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eff8c4c762ffc656d3b4180b45406727334fcce1 | # Dataset Card for "OxfordFlowers_test_facebook_opt_350m_Visclues_ns_6149_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordFlowers_test_facebook_opt_350m_Visclues_ns_6149_random | [
"region:us"
]
| 2023-02-04T04:46:17+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 270234974.375, "num_examples": 6149}, {"name": "fewshot_3_bs_16", "num_bytes": 274952857.375, "num_examples": 6149}], "download_size": 534121686, "dataset_size": 545187831.75}} | 2023-02-04T06:55:41+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OxfordFlowers_test_facebook_opt_350m_Visclues_ns_6149_random"
More Information needed | [
"# Dataset Card for \"OxfordFlowers_test_facebook_opt_350m_Visclues_ns_6149_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordFlowers_test_facebook_opt_350m_Visclues_ns_6149_random\"\n\nMore Information needed"
]
| [
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|
9ceb37b7773d4e25acc49962aec3ae7dd2e8141b | # range3/wiki40b-ja
This dataset consists of three parquet files from the wiki40b dataset with only Japanese data extracted. It is generated by the following python code.
このデータセットは、wiki40bデータセットの日本語データのみを抽出した3つのparquetファイルで構成されます。以下のpythonコードによって生成しています。
```py
import datasets
dss = datasets.load_dataset(
"wiki40b",
"ja",
beam_runner="DirectRunner",
)
for split,ds in dss.items():
ds.to_parquet(f"wikipedia-ja-20230101/{split}.parquet")
``` | range3/wiki40b-ja | [
"task_categories:text-generation",
"task_categories:fill-mask",
"language:ja",
"region:us"
]
| 2023-02-04T04:54:17+00:00 | {"language": ["ja"], "task_categories": ["text-generation", "fill-mask"]} | 2023-02-04T05:44:21+00:00 | []
| [
"ja"
]
| TAGS
#task_categories-text-generation #task_categories-fill-mask #language-Japanese #region-us
| # range3/wiki40b-ja
This dataset consists of three parquet files from the wiki40b dataset with only Japanese data extracted. It is generated by the following python code.
このデータセットは、wiki40bデータセットの日本語データのみを抽出した3つのparquetファイルで構成されます。以下のpythonコードによって生成しています。
| [
"# range3/wiki40b-ja\n\nThis dataset consists of three parquet files from the wiki40b dataset with only Japanese data extracted. It is generated by the following python code.\n\nこのデータセットは、wiki40bデータセットの日本語データのみを抽出した3つのparquetファイルで構成されます。以下のpythonコードによって生成しています。"
]
| [
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #language-Japanese #region-us \n",
"# range3/wiki40b-ja\n\nThis dataset consists of three parquet files from the wiki40b dataset with only Japanese data extracted. It is generated by the following python code.\n\nこのデータセットは、wiki40bデータセットの日本語データのみを抽出した3つのparquetファイルで構成されます。以下のpythonコードによって生成しています。"
]
| [
34,
77
]
| [
"passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #language-Japanese #region-us \n# range3/wiki40b-ja\n\nThis dataset consists of three parquet files from the wiki40b dataset with only Japanese data extracted. It is generated by the following python code.\n\nこのデータセットは、wiki40bデータセットの日本語データのみを抽出した3つのparquetファイルで構成されます。以下のpythonコードによって生成しています。"
]
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|
f3c9cf70baafecd7c693609622fdd680be16a61f | # Dataset Card for "OxfordFlowers_test_facebook_opt_1.3b_Visclues_ns_6149_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordFlowers_test_facebook_opt_1.3b_Visclues_ns_6149_random | [
"region:us"
]
| 2023-02-04T05:02:19+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 270233527.375, "num_examples": 6149}, {"name": "fewshot_3_bs_16", "num_bytes": 274949398.375, "num_examples": 6149}], "download_size": 534137349, "dataset_size": 545182925.75}} | 2023-02-04T07:27:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OxfordFlowers_test_facebook_opt_1.3b_Visclues_ns_6149_random"
More Information needed | [
"# Dataset Card for \"OxfordFlowers_test_facebook_opt_1.3b_Visclues_ns_6149_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordFlowers_test_facebook_opt_1.3b_Visclues_ns_6149_random\"\n\nMore Information needed"
]
| [
6,
36
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordFlowers_test_facebook_opt_1.3b_Visclues_ns_6149_random\"\n\nMore Information needed"
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|
5f83e950d0581df275911f8bed66939e361e697f | # range3/cc100-ja
This dataset consists of parquet files from the cc100 dataset with only the Japanese language extracted and sharded.
このデータセットは、cc100データセットの日本語のみを抽出し、シャーディングしたparquetファイルで構成されます。 | range3/cc100-ja | [
"task_categories:text-generation",
"task_categories:fill-mask",
"language:ja",
"license:unknown",
"region:us"
]
| 2023-02-04T05:10:34+00:00 | {"language": ["ja"], "license": "unknown", "task_categories": ["text-generation", "fill-mask"]} | 2023-02-04T05:43:32+00:00 | []
| [
"ja"
]
| TAGS
#task_categories-text-generation #task_categories-fill-mask #language-Japanese #license-unknown #region-us
| # range3/cc100-ja
This dataset consists of parquet files from the cc100 dataset with only the Japanese language extracted and sharded.
このデータセットは、cc100データセットの日本語のみを抽出し、シャーディングしたparquetファイルで構成されます。 | [
"# range3/cc100-ja\nThis dataset consists of parquet files from the cc100 dataset with only the Japanese language extracted and sharded.\n\nこのデータセットは、cc100データセットの日本語のみを抽出し、シャーディングしたparquetファイルで構成されます。"
]
| [
"TAGS\n#task_categories-text-generation #task_categories-fill-mask #language-Japanese #license-unknown #region-us \n",
"# range3/cc100-ja\nThis dataset consists of parquet files from the cc100 dataset with only the Japanese language extracted and sharded.\n\nこのデータセットは、cc100データセットの日本語のみを抽出し、シャーディングしたparquetファイルで構成されます。"
]
| [
41,
61
]
| [
"passage: TAGS\n#task_categories-text-generation #task_categories-fill-mask #language-Japanese #license-unknown #region-us \n# range3/cc100-ja\nThis dataset consists of parquet files from the cc100 dataset with only the Japanese language extracted and sharded.\n\nこのデータセットは、cc100データセットの日本語のみを抽出し、シャーディングしたparquetファイルで構成されます。"
]
| [
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|
44c81c75462c0440236ecbfb47c992d7857e5ace | # Dataset Card for "OxfordFlowers_test_facebook_opt_2.7b_Visclues_ns_6149_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordFlowers_test_facebook_opt_2.7b_Visclues_ns_6149_random | [
"region:us"
]
| 2023-02-04T05:27:26+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 270234457.375, "num_examples": 6149}, {"name": "fewshot_3_bs_16", "num_bytes": 274950362.375, "num_examples": 6149}], "download_size": 534140897, "dataset_size": 545184819.75}} | 2023-02-04T08:17:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OxfordFlowers_test_facebook_opt_2.7b_Visclues_ns_6149_random"
More Information needed | [
"# Dataset Card for \"OxfordFlowers_test_facebook_opt_2.7b_Visclues_ns_6149_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordFlowers_test_facebook_opt_2.7b_Visclues_ns_6149_random\"\n\nMore Information needed"
]
| [
6,
37
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordFlowers_test_facebook_opt_2.7b_Visclues_ns_6149_random\"\n\nMore Information needed"
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|
cbf55f4429c983246b194fec3ff9195d229c3dd9 | # Dataset Card for "nowiki_abstract_second_scrape_split2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jkorsvik/nowiki_abstract_second_scrape_split2 | [
"region:us"
]
| 2023-02-04T06:39:17+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "date_scraped", "dtype": "string"}, {"name": "headline", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "ingress", "dtype": "string"}, {"name": "article", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 758224560, "num_examples": 201819}], "download_size": 358042111, "dataset_size": 758224560}} | 2023-02-04T06:39:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "nowiki_abstract_second_scrape_split2"
More Information needed | [
"# Dataset Card for \"nowiki_abstract_second_scrape_split2\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"nowiki_abstract_second_scrape_split2\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"nowiki_abstract_second_scrape_split2\"\n\nMore Information needed"
]
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|
6b76992be330318546a1797e6c01dba975f4d613 | # Dataset Card for "boostcamp-docvqa-v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Ssunbell/boostcamp-docvqa-v4 | [
"region:us"
]
| 2023-02-04T08:02:32+00:00 | {"dataset_info": {"features": [{"name": "questionId", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "sequence": {"sequence": {"sequence": {"sequence": "uint8"}}}}, {"name": "docId", "dtype": "int64"}, {"name": "ucsf_document_id", "dtype": "string"}, {"name": "ucsf_document_page_no", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "data_split", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "boxes", "sequence": {"sequence": "int64"}}], "splits": [{"name": "train", "num_bytes": 6381951489, "num_examples": 39454}, {"name": "val", "num_bytes": 869383194, "num_examples": 5349}], "download_size": 2582271500, "dataset_size": 7251334683}} | 2023-02-04T10:39:51+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "boostcamp-docvqa-v4"
More Information needed | [
"# Dataset Card for \"boostcamp-docvqa-v4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"boostcamp-docvqa-v4\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"boostcamp-docvqa-v4\"\n\nMore Information needed"
]
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]
|
5d075b9f87a3ed9a9fbb1dc9e15443167513ba19 |
This dataset was created by:
* Starting with the [Dialog Inpainting](https://github.com/google-research/dialog-inpainting) dataset
* Labeling the turns of each dialog with `User: ` and `Assistant: `
* Filtering using spaCy, using code similar to the following (written by https://huggingface.co/ontocord):
```
import pandas as pd
import spacy
try:
if sci is None: pass
except:
sci = spacy.load("en_ner_craft_md")
data = pd.read_parquet('data.parquet', engine='pyarrow')
for a in data['labeleddialog']:
a = a.replace("this article", "this subject").replace("()", "").replace(" ", " ")
if 'novel' in a or ' story' in a or 'movie' in a or 'film' in a or 'music' in a:
#print ('###arts\n', a)
continue
if ' game' in a or 'sports' in a or 'football' in a or 'soccer' in a or 'baseball' in a or 'basketball' in a:
#print ('###sports\n', a)
continue
if 'population' in a or 'territory' in a or 'village' in a or 'country' in a or 'county' in a:
#print ('###place\n', a)
continue
if 'ingredient' in a or 'food' in a or 'recipe' in a:
#print ('###recipe\n', a)
continue
if ' rights' in a or ' court ' in a or ' criminal ' in a or ' verdict ' in a or ' guilt ' in a or ' legislat' in a:
#print ('###law\n', a)
continue
doc = sci(a)
j = 0
for ent in doc.ents:
if ent.label == 'SO' or (ent.label == 'CHEBI' and len(ent.text) > 5):
j+= 1
if j > 3:
print ('###biomed\n',a)
break
#print (ent.label, ent.text)
```
* Filtering using BERT, using the following code:
```
from transformers import pipeline
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
classifier(page_titles, ["Biomedical", "Non-biomedical"])
# Dialogs with page titles with `prob < 0.7` were dropped.
prob = classification_result["scores"][classification_result["labels"].index("Biomedical")]
``` | ericyu3/openassistant_inpainted_dialogs_5k_biomedical | [
"size_categories:1K<n<10K",
"license:apache-2.0",
"region:us"
]
| 2023-02-04T08:41:50+00:00 | {"license": "apache-2.0", "size_categories": ["1K<n<10K"]} | 2023-02-06T00:26:21+00:00 | []
| []
| TAGS
#size_categories-1K<n<10K #license-apache-2.0 #region-us
|
This dataset was created by:
* Starting with the Dialog Inpainting dataset
* Labeling the turns of each dialog with 'User: ' and 'Assistant: '
* Filtering using spaCy, using code similar to the following (written by URL
* Filtering using BERT, using the following code:
| []
| [
"TAGS\n#size_categories-1K<n<10K #license-apache-2.0 #region-us \n"
]
| [
26
]
| [
"passage: TAGS\n#size_categories-1K<n<10K #license-apache-2.0 #region-us \n"
]
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|
9145c144a0b1fb16763a8494cdff6e8fa87159ab | # Dataset Card for "nowiki_abstract_second_scrape_split3"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jkorsvik/nowiki_abstract_second_scrape_split3 | [
"region:us"
]
| 2023-02-04T10:10:30+00:00 | {"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "date_scraped", "dtype": "string"}, {"name": "headline", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "ingress", "dtype": "string"}, {"name": "article", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 631468644, "num_examples": 201819}], "download_size": 270168559, "dataset_size": 631468644}} | 2023-02-04T10:10:55+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "nowiki_abstract_second_scrape_split3"
More Information needed | [
"# Dataset Card for \"nowiki_abstract_second_scrape_split3\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"nowiki_abstract_second_scrape_split3\"\n\nMore Information needed"
]
| [
6,
26
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"nowiki_abstract_second_scrape_split3\"\n\nMore Information needed"
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|
650fdb08b147a44c7cf3a3697be4a4dcfec8875e | # Mawqif: A Multi-label Arabic Dataset for Target-specific Stance Detection
- *Mawqif* is the first Arabic dataset that can be used for target-specific stance detection.
- This is a multi-label dataset where each data point is annotated for stance, sentiment, and sarcasm.
- We benchmark *Mawqif* dataset on the stance detection task and evaluate the performance of four BERT-based models. Our best model achieves a macro-F1 of 78.89\%.
# Mawqif Statistics
- This dataset consists of **4,121** tweets in multi-dialectal Arabic. Each tweet is annotated with a stance toward one of three targets: “COVID-19 vaccine,” “digital transformation,” and “women empowerment.” In addition, it is annotated with sentiment and sarcasm polarities.
- The following figure illustrates the labels’ distribution across all targets, and the distribution per target.
<img width="738" alt="dataStat-2" src="https://user-images.githubusercontent.com/31368075/188299057-54d04e87-802d-4b0e-b7c6-56bdc1078284.png">
# Interactive Visualization
To browse an interactive visualization of the *Mawqif* dataset, please click [here](https://public.tableau.com/views/MawqifDatasetDashboard/Dashboard1?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link)
- *You can click on visualization components to filter the data by target and by class. **For example,** you can click on “women empowerment" and "against" to get the information of tweets that express against women empowerment.*
# Citation
If you feel our paper and resources are useful, please consider citing our work!
```
@inproceedings{alturayeif-etal-2022-mawqif,
title = "Mawqif: A Multi-label {A}rabic Dataset for Target-specific Stance Detection",
author = "Alturayeif, Nora Saleh and
Luqman, Hamzah Abdullah and
Ahmed, Moataz Aly Kamaleldin",
booktitle = "Proceedings of the The Seventh Arabic Natural Language Processing Workshop (WANLP)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.wanlp-1.16",
pages = "174--184"
}
``` | NoraAlt/Mawqif_Stance-Detection | [
"task_categories:text-classification",
"size_categories:1K<n<10K",
"language:ar",
"Stance Detection",
"Sentiment Analysis",
"Sarcasm Detection",
"region:us"
]
| 2023-02-04T10:21:10+00:00 | {"language": ["ar"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "Mawqif: Stance Detection", "tags": ["Stance Detection", "Sentiment Analysis", "Sarcasm Detection"]} | 2024-01-18T10:11:13+00:00 | []
| [
"ar"
]
| TAGS
#task_categories-text-classification #size_categories-1K<n<10K #language-Arabic #Stance Detection #Sentiment Analysis #Sarcasm Detection #region-us
| # Mawqif: A Multi-label Arabic Dataset for Target-specific Stance Detection
- *Mawqif* is the first Arabic dataset that can be used for target-specific stance detection.
- This is a multi-label dataset where each data point is annotated for stance, sentiment, and sarcasm.
- We benchmark *Mawqif* dataset on the stance detection task and evaluate the performance of four BERT-based models. Our best model achieves a macro-F1 of 78.89\%.
# Mawqif Statistics
- This dataset consists of 4,121 tweets in multi-dialectal Arabic. Each tweet is annotated with a stance toward one of three targets: “COVID-19 vaccine,” “digital transformation,” and “women empowerment.” In addition, it is annotated with sentiment and sarcasm polarities.
- The following figure illustrates the labels’ distribution across all targets, and the distribution per target.
<img width="738" alt="dataStat-2" src="URL
# Interactive Visualization
To browse an interactive visualization of the *Mawqif* dataset, please click here
- *You can click on visualization components to filter the data by target and by class. For example, you can click on “women empowerment" and "against" to get the information of tweets that express against women empowerment.*
If you feel our paper and resources are useful, please consider citing our work!
| [
"# Mawqif: A Multi-label Arabic Dataset for Target-specific Stance Detection\n\n- *Mawqif* is the first Arabic dataset that can be used for target-specific stance detection. \n\n- This is a multi-label dataset where each data point is annotated for stance, sentiment, and sarcasm.\n\n- We benchmark *Mawqif* dataset on the stance detection task and evaluate the performance of four BERT-based models. Our best model achieves a macro-F1 of 78.89\\%.",
"# Mawqif Statistics\n\n- This dataset consists of 4,121 tweets in multi-dialectal Arabic. Each tweet is annotated with a stance toward one of three targets: “COVID-19 vaccine,” “digital transformation,” and “women empowerment.” In addition, it is annotated with sentiment and sarcasm polarities.\n\n- The following figure illustrates the labels’ distribution across all targets, and the distribution per target.\n\n<img width=\"738\" alt=\"dataStat-2\" src=\"URL",
"# Interactive Visualization\n\nTo browse an interactive visualization of the *Mawqif* dataset, please click here\n- *You can click on visualization components to filter the data by target and by class. For example, you can click on “women empowerment\" and \"against\" to get the information of tweets that express against women empowerment.* \n\n\nIf you feel our paper and resources are useful, please consider citing our work!"
]
| [
"TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Arabic #Stance Detection #Sentiment Analysis #Sarcasm Detection #region-us \n",
"# Mawqif: A Multi-label Arabic Dataset for Target-specific Stance Detection\n\n- *Mawqif* is the first Arabic dataset that can be used for target-specific stance detection. \n\n- This is a multi-label dataset where each data point is annotated for stance, sentiment, and sarcasm.\n\n- We benchmark *Mawqif* dataset on the stance detection task and evaluate the performance of four BERT-based models. Our best model achieves a macro-F1 of 78.89\\%.",
"# Mawqif Statistics\n\n- This dataset consists of 4,121 tweets in multi-dialectal Arabic. Each tweet is annotated with a stance toward one of three targets: “COVID-19 vaccine,” “digital transformation,” and “women empowerment.” In addition, it is annotated with sentiment and sarcasm polarities.\n\n- The following figure illustrates the labels’ distribution across all targets, and the distribution per target.\n\n<img width=\"738\" alt=\"dataStat-2\" src=\"URL",
"# Interactive Visualization\n\nTo browse an interactive visualization of the *Mawqif* dataset, please click here\n- *You can click on visualization components to filter the data by target and by class. For example, you can click on “women empowerment\" and \"against\" to get the information of tweets that express against women empowerment.* \n\n\nIf you feel our paper and resources are useful, please consider citing our work!"
]
| [
52,
123,
123,
100
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-1K<n<10K #language-Arabic #Stance Detection #Sentiment Analysis #Sarcasm Detection #region-us \n# Mawqif: A Multi-label Arabic Dataset for Target-specific Stance Detection\n\n- *Mawqif* is the first Arabic dataset that can be used for target-specific stance detection. \n\n- This is a multi-label dataset where each data point is annotated for stance, sentiment, and sarcasm.\n\n- We benchmark *Mawqif* dataset on the stance detection task and evaluate the performance of four BERT-based models. Our best model achieves a macro-F1 of 78.89\\%.# Mawqif Statistics\n\n- This dataset consists of 4,121 tweets in multi-dialectal Arabic. Each tweet is annotated with a stance toward one of three targets: “COVID-19 vaccine,” “digital transformation,” and “women empowerment.” In addition, it is annotated with sentiment and sarcasm polarities.\n\n- The following figure illustrates the labels’ distribution across all targets, and the distribution per target.\n\n<img width=\"738\" alt=\"dataStat-2\" src=\"URL# Interactive Visualization\n\nTo browse an interactive visualization of the *Mawqif* dataset, please click here\n- *You can click on visualization components to filter the data by target and by class. For example, you can click on “women empowerment\" and \"against\" to get the information of tweets that express against women empowerment.* \n\n\nIf you feel our paper and resources are useful, please consider citing our work!"
]
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|
3454631364656f2cd2f2a2ca97a30f850030b927 | # Dataset Card for "discord4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tomaxe/discord4 | [
"region:us"
]
| 2023-02-04T10:37:08+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 51865891, "num_examples": 569785}, {"name": "validation", "num_bytes": 8713, "num_examples": 100}], "download_size": 28882054, "dataset_size": 51874604}} | 2023-02-04T10:37:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "discord4"
More Information needed | [
"# Dataset Card for \"discord4\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"discord4\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"discord4\"\n\nMore Information needed"
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|
c99df89198a7f588a74c2d673630eb90e03ef050 | # Dataset Card for "boostcamp-docvqa-v4-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Ssunbell/boostcamp-docvqa-v4-test | [
"region:us"
]
| 2023-02-04T10:39:55+00:00 | {"dataset_info": {"features": [{"name": "questionId", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "sequence": {"sequence": {"sequence": {"sequence": "uint8"}}}}, {"name": "docId", "dtype": "int64"}, {"name": "ucsf_document_id", "dtype": "string"}, {"name": "ucsf_document_page_no", "dtype": "string"}, {"name": "data_split", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "boxes", "sequence": {"sequence": "int64"}}], "splits": [{"name": "test", "num_bytes": 843104716, "num_examples": 5188}], "download_size": 297218666, "dataset_size": 843104716}} | 2023-02-04T10:41:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "boostcamp-docvqa-v4-test"
More Information needed | [
"# Dataset Card for \"boostcamp-docvqa-v4-test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"boostcamp-docvqa-v4-test\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"boostcamp-docvqa-v4-test\"\n\nMore Information needed"
]
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|
b861d67183c66dc44717903ce289a03e29d05004 | # Dataset Card for "nowiki_second_scrape_merged"
## 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:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@jkorsvik](https://github.com/jkorsvik) for adding this dataset.
| jkorsvik/nowiki_second_scrape_merged | [
"task_categories:summarization",
"task_categories:text2text-generation",
"task_ids:text-simplification",
"annotations_creators:jkorsvik",
"annotations_creators:machine-generated",
"language_creators:found",
"language_creators:jkorsvik",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:no",
"language:nob",
"Norwegian wikipedia scraped with links from nowiki dump:<https://dumps.wikimedia.org/nowiki/20230120/>",
"region:us"
]
| 2023-02-04T10:42:26+00:00 | {"annotations_creators": ["jkorsvik", "machine-generated"], "language_creators": ["found", "jkorsvik"], "language": [false, "nob"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["summarization", "text2text-generation"], "task_ids": ["text-simplification"], "pretty_name": "nowiki_second_scrape_merged", "tags": ["Norwegian wikipedia scraped with links from nowiki dump:<https://dumps.wikimedia.org/nowiki/20230120/>"], "dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "date_scraped", "dtype": "string"}, {"name": "headline", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "ingress", "dtype": "string"}, {"name": "article", "dtype": "string"}, {"name": "abstract", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2321106744, "num_examples": 601040}], "download_size": 1109052656, "dataset_size": 2321106744}} | 2023-02-04T10:47:58+00:00 | []
| [
"no",
"nob"
]
| TAGS
#task_categories-summarization #task_categories-text2text-generation #task_ids-text-simplification #annotations_creators-jkorsvik #annotations_creators-machine-generated #language_creators-found #language_creators-jkorsvik #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Norwegian #language-Norwegian Bokmål #Norwegian wikipedia scraped with links from nowiki dump-<https-//dumps.wikimedia.org/nowiki/20230120/> #region-us
| # Dataset Card for "nowiki_second_scrape_merged"
## 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
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @jkorsvik for adding this dataset.
| [
"# Dataset Card for \"nowiki_second_scrape_merged\"",
"## Table of Contents\n\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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\nThanks to @jkorsvik for adding this dataset."
