sha stringlengths 40 40 | text stringlengths 1 13.4M | id stringlengths 2 117 | tags listlengths 1 7.91k | created_at stringlengths 25 25 | metadata stringlengths 2 875k | last_modified stringlengths 25 25 | arxiv listlengths 0 25 | languages listlengths 0 7.91k | tags_str stringlengths 17 159k | text_str stringlengths 1 447k | text_lists listlengths 0 352 | processed_texts listlengths 1 353 | tokens_length listlengths 1 353 | input_texts listlengths 1 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
a2145fcd66bb404d4c04b04436dad191394eae32 | # Dataset Card for "squad_context_v3_train_30_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_context_v3_train_30_eval_10 | [
"region:us"
] | 2023-09-26T07:07:42+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "context_i... | 2023-09-26T07:07:49+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_context_v3_train_30_eval_10"
More Information needed | [
"# Dataset Card for \"squad_context_v3_train_30_eval_10\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"squad_context_v3_train_30_eval_10\"\n\nMore Information needed"
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d54b2badad8f79b757af61a26fffc2c7c63a35ba | # Dataset Card for "squad_wrong_title_v3_train_30_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_wrong_title_v3_train_30_eval_10 | [
"region:us"
] | 2023-09-26T07:07:52+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "context_i... | 2023-09-26T07:07:57+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_wrong_title_v3_train_30_eval_10"
More Information needed | [
"# Dataset Card for \"squad_wrong_title_v3_train_30_eval_10\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"squad_wrong_title_v3_train_30_eval_10\"\n\nMore Information needed"
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8f65fde954cd8cd8676781f487692d3d89491f28 | # Dataset Card for "squad_no_title_v3_train_30_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_no_title_v3_train_30_eval_10 | [
"region:us"
] | 2023-09-26T07:07:58+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "context_i... | 2023-09-26T07:08:04+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_no_title_v3_train_30_eval_10"
More Information needed | [
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d5310f2a684c52468b22c8c2904b4554fcfa60af |
# Dataset of aoba_moca/青葉モカ/아오바모카 (BanG Dream!)
This is the dataset of aoba_moca/青葉モカ/아오바모카 (BanG Dream!), containing 416 images and their tags.
The core tags of this character are `short_hair, grey_hair, bangs, blue_eyes`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv, ... | CyberHarem/aoba_moca_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T07:10:29+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T16:49:05+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of aoba\_moca/青葉モカ/아오바모카 (BanG Dream!)
==============================================
This is the dataset of aoba\_moca/青葉モカ/아오바모카 (BanG Dream!), containing 416 images and their tags.
The core tags of this character are 'short\_hair, grey\_hair, bangs, blue\_eyes', which are pruned in this dataset.
Images... | [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
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28aee548f21b893e855e19c348a1ff2a110ae175 | ## Textbooks are all you need : A SciPhi Collection
Dataset Description
With LLMs, we can create a fully open-source Library of Alexandria.
As a first attempt, we have generated 650,000 unique textbook samples from a diverse span of courses, kindergarten through graduate school.
These are open source samples, which... | SciPhi/textbooks-are-all-you-need-lite | [
"license:llama2",
"region:us"
] | 2023-09-26T07:14:12+00:00 | {"license": "llama2", "dataset_info": {"features": [{"name": "formatted_prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}, {"name": "first_task", "dtype": "string"}, {"name": "second_task", "dtype": "string"}, {"name": "last_task", "dtype": "string"}, {"name": "notes", "dtype": "string"}, {"name": ... | 2023-09-30T20:57:36+00:00 | [] | [] | TAGS
#license-llama2 #region-us
| ## Textbooks are all you need : A SciPhi Collection
Dataset Description
With LLMs, we can create a fully open-source Library of Alexandria.
As a first attempt, we have generated 650,000 unique textbook samples from a diverse span of courses, kindergarten through graduate school.
These are open source samples, which... | [
"## Textbooks are all you need : A SciPhi Collection\n\nDataset Description\n\nWith LLMs, we can create a fully open-source Library of Alexandria.\n\nAs a first attempt, we have generated 650,000 unique textbook samples from a diverse span of courses, kindergarten through graduate school.\n\nThese are open source s... | [
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93f768e00352194efc5c04aebc332e0d05704860 | # Dataset Card for "headlines"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jtatman/headlines | [
"region:us"
] | 2023-09-26T07:26:46+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 80263469, "num_examples": 1662297}], "download_size": 62717748, "dataset_size": 80263469}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-09-26T07:27:15+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "headlines"
More Information needed | [
"# Dataset Card for \"headlines\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"headlines\"\n\nMore Information needed"
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aabcaace0e78f68370753e255e45399385403a5c | # Dataset Card for "test_fboolq"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | manu/french_boolq | [
"region:us"
] | 2023-09-26T07:30:49+00:00 | {"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "passage", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "test", "num_bytes": 153880, "num_examples": 178}, {"name": "valid", "num_bytes": 7038, "num_examples": 10}], "download_size": 64042, "dataset_size": ... | 2023-11-14T08:58:37+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "test_fboolq"
More Information needed | [
"# Dataset Card for \"test_fboolq\"\n\nMore Information needed"
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"# Dataset Card for \"test_fboolq\"\n\nMore Information needed"
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"passage: TAGS\n#region-us \n# Dataset Card for \"test_fboolq\"\n\nMore Information needed"
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b2bd9721e77b4c95ff79a6f01831c63b95795a96 | # Dataset Card for "NewData"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | Zaid/NewData | [
"region:us"
] | 2023-09-26T07:44:37+00:00 | {"dataset_info": {"features": [{"name": "Name", "dtype": "string"}, {"name": "Age", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "female", "1": "male"}}}}], "splits": [{"name": "train", "num_bytes": 50, "num_examples": 2}], "download_size": 1182, "dataset_size": 50}} | 2023-09-26T07:44:43+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "NewData"
More Information needed | [
"# Dataset Card for \"NewData\"\n\nMore Information needed"
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c0825dbaf1838dd5d38a8f903f3353a4661aa5db |
Dataset from: http://websail-fe.cs.northwestern.edu/TabEL | MikeXydas/wikitable | [
"license:mit",
"region:us"
] | 2023-09-26T07:53:31+00:00 | {"license": "mit"} | 2023-09-26T08:21:03+00:00 | [] | [] | TAGS
#license-mit #region-us
|
Dataset from: URL | [] | [
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11
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d14d45b0ae44f408604e3ec309433ddf19c4260d | # Argument mining from Tweets related to COVID-19.
This repository contains a dataset for SMM4H'22 Task 2: Classification of stance and premise in tweets about health mandates (COVID-19).
Data includes:
- [Train](train) and [test](data/test/smm4h) data for SMM4H 2022 Task 2: tweets annotated for stance and premise pre... | veranchos/arg_mining_tweets | [
"license:afl-3.0",
"region:us"
] | 2023-09-26T07:55:52+00:00 | {"license": "afl-3.0"} | 2023-09-27T07:30:45+00:00 | [] | [] | TAGS
#license-afl-3.0 #region-us
| # Argument mining from Tweets related to COVID-19.
This repository contains a dataset for SMM4H'22 Task 2: Classification of stance and premise in tweets about health mandates (COVID-19).
Data includes:
- Train and test data for SMM4H 2022 Task 2: tweets annotated for stance and premise prediction on three claims abou... | [
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a2a30a6d4b6119d76efd23d0f3c4c8ac5101323f |
# OCR Barcodes Detection
The dataset consists of images of various **grocery goods** that have **barcode labels**. Each image in the dataset is annotated with polygons around the barcode labels. Additionally, Optical Character Recognition (**OCR**) has been performed on each bounding box to extract the barcode numbers... | TrainingDataPro/ocr-barcodes-detection | [
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"code",
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"region:us"
] | 2023-09-26T08:02:16+00:00 | {"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-to-text"], "tags": ["code", "finance"], "dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "name", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "mask", "dtype": "image"}, {"name": "width", "dtype": "... | 2023-10-09T06:28:23+00:00 | [] | [
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] | TAGS
#task_categories-image-to-text #language-English #license-cc-by-nc-nd-4.0 #code #finance #region-us
|
# OCR Barcodes Detection
The dataset consists of images of various grocery goods that have barcode labels. Each image in the dataset is annotated with polygons around the barcode labels. Additionally, Optical Character Recognition (OCR) has been performed on each bounding box to extract the barcode numbers.
The datas... | [
"# OCR Barcodes Detection\nThe dataset consists of images of various grocery goods that have barcode labels. Each image in the dataset is annotated with polygons around the barcode labels. Additionally, Optical Character Recognition (OCR) has been performed on each bounding box to extract the barcode numbers.\n\nTh... | [
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2b2044305e428bde11c7ea7e7d4ebbd81270b56d | # Dataset Card for "sv_corpora_parliament_processed"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | ALIGHASEMI931/sv_corpora_parliament_processed | [
"region:us"
] | 2023-09-26T08:03:18+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 292351437, "num_examples": 1892723}], "download_size": 161955796, "dataset_size": 292351437}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-09-26T08:26:36+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "sv_corpora_parliament_processed"
More Information needed | [
"# Dataset Card for \"sv_corpora_parliament_processed\"\n\nMore Information needed"
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dfc6b1c4768b14516eaa04ec6adfba98bd7613d2 | # Dataset Card for "squad_title_v4_train_30_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_title_v4_train_30_eval_10 | [
"region:us"
] | 2023-09-26T08:04:51+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "context_i... | 2023-09-26T08:49:20+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_title_v4_train_30_eval_10"
More Information needed | [
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f11390f55d1eed2ea6a5ddd0a40c1b09a03c9d9d |
# gandalf_summarization
This is a dataset of _indirect_ prompt injections from [Gandalf](https://gandalf.lakera.ai/) by [Lakera](https://www.lakera.ai/), specifically from the Adventure 4 level ([link](https://gandalf.lakera.ai/adventures), although not... | Lakera/gandalf_summarization | [
"license:mit",
"region:us"
] | 2023-09-26T08:06:29+00:00 | {"license": "mit", "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "gandalf_answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 103686, "num_examples": 114}, {"name": "validation", "num_bytes": 7635, "num_examples": 13}, {"name": "test", "num_bytes": 8763, "num_examples":... | 2023-10-02T08:25:52+00:00 | [] | [] | TAGS
#license-mit #region-us
|
# gandalf_summarization
.
Note that we might update the dataset occasionally by cleaning the data or adding more sam... | [
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bd7f55760e72ae59fdedad4c3dc6c0f26f7ef467 |
# Dataset of hazawa_tsugumi/羽沢つぐみ (BanG Dream!)
This is the dataset of hazawa_tsugumi/羽沢つぐみ (BanG Dream!), containing 297 images and their tags.
The core tags of this character are `brown_hair, short_hair, brown_eyes, bangs`, which are pruned in this dataset.
Images are crawled from many sites (e.g. danbooru, pixiv... | CyberHarem/hazawa_tsugumi_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T08:13:13+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T18:17:58+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of hazawa\_tsugumi/羽沢つぐみ (BanG Dream!)
==============================================
This is the dataset of hazawa\_tsugumi/羽沢つぐみ (BanG Dream!), containing 297 images and their tags.
The core tags of this character are 'brown\_hair, short\_hair, brown\_eyes, bangs', which are pruned in this dataset.
Imag... | [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
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f07b10efbea8cb5f23af7a0942cdcf815579bc7d | # Dataset Card for "squad_baseline_v4_train_30_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_baseline_v4_train_30_eval_10 | [
"region:us"
] | 2023-09-26T08:15:41+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "inputs", ... | 2023-09-26T08:49:00+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_baseline_v4_train_30_eval_10"
More Information needed | [
"# Dataset Card for \"squad_baseline_v4_train_30_eval_10\"\n\nMore Information needed"
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696401ee731c9f90cdb9426b7b51196473bf34dd | # Dataset Card for "squad_context_v4_train_30_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_context_v4_train_30_eval_10 | [
"region:us"
] | 2023-09-26T08:21:04+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "inputs", ... | 2023-09-26T08:49:11+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_context_v4_train_30_eval_10"
More Information needed | [
"# Dataset Card for \"squad_context_v4_train_30_eval_10\"\n\nMore Information needed"
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38f634103ddc93f8ad1efc5dbba0e64af95ff6b1 | # Dataset Card for "articles_87_07"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | jtatman/articles_87_07 | [
"region:us"
] | 2023-09-26T08:23:29+00:00 | {"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 517874639, "num_examples": 4344588}], "download_size": 372405322, "dataset_size": 517874639}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]} | 2023-09-26T08:26:13+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "articles_87_07"
More Information needed | [
"# Dataset Card for \"articles_87_07\"\n\nMore Information needed"
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"# Dataset Card for \"articles_87_07\"\n\nMore Information needed"
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ff50d7122f47f8aaf37069a718cb3f3d0aa02cf3 |
# CAPP (case law from appeal courts and courts of first instance)
[Documentary collection of case law from appeal courts and courts of first instance](https://www.data.gouv.fr/en/datasets/capp/), including
a selection of decisions in civil and criminal matters.
Decisions are selected by the courts in accordance with ... | Nicolas-BZRD/CAPP_opendata | [
"size_categories:10K<n<100K",
"language:fr",
"license:odc-by",
"legal",
"region:us"
] | 2023-09-26T08:33:24+00:00 | {"language": ["fr"], "license": "odc-by", "size_categories": ["10K<n<100K"], "pretty_name": "Fonds documentaire de jurisprudence des cours d\u2019appel et des juridictions de premier degr\u00e9", "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"featu... | 2023-09-28T09:07:32+00:00 | [] | [
"fr"
] | TAGS
#size_categories-10K<n<100K #language-French #license-odc-by #legal #region-us
|
# CAPP (case law from appeal courts and courts of first instance)
Documentary collection of case law from appeal courts and courts of first instance, including
a selection of decisions in civil and criminal matters.
Decisions are selected by the courts in accordance with decree no. 2005-13 of January 7, 2005, amendin... | [
"# CAPP (case law from appeal courts and courts of first instance)\n\nDocumentary collection of case law from appeal courts and courts of first instance, including\na selection of decisions in civil and criminal matters.\nDecisions are selected by the courts in accordance with decree no. 2005-13 of January 7, 2005,... | [
"TAGS\n#size_categories-10K<n<100K #language-French #license-odc-by #legal #region-us \n",
"# CAPP (case law from appeal courts and courts of first instance)\n\nDocumentary collection of case law from appeal courts and courts of first instance, including\na selection of decisions in civil and criminal matters.\nD... | [
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"passage: TAGS\n#size_categories-10K<n<100K #language-French #license-odc-by #legal #region-us \n# CAPP (case law from appeal courts and courts of first instance)\n\nDocumentary collection of case law from appeal courts and courts of first instance, including\na selection of decisions in civil and criminal matters.... |
a433b163be0340c74fa2fca7fc2dc42aa76bd083 | # Dataset Card for "squad_wrong_title_v4_train_30_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_wrong_title_v4_train_30_eval_10 | [
"region:us"
] | 2023-09-26T08:34:17+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "context_i... | 2023-09-26T08:49:32+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_wrong_title_v4_train_30_eval_10"
More Information needed | [
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e6b43a591f6cd046788db4243ef8bc4a1c2838da | # Dataset Card for "squad_no_title_v4_train_30_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_no_title_v4_train_30_eval_10 | [
"region:us"
] | 2023-09-26T08:34:24+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "context_i... | 2023-09-26T08:49:41+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_no_title_v4_train_30_eval_10"
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7b5d9026b38bf4fed400552e0043d7e02aa15b61 | # Dataset Card for "squad_no_title_strict_v4_train_30_eval_10"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | tyzhu/squad_no_title_strict_v4_train_30_eval_10 | [
"region:us"
] | 2023-09-26T08:37:46+00:00 | {"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "question", "dtype": "string"}, {"name": "answers", "sequence": [{"name": "text", "dtype": "string"}, {"name": "answer_start", "dtype": "int32"}]}, {"name": "context_i... | 2023-09-26T08:51:47+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "squad_no_title_strict_v4_train_30_eval_10"
More Information needed | [
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8a39ff3da4c9831fafe7f7b0732b6aeccf30ec40 |
# Dataset of okusawa_misaki/奥沢美咲/오쿠사와미사키 (BanG Dream!)
This is the dataset of okusawa_misaki/奥沢美咲/오쿠사와미사키 (BanG Dream!), containing 374 images and their tags.
