11+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_1000"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_1000\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_1000\"\n\nMore Information needed"
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_350m_VQAv2_visclues_ns_1000\"\n\nMore Information needed"
]
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|
034beb7c55289273e23c00633cf3c26fe55c38e8 | 7 datasets used for RSE
All contains sentence pairs and their respective relations. | binwang/RSE-sentence-relational-data | [
"region:us"
]
| 2023-02-15T03:05:15+00:00 | {} | 2023-02-15T03:25:34+00:00 | []
| []
| TAGS
#region-us
| 7 datasets used for RSE
All contains sentence pairs and their respective relations. | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
| [
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|
bae822a80b5b6b6ba58ff78cdd08ac25888a505b | # Dataset Card for "VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_1000 | [
"region:us"
]
| 2023-02-15T03:24:28+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_8", "num_bytes": 25491982, "num_examples": 1000}], "download_size": 4915915, "dataset_size": 25491982}} | 2023-02-15T03:24:32+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_1000"
More Information needed | [
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_1000\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_1000\"\n\nMore Information needed"
]
| [
6,
42
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"VQAv2_sample_validation_facebook_opt_1.3b_VQAv2_visclues_ns_1000\"\n\nMore Information needed"
]
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|
5f72aa2ee768ef6df7368bb78dcca0a654e0ff6c | Static split of Anthropic's Helpful Harmless dataset. Contains base-online and rejection sampled outputs. | Dahoas/static-hh | [
"region:us"
]
| 2023-02-15T03:53:36+00:00 | {"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "response", "dtype": "string"}, {"name": "chosen", "dtype": "string"}, {"name": "rejected", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 143664651, "num_examples": 96256}, {"name": "test", "num_bytes": 7649255, "num_examples": 5103}], "download_size": 90825631, "dataset_size": 151313906}} | 2023-03-06T00:11:55+00:00 | []
| []
| TAGS
#region-us
| Static split of Anthropic's Helpful Harmless dataset. Contains base-online and rejection sampled outputs. | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
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