]
| [
"TAGS\n#task_categories-summarization #task_categories-text2text-generation #task_ids-text-simplification #annotations_creators-jkorsvik #annotations_creators-machine-generated #language_creators-found #language_creators-jkorsvik #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Norwegian #language-Norwegian Bokmål #Norwegian wikipedia scraped with links from nowiki dump-<https-//dumps.wikimedia.org/nowiki/20230120/> #region-us \n",
"# Dataset Card for \"nowiki_second_scrape_merged\"",
"## Table of Contents\n\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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\nThanks to @jkorsvik for adding this dataset."
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"passage: TAGS\n#task_categories-summarization #task_categories-text2text-generation #task_ids-text-simplification #annotations_creators-jkorsvik #annotations_creators-machine-generated #language_creators-found #language_creators-jkorsvik #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-Norwegian #language-Norwegian Bokmål #Norwegian wikipedia scraped with links from nowiki dump-<https-//dumps.wikimedia.org/nowiki/20230120/> #region-us \n# Dataset Card for \"nowiki_second_scrape_merged\"## Table of Contents\n\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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:### Dataset Summary### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions\n\nThanks to @jkorsvik for adding this dataset."
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|
7c3bb9c344fd0c9ea4780fb4ec49ee4dcbb5b903 | # Dataset Card for "beijingopera-trainorigin"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | AkikoOu/beijingopera-trainorigin | [
"region:us"
]
| 2023-02-04T11:21:40+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 343446.0, "num_examples": 9}], "download_size": 342528, "dataset_size": 343446.0}} | 2023-02-04T11:21:45+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "beijingopera-trainorigin"
More Information needed | [
"# Dataset Card for \"beijingopera-trainorigin\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"beijingopera-trainorigin\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"beijingopera-trainorigin\"\n\nMore Information needed"
]
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|
46865e0bc989f0b010218ec2c590b4703d0e2a2f | From https://github.com/audioset/ontology
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
| xfh/ontology_image_audio_2k | [
"region:us"
]
| 2023-02-04T11:45:49+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "audio", "dtype": "audio"}, {"name": "tag", "dtype": "string"}, {"name": "text_id", "dtype": "string"}], "splits": [{"name": "train", "num_examples": 2403}]}} | 2023-02-06T02:46:05+00:00 | []
| []
| TAGS
#region-us
| From URL
More Information needed
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
f17e55b8f60da2f32720c1a16e4b6149145c662f | https://github.com/microsoft/LoNLI
```bibtex
@article{Tarunesh2021TrustingRO,
title={Trusting RoBERTa over BERT: Insights from CheckListing the Natural Language Inference Task},
author={Ishan Tarunesh and Somak Aditya and Monojit Choudhury},
journal={ArXiv},
year={2021},
volume={abs/2107.07229}
}
``` | tasksource/lonli | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"language:en",
"license:mit",
"region:us"
]
| 2023-02-04T14:48:11+00:00 | {"language": ["en"], "license": "mit", "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"]} | 2023-05-31T07:41:36+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_ids-natural-language-inference #language-English #license-mit #region-us
| URL
| []
| [
"TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #language-English #license-mit #region-us \n"
]
| [
39
]
| [
"passage: TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #language-English #license-mit #region-us \n"
]
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30af7c6761f5c2b87261aa81bd05d4a51e4c271b | # Dataset Card for "FGVC_Aircraft_test_facebook_opt_350m_Attributes_Caption_ns_3333_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/FGVC_Aircraft_test_facebook_opt_350m_Attributes_Caption_ns_3333_random | [
"region:us"
]
| 2023-02-04T15:47:35+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 300147629.375, "num_examples": 3333}, {"name": "fewshot_3_bs_16", "num_bytes": 301866570.375, "num_examples": 3333}], "download_size": 595044400, "dataset_size": 602014199.75}} | 2023-02-04T16:13:52+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "FGVC_Aircraft_test_facebook_opt_350m_Attributes_Caption_ns_3333_random"
More Information needed | [
"# Dataset Card for \"FGVC_Aircraft_test_facebook_opt_350m_Attributes_Caption_ns_3333_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_facebook_opt_350m_Attributes_Caption_ns_3333_random\"\n\nMore Information needed"
]
| [
6,
40
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_facebook_opt_350m_Attributes_Caption_ns_3333_random\"\n\nMore Information needed"
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|
196055cf3335442ce66b4d46dcb2715a5e4d5d06 | # Dataset Card for "FGVC_Aircraft_test_facebook_opt_1.3b_Attributes_Caption_ns_3333_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/FGVC_Aircraft_test_facebook_opt_1.3b_Attributes_Caption_ns_3333_random | [
"region:us"
]
| 2023-02-04T15:55:44+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 300147552.375, "num_examples": 3333}, {"name": "fewshot_3_bs_16", "num_bytes": 301866337.375, "num_examples": 3333}], "download_size": 595047424, "dataset_size": 602013889.75}} | 2023-02-04T16:27:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "FGVC_Aircraft_test_facebook_opt_1.3b_Attributes_Caption_ns_3333_random"
More Information needed | [
"# Dataset Card for \"FGVC_Aircraft_test_facebook_opt_1.3b_Attributes_Caption_ns_3333_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_facebook_opt_1.3b_Attributes_Caption_ns_3333_random\"\n\nMore Information needed"
]
| [
6,
40
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_facebook_opt_1.3b_Attributes_Caption_ns_3333_random\"\n\nMore Information needed"
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|
2e762d1f58f3e61511136bdbc4b27a5384d14c73 | # Dataset Card for "bookcorpus_compact_1024_shard9_of_10_meta"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | saibo/bookcorpus_compact_1024_shard9_of_10_meta | [
"region:us"
]
| 2023-02-04T15:59:14+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "concept_with_offset", "dtype": "string"}, {"name": "cid_arrangement", "sequence": "int32"}, {"name": "schema_lengths", "sequence": "int64"}, {"name": "topic_entity_mask", "sequence": "int64"}, {"name": "text_lengths", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 7675706871, "num_examples": 61605}], "download_size": 1683788529, "dataset_size": 7675706871}} | 2023-02-04T16:01:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_compact_1024_shard9_of_10_meta"
More Information needed | [
"# Dataset Card for \"bookcorpus_compact_1024_shard9_of_10_meta\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_compact_1024_shard9_of_10_meta\"\n\nMore Information needed"
]
| [
6,
29
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_compact_1024_shard9_of_10_meta\"\n\nMore Information needed"
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|
4320043296a1f2cbf7260dbcdb00aa29e4b848dd | # Dataset Card for "devign_with_norm_vul_lines"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | EddieChen372/devign_with_norm_vul_lines | [
"region:us"
]
| 2023-02-04T16:28:55+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "func", "dtype": "string"}, {"name": "target", "dtype": "bool"}, {"name": "project", "dtype": "string"}, {"name": "commit_id", "dtype": "string"}, {"name": "func_clean", "dtype": "string"}, {"name": "vul_lines", "struct": [{"name": "code", "sequence": "string"}, {"name": "line_no", "sequence": "int64"}]}, {"name": "normalized_func", "dtype": "string"}, {"name": "lines", "sequence": "string"}, {"name": "label", "sequence": "int64"}, {"name": "line_no", "sequence": {"sequence": "int64"}}], "splits": [{"name": "test", "num_bytes": 22801956, "num_examples": 2732}, {"name": "train", "num_bytes": 183794878, "num_examples": 21854}, {"name": "validation", "num_bytes": 22451009, "num_examples": 2732}], "download_size": 72100845, "dataset_size": 229047843}} | 2023-02-04T16:35:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "devign_with_norm_vul_lines"
More Information needed | [
"# Dataset Card for \"devign_with_norm_vul_lines\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"devign_with_norm_vul_lines\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"devign_with_norm_vul_lines\"\n\nMore Information needed"
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|
a2a41a2fb50456f359b2f08763675a716cc5b2d3 | # Dataset Card for "sample_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | BhavyaMuni/sample_dataset | [
"region:us"
]
| 2023-02-04T16:37:27+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 627787, "num_examples": 387}], "download_size": 259828, "dataset_size": 627787}} | 2023-02-04T16:37:31+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sample_dataset"
More Information needed | [
"# Dataset Card for \"sample_dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sample_dataset\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sample_dataset\"\n\nMore Information needed"
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|
e9763a4df23c93d6783fe5dcc881a5729a2e377c | # Dataset Card for "mnist_sorted_v0.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jlbaker361/mnist_sorted_v0.0 | [
"region:us"
]
| 2023-02-04T17:09:24+00:00 | {"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "sequence", "sequence": "int64"}, {"name": "occurence", "dtype": "int64"}, {"name": "split", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 84223889, "num_examples": 68614}], "download_size": 12695868, "dataset_size": 84223889}} | 2023-02-04T17:09:27+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mnist_sorted_v0.0"
More Information needed | [
"# Dataset Card for \"mnist_sorted_v0.0\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mnist_sorted_v0.0\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mnist_sorted_v0.0\"\n\nMore Information needed"
]
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|
bd980e88405e0c3cbf3880f6a670dcd047384661 |
# Dataset Card for *BioLeaflets* Dataset
## 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 link](https://github.com/bayer-science-for-a-better-life/data2text-bioleaflets)
- **Paper:** [ACL Anthology](https://aclanthology.org/2021.inlg-1.40/)
- **Leaderboard:** [Papers with Code leaderboard for BioLeaflets Dataset](https://paperswithcode.com/dataset/bioleaflets)
- **Point of Contact:** [Ruslan Yermakov](https://github.com/wingedRuslan)
### Dataset Summary
*BioLeaflets* is a biomedical dataset for Data2Text generation. It is a corpus of 1,336 package leaflets of medicines authorised in Europe, which were obtained by scraping the European Medicines Agency (EMA) website.
Package leaflets are included in the packaging of medicinal products and contain information to help patients use the product safely and appropriately.
This dataset comprises the large majority (∼ 90%) of medicinal products authorised through the centralised procedure in Europe as of January 2021.
For more detailed information, please read the paper at [ACL Anthology](https://aclanthology.org/2021.inlg-1.40/).
### Supported Tasks and Leaderboards
BioLeaflets proposes a **conditional generation task** (data-to-text) in the biomedical domain: given an ordered set of entities as source, the *goal* is to produce a multi-sentence section.
Successful generation thus requires the model to learn specific syntax, terminology, and writing style from the corpus. Alternatively, the dataset might be used for **named-entity recognition task**: given text, detect medical entities.
The dataset provides an extensive description of medicinal products and thus supports a plain **language modeling task** focused on biomedical data.
### Languages
Monolingual - en.
## Dataset Structure
### Data Instances
For each instance (leaflet), there is a unique ID, URL, Product_Name, and textual information clearly describing the medicine.
The content of each section is not standardized (NO template), yet it is still well-structured.
Each document contains six sections:
1) What is the product and what is it used for
2) What you need to know before you take the product
3) Product usage instructions
4) possible side effects
5) product storage conditions
6) other information
Every section is represented as a dictionary with the 'Title', 'Section_Content', and 'Entity_Recognition' as keys.
The content of each section is lower-cased and tokenized by treating all special characters as separate tokens.
### Data Fields
- `ID`: a string representing a unique ID assigned to a leaflet
- `URL`: a string containing the link to the article on the European Medicines Agency (EMA) website
- `Product Name`: a string, the name of the medicine
- `Full Content`: a string covering the full content of the article available at URL
- `Section 1`: a dictionary including section 1 content and associated medical entities
- `Section 2`: a dictionary including section 2 content and associated medical entities
- `Section 3`: a dictionary including section 3 content and associated medical entities
- `Section 4`: a dictionary including section 4 content and associated medical entities
- `Section 5`: a dictionary including section 5 content and associated medical entities
- `Section 6`: a dictionary including section 6 content and associated medical entities
### Data Splits
We randomly split the dataset into training (80%), development (10%), and test (10%) set. Duplicates are removed.
## Dataset Creation
### Curation Rationale
Introduce a new biomedical dataset (BioLeaflets), which could serve as a benchmark for biomedical text generation models.
BioLeaflets proposes a conditional generation task: given an ordered set of entities as source, the goal is to produce a multi-sentence section.
### Source Data
#### Initial Data Collection and Normalization
The dataset was obtained by scraping the European Medicines Agency (EMA) website.
Each leaflet has an URL associated with it to the article on the EMA website.
#### Who are the source language producers?
Labeling experts with domain knowledge produced factual information.
### Annotations
#### Annotation process
To create the required input for data-to-text generation, we augment each document by leveraging named entity recognition (NER).
We combine two NER frameworks: Amazon Comprehend Medical (commercial) and Stanford Stanza (open-sourced).
Additionally, we treat all digits as entities and add the medicine name as the first entity
#### Who are the annotators?
Machine-generated: ensemble of state-of-the-art named entity recognition (NER) models.
### Personal and Sensitive Information
[Not included / Not present]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop models that can automatically generate long paragraphs of text as well as to facilitate the development of NLP models in the biomedical domain.
The main challenges of this dataset for D2T generation are multi-sentence and multi-section target text, small sample size, specialized medical vocabulary, and syntax.
### Discussion of Biases
Package leaflets are published for medicinal products approved in the European Union (EU).
They are included in the packaging of medicinal products and contain information to help patients use the product safely and appropriately.
The dataset represents factual information produced by labeling experts and validated before publishing. Hence, biases of any kind are not present in the dataset.
Package leaflets are required to be written in a way that is clear and understandable.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
The data was originally collected by Ruslan Yermakov<sup>*</sup>, Nicholas Drago, and Angelo Ziletti at Bayer AG (Decision Science & Advanced Analytics unit). The code is made publicly available at [github link](https://github.com/bayer-science-for-a-better-life/data2text-bioleaflets)
<sup>*</sup> Work done during internship.
### Licensing Information
The *BioLeaflets* dataset is released under the [Apache-2.0 License](http://www.apache.org/licenses/LICENSE-2.0).
### Citation Information
@inproceedings{yermakov-etal-2021-biomedical,
title = "Biomedical Data-to-Text Generation via Fine-Tuning Transformers",
author = "Yermakov, Ruslan and
Drago, Nicholas and
Ziletti, Angelo",
booktitle = "Proceedings of the 14th International Conference on Natural Language Generation",
month = aug,
year = "2021",
address = "Aberdeen, Scotland, UK",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.inlg-1.40",
pages = "364--370",
abstract = "Data-to-text (D2T) generation in the biomedical domain is a promising - yet mostly unexplored - field of research. Here, we apply neural models for D2T generation to a real-world dataset consisting of package leaflets of European medicines. We show that fine-tuned transformers are able to generate realistic, multi-sentence text from data in the biomedical domain, yet have important limitations. We also release a new dataset (BioLeaflets) for benchmarking D2T generation models in the biomedical domain.",
}
### Contributions
Thanks to [@wingedRuslan](https://github.com/wingedRuslan) for adding this dataset.
| ruslan/bioleaflets-biomedical-ner | [
"task_categories:text-generation",
"task_categories:text2text-generation",
"task_ids:language-modeling",
"annotations_creators:machine-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"bio",
"medical",
"biomedical",
"bioleaflets",
"medicine",
"data2text",
"data-to-text",
"region:us"
]
| 2023-02-04T17:29:52+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["apache-2.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-generation", "text2text-generation"], "task_ids": ["language-modeling"], "pretty_name": "bioleaflets-biomedical-ner", "tags": ["bio", "medical", "biomedical", "bioleaflets", "medicine", "data2text", "data-to-text"]} | 2023-02-08T20:34:51+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-generation #task_categories-text2text-generation #task_ids-language-modeling #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-apache-2.0 #bio #medical #biomedical #bioleaflets #medicine #data2text #data-to-text #region-us
|
# Dataset Card for *BioLeaflets* Dataset
## 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 link
- Paper: ACL Anthology
- Leaderboard: Papers with Code leaderboard for BioLeaflets Dataset
- Point of Contact: Ruslan Yermakov
### Dataset Summary
*BioLeaflets* is a biomedical dataset for Data2Text generation. It is a corpus of 1,336 package leaflets of medicines authorised in Europe, which were obtained by scraping the European Medicines Agency (EMA) website.
Package leaflets are included in the packaging of medicinal products and contain information to help patients use the product safely and appropriately.
This dataset comprises the large majority (∼ 90%) of medicinal products authorised through the centralised procedure in Europe as of January 2021.
For more detailed information, please read the paper at ACL Anthology.
### Supported Tasks and Leaderboards
BioLeaflets proposes a conditional generation task (data-to-text) in the biomedical domain: given an ordered set of entities as source, the *goal* is to produce a multi-sentence section.
Successful generation thus requires the model to learn specific syntax, terminology, and writing style from the corpus. Alternatively, the dataset might be used for named-entity recognition task: given text, detect medical entities.
The dataset provides an extensive description of medicinal products and thus supports a plain language modeling task focused on biomedical data.
### Languages
Monolingual - en.
## Dataset Structure
### Data Instances
For each instance (leaflet), there is a unique ID, URL, Product_Name, and textual information clearly describing the medicine.
The content of each section is not standardized (NO template), yet it is still well-structured.
Each document contains six sections:
1) What is the product and what is it used for
2) What you need to know before you take the product
3) Product usage instructions
4) possible side effects
5) product storage conditions
6) other information
Every section is represented as a dictionary with the 'Title', 'Section_Content', and 'Entity_Recognition' as keys.
The content of each section is lower-cased and tokenized by treating all special characters as separate tokens.
### Data Fields
- 'ID': a string representing a unique ID assigned to a leaflet
- 'URL': a string containing the link to the article on the European Medicines Agency (EMA) website
- 'Product Name': a string, the name of the medicine
- 'Full Content': a string covering the full content of the article available at URL
- 'Section 1': a dictionary including section 1 content and associated medical entities
- 'Section 2': a dictionary including section 2 content and associated medical entities
- 'Section 3': a dictionary including section 3 content and associated medical entities
- 'Section 4': a dictionary including section 4 content and associated medical entities
- 'Section 5': a dictionary including section 5 content and associated medical entities
- 'Section 6': a dictionary including section 6 content and associated medical entities
### Data Splits
We randomly split the dataset into training (80%), development (10%), and test (10%) set. Duplicates are removed.
## Dataset Creation
### Curation Rationale
Introduce a new biomedical dataset (BioLeaflets), which could serve as a benchmark for biomedical text generation models.
BioLeaflets proposes a conditional generation task: given an ordered set of entities as source, the goal is to produce a multi-sentence section.
### Source Data
#### Initial Data Collection and Normalization
The dataset was obtained by scraping the European Medicines Agency (EMA) website.
Each leaflet has an URL associated with it to the article on the EMA website.
#### Who are the source language producers?
Labeling experts with domain knowledge produced factual information.
### Annotations
#### Annotation process
To create the required input for data-to-text generation, we augment each document by leveraging named entity recognition (NER).
We combine two NER frameworks: Amazon Comprehend Medical (commercial) and Stanford Stanza (open-sourced).
Additionally, we treat all digits as entities and add the medicine name as the first entity
#### Who are the annotators?
Machine-generated: ensemble of state-of-the-art named entity recognition (NER) models.
### Personal and Sensitive Information
[Not included / Not present]
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to help develop models that can automatically generate long paragraphs of text as well as to facilitate the development of NLP models in the biomedical domain.
The main challenges of this dataset for D2T generation are multi-sentence and multi-section target text, small sample size, specialized medical vocabulary, and syntax.
### Discussion of Biases
Package leaflets are published for medicinal products approved in the European Union (EU).
They are included in the packaging of medicinal products and contain information to help patients use the product safely and appropriately.
The dataset represents factual information produced by labeling experts and validated before publishing. Hence, biases of any kind are not present in the dataset.
Package leaflets are required to be written in a way that is clear and understandable.
### Other Known Limitations
[N/A]
## Additional Information
### Dataset Curators
The data was originally collected by Ruslan Yermakov<sup>*</sup>, Nicholas Drago, and Angelo Ziletti at Bayer AG (Decision Science & Advanced Analytics unit). The code is made publicly available at github link
<sup>*</sup> Work done during internship.
### Licensing Information
The *BioLeaflets* dataset is released under the Apache-2.0 License.
@inproceedings{yermakov-etal-2021-biomedical,
title = "Biomedical Data-to-Text Generation via Fine-Tuning Transformers",
author = "Yermakov, Ruslan and
Drago, Nicholas and
Ziletti, Angelo",
booktitle = "Proceedings of the 14th International Conference on Natural Language Generation",
month = aug,
year = "2021",
address = "Aberdeen, Scotland, UK",
publisher = "Association for Computational Linguistics",
url = "URL
pages = "364--370",
abstract = "Data-to-text (D2T) generation in the biomedical domain is a promising - yet mostly unexplored - field of research. Here, we apply neural models for D2T generation to a real-world dataset consisting of package leaflets of European medicines. We show that fine-tuned transformers are able to generate realistic, multi-sentence text from data in the biomedical domain, yet have important limitations. We also release a new dataset (BioLeaflets) for benchmarking D2T generation models in the biomedical domain.",
}
### Contributions
Thanks to @wingedRuslan for adding this dataset.
| [
"# Dataset Card for *BioLeaflets* Dataset",
"## 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:\n- Repository: GitHub link\n- Paper: ACL Anthology\n- Leaderboard: Papers with Code leaderboard for BioLeaflets Dataset\n- Point of Contact: Ruslan Yermakov",
"### Dataset Summary\n\n*BioLeaflets* is a biomedical dataset for Data2Text generation. It is a corpus of 1,336 package leaflets of medicines authorised in Europe, which were obtained by scraping the European Medicines Agency (EMA) website. \nPackage leaflets are included in the packaging of medicinal products and contain information to help patients use the product safely and appropriately. \nThis dataset comprises the large majority (∼ 90%) of medicinal products authorised through the centralised procedure in Europe as of January 2021. \nFor more detailed information, please read the paper at ACL Anthology.",
"### Supported Tasks and Leaderboards\n\nBioLeaflets proposes a conditional generation task (data-to-text) in the biomedical domain: given an ordered set of entities as source, the *goal* is to produce a multi-sentence section. \nSuccessful generation thus requires the model to learn specific syntax, terminology, and writing style from the corpus. Alternatively, the dataset might be used for named-entity recognition task: given text, detect medical entities. \nThe dataset provides an extensive description of medicinal products and thus supports a plain language modeling task focused on biomedical data.",
"### Languages\n\nMonolingual - en.",
"## Dataset Structure",
"### Data Instances\n\nFor each instance (leaflet), there is a unique ID, URL, Product_Name, and textual information clearly describing the medicine. \nThe content of each section is not standardized (NO template), yet it is still well-structured. \nEach document contains six sections: \n1) What is the product and what is it used for\n2) What you need to know before you take the product\n3) Product usage instructions\n4) possible side effects\n5) product storage conditions\n6) other information\n\nEvery section is represented as a dictionary with the 'Title', 'Section_Content', and 'Entity_Recognition' as keys. \nThe content of each section is lower-cased and tokenized by treating all special characters as separate tokens.",
"### Data Fields\n\n- 'ID': a string representing a unique ID assigned to a leaflet\n- 'URL': a string containing the link to the article on the European Medicines Agency (EMA) website \n- 'Product Name': a string, the name of the medicine\n- 'Full Content': a string covering the full content of the article available at URL\n- 'Section 1': a dictionary including section 1 content and associated medical entities\n- 'Section 2': a dictionary including section 2 content and associated medical entities\n- 'Section 3': a dictionary including section 3 content and associated medical entities\n- 'Section 4': a dictionary including section 4 content and associated medical entities\n- 'Section 5': a dictionary including section 5 content and associated medical entities\n- 'Section 6': a dictionary including section 6 content and associated medical entities",
"### Data Splits\n\nWe randomly split the dataset into training (80%), development (10%), and test (10%) set. Duplicates are removed.",
"## Dataset Creation",
"### Curation Rationale\n\nIntroduce a new biomedical dataset (BioLeaflets), which could serve as a benchmark for biomedical text generation models. \nBioLeaflets proposes a conditional generation task: given an ordered set of entities as source, the goal is to produce a multi-sentence section.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nThe dataset was obtained by scraping the European Medicines Agency (EMA) website. \nEach leaflet has an URL associated with it to the article on the EMA website.",
"#### Who are the source language producers?\n\nLabeling experts with domain knowledge produced factual information.",
"### Annotations",
"#### Annotation process\n\nTo create the required input for data-to-text generation, we augment each document by leveraging named entity recognition (NER). \nWe combine two NER frameworks: Amazon Comprehend Medical (commercial) and Stanford Stanza (open-sourced). \nAdditionally, we treat all digits as entities and add the medicine name as the first entity",
"#### Who are the annotators?\n\nMachine-generated: ensemble of state-of-the-art named entity recognition (NER) models.",
"### Personal and Sensitive Information\n\n[Not included / Not present]",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThe purpose of this dataset is to help develop models that can automatically generate long paragraphs of text as well as to facilitate the development of NLP models in the biomedical domain. \nThe main challenges of this dataset for D2T generation are multi-sentence and multi-section target text, small sample size, specialized medical vocabulary, and syntax.",
"### Discussion of Biases\n\nPackage leaflets are published for medicinal products approved in the European Union (EU). \nThey are included in the packaging of medicinal products and contain information to help patients use the product safely and appropriately. \nThe dataset represents factual information produced by labeling experts and validated before publishing. Hence, biases of any kind are not present in the dataset. \nPackage leaflets are required to be written in a way that is clear and understandable.",
"### Other Known Limitations\n\n[N/A]",
"## Additional Information",
"### Dataset Curators\n\nThe data was originally collected by Ruslan Yermakov<sup>*</sup>, Nicholas Drago, and Angelo Ziletti at Bayer AG (Decision Science & Advanced Analytics unit). The code is made publicly available at github link\n\n<sup>*</sup> Work done during internship.",
"### Licensing Information\n\nThe *BioLeaflets* dataset is released under the Apache-2.0 License. \n\n\n\n @inproceedings{yermakov-etal-2021-biomedical,\n title = \"Biomedical Data-to-Text Generation via Fine-Tuning Transformers\",\n author = \"Yermakov, Ruslan and\n Drago, Nicholas and\n Ziletti, Angelo\",\n booktitle = \"Proceedings of the 14th International Conference on Natural Language Generation\",\n month = aug,\n year = \"2021\",\n address = \"Aberdeen, Scotland, UK\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"364--370\",\n abstract = \"Data-to-text (D2T) generation in the biomedical domain is a promising - yet mostly unexplored - field of research. Here, we apply neural models for D2T generation to a real-world dataset consisting of package leaflets of European medicines. We show that fine-tuned transformers are able to generate realistic, multi-sentence text from data in the biomedical domain, yet have important limitations. We also release a new dataset (BioLeaflets) for benchmarking D2T generation models in the biomedical domain.\",\n }",
"### Contributions\n\nThanks to @wingedRuslan for adding this dataset."