The core tags of this character are `bangs, black_hair, hair_ornament, blue_eyes, hairclip, medium_hair, long_hair`, which are pruned in this dataset.
Images... | CyberHarem/okusawa_misaki_bangdream | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T08:55:03+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2024-01-15T16:38:57+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of okusawa\_misaki/奥沢美咲/오쿠사와미사키 (BanG Dream!)
=====================================================
This is the dataset of okusawa\_misaki/奥沢美咲/오쿠사와미사키 (BanG Dream!), containing 374 images and their tags.
The core tags of this character are 'bangs, black\_hair, hair\_ornament, blue\_eyes, hairclip, medium\_... | [
"### Load Raw Dataset with Waifuc\n\n\nWe provide raw dataset (including tagged images) for waifuc loading. If you need this, just run the following code\n\n\nList of Clusters\n----------------\n\n\nList of tag clustering result, maybe some outfits can be mined here.",
"### Raw Text Version",
"### Table Version... | [
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a384718d5b545c79a92fcfaf27aba5163a2f9702 |
# Dataset Card for turkish-nlp-suite/vitamins-supplements-NER
<img src="https://raw.githubusercontent.com/turkish-nlp-suite/.github/main/profile/supplementsNER.png" width="20%" height="20%">
### Dataset Description
- **Repository:** [Vitamins and Supplements NER Dataset](https://github.com/turkish-nlp-suite/Vitami... | turkish-nlp-suite/vitamins-supplements-NER | [
"task_categories:token-classification",
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"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:tr",
"license:cc-by-sa-4.0",
"region:us"
] | 2023-09-26T08:58:33+00:00 | {"language": ["tr"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_categories": ["token-classification"], "task_ids": ["named-entity-recognition"], "pretty_name": "Vitamins and Supplements NER Dataset"} | 2023-09-26T11:26:31+00:00 | [] | [
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| Dataset Card for turkish-nlp-suite/vitamins-supplements-NER
===========================================================
<img src="URL width="20%" height="20%">
### Dataset Description
* Repository: Vitamins and Supplements NER Dataset
* Paper: ACL link
* Dataset: Vitamins and Supplements NER Dataset
* Domain: E-c... | [
"### Dataset Description\n\n\n* Repository: Vitamins and Supplements NER Dataset\n* Paper: ACL link\n* Dataset: Vitamins and Supplements NER Dataset\n* Domain: E-commerce, customer reviews, medical",
"### Dataset Summary\n\n\nThe Vitamins and Supplements NER Dataset is a NER dataset containing customer reviews wi... | [
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a13b4a9931181177c379988aea11c3735b7e086f |
# Dataset of Miyauchi Renge
This is the dataset of Miyauchi Renge, containing 299 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs... | CyberHarem/miyauchi_renge_nonnonbiyori | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T09:02:34+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2023-09-27T17:11:53+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of Miyauchi Renge
=========================
This is the dataset of Miyauchi Renge, containing 299 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
| [] | [
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dacd13d2f2ef149e3582c7c2713785d47b1694bb | # Dataset Card for "pubmed_subset_wiki_5p"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) | zxvix/pubmed_subset_wiki_5p | [
"region:us"
] | 2023-09-26T09:09:28+00:00 | {"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2997216394.9753833, "num_examples": 1052579}, {"name": "test", "nu... | 2023-09-26T09:10:53+00:00 | [] | [] | TAGS
#region-us
| # Dataset Card for "pubmed_subset_wiki_5p"
More Information needed | [
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f6993002f94f04895c95a2b1f161e0d57dae931b |
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
This dataset contains the German and Polish names for almost 10k places in Poland. It has been generated using [this code](https://github.com/DebasishDhal/Minor-Stuff/blob/main/p... | DebasishDhal99/german-polish-paired-placenames | [
"task_categories:translation",
"size_categories:1K<n<10K",
"language:de",
"language:pl",
"license:mit",
"history",
"region:us"
] | 2023-09-26T09:24:06+00:00 | {"language": ["de", "pl"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["translation"], "tags": ["history"]} | 2023-09-28T12:12:12+00:00 | [] | [
"de",
"pl"
] | TAGS
#task_categories-translation #size_categories-1K<n<10K #language-German #language-Polish #license-mit #history #region-us
|
## Dataset Description
- Homepage:
- Repository:
- Paper:
- Leaderboard:
- Point of Contact:
### Dataset Summary
This dataset contains the German and Polish names for almost 10k places in Poland. It has been generated using this code.
Many of these names are related to each other. Some German names are literal... | [
"## Dataset Description\n\n- Homepage: \n- Repository: \n- Paper: \n- Leaderboard: \n- Point of Contact:",
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a7272ccd958d27ebcbd934ee8f48ebbec885c996 | https://www.youtube.com/watch?v=qo5ubQadvfs | lunarflu/Bringing-SoTA-Diffusion-Models-to-the-Masses-with-diffusers | [
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301f0b6ed2457675c33849697e411c8cbdacc91c |
# Dataset of Ichijou Hotaru
This is the dataset of Ichijou Hotaru, containing 299 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by [DeepGHS Team](https://github.com/deepghs)([huggingface organization](https://huggingface.co/deepghs... | CyberHarem/ichijou_hotaru_nonnonbiyori | [
"task_categories:text-to-image",
"size_categories:n<1K",
"license:mit",
"art",
"not-for-all-audiences",
"region:us"
] | 2023-09-26T09:50:39+00:00 | {"license": "mit", "size_categories": ["n<1K"], "task_categories": ["text-to-image"], "tags": ["art", "not-for-all-audiences"]} | 2023-09-27T17:55:03+00:00 | [] | [] | TAGS
#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us
| Dataset of Ichijou Hotaru
=========================
This is the dataset of Ichijou Hotaru, containing 299 images and their tags.
Images are crawled from many sites (e.g. danbooru, pixiv, zerochan ...), the auto-crawling system is powered by DeepGHS Team(huggingface organization).
| [] | [
"TAGS\n#task_categories-text-to-image #size_categories-n<1K #license-mit #art #not-for-all-audiences #region-us \n"
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44
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62eed1193101960eff4d9819e8fd1136b3693f94 |
# code_mixed_jv_id
Sentiment analysis and machine translation data for Javanese and Indonesian.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@article{Tho_2021,
doi = {10.1088/1742-6596/1869/1/012084},
url = {https://doi.org/10.10... | SEACrowd/code_mixed_jv_id | [
"language:jav",
"language:ind",
"sentiment-analysis",
"machine-translation",
"region:us"
] | 2023-09-26T10:00:58+00:00 | {"language": ["jav", "ind"], "tags": ["sentiment-analysis", "machine-translation"]} | 2023-09-26T11:28:06+00:00 | [] | [
"jav",
"ind"
] | TAGS
#language-Javanese #language-Indonesian #sentiment-analysis #machine-translation #region-us
|
# code_mixed_jv_id
Sentiment analysis and machine translation data for Javanese and Indonesian.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
cc_by_3.0
## Homepage
URL
### NusaCatalogue
For easy indexing and metadata: URL | [
"# code_mixed_jv_id\n\nSentiment analysis and machine translation data for Javanese and Indonesian.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
"## License\n\ncc_by_3.0",
"## Homepage\n\nURL",
"### NusaCatalogue\n\nFor easy indexing a... | [
"TAGS\n#language-Javanese #language-Indonesian #sentiment-analysis #machine-translation #region-us \n",
"# code_mixed_jv_id\n\nSentiment analysis and machine translation data for Javanese and Indonesian.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_da... | [
27,
24,
35,
8,
3,
16
] | [
"passage: TAGS\n#language-Javanese #language-Indonesian #sentiment-analysis #machine-translation #region-us \n# code_mixed_jv_id\n\nSentiment analysis and machine translation data for Javanese and Indonesian.## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_datas... |
694276ba32833a4ccfbf2d5c2520a32b99183d16 |
# Customers Reviews on Banks ⭐️
The Reviews on Banks Dataset is a comprehensive collection of **20,000** the most recent customer reviews on **48** US banks.
This dataset containing diverse reviews on multiple banks, can be useful for *sentiment analysis, assessing geographical variations in customer satisfaction, a... | TrainingDataPro/customers-reviews-on-banks | [
"task_categories:text-classification",
"language:en",
"license:cc-by-nc-nd-4.0",
"code",
"finance",
"region:us"
] | 2023-09-26T10:05:11+00:00 | {"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["text-classification"], "tags": ["code", "finance"]} | 2023-09-26T10:08:32+00:00 | [] | [
"en"
] | TAGS
#task_categories-text-classification #language-English #license-cc-by-nc-nd-4.0 #code #finance #region-us
|
# Customers Reviews on Banks ⭐️
The Reviews on Banks Dataset is a comprehensive collection of 20,000 the most recent customer reviews on 48 US banks.
This dataset containing diverse reviews on multiple banks, can be useful for *sentiment analysis, assessing geographical variations in customer satisfaction, and explo... | [
"# Customers Reviews on Banks ⭐️\n\nThe Reviews on Banks Dataset is a comprehensive collection of 20,000 the most recent customer reviews on 48 US banks.\n\nThis dataset containing diverse reviews on multiple banks, can be useful for *sentiment analysis, assessing geographical variations in customer satisfaction, a... | [
"TAGS\n#task_categories-text-classification #language-English #license-cc-by-nc-nd-4.0 #code #finance #region-us \n",
"# Customers Reviews on Banks ⭐️\n\nThe Reviews on Banks Dataset is a comprehensive collection of 20,000 the most recent customer reviews on 48 US banks.\n\nThis dataset containing diverse reviews... | [
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] | [
"passage: TAGS\n#task_categories-text-classification #language-English #license-cc-by-nc-nd-4.0 #code #finance #region-us \n# Customers Reviews on Banks ⭐️\n\nThe Reviews on Banks Dataset is a comprehensive collection of 20,000 the most recent customer reviews on 48 US banks.\n\nThis dataset containing diverse revi... |
e80a5de5735b9d4cc96025af75cfa9a30e96ef38 |
# indspeech_teldialog_svcsr
This is the first Indonesian speech dataset for small vocabulary continuous speech recognition (SVCSR).
The data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advanced
Telecommunication Research Institute International (ATR) Japan and Band... | SEACrowd/indspeech_teldialog_svcsr | [
"language:ind",
"speech-recognition",
"region:us"
] | 2023-09-26T10:11:12+00:00 | {"language": ["ind"], "tags": ["speech-recognition"]} | 2023-09-26T11:28:10+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #speech-recognition #region-us
|
# indspeech_teldialog_svcsr
This is the first Indonesian speech dataset for small vocabulary continuous speech recognition (SVCSR).
The data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advanced
Telecommunication Research Institute International (ATR) Japan and Band... | [
"# indspeech_teldialog_svcsr\n\nThis is the first Indonesian speech dataset for small vocabulary continuous speech recognition (SVCSR).\n\nThe data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advanced\n\nTelecommunication Research Institute International (ATR) Japa... | [
"TAGS\n#language-Indonesian #speech-recognition #region-us \n",
"# indspeech_teldialog_svcsr\n\nThis is the first Indonesian speech dataset for small vocabulary continuous speech recognition (SVCSR).\n\nThe data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Advance... | [
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379,
35,
11,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #speech-recognition #region-us \n# indspeech_teldialog_svcsr\n\nThis is the first Indonesian speech dataset for small vocabulary continuous speech recognition (SVCSR).\n\nThe data was developed by TELKOMRisTI (R&D Division, PT Telekomunikasi Indonesia) in collaboration with Adva... |
d9d0346b4b8ad4ff3c27990774f4330e8de7e648 |
# kamus_alay
Kamus Alay provide a lexicon for text normalization of Indonesian colloquial words.
It contains 3,592 unique colloquial words-also known as “bahasa alay” -and manually annotated them
with the normalized form. We built this lexicon from Instagram comments provided by Septiandri & Wibisono (2017)
## Dat... | SEACrowd/kamus_alay | [
"language:ind",
"license:unknown",
"morphological-inflection",
"region:us"
] | 2023-09-26T10:11:16+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["morphological-inflection"]} | 2023-09-26T11:28:13+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #morphological-inflection #region-us
|
# kamus_alay
Kamus Alay provide a lexicon for text normalization of Indonesian colloquial words.
It contains 3,592 unique colloquial words-also known as “bahasa alay” -and manually annotated them
with the normalized form. We built this lexicon from Instagram comments provided by Septiandri & Wibisono (2017)
## Dat... | [
"# kamus_alay\n\nKamus Alay provide a lexicon for text normalization of Indonesian colloquial words.\n\nIt contains 3,592 unique colloquial words-also known as “bahasa alay” -and manually annotated them\n\nwith the normalized form. We built this lexicon from Instagram comments provided by Septiandri & Wibisono (201... | [
"TAGS\n#language-Indonesian #license-unknown #morphological-inflection #region-us \n",
"# kamus_alay\n\nKamus Alay provide a lexicon for text normalization of Indonesian colloquial words.\n\nIt contains 3,592 unique colloquial words-also known as “bahasa alay” -and manually annotated them\n\nwith the normalized f... | [
25,
78,
35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-unknown #morphological-inflection #region-us \n# kamus_alay\n\nKamus Alay provide a lexicon for text normalization of Indonesian colloquial words.\n\nIt contains 3,592 unique colloquial words-also known as “bahasa alay” -and manually annotated them\n\nwith the normalize... |
c299fb355fc1e7865093b485e3a845b1cefe6d43 |
# indolem_ner_ugm
NER UGM is a Named Entity Recognition dataset that comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organization, location, time, and quantity.
## Dataset Usage
Run `pip install nusacrowd` before loading t... | SEACrowd/indolem_ner_ugm | [
"language:ind",
"license:cc-by-4.0",
"named-entity-recognition",
"region:us"
] | 2023-09-26T10:11:17+00:00 | {"language": ["ind"], "license": "cc-by-4.0", "tags": ["named-entity-recognition"]} | 2023-09-26T11:28:37+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-cc-by-4.0 #named-entity-recognition #region-us
|
# indolem_ner_ugm
NER UGM is a Named Entity Recognition dataset that comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organization, location, time, and quantity.
## Dataset Usage
Run 'pip install nusacrowd' before loading t... | [
"# indolem_ner_ugm\n\nNER UGM is a Named Entity Recognition dataset that comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organization, location, time, and quantity.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' befo... | [
"TAGS\n#language-Indonesian #license-cc-by-4.0 #named-entity-recognition #region-us \n",
"# indolem_ner_ugm\n\nNER UGM is a Named Entity Recognition dataset that comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organizati... | [
30,
66,
35,
6,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-cc-by-4.0 #named-entity-recognition #region-us \n# indolem_ner_ugm\n\nNER UGM is a Named Entity Recognition dataset that comprises 2,343 sentences from news articles, and was constructed at the University of Gajah Mada based on five named entity classes: person, organiz... |
a3b0be7a80b62edc507215c55a4d349a4a1adac3 |
# id_hoax_news
This research proposes to build an automatic hoax news detection and collects 250 pages of hoax and valid news articles in Indonesian language.
Each data sample is annotated by three reviewers and the final taggings are obtained by voting of those three reviewers.