]
| [
"TAGS\n#task_categories-text-generation #task_categories-text2text-generation #task_ids-language-modeling #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-apache-2.0 #bio #medical #biomedical #bioleaflets #medicine #data2text #data-to-text #region-us \n",
"# Dataset Card for *BioLeaflets* Dataset",
"## 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:\n- Repository: GitHub link\n- Paper: ACL Anthology\n- Leaderboard: Papers with Code leaderboard for BioLeaflets Dataset\n- Point of Contact: Ruslan Yermakov",
"### Dataset Summary\n\n*BioLeaflets* is a biomedical dataset for Data2Text generation. It is a corpus of 1,336 package leaflets of medicines authorised in Europe, which were obtained by scraping the European Medicines Agency (EMA) website. \nPackage leaflets are included in the packaging of medicinal products and contain information to help patients use the product safely and appropriately. \nThis dataset comprises the large majority (∼ 90%) of medicinal products authorised through the centralised procedure in Europe as of January 2021. \nFor more detailed information, please read the paper at ACL Anthology.",
"### Supported Tasks and Leaderboards\n\nBioLeaflets proposes a conditional generation task (data-to-text) in the biomedical domain: given an ordered set of entities as source, the *goal* is to produce a multi-sentence section. \nSuccessful generation thus requires the model to learn specific syntax, terminology, and writing style from the corpus. Alternatively, the dataset might be used for named-entity recognition task: given text, detect medical entities. \nThe dataset provides an extensive description of medicinal products and thus supports a plain language modeling task focused on biomedical data.",
"### Languages\n\nMonolingual - en.",
"## Dataset Structure",
"### Data Instances\n\nFor each instance (leaflet), there is a unique ID, URL, Product_Name, and textual information clearly describing the medicine. \nThe content of each section is not standardized (NO template), yet it is still well-structured. \nEach document contains six sections: \n1) What is the product and what is it used for\n2) What you need to know before you take the product\n3) Product usage instructions\n4) possible side effects\n5) product storage conditions\n6) other information\n\nEvery section is represented as a dictionary with the 'Title', 'Section_Content', and 'Entity_Recognition' as keys. \nThe content of each section is lower-cased and tokenized by treating all special characters as separate tokens.",
"### Data Fields\n\n- 'ID': a string representing a unique ID assigned to a leaflet\n- 'URL': a string containing the link to the article on the European Medicines Agency (EMA) website \n- 'Product Name': a string, the name of the medicine\n- 'Full Content': a string covering the full content of the article available at URL\n- 'Section 1': a dictionary including section 1 content and associated medical entities\n- 'Section 2': a dictionary including section 2 content and associated medical entities\n- 'Section 3': a dictionary including section 3 content and associated medical entities\n- 'Section 4': a dictionary including section 4 content and associated medical entities\n- 'Section 5': a dictionary including section 5 content and associated medical entities\n- 'Section 6': a dictionary including section 6 content and associated medical entities",
"### Data Splits\n\nWe randomly split the dataset into training (80%), development (10%), and test (10%) set. Duplicates are removed.",
"## Dataset Creation",
"### Curation Rationale\n\nIntroduce a new biomedical dataset (BioLeaflets), which could serve as a benchmark for biomedical text generation models. \nBioLeaflets proposes a conditional generation task: given an ordered set of entities as source, the goal is to produce a multi-sentence section.",
"### Source Data",
"#### Initial Data Collection and Normalization\n\nThe dataset was obtained by scraping the European Medicines Agency (EMA) website. \nEach leaflet has an URL associated with it to the article on the EMA website.",
"#### Who are the source language producers?\n\nLabeling experts with domain knowledge produced factual information.",
"### Annotations",
"#### Annotation process\n\nTo create the required input for data-to-text generation, we augment each document by leveraging named entity recognition (NER). \nWe combine two NER frameworks: Amazon Comprehend Medical (commercial) and Stanford Stanza (open-sourced). \nAdditionally, we treat all digits as entities and add the medicine name as the first entity",
"#### Who are the annotators?\n\nMachine-generated: ensemble of state-of-the-art named entity recognition (NER) models.",
"### Personal and Sensitive Information\n\n[Not included / Not present]",
"## Considerations for Using the Data",
"### Social Impact of Dataset\n\nThe purpose of this dataset is to help develop models that can automatically generate long paragraphs of text as well as to facilitate the development of NLP models in the biomedical domain. \nThe main challenges of this dataset for D2T generation are multi-sentence and multi-section target text, small sample size, specialized medical vocabulary, and syntax.",
"### Discussion of Biases\n\nPackage leaflets are published for medicinal products approved in the European Union (EU). \nThey are included in the packaging of medicinal products and contain information to help patients use the product safely and appropriately. \nThe dataset represents factual information produced by labeling experts and validated before publishing. Hence, biases of any kind are not present in the dataset. \nPackage leaflets are required to be written in a way that is clear and understandable.",
"### Other Known Limitations\n\n[N/A]",
"## Additional Information",
"### Dataset Curators\n\nThe data was originally collected by Ruslan Yermakov<sup>*</sup>, Nicholas Drago, and Angelo Ziletti at Bayer AG (Decision Science & Advanced Analytics unit). The code is made publicly available at github link\n\n<sup>*</sup> Work done during internship.",
"### Licensing Information\n\nThe *BioLeaflets* dataset is released under the Apache-2.0 License. \n\n\n\n @inproceedings{yermakov-etal-2021-biomedical,\n title = \"Biomedical Data-to-Text Generation via Fine-Tuning Transformers\",\n author = \"Yermakov, Ruslan and\n Drago, Nicholas and\n Ziletti, Angelo\",\n booktitle = \"Proceedings of the 14th International Conference on Natural Language Generation\",\n month = aug,\n year = \"2021\",\n address = \"Aberdeen, Scotland, UK\",\n publisher = \"Association for Computational Linguistics\",\n url = \"URL\n pages = \"364--370\",\n abstract = \"Data-to-text (D2T) generation in the biomedical domain is a promising - yet mostly unexplored - field of research. Here, we apply neural models for D2T generation to a real-world dataset consisting of package leaflets of European medicines. We show that fine-tuned transformers are able to generate realistic, multi-sentence text from data in the biomedical domain, yet have important limitations. We also release a new dataset (BioLeaflets) for benchmarking D2T generation models in the biomedical domain.\",\n }",
"### Contributions\n\nThanks to @wingedRuslan for adding this dataset."
]
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"passage: TAGS\n#task_categories-text-generation #task_categories-text2text-generation #task_ids-language-modeling #annotations_creators-machine-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-English #license-apache-2.0 #bio #medical #biomedical #bioleaflets #medicine #data2text #data-to-text #region-us \n# Dataset Card for *BioLeaflets* Dataset## 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:\n- Repository: GitHub link\n- Paper: ACL Anthology\n- Leaderboard: Papers with Code leaderboard for BioLeaflets Dataset\n- Point of Contact: Ruslan Yermakov### Dataset Summary\n\n*BioLeaflets* is a biomedical dataset for Data2Text generation. It is a corpus of 1,336 package leaflets of medicines authorised in Europe, which were obtained by scraping the European Medicines Agency (EMA) website. \nPackage leaflets are included in the packaging of medicinal products and contain information to help patients use the product safely and appropriately. \nThis dataset comprises the large majority (∼ 90%) of medicinal products authorised through the centralised procedure in Europe as of January 2021. \nFor more detailed information, please read the paper at ACL Anthology.",
"passage: ### Supported Tasks and Leaderboards\n\nBioLeaflets proposes a conditional generation task (data-to-text) in the biomedical domain: given an ordered set of entities as source, the *goal* is to produce a multi-sentence section. \nSuccessful generation thus requires the model to learn specific syntax, terminology, and writing style from the corpus. Alternatively, the dataset might be used for named-entity recognition task: given text, detect medical entities. \nThe dataset provides an extensive description of medicinal products and thus supports a plain language modeling task focused on biomedical data.### Languages\n\nMonolingual - en.## Dataset Structure### Data Instances\n\nFor each instance (leaflet), there is a unique ID, URL, Product_Name, and textual information clearly describing the medicine. \nThe content of each section is not standardized (NO template), yet it is still well-structured. \nEach document contains six sections: \n1) What is the product and what is it used for\n2) What you need to know before you take the product\n3) Product usage instructions\n4) possible side effects\n5) product storage conditions\n6) other information\n\nEvery section is represented as a dictionary with the 'Title', 'Section_Content', and 'Entity_Recognition' as keys. \nThe content of each section is lower-cased and tokenized by treating all special characters as separate tokens.### Data Fields\n\n- 'ID': a string representing a unique ID assigned to a leaflet\n- 'URL': a string containing the link to the article on the European Medicines Agency (EMA) website \n- 'Product Name': a string, the name of the medicine\n- 'Full Content': a string covering the full content of the article available at URL\n- 'Section 1': a dictionary including section 1 content and associated medical entities\n- 'Section 2': a dictionary including section 2 content and associated medical entities\n- 'Section 3': a dictionary including section 3 content and associated medical entities\n- 'Section 4': a dictionary including section 4 content and associated medical entities\n- 'Section 5': a dictionary including section 5 content and associated medical entities\n- 'Section 6': a dictionary including section 6 content and associated medical entities### Data Splits\n\nWe randomly split the dataset into training (80%), development (10%), and test (10%) set. Duplicates are removed.## Dataset Creation### Curation Rationale\n\nIntroduce a new biomedical dataset (BioLeaflets), which could serve as a benchmark for biomedical text generation models. \nBioLeaflets proposes a conditional generation task: given an ordered set of entities as source, the goal is to produce a multi-sentence section.### Source Data",
"passage: #### Initial Data Collection and Normalization\n\nThe dataset was obtained by scraping the European Medicines Agency (EMA) website. \nEach leaflet has an URL associated with it to the article on the EMA website.#### Who are the source language producers?\n\nLabeling experts with domain knowledge produced factual information.### Annotations#### Annotation process\n\nTo create the required input for data-to-text generation, we augment each document by leveraging named entity recognition (NER). \nWe combine two NER frameworks: Amazon Comprehend Medical (commercial) and Stanford Stanza (open-sourced). \nAdditionally, we treat all digits as entities and add the medicine name as the first entity#### Who are the annotators?\n\nMachine-generated: ensemble of state-of-the-art named entity recognition (NER) models.### Personal and Sensitive Information\n\n[Not included / Not present]## Considerations for Using the Data### Social Impact of Dataset\n\nThe purpose of this dataset is to help develop models that can automatically generate long paragraphs of text as well as to facilitate the development of NLP models in the biomedical domain. \nThe main challenges of this dataset for D2T generation are multi-sentence and multi-section target text, small sample size, specialized medical vocabulary, and syntax.### Discussion of Biases\n\nPackage leaflets are published for medicinal products approved in the European Union (EU). \nThey are included in the packaging of medicinal products and contain information to help patients use the product safely and appropriately. \nThe dataset represents factual information produced by labeling experts and validated before publishing. Hence, biases of any kind are not present in the dataset. \nPackage leaflets are required to be written in a way that is clear and understandable.### Other Known Limitations\n\n[N/A]## Additional Information### Dataset Curators\n\nThe data was originally collected by Ruslan Yermakov<sup>*</sup>, Nicholas Drago, and Angelo Ziletti at Bayer AG (Decision Science & Advanced Analytics unit). The code is made publicly available at github link\n\n<sup>*</sup> Work done during internship."
]
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|
c9988951aa6571824b20888f8ba8201953d44df9 | https://github.com/verypluming/HELP
```bib
@InProceedings{yanaka-EtAl:2019:starsem,
author = {Yanaka, Hitomi and Mineshima, Koji and Bekki, Daisuke and Inui, Kentaro and Sekine, Satoshi and Abzianidze, Lasha and Bos, Johan},
title = {HELP: A Dataset for Identifying Shortcomings of Neural Models in Monotonicity Reasoning},
booktitle = {Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM2019)},
year = {2019},
}
``` | metaeval/help-nli | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"language:en",
"license:cc",
"region:us"
]
| 2023-02-04T18:07:35+00:00 | {"language": ["en"], "license": "cc", "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"]} | 2023-05-31T07:57:01+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_ids-natural-language-inference #language-English #license-cc #region-us
| URL
| []
| [
"TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #language-English #license-cc #region-us \n"
]
| [
39
]
| [
"passage: TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #language-English #license-cc #region-us \n"
]
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|
cbf17036f91ebc9d2b4e6c1eeff30dace5398452 | # Dataset Card for "origa_segmet"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ferferefer/origa_segmet | [
"region:us"
]
| 2023-02-04T18:46:23+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 7092060.0, "num_examples": 585}, {"name": "validation", "num_bytes": 758584.0, "num_examples": 65}], "download_size": 7875129, "dataset_size": 7850644.0}} | 2023-04-18T15:00:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "origa_segmet"
More Information needed | [
"# Dataset Card for \"origa_segmet\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"origa_segmet\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"origa_segmet\"\n\nMore Information needed"
]
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|
6101cd85ea986419793772cb79c15e810913a1dc | # Dataset Card for "wiki-nds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | danielpleus/wiki-nds | [
"region:us"
]
| 2023-02-04T19:00:57+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 92432660, "num_examples": 84158}], "download_size": 47740161, "dataset_size": 92432660}} | 2023-02-04T19:06:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "wiki-nds"
More Information needed | [
"# Dataset Card for \"wiki-nds\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"wiki-nds\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"wiki-nds\"\n\nMore Information needed"
]
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|
f3396c3fe34b7f05e97222f465b153ac096bc5b5 | # Dataset Card for "test_temp"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MartinKu/test_temp | [
"region:us"
]
| 2023-02-04T21:33:29+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "S_V", "sequence": "string"}, {"name": "S_V_position", "sequence": "int64"}, {"name": "O_C", "sequence": "string"}, {"name": "O_C_position", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 9100, "num_examples": 40}], "download_size": 8683, "dataset_size": 9100}} | 2023-02-08T06:59:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test_temp"
More Information needed | [
"# Dataset Card for \"test_temp\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test_temp\"\n\nMore Information needed"
]
| [
6,
13
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test_temp\"\n\nMore Information needed"
]
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8a24aaff4b3313cabaf75056e1f1a2ca5b68d957 | # Dataset Card for "OxfordPets_test_facebook_opt_6.7b_Attributes_Caption_ns_3669_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordPets_test_facebook_opt_6.7b_Attributes_Caption_ns_3669_random | [
"region:us"
]
| 2023-02-04T21:44:27+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 122168243.375, "num_examples": 3669}, {"name": "fewshot_3_bs_16", "num_bytes": 124209771.375, "num_examples": 3669}, {"name": "fewshot_0_bs_16", "num_bytes": 121141370.375, "num_examples": 3669}], "download_size": 358990525, "dataset_size": 367519385.125}} | 2023-02-06T05:17:15+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OxfordPets_test_facebook_opt_6.7b_Attributes_Caption_ns_3669_random"
More Information needed | [
"# Dataset Card for \"OxfordPets_test_facebook_opt_6.7b_Attributes_Caption_ns_3669_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordPets_test_facebook_opt_6.7b_Attributes_Caption_ns_3669_random\"\n\nMore Information needed"
]
| [
6,
38
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordPets_test_facebook_opt_6.7b_Attributes_Caption_ns_3669_random\"\n\nMore Information needed"
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|
cf0ff9fa2ae42eea2257b0dd1214d8a8577e9387 | # Dataset Card for "OxfordPets_test_facebook_opt_6.7b_Visclues_ns_3669_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordPets_test_facebook_opt_6.7b_Visclues_ns_3669_random | [
"region:us"
]
| 2023-02-04T21:57:11+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 122804715.375, "num_examples": 3669}, {"name": "fewshot_3_bs_16", "num_bytes": 125493759.375, "num_examples": 3669}, {"name": "fewshot_0_bs_16", "num_bytes": 121471156.375, "num_examples": 3669}], "download_size": 359901253, "dataset_size": 369769631.125}} | 2023-02-06T05:28:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OxfordPets_test_facebook_opt_6.7b_Visclues_ns_3669_random"
More Information needed | [
"# Dataset Card for \"OxfordPets_test_facebook_opt_6.7b_Visclues_ns_3669_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordPets_test_facebook_opt_6.7b_Visclues_ns_3669_random\"\n\nMore Information needed"
]
| [
6,
35
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordPets_test_facebook_opt_6.7b_Visclues_ns_3669_random\"\n\nMore Information needed"
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|
28f6377542a0f9bf9b5b09104233fb03ffe210d0 |
# PWESuite-Eval
Dataset composed of multiple smaller datasets used for the evaluation of phonetic word embeddings.
See code for evaluation [here](https://github.com/zouharvi/pwesuite).
Used datasets:
- [CMU Pronunciation dictionary](http://www.speech.cs.cmu.edu/cgi-bin/cmudict)
- [CC-100](https://data.statmt.org/cc-100/)
- [CogNet v0](https://aclanthology.org/P19-1302/)
- [Vitz and Winkler (1973)](https://www.sciencedirect.com/science/article/pii/S0022537173800167)
Authors:
- Vilém Zouhar (ETH Zürich, [contact](mailto:[email protected]))
- Kalvin Chang (CMU LTI, [contact](mailto:[email protected]))
- Chenxuan Cui (CMU LTI, [contact](mailto:[email protected]))
- Nathaniel Robinson (CMU LTI, [contact](mailto:[email protected]))
- Nathaniel Carlson (BYU, [contact](mailto:[email protected]))
- David Mortensen (CMU LTI, [contact](mailto:[email protected]))
If you use this dataset/evaluation, please cite:
```
@article{zouhar2023pwesuite,
title={{PWESuite}: {P}honetic Word Embeddings and Tasks They Facilitate},
author={Zouhar, Vil{\'e}m and Chang, Kalvin and Cui, Chenxuan and Carlson, Nathaniel and Robinson, Nathaniel and Sachan, Mrinmaya and Mortensen, David},
journal={arXiv preprint arXiv:2304.02541},
year={2023},
url={https://arxiv.org/abs/2304.02541}
}
``` | zouharvi/pwesuite-eval | [
"multilinguality:multilingual",
"size_categories:100K<n<1M",
"language:en",
"language:am",
"language:bn",
"language:sw",
"language:uz",
"language:es",
"language:pl",
"language:fr",
"language:de",
"license:apache-2.0",
"words",
"word",
"embedding",
"phonetic",
"phonological",
"cognates",
"rhyme",
"analogy",
"arxiv:2304.02541",
"region:us"
]
| 2023-02-04T22:04:58+00:00 | {"language": ["en", "am", "bn", "sw", "uz", "es", "pl", "fr", "de"], "license": "apache-2.0", "multilinguality": ["multilingual"], "size_categories": ["100K<n<1M"], "pretty_name": "PWESuite Evaluation v1", "tags": ["words", "word", "embedding", "phonetic", "phonological", "cognates", "rhyme", "analogy"], "dataset_info": {"features": [{"name": "token_ort", "dtype": "string"}, {"name": "token_ipa", "dtype": "string"}, {"name": "token_arp", "dtype": "string"}, {"name": "lang", "dtype": "string"}, {"name": "purpose", "dtype": "string"}], "splits": [{"name": "train", "num_examples": 1738008}]}} | 2023-10-11T16:14:09+00:00 | [
"2304.02541"
]
| [
"en",
"am",
"bn",
"sw",
"uz",
"es",
"pl",
"fr",
"de"
]
| TAGS
#multilinguality-multilingual #size_categories-100K<n<1M #language-English #language-Amharic #language-Bengali #language-Swahili (macrolanguage) #language-Uzbek #language-Spanish #language-Polish #language-French #language-German #license-apache-2.0 #words #word #embedding #phonetic #phonological #cognates #rhyme #analogy #arxiv-2304.02541 #region-us
|
# PWESuite-Eval
Dataset composed of multiple smaller datasets used for the evaluation of phonetic word embeddings.
See code for evaluation here.
Used datasets:
- CMU Pronunciation dictionary
- CC-100
- CogNet v0
- Vitz and Winkler (1973)
Authors:
- Vilém Zouhar (ETH Zürich, contact)
- Kalvin Chang (CMU LTI, contact)
- Chenxuan Cui (CMU LTI, contact)
- Nathaniel Robinson (CMU LTI, contact)
- Nathaniel Carlson (BYU, contact)
- David Mortensen (CMU LTI, contact)
If you use this dataset/evaluation, please cite:
| [
"# PWESuite-Eval\n\nDataset composed of multiple smaller datasets used for the evaluation of phonetic word embeddings.\nSee code for evaluation here.\n\nUsed datasets:\n- CMU Pronunciation dictionary\n- CC-100\n- CogNet v0\n- Vitz and Winkler (1973)\n\nAuthors:\n- Vilém Zouhar (ETH Zürich, contact)\n- Kalvin Chang (CMU LTI, contact)\n- Chenxuan Cui (CMU LTI, contact)\n- Nathaniel Robinson (CMU LTI, contact)\n- Nathaniel Carlson (BYU, contact)\n- David Mortensen (CMU LTI, contact)\n\nIf you use this dataset/evaluation, please cite:"
]
| [
"TAGS\n#multilinguality-multilingual #size_categories-100K<n<1M #language-English #language-Amharic #language-Bengali #language-Swahili (macrolanguage) #language-Uzbek #language-Spanish #language-Polish #language-French #language-German #license-apache-2.0 #words #word #embedding #phonetic #phonological #cognates #rhyme #analogy #arxiv-2304.02541 #region-us \n",
"# PWESuite-Eval\n\nDataset composed of multiple smaller datasets used for the evaluation of phonetic word embeddings.\nSee code for evaluation here.\n\nUsed datasets:\n- CMU Pronunciation dictionary\n- CC-100\n- CogNet v0\n- Vitz and Winkler (1973)\n\nAuthors:\n- Vilém Zouhar (ETH Zürich, contact)\n- Kalvin Chang (CMU LTI, contact)\n- Chenxuan Cui (CMU LTI, contact)\n- Nathaniel Robinson (CMU LTI, contact)\n- Nathaniel Carlson (BYU, contact)\n- David Mortensen (CMU LTI, contact)\n\nIf you use this dataset/evaluation, please cite:"
]
| [
120,
157
]
| [
"passage: TAGS\n#multilinguality-multilingual #size_categories-100K<n<1M #language-English #language-Amharic #language-Bengali #language-Swahili (macrolanguage) #language-Uzbek #language-Spanish #language-Polish #language-French #language-German #license-apache-2.0 #words #word #embedding #phonetic #phonological #cognates #rhyme #analogy #arxiv-2304.02541 #region-us \n# PWESuite-Eval\n\nDataset composed of multiple smaller datasets used for the evaluation of phonetic word embeddings.\nSee code for evaluation here.\n\nUsed datasets:\n- CMU Pronunciation dictionary\n- CC-100\n- CogNet v0\n- Vitz and Winkler (1973)\n\nAuthors:\n- Vilém Zouhar (ETH Zürich, contact)\n- Kalvin Chang (CMU LTI, contact)\n- Chenxuan Cui (CMU LTI, contact)\n- Nathaniel Robinson (CMU LTI, contact)\n- Nathaniel Carlson (BYU, contact)\n- David Mortensen (CMU LTI, contact)\n\nIf you use this dataset/evaluation, please cite:"
]
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|
3d4236f8988fe37444b326ffbba78137866e8c5d | # Dataset Card for "Intent-Classification-large"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | dipesh/Intent-Classification-large | [
"region:us"
]
| 2023-02-04T22:17:57+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "intent", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "others", "1": "places near me", "2": "send whatsapp message", "3": "greet and hello hi kind of things, general check in", "4": "play games", "5": "tell me news", "6": "covid cases", "7": "tell me about", "8": "volume control", "9": "open website", "10": "play on youtube", "11": "tell me joke", "12": "send email", "13": "goodbye", "14": "take screenshot", "15": "download youtube video", "16": "asking weather", "17": "asking date", "18": "asking time", "19": "i am bored", "20": "click photo", "21": "what can you do"}}}}], "splits": [{"name": "train", "num_bytes": 1594125, "num_examples": 15311}, {"name": "validation", "num_bytes": 175519, "num_examples": 1702}], "download_size": 677155, "dataset_size": 1769644}} | 2023-02-04T22:18:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Intent-Classification-large"
More Information needed | [
"# Dataset Card for \"Intent-Classification-large\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Intent-Classification-large\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Intent-Classification-large\"\n\nMore Information needed"
]
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|
e396b6888fd059c8a24fa68a37861fadf059eff2 | # Dataset Card for "DTD_parition1_test_facebook_opt_6.7b_Attributes_Caption_ns_1880_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/DTD_parition1_test_facebook_opt_6.7b_Attributes_Caption_ns_1880_random | [
"region:us"
]
| 2023-02-04T22:40:07+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 92259840.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93271918.0, "num_examples": 1880}], "download_size": 181110966, "dataset_size": 185531758.0}} | 2023-02-04T22:57:35+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DTD_parition1_test_facebook_opt_6.7b_Attributes_Caption_ns_1880_random"
More Information needed | [
"# Dataset Card for \"DTD_parition1_test_facebook_opt_6.7b_Attributes_Caption_ns_1880_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_facebook_opt_6.7b_Attributes_Caption_ns_1880_random\"\n\nMore Information needed"
]
| [
6,
40
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_facebook_opt_6.7b_Attributes_Caption_ns_1880_random\"\n\nMore Information needed"
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06c84e97bd9360dbf93770538c1cda0b8f332a22 | # Dataset Card for "DTD_parition1_test_facebook_opt_6.7b_Visclues_ns_1880_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/DTD_parition1_test_facebook_opt_6.7b_Visclues_ns_1880_random | [
"region:us"
]
| 2023-02-04T22:47:57+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 92562741.0, "num_examples": 1880}, {"name": "fewshot_3_bs_16", "num_bytes": 93877293.0, "num_examples": 1880}], "download_size": 181620779, "dataset_size": 186440034.0}} | 2023-02-04T23:09:02+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "DTD_parition1_test_facebook_opt_6.7b_Visclues_ns_1880_random"
More Information needed | [
"# Dataset Card for \"DTD_parition1_test_facebook_opt_6.7b_Visclues_ns_1880_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"DTD_parition1_test_facebook_opt_6.7b_Visclues_ns_1880_random\"\n\nMore Information needed"
]
| [
6,
37
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"DTD_parition1_test_facebook_opt_6.7b_Visclues_ns_1880_random\"\n\nMore Information needed"
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|
bf46422e48a9a5972b232439870c4650497e1175 |
# Video-to-Video Dataset
This is a dataset for video-to-video.
You have not to worry about this copyright if you read the outline of license.
# Outline of License
This is under Unity-Chan License. The outline is as follow:
- You can use this for commercial purpose.
- You must display "Song/Motion: © Unity Technologies Japan/UCL." in your work.
The official guideline is [here](https://unity-chan.com/contents/guideline_en/).
Please read it.
# Copyrights
## 3D Model
This model is CC-0.
[More](https://vroid.pixiv.help/hc/ja/articles/360012381793-AvatarSample-D)
## Song
Unity Technologies Japan/UCL has the copyright of the song.
[More](https://unity-chan.com/download/releaseNote.php?id=imagealbum-vol1&lang=en)
## Motion
Unity Technologies Japan/UCL has the copyright of the motion.
# Contact
Plese use the Community function.
| alfredplpl/video-to-video-dataset | [
"language:ja",
"language:en",
"license:other",
"region:us"
]
| 2023-02-04T23:52:54+00:00 | {"language": ["ja", "en"], "license": "other"} | 2023-02-05T05:33:49+00:00 | []
| [
"ja",
"en"
]
| TAGS
#language-Japanese #language-English #license-other #region-us
|
# Video-to-Video Dataset
This is a dataset for video-to-video.
You have not to worry about this copyright if you read the outline of license.
# Outline of License
This is under Unity-Chan License. The outline is as follow:
- You can use this for commercial purpose.
- You must display "Song/Motion: © Unity Technologies Japan/UCL." in your work.
The official guideline is here.
Please read it.
# Copyrights
## 3D Model
This model is CC-0.
More
## Song
Unity Technologies Japan/UCL has the copyright of the song.
More
!Unite in the sky
## Motion
Unity Technologies Japan/UCL has the copyright of the motion.
# Contact
Plese use the Community function.
| [
"# Video-to-Video Dataset\n\nThis is a dataset for video-to-video.\nYou have not to worry about this copyright if you read the outline of license.",
"# Outline of License\n\nThis is under Unity-Chan License. The outline is as follow:\n\n- You can use this for commercial purpose.\n- You must display \"Song/Motion: © Unity Technologies Japan/UCL.\" in your work.\n\nThe official guideline is here.\nPlease read it.",
"# Copyrights",
"## 3D Model\nThis model is CC-0.\nMore",
"## Song\nUnity Technologies Japan/UCL has the copyright of the song.\nMore\n\n!Unite in the sky",
"## Motion\nUnity Technologies Japan/UCL has the copyright of the motion.",
"# Contact\n\nPlese use the Community function."
]
| [
"TAGS\n#language-Japanese #language-English #license-other #region-us \n",
"# Video-to-Video Dataset\n\nThis is a dataset for video-to-video.\nYou have not to worry about this copyright if you read the outline of license.",
"# Outline of License\n\nThis is under Unity-Chan License. The outline is as follow:\n\n- You can use this for commercial purpose.\n- You must display \"Song/Motion: © Unity Technologies Japan/UCL.\" in your work.\n\nThe official guideline is here.\nPlease read it.",
"# Copyrights",
"## 3D Model\nThis model is CC-0.\nMore",
"## Song\nUnity Technologies Japan/UCL has the copyright of the song.\nMore\n\n!Unite in the sky",
"## Motion\nUnity Technologies Japan/UCL has the copyright of the motion.",
"# Contact\n\nPlese use the Community function."
]
| [
21,
37,
64,
3,
11,
23,
16,
9
]
| [
"passage: TAGS\n#language-Japanese #language-English #license-other #region-us \n# Video-to-Video Dataset\n\nThis is a dataset for video-to-video.\nYou have not to worry about this copyright if you read the outline of license.# Outline of License\n\nThis is under Unity-Chan License. The outline is as follow:\n\n- You can use this for commercial purpose.\n- You must display \"Song/Motion: © Unity Technologies Japan/UCL.\" in your work.\n\nThe official guideline is here.\nPlease read it.# Copyrights## 3D Model\nThis model is CC-0.\nMore## Song\nUnity Technologies Japan/UCL has the copyright of the song.\nMore\n\n!Unite in the sky## Motion\nUnity Technologies Japan/UCL has the copyright of the motion.# Contact\n\nPlese use the Community function."
]
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|
a6f833b5ef32dea49cd5c5fc49c9358b22ec82fa | # Dataset Card for "Caltech101_with_background_test_facebook_opt_6.7b_Attributes_Caption_ns_6084_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Caltech101_with_background_test_facebook_opt_6.7b_Attributes_Caption_ns_6084_random | [
"region:us"
]
| 2023-02-04T23:57:40+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 102753879.5, "num_examples": 6084}, {"name": "fewshot_3_bs_16", "num_bytes": 105999857.5, "num_examples": 6084}], "download_size": 193316942, "dataset_size": 208753737.0}} | 2023-02-05T01:43:57+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Caltech101_with_background_test_facebook_opt_6.7b_Attributes_Caption_ns_6084_random"
More Information needed | [
"# Dataset Card for \"Caltech101_with_background_test_facebook_opt_6.7b_Attributes_Caption_ns_6084_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Caltech101_with_background_test_facebook_opt_6.7b_Attributes_Caption_ns_6084_random\"\n\nMore Information needed"
]
| [
6,
43
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_with_background_test_facebook_opt_6.7b_Attributes_Caption_ns_6084_random\"\n\nMore Information needed"
]
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|
04bec310672fa3028655dc0f9abafef8c9336b65 | # Dataset Card for "Caltech101_with_background_test_facebook_opt_6.7b_Visclues_ns_6084_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Caltech101_with_background_test_facebook_opt_6.7b_Visclues_ns_6084_random | [
"region:us"
]
| 2023-02-05T00:44:25+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 103748347.5, "num_examples": 6084}, {"name": "fewshot_3_bs_16", "num_bytes": 107978514.5, "num_examples": 6084}], "download_size": 195080607, "dataset_size": 211726862.0}} | 2023-02-05T02:59:54+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Caltech101_with_background_test_facebook_opt_6.7b_Visclues_ns_6084_random"
More Information needed | [
"# Dataset Card for \"Caltech101_with_background_test_facebook_opt_6.7b_Visclues_ns_6084_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Caltech101_with_background_test_facebook_opt_6.7b_Visclues_ns_6084_random\"\n\nMore Information needed"
]
| [
6,
40
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_with_background_test_facebook_opt_6.7b_Visclues_ns_6084_random\"\n\nMore Information needed"
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|
913c95c8d302c88ef0704cbbd2d0e0c7fd3d7daf | # AutoTrain Dataset for project: square-count-classifier
## Dataset Description
This dataset has been automatically processed by AutoTrain for project square-count-classifier.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<28x28 L PIL image>",
"target": 0
},
{
"image": "<28x28 L PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['green', 'red'], 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 | 394 |
| valid | 40 |
| dhavala/autotrain-data-square-count-classifier | [
"task_categories:image-classification",
"region:us"
]
| 2023-02-05T02:16:57+00:00 | {"task_categories": ["image-classification"]} | 2023-02-05T02:22:19+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: square-count-classifier
======================================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project square-count-classifier.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-image-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
17,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
-0.13260804116725922,
0.1650877296924591,
0.002344331005588174,
0.055275481194257736,
0.0935901552438736,
0.11733019351959229,
-0.003920542076230049,
0.14625874161720276,
0.1256745159626007,
0.016513284295797348,
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|
7719ef590e024f1d2f69c986e1f76ece1ecc1190 | # Dataset Card for "boostcamp-docvqa-v5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Ssunbell/boostcamp-docvqa-v5 | [
"region:us"
]
| 2023-02-05T02:50:57+00:00 | {"dataset_info": {"features": [{"name": "questionId", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "sequence": {"sequence": {"sequence": "uint8"}}}, {"name": "docId", "dtype": "int64"}, {"name": "ucsf_document_id", "dtype": "string"}, {"name": "ucsf_document_page_no", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "data_split", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "boxes", "sequence": {"sequence": "int64"}}], "splits": [{"name": "train", "num_bytes": 6381793673, "num_examples": 39454}, {"name": "val", "num_bytes": 869361798, "num_examples": 5349}], "download_size": 2578655464, "dataset_size": 7251155471}} | 2023-02-05T03:01:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "boostcamp-docvqa-v5"
More Information needed | [
"# Dataset Card for \"boostcamp-docvqa-v5\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"boostcamp-docvqa-v5\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"boostcamp-docvqa-v5\"\n\nMore Information needed"
]
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|
ae29eeddc3350ad0adb3cb89f7da637ee80b9dc6 | # Dataset Card for "boostcamp-docvqa-v5-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Ssunbell/boostcamp-docvqa-v5-test | [
"region:us"
]
| 2023-02-05T03:01:54+00:00 | {"dataset_info": {"features": [{"name": "questionId", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "sequence": {"sequence": {"sequence": "uint8"}}}, {"name": "docId", "dtype": "int64"}, {"name": "ucsf_document_id", "dtype": "string"}, {"name": "ucsf_document_page_no", "dtype": "string"}, {"name": "data_split", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "boxes", "sequence": {"sequence": "int64"}}], "splits": [{"name": "test", "num_bytes": 843083964, "num_examples": 5188}], "download_size": 296859136, "dataset_size": 843083964}} | 2023-02-05T03:03:26+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "boostcamp-docvqa-v5-test"
More Information needed | [
"# Dataset Card for \"boostcamp-docvqa-v5-test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"boostcamp-docvqa-v5-test\"\n\nMore Information needed"
]
| [
6,
21
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"boostcamp-docvqa-v5-test\"\n\nMore Information needed"
]
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|
aa57749de94d6104ddb34dc5109abdb60b5d5a27 | # Dataset Card for "FGVC_Aircraft_test_facebook_opt_6.7b_Attributes_Caption_ns_3333_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/FGVC_Aircraft_test_facebook_opt_6.7b_Attributes_Caption_ns_3333_random | [
"region:us"
]
| 2023-02-05T03:22:37+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 300148506.375, "num_examples": 3333}, {"name": "fewshot_3_bs_16", "num_bytes": 301866097.375, "num_examples": 3333}], "download_size": 590830197, "dataset_size": 602014603.75}} | 2023-02-05T04:21:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "FGVC_Aircraft_test_facebook_opt_6.7b_Attributes_Caption_ns_3333_random"
More Information needed | [
"# Dataset Card for \"FGVC_Aircraft_test_facebook_opt_6.7b_Attributes_Caption_ns_3333_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_facebook_opt_6.7b_Attributes_Caption_ns_3333_random\"\n\nMore Information needed"
]
| [
6,
40
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_facebook_opt_6.7b_Attributes_Caption_ns_3333_random\"\n\nMore Information needed"
]
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0648b56590979c5661bfb55569a0a247612f217e | # Dataset Card for "FGVC_Aircraft_test_facebook_opt_6.7b_Visclues_ns_3333_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/FGVC_Aircraft_test_facebook_opt_6.7b_Visclues_ns_3333_random | [
"region:us"
]
| 2023-02-05T03:49:11+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 300686675.375, "num_examples": 3333}, {"name": "fewshot_3_bs_16", "num_bytes": 302944193.375, "num_examples": 3333}], "download_size": 591533331, "dataset_size": 603630868.75}} | 2023-02-05T05:02:10+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "FGVC_Aircraft_test_facebook_opt_6.7b_Visclues_ns_3333_random"
More Information needed | [
"# Dataset Card for \"FGVC_Aircraft_test_facebook_opt_6.7b_Visclues_ns_3333_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"FGVC_Aircraft_test_facebook_opt_6.7b_Visclues_ns_3333_random\"\n\nMore Information needed"
]
| [
6,
37
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| [
"passage: TAGS\n#region-us \n# Dataset Card for \"FGVC_Aircraft_test_facebook_opt_6.7b_Visclues_ns_3333_random\"\n\nMore Information needed"
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|
68bd22702024d7436061b4ed7d240deb62ddf001 | # Dataset Card for "OxfordFlowers_test_facebook_opt_6.7b_Attributes_Caption_ns_6149_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordFlowers_test_facebook_opt_6.7b_Attributes_Caption_ns_6149_random | [
"region:us"
]
| 2023-02-05T05:46:03+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 269126072.375, "num_examples": 6149}, {"name": "fewshot_3_bs_16", "num_bytes": 272734326.375, "num_examples": 6149}], "download_size": 524724011, "dataset_size": 541860398.75}} | 2023-02-05T07:43:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OxfordFlowers_test_facebook_opt_6.7b_Attributes_Caption_ns_6149_random"
More Information needed | [
"# Dataset Card for \"OxfordFlowers_test_facebook_opt_6.7b_Attributes_Caption_ns_6149_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordFlowers_test_facebook_opt_6.7b_Attributes_Caption_ns_6149_random\"\n\nMore Information needed"
]
| [
6,
39
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordFlowers_test_facebook_opt_6.7b_Attributes_Caption_ns_6149_random\"\n\nMore Information needed"
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|
10f33a1b8068336360d90d6edde7f6d157b266c4 | # Dataset Card for "OxfordFlowers_test_facebook_opt_6.7b_Visclues_ns_6149_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/OxfordFlowers_test_facebook_opt_6.7b_Visclues_ns_6149_random | [
"region:us"
]
| 2023-02-05T06:38:10+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 270234427.375, "num_examples": 6149}, {"name": "fewshot_3_bs_16", "num_bytes": 274949239.375, "num_examples": 6149}], "download_size": 526197349, "dataset_size": 545183666.75}} | 2023-02-05T09:10:17+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "OxfordFlowers_test_facebook_opt_6.7b_Visclues_ns_6149_random"
More Information needed | [
"# Dataset Card for \"OxfordFlowers_test_facebook_opt_6.7b_Visclues_ns_6149_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"OxfordFlowers_test_facebook_opt_6.7b_Visclues_ns_6149_random\"\n\nMore Information needed"
]
| [
6,
36
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"OxfordFlowers_test_facebook_opt_6.7b_Visclues_ns_6149_random\"\n\nMore Information needed"
]
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|
3db68af54c50ccb3bc8d9bc00124886f14d6847d | # Dataset for project: quick-summarization
## Dataset Description
This dataset has been trained for project quick-summarization.
### Languages
The BCP-47 code for the dataset's language is en.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"text": "Ever noticed how plane seats appear to be getting smaller and smaller? With increasing numbers of people taking to the skies, some experts are questioning if having such packed out planes is putting passengers at risk. They say that the shrinking space on aeroplanes is not only uncomfortable - it's putting our health and safety in danger. More than squabbling over the arm rest, shrinking space on planes putting our health and safety in danger? This week, a U.S consumer advisory group set up by the Department of Transportation said at a public hearing that while the government is happy to set standards for animals flying on planes, it doesn't stipulate a minimum amount of space for humans. 'In a world where animals have more rights to space and food than humans,' said Charlie Leocha, consumer representative on the committee.\u00a0'It is time that the DOT and FAA take a stand for humane treatment of passengers.' But could crowding on planes lead to more serious issues than fighting for space in the overhead lockers, crashing elbows and seat back kicking? Tests conducted by the FAA use planes with a 31 inch pitch, a standard which on some airlines has decreased . Many economy seats on United Airlines have 30 inches of room, while some airlines offer as little as 28 inches . Cynthia Corbertt, a human factors researcher with the Federal Aviation Administration, that it conducts tests on how quickly passengers can leave a plane. But these tests are conducted using planes with 31 inches between each row of seats, a standard which on some airlines has decreased, reported the Detroit News. The distance between two seats from one point on a seat to the same point on the seat behind it is known as the pitch. While most airlines stick to a pitch of 31 inches or above, some fall below this. While United Airlines has 30 inches of space, Gulf Air economy seats have between 29 and 32 inches, Air Asia offers 29 inches and Spirit Airlines offers just 28 inches. British Airways has a seat pitch of 31 inches, while easyJet has 29 inches, Thomson's short haul seat pitch is 28 inches, and Virgin Atlantic's is 30-31.",
"target": "Experts question if packed out planes are putting passengers at risk.\nU.S consumer advisory group says minimum space must be stipulated.\nSafety tests conducted on planes with more leg room than airlines offer."
},
{
"text": "A drunk teenage boy had to be rescued by security after jumping into a lions' enclosure at a zoo in western India. Rahul Kumar, 17, clambered over the enclosure fence at the\u00a0Kamla Nehru Zoological Park in Ahmedabad, and began running towards the animals, shouting he would 'kill them'. Mr Kumar explained afterwards that he was drunk and 'thought I'd stand a good chance' against the predators. Next level drunk: Intoxicated Rahul Kumar, 17, climbed into the lions' enclosure at a zoo in Ahmedabad and began running towards the animals shouting 'Today I kill a lion!' Mr Kumar had been sitting near the enclosure when he suddenly made a dash for the lions, surprising zoo security. The intoxicated teenager ran towards the lions, shouting: 'Today I kill a lion or a lion kills me!' A zoo spokesman said: 'Guards had earlier spotted him close to the enclosure but had no idea he was planing to enter it. 'Fortunately, there are eight moats to cross before getting to where the lions usually are and he fell into the second one, allowing guards to catch up with him and take him out. 'We then handed him over to the police.' Brave fool: Fortunately, Mr Kumar fell into a moat as he ran towards the lions and could be rescued by zoo security staff before reaching the animals (stock image) Kumar later explained: 'I don't really know why I did it. 'I was drunk and thought I'd stand a good chance.' A police spokesman said: 'He has been cautioned and will be sent for psychiatric evaluation. 'Fortunately for him, the lions were asleep and the zoo guards acted quickly enough to prevent a tragedy similar to that in Delhi.' Last year a 20-year-old man was mauled to death by a tiger in the Indian capital after climbing into its enclosure at the city zoo.",
"target": "Drunk teenage boy climbed into lion enclosure at zoo in west India.\nRahul Kumar, 17, ran towards animals shouting 'Today I kill a lion!'\nFortunately he fell into a moat before reaching lions and was rescued."
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"text": "Value(dtype='string', id=None)",
"target": "Value(dtype='string', 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 | 7507 |
| valid | 2491 |
| Kaludi/data-quick-summarization | [
"task_categories:summarization",
"language:en",
"region:us"
]
| 2023-02-05T08:28:09+00:00 | {"language": ["en"], "task_categories": ["summarization"]} | 2023-02-05T20:42:13+00:00 | []
| [
"en"
]
| TAGS
#task_categories-summarization #language-English #region-us
| Dataset for project: quick-summarization
========================================
Dataset Description
-------------------
This dataset has been trained for project quick-summarization.