## Dataset Usage
Run `pip install n... | SEACrowd/id_hoax_news | [
"language:ind",
"hoax-news-classification",
"region:us"
] | 2023-09-26T10:11:17+00:00 | {"language": ["ind"], "tags": ["hoax-news-classification"]} | 2023-09-26T11:28:34+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #hoax-news-classification #region-us
|
# id_hoax_news
This research proposes to build an automatic hoax news detection and collects 250 pages of hoax and valid news articles in Indonesian language.
Each data sample is annotated by three reviewers and the final taggings are obtained by voting of those three reviewers.
## Dataset Usage
Run 'pip install n... | [
"# id_hoax_news\n\nThis research proposes to build an automatic hoax news detection and collects 250 pages of hoax and valid news articles in Indonesian language.\n\nEach data sample is annotated by three reviewers and the final taggings are obtained by voting of those three reviewers.",
"## Dataset Usage\n\nRun ... | [
"TAGS\n#language-Indonesian #hoax-news-classification #region-us \n",
"# id_hoax_news\n\nThis research proposes to build an automatic hoax news detection and collects 250 pages of hoax and valid news articles in Indonesian language.\n\nEach data sample is annotated by three reviewers and the final taggings are ob... | [
19,
66,
35,
7,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #hoax-news-classification #region-us \n# id_hoax_news\n\nThis research proposes to build an automatic hoax news detection and collects 250 pages of hoax and valid news articles in Indonesian language.\n\nEach data sample is annotated by three reviewers and the final taggings are... |
913f67e6d0d2c18bed800efbb5ae887790ae2f67 |
# indspeech_news_ethnicsr
INDspeech_NEWS_EthnicSR is a collection of Indonesian ethnic speech corpora for Javanese and Sundanese for Indonesian ethnic speech recognition. It was developed in 2012 by the Nara Institute of Science and Technology (NAIST, Japan) in collaboration with the Bandung Institute of Technology (... | SEACrowd/indspeech_news_ethnicsr | [
"language:sun",
"language:jav",
"speech-recognition",
"region:us"
] | 2023-09-26T10:11:18+00:00 | {"language": ["sun", "jav"], "tags": ["speech-recognition"]} | 2023-09-26T11:28:44+00:00 | [] | [
"sun",
"jav"
] | TAGS
#language-Sundanese #language-Javanese #speech-recognition #region-us
|
# indspeech_news_ethnicsr
INDspeech_NEWS_EthnicSR is a collection of Indonesian ethnic speech corpora for Javanese and Sundanese for Indonesian ethnic speech recognition. It was developed in 2012 by the Nara Institute of Science and Technology (NAIST, Japan) in collaboration with the Bandung Institute of Technology (... | [
"# indspeech_news_ethnicsr\n\nINDspeech_NEWS_EthnicSR is a collection of Indonesian ethnic speech corpora for Javanese and Sundanese for Indonesian ethnic speech recognition. It was developed in 2012 by the Nara Institute of Science and Technology (NAIST, Japan) in collaboration with the Bandung Institute of Techno... | [
"TAGS\n#language-Sundanese #language-Javanese #speech-recognition #region-us \n",
"# indspeech_news_ethnicsr\n\nINDspeech_NEWS_EthnicSR is a collection of Indonesian ethnic speech corpora for Javanese and Sundanese for Indonesian ethnic speech recognition. It was developed in 2012 by the Nara Institute of Science... | [
24,
91,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Sundanese #language-Javanese #speech-recognition #region-us \n# indspeech_news_ethnicsr\n\nINDspeech_NEWS_EthnicSR is a collection of Indonesian ethnic speech corpora for Javanese and Sundanese for Indonesian ethnic speech recognition. It was developed in 2012 by the Nara Institute of Scie... |
eada1ba2f305fa0ca1f931252b85a2092a122c49 |
# cc100
This corpus is an attempt to recreate the dataset used for training
XLM-R. This corpus comprises of monolingual data for 100+ languages and
also includes data for romanized languages (indicated by *_rom). This
was constructed using the urls and paragraph indices provided by the
... | SEACrowd/cc100 | [
"language:ind",
"language:jav",
"language:sun",
"license:mit",
"self-supervised-pretraining",
"region:us"
] | 2023-09-26T10:11:18+00:00 | {"language": ["ind", "jav", "sun"], "license": "mit", "tags": ["self-supervised-pretraining"]} | 2023-09-26T11:28:40+00:00 | [] | [
"ind",
"jav",
"sun"
] | TAGS
#language-Indonesian #language-Javanese #language-Sundanese #license-mit #self-supervised-pretraining #region-us
|
# cc100
This corpus is an attempt to recreate the dataset used for training
XLM-R. This corpus comprises of monolingual data for 100+ languages and
also includes data for romanized languages (indicated by *_rom). This
was constructed using the urls and paragraph indices provided by the
... | [
"# cc100\n\nThis corpus is an attempt to recreate the dataset used for training\n\n XLM-R. This corpus comprises of monolingual data for 100+ languages and\n\n also includes data for romanized languages (indicated by *_rom). This\n\n was constructed using the urls and paragraph indices provided... | [
"TAGS\n#language-Indonesian #language-Javanese #language-Sundanese #license-mit #self-supervised-pretraining #region-us \n",
"# cc100\n\nThis corpus is an attempt to recreate the dataset used for training\n\n XLM-R. This corpus comprises of monolingual data for 100+ languages and\n\n also includes d... | [
36,
151,
35,
3,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #language-Javanese #language-Sundanese #license-mit #self-supervised-pretraining #region-us \n# cc100\n\nThis corpus is an attempt to recreate the dataset used for training\n\n XLM-R. This corpus comprises of monolingual data for 100+ languages and\n\n also include... |
f6f233e62c3d2559c6a74ee4e84b3fb2a6a31341 |
# minangnlp_mt
In this work, we create Minangkabau–Indonesian (MIN-ID) parallel corpus by using Wikipedia. We obtain 224,180 Minangkabau and
510,258 Indonesian articles, and align documents through title matching, resulting in 111,430 MINID document pairs.
After that, we do sentence segmentation based on simple pun... | SEACrowd/minangnlp_mt | [
"language:min",
"language:ind",
"license:mit",
"machine-translation",
"region:us"
] | 2023-09-26T10:11:19+00:00 | {"language": ["min", "ind"], "license": "mit", "tags": ["machine-translation"]} | 2023-09-26T11:29:22+00:00 | [] | [
"min",
"ind"
] | TAGS
#language-Minangkabau #language-Indonesian #license-mit #machine-translation #region-us
|
# minangnlp_mt
In this work, we create Minangkabau–Indonesian (MIN-ID) parallel corpus by using Wikipedia. We obtain 224,180 Minangkabau and
510,258 Indonesian articles, and align documents through title matching, resulting in 111,430 MINID document pairs.
After that, we do sentence segmentation based on simple pun... | [
"# minangnlp_mt\n\nIn this work, we create Minangkabau–Indonesian (MIN-ID) parallel corpus by using Wikipedia. We obtain 224,180 Minangkabau and\n\n510,258 Indonesian articles, and align documents through title matching, resulting in 111,430 MINID document pairs.\n\nAfter that, we do sentence segmentation based on ... | [
"TAGS\n#language-Minangkabau #language-Indonesian #license-mit #machine-translation #region-us \n",
"# minangnlp_mt\n\nIn this work, we create Minangkabau–Indonesian (MIN-ID) parallel corpus by using Wikipedia. We obtain 224,180 Minangkabau and\n\n510,258 Indonesian articles, and align documents through title mat... | [
27,
406,
35,
3,
3,
16
] | [
"passage: TAGS\n#language-Minangkabau #language-Indonesian #license-mit #machine-translation #region-us \n# minangnlp_mt\n\nIn this work, we create Minangkabau–Indonesian (MIN-ID) parallel corpus by using Wikipedia. We obtain 224,180 Minangkabau and\n\n510,258 Indonesian articles, and align documents through title ... |
5ccd9bca00208ae3fc2e10c181d5af32c3243264 |
# liputan6
A large-scale Indonesian summarization dataset consisting of harvested articles from Liputan6.com, an online news portal, resulting in 215,827 document-summary pairs.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@inproceed... | SEACrowd/liputan6 | [
"language:ind",
"summarization",
"region:us"
] | 2023-09-26T10:11:20+00:00 | {"language": ["ind"], "tags": ["summarization"]} | 2023-09-26T11:30:04+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #summarization #region-us
|
# liputan6
A large-scale Indonesian summarization dataset consisting of harvested articles from URL, an online news portal, resulting in 215,827 document-summary pairs.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
CC-BY-SA 4.0
## Homepag... | [
"# liputan6\n\nA large-scale Indonesian summarization dataset consisting of harvested articles from URL, an online news portal, resulting in 215,827 document-summary pairs.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
"## License\n\nCC-BY-... | [
"TAGS\n#language-Indonesian #summarization #region-us \n",
"# liputan6\n\nA large-scale Indonesian summarization dataset consisting of harvested articles from URL, an online news portal, resulting in 215,827 document-summary pairs.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset th... | [
15,
44,
35,
8,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #summarization #region-us \n# liputan6\n\nA large-scale Indonesian summarization dataset consisting of harvested articles from URL, an online news portal, resulting in 215,827 document-summary pairs.## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset throu... |
cfdfeb745b3f1ba67b9730220ba5d7678cebdb2c |
# indolem_ntp
NTP (Next Tweet prediction) is one of the comprehensive Indonesian benchmarks that given a list of tweets and an option, we predict if the option is the next tweet or not.
This task is similar to the next sentence prediction (NSP) task used to train BERT (Devlin et al., 2019).
In NTP, each instance co... | SEACrowd/indolem_ntp | [
"language:ind",
"license:cc-by-4.0",
"next-sentence-prediction",
"arxiv:2011.00677",
"region:us"
] | 2023-09-26T10:11:20+00:00 | {"language": ["ind"], "license": "cc-by-4.0", "tags": ["next-sentence-prediction"]} | 2023-09-26T11:30:22+00:00 | [
"2011.00677"
] | [
"ind"
] | TAGS
#language-Indonesian #license-cc-by-4.0 #next-sentence-prediction #arxiv-2011.00677 #region-us
|
# indolem_ntp
NTP (Next Tweet prediction) is one of the comprehensive Indonesian benchmarks that given a list of tweets and an option, we predict if the option is the next tweet or not.
This task is similar to the next sentence prediction (NSP) task used to train BERT (Devlin et al., 2019).
In NTP, each instance co... | [
"# indolem_ntp\n\nNTP (Next Tweet prediction) is one of the comprehensive Indonesian benchmarks that given a list of tweets and an option, we predict if the option is the next tweet or not.\n\nThis task is similar to the next sentence prediction (NSP) task used to train BERT (Devlin et al., 2019).\n\nIn NTP, each i... | [
"TAGS\n#language-Indonesian #license-cc-by-4.0 #next-sentence-prediction #arxiv-2011.00677 #region-us \n",
"# indolem_ntp\n\nNTP (Next Tweet prediction) is one of the comprehensive Indonesian benchmarks that given a list of tweets and an option, we predict if the option is the next tweet or not.\n\nThis task is s... | [
38,
141,
35,
6,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-cc-by-4.0 #next-sentence-prediction #arxiv-2011.00677 #region-us \n# indolem_ntp\n\nNTP (Next Tweet prediction) is one of the comprehensive Indonesian benchmarks that given a list of tweets and an option, we predict if the option is the next tweet or not.\n\nThis task i... |
0fde15975bd4b260ce5e31dde82dbe5fcbaed13f |
# covost2
CoVoST2 is a large-scale multilingual speech translation corpus covering translations from 21 languages to English
and from English into 15 languages. The dataset is created using Mozilla's open-source Common Voice database of
crowdsourced voice recordings. There are 2,900 hours of speech represented in t... | SEACrowd/covost2 | [
"language:ind",
"language:eng",
"speech-to-text-translation",
"machine-translation",
"region:us"
] | 2023-09-26T10:11:21+00:00 | {"language": ["ind", "eng"], "tags": ["speech-to-text-translation", "machine-translation"]} | 2023-09-26T11:31:13+00:00 | [] | [
"ind",
"eng"
] | TAGS
#language-Indonesian #language-English #speech-to-text-translation #machine-translation #region-us
|
# covost2
CoVoST2 is a large-scale multilingual speech translation corpus covering translations from 21 languages to English
and from English into 15 languages. The dataset is created using Mozilla's open-source Common Voice database of
crowdsourced voice recordings. There are 2,900 hours of speech represented in t... | [
"# covost2\n\nCoVoST2 is a large-scale multilingual speech translation corpus covering translations from 21 languages to English\n\nand from English into 15 languages. The dataset is created using Mozilla's open-source Common Voice database of\n\ncrowdsourced voice recordings. There are 2,900 hours of speech repres... | [
"TAGS\n#language-Indonesian #language-English #speech-to-text-translation #machine-translation #region-us \n",
"# covost2\n\nCoVoST2 is a large-scale multilingual speech translation corpus covering translations from 21 languages to English\n\nand from English into 15 languages. The dataset is created using Mozill... | [
30,
75,
35,
7,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #language-English #speech-to-text-translation #machine-translation #region-us \n# covost2\n\nCoVoST2 is a large-scale multilingual speech translation corpus covering translations from 21 languages to English\n\nand from English into 15 languages. The dataset is created using Moz... |
2fd65a143cad293407d4c91dc590d3ab7a7c1535 |
# kopi_cc_news
KoPI(Korpus Perayapan Indonesia)-CC_News is Indonesian Only Extract from CC NEWS Common Crawl from 2016-2022(july) ,each snapshots get extracted using warcio,trafilatura and filter using fasttext
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_datas... | SEACrowd/kopi_cc_news | [
"language:ind",
"self-supervised-pretraining",
"region:us"
] | 2023-09-26T10:11:21+00:00 | {"language": ["ind"], "tags": ["self-supervised-pretraining"]} | 2023-09-26T11:31:04+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #self-supervised-pretraining #region-us
|
# kopi_cc_news
KoPI(Korpus Perayapan Indonesia)-CC_News is Indonesian Only Extract from CC NEWS Common Crawl from 2016-2022(july) ,each snapshots get extracted using warcio,trafilatura and filter using fasttext
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_datas... | [
"# kopi_cc_news\n\nKoPI(Korpus Perayapan Indonesia)-CC_News is Indonesian Only Extract from CC NEWS Common Crawl from 2016-2022(july) ,each snapshots get extracted using warcio,trafilatura and filter using fasttext",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's... | [
"TAGS\n#language-Indonesian #self-supervised-pretraining #region-us \n",
"# kopi_cc_news\n\nKoPI(Korpus Perayapan Indonesia)-CC_News is Indonesian Only Extract from CC NEWS Common Crawl from 2016-2022(july) ,each snapshots get extracted using warcio,trafilatura and filter using fasttext",
"## Dataset Usage\n\nR... | [
20,
60,
35,
4,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #self-supervised-pretraining #region-us \n# kopi_cc_news\n\nKoPI(Korpus Perayapan Indonesia)-CC_News is Indonesian Only Extract from CC NEWS Common Crawl from 2016-2022(july) ,each snapshots get extracted using warcio,trafilatura and filter using fasttext## Dataset Usage\n\nRun ... |
19033d95763a2b0351699b61fc28cf97a861abe6 |
# kopi_cc
KoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian Only Extract from Common Crawl snapshots ,each snapshots get extracted using ungoliant and get extra "filtering" using deduplication technique
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.... | SEACrowd/kopi_cc | [
"language:ind",
"self-supervised-pretraining",
"arxiv:2201.06642",
"region:us"
] | 2023-09-26T10:11:21+00:00 | {"language": ["ind"], "tags": ["self-supervised-pretraining"]} | 2023-09-26T11:30:57+00:00 | [
"2201.06642"
] | [
"ind"
] | TAGS
#language-Indonesian #self-supervised-pretraining #arxiv-2201.06642 #region-us
|
# kopi_cc
KoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian Only Extract from Common Crawl snapshots ,each snapshots get extracted using ungoliant and get extra "filtering" using deduplication technique
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.... | [
"# kopi_cc\n\nKoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian Only Extract from Common Crawl snapshots ,each snapshots get extracted using ungoliant and get extra \"filtering\" using deduplication technique",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's '... | [
"TAGS\n#language-Indonesian #self-supervised-pretraining #arxiv-2201.06642 #region-us \n",
"# kopi_cc\n\nKoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian Only Extract from Common Crawl snapshots ,each snapshots get extracted using ungoliant and get extra \"filtering\" using deduplication technique",
"## Da... | [
29,
56,
35,
4,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #self-supervised-pretraining #arxiv-2201.06642 #region-us \n# kopi_cc\n\nKoPI-CC (Korpus Perayapan Indonesia)-CC is Indonesian Only Extract from Common Crawl snapshots ,each snapshots get extracted using ungoliant and get extra \"filtering\" using deduplication technique## Datas... |
36b97226960c35a3744f35025aeefe67ff3f2322 |
# indonli
This dataset is designed for Natural Language Inference NLP task. It is designed to provide a challenging test-bed
for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural
changes, idioms, or temporal and spatial reasoning.