### Languages
The BCP-47 code for the dataset's language is en.
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 en.\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-summarization #language-English #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\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:"
]
| [
20,
26,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-summarization #language-English #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is en.\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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|
be85d4d2987cdbc212484ce2a1fccd4ec450510e | # Dataset Card for "test1-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | futuristixa/test1-dataset | [
"region:us"
]
| 2023-02-05T09:04:39+00:00 | {"dataset_info": {"features": [{"name": "review", "dtype": "string"}, {"name": "review_length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1252876.2642514652, "num_examples": 3378}, {"name": "validation", "num_bytes": 139455.7357485349, "num_examples": 376}], "download_size": 0, "dataset_size": 1392332.0}} | 2023-02-05T10:15:25+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "test1-dataset"
More Information needed | [
"# Dataset Card for \"test1-dataset\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"test1-dataset\"\n\nMore Information needed"
]
| [
6,
14
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"test1-dataset\"\n\nMore Information needed"
]
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|
cffce0c535b77b35d90cd867f537f9f1152cf55c | # DreamBank - Dreams
The dataset is a collection of ~30k 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. The vast majority of the series are in the
English language, but a small part of the are in German. These series are indicated by the presence of `.de` in their name.
## 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`
## 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
- german-f.de: 397
- german-m.de: 140
- 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
- vonuslar.de: 6094
- wedding: 65
- west_coast_teens: 89
- zurich-f.de: 164
- zurich-m.de: 135 | DReAMy-lib/DreamBank-dreams | [
"task_categories:text-generation",
"size_categories:10K<n<100K",
"language:en",
"language:de",
"license:apache-2.0",
"region:us"
]
| 2023-02-05T09:56:21+00:00 | {"language": ["en", "de"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "dataset_info": {"features": [{"name": "dreams", "dtype": "string"}, {"name": "series", "dtype": "string"}, {"name": "description", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 27263345, "num_examples": 29345}], "download_size": 15525739, "dataset_size": 27263345}} | 2023-02-05T09:58:42+00:00 | []
| [
"en",
"de"
]
| TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #language-German #license-apache-2.0 #region-us
| # DreamBank - Dreams
The dataset is a collection of ~30k 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. The vast majority of the series are in the
English language, but a small part of the are in German. These series are indicated by the presence of '.de' in their name.
## 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'
## 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
- URL: 397
- URL: 140
- 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
- URL: 6094
- wedding: 65
- west_coast_teens: 89
- URL: 164
- URL: 135 | [
"# DreamBank - Dreams\n\nThe dataset is a collection of ~30k 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. The vast majority of the series are in the \nEnglish language, but a small part of the are in German. These series are indicated by the presence of '.de' in their name.",
"## 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'",
"## 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- URL: 397\n- URL: 140\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- URL: 6094\n- wedding: 65\n- west_coast_teens: 89\n- URL: 164\n- URL: 135"
]
| [
"TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #language-German #license-apache-2.0 #region-us \n",
"# DreamBank - Dreams\n\nThe dataset is a collection of ~30k 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. The vast majority of the series are in the \nEnglish language, but a small part of the are in German. These series are indicated by the presence of '.de' in their name.",
"## 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'",
"## 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- URL: 397\n- URL: 140\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- URL: 6094\n- wedding: 65\n- west_coast_teens: 89\n- URL: 164\n- URL: 135"
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"passage: TAGS\n#task_categories-text-generation #size_categories-10K<n<100K #language-English #language-German #license-apache-2.0 #region-us \n# DreamBank - Dreams\n\nThe dataset is a collection of ~30k 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. The vast majority of the series are in the \nEnglish language, but a small part of the are in German. These series are indicated by the presence of '.de' in their name.## 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'"
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|
acbcd60a205f27e1de9a9d46838528fecb876fc9 | persianConversation | Kamtera/Persian-conversational-dataset | [
"task_categories:conversational",
"task_categories:text-generation",
"language:fa",
"license:apache-2.0",
"region:us"
]
| 2023-02-05T10:12:23+00:00 | {"language": ["fa"], "license": "apache-2.0", "task_categories": ["conversational", "text-generation"], "pretty_name": "persianConversation"} | 2023-04-04T07:19:27+00:00 | []
| [
"fa"
]
| TAGS
#task_categories-conversational #task_categories-text-generation #language-Persian #license-apache-2.0 #region-us
| persianConversation | []
| [
"TAGS\n#task_categories-conversational #task_categories-text-generation #language-Persian #license-apache-2.0 #region-us \n"
]
| [
40
]
| [
"passage: TAGS\n#task_categories-conversational #task_categories-text-generation #language-Persian #license-apache-2.0 #region-us \n"
]
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|
0ef85426a9b6765dca712c0d45cc55d42ef18fe7 | tomi dataset (theory of mind question answering) recasted as natural language inference
https://colab.research.google.com/drive/1J_RqDSw9iPxJSBvCJu-VRbjXnrEjKVvr?usp=sharing
```
@article{sileo2023tasksource,
title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation},
author={Sileo, Damien},
url= {https://arxiv.org/abs/2301.05948},
journal={arXiv preprint arXiv:2301.05948},
year={2023}
}
@inproceedings{le-etal-2019-revisiting,
title = "Revisiting the Evaluation of Theory of Mind through Question Answering",
author = "Le, Matthew and
Boureau, Y-Lan and
Nickel, Maximilian",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/D19-1598",
doi = "10.18653/v1/D19-1598",
pages = "5872--5877"
}
``` | tasksource/tomi-nli | [
"task_categories:text-classification",
"task_ids:natural-language-inference",
"language:en",
"license:gpl-3.0",
"arxiv:2301.05948",
"region:us"
]
| 2023-02-05T10:40:34+00:00 | {"language": ["en"], "license": "gpl-3.0", "task_categories": ["text-classification"], "task_ids": ["natural-language-inference"]} | 2023-02-09T21:05:13+00:00 | [
"2301.05948"
]
| [
"en"
]
| TAGS
#task_categories-text-classification #task_ids-natural-language-inference #language-English #license-gpl-3.0 #arxiv-2301.05948 #region-us
| tomi dataset (theory of mind question answering) recasted as natural language inference
URL
| []
| [
"TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #language-English #license-gpl-3.0 #arxiv-2301.05948 #region-us \n"
]
| [
51
]
| [
"passage: TAGS\n#task_categories-text-classification #task_ids-natural-language-inference #language-English #license-gpl-3.0 #arxiv-2301.05948 #region-us \n"
]
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|
75cafe8ddb7ed77d5712ed27b6388b385899315c | Images trained for my [phantom diffusion](https://huggingface.co/Phantom-Artist/phantom-diffusion) series.
Since they are all AI generated images that are public domain under the US law, I claim it is legal to redistribute them as public domain.
However, they might have copyright in your/their original country.
Still, many countries including Japan allow us to use them for training an AI under their copyrights law, and because all the artists here are from Japan, I assume it should be allowed to reuse it for training globally. | Phantom-Artist/phantom-diffusion-dataset | [
"size_categories:n<1K",
"language:en",
"language:ja",
"license:cc0-1.0",
"region:us"
]
| 2023-02-05T11:06:08+00:00 | {"language": ["en", "ja"], "license": "cc0-1.0", "size_categories": ["n<1K"]} | 2023-02-05T11:13:34+00:00 | []
| [
"en",
"ja"
]
| TAGS
#size_categories-n<1K #language-English #language-Japanese #license-cc0-1.0 #region-us
| Images trained for my phantom diffusion series.
Since they are all AI generated images that are public domain under the US law, I claim it is legal to redistribute them as public domain.
However, they might have copyright in your/their original country.
Still, many countries including Japan allow us to use them for training an AI under their copyrights law, and because all the artists here are from Japan, I assume it should be allowed to reuse it for training globally. | []
| [
"TAGS\n#size_categories-n<1K #language-English #language-Japanese #license-cc0-1.0 #region-us \n"
]
| [
34
]
| [
"passage: TAGS\n#size_categories-n<1K #language-English #language-Japanese #license-cc0-1.0 #region-us \n"
]
| [
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|
1a9eff18797ee36426711ce0bcce52225e02307e | Embeddings derived from business descriptions of S&P500 companies using sentence-BERT, SentenceTransformer('all-MiniLM-L6-v2') to be exact. For more info on evaluation of sentence transformers (specifcailly the huge GPT-3 versus smaller models see: https://twitter.com/Nils_Reimers/status/1487014195568775173) | beanjar/sp500-business-description-sentence-bert-embeddings | [
"license:mit",
"region:us"
]
| 2023-02-05T11:19:03+00:00 | {"license": "mit"} | 2023-02-05T11:38:49+00:00 | []
| []
| TAGS
#license-mit #region-us
| Embeddings derived from business descriptions of S&P500 companies using sentence-BERT, SentenceTransformer('all-MiniLM-L6-v2') to be exact. For more info on evaluation of sentence transformers (specifcailly the huge GPT-3 versus smaller models see: URL | []
| [
"TAGS\n#license-mit #region-us \n"
]
| [
11
]
| [
"passage: TAGS\n#license-mit #region-us \n"
]
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|
225ef41e13b1c9e1d544aec8e809140e635effb7 | # Dataset Card for "sidewalk-imagery"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | shahardekel/sidewalk-imagery | [
"region:us"
]
| 2023-02-05T12:06:15+00:00 | {"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 86083036.0, "num_examples": 10}], "download_size": 7066127, "dataset_size": 86083036.0}} | 2023-02-05T12:06:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sidewalk-imagery"
More Information needed | [
"# Dataset Card for \"sidewalk-imagery\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sidewalk-imagery\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sidewalk-imagery\"\n\nMore Information needed"
]
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|
e913e2d4075f89f6c6eb14fadc36e072c006bb4d | # AutoTrain Dataset for project: big-data-chest
## Dataset Description
This dataset has been automatically processed by AutoTrain for project big-data-chest.
### Languages
The BCP-47 code for the dataset's language is unk.
## Dataset Structure
### Data Instances
A sample from this dataset looks as follows:
```json
[
{
"image": "<2090x1858 L PIL image>",
"target": 0
},
{
"image": "<1422x1152 L PIL image>",
"target": 0
}
]
```
### Dataset Fields
The dataset has the following fields (also called "features"):
```json
{
"image": "Image(decode=True, id=None)",
"target": "ClassLabel(names=['NORMAL', 'PNEUMONIA'], 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 | 298 |
| valid | 198 |
| MartinLubenov/autotrain-data-big-data-chest | [
"task_categories:image-classification",
"region:us"
]
| 2023-02-05T12:14:31+00:00 | {"task_categories": ["image-classification"]} | 2023-02-05T13:01:33+00:00 | []
| []
| TAGS
#task_categories-image-classification #region-us
| AutoTrain Dataset for project: big-data-chest
=============================================
Dataset Description
-------------------
This dataset has been automatically processed by AutoTrain for project big-data-chest.
### Languages
The BCP-47 code for the dataset's language is unk.
Dataset Structure
-----------------
### Data Instances
A sample from this dataset looks as follows:
### Dataset Fields
The dataset has the following fields (also called "features"):
### Dataset Splits
This dataset is split into a train and validation split. The split sizes are as follow:
| [
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
"TAGS\n#task_categories-image-classification #region-us \n",
"### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------",
"### Data Instances\n\n\nA sample from this dataset looks as follows:",
"### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):",
"### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
17,
27,
17,
23,
27
]
| [
"passage: TAGS\n#task_categories-image-classification #region-us \n### Languages\n\n\nThe BCP-47 code for the dataset's language is unk.\n\n\nDataset Structure\n-----------------### Data Instances\n\n\nA sample from this dataset looks as follows:### Dataset Fields\n\n\nThe dataset has the following fields (also called \"features\"):### Dataset Splits\n\n\nThis dataset is split into a train and validation split. The split sizes are as follow:"
]
| [
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|
42bd36ef98feca9b38f038f118e85538c2dbdb55 | # Dataset Card for "mozilla-vie-speech2text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ademax/mozilla-vie-speech2text | [
"region:us"
]
| 2023-02-05T16:39:09+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 351039045.6835097, "num_examples": 14338}, {"name": "test", "num_bytes": 25069782.960490286, "num_examples": 1000}], "download_size": 365566062, "dataset_size": 376108828.644}} | 2023-02-06T04:07:48+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "mozilla-vie-speech2text"
More Information needed | [
"# Dataset Card for \"mozilla-vie-speech2text\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"mozilla-vie-speech2text\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"mozilla-vie-speech2text\"\n\nMore Information needed"
]
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|
1c937fd68293095dc38dc40253ce4a522f410de3 | # Dataset Card for "vivos-vie-speech2text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ademax/vivos-vie-speech2text | [
"region:us"
]
| 2023-02-05T16:49:25+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}, {"name": "raw_transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1665722954.5, "num_examples": 11420}, {"name": "test", "num_bytes": 141949259.0, "num_examples": 1000}], "download_size": 1776326496, "dataset_size": 1807672213.5}} | 2023-08-26T03:21:56+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "vivos-vie-speech2text"
More Information needed | [
"# Dataset Card for \"vivos-vie-speech2text\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"vivos-vie-speech2text\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"vivos-vie-speech2text\"\n\nMore Information needed"
]
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|
0c967e194d7079f029907148e327fe9ecc55de40 | # Dataset Card for "tatoeba-nds"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | danielpleus/tatoeba-nds | [
"region:us"
]
| 2023-02-05T16:51:28+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 692245, "num_examples": 18101}], "download_size": 478178, "dataset_size": 692245}} | 2023-02-05T16:52:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "tatoeba-nds"
More Information needed | [
"# Dataset Card for \"tatoeba-nds\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"tatoeba-nds\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"tatoeba-nds\"\n\nMore Information needed"
]
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|
55726e3d237abdffd011e5f393f6eb3d96f660ca | # Dataset Card for "faces"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | SDbiaseval/faces | [
"region:us"
]
| 2023-02-05T17:25:53+00:00 | {"dataset_info": {"features": [{"name": "model", "dtype": "string"}, {"name": "adjective", "dtype": "string"}, {"name": "profession", "dtype": "string"}, {"name": "no", "dtype": "int32"}, {"name": "image_name", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3432470489.253643, "num_examples": 88708}], "download_size": 1970670181, "dataset_size": 3432470489.253643}} | 2023-02-05T17:53:23+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "faces"
More Information needed | [
"# Dataset Card for \"faces\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"faces\"\n\nMore Information needed"
]
| [
6,
12
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"faces\"\n\nMore Information needed"
]
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]
|
07bbbcc551794e7b5b9343788d9500809b486681 |
# Dataset Card for faces
## 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:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | stable-bias/faces | [
"language:en",
"license:cc-by-sa-4.0",
"region:us"
]
| 2023-02-05T17:27:44+00:00 | {"language": ["en"], "license": "cc-by-sa-4.0", "dataset_info": {"features": [{"name": "model", "dtype": "string"}, {"name": "adjective", "dtype": "string"}, {"name": "profession", "dtype": "string"}, {"name": "no", "dtype": "int32"}, {"name": "image_name", "dtype": "string"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3432470489.253643, "num_examples": 88708}], "download_size": 1970670181, "dataset_size": 3432470489.253643}} | 2023-08-11T10:11:20+00:00 | []
| [
"en"
]
| TAGS
#language-English #license-cc-by-sa-4.0 #region-us
|
# Dataset Card for faces
## 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
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @github-username for adding this dataset. | [
"# Dataset Card for faces",
"## 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\nThanks to @github-username for adding this dataset."
]
| [
"TAGS\n#language-English #license-cc-by-sa-4.0 #region-us \n",
"# Dataset Card for faces",
"## 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\nThanks to @github-username for adding this dataset."
]
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"passage: TAGS\n#language-English #license-cc-by-sa-4.0 #region-us \n# Dataset Card for faces## 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:### Dataset Summary### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions\n\nThanks to @github-username for adding this dataset."
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|
7310f5d3a185ee835cc5e5f715c574d51c5d93db |
# Dataset Card for feature vector embeddings of the 20newsgroup dataset
## 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:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains vector embeddings of the [20newsgroups dataset](http://qwone.com/~jason/20Newsgroups/).
The embeddings were created with the [Sentence Transformers library](https://www.sbert.net/index.html) using the `multi-qa-MiniLM-L6-cos-v1` model.
### 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
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | fscheffczyk/20newsgroups_embeddings | [
"task_categories:feature-extraction",
"task_categories:sentence-similarity",
"task_categories:question-answering",
"multilinguality:monolingual",
"size_categories:unknown",
"language:en",
"news",
"20newsgroups",
"region:us"
]
| 2023-02-05T17:48:30+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": [{"20newsgroups dataset": "http://qwone.com/~jason/20Newsgroups/"}], "task_categories": ["feature-extraction", "sentence-similarity", "question-answering"], "task_ids": [], "pretty_name": "Feature vector embeddings of the 20newsgroup dataset", "tags": ["news", "20newsgroups"]} | 2023-02-05T17:59:34+00:00 | []
| [
"en"
]
| TAGS
#task_categories-feature-extraction #task_categories-sentence-similarity #task_categories-question-answering #multilinguality-monolingual #size_categories-unknown #language-English #news #20newsgroups #region-us
|
# Dataset Card for feature vector embeddings of the 20newsgroup dataset
## 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
This dataset contains vector embeddings of the 20newsgroups dataset.
The embeddings were created with the Sentence Transformers library using the 'multi-qa-MiniLM-L6-cos-v1' model.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @github-username for adding this dataset. | [
"# Dataset Card for feature vector embeddings of the 20newsgroup dataset",
"## 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary\n\nThis dataset contains vector embeddings of the 20newsgroups dataset.\nThe embeddings were created with the Sentence Transformers library using the 'multi-qa-MiniLM-L6-cos-v1' model.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\nThanks to @github-username for adding this dataset."
]
| [
"TAGS\n#task_categories-feature-extraction #task_categories-sentence-similarity #task_categories-question-answering #multilinguality-monolingual #size_categories-unknown #language-English #news #20newsgroups #region-us \n",
"# Dataset Card for feature vector embeddings of the 20newsgroup dataset",
"## 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary\n\nThis dataset contains vector embeddings of the 20newsgroups dataset.\nThe embeddings were created with the Sentence Transformers library using the 'multi-qa-MiniLM-L6-cos-v1' model.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\nThanks to @github-username for adding this dataset."
]
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"passage: TAGS\n#task_categories-feature-extraction #task_categories-sentence-similarity #task_categories-question-answering #multilinguality-monolingual #size_categories-unknown #language-English #news #20newsgroups #region-us \n# Dataset Card for feature vector embeddings of the 20newsgroup dataset## 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:### Dataset Summary\n\nThis dataset contains vector embeddings of the 20newsgroups dataset.\nThe embeddings were created with the Sentence Transformers library using the 'multi-qa-MiniLM-L6-cos-v1' model.### Supported Tasks and Leaderboards### Languages## Dataset Structure### Data Instances### Data Fields### Data Splits## Dataset Creation### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information## Considerations for Using the Data### Social Impact of Dataset### Discussion of Biases### Other Known Limitations## Additional Information### Dataset Curators### Licensing Information### Contributions\n\nThanks to @github-username for adding this dataset."
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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] | enankobh1/ASR_primock_data | [
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| 2023-02-05T18:10:51+00:00 | {} | 2023-02-06T18:27:49+00:00 | []
| []
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# Dataset Card for Dataset Name
## Dataset Description
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- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
### 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
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|
da22d598c8a586a169a5c61a33296ebece08c707 |
# Dataset Card for feature vector embeddings of the 20newsgroup dataset
## 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:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains dimensional reduced vector embeddings of the [20newsgroups dataset](http://qwone.com/~jason/20Newsgroups/). This dataset contains two dimensions.
The dimensional reduced embeddings were created with the [TruncatedSVD function](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html#sklearn.decomposition.TruncatedSVD) from the [scikit-learn library](https://scikit-learn.org/stable/index.html).
These reduced feature vectors are based on the [fscheffczyk/20newsgroup_embeddings dataset](https://huggingface.co/datasets/fscheffczyk/20newsgroups_embeddings).
### 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
Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset. | fscheffczyk/2D_20newsgroups_embeddings | [
"task_categories:feature-extraction",
"task_categories:sentence-similarity",
"task_categories:question-answering",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|fscheffczyk/20newsgroups_embeddings",
"language:en",
"news",
"20newsgroups",
"region:us"
]
| 2023-02-05T18:52:06+00:00 | {"annotations_creators": [], "language_creators": [], "language": ["en"], "license": [], "multilinguality": ["monolingual"], "size_categories": ["unknown"], "source_datasets": ["extended|fscheffczyk/20newsgroups_embeddings"], "task_categories": ["feature-extraction", "sentence-similarity", "question-answering"], "task_ids": [], "pretty_name": "Dimensional reduced feature vector embeddings of the 20newsgroup dataset", "tags": ["news", "20newsgroups"]} | 2023-02-05T18:57:29+00:00 | []
| [
"en"
]
| TAGS
#task_categories-feature-extraction #task_categories-sentence-similarity #task_categories-question-answering #multilinguality-monolingual #size_categories-unknown #source_datasets-extended|fscheffczyk/20newsgroups_embeddings #language-English #news #20newsgroups #region-us
|
# Dataset Card for feature vector embeddings of the 20newsgroup dataset
## 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
This dataset contains dimensional reduced vector embeddings of the 20newsgroups dataset. This dataset contains two dimensions.
The dimensional reduced embeddings were created with the TruncatedSVD function from the scikit-learn library.
These reduced feature vectors are based on the fscheffczyk/20newsgroup_embeddings dataset.
### Supported Tasks and Leaderboards
### Languages
## Dataset Structure
### Data Instances
### Data Fields
### Data Splits
## Dataset Creation
### Curation Rationale
### Source Data
#### Initial Data Collection and Normalization
#### Who are the source language producers?
### Annotations
#### Annotation process
#### Who are the annotators?
### Personal and Sensitive Information
## Considerations for Using the Data
### Social Impact of Dataset
### Discussion of Biases
### Other Known Limitations
## Additional Information
### Dataset Curators
### Licensing Information
### Contributions
Thanks to @github-username for adding this dataset. | [
"# Dataset Card for feature vector embeddings of the 20newsgroup dataset",
"## 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary\n\nThis dataset contains dimensional reduced vector embeddings of the 20newsgroups dataset. This dataset contains two dimensions. \n\nThe dimensional reduced embeddings were created with the TruncatedSVD function from the scikit-learn library. \nThese reduced feature vectors are based on the fscheffczyk/20newsgroup_embeddings dataset.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\nThanks to @github-username for adding this dataset."
]
| [
"TAGS\n#task_categories-feature-extraction #task_categories-sentence-similarity #task_categories-question-answering #multilinguality-monolingual #size_categories-unknown #source_datasets-extended|fscheffczyk/20newsgroups_embeddings #language-English #news #20newsgroups #region-us \n",
"# Dataset Card for feature vector embeddings of the 20newsgroup dataset",
"## 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:",
"### Dataset Summary\n\nThis dataset contains dimensional reduced vector embeddings of the 20newsgroups dataset. This dataset contains two dimensions. \n\nThe dimensional reduced embeddings were created with the TruncatedSVD function from the scikit-learn library. \nThese reduced feature vectors are based on the fscheffczyk/20newsgroup_embeddings dataset.",
"### Supported Tasks and Leaderboards",
"### Languages",
"## Dataset Structure",
"### Data Instances",
"### Data Fields",
"### Data Splits",
"## Dataset Creation",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information",
"## Considerations for Using the Data",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations",
"## Additional Information",
"### Dataset Curators",
"### Licensing Information",
"### Contributions\n\nThanks to @github-username for adding this dataset."
]
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19,
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| [
"passage: TAGS\n#task_categories-feature-extraction #task_categories-sentence-similarity #task_categories-question-answering #multilinguality-monolingual #size_categories-unknown #source_datasets-extended|fscheffczyk/20newsgroups_embeddings #language-English #news #20newsgroups #region-us \n# Dataset Card for feature vector embeddings of the 20newsgroup dataset## 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:\n- Repository:\n- Paper:\n- Leaderboard:\n- Point of Contact:### Dataset Summary\n\nThis dataset contains dimensional reduced vector embeddings of the 20newsgroups dataset. This dataset contains two dimensions. \n\nThe dimensional reduced embeddings were created with the TruncatedSVD function from the scikit-learn library. \nThese reduced feature vectors are based on the fscheffczyk/20newsgroup_embeddings dataset.### 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"
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|
e4e6b87898b041b9fd0171d5313fc6fc01681468 | # Dataset Card for "selector_output_preprocessed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Reza-Madani/selector_output_preprocessed | [
"region:us"
]
| 2023-02-05T19:45:36+00:00 | {"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "dtype": "int64"}, {"name": "token_type_ids", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 1139584, "num_examples": 9824}], "download_size": 139270, "dataset_size": 1139584}} | 2023-02-05T19:45:46+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "selector_output_preprocessed"
More Information needed | [
"# Dataset Card for \"selector_output_preprocessed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"selector_output_preprocessed\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"selector_output_preprocessed\"\n\nMore Information needed"
]
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|
dc0a2517058a8e8f7873392f0a55ca30d98b04a4 | # Dataset Card for "yolochess_lichess-elite_2211"
Source: https://database.nikonoel.fr/ - filtered from https://database.lichess.org for November 2022
Features:
- fen = Chess board position in [FEN](https://en.wikipedia.org/wiki/Forsyth%E2%80%93Edwards_Notation) format
- move = Move played by a strong human player in this position
- result = Final result of the match
- eco = [ECO](https://en.wikipedia.org/wiki/Encyclopaedia_of_Chess_Openings)-code of the Opening played
Samples: 22.1 million | jrahn/yolochess_lichess-elite_2211 | [
"task_categories:text-classification",
"task_categories:reinforcement-learning",
"size_categories:10M<n<100M",
"license:cc",
"chess",
"region:us"
]
| 2023-02-05T20:51:21+00:00 | {"license": "cc", "size_categories": ["10M<n<100M"], "task_categories": ["text-classification", "reinforcement-learning"], "dataset_info": {"features": [{"name": "fen", "dtype": "string"}, {"name": "move", "dtype": "string"}, {"name": "result", "dtype": "string"}, {"name": "eco", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1794337922, "num_examples": 22116598}], "download_size": 1044871571, "dataset_size": 1794337922}, "tags": ["chess"]} | 2023-02-08T07:19:54+00:00 | []
| []
| TAGS
#task_categories-text-classification #task_categories-reinforcement-learning #size_categories-10M<n<100M #license-cc #chess #region-us
| # Dataset Card for "yolochess_lichess-elite_2211"
Source: URL - filtered from URL for November 2022
Features:
- fen = Chess board position in FEN format
- move = Move played by a strong human player in this position
- result = Final result of the match
- eco = ECO-code of the Opening played
Samples: 22.1 million | [
"# Dataset Card for \"yolochess_lichess-elite_2211\"\n\nSource: URL - filtered from URL for November 2022 \n\nFeatures:\n- fen = Chess board position in FEN format\n- move = Move played by a strong human player in this position\n- result = Final result of the match\n- eco = ECO-code of the Opening played\n\nSamples: 22.1 million"
]
| [
"TAGS\n#task_categories-text-classification #task_categories-reinforcement-learning #size_categories-10M<n<100M #license-cc #chess #region-us \n",
"# Dataset Card for \"yolochess_lichess-elite_2211\"\n\nSource: URL - filtered from URL for November 2022 \n\nFeatures:\n- fen = Chess board position in FEN format\n- move = Move played by a strong human player in this position\n- result = Final result of the match\n- eco = ECO-code of the Opening played\n\nSamples: 22.1 million"
]
| [
49,
84
]
| [
"passage: TAGS\n#task_categories-text-classification #task_categories-reinforcement-learning #size_categories-10M<n<100M #license-cc #chess #region-us \n# Dataset Card for \"yolochess_lichess-elite_2211\"\n\nSource: URL - filtered from URL for November 2022 \n\nFeatures:\n- fen = Chess board position in FEN format\n- move = Move played by a strong human player in this position\n- result = Final result of the match\n- eco = ECO-code of the Opening played\n\nSamples: 22.1 million"
]
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|
470866e0e33914e3aaed70c5dd58afc49621239a | # Dataset Card for "sentiment-analysis-finetune"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | racro/sentiment-analysis-finetune | [
"region:us"
]
| 2023-02-05T21:28:45+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 119146, "num_examples": 751}], "download_size": 0, "dataset_size": 119146}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-10-16T18:46:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "sentiment-analysis-finetune"
More Information needed | [
"# Dataset Card for \"sentiment-analysis-finetune\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"sentiment-analysis-finetune\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"sentiment-analysis-finetune\"\n\nMore Information needed"
]
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|
cc66c5efce8b1e13473ab99064d276b36d4c5583 | # Dataset Card for "bert-mini-predictor-output"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Reza-Madani/bert-mini-predictor-output | [
"region:us"
]
| 2023-02-05T22:55:18+00:00 | {"dataset_info": {"features": [{"name": "predictions", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 78592, "num_examples": 9824}], "download_size": 6138, "dataset_size": 78592}} | 2023-02-05T22:55:22+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bert-mini-predictor-output"
More Information needed | [
"# Dataset Card for \"bert-mini-predictor-output\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bert-mini-predictor-output\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bert-mini-predictor-output\"\n\nMore Information needed"
]
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|
44df4f82f6af47ad7f746bc1120097066e538457 | # Dataset Card for "small-coco"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | RIW/small-coco | [
"region:us"
]
| 2023-02-06T00:57:39+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "key", "dtype": "string"}, {"name": "status", "dtype": "string"}, {"name": "error_message", "dtype": "null"}, {"name": "width", "dtype": "int64"}, {"name": "height", "dtype": "int64"}, {"name": "original_width", "dtype": "int64"}, {"name": "original_height", "dtype": "int64"}, {"name": "exif", "dtype": "string"}, {"name": "sha256", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1946738057.45, "num_examples": 9890}, {"name": "validation", "num_bytes": 1953823510.0, "num_examples": 9893}], "download_size": 1313384992, "dataset_size": 3900561567.45}} | 2023-02-06T01:06:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "small-coco"
More Information needed | [
"# Dataset Card for \"small-coco\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"small-coco\"\n\nMore Information needed"
]
| [
6,
15
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"small-coco\"\n\nMore Information needed"
]
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|
9a298a58b021256922e359d8d59737383c14a281 | # Dataset Card for "Caltech101_not_background_test_facebook_opt_6.7b_Attributes_Caption_ns_5647_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_6.7b_Attributes_Caption_ns_5647_random | [
"region:us"
]
| 2023-02-06T01:05:26+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 85893435.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 88898369.125, "num_examples": 5647}], "download_size": 160743632, "dataset_size": 174791804.25}} | 2023-02-06T02:44:47+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Caltech101_not_background_test_facebook_opt_6.7b_Attributes_Caption_ns_5647_random"
More Information needed | [
"# Dataset Card for \"Caltech101_not_background_test_facebook_opt_6.7b_Attributes_Caption_ns_5647_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Caltech101_not_background_test_facebook_opt_6.7b_Attributes_Caption_ns_5647_random\"\n\nMore Information needed"
]
| [
6,
43
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_6.7b_Attributes_Caption_ns_5647_random\"\n\nMore Information needed"
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|
393f7c68e114ac599f1cabe6ca00fc676ecfb4b4 | # Dataset Card for "Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647_random"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647_random | [
"region:us"
]
| 2023-02-06T01:49:36+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_1_bs_16", "num_bytes": 86816009.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 90735172.125, "num_examples": 5647}], "download_size": 162358362, "dataset_size": 177551181.25}} | 2023-02-06T03:55:04+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647_random"
More Information needed | [
"# Dataset Card for \"Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647_random\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647_random\"\n\nMore Information needed"
]
| [
6,
40
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647_random\"\n\nMore Information needed"
]
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|
34cb8006acbb117a169204b16728928c7faa5a63 | # Dataset Card for "ko-conversation-sentiment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mk9165/ko-conversation-sentiment | [
"region:us"
]
| 2023-02-06T04:25:10+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label1", "dtype": "string"}, {"name": "label2", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 25376313, "num_examples": 51630}, {"name": "test", "num_bytes": 3144865, "num_examples": 6641}], "download_size": 13431512, "dataset_size": 28521178}} | 2023-02-06T04:25:18+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "ko-conversation-sentiment"
More Information needed | [
"# Dataset Card for \"ko-conversation-sentiment\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"ko-conversation-sentiment\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"ko-conversation-sentiment\"\n\nMore Information needed"
]
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|
e2ece7cd92a906971f9b7a1e68f2865a24146440 | # Dataset Card for "Caltech101_not_background_test_facebook_opt_6.7b_Attributes_Caption_ns_5647"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_6.7b_Attributes_Caption_ns_5647 | [
"region:us"
]
| 2023-02-06T04:28:50+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 84389801.125, "num_examples": 5647}, {"name": "fewshot_1_bs_16", "num_bytes": 85882598.125, "num_examples": 5647}, {"name": "fewshot_3_bs_16", "num_bytes": 88873041.125, "num_examples": 5647}], "download_size": 237858362, "dataset_size": 259145440.375}} | 2023-02-06T16:49:21+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Caltech101_not_background_test_facebook_opt_6.7b_Attributes_Caption_ns_5647"
More Information needed | [
"# Dataset Card for \"Caltech101_not_background_test_facebook_opt_6.7b_Attributes_Caption_ns_5647\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Caltech101_not_background_test_facebook_opt_6.7b_Attributes_Caption_ns_5647\"\n\nMore Information needed"
]
| [
6,
40
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_6.7b_Attributes_Caption_ns_5647\"\n\nMore Information needed"
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|
f6d37acc6c3c9eb6184f9a2b7463bf08bd77e546 | # Dataset Card for "Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Multimodal-Fatima/Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647 | [
"region:us"
]
| 2023-02-06T05:04:03+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "image", "dtype": "image"}, {"name": "prompt", "dtype": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}, {"name": "scores", "sequence": "float64"}], "splits": [{"name": "fewshot_0_bs_16", "num_bytes": 84854811.125, "num_examples": 5647}, {"name": "fewshot_1_bs_16", "num_bytes": 86808883.125, "num_examples": 5647}], "download_size": 158701717, "dataset_size": 171663694.25}} | 2023-02-06T15:56:03+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647"
More Information needed | [
"# Dataset Card for \"Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647\"\n\nMore Information needed"
]
| [
6,
37
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Caltech101_not_background_test_facebook_opt_6.7b_Visclues_ns_5647\"\n\nMore Information needed"
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|
363f21afd8e16507c7e85676d244647a0eea31cc | Ongoing work in extracting the largest amount of text data from the internet!
text-dataset.txt is a placeholder dataset, it's made from a miiverse archive as a parsing demo. | BirdL/TheLandfill | [
"region:us"
]
| 2023-02-06T05:20:12+00:00 | {} | 2023-02-06T05:25:27+00:00 | []
| []
| TAGS
#region-us
| Ongoing work in extracting the largest amount of text data from the internet!
URL is a placeholder dataset, it's made from a miiverse archive as a parsing demo. | []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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60efb980f44203ce28dc60eeed4b77dffa78c001 | # 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] | flow3rdown/MARS | [
"language:en",
"region:us"
]
| 2023-02-06T05:57:19+00:00 | {"language": ["en"]} | 2023-02-06T05:59:27+00:00 | []
| [
"en"
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| TAGS
#language-English #region-us
| # 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
### 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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|
bf15bd217b87ec6a1f9ea91c30809fa95ef78520 | [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
| xfh/lexica_6k | [
"region:us"
]
| 2023-02-06T06:37:28+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "width", "dtype": "int32"}, {"name": "height", "dtype": "int32"}, {"name": "md5", "dtype": "string"}, {"name": "tag", "dtype": "string"}], "splits": [{"name": "train", "num_examples": 12048}]}} | 2023-02-06T08:17:40+00:00 | []
| []
| TAGS
#region-us
| More Information needed
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
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|
49c279a413d54ac000ab2af416c0c14fbfb0db6d | # Dataset Card for "female-it-engineer"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | abhijit1247/female-it-engineer | [
"region:us"
]
| 2023-02-06T07:33:36+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 436309940.896, "num_examples": 5509}], "download_size": 509782344, "dataset_size": 436309940.896}} | 2023-02-06T07:34:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "female-it-engineer"
More Information needed | [
"# Dataset Card for \"female-it-engineer\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"female-it-engineer\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"female-it-engineer\"\n\nMore Information needed"
]
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|
61176010c9e5215b71482a289d40b36461ef2f7d | # Dataset Card for "Fin_Corpus_News"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | FINDA-FIT/Fin_Corpus_News | [
"region:us"
]
| 2023-02-06T07:46:04+00:00 | {"dataset_info": {"features": [{"name": "ID", "dtype": "string"}, {"name": "CONTEXT", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2383214453, "num_examples": 765104}], "download_size": 1335325915, "dataset_size": 2383214453}} | 2023-02-06T07:47:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "Fin_Corpus_News"
More Information needed | [
"# Dataset Card for \"Fin_Corpus_News\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"Fin_Corpus_News\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"Fin_Corpus_News\"\n\nMore Information needed"
]
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|
3b949997bc8b1f4a33e406ff895bea959ed540dc | # RTV SLO Comments - Top 5 Users - Without OOC
## Other related models
| Includes the OOC class? | 5 classes | 10 classes | 20 classes | 50 classes | 100 classes |
| ------------------------| --------- | ---------- | ---------- | ---------- | ----------- |
| No | [link](https://huggingface.co/datasets/gregorgabrovsek/RTVCommentsTop5UsersWithoutOOC) | [link](https://huggingface.co/datasets/gregorgabrovsek/RTVCommentsTop10UsersWithoutOOC) | [link](https://huggingface.co/datasets/gregorgabrovsek/RTVCommentsTop20UsersWithoutOOC) | [link](https://huggingface.co/datasets/gregorgabrovsek/RTVCommentsTop50UsersWithoutOOC) | [link](https://huggingface.co/datasets/gregorgabrovsek/RTVCommentsTop100UsersWithoutOOC) |
| Yes | [link](https://huggingface.co/datasets/gregorgabrovsek/RTVCommentsTop5UsersWithOOC) | [link](https://huggingface.co/datasets/gregorgabrovsek/RTVCommentsTop10UsersWithOOC) | [link](https://huggingface.co/datasets/gregorgabrovsek/RTVCommentsTop20UsersWithOOC) | [link](https://huggingface.co/datasets/gregorgabrovsek/RTVCommentsTop50UsersWithOOC) | [link](https://huggingface.co/datasets/gregorgabrovsek/RTVCommentsTop100UsersWithOOC) | | gregorgabrovsek/RTVCommentsTop5UsersWithoutOOC | [
"region:us"
]
| 2023-02-06T08:31:19+00:00 | {} | 2023-08-21T21:07:40+00:00 | []
| []
| TAGS
#region-us
| RTV SLO Comments - Top 5 Users - Without OOC
============================================
Other related models
--------------------
| []
| [
"TAGS\n#region-us \n"
]
| [
6
]
| [
"passage: TAGS\n#region-us \n"
]
| [
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|
4f91c7f84406b33fbf510d2b5b36bcbe209fc0d6 | # Dataset Card for "catalan_commonvoice_first15hr"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | shields/catalan_commonvoice_first15hr | [
"region:us"
]
| 2023-02-06T11:08:42+00:00 | {"dataset_info": {"features": [{"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 48000}}}, {"name": "sentence", "dtype": "string"}, {"name": "up_votes", "dtype": "int64"}, {"name": "down_votes", "dtype": "int64"}, {"name": "age", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "segment", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 287860990.0, "num_examples": 7000}, {"name": "val", "num_bytes": 118983362.0, "num_examples": 3000}], "download_size": 386267835, "dataset_size": 406844352.0}} | 2023-02-06T11:09:34+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "catalan_commonvoice_first15hr"
More Information needed | [
"# Dataset Card for \"catalan_commonvoice_first15hr\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"catalan_commonvoice_first15hr\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"catalan_commonvoice_first15hr\"\n\nMore Information needed"
]
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|
429b7e8594bd3eca014c0fef73af83294584354b | # Dataset Card for "vlsp-vie-speech2text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ademax/vlsp-vie-speech2text | [
"region:us"
]
| 2023-02-06T11:11:16+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11442518990.931095, "num_examples": 55427}, {"name": "test", "num_bytes": 203388934.0569054, "num_examples": 1000}], "download_size": 11650527572, "dataset_size": 11645907924.988}} | 2023-02-06T12:24:42+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "vlsp-vie-speech2text"
More Information needed | [
"# Dataset Card for \"vlsp-vie-speech2text\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"vlsp-vie-speech2text\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"vlsp-vie-speech2text\"\n\nMore Information needed"
]
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|
e81d9f42cd2cfffc335e7e9102afa6dd7297c843 | # Dataset Card for "fpt-vie-speech2text"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ademax/fpt-vie-speech2text | [
"region:us"
]
| 2023-02-06T11:44:27+00:00 | {"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 807889708.0629112, "num_examples": 24921}, {"name": "test", "num_bytes": 32503643.295088924, "num_examples": 1000}], "download_size": 819824750, "dataset_size": 840393351.358}} | 2023-02-06T11:46:12+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "fpt-vie-speech2text"
More Information needed | [
"# Dataset Card for \"fpt-vie-speech2text\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"fpt-vie-speech2text\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"fpt-vie-speech2text\"\n\nMore Information needed"
]
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|
d8343e497dc9a771fa5678d43490c0530eb7175a | # Dataset Card for "catalan_commonvoice_first15hr_processed_with_noise"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | shields/catalan_commonvoice_first15hr_processed_with_noise | [
"region:us"
]
| 2023-02-06T12:18:04+00:00 | {"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 6723710888, "num_examples": 7000}, {"name": "val", "num_bytes": 2881592776, "num_examples": 3000}], "download_size": 1776942256, "dataset_size": 9605303664}} | 2023-02-06T12:23:28+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "catalan_commonvoice_first15hr_processed_with_noise"
More Information needed | [
"# Dataset Card for \"catalan_commonvoice_first15hr_processed_with_noise\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"catalan_commonvoice_first15hr_processed_with_noise\"\n\nMore Information needed"
]
| [
6,
30
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"catalan_commonvoice_first15hr_processed_with_noise\"\n\nMore Information needed"
]
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|
4953f974b130e8be5d3372701633904c84aa63e4 | # Dataset Card for "catalan_commonvoice_first15hr_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | shields/catalan_commonvoice_first15hr_processed | [
"region:us"
]
| 2023-02-06T12:39:56+00:00 | {"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 6723710888, "num_examples": 7000}, {"name": "val", "num_bytes": 2881592776, "num_examples": 3000}], "download_size": 1776942256, "dataset_size": 9605303664}} | 2023-02-06T12:45:19+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "catalan_commonvoice_first15hr_processed"
More Information needed | [
"# Dataset Card for \"catalan_commonvoice_first15hr_processed\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"catalan_commonvoice_first15hr_processed\"\n\nMore Information needed"
]
| [
6,
25
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"catalan_commonvoice_first15hr_processed\"\n\nMore Information needed"
]
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|
da8e6559c52dd5bfc14e8bd1bd85f0090cadabcc |
## ita2medieval
The **ita2medieval** dataset contains sentences from medieval italian along with paraphrases in contemporary italian (approximately 6.5k pairs in total). The medieval italian sentences are extracted from texts by Dante, Petrarca, Guinizelli and Cavalcanti.
It is intended to perform text-style-transfer from contemporary to medieval italian and vice-versa.
## Loading the dataset
```
from datasets import load_dataset
dataset = load_dataset("leobertolazzi/ita2medieval")
```
Note: due to the small size of the dataset there are no predefined train and test splits.
## Dataset creation
**ita2medieval** was created by scraping [letteritaliana.weebly.com](https://letteritaliana.weebly.com/). | leobertolazzi/ita2medieval | [
"task_categories:text2text-generation",
"size_categories:1K<n<10K",
"language:it",
"region:us"
]
| 2023-02-06T12:40:21+00:00 | {"language": ["it"], "size_categories": ["1K<n<10K"], "task_categories": ["text2text-generation"]} | 2023-02-06T13:00:18+00:00 | []
| [
"it"
]
| TAGS
#task_categories-text2text-generation #size_categories-1K<n<10K #language-Italian #region-us
|
## ita2medieval
The ita2medieval dataset contains sentences from medieval italian along with paraphrases in contemporary italian (approximately 6.5k pairs in total). The medieval italian sentences are extracted from texts by Dante, Petrarca, Guinizelli and Cavalcanti.
It is intended to perform text-style-transfer from contemporary to medieval italian and vice-versa.
## Loading the dataset
Note: due to the small size of the dataset there are no predefined train and test splits.
## Dataset creation
ita2medieval was created by scraping URL. | [
"## ita2medieval\nThe ita2medieval dataset contains sentences from medieval italian along with paraphrases in contemporary italian (approximately 6.5k pairs in total). The medieval italian sentences are extracted from texts by Dante, Petrarca, Guinizelli and Cavalcanti.\n\nIt is intended to perform text-style-transfer from contemporary to medieval italian and vice-versa.",
"## Loading the dataset\n\n\n\nNote: due to the small size of the dataset there are no predefined train and test splits.",
"## Dataset creation\n\nita2medieval was created by scraping URL."
]
| [
"TAGS\n#task_categories-text2text-generation #size_categories-1K<n<10K #language-Italian #region-us \n",
"## ita2medieval\nThe ita2medieval dataset contains sentences from medieval italian along with paraphrases in contemporary italian (approximately 6.5k pairs in total). The medieval italian sentences are extracted from texts by Dante, Petrarca, Guinizelli and Cavalcanti.\n\nIt is intended to perform text-style-transfer from contemporary to medieval italian and vice-versa.",
"## Loading the dataset\n\n\n\nNote: due to the small size of the dataset there are no predefined train and test splits.",
"## Dataset creation\n\nita2medieval was created by scraping URL."
]
| [
36,
91,
29,
16
]
| [
"passage: TAGS\n#task_categories-text2text-generation #size_categories-1K<n<10K #language-Italian #region-us \n## ita2medieval\nThe ita2medieval dataset contains sentences from medieval italian along with paraphrases in contemporary italian (approximately 6.5k pairs in total). The medieval italian sentences are extracted from texts by Dante, Petrarca, Guinizelli and Cavalcanti.\n\nIt is intended to perform text-style-transfer from contemporary to medieval italian and vice-versa.## Loading the dataset\n\n\n\nNote: due to the small size of the dataset there are no predefined train and test splits.## Dataset creation\n\nita2medieval was created by scraping URL."
]
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|
f5546ef1e91613ffdb1ae9b339b3462e8aa02e23 | # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by [AutoTrain](https://huggingface.co/autotrain) for the following task and dataset:
* Task: Summarization
* Model: knkarthick/MEETING_SUMMARY
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's [automatic model evaluator](https://huggingface.co/spaces/autoevaluate/model-evaluator).
## Contributions
Thanks to [@TheAlphaQ](https://huggingface.co/TheAlphaQ) for evaluating this model. | autoevaluate/autoeval-eval-samsum-samsum-6999f5-3301091732 | [
"autotrain",
"evaluation",
"region:us"
]
| 2023-02-06T13:31:31+00:00 | {"type": "predictions", "tags": ["autotrain", "evaluation"], "datasets": ["samsum"], "eval_info": {"task": "summarization", "model": "knkarthick/MEETING_SUMMARY", "metrics": [], "dataset_name": "samsum", "dataset_config": "samsum", "dataset_split": "test", "col_mapping": {"text": "dialogue", "target": "summary"}}} | 2023-02-06T13:33:58+00:00 | []
| []
| TAGS
#autotrain #evaluation #region-us
| # Dataset Card for AutoTrain Evaluator
This repository contains model predictions generated by AutoTrain for the following task and dataset:
* Task: Summarization
* Model: knkarthick/MEETING_SUMMARY
* Dataset: samsum
* Config: samsum
* Split: test
To run new evaluation jobs, visit Hugging Face's automatic model evaluator.