## Dataset Usage
Run... | SEACrowd/indonli | [
"language:ind",
"textual-entailment",
"region:us"
] | 2023-09-26T10:11:21+00:00 | {"language": ["ind"], "tags": ["textual-entailment"]} | 2023-09-26T11:30:50+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #textual-entailment #region-us
|
# indonli
This dataset is designed for Natural Language Inference NLP task. It is designed to provide a challenging test-bed
for Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural
changes, idioms, or temporal and spatial reasoning.
## Dataset Usage
Run... | [
"# indonli\n\nThis dataset is designed for Natural Language Inference NLP task. It is designed to provide a challenging test-bed\n\nfor Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural\n\nchanges, idioms, or temporal and spatial reasoning.",
"## Data... | [
"TAGS\n#language-Indonesian #textual-entailment #region-us \n",
"# indonli\n\nThis dataset is designed for Natural Language Inference NLP task. It is designed to provide a challenging test-bed\n\nfor Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structural\n... | [
18,
68,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #textual-entailment #region-us \n# indonli\n\nThis dataset is designed for Natural Language Inference NLP task. It is designed to provide a challenging test-bed\n\nfor Indonesian NLI by explicitly incorporating various linguistic phenomena such as numerical reasoning, structura... |
53ef65fb16ef45c7d0cc5e6528a6a1d9c1db0ad9 |
# singgalang
Rule-based annotation Indonesian NER Dataset of 48,957 sentences or 1,478,286 tokens.
Annotation conforms the Stanford-NER format (https://stanfordnlp.github.io/CoreNLP/ner.html) for 3 NER tags of Person, Organisation, and Place.
This dataset consists of 41,297, 14,770, and 82,179 tokens of entity (res... | SEACrowd/singgalang | [
"language:ind",
"named-entity-recognition",
"region:us"
] | 2023-09-26T10:11:21+00:00 | {"language": ["ind"], "tags": ["named-entity-recognition"]} | 2023-09-26T11:30:41+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #named-entity-recognition #region-us
|
# singgalang
Rule-based annotation Indonesian NER Dataset of 48,957 sentences or 1,478,286 tokens.
Annotation conforms the Stanford-NER format (URL for 3 NER tags of Person, Organisation, and Place.
This dataset consists of 41,297, 14,770, and 82,179 tokens of entity (respectively) from over 14, 6, and 5 rules.
##... | [
"# singgalang\n\nRule-based annotation Indonesian NER Dataset of 48,957 sentences or 1,478,286 tokens.\n\nAnnotation conforms the Stanford-NER format (URL for 3 NER tags of Person, Organisation, and Place.\n\nThis dataset consists of 41,297, 14,770, and 82,179 tokens of entity (respectively) from over 14, 6, and 5 ... | [
"TAGS\n#language-Indonesian #named-entity-recognition #region-us \n",
"# singgalang\n\nRule-based annotation Indonesian NER Dataset of 48,957 sentences or 1,478,286 tokens.\n\nAnnotation conforms the Stanford-NER format (URL for 3 NER tags of Person, Organisation, and Place.\n\nThis dataset consists of 41,297, 14... | [
21,
92,
35,
67,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #named-entity-recognition #region-us \n# singgalang\n\nRule-based annotation Indonesian NER Dataset of 48,957 sentences or 1,478,286 tokens.\n\nAnnotation conforms the Stanford-NER format (URL for 3 NER tags of Person, Organisation, and Place.\n\nThis dataset consists of 41,297,... |
e34fa7dcbc8993bf1e0bb9e94dde913f20764693 |
# xsid
XSID is a new benchmark for cross-lingual (X) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@inproceedings{van-der-goot-... | SEACrowd/xsid | [
"language:ind",
"intent-classification",
"pos-tagging",
"region:us"
] | 2023-09-26T10:11:23+00:00 | {"language": ["ind"], "tags": ["intent-classification", "pos-tagging"]} | 2023-09-26T11:32:38+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #intent-classification #pos-tagging #region-us
|
# xsid
XSID is a new benchmark for cross-lingual (X) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
CC-BY-SA 4.0
## Homepage
URL
#... | [
"# xsid\n\nXSID is a new benchmark for cross-lingual (X) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
"## License\n\nCC-BY-SA 4.0",
... | [
"TAGS\n#language-Indonesian #intent-classification #pos-tagging #region-us \n",
"# xsid\n\nXSID is a new benchmark for cross-lingual (X) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading th... | [
22,
41,
35,
8,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #intent-classification #pos-tagging #region-us \n# xsid\n\nXSID is a new benchmark for cross-lingual (X) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect.## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the d... |
ccaaba6916ae4874ad223e29138107411fb021b0 |
# id_abusive
The ID_ABUSIVE dataset is collection of 2,016 informal abusive tweets in Indonesian language,
designed for sentiment analysis NLP task. This dataset is crawled from Twitter, and then filtered
and labelled manually by 20 volunteer annotators. The dataset labelled into three labels namely
not abusive la... | SEACrowd/id_abusive | [
"language:ind",
"sentiment-analysis",
"region:us"
] | 2023-09-26T10:11:23+00:00 | {"language": ["ind"], "tags": ["sentiment-analysis"]} | 2023-09-26T11:32:46+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #sentiment-analysis #region-us
|
# id_abusive
The ID_ABUSIVE dataset is collection of 2,016 informal abusive tweets in Indonesian language,
designed for sentiment analysis NLP task. This dataset is crawled from Twitter, and then filtered
and labelled manually by 20 volunteer annotators. The dataset labelled into three labels namely
not abusive la... | [
"# id_abusive\n\nThe ID_ABUSIVE dataset is collection of 2,016 informal abusive tweets in Indonesian language,\n\ndesigned for sentiment analysis NLP task. This dataset is crawled from Twitter, and then filtered\n\nand labelled manually by 20 volunteer annotators. The dataset labelled into three labels namely\n\nno... | [
"TAGS\n#language-Indonesian #sentiment-analysis #region-us \n",
"# id_abusive\n\nThe ID_ABUSIVE dataset is collection of 2,016 informal abusive tweets in Indonesian language,\n\ndesigned for sentiment analysis NLP task. This dataset is crawled from Twitter, and then filtered\n\nand labelled manually by 20 volunte... | [
17,
89,
35,
17,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #sentiment-analysis #region-us \n# id_abusive\n\nThe ID_ABUSIVE dataset is collection of 2,016 informal abusive tweets in Indonesian language,\n\ndesigned for sentiment analysis NLP task. This dataset is crawled from Twitter, and then filtered\n\nand labelled manually by 20 volu... |
856ddcb778b0c58d8872908bb6fdb606eb2ca755 |
# indocollex
IndoCollex: A Testbed for Morphological Transformation of Indonesian Colloquial Words
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@inproceedings{wibowo-etal-2021-indocollex,
title = "{I}ndo{C}ollex: A Testbed for Mo... | SEACrowd/indocollex | [
"language:ind",
"morphological-inflection",
"region:us"
] | 2023-09-26T10:11:23+00:00 | {"language": ["ind"], "tags": ["morphological-inflection"]} | 2023-09-26T11:32:21+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #morphological-inflection #region-us
|
# indocollex
IndoCollex: A Testbed for Morphological Transformation of Indonesian Colloquial Words
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
CC BY-SA 4.0
## Homepage
URL
### NusaCatalogue
For easy indexing and metadata: URL | [
"# indocollex\n\nIndoCollex: A Testbed for Morphological Transformation of Indonesian Colloquial Words",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
"## License\n\nCC BY-SA 4.0",
"## Homepage\n\nURL",
"### NusaCatalogue\n\nFor easy inde... | [
"TAGS\n#language-Indonesian #morphological-inflection #region-us \n",
"# indocollex\n\nIndoCollex: A Testbed for Morphological Transformation of Indonesian Colloquial Words",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
"## License\n\nCC B... | [
18,
25,
35,
7,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #morphological-inflection #region-us \n# indocollex\n\nIndoCollex: A Testbed for Morphological Transformation of Indonesian Colloquial Words## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.## License\n\nCC BY-SA 4.0#... |
509f41151976122626af640e2d9dd75e6e14e57a |
# xl_sum
XL-Sum is a large-scale multilingual summarization dataset that covers 45 languages including Indonesian text summarization.
The dataset is based on article-summary pairs from BBC, is highly abstractive, concise, and of high quality, as indicated by human and intrinsic evaluation.
## Dataset Usage
Run `pi... | SEACrowd/xl_sum | [
"language:ind",
"language:eng",
"summarization",
"region:us"
] | 2023-09-26T10:11:23+00:00 | {"language": ["ind", "eng"], "tags": ["summarization"]} | 2023-09-26T11:32:30+00:00 | [] | [
"ind",
"eng"
] | TAGS
#language-Indonesian #language-English #summarization #region-us
|
# xl_sum
XL-Sum is a large-scale multilingual summarization dataset that covers 45 languages including Indonesian text summarization.
The dataset is based on article-summary pairs from BBC, is highly abstractive, concise, and of high quality, as indicated by human and intrinsic evaluation.
## Dataset Usage
Run 'pi... | [
"# xl_sum\n\nXL-Sum is a large-scale multilingual summarization dataset that covers 45 languages including Indonesian text summarization.\n\nThe dataset is based on article-summary pairs from BBC, is highly abstractive, concise, and of high quality, as indicated by human and intrinsic evaluation.",
"## Dataset Us... | [
"TAGS\n#language-Indonesian #language-English #summarization #region-us \n",
"# xl_sum\n\nXL-Sum is a large-scale multilingual summarization dataset that covers 45 languages including Indonesian text summarization.\n\nThe dataset is based on article-summary pairs from BBC, is highly abstractive, concise, and of h... | [
19,
77,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #language-English #summarization #region-us \n# xl_sum\n\nXL-Sum is a large-scale multilingual summarization dataset that covers 45 languages including Indonesian text summarization.\n\nThe dataset is based on article-summary pairs from BBC, is highly abstractive, concise, and o... |
585353da32f8996383a708ddc26a0e427a1aade2 |
# emotcmt
EmotCMT is an emotion classification Indonesian-English code-mixing dataset created through an Indonesian-English code-mixed Twitter data pipeline consisting of 4 processing steps, i.e., tokenization, language identification, lexical normalization, and translation. The dataset consists of 825 tweets, 22.736... | SEACrowd/emotcmt | [
"language:ind",
"license:mit",
"emotion-classification",
"region:us"
] | 2023-09-26T10:11:24+00:00 | {"language": ["ind"], "license": "mit", "tags": ["emotion-classification"]} | 2023-09-26T11:33:23+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-mit #emotion-classification #region-us
|
# emotcmt
EmotCMT is an emotion classification Indonesian-English code-mixing dataset created through an Indonesian-English code-mixed Twitter data pipeline consisting of 4 processing steps, i.e., tokenization, language identification, lexical normalization, and translation. The dataset consists of 825 tweets, 22.736... | [
"# emotcmt\n\nEmotCMT is an emotion classification Indonesian-English code-mixing dataset created through an Indonesian-English code-mixed Twitter data pipeline consisting of 4 processing steps, i.e., tokenization, language identification, lexical normalization, and translation. The dataset consists of 825 tweets, ... | [
"TAGS\n#language-Indonesian #license-mit #emotion-classification #region-us \n",
"# emotcmt\n\nEmotCMT is an emotion classification Indonesian-English code-mixing dataset created through an Indonesian-English code-mixed Twitter data pipeline consisting of 4 processing steps, i.e., tokenization, language identific... | [
22,
133,
35,
3,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-mit #emotion-classification #region-us \n# emotcmt\n\nEmotCMT is an emotion classification Indonesian-English code-mixing dataset created through an Indonesian-English code-mixed Twitter data pipeline consisting of 4 processing steps, i.e., tokenization, language identi... |
9104368d0fdaa011aaa16883b9fd83fbc21a3884 |
# bible_su_id
Bible Su-Id is a machine translation dataset containing Indonesian-Sundanese parallel sentences collected from the bible. As there is no existing parallel corpus for Sundanese and Indonesian, we create a new dataset for Sundanese and Indonesian translation generated from the Bible. We create a verse-ali... | SEACrowd/bible_su_id | [
"language:ind",
"language:sun",
"machine-translation",
"region:us"
] | 2023-09-26T10:11:24+00:00 | {"language": ["ind", "sun"], "tags": ["machine-translation"]} | 2023-09-26T11:33:31+00:00 | [] | [
"ind",
"sun"
] | TAGS
#language-Indonesian #language-Sundanese #machine-translation #region-us
|
# bible_su_id
Bible Su-Id is a machine translation dataset containing Indonesian-Sundanese parallel sentences collected from the bible. As there is no existing parallel corpus for Sundanese and Indonesian, we create a new dataset for Sundanese and Indonesian translation generated from the Bible. We create a verse-ali... | [
"# bible_su_id\n\nBible Su-Id is a machine translation dataset containing Indonesian-Sundanese parallel sentences collected from the bible. As there is no existing parallel corpus for Sundanese and Indonesian, we create a new dataset for Sundanese and Indonesian translation generated from the Bible. We create a ver... | [
"TAGS\n#language-Indonesian #language-Sundanese #machine-translation #region-us \n",
"# bible_su_id\n\nBible Su-Id is a machine translation dataset containing Indonesian-Sundanese parallel sentences collected from the bible. As there is no existing parallel corpus for Sundanese and Indonesian, we create a new dat... | [
22,
111,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #language-Sundanese #machine-translation #region-us \n# bible_su_id\n\nBible Su-Id is a machine translation dataset containing Indonesian-Sundanese parallel sentences collected from the bible. As there is no existing parallel corpus for Sundanese and Indonesian, we create a new ... |
71e8e3397b0b3d689419c5d67a8e3814e5514185 |
# su_id_asr
Sundanese ASR training data set containing ~220K utterances.
This dataset was collected by Google in Indonesia.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@inproceedings{sodimana18_sltu,
author={Keshan Sodimana and P... | SEACrowd/su_id_asr | [
"language:sun",
"speech-recognition",
"region:us"
] | 2023-09-26T10:11:24+00:00 | {"language": ["sun"], "tags": ["speech-recognition"]} | 2023-09-26T11:33:07+00:00 | [] | [
"sun"
] | TAGS
#language-Sundanese #speech-recognition #region-us
|
# su_id_asr
Sundanese ASR training data set containing ~220K utterances.
This dataset was collected by Google in Indonesia.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
Attribution-ShareAlike 4.0 International.
## Homepage
URL
### Nus... | [
"# su_id_asr\n\nSundanese ASR training data set containing ~220K utterances.\n\nThis dataset was collected by Google in Indonesia.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
"## License\n\nAttribution-ShareAlike 4.0 International.",
"#... | [
"TAGS\n#language-Sundanese #speech-recognition #region-us \n",
"# su_id_asr\n\nSundanese ASR training data set containing ~220K utterances.\n\nThis dataset was collected by Google in Indonesia.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
... | [
19,
34,
35,
8,
3,
16
] | [
"passage: TAGS\n#language-Sundanese #speech-recognition #region-us \n# su_id_asr\n\nSundanese ASR training data set containing ~220K utterances.\n\nThis dataset was collected by Google in Indonesia.## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.## Lic... |
1049e5c9935005470d6408ccb5036e4334d10432 |
# cod
Cross-lingual Outline-based Dialogue (COD) is a dataset comprised of manually generated, localized, and cross-lingually aligned Task-Oriented-Dialogue (TOD) data that served as the source of dialogue prompts.
COD enables natural language understanding, dialogue state tracking, and end-to-end dialogue modeling ... | SEACrowd/cod | [
"language:ind",
"license:unknown",
"dialogue-system",
"region:us"
] | 2023-09-26T10:11:25+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["dialogue-system"]} | 2023-09-26T11:33:43+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #dialogue-system #region-us
|
# cod
Cross-lingual Outline-based Dialogue (COD) is a dataset comprised of manually generated, localized, and cross-lingually aligned Task-Oriented-Dialogue (TOD) data that served as the source of dialogue prompts.
COD enables natural language understanding, dialogue state tracking, and end-to-end dialogue modeling ... | [
"# cod\n\nCross-lingual Outline-based Dialogue (COD) is a dataset comprised of manually generated, localized, and cross-lingually aligned Task-Oriented-Dialogue (TOD) data that served as the source of dialogue prompts.\n\nCOD enables natural language understanding, dialogue state tracking, and end-to-end dialogue m... | [
"TAGS\n#language-Indonesian #license-unknown #dialogue-system #region-us \n",
"# cod\n\nCross-lingual Outline-based Dialogue (COD) is a dataset comprised of manually generated, localized, and cross-lingually aligned Task-Oriented-Dialogue (TOD) data that served as the source of dialogue prompts.\n\nCOD enables na... | [
24,
187,
35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-unknown #dialogue-system #region-us \n# cod\n\nCross-lingual Outline-based Dialogue (COD) is a dataset comprised of manually generated, localized, and cross-lingually aligned Task-Oriented-Dialogue (TOD) data that served as the source of dialogue prompts.\n\nCOD enables... |
047aa434f4621e49baa636de127a0b15f41721b1 |
# nusatranslation_mt
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. Wh... | SEACrowd/nusatranslation_mt | [
"language:ind",
"language:btk",
"language:bew",
"language:bug",
"language:jav",
"language:mad",
"language:mak",
"language:min",
"language:mui",
"language:rej",
"language:sun",
"machine-translation",
"region:us"
] | 2023-09-26T10:11:25+00:00 | {"language": ["ind", "btk", "bew", "bug", "jav", "mad", "mak", "min", "mui", "rej", "sun"], "tags": ["machine-translation"]} | 2023-09-26T11:33:56+00:00 | [] | [
"ind",
"btk",
"bew",
"bug",
"jav",
"mad",
"mak",
"min",
"mui",
"rej",
"sun"
] | TAGS
#language-Indonesian #language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #machine-translation #region-us
|
# nusatranslation_mt
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. Wh... | [
"# nusatranslation_mt\n\nDemocratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translati... | [
"TAGS\n#language-Indonesian #language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #machine-translation #region-us \n",
"# nusatranslation_mt\n\nDemocratizing access to natural language pro... | [
71,
373,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #machine-translation #region-us \n# nusatranslation_mt\n\nDemocratizing access to natural language ... |
cad48826bb47f98fe6012dc864cf796cbf7954d5 |
# indolem_ud_id_gsd
The Indonesian-GSD treebank consists of 5598 sentences and 122k words split into train/dev/test of 97k/12k/11k words.
The treebank was originally converted from the content head version of the universal dependency treebank v2.0 (legacy) in 2015.In order to comply with the latest Indonesian annota... | SEACrowd/indolem_ud_id_gsd | [
"language:ind",
"license:cc-by-4.0",
"dependency-parsing",
"arxiv:2011.00677",
"region:us"
] | 2023-09-26T10:11:25+00:00 | {"language": ["ind"], "license": "cc-by-4.0", "tags": ["dependency-parsing"]} | 2023-09-26T11:34:22+00:00 | [
"2011.00677"
] | [
"ind"
] | TAGS
#language-Indonesian #license-cc-by-4.0 #dependency-parsing #arxiv-2011.00677 #region-us
|
# indolem_ud_id_gsd
The Indonesian-GSD treebank consists of 5598 sentences and 122k words split into train/dev/test of 97k/12k/11k words.
The treebank was originally converted from the content head version of the universal dependency treebank v2.0 (legacy) in 2015.In order to comply with the latest Indonesian annota... | [
"# indolem_ud_id_gsd\n\nThe Indonesian-GSD treebank consists of 5598 sentences and 122k words split into train/dev/test of 97k/12k/11k words.\n\nThe treebank was originally converted from the content head version of the universal dependency treebank v2.0 (legacy) in 2015.In order to comply with the latest Indonesia... | [
"TAGS\n#language-Indonesian #license-cc-by-4.0 #dependency-parsing #arxiv-2011.00677 #region-us \n",
"# indolem_ud_id_gsd\n\nThe Indonesian-GSD treebank consists of 5598 sentences and 122k words split into train/dev/test of 97k/12k/11k words.\n\nThe treebank was originally converted from the content head version ... | [
35,
110,
35,
6,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-cc-by-4.0 #dependency-parsing #arxiv-2011.00677 #region-us \n# indolem_ud_id_gsd\n\nThe Indonesian-GSD treebank consists of 5598 sentences and 122k words split into train/dev/test of 97k/12k/11k words.\n\nThe treebank was originally converted from the content head versi... |
8f8d997632c32c08877eb6ac0f83c6a0ee6113e8 |
# idn_tagged_corpus_csui
Idn-tagged-corpus-CSUI is a POS tagging dataset contains about 10,000 sentences, collected from the PAN Localization Project tagged with 23 POS tag classes.
The POS tagset is created through a detailed study and analysis of existing tagsets and the manual tagging of an Indonesian corpus.
Id... | SEACrowd/idn_tagged_corpus_csui | [
"language:ind",
"pos-tagging",
"region:us"
] | 2023-09-26T10:11:27+00:00 | {"language": ["ind"], "tags": ["pos-tagging"]} | 2023-09-26T11:35:14+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #pos-tagging #region-us
|
# idn_tagged_corpus_csui
Idn-tagged-corpus-CSUI is a POS tagging dataset contains about 10,000 sentences, collected from the PAN Localization Project tagged with 23 POS tag classes.
The POS tagset is created through a detailed study and analysis of existing tagsets and the manual tagging of an Indonesian corpus.
Id... | [
"# idn_tagged_corpus_csui\n\nIdn-tagged-corpus-CSUI is a POS tagging dataset contains about 10,000 sentences, collected from the PAN Localization Project tagged with 23 POS tag classes.\n\nThe POS tagset is created through a detailed study and analysis of existing tagsets and the manual tagging of an Indonesian cor... | [
"TAGS\n#language-Indonesian #pos-tagging #region-us \n",
"# idn_tagged_corpus_csui\n\nIdn-tagged-corpus-CSUI is a POS tagging dataset contains about 10,000 sentences, collected from the PAN Localization Project tagged with 23 POS tag classes.\n\nThe POS tagset is created through a detailed study and analysis of e... | [
16,
112,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #pos-tagging #region-us \n# idn_tagged_corpus_csui\n\nIdn-tagged-corpus-CSUI is a POS tagging dataset contains about 10,000 sentences, collected from the PAN Localization Project tagged with 23 POS tag classes.\n\nThe POS tagset is created through a detailed study and analysis o... |
cf1bc2c706111e9283c8126cec71f7063819f64c |
# indolem_sentiment
IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.
This dataset is based on binary classificat... | SEACrowd/indolem_sentiment | [
"language:ind",
"sentiment-analysis",
"arxiv:2011.00677",
"region:us"
] | 2023-09-26T10:11:27+00:00 | {"language": ["ind"], "tags": ["sentiment-analysis"]} | 2023-10-17T12:31:29+00:00 | [
"2011.00677"
] | [
"ind"
] | TAGS
#language-Indonesian #sentiment-analysis #arxiv-2011.00677 #region-us
|
# indolem_sentiment
IndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.
This dataset is based on binary classificat... | [
"# indolem_sentiment\n\nIndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP tasks: morpho-syntax, semantics, and discourse.\n\n\n\nThis dataset is based on binary c... | [
"TAGS\n#language-Indonesian #sentiment-analysis #arxiv-2011.00677 #region-us \n",
"# indolem_sentiment\n\nIndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP task... | [
25,
139,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #sentiment-analysis #arxiv-2011.00677 #region-us \n# indolem_sentiment\n\nIndoLEM (Indonesian Language Evaluation Montage) is a comprehensive Indonesian benchmark that comprises of seven tasks for the Indonesian language. This benchmark is categorized into three pillars of NLP t... |
80974f6bd42143837a60e09dae5eea9d5c9f8c40 |
# talpco
The TUFS Asian Language Parallel Corpus (TALPCo) is an open parallel corpus consisting of Japanese sentences
and their translations into Korean, Burmese (Myanmar; the official language of the Republic of the Union of Myanmar),
Malay (the national language of Malaysia, Singapore and Brunei), Indonesian, Tha... | SEACrowd/talpco | [
"language:eng",
"language:ind",
"language:jpn",
"language:kor",
"language:myn",
"language:tha",
"language:vie",
"language:zsm",
"machine-translation",
"region:us"
] | 2023-09-26T10:11:28+00:00 | {"language": ["eng", "ind", "jpn", "kor", "myn", "tha", "vie", "zsm"], "tags": ["machine-translation"]} | 2023-09-26T11:35:36+00:00 | [] | [
"eng",
"ind",
"jpn",
"kor",
"myn",
"tha",
"vie",
"zsm"
] | TAGS
#language-English #language-Indonesian #language-Japanese #language-Korean #language-myn #language-Thai #language-Vietnamese #language-Standard Malay #machine-translation #region-us
|
# talpco
The TUFS Asian Language Parallel Corpus (TALPCo) is an open parallel corpus consisting of Japanese sentences
and their translations into Korean, Burmese (Myanmar; the official language of the Republic of the Union of Myanmar),
Malay (the national language of Malaysia, Singapore and Brunei), Indonesian, Tha... | [
"# talpco\n\nThe TUFS Asian Language Parallel Corpus (TALPCo) is an open parallel corpus consisting of Japanese sentences\n\nand their translations into Korean, Burmese (Myanmar; the official language of the Republic of the Union of Myanmar),\n\nMalay (the national language of Malaysia, Singapore and Brunei), Indon... | [
"TAGS\n#language-English #language-Indonesian #language-Japanese #language-Korean #language-myn #language-Thai #language-Vietnamese #language-Standard Malay #machine-translation #region-us \n",
"# talpco\n\nThe TUFS Asian Language Parallel Corpus (TALPCo) is an open parallel corpus consisting of Japanese sentence... | [
53,
74,
35,
6,
3,
16
] | [
"passage: TAGS\n#language-English #language-Indonesian #language-Japanese #language-Korean #language-myn #language-Thai #language-Vietnamese #language-Standard Malay #machine-translation #region-us \n# talpco\n\nThe TUFS Asian Language Parallel Corpus (TALPCo) is an open parallel corpus consisting of Japanese sente... |
6aa7cb6fdb069ccbb3652c27456205b7f373559d |
# id_short_answer_grading
Indonesian short answers for Biology and Geography subjects from 534 respondents where the answer grading was done by 7 experts.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@article{
JLK,
author = {... | SEACrowd/id_short_answer_grading | [
"language:ind",
"license:unknown",
"short-answer-grading",
"region:us"
] | 2023-09-26T10:11:58+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["short-answer-grading"]} | 2023-09-26T11:28:15+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #short-answer-grading #region-us
|
# id_short_answer_grading
Indonesian short answers for Biology and Geography subjects from 534 respondents where the answer grading was done by 7 experts.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
Unknown
## Homepage
URL
### NusaCat... | [
"# id_short_answer_grading\n\nIndonesian short answers for Biology and Geography subjects from 534 respondents where the answer grading was done by 7 experts.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
"## License\n\nUnknown",
"## Home... | [
"TAGS\n#language-Indonesian #license-unknown #short-answer-grading #region-us \n",
"# id_short_answer_grading\n\nIndonesian short answers for Biology and Geography subjects from 534 respondents where the answer grading was done by 7 experts.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the ... | [
26,
39,
35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-unknown #short-answer-grading #region-us \n# id_short_answer_grading\n\nIndonesian short answers for Biology and Geography subjects from 534 respondents where the answer grading was done by 7 experts.## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dat... |
7444bbc3b8953150a4ec4061449cc9e4d75c4a4d |
# tico_19
TICO-19 (Translation Initiative for COVID-19) is sampled from a variety of public sources containing
COVID-19 related content, representing different domains (e.g., news, wiki articles, and others). TICO-19
includes 30 documents (3071 sentences, 69.7k words) translated from English into 36 languages: Am... | SEACrowd/tico_19 | [
"language:ind",
"language:ara",
"language:spa",
"language:fra",
"language:hin",
"language:por",
"language:rus",
"language:zho",
"language:eng",
"machine-translation",
"region:us"
] | 2023-09-26T10:12:01+00:00 | {"language": ["ind", "ara", "spa", "fra", "hin", "por", "rus", "zho", "eng"], "tags": ["machine-translation"]} | 2023-09-26T11:28:20+00:00 | [] | [
"ind",
"ara",
"spa",
"fra",
"hin",
"por",
"rus",
"zho",
"eng"
] | TAGS
#language-Indonesian #language-Arabic #language-Spanish #language-French #language-Hindi #language-Portuguese #language-Russian #language-Chinese #language-English #machine-translation #region-us
|
# tico_19
TICO-19 (Translation Initiative for COVID-19) is sampled from a variety of public sources containing
COVID-19 related content, representing different domains (e.g., news, wiki articles, and others). TICO-19
includes 30 documents (3071 sentences, 69.7k words) translated from English into 36 languages: Am... | [
"# tico_19\n\nTICO-19 (Translation Initiative for COVID-19) is sampled from a variety of public sources containing \n\nCOVID-19 related content, representing different domains (e.g., news, wiki articles, and others). TICO-19 \n\nincludes 30 documents (3071 sentences, 69.7k words) translated from English into 36 lan... | [
"TAGS\n#language-Indonesian #language-Arabic #language-Spanish #language-French #language-Hindi #language-Portuguese #language-Russian #language-Chinese #language-English #machine-translation #region-us \n",
"# tico_19\n\nTICO-19 (Translation Initiative for COVID-19) is sampled from a variety of public sources co... | [
56,
208,
35,
4,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #language-Arabic #language-Spanish #language-French #language-Hindi #language-Portuguese #language-Russian #language-Chinese #language-English #machine-translation #region-us \n# tico_19\n\nTICO-19 (Translation Initiative for COVID-19) is sampled from a variety of public sources... |
fa37f4a346913f7a3574d9e3151578394342368c |
# bible_jv_id
Analogous to the En ↔ Id and Su ↔ Id datasets, we create a new dataset for Javanese and Indonesian translation generated from the verse-aligned Bible parallel corpus with the same split setting. In terms of size, both the Su ↔ Id and Jv ↔ Id datasets are much smaller compared to the En ↔ Id dataset, bec... | SEACrowd/bible_jv_id | [
"language:ind",
"language:jav",
"machine-translation",
"region:us"
] | 2023-09-26T10:12:06+00:00 | {"language": ["ind", "jav"], "tags": ["machine-translation"]} | 2023-09-26T11:28:24+00:00 | [] | [
"ind",
"jav"
] | TAGS
#language-Indonesian #language-Javanese #machine-translation #region-us
|
# bible_jv_id
Analogous to the En ↔ Id and Su ↔ Id datasets, we create a new dataset for Javanese and Indonesian translation generated from the verse-aligned Bible parallel corpus with the same split setting. In terms of size, both the Su ↔ Id and Jv ↔ Id datasets are much smaller compared to the En ↔ Id dataset, bec... | [
"# bible_jv_id\n\nAnalogous to the En ↔ Id and Su ↔ Id datasets, we create a new dataset for Javanese and Indonesian translation generated from the verse-aligned Bible parallel corpus with the same split setting. In terms of size, both the Su ↔ Id and Jv ↔ Id datasets are much smaller compared to the En ↔ Id datase... | [
"TAGS\n#language-Indonesian #language-Javanese #machine-translation #region-us \n",
"# bible_jv_id\n\nAnalogous to the En ↔ Id and Su ↔ Id datasets, we create a new dataset for Javanese and Indonesian translation generated from the verse-aligned Bible parallel corpus with the same split setting. In terms of size,... | [
21,
115,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #language-Javanese #machine-translation #region-us \n# bible_jv_id\n\nAnalogous to the En ↔ Id and Su ↔ Id datasets, we create a new dataset for Javanese and Indonesian translation generated from the verse-aligned Bible parallel corpus with the same split setting. In terms of si... |
2bf7b09bf685e06ae097f075634b55ed6d63c7ca |
# indosum
INDOSUM is a new benchmark dataset for Indonesian text summarization.
The dataset consists of news articles and manually constructed summaries.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@INPROCEEDINGS{8629109,
author... | SEACrowd/indosum | [
"language:ind",
"summarization",
"region:us"
] | 2023-09-26T10:12:11+00:00 | {"language": ["ind"], "tags": ["summarization"]} | 2023-09-26T11:28:30+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #summarization #region-us
|
# indosum
INDOSUM is a new benchmark dataset for Indonesian text summarization.
The dataset consists of news articles and manually constructed summaries.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
Apache License, Version 2.0
## Homep... | [
"# indosum\n\nINDOSUM is a new benchmark dataset for Indonesian text summarization. \n\nThe dataset consists of news articles and manually constructed summaries.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
"## License\n\nApache License, V... | [
"TAGS\n#language-Indonesian #summarization #region-us \n",
"# indosum\n\nINDOSUM is a new benchmark dataset for Indonesian text summarization. \n\nThe dataset consists of news articles and manually constructed summaries.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through Huggi... | [
15,
37,
35,
8,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #summarization #region-us \n# indosum\n\nINDOSUM is a new benchmark dataset for Indonesian text summarization. \n\nThe dataset consists of news articles and manually constructed summaries.## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingF... |
f807f5684d9dc22f124745f6772c1d5c8e9c453c |
# id_hsd_nofaaulia
There have been many studies on detecting hate speech in short documents like Twitter data. But to our knowledge, research on long documents is rare, we suppose that the difficulty is increasing due to the possibility of the message of the text may be hidden. In this research, we explore in detecti... | SEACrowd/id_hsd_nofaaulia | [
"language:ind",
"license:unknown",
"sentiment-analysis",
"region:us"
] | 2023-09-26T10:12:47+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["sentiment-analysis"]} | 2023-09-26T11:28:47+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #sentiment-analysis #region-us
|
# id_hsd_nofaaulia
There have been many studies on detecting hate speech in short documents like Twitter data. But to our knowledge, research on long documents is rare, we suppose that the difficulty is increasing due to the possibility of the message of the text may be hidden. In this research, we explore in detecti... | [
"# id_hsd_nofaaulia\n\nThere have been many studies on detecting hate speech in short documents like Twitter data. But to our knowledge, research on long documents is rare, we suppose that the difficulty is increasing due to the possibility of the message of the text may be hidden. In this research, we explore in d... | [
"TAGS\n#language-Indonesian #license-unknown #sentiment-analysis #region-us \n",
"# id_hsd_nofaaulia\n\nThere have been many studies on detecting hate speech in short documents like Twitter data. But to our knowledge, research on long documents is rare, we suppose that the difficulty is increasing due to the poss... | [
24,
95,
35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-unknown #sentiment-analysis #region-us \n# id_hsd_nofaaulia\n\nThere have been many studies on detecting hate speech in short documents like Twitter data. But to our knowledge, research on long documents is rare, we suppose that the difficulty is increasing due to the p... |
0f1a752fbc658a682c2eb028aa88c68922f6caa5 |
# nllb_seed
No Language Left Behind Seed Data
NLLB Seed is a set of professionally-translated sentences in the Wikipedia domain. Data for NLLB-Seed was sampled from Wikimedia’s List of articles every Wikipedia should have, a collection of topics in different fields of knowledge and human activity. NLLB-Seed consists... | SEACrowd/nllb_seed | [
"language:ace",
"language:bjn",
"language:bug",
"language:eng",
"machine-translation",
"region:us"
] | 2023-09-26T10:12:50+00:00 | {"language": ["ace", "bjn", "bug", "eng"], "tags": ["machine-translation"]} | 2023-09-26T11:28:51+00:00 | [] | [
"ace",
"bjn",
"bug",
"eng"
] | TAGS
#language-Achinese #language-Banjar #language-Buginese #language-English #machine-translation #region-us
|
# nllb_seed
No Language Left Behind Seed Data
NLLB Seed is a set of professionally-translated sentences in the Wikipedia domain. Data for NLLB-Seed was sampled from Wikimedia’s List of articles every Wikipedia should have, a collection of topics in different fields of knowledge and human activity. NLLB-Seed consists... | [
"# nllb_seed\n\nNo Language Left Behind Seed Data\n\nNLLB Seed is a set of professionally-translated sentences in the Wikipedia domain. Data for NLLB-Seed was sampled from Wikimedia’s List of articles every Wikipedia should have, a collection of topics in different fields of knowledge and human activity. NLLB-Seed ... | [
"TAGS\n#language-Achinese #language-Banjar #language-Buginese #language-English #machine-translation #region-us \n",
"# nllb_seed\n\nNo Language Left Behind Seed Data\n\nNLLB Seed is a set of professionally-translated sentences in the Wikipedia domain. Data for NLLB-Seed was sampled from Wikimedia’s List of artic... | [
32,
141,
35,
8,
3,
16
] | [
"passage: TAGS\n#language-Achinese #language-Banjar #language-Buginese #language-English #machine-translation #region-us \n# nllb_seed\n\nNo Language Left Behind Seed Data\n\nNLLB Seed is a set of professionally-translated sentences in the Wikipedia domain. Data for NLLB-Seed was sampled from Wikimedia’s List of ar... |
e9c204625c60b9b3a59a9c9f6b36935283360ea0 |
# indo4b
Indo4B is a large-scale Indonesian self-supervised pre-training corpus
consists of around 3.6B words, with around 250M sentences. The corpus
covers both formal and colloquial Indonesian sentences compiled from
12 sources, of which two cover Indonesian colloquial language, eight
cover for... | SEACrowd/indo4b | [
"language:ind",
"self-supervised-pretraining",
"region:us"
] | 2023-09-26T10:12:56+00:00 | {"language": ["ind"], "tags": ["self-supervised-pretraining"]} | 2023-09-26T11:28:57+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #self-supervised-pretraining #region-us
|
# indo4b
Indo4B is a large-scale Indonesian self-supervised pre-training corpus
consists of around 3.6B words, with around 250M sentences. The corpus
covers both formal and colloquial Indonesian sentences compiled from
12 sources, of which two cover Indonesian colloquial language, eight
cover for... | [
"# indo4b\n\nIndo4B is a large-scale Indonesian self-supervised pre-training corpus\n\n consists of around 3.6B words, with around 250M sentences. The corpus\n\n covers both formal and colloquial Indonesian sentences compiled from \n\n 12 sources, of which two cover Indonesian colloquial language, eight\n\... | [
"TAGS\n#language-Indonesian #self-supervised-pretraining #region-us \n",
"# indo4b\n\nIndo4B is a large-scale Indonesian self-supervised pre-training corpus\n\n consists of around 3.6B words, with around 250M sentences. The corpus\n\n covers both formal and colloquial Indonesian sentences compiled from \n\n... | [
20,
92,
35,
4,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #self-supervised-pretraining #region-us \n# indo4b\n\nIndo4B is a large-scale Indonesian self-supervised pre-training corpus\n\n consists of around 3.6B words, with around 250M sentences. The corpus\n\n covers both formal and colloquial Indonesian sentences compiled from \... |
dff73232e2332e1a26865b3e947df75ccd51e77b |
# wrete
WReTe, The Wiki Revision Edits Textual Entailment dataset (Setya and Mahendra, 2018) consists of 450 sentence pairs constructed from Wikipedia revision history. The dataset contains pairs of sentences and binary semantic relations between the pairs. The data are labeled as entailed when the meaning of the sec... | SEACrowd/wrete | [
"language:ind",
"textual-entailment",
"region:us"
] | 2023-09-26T10:13:01+00:00 | {"language": ["ind"], "tags": ["textual-entailment"]} | 2023-09-26T11:29:01+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #textual-entailment #region-us
|
# wrete
WReTe, The Wiki Revision Edits Textual Entailment dataset (Setya and Mahendra, 2018) consists of 450 sentence pairs constructed from Wikipedia revision history. The dataset contains pairs of sentences and binary semantic relations between the pairs. The data are labeled as entailed when the meaning of the sec... | [
"# wrete\n\nWReTe, The Wiki Revision Edits Textual Entailment dataset (Setya and Mahendra, 2018) consists of 450 sentence pairs constructed from Wikipedia revision history. The dataset contains pairs of sentences and binary semantic relations between the pairs. The data are labeled as entailed when the meaning of t... | [
"TAGS\n#language-Indonesian #textual-entailment #region-us \n",
"# wrete\n\nWReTe, The Wiki Revision Edits Textual Entailment dataset (Setya and Mahendra, 2018) consists of 450 sentence pairs constructed from Wikipedia revision history. The dataset contains pairs of sentences and binary semantic relations between... | [
18,
93,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #textual-entailment #region-us \n# wrete\n\nWReTe, The Wiki Revision Edits Textual Entailment dataset (Setya and Mahendra, 2018) consists of 450 sentence pairs constructed from Wikipedia revision history. The dataset contains pairs of sentences and binary semantic relations betw... |
bd6d67fc5732f4f774d4e6441c60e4c730c10570 |
# multilexnorm
MULTILEXNPRM is a new benchmark dataset for multilingual lexical normalization
including 12 language variants,
we here specifically work on the Indonisian-english language.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
``... | SEACrowd/multilexnorm | [
"language:ind",
"multilexnorm",
"region:us"
] | 2023-09-26T10:13:05+00:00 | {"language": ["ind"], "tags": ["multilexnorm"]} | 2023-09-26T11:29:08+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #multilexnorm #region-us
|
# multilexnorm
MULTILEXNPRM is a new benchmark dataset for multilingual lexical normalization
including 12 language variants,
we here specifically work on the Indonisian-english language.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
CC-... | [
"# multilexnorm\n\nMULTILEXNPRM is a new benchmark dataset for multilingual lexical normalization\n\nincluding 12 language variants,\n\nwe here specifically work on the Indonisian-english language.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'."... | [
"TAGS\n#language-Indonesian #multilexnorm #region-us \n",
"# multilexnorm\n\nMULTILEXNPRM is a new benchmark dataset for multilingual lexical normalization\n\nincluding 12 language variants,\n\nwe here specifically work on the Indonisian-english language.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' befor... | [
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"passage: TAGS\n#language-Indonesian #multilexnorm #region-us \n# multilexnorm\n\nMULTILEXNPRM is a new benchmark dataset for multilingual lexical normalization\n\nincluding 12 language variants,\n\nwe here specifically work on the Indonisian-english language.## Dataset Usage\n\nRun 'pip install nusacrowd' before l... |
d959fa3e7b109d4e2a539187b74e120bdb13bfde |
# jadi_ide
The JaDi-Ide dataset is a Twitter dataset for Javanese dialect identification, containing 16,498
data samples. The dialect is classified into `Standard Javanese`, `Ngapak Javanese`, and `East
Javanese` dialects.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFa... | SEACrowd/jadi_ide | [
"language:ind",
"license:unknown",
"emotion-classification",
"region:us"
] | 2023-09-26T10:13:15+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["emotion-classification"]} | 2023-09-26T11:29:15+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #emotion-classification #region-us
|
# jadi_ide
The JaDi-Ide dataset is a Twitter dataset for Javanese dialect identification, containing 16,498
data samples. The dialect is classified into 'Standard Javanese', 'Ngapak Javanese', and 'East
Javanese' dialects.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFa... | [
"# jadi_ide\n\nThe JaDi-Ide dataset is a Twitter dataset for Javanese dialect identification, containing 16,498 \n\ndata samples. The dialect is classified into 'Standard Javanese', 'Ngapak Javanese', and 'East \n\nJavanese' dialects.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset t... | [
"TAGS\n#language-Indonesian #license-unknown #emotion-classification #region-us \n",
"# jadi_ide\n\nThe JaDi-Ide dataset is a Twitter dataset for Javanese dialect identification, containing 16,498 \n\ndata samples. The dialect is classified into 'Standard Javanese', 'Ngapak Javanese', and 'East \n\nJavanese' dial... | [
24,
63,
35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-unknown #emotion-classification #region-us \n# jadi_ide\n\nThe JaDi-Ide dataset is a Twitter dataset for Javanese dialect identification, containing 16,498 \n\ndata samples. The dialect is classified into 'Standard Javanese', 'Ngapak Javanese', and 'East \n\nJavanese' d... |
0a0b117d1414a225e4553c46ca22deb1dd2bc05d |
# id_abusive_news_comment
Abusive language is an expression used by a person with insulting delivery of any person's aspect.
In the modern era, the use of harsh words is often found on the internet, one of them is in the comment section of online news articles which contains harassment, insult, or a curse.
An abusi... | SEACrowd/id_abusive_news_comment | [
"language:ind",
"sentiment-analysis",
"region:us"
] | 2023-09-26T10:13:18+00:00 | {"language": ["ind"], "tags": ["sentiment-analysis"]} | 2023-09-26T11:29:19+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #sentiment-analysis #region-us
|
# id_abusive_news_comment
Abusive language is an expression used by a person with insulting delivery of any person's aspect.
In the modern era, the use of harsh words is often found on the internet, one of them is in the comment section of online news articles which contains harassment, insult, or a curse.
An abusi... | [
"# id_abusive_news_comment\n\nAbusive language is an expression used by a person with insulting delivery of any person's aspect.\n\nIn the modern era, the use of harsh words is often found on the internet, one of them is in the comment section of online news articles which contains harassment, insult, or a curse.\n... | [
"TAGS\n#language-Indonesian #sentiment-analysis #region-us \n",
"# id_abusive_news_comment\n\nAbusive language is an expression used by a person with insulting delivery of any person's aspect.\n\nIn the modern era, the use of harsh words is often found on the internet, one of them is in the comment section of onl... | [
17,
114,
35,
10,
3,
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] | [
"passage: TAGS\n#language-Indonesian #sentiment-analysis #region-us \n# id_abusive_news_comment\n\nAbusive language is an expression used by a person with insulting delivery of any person's aspect.\n\nIn the modern era, the use of harsh words is often found on the internet, one of them is in the comment section of ... |
1af4126fe1546d7c041c6535ec4e3a3c4eeca4e8 |
# id_sts
SemEval is a series of international natural language processing (NLP) research workshops whose mission is
to advance the current state of the art in semantic analysis and to help create high-quality annotated datasets in a
range of increasingly challenging problems in natural language semantics. This is a... | SEACrowd/id_sts | [
"language:ind",
"license:unknown",
"semantic-similarity",
"region:us"
] | 2023-09-26T10:13:25+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["semantic-similarity"]} | 2023-09-26T11:29:25+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #semantic-similarity #region-us
|
# id_sts
SemEval is a series of international natural language processing (NLP) research workshops whose mission is
to advance the current state of the art in semantic analysis and to help create high-quality annotated datasets in a
range of increasingly challenging problems in natural language semantics. This is a... | [
"# id_sts\n\nSemEval is a series of international natural language processing (NLP) research workshops whose mission is\n\nto advance the current state of the art in semantic analysis and to help create high-quality annotated datasets in a\n\nrange of increasingly challenging problems in natural language semantics.... | [
"TAGS\n#language-Indonesian #license-unknown #semantic-similarity #region-us \n",
"# id_sts\n\nSemEval is a series of international natural language processing (NLP) research workshops whose mission is\n\nto advance the current state of the art in semantic analysis and to help create high-quality annotated datase... | [
25,
96,
35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-unknown #semantic-similarity #region-us \n# id_sts\n\nSemEval is a series of international natural language processing (NLP) research workshops whose mission is\n\nto advance the current state of the art in semantic analysis and to help create high-quality annotated dat... |
73ba83b707fbaa6c04f54b8baab9bdbdbf2fe25d |
# hoasa
HoASA: An aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel aggregator platform, AiryRooms.
The dataset covers ten different aspects of hotel quality. Similar to the CASA dataset, each review is labeled with a single sentiment label for each aspect.
There are four ... | SEACrowd/hoasa | [
"language:ind",
"aspect-based-sentiment-analysis",
"region:us"
] | 2023-09-26T10:13:28+00:00 | {"language": ["ind"], "tags": ["aspect-based-sentiment-analysis"]} | 2023-09-26T11:29:28+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #aspect-based-sentiment-analysis #region-us
|
# hoasa
HoASA: An aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel aggregator platform, AiryRooms.
The dataset covers ten different aspects of hotel quality. Similar to the CASA dataset, each review is labeled with a single sentiment label for each aspect.
There are four ... | [
"# hoasa\n\nHoASA: An aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel aggregator platform, AiryRooms.\n\nThe dataset covers ten different aspects of hotel quality. Similar to the CASA dataset, each review is labeled with a single sentiment label for each aspect.\n\nThere... | [
"TAGS\n#language-Indonesian #aspect-based-sentiment-analysis #region-us \n",
"# hoasa\n\nHoASA: An aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel aggregator platform, AiryRooms.\n\nThe dataset covers ten different aspects of hotel quality. Similar to the CASA dataset,... | [
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35,
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3,
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] | [
"passage: TAGS\n#language-Indonesian #aspect-based-sentiment-analysis #region-us \n# hoasa\n\nHoASA: An aspect-based sentiment analysis dataset consisting of hotel reviews collected from the hotel aggregator platform, AiryRooms.\n\nThe dataset covers ten different aspects of hotel quality. Similar to the CASA datas... |
b7463ab8cc5afb6419bfaa956066cb45e92e6346 |
# nusaparagraph_rhetoric
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation... | SEACrowd/nusaparagraph_rhetoric | [
"language:btk",
"language:bew",
"language:bug",
"language:jav",
"language:mad",
"language:mak",
"language:min",
"language:mui",
"language:rej",
"language:sun",
"rhetoric-mode-classification",
"region:us"
] | 2023-09-26T10:13:32+00:00 | {"language": ["btk", "bew", "bug", "jav", "mad", "mak", "min", "mui", "rej", "sun"], "tags": ["rhetoric-mode-classification"]} | 2023-09-26T11:29:33+00:00 | [] | [
"btk",
"bew",
"bug",
"jav",
"mad",
"mak",
"min",
"mui",
"rej",
"sun"
] | TAGS
#language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #rhetoric-mode-classification #region-us
|
# nusaparagraph_rhetoric
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation... | [
"# nusaparagraph_rhetoric\n\nDemocratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document trans... | [
"TAGS\n#language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #rhetoric-mode-classification #region-us \n",
"# nusaparagraph_rhetoric\n\nDemocratizing access to natural language processing ... | [
71,
375,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #rhetoric-mode-classification #region-us \n# nusaparagraph_rhetoric\n\nDemocratizing access to natural language processi... |
c5b6453c5abc5ce20a8144349e41485d1ddf66ae |
# term_a
TermA is a span-extraction dataset collected from the hotel aggregator platform, AiryRooms
(Septiandri and Sutiono, 2019; Fernando et al.,
2019) consisting of thousands of hotel reviews,each containing a span label for aspect
and sentiment words representing the opinion of the reviewer on the correspondin... | SEACrowd/term_a | [
"language:ind",
"keyword-tagging",
"region:us"
] | 2023-09-26T10:13:44+00:00 | {"language": ["ind"], "tags": ["keyword-tagging"]} | 2023-09-26T11:29:41+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #keyword-tagging #region-us
|
# term_a
TermA is a span-extraction dataset collected from the hotel aggregator platform, AiryRooms
(Septiandri and Sutiono, 2019; Fernando et al.,
2019) consisting of thousands of hotel reviews,each containing a span label for aspect
and sentiment words representing the opinion of the reviewer on the correspondin... | [
"# term_a\n\nTermA is a span-extraction dataset collected from the hotel aggregator platform, AiryRooms\n\n(Septiandri and Sutiono, 2019; Fernando et al.,\n\n2019) consisting of thousands of hotel reviews,each containing a span label for aspect\n\nand sentiment words representing the opinion of the reviewer on the ... | [
"TAGS\n#language-Indonesian #keyword-tagging #region-us \n",
"# term_a\n\nTermA is a span-extraction dataset collected from the hotel aggregator platform, AiryRooms\n\n(Septiandri and Sutiono, 2019; Fernando et al.,\n\n2019) consisting of thousands of hotel reviews,each containing a span label for aspect\n\nand s... | [
17,
109,
35,
10,
3,
16
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"passage: TAGS\n#language-Indonesian #keyword-tagging #region-us \n# term_a\n\nTermA is a span-extraction dataset collected from the hotel aggregator platform, AiryRooms\n\n(Septiandri and Sutiono, 2019; Fernando et al.,\n\n2019) consisting of thousands of hotel reviews,each containing a span label for aspect\n\nan... |
4c23b73760b1e26eac39ae4442026c3cb0aa59af |
# id_multilabel_hs
The ID_MULTILABEL_HS dataset is collection of 13,169 tweets in Indonesian language,
designed for hate speech detection NLP task. This dataset is combination from previous research and newly crawled data from Twitter.
This is a multilabel dataset with label details as follows:
-HS : hate speech l... | SEACrowd/id_multilabel_hs | [
"language:ind",
"aspect-based-sentiment-analysis",
"region:us"
] | 2023-09-26T10:13:49+00:00 | {"language": ["ind"], "tags": ["aspect-based-sentiment-analysis"]} | 2023-09-26T11:29:46+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #aspect-based-sentiment-analysis #region-us
|
# id_multilabel_hs
The ID_MULTILABEL_HS dataset is collection of 13,169 tweets in Indonesian language,
designed for hate speech detection NLP task. This dataset is combination from previous research and newly crawled data from Twitter.
This is a multilabel dataset with label details as follows:
-HS : hate speech l... | [
"# id_multilabel_hs\n\nThe ID_MULTILABEL_HS dataset is collection of 13,169 tweets in Indonesian language,\n\ndesigned for hate speech detection NLP task. This dataset is combination from previous research and newly crawled data from Twitter.\n\nThis is a multilabel dataset with label details as follows:\n\n-HS : h... | [
"TAGS\n#language-Indonesian #aspect-based-sentiment-analysis #region-us \n",
"# id_multilabel_hs\n\nThe ID_MULTILABEL_HS dataset is collection of 13,169 tweets in Indonesian language,\n\ndesigned for hate speech detection NLP task. This dataset is combination from previous research and newly crawled data from Twi... | [
21,
231,
35,
14,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #aspect-based-sentiment-analysis #region-us \n# id_multilabel_hs\n\nThe ID_MULTILABEL_HS dataset is collection of 13,169 tweets in Indonesian language,\n\ndesigned for hate speech detection NLP task. This dataset is combination from previous research and newly crawled data from ... |
c8ec372d6cef38a42929a1e42a9dff728e20c9be |
# indo_puisi
Puisi is an Indonesian poetic form. The dataset was collected by scraping various websites. It contains 7223 Indonesian puisi along with the title and author.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
```
## License... | SEACrowd/indo_puisi | [
"language:ind",
"self-supervised-pretraining",
"region:us"
] | 2023-09-26T10:13:53+00:00 | {"language": ["ind"], "tags": ["self-supervised-pretraining"]} | 2023-09-26T11:29:49+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #self-supervised-pretraining #region-us
|
# indo_puisi
Puisi is an Indonesian poetic form. The dataset was collected by scraping various websites. It contains 7223 Indonesian puisi along with the title and author.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
Creative Commons Attr... | [
"# indo_puisi\n\nPuisi is an Indonesian poetic form. The dataset was collected by scraping various websites. It contains 7223 Indonesian puisi along with the title and author.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
"## License\n\nCre... | [
"TAGS\n#language-Indonesian #self-supervised-pretraining #region-us \n",
"# indo_puisi\n\nPuisi is an Indonesian poetic form. The dataset was collected by scraping various websites. It contains 7223 Indonesian puisi along with the title and author.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loadi... | [
20,
42,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #self-supervised-pretraining #region-us \n# indo_puisi\n\nPuisi is an Indonesian poetic form. The dataset was collected by scraping various websites. It contains 7223 Indonesian puisi along with the title and author.## Dataset Usage\n\nRun 'pip install nusacrowd' before loading ... |
cb5a60f45a592643cefd61707a95b38f6f752418 |
# stif_indonesia
STIF-Indonesia is formal-informal (bahasa baku - bahasa alay/slang) style transfer for Indonesian. Texts were collected from Twitter. Then, native speakers were aksed to transform the text into formal style.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace... | SEACrowd/stif_indonesia | [
"language:ind",
"license:mit",
"paraphrasing",
"region:us"
] | 2023-09-26T10:13:58+00:00 | {"language": ["ind"], "license": "mit", "tags": ["paraphrasing"]} | 2023-09-26T11:29:52+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-mit #paraphrasing #region-us
|
# stif_indonesia
STIF-Indonesia is formal-informal (bahasa baku - bahasa alay/slang) style transfer for Indonesian. Texts were collected from Twitter. Then, native speakers were aksed to transform the text into formal style.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace... | [
"# stif_indonesia\n\nSTIF-Indonesia is formal-informal (bahasa baku - bahasa alay/slang) style transfer for Indonesian. Texts were collected from Twitter. Then, native speakers were aksed to transform the text into formal style.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through... | [
"TAGS\n#language-Indonesian #license-mit #paraphrasing #region-us \n",
"# stif_indonesia\n\nSTIF-Indonesia is formal-informal (bahasa baku - bahasa alay/slang) style transfer for Indonesian. Texts were collected from Twitter. Then, native speakers were aksed to transform the text into formal style.",
"## Datase... | [
20,
59,
35,
3,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-mit #paraphrasing #region-us \n# stif_indonesia\n\nSTIF-Indonesia is formal-informal (bahasa baku - bahasa alay/slang) style transfer for Indonesian. Texts were collected from Twitter. Then, native speakers were aksed to transform the text into formal style.## Dataset U... |
89aa4bf206393feeaad23cb44d2343070072adc3 |
# identic
IDENTIC is an Indonesian-English parallel corpus for research purposes.
The corpus is a bilingual corpus paired with English. The aim of this work is to build and provide
researchers a proper Indonesian-English textual data set and also to promote research in this language pair.
The corpus contains texts... | SEACrowd/identic | [
"language:ind",
"language:eng",
"machine-translation",
"pos-tagging",
"region:us"
] | 2023-09-26T10:14:01+00:00 | {"language": ["ind", "eng"], "tags": ["machine-translation", "pos-tagging"]} | 2023-09-26T11:29:56+00:00 | [] | [
"ind",
"eng"
] | TAGS
#language-Indonesian #language-English #machine-translation #pos-tagging #region-us
|
# identic
IDENTIC is an Indonesian-English parallel corpus for research purposes.
The corpus is a bilingual corpus paired with English. The aim of this work is to build and provide
researchers a proper Indonesian-English textual data set and also to promote research in this language pair.
The corpus contains texts... | [
"# identic\n\nIDENTIC is an Indonesian-English parallel corpus for research purposes.\n\nThe corpus is a bilingual corpus paired with English. The aim of this work is to build and provide\n\nresearchers a proper Indonesian-English textual data set and also to promote research in this language pair.\n\nThe corpus co... | [
"TAGS\n#language-Indonesian #language-English #machine-translation #pos-tagging #region-us \n",
"# identic\n\nIDENTIC is an Indonesian-English parallel corpus for research purposes.\n\nThe corpus is a bilingual corpus paired with English. The aim of this work is to build and provide\n\nresearchers a proper Indone... | [
25,
112,
35,
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"passage: TAGS\n#language-Indonesian #language-English #machine-translation #pos-tagging #region-us \n# identic\n\nIDENTIC is an Indonesian-English parallel corpus for research purposes.\n\nThe corpus is a bilingual corpus paired with English. The aim of this work is to build and provide\n\nresearchers a proper Ind... |
d15bc7196a41403c4472a488e42123929bd66b8b |
# ted_en_id
TED En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the TED talk transcripts. We split the dataset and use 75% as the training set, 10% as the validation set, and 15% as the test set. Each of the datasets is evaluated in both directions, i.e., English... | SEACrowd/ted_en_id | [
"language:ind",
"language:eng",
"machine-translation",
"region:us"
] | 2023-09-26T10:14:05+00:00 | {"language": ["ind", "eng"], "tags": ["machine-translation"]} | 2023-09-26T11:30:00+00:00 | [] | [
"ind",
"eng"
] | TAGS
#language-Indonesian #language-English #machine-translation #region-us
|
# ted_en_id
TED En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the TED talk transcripts. We split the dataset and use 75% as the training set, 10% as the validation set, and 15% as the test set. Each of the datasets is evaluated in both directions, i.e., English... | [
"# ted_en_id\n\nTED En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the TED talk transcripts. We split the dataset and use 75% as the training set, 10% as the validation set, and 15% as the test set. Each of the datasets is evaluated in both directions, i.e., E... | [
"TAGS\n#language-Indonesian #language-English #machine-translation #region-us \n",
"# ted_en_id\n\nTED En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the TED talk transcripts. We split the dataset and use 75% as the training set, 10% as the validation set, a... | [
20,
108,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #language-English #machine-translation #region-us \n# ted_en_id\n\nTED En-Id is a machine translation dataset containing Indonesian-English parallel sentences collected from the TED talk transcripts. We split the dataset and use 75% as the training set, 10% as the validation set... |
d87ebc684f8217588cc14a4ea341279e51624f2e |
# indo_general_mt_en_id
"In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language,
and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic.
In this paper, we show otherwise by... | SEACrowd/indo_general_mt_en_id | [
"language:ind",
"machine-translation",
"region:us"
] | 2023-09-26T10:14:14+00:00 | {"language": ["ind"], "tags": ["machine-translation"]} | 2023-09-26T11:30:08+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #machine-translation #region-us
|
# indo_general_mt_en_id
"In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language,
and therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic.
In this paper, we show otherwise by... | [
"# indo_general_mt_en_id\n\n\"In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language,\n\nand therefore applying Neural Machine Translation (NMT) which typically requires large training dataset proves to be problematic.\n\nIn this paper, we show o... | [
"TAGS\n#language-Indonesian #machine-translation #region-us \n",
"# indo_general_mt_en_id\n\n\"In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language,\n\nand therefore applying Neural Machine Translation (NMT) which typically requires large tra... | [
16,
192,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #machine-translation #region-us \n# indo_general_mt_en_id\n\n\"In the context of Machine Translation (MT) from-and-to English, Bahasa Indonesia has been considered a low-resource language,\n\nand therefore applying Neural Machine Translation (NMT) which typically requires large ... |
902c77c37a09d351e4a34a5b4491c68090385088 |
# id_stance
Stance Classification Towards Political Figures on Blog Writing.
This dataset contains dataset from the second research, which is combined from the first research and new dataset.
The dataset consist of 337 data, about five target and every target have 1 different event.
Two label are used: 'For' and '... | SEACrowd/id_stance | [
"language:ind",
"textual-entailment",
"region:us"
] | 2023-09-26T10:14:19+00:00 | {"language": ["ind"], "tags": ["textual-entailment"]} | 2023-09-26T11:30:12+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #textual-entailment #region-us
|
# id_stance
Stance Classification Towards Political Figures on Blog Writing.
This dataset contains dataset from the second research, which is combined from the first research and new dataset.
The dataset consist of 337 data, about five target and every target have 1 different event.
Two label are used: 'For' and '... | [
"# id_stance\n\nStance Classification Towards Political Figures on Blog Writing.\n\nThis dataset contains dataset from the second research, which is combined from the first research and new dataset.\n\nThe dataset consist of 337 data, about five target and every target have 1 different event.\n\nTwo label are used:... | [
"TAGS\n#language-Indonesian #textual-entailment #region-us \n",
"# id_stance\n\nStance Classification Towards Political Figures on Blog Writing.\n\nThis dataset contains dataset from the second research, which is combined from the first research and new dataset.\n\nThe dataset consist of 337 data, about five targ... | [
18,
114,
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3,
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] | [
"passage: TAGS\n#language-Indonesian #textual-entailment #region-us \n# id_stance\n\nStance Classification Towards Political Figures on Blog Writing.\n\nThis dataset contains dataset from the second research, which is combined from the first research and new dataset.\n\nThe dataset consist of 337 data, about five t... |
050d24a7a4b304da82495d71d33be6ce28bdd321 |
# emot
EmoT is an emotion classification dataset collected from the social media platform Twitter. The dataset consists of around 4000 Indonesian colloquial language tweets, covering five different emotion labels: anger, fear, happiness, love, and sadness.
EmoT dataset is splitted into 3 sets with 3521 train, 440 va... | SEACrowd/emot | [
"language:ind",
"emotion-classification",
"region:us"
] | 2023-09-26T10:14:23+00:00 | {"language": ["ind"], "tags": ["emotion-classification"]} | 2023-09-26T11:30:16+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #emotion-classification #region-us
|
# emot
EmoT is an emotion classification dataset collected from the social media platform Twitter. The dataset consists of around 4000 Indonesian colloquial language tweets, covering five different emotion labels: anger, fear, happiness, love, and sadness.
EmoT dataset is splitted into 3 sets with 3521 train, 440 va... | [
"# emot\n\nEmoT is an emotion classification dataset collected from the social media platform Twitter. The dataset consists of around 4000 Indonesian colloquial language tweets, covering five different emotion labels: anger, fear, happiness, love, and sadness.\n\nEmoT dataset is splitted into 3 sets with 3521 train... | [
"TAGS\n#language-Indonesian #emotion-classification #region-us \n",
"# emot\n\nEmoT is an emotion classification dataset collected from the social media platform Twitter. The dataset consists of around 4000 Indonesian colloquial language tweets, covering five different emotion labels: anger, fear, happiness, love... | [
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] | [
"passage: TAGS\n#language-Indonesian #emotion-classification #region-us \n# emot\n\nEmoT is an emotion classification dataset collected from the social media platform Twitter. The dataset consists of around 4000 Indonesian colloquial language tweets, covering five different emotion labels: anger, fear, happiness, l... |
1d82e36c37d0151c2adccdf64c80d5aeec26d21d |
# imdb_jv
Javanese Imdb Movie Reviews Dataset is a Javanese version of the IMDb Movie Reviews dataset by translating the original English dataset to Javanese.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@inproceedings{wongso2021caus... | SEACrowd/imdb_jv | [
"language:ind",
"license:unknown",
"sentiment-analysis",
"region:us"
] | 2023-09-26T10:14:28+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["sentiment-analysis"]} | 2023-09-26T11:30:19+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #sentiment-analysis #region-us
|
# imdb_jv
Javanese Imdb Movie Reviews Dataset is a Javanese version of the IMDb Movie Reviews dataset by translating the original English dataset to Javanese.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
Unknown
## Homepage
URL
### Nus... | [
"# imdb_jv\n\nJavanese Imdb Movie Reviews Dataset is a Javanese version of the IMDb Movie Reviews dataset by translating the original English dataset to Javanese.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
"## License\n\nUnknown",
"## ... | [
"TAGS\n#language-Indonesian #license-unknown #sentiment-analysis #region-us \n",
"# imdb_jv\n\nJavanese Imdb Movie Reviews Dataset is a Javanese version of the IMDb Movie Reviews dataset by translating the original English dataset to Javanese.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading th... | [
24,
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35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-unknown #sentiment-analysis #region-us \n# imdb_jv\n\nJavanese Imdb Movie Reviews Dataset is a Javanese version of the IMDb Movie Reviews dataset by translating the original English dataset to Javanese.## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the d... |
e585b7a52f38ecedf1f7635638a51e336350be72 |
# id_hatespeech
The ID Hatespeech dataset is collection of 713 tweets related to a political event, the Jakarta Governor Election 2017
designed for hate speech detection NLP task. This dataset is crawled from Twitter, and then filtered
and annotated manually. The dataset labelled into two; HS if the tweet contains ... | SEACrowd/id_hatespeech | [
"language:ind",
"license:unknown",
"sentiment-analysis",
"region:us"
] | 2023-09-26T10:14:33+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["sentiment-analysis"]} | 2023-09-26T11:30:25+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #sentiment-analysis #region-us
|
# id_hatespeech
The ID Hatespeech dataset is collection of 713 tweets related to a political event, the Jakarta Governor Election 2017
designed for hate speech detection NLP task. This dataset is crawled from Twitter, and then filtered
and annotated manually. The dataset labelled into two; HS if the tweet contains ... | [
"# id_hatespeech\n\nThe ID Hatespeech dataset is collection of 713 tweets related to a political event, the Jakarta Governor Election 2017\n\ndesigned for hate speech detection NLP task. This dataset is crawled from Twitter, and then filtered\n\nand annotated manually. The dataset labelled into two; HS if the tweet... | [
"TAGS\n#language-Indonesian #license-unknown #sentiment-analysis #region-us \n",
"# id_hatespeech\n\nThe ID Hatespeech dataset is collection of 713 tweets related to a political event, the Jakarta Governor Election 2017\n\ndesigned for hate speech detection NLP task. This dataset is crawled from Twitter, and then... | [
24,
86,
35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-unknown #sentiment-analysis #region-us \n# id_hatespeech\n\nThe ID Hatespeech dataset is collection of 713 tweets related to a political event, the Jakarta Governor Election 2017\n\ndesigned for hate speech detection NLP task. This dataset is crawled from Twitter, and t... |
8fbec72b1a280b83c24cae5a2c7144a126d39e23 |
# indo4b_plus
Indo4B-Plus is an extension of Indo4B, a large-scale Indonesian self-supervised pre-training corpus.
Indo4B-Plus extend Indo4B by adding two low-resource Indonesian local languages to the corpus, i.e., Sundanese and Javanese.
Indo4B-Plus adds 82,582,025 words (∼2.07%) of Sundanese sentences a... | SEACrowd/indo4b_plus | [
"language:ind",
"language:sun",
"language:jav",
"self-supervised-pretraining",
"region:us"
] | 2023-09-26T10:14:35+00:00 | {"language": ["ind", "sun", "jav"], "tags": ["self-supervised-pretraining"]} | 2023-09-26T11:30:29+00:00 | [] | [
"ind",
"sun",
"jav"
] | TAGS
#language-Indonesian #language-Sundanese #language-Javanese #self-supervised-pretraining #region-us
|
# indo4b_plus
Indo4B-Plus is an extension of Indo4B, a large-scale Indonesian self-supervised pre-training corpus.
Indo4B-Plus extend Indo4B by adding two low-resource Indonesian local languages to the corpus, i.e., Sundanese and Javanese.
Indo4B-Plus adds 82,582,025 words (∼2.07%) of Sundanese sentences a... | [
"# indo4b_plus\n\nIndo4B-Plus is an extension of Indo4B, a large-scale Indonesian self-supervised pre-training corpus. \n\n Indo4B-Plus extend Indo4B by adding two low-resource Indonesian local languages to the corpus, i.e., Sundanese and Javanese.\n\n Indo4B-Plus adds 82,582,025 words (∼2.07%) of Sundanese s... | [
"TAGS\n#language-Indonesian #language-Sundanese #language-Javanese #self-supervised-pretraining #region-us \n",
"# indo4b_plus\n\nIndo4B-Plus is an extension of Indo4B, a large-scale Indonesian self-supervised pre-training corpus. \n\n Indo4B-Plus extend Indo4B by adding two low-resource Indonesian local langu... | [
31,
110,
35,
4,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #language-Sundanese #language-Javanese #self-supervised-pretraining #region-us \n# indo4b_plus\n\nIndo4B-Plus is an extension of Indo4B, a large-scale Indonesian self-supervised pre-training corpus. \n\n Indo4B-Plus extend Indo4B by adding two low-resource Indonesian local la... |
e2d78a2410362bde93439a2026121b45b76a38cf |
# indocoref
Dataset contains articles from Wikipedia Bahasa Indonesia which fulfill these conditions:
- The pages contain many noun phrases, which the authors subjectively pick: (i) fictional plots, e.g., subtitles for films,
TV show episodes, and novel stories; (ii) biographies (incl. fictional characters); and ... | SEACrowd/indocoref | [
"language:ind",
"license:mit",
"coreference-resolution",
"region:us"
] | 2023-09-26T10:14:40+00:00 | {"language": ["ind"], "license": "mit", "tags": ["coreference-resolution"]} | 2023-09-26T11:30:32+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-mit #coreference-resolution #region-us
|
# indocoref
Dataset contains articles from Wikipedia Bahasa Indonesia which fulfill these conditions:
- The pages contain many noun phrases, which the authors subjectively pick: (i) fictional plots, e.g., subtitles for films,
TV show episodes, and novel stories; (ii) biographies (incl. fictional characters); and ... | [
"# indocoref\n\nDataset contains articles from Wikipedia Bahasa Indonesia which fulfill these conditions:\n\n- The pages contain many noun phrases, which the authors subjectively pick: (i) fictional plots, e.g., subtitles for films,\n\n TV show episodes, and novel stories; (ii) biographies (incl. fictional charact... | [
"TAGS\n#language-Indonesian #license-mit #coreference-resolution #region-us \n",
"# indocoref\n\nDataset contains articles from Wikipedia Bahasa Indonesia which fulfill these conditions:\n\n- The pages contain many noun phrases, which the authors subjectively pick: (i) fictional plots, e.g., subtitles for films,\... | [
22,
296,
35,
3,
3,
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] | [
"passage: TAGS\n#language-Indonesian #license-mit #coreference-resolution #region-us \n# indocoref\n\nDataset contains articles from Wikipedia Bahasa Indonesia which fulfill these conditions:\n\n- The pages contain many noun phrases, which the authors subjectively pick: (i) fictional plots, e.g., subtitles for film... |
63c7f78fc2f048059250bebbebc3c5861e8744a2 |
# nusaparagraph_emot
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. Wh... | SEACrowd/nusaparagraph_emot | [
"language:btk",
"language:bew",
"language:bug",
"language:jav",
"language:mad",
"language:mak",
"language:min",
"language:mui",
"language:rej",
"language:sun",
"emotion-classification",
"region:us"
] | 2023-09-26T10:14:43+00:00 | {"language": ["btk", "bew", "bug", "jav", "mad", "mak", "min", "mui", "rej", "sun"], "tags": ["emotion-classification"]} | 2023-09-26T11:30:37+00:00 | [] | [
"btk",
"bew",
"bug",
"jav",
"mad",
"mak",
"min",
"mui",
"rej",
"sun"
] | TAGS
#language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #emotion-classification #region-us
|
# nusaparagraph_emot
Democratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translation. Wh... | [
"# nusaparagraph_emot\n\nDemocratizing access to natural language processing (NLP) technology is crucial, especially for underrepresented and extremely low-resource languages. Previous research has focused on developing labeled and unlabeled corpora for these languages through online scraping and document translati... | [
"TAGS\n#language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #emotion-classification #region-us \n",
"# nusaparagraph_emot\n\nDemocratizing access to natural language processing (NLP) tech... | [
67,
388,
35,
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3,
16
] | [
"passage: TAGS\n#language-btk #language-Betawi #language-Buginese #language-Javanese #language-Madurese #language-Makasar #language-Minangkabau #language-Musi #language-Rejang #language-Sundanese #emotion-classification #region-us \n# nusaparagraph_emot\n\nDemocratizing access to natural language processing (NLP) t... |
ed93262450133e410d1c783447ce37b04d0b1740 |
# cvss
CVSS is a massively multilingual-to-English speech-to-speech translation corpus,
covering sentence-level parallel speech-to-speech translation pairs from 21
languages into English.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
``... | SEACrowd/cvss | [
"language:ind",
"language:eng",
"speech-to-speech-translation",
"region:us"
] | 2023-09-26T10:14:52+00:00 | {"language": ["ind", "eng"], "tags": ["speech-to-speech-translation"]} | 2023-09-26T11:30:46+00:00 | [] | [
"ind",
"eng"
] | TAGS
#language-Indonesian #language-English #speech-to-speech-translation #region-us
|
# cvss
CVSS is a massively multilingual-to-English speech-to-speech translation corpus,
covering sentence-level parallel speech-to-speech translation pairs from 21
languages into English.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
CC-... | [
"# cvss\n\nCVSS is a massively multilingual-to-English speech-to-speech translation corpus,\n\ncovering sentence-level parallel speech-to-speech translation pairs from 21\n\nlanguages into English.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'."... | [
"TAGS\n#language-Indonesian #language-English #speech-to-speech-translation #region-us \n",
"# cvss\n\nCVSS is a massively multilingual-to-English speech-to-speech translation corpus,\n\ncovering sentence-level parallel speech-to-speech translation pairs from 21\n\nlanguages into English.",
"## Dataset Usage\n\... | [
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"passage: TAGS\n#language-Indonesian #language-English #speech-to-speech-translation #region-us \n# cvss\n\nCVSS is a massively multilingual-to-English speech-to-speech translation corpus,\n\ncovering sentence-level parallel speech-to-speech translation pairs from 21\n\nlanguages into English.## Dataset Usage\n\nRu... |
984b6df9d09e120547582070cc9ef2a65fb7483d |
# local_id_abusive
This dataset is for abusive and hate speech detection, using Twitter text containing Javanese and Sundanese words.
(from the publication source)
The Indonesian local language dataset collection was conducted using Twitter search API to collect the tweets and then
implemented using Tweepy Libra... | SEACrowd/local_id_abusive | [
"language:jav",
"language:sun",
"license:unknown",
"aspect-based-sentiment-analysis",
"region:us"
] | 2023-09-26T10:15:02+00:00 | {"language": ["jav", "sun"], "license": "unknown", "tags": ["aspect-based-sentiment-analysis"]} | 2023-09-26T11:30:53+00:00 | [] | [
"jav",
"sun"
] | TAGS
#language-Javanese #language-Sundanese #license-unknown #aspect-based-sentiment-analysis #region-us
|
# local_id_abusive
This dataset is for abusive and hate speech detection, using Twitter text containing Javanese and Sundanese words.
(from the publication source)
The Indonesian local language dataset collection was conducted using