## Contributions
Thanks to @TheAlphaQ for evaluating this model. | [
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: knkarthick/MEETING_SUMMARY\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @TheAlphaQ for evaluating this model."
]
| [
"TAGS\n#autotrain #evaluation #region-us \n",
"# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: knkarthick/MEETING_SUMMARY\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.",
"## Contributions\n\nThanks to @TheAlphaQ for evaluating this model."
]
| [
13,
88,
17
]
| [
"passage: TAGS\n#autotrain #evaluation #region-us \n# Dataset Card for AutoTrain Evaluator\n\nThis repository contains model predictions generated by AutoTrain for the following task and dataset:\n\n* Task: Summarization\n* Model: knkarthick/MEETING_SUMMARY\n* Dataset: samsum\n* Config: samsum\n* Split: test\n\nTo run new evaluation jobs, visit Hugging Face's automatic model evaluator.## Contributions\n\nThanks to @TheAlphaQ for evaluating this model."
]
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|
3dc8695adf6831109645ca26ce71440aa1c1e763 | Images trained for my [phantom diffusion s2](https://huggingface.co/Phantom-Artist/phantom-diffusion-s2) series.
Since they are all AI generated images that are public domain under the US law, I claim it is legal to redistribute them as public domain.
However, they might have copyright in your/their original country.
Still, many countries including Japan allow us to use them for training an AI under their copyrights law, and because all the artists here are from Japan, I assume it should be allowed to reuse it for training globally.
| Phantom-Artist/phantom-diffusion-s2-dataset | [
"license:cc0-1.0",
"region:us"
]
| 2023-02-06T13:35:24+00:00 | {"license": "cc0-1.0"} | 2023-02-06T13:39:34+00:00 | []
| []
| TAGS
#license-cc0-1.0 #region-us
| Images trained for my phantom diffusion s2 series.
Since they are all AI generated images that are public domain under the US law, I claim it is legal to redistribute them as public domain.
However, they might have copyright in your/their original country.
Still, many countries including Japan allow us to use them for training an AI under their copyrights law, and because all the artists here are from Japan, I assume it should be allowed to reuse it for training globally.
| []
| [
"TAGS\n#license-cc0-1.0 #region-us \n"
]
| [
14
]
| [
"passage: TAGS\n#license-cc0-1.0 #region-us \n"
]
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|
da96923fa7771a2cc4537bb5e17d324a5ea1dd69 | # Dataset Card for "boostcamp-docvqa-marker"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Ssunbell/boostcamp-docvqa-marker | [
"region:us"
]
| 2023-02-06T13:48:16+00:00 | {"dataset_info": {"features": [{"name": "questionId", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "sequence": {"sequence": {"sequence": "uint8"}}}, {"name": "docId", "dtype": "int64"}, {"name": "ucsf_document_id", "dtype": "string"}, {"name": "ucsf_document_page_no", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "data_split", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "boxes", "sequence": {"sequence": "int64"}}, {"name": "question_m", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6384754744, "num_examples": 39454}, {"name": "val", "num_bytes": 869779916, "num_examples": 5349}], "download_size": 2580109440, "dataset_size": 7254534660}} | 2023-02-06T15:18:58+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "boostcamp-docvqa-marker"
More Information needed | [
"# Dataset Card for \"boostcamp-docvqa-marker\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"boostcamp-docvqa-marker\"\n\nMore Information needed"
]
| [
6,
20
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"boostcamp-docvqa-marker\"\n\nMore Information needed"
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|
db3142686aca5ed32b1658df8b97c1fe2fe41e58 | # Dataset Card for "boostcamp-docvqa-marker-test"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Ssunbell/boostcamp-docvqa-marker-test | [
"region:us"
]
| 2023-02-06T13:48:25+00:00 | {"dataset_info": {"features": [{"name": "questionId", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "sequence": {"sequence": {"sequence": "uint8"}}}, {"name": "docId", "dtype": "int64"}, {"name": "ucsf_document_id", "dtype": "string"}, {"name": "ucsf_document_page_no", "dtype": "string"}, {"name": "data_split", "dtype": "string"}, {"name": "words", "sequence": "string"}, {"name": "boxes", "sequence": {"sequence": "int64"}}, {"name": "question_m", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 843485050, "num_examples": 5188}], "download_size": 296954981, "dataset_size": 843485050}} | 2023-02-06T15:33:30+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "boostcamp-docvqa-marker-test"
More Information needed | [
"# Dataset Card for \"boostcamp-docvqa-marker-test\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"boostcamp-docvqa-marker-test\"\n\nMore Information needed"
]
| [
6,
22
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"boostcamp-docvqa-marker-test\"\n\nMore Information needed"
]
| [
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|
3b8e27543cd0bafc236d43c57bdae4e6b5c1689b |
# Dataset Card for Media-Bias-Identification-Benchmark
## Table of Contents
- [Dataset Card for Media-Bias-Identification-Benchmark](#dataset-card-for-mbib)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Tasks and Information](#tasks-and-information)
- [Baseline](#baseline)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [cognitive-bias](#cognitive-bias)
- [Data Fields](#data-fields)
- [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)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/Media-Bias-Group/Media-Bias-Identification-Benchmark
- **Repository:** https://github.com/Media-Bias-Group/Media-Bias-Identification-Benchmark
- **Paper:** https://doi.org/10.1145/3539618.3591882
- **Point of Contact:** [Martin Wessel](mailto:[email protected])
### Baseline
<table>
<tr><td><b>Task</b></td><td><b>Model</b></td><td><b>Micro F1</b></td><td><b>Macro F1</b></td></tr>
<td>cognitive-bias</td> <td> ConvBERT/ConvBERT</td> <td>0.7126</td> <td> 0.7664</td></tr>
<td>fake-news</td> <td>Bart/RoBERTa-T</td> <td>0.6811</td> <td> 0.7533</td> </tr>
<td>gender-bias</td> <td> RoBERTa-T/ELECTRA</td> <td>0.8334</td> <td>0.8211</td> </tr>
<td>hate-speech</td> <td>RoBERTA-T/Bart</td> <td>0.8897</td> <td> 0.7310</td> </tr>
<td>linguistic-bias</td> <td> ConvBERT/Bart </td> <td> 0.7044 </td> <td> 0.4995 </td> </tr>
<td>political-bias</td> <td> ConvBERT/ConvBERT </td> <td> 0.7041 </td> <td> 0.7110 </td> </tr>
<td>racial-bias</td> <td> ConvBERT/ELECTRA </td> <td> 0.8772 </td> <td> 0.6170 </td> </tr>
<td>text-leve-bias</td> <td> ConvBERT/ConvBERT </td> <td> 0.7697</td> <td> 0.7532 </td> </tr>
</table>
### Languages
All datasets are in English
## Dataset Structure
### Data Instances
#### cognitive-bias
An example of one training instance looks as follows.
```json
{
"text": "A defense bill includes language that would require military hospitals to provide abortions on demand",
"label": 1
}
```
### Data Fields
- `text`: a sentence from various sources (eg., news articles, twitter, other social media).
- `label`: binary indicator of bias (0 = unbiased, 1 = biased)
## Considerations for Using the Data
### Social Impact of Dataset
We believe that MBIB offers a new common ground
for research in the domain, especially given the rising amount of
(research) attention directed toward media bias
### Citation Information
```
@inproceedings{
title = {Introducing MBIB - the first Media Bias Identification Benchmark Task and Dataset Collection},
author = {Wessel, Martin and Spinde, Timo and Horych, Tomáš and Ruas, Terry and Aizawa, Akiko and Gipp, Bela},
year = {2023},
note = {[in review]}
}
``` | mediabiasgroup/mbib-base | [
"task_categories:text-classification",
"size_categories:1M<n<10M",
"language:en",
"license:cc-by-nc-nd-4.0",
"media",
"mediabias",
"media-bias",
"media bias",
"region:us"
]
| 2023-02-06T13:51:22+00:00 | {"language": ["en"], "license": "cc-by-nc-nd-4.0", "size_categories": ["1M<n<10M"], "task_categories": ["text-classification"], "tags": ["media", "mediabias", "media-bias", "media bias"], "dataset_info": {"config_name": "plain_text", "splits": [{"name": "cognitive_bias"}, {"name": "fake_news"}, {"name": "gender_bias"}, {"name": "hate_speech"}, {"name": "linguistic_bias"}, {"name": "political_bias"}, {"name": "racial_bias"}, {"name": "text_level_bias"}]}, "configs": [{"config_name": "default", "data_files": [{"split": "cognitive_bias", "path": "mbib-aggregated/cognitive-bias.csv"}, {"split": "fake_news", "path": "mbib-aggregated/fake-news.csv"}, {"split": "gender_bias", "path": "mbib-aggregated/gender-bias.csv"}, {"split": "hate_speech", "path": "mbib-aggregated/hate-speech.csv"}, {"split": "linguistic_bias", "path": "mbib-aggregated/linguistic-bias.csv"}, {"split": "political_bias", "path": "mbib-aggregated/political-bias.csv"}, {"split": "racial_bias", "path": "mbib-aggregated/racial-bias.csv"}, {"split": "text_level_bias", "path": "mbib-aggregated/text-level-bias.csv"}]}]} | 2024-02-06T15:57:17+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #size_categories-1M<n<10M #language-English #license-cc-by-nc-nd-4.0 #media #mediabias #media-bias #media bias #region-us
| Dataset Card for Media-Bias-Identification-Benchmark
====================================================
Table of Contents
-----------------
* Dataset Card for Media-Bias-Identification-Benchmark
+ Table of Contents
+ Dataset Description
- Dataset Summary
- Tasks and Information
- Baseline
- Languages
+ Dataset Structure
- Data Instances
* cognitive-bias
- Data Fields
+ Considerations for Using the Data
- Social Impact of Dataset
- Discussion of Biases
- Other Known Limitations
- Citation Information
- Contributions
Dataset Description
-------------------
* Homepage: URL
* Repository: URL
* Paper: URL
* Point of Contact: Martin Wessel
### Baseline
### Languages
All datasets are in English
Dataset Structure
-----------------
### Data Instances
#### cognitive-bias
An example of one training instance looks as follows.
### Data Fields
* 'text': a sentence from various sources (eg., news articles, twitter, other social media).
* 'label': binary indicator of bias (0 = unbiased, 1 = biased)
Considerations for Using the Data
---------------------------------
### Social Impact of Dataset
We believe that MBIB offers a new common ground
for research in the domain, especially given the rising amount of
(research) attention directed toward media bias
| [
"### Baseline",
"### Languages\n\n\nAll datasets are in English\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### cognitive-bias\n\n\nAn example of one training instance looks as follows.",
"### Data Fields\n\n\n* 'text': a sentence from various sources (eg., news articles, twitter, other social media).\n* 'label': binary indicator of bias (0 = unbiased, 1 = biased)\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nWe believe that MBIB offers a new common ground\nfor research in the domain, especially given the rising amount of\n(research) attention directed toward media bias"
]
| [
"TAGS\n#task_categories-text-classification #size_categories-1M<n<10M #language-English #license-cc-by-nc-nd-4.0 #media #mediabias #media-bias #media bias #region-us \n",
"### Baseline",
"### Languages\n\n\nAll datasets are in English\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### cognitive-bias\n\n\nAn example of one training instance looks as follows.",
"### Data Fields\n\n\n* 'text': a sentence from various sources (eg., news articles, twitter, other social media).\n* 'label': binary indicator of bias (0 = unbiased, 1 = biased)\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset\n\n\nWe believe that MBIB offers a new common ground\nfor research in the domain, especially given the rising amount of\n(research) attention directed toward media bias"
]
| [
61,
4,
18,
6,
17,
60,
41
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-1M<n<10M #language-English #license-cc-by-nc-nd-4.0 #media #mediabias #media-bias #media bias #region-us \n### Baseline### Languages\n\n\nAll datasets are in English\n\n\nDataset Structure\n-----------------### Data Instances#### cognitive-bias\n\n\nAn example of one training instance looks as follows.### Data Fields\n\n\n* 'text': a sentence from various sources (eg., news articles, twitter, other social media).\n* 'label': binary indicator of bias (0 = unbiased, 1 = biased)\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset\n\n\nWe believe that MBIB offers a new common ground\nfor research in the domain, especially given the rising amount of\n(research) attention directed toward media bias"
]
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|
7742bbffa1e85e5a95bbd583cd0132fb11e9f279 | # Dataset Card for "dreambooth-bee-images"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Zenodia/dreambooth-bee-images | [
"region:us"
]
| 2023-02-06T14:12:50+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1640816.0, "num_examples": 6}], "download_size": 1626376, "dataset_size": 1640816.0}} | 2023-02-06T14:18:36+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dreambooth-bee-images"
More Information needed | [
"# Dataset Card for \"dreambooth-bee-images\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dreambooth-bee-images\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"dreambooth-bee-images\"\n\nMore Information needed"
]
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|
b8502adb4a7d804c759e799df95c2fafb6df8139 | ## Dataset instance structure
{'audio': {'path': '/path/to/wav.wav',
'array': array([wav numpy array]), dtype=float32),
'sampling_rate': 16000},
'transcription': 'транскрипция'}
## Dataset audio info
- 16000 Hz
- wav
- mono
- Russian speech from audiobooks
## Citation
@misc{sova2021rudevices,
author = {Zubarev, Egor and Moskalets, Timofey and SOVA.ai},
title = {SOVA RuDevices Dataset: free public STT/ASR dataset with manually annotated live speech},
publisher = {GitHub},
journal = {GitHub repository},
year = {2021},
howpublished = {\url{https://github.com/sovaai/sova-dataset}},
} | dangrebenkin/sova_rudevices_audiobooks | [
"license:apache-2.0",
"region:us"
]
| 2023-02-06T14:15:49+00:00 | {"license": "apache-2.0", "dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "transcription", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 29311788948.030003, "num_examples": 182835}, {"name": "test", "num_bytes": 3181370673.15, "num_examples": 20315}], "download_size": 29701298876, "dataset_size": 32493159621.180004}} | 2023-02-06T19:22:04+00:00 | []
| []
| TAGS
#license-apache-2.0 #region-us
| ## Dataset instance structure
{'audio': {'path': '/path/to/URL',
'array': array([wav numpy array]), dtype=float32),
'sampling_rate': 16000},
'transcription': 'транскрипция'}
## Dataset audio info
- 16000 Hz
- wav
- mono
- Russian speech from audiobooks
@misc{sova2021rudevices,
author = {Zubarev, Egor and Moskalets, Timofey and URL},
title = {SOVA RuDevices Dataset: free public STT/ASR dataset with manually annotated live speech},
publisher = {GitHub},
journal = {GitHub repository},
year = {2021},
howpublished = {\url{URL
} | [
"## Dataset instance structure\n{'audio': {'path': '/path/to/URL',\n 'array': array([wav numpy array]), dtype=float32),\n 'sampling_rate': 16000},\n 'transcription': 'транскрипция'}",
"## Dataset audio info\n- 16000 Hz\n- wav\n- mono\n- Russian speech from audiobooks\n@misc{sova2021rudevices,\n author = {Zubarev, Egor and Moskalets, Timofey and URL},\n title = {SOVA RuDevices Dataset: free public STT/ASR dataset with manually annotated live speech},\n publisher = {GitHub},\n journal = {GitHub repository},\n year = {2021},\n howpublished = {\\url{URL\n}"
]
| [
"TAGS\n#license-apache-2.0 #region-us \n",
"## Dataset instance structure\n{'audio': {'path': '/path/to/URL',\n 'array': array([wav numpy array]), dtype=float32),\n 'sampling_rate': 16000},\n 'transcription': 'транскрипция'}",
"## Dataset audio info\n- 16000 Hz\n- wav\n- mono\n- Russian speech from audiobooks\n@misc{sova2021rudevices,\n author = {Zubarev, Egor and Moskalets, Timofey and URL},\n title = {SOVA RuDevices Dataset: free public STT/ASR dataset with manually annotated live speech},\n publisher = {GitHub},\n journal = {GitHub repository},\n year = {2021},\n howpublished = {\\url{URL\n}"
]
| [
14,
73,
119
]
| [
"passage: TAGS\n#license-apache-2.0 #region-us \n## Dataset instance structure\n{'audio': {'path': '/path/to/URL',\n 'array': array([wav numpy array]), dtype=float32),\n 'sampling_rate': 16000},\n 'transcription': 'транскрипция'}## Dataset audio info\n- 16000 Hz\n- wav\n- mono\n- Russian speech from audiobooks\n@misc{sova2021rudevices,\n author = {Zubarev, Egor and Moskalets, Timofey and URL},\n title = {SOVA RuDevices Dataset: free public STT/ASR dataset with manually annotated live speech},\n publisher = {GitHub},\n journal = {GitHub repository},\n year = {2021},\n howpublished = {\\url{URL\n}"
]
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678ae2f35e12df2a5540b538d8257732cd26160e |
# 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] | LorenzoG27/data_principals | [
"region:us"
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| 2023-02-06T15:00:47+00:00 | {} | 2023-02-06T15:27:14+00:00 | []
| []
| TAGS
#region-us
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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
### 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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|
93552494f53a4ca0687a1c151f0f57a641a8c080 |
# Dataset Card for NeuMARCO
## Dataset Description
- **Website:** https://neuclir.github.io/
### Dataset Summary
This is the dataset created for TREC 2022 NeuCLIR Track. The collection consists of documents from [`msmarco-passage`](https://ir-datasets.com/msmarco-passage) translated into
Chinese, Persian, and Russian.
### Languages
- Chinese
- Persian
- Russian
## Dataset Structure
### Data Instances
| Split | Documents |
|-----------------|----------:|
| `fas` (Persian) | 8.8M |
| `rus` (Russian) | 8.8M |
| `zho` (Chinese) | 8.8M |
### Data Fields
- `doc_id`: unique identifier for this document
- `text`: translated passage text
## Dataset Usage
Using 🤗 Datasets:
```python
from datasets import load_dataset
dataset = load_dataset('neuclir/neumarco')
dataset['fas'] # Persian passages
dataset['rus'] # Russian passages
dataset['zho'] # Chinese passages
```
| neuclir/neumarco | [
"task_categories:text-retrieval",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:multilingual",
"size_categories:1M<n<10M",
"source_datasets:extended|irds/msmarco-passage",
"language:fa",
"language:ru",
"language:zh",
"region:us"
]
| 2023-02-06T15:19:57+00:00 | {"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["fa", "ru", "zh"], "multilinguality": ["multilingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["extended|irds/msmarco-passage"], "task_categories": ["text-retrieval"], "pretty_name": "NeuMARCO", "tags": []} | 2023-02-06T16:16:37+00:00 | []
| [
"fa",
"ru",
"zh"
]
| TAGS
#task_categories-text-retrieval #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-extended|irds/msmarco-passage #language-Persian #language-Russian #language-Chinese #region-us
| Dataset Card for NeuMARCO
=========================
Dataset Description
-------------------
* Website: URL
### Dataset Summary
This is the dataset created for TREC 2022 NeuCLIR Track. The collection consists of documents from 'msmarco-passage' translated into
Chinese, Persian, and Russian.
### Languages
* Chinese
* Persian
* Russian
Dataset Structure
-----------------
### Data Instances
### Data Fields
* 'doc\_id': unique identifier for this document
* 'text': translated passage text
Dataset Usage
-------------
Using Datasets:
| [
"### Dataset Summary\n\n\nThis is the dataset created for TREC 2022 NeuCLIR Track. The collection consists of documents from 'msmarco-passage' translated into\nChinese, Persian, and Russian.",
"### Languages\n\n\n* Chinese\n* Persian\n* Russian\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields\n\n\n* 'doc\\_id': unique identifier for this document\n* 'text': translated passage text\n\n\nDataset Usage\n-------------\n\n\nUsing Datasets:"
]
| [
"TAGS\n#task_categories-text-retrieval #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-extended|irds/msmarco-passage #language-Persian #language-Russian #language-Chinese #region-us \n",
"### Dataset Summary\n\n\nThis is the dataset created for TREC 2022 NeuCLIR Track. The collection consists of documents from 'msmarco-passage' translated into\nChinese, Persian, and Russian.",
"### Languages\n\n\n* Chinese\n* Persian\n* Russian\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"### Data Fields\n\n\n* 'doc\\_id': unique identifier for this document\n* 'text': translated passage text\n\n\nDataset Usage\n-------------\n\n\nUsing Datasets:"
]
| [
97,
48,
18,
6,
42
]
| [
"passage: TAGS\n#task_categories-text-retrieval #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-multilingual #size_categories-1M<n<10M #source_datasets-extended|irds/msmarco-passage #language-Persian #language-Russian #language-Chinese #region-us \n### Dataset Summary\n\n\nThis is the dataset created for TREC 2022 NeuCLIR Track. The collection consists of documents from 'msmarco-passage' translated into\nChinese, Persian, and Russian.### Languages\n\n\n* Chinese\n* Persian\n* Russian\n\n\nDataset Structure\n-----------------### Data Instances### Data Fields\n\n\n* 'doc\\_id': unique identifier for this document\n* 'text': translated passage text\n\n\nDataset Usage\n-------------\n\n\nUsing Datasets:"
]
| [
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|
c68ac8e5f2418f75df2e7dc37cf902b443a83f6f | # Dataset Card for "model_cards_with_readmes"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | davanstrien/model_cards_with_readmes | [
"region:us"
]
| 2023-02-06T15:35:19+00:00 | {"dataset_info": {"features": [{"name": "repo_id", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "model_type", "dtype": "string"}, {"name": "files_per_repo", "dtype": "int64"}, {"name": "downloads_30d", "dtype": "int64"}, {"name": "library", "dtype": "string"}, {"name": "likes", "dtype": "int64"}, {"name": "pipeline", "dtype": "string"}, {"name": "pytorch", "dtype": "bool"}, {"name": "tensorflow", "dtype": "bool"}, {"name": "jax", "dtype": "bool"}, {"name": "license", "dtype": "string"}, {"name": "languages", "dtype": "string"}, {"name": "datasets", "dtype": "string"}, {"name": "co2", "dtype": "string"}, {"name": "prs_count", "dtype": "int64"}, {"name": "prs_open", "dtype": "int64"}, {"name": "prs_merged", "dtype": "int64"}, {"name": "prs_closed", "dtype": "int64"}, {"name": "discussions_count", "dtype": "int64"}, {"name": "discussions_open", "dtype": "int64"}, {"name": "discussions_closed", "dtype": "int64"}, {"name": "tags", "dtype": "string"}, {"name": "has_model_index", "dtype": "bool"}, {"name": "has_metadata", "dtype": "bool"}, {"name": "has_text", "dtype": "bool"}, {"name": "text_length", "dtype": "int64"}, {"name": "is_nc", "dtype": "bool"}, {"name": "readme", "dtype": "string"}, {"name": "hash", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 91746845.07931802, "num_examples": 29806}], "download_size": 37088334, "dataset_size": 91746845.07931802}} | 2023-02-16T22:35:39+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "model_cards_with_readmes"
More Information needed | [
"# Dataset Card for \"model_cards_with_readmes\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"model_cards_with_readmes\"\n\nMore Information needed"
]
| [
6,
19
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"model_cards_with_readmes\"\n\nMore Information needed"
]
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|
3f5b3917888600c2dc377e670ff22ecf38f637a0 | # Dataset Card for "bookcorpus_ALL_SV"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MartinKu/bookcorpus_ALL_SV | [
"region:us"
]
| 2023-02-06T15:54:32+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2210939478, "num_examples": 111661463}], "download_size": 1422662083, "dataset_size": 2210939478}} | 2023-02-09T19:44:07+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_ALL_SV"
More Information needed | [
"# Dataset Card for \"bookcorpus_ALL_SV\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_ALL_SV\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_ALL_SV\"\n\nMore Information needed"
]
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|
c47327ea18e632ab2a4b2f54d51c557175d99df1 | # Dataset Card for "bookcorpus_ALL_OC"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | MartinKu/bookcorpus_ALL_OC | [
"region:us"
]
| 2023-02-06T15:55:56+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2991397965, "num_examples": 100095502}], "download_size": 2020456946, "dataset_size": 2991397965}} | 2023-02-09T20:09:14+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "bookcorpus_ALL_OC"
More Information needed | [
"# Dataset Card for \"bookcorpus_ALL_OC\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"bookcorpus_ALL_OC\"\n\nMore Information needed"
]
| [
6,
17
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"bookcorpus_ALL_OC\"\n\nMore Information needed"
]
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1302d18f3afcd82fbad91907b9578aa89ec4991b | # Dataset Card for "amazon-shoe-reviews"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | mesmalif/amazon-shoe-reviews | [
"region:us"
]
| 2023-02-06T16:06:43+00:00 | {"dataset_info": {"features": [{"name": "marketplace", "dtype": "string"}, {"name": "customer_id", "dtype": "string"}, {"name": "review_id", "dtype": "string"}, {"name": "product_id", "dtype": "string"}, {"name": "product_parent", "dtype": "string"}, {"name": "product_title", "dtype": "string"}, {"name": "product_category", "dtype": "string"}, {"name": "labels", "dtype": "int64"}, {"name": "helpful_votes", "dtype": "int64"}, {"name": "total_votes", "dtype": "int64"}, {"name": "vine", "dtype": "int64"}, {"name": "verified_purchase", "dtype": "int64"}, {"name": "review_headline", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "review_date", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 34784832.6, "num_examples": 90000}, {"name": "test", "num_bytes": 3864981.4, "num_examples": 10000}], "download_size": 21283157, "dataset_size": 38649814.0}} | 2023-02-06T16:07:08+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "amazon-shoe-reviews"
More Information needed | [
"# Dataset Card for \"amazon-shoe-reviews\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"amazon-shoe-reviews\"\n\nMore Information needed"
]
| [
6,
18
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"amazon-shoe-reviews\"\n\nMore Information needed"
]
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]
|
a79260be92ca6279d0b6edb1e2654efc4c454f2d |
# ml4pubmed/pubmed-classification-20k
- 20k subset of pubmed text classification from course | ml4pubmed/pubmed-classification-20k | [
"task_categories:text-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"pubmed",
"region:us"
]
| 2023-02-06T16:16:31+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "tags": ["pubmed"]} | 2023-02-17T06:31:13+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #pubmed #region-us
|
# ml4pubmed/pubmed-classification-20k
- 20k subset of pubmed text classification from course | [
"# ml4pubmed/pubmed-classification-20k \n\n\n- 20k subset of pubmed text classification from course"
]
| [
"TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #pubmed #region-us \n",
"# ml4pubmed/pubmed-classification-20k \n\n\n- 20k subset of pubmed text classification from course"
]
| [
44,
26
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-10K<n<100K #language-English #license-apache-2.0 #pubmed #region-us \n# ml4pubmed/pubmed-classification-20k \n\n\n- 20k subset of pubmed text classification from course"
]
| [
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|
afab1080849081e976fd0dd4948d720f5b2a2555 | # Dataset Card for "dreambooth-mooncake"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Zenodia/dreambooth-mooncake | [
"region:us"
]
| 2023-02-06T16:20:02+00:00 | {"dataset_info": {"features": [{"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 7535176.0, "num_examples": 15}], "download_size": 7499175, "dataset_size": 7535176.0}} | 2023-02-06T16:20:09+00:00 | []
| []
| TAGS
#region-us
| # Dataset Card for "dreambooth-mooncake"
More Information needed | [
"# Dataset Card for \"dreambooth-mooncake\"\n\nMore Information needed"
]
| [
"TAGS\n#region-us \n",
"# Dataset Card for \"dreambooth-mooncake\"\n\nMore Information needed"
]
| [
6,
16
]
| [
"passage: TAGS\n#region-us \n# Dataset Card for \"dreambooth-mooncake\"\n\nMore Information needed"
]
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|
1f7e8ab74762318dd169d32b68e80c7202a2b32e |
# ml4pubmed/pubmed-text-classification-cased
A parsed/cleaned version of the source data retaining case. | ml4pubmed/pubmed-text-classification-cased | [
"task_categories:text-classification",
"size_categories:1M<n<10M",
"source_datasets:pubmed",
"language:en",
"license:apache-2.0",
"pubmed",
"region:us"
]
| 2023-02-06T16:20:15+00:00 | {"language": ["en"], "license": "apache-2.0", "size_categories": ["1M<n<10M"], "source_datasets": "pubmed", "task_categories": ["text-classification"], "tags": ["pubmed"]} | 2023-02-06T16:43:19+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #size_categories-1M<n<10M #source_datasets-pubmed #language-English #license-apache-2.0 #pubmed #region-us
|
# ml4pubmed/pubmed-text-classification-cased
A parsed/cleaned version of the source data retaining case. | [
"# ml4pubmed/pubmed-text-classification-cased\n\nA parsed/cleaned version of the source data retaining case."
]
| [
"TAGS\n#task_categories-text-classification #size_categories-1M<n<10M #source_datasets-pubmed #language-English #license-apache-2.0 #pubmed #region-us \n",
"# ml4pubmed/pubmed-text-classification-cased\n\nA parsed/cleaned version of the source data retaining case."
]
| [
53,
32
]
| [
"passage: TAGS\n#task_categories-text-classification #size_categories-1M<n<10M #source_datasets-pubmed #language-English #license-apache-2.0 #pubmed #region-us \n# ml4pubmed/pubmed-text-classification-cased\n\nA parsed/cleaned version of the source data retaining case."
]
| [
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|
50a06885204f7b45e953033eb08904c5a0d5bafe |
## STR-2022: Dataset Description
The dataset consists of 5500 English sentence pairs that are scored and ranked on a relatedness scale ranging from 0 (least related) to 1 (most related).
## Loading the Dataset
- The sentence pairs, and associated scores, are in the file sem_text_rel_ranked.csv in the root directory. The CSV file can be read using:
```python
import pandas as pd
str = pd.read_csv('sem_text_rel_ranked.csv')
row = str.loc[0]
sent1, sent2 = row['Text'].split("\n")
score = row['Score']
```
- Relevant columns:
- Text: Sentence pair, separated by the newline character.
- Score: The semantic relatedness score between 0 and 1.
- Additionally:
- the SourceID column indicates the source dataset from which the sentence pair was drawn (see Table 2 of our paper)
- The SubsetID column indicates the sampling strategy used for the source dataset
- and the PairID is a unique identifier for each pair that also indicates its Source and Subset.
## Why Semantic Relatedness?
Closeness of meaning can be of two kinds: semantic relatedness and semantic similarity. Two sentences are considered semantically similar when they have a paraphrasal or entailment relation, whereas relatedness accounts for all of the commonalities that can exist between two sentences. Semantic relatedness is central to textual coherence and narrative structure. Automatically determining semantic relatedness has many applications such as question answering, plagiarism detection, text generation (say in personal assistants and chat bots), and summarization.
Prior NLP work has focused on semantic similarity (a small subset of semantic relatedness), largely because of a dearth of datasets. In this paper, we present the first manually annotated dataset of sentence--sentence semantic relatedness. It includes fine-grained scores of relatedness from 0 (least related) to 1 (most related) for 5,500 English sentence pairs. The sentences are taken from diverse sources and thus also have diverse sentence structures, varying amounts of lexical overlap, and varying formality.
## Comparative Annotations and Best-Worst Scaling
Most existing sentence-sentence similarity datasets were annotated, one item at a time, using coarse rating labels such as integer values between 1 and 5\@ representing coarse degrees of closeness. It is well documented that such approaches suffer from inter- and intra-annotator inconsistency, scale region bias, and issues arising due to the fixed granularity.
The relatedness scores for our dataset were, instead, obtained using a __comparative__ annotation schema. In comparative annotations, two (or more) items are presented together and the annotator has to determine which is greater with respect to the metric of interest.
Specifically, we use Best-Worst Scaling, a comparative annotation method}, which has been shown to produce reliable scores with fewer annotations in other NLP tasks. We use scripts from https://saifmohammad.com/WebPages/BestWorst.html to obtain relatedness scores from our annotations.
## Citing our work
Please use the following BibTex entry to cite us if you use our dataset or any of the [associated analyses](https://arxiv.org/abs/2110.04845):
```
@inproceedings{abdalla2023makes,
title={What Makes Sentences Semantically Related: A Textual Relatedness Dataset and Empirical Study},
author={Abdalla, Mohamed and Vishnubhotla, Krishnapriya and Mohammad, Saif M.},
year={2023},
address = {Dubrovnik, Croatia},
publisher = "Association for Computational Linguistics",
booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume"
}
```
## Ethics Statement
Any dataset of semantic relatedness entails several ethical considerations. We talk about this in Section 10 of [our paper](https://arxiv.org/abs/2110.04845).
## Relevant Links
- [GitHub repository](https://github.com/Priya22/semantic-textual-relatedness)
- [Zenodo page](https://zenodo.org/record/7599667)
## Creators
- [Mohamed Abdalla](https://www.cs.toronto.edu/~msa/index_all.html) (University of Toronto)
- [Krishnapriya Vishnubhotla](https://priya22.github.io/) (University of Toronto)
- [Saif M. Mohammad](http://saifmohammad.com/) (National Research Council Canada)
**Contact**: [email protected], [email protected], [email protected] | vkpriya/str-2022 | [
"task_categories:sentence-similarity",
"size_categories:1K<n<10K",
"language:en",
"license:other",
"#sentence-relatedness",
"#semantic-similarity",
"#semantic-relatedness",
"arxiv:2110.04845",
"region:us"
]
| 2023-02-06T17:16:38+00:00 | {"language": ["en"], "license": "other", "size_categories": ["1K<n<10K"], "task_categories": ["sentence-similarity"], "pretty_name": "str-2022", "tags": ["#sentence-relatedness", "#semantic-similarity", "#semantic-relatedness"]} | 2023-02-06T17:50:10+00:00 | [
"2110.04845"
]
| [
"en"
]
| TAGS
#task_categories-sentence-similarity #size_categories-1K<n<10K #language-English #license-other ##sentence-relatedness ##semantic-similarity ##semantic-relatedness #arxiv-2110.04845 #region-us
|
## STR-2022: Dataset Description
The dataset consists of 5500 English sentence pairs that are scored and ranked on a relatedness scale ranging from 0 (least related) to 1 (most related).
## Loading the Dataset
- The sentence pairs, and associated scores, are in the file sem_text_rel_ranked.csv in the root directory. The CSV file can be read using:
- Relevant columns:
- Text: Sentence pair, separated by the newline character.
- Score: The semantic relatedness score between 0 and 1.
- Additionally:
- the SourceID column indicates the source dataset from which the sentence pair was drawn (see Table 2 of our paper)
- The SubsetID column indicates the sampling strategy used for the source dataset
- and the PairID is a unique identifier for each pair that also indicates its Source and Subset.
## Why Semantic Relatedness?
Closeness of meaning can be of two kinds: semantic relatedness and semantic similarity. Two sentences are considered semantically similar when they have a paraphrasal or entailment relation, whereas relatedness accounts for all of the commonalities that can exist between two sentences. Semantic relatedness is central to textual coherence and narrative structure. Automatically determining semantic relatedness has many applications such as question answering, plagiarism detection, text generation (say in personal assistants and chat bots), and summarization.
Prior NLP work has focused on semantic similarity (a small subset of semantic relatedness), largely because of a dearth of datasets. In this paper, we present the first manually annotated dataset of sentence--sentence semantic relatedness. It includes fine-grained scores of relatedness from 0 (least related) to 1 (most related) for 5,500 English sentence pairs. The sentences are taken from diverse sources and thus also have diverse sentence structures, varying amounts of lexical overlap, and varying formality.
## Comparative Annotations and Best-Worst Scaling
Most existing sentence-sentence similarity datasets were annotated, one item at a time, using coarse rating labels such as integer values between 1 and 5\@ representing coarse degrees of closeness. It is well documented that such approaches suffer from inter- and intra-annotator inconsistency, scale region bias, and issues arising due to the fixed granularity.
The relatedness scores for our dataset were, instead, obtained using a __comparative__ annotation schema. In comparative annotations, two (or more) items are presented together and the annotator has to determine which is greater with respect to the metric of interest.
Specifically, we use Best-Worst Scaling, a comparative annotation method}, which has been shown to produce reliable scores with fewer annotations in other NLP tasks. We use scripts from URL to obtain relatedness scores from our annotations.
## Citing our work
Please use the following BibTex entry to cite us if you use our dataset or any of the associated analyses:
## Ethics Statement
Any dataset of semantic relatedness entails several ethical considerations. We talk about this in Section 10 of our paper.
## Relevant Links
- GitHub repository
- Zenodo page
## Creators
- Mohamed Abdalla (University of Toronto)
- Krishnapriya Vishnubhotla (University of Toronto)
- Saif M. Mohammad (National Research Council Canada)
Contact: msa@URL, vkpriya@URL, saif.mohammad@URL | [
"## STR-2022: Dataset Description\n\nThe dataset consists of 5500 English sentence pairs that are scored and ranked on a relatedness scale ranging from 0 (least related) to 1 (most related).",
"## Loading the Dataset\n- The sentence pairs, and associated scores, are in the file sem_text_rel_ranked.csv in the root directory. The CSV file can be read using:\n\n \n\n- Relevant columns: \n\n - Text: Sentence pair, separated by the newline character.\n - Score: The semantic relatedness score between 0 and 1. \n\n- Additionally:\n - the SourceID column indicates the source dataset from which the sentence pair was drawn (see Table 2 of our paper)\n - The SubsetID column indicates the sampling strategy used for the source dataset\n - and the PairID is a unique identifier for each pair that also indicates its Source and Subset.",
"## Why Semantic Relatedness?\nCloseness of meaning can be of two kinds: semantic relatedness and semantic similarity. Two sentences are considered semantically similar when they have a paraphrasal or entailment relation, whereas relatedness accounts for all of the commonalities that can exist between two sentences. Semantic relatedness is central to textual coherence and narrative structure. Automatically determining semantic relatedness has many applications such as question answering, plagiarism detection, text generation (say in personal assistants and chat bots), and summarization. \n\nPrior NLP work has focused on semantic similarity (a small subset of semantic relatedness), largely because of a dearth of datasets. In this paper, we present the first manually annotated dataset of sentence--sentence semantic relatedness. It includes fine-grained scores of relatedness from 0 (least related) to 1 (most related) for 5,500 English sentence pairs. The sentences are taken from diverse sources and thus also have diverse sentence structures, varying amounts of lexical overlap, and varying formality.",
"## Comparative Annotations and Best-Worst Scaling \nMost existing sentence-sentence similarity datasets were annotated, one item at a time, using coarse rating labels such as integer values between 1 and 5\\@ representing coarse degrees of closeness. It is well documented that such approaches suffer from inter- and intra-annotator inconsistency, scale region bias, and issues arising due to the fixed granularity.\n\nThe relatedness scores for our dataset were, instead, obtained using a __comparative__ annotation schema. In comparative annotations, two (or more) items are presented together and the annotator has to determine which is greater with respect to the metric of interest.\n\nSpecifically, we use Best-Worst Scaling, a comparative annotation method}, which has been shown to produce reliable scores with fewer annotations in other NLP tasks. We use scripts from URL to obtain relatedness scores from our annotations.",
"## Citing our work \nPlease use the following BibTex entry to cite us if you use our dataset or any of the associated analyses:",
"## Ethics Statement\nAny dataset of semantic relatedness entails several ethical considerations. We talk about this in Section 10 of our paper.",
"## Relevant Links\n- GitHub repository\n- Zenodo page",
"## Creators\n- Mohamed Abdalla (University of Toronto)\n- Krishnapriya Vishnubhotla (University of Toronto)\n- Saif M. Mohammad (National Research Council Canada)\n\nContact: msa@URL, vkpriya@URL, saif.mohammad@URL"
]
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"TAGS\n#task_categories-sentence-similarity #size_categories-1K<n<10K #language-English #license-other ##sentence-relatedness ##semantic-similarity ##semantic-relatedness #arxiv-2110.04845 #region-us \n",
"## STR-2022: Dataset Description\n\nThe dataset consists of 5500 English sentence pairs that are scored and ranked on a relatedness scale ranging from 0 (least related) to 1 (most related).",
"## Loading the Dataset\n- The sentence pairs, and associated scores, are in the file sem_text_rel_ranked.csv in the root directory. The CSV file can be read using:\n\n \n\n- Relevant columns: \n\n - Text: Sentence pair, separated by the newline character.\n - Score: The semantic relatedness score between 0 and 1. \n\n- Additionally:\n - the SourceID column indicates the source dataset from which the sentence pair was drawn (see Table 2 of our paper)\n - The SubsetID column indicates the sampling strategy used for the source dataset\n - and the PairID is a unique identifier for each pair that also indicates its Source and Subset.",
"## Why Semantic Relatedness?\nCloseness of meaning can be of two kinds: semantic relatedness and semantic similarity. Two sentences are considered semantically similar when they have a paraphrasal or entailment relation, whereas relatedness accounts for all of the commonalities that can exist between two sentences. Semantic relatedness is central to textual coherence and narrative structure. Automatically determining semantic relatedness has many applications such as question answering, plagiarism detection, text generation (say in personal assistants and chat bots), and summarization. \n\nPrior NLP work has focused on semantic similarity (a small subset of semantic relatedness), largely because of a dearth of datasets. In this paper, we present the first manually annotated dataset of sentence--sentence semantic relatedness. It includes fine-grained scores of relatedness from 0 (least related) to 1 (most related) for 5,500 English sentence pairs. The sentences are taken from diverse sources and thus also have diverse sentence structures, varying amounts of lexical overlap, and varying formality.",
"## Comparative Annotations and Best-Worst Scaling \nMost existing sentence-sentence similarity datasets were annotated, one item at a time, using coarse rating labels such as integer values between 1 and 5\\@ representing coarse degrees of closeness. It is well documented that such approaches suffer from inter- and intra-annotator inconsistency, scale region bias, and issues arising due to the fixed granularity.\n\nThe relatedness scores for our dataset were, instead, obtained using a __comparative__ annotation schema. In comparative annotations, two (or more) items are presented together and the annotator has to determine which is greater with respect to the metric of interest.\n\nSpecifically, we use Best-Worst Scaling, a comparative annotation method}, which has been shown to produce reliable scores with fewer annotations in other NLP tasks. We use scripts from URL to obtain relatedness scores from our annotations.",
"## Citing our work \nPlease use the following BibTex entry to cite us if you use our dataset or any of the associated analyses:",
"## Ethics Statement\nAny dataset of semantic relatedness entails several ethical considerations. We talk about this in Section 10 of our paper.",
"## Relevant Links\n- GitHub repository\n- Zenodo page",
"## Creators\n- Mohamed Abdalla (University of Toronto)\n- Krishnapriya Vishnubhotla (University of Toronto)\n- Saif M. Mohammad (National Research Council Canada)\n\nContact: msa@URL, vkpriya@URL, saif.mohammad@URL"
]
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|
898a4636a13c66b4c1db9140121b186af5df0493 |
# Dataset Card for "quora"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.kaggle.com/c/quora-question-pairs](https://www.kaggle.com/c/quora-question-pairs)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 55.48 MB
- **Size of the generated dataset:** 55.46 MB
- **Total amount of disk used:** 110.94 MB
### Dataset Summary
The Quora dataset is composed of question pairs, and the task is to determine if the questions are paraphrases of each other (have the same meaning).
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 55.48 MB
- **Size of the generated dataset:** 55.46 MB
- **Total amount of disk used:** 110.94 MB
### Data Fields
The data fields are the same among all splits.
### Data Splits
| name |train |
|-------|-----:|
|default|404290|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[Quora Term of Service](https://www.quora.com/about/tos), no commercial use.
### Citation Information
Unknown.
| chenghao/quora_questions | [
"task_categories:text-classification",
"task_ids:semantic-similarity-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:other",
"region:us"
]
| 2023-02-06T17:16:39+00:00 | {"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": "other", "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["semantic-similarity-classification"], "pretty_name": "Quora Questions", "dataset_info": {"features": [{"name": "questions", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 51635953, "num_examples": 808580}], "download_size": 31079310, "dataset_size": 51635953}} | 2023-02-06T17:23:12+00:00 | []
| [
"en"
]
| TAGS
#task_categories-text-classification #task_ids-semantic-similarity-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-other #region-us
| Dataset Card for "quora"
========================
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:
* Point of Contact:
* Size of downloaded dataset files: 55.48 MB
* Size of the generated dataset: 55.46 MB
* Total amount of disk used: 110.94 MB
### Dataset Summary
The Quora dataset is composed of question pairs, and the task is to determine if the questions are paraphrases of each other (have the same meaning).
### Supported Tasks and Leaderboards
### Languages
Dataset Structure
-----------------
### Data Instances
#### default
* Size of downloaded dataset files: 55.48 MB
* Size of the generated dataset: 55.46 MB
* Total amount of disk used: 110.94 MB
### Data Fields
The data fields are the same among all splits.
### 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
Quora Term of Service, no commercial use.
Unknown.
| [
"### Dataset Summary\n\n\nThe Quora dataset is composed of question pairs, and the task is to determine if the questions are paraphrases of each other (have the same meaning).",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### default\n\n\n* Size of downloaded dataset files: 55.48 MB\n* Size of the generated dataset: 55.46 MB\n* Total amount of disk used: 110.94 MB",
"### Data Fields\n\n\nThe data fields are the same among all splits.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nQuora Term of Service, no commercial use.\n\n\nUnknown."
]
| [
"TAGS\n#task_categories-text-classification #task_ids-semantic-similarity-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-other #region-us \n",
"### Dataset Summary\n\n\nThe Quora dataset is composed of question pairs, and the task is to determine if the questions are paraphrases of each other (have the same meaning).",
"### Supported Tasks and Leaderboards",
"### Languages\n\n\nDataset Structure\n-----------------",
"### Data Instances",
"#### default\n\n\n* Size of downloaded dataset files: 55.48 MB\n* Size of the generated dataset: 55.46 MB\n* Total amount of disk used: 110.94 MB",
"### Data Fields\n\n\nThe data fields are the same among all splits.",
"### Data Splits\n\n\n\nDataset Creation\n----------------",
"### Curation Rationale",
"### Source Data",
"#### Initial Data Collection and Normalization",
"#### Who are the source language producers?",
"### Annotations",
"#### Annotation process",
"#### Who are the annotators?",
"### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------",
"### Social Impact of Dataset",
"### Discussion of Biases",
"### Other Known Limitations\n\n\nAdditional Information\n----------------------",
"### Dataset Curators",
"### Licensing Information\n\n\nQuora Term of Service, no commercial use.\n\n\nUnknown."
]
| [
90,
41,
10,
11,
6,
40,
17,
11,
7,
4,
10,
10,
5,
5,
9,
18,
7,
8,
14,
6,
21
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| [
"passage: TAGS\n#task_categories-text-classification #task_ids-semantic-similarity-classification #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #source_datasets-original #language-English #license-other #region-us \n### Dataset Summary\n\n\nThe Quora dataset is composed of question pairs, and the task is to determine if the questions are paraphrases of each other (have the same meaning).### Supported Tasks and Leaderboards### Languages\n\n\nDataset Structure\n-----------------### Data Instances#### default\n\n\n* Size of downloaded dataset files: 55.48 MB\n* Size of the generated dataset: 55.46 MB\n* Total amount of disk used: 110.94 MB### Data Fields\n\n\nThe data fields are the same among all splits.### Data Splits\n\n\n\nDataset Creation\n----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization#### Who are the source language producers?### Annotations#### Annotation process#### Who are the annotators?### Personal and Sensitive Information\n\n\nConsiderations for Using the Data\n---------------------------------### Social Impact of Dataset### Discussion of Biases### Other Known Limitations\n\n\nAdditional Information\n----------------------### Dataset Curators### Licensing Information\n\n\nQuora Term of Service, no commercial use.\n\n\nUnknown."
]
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|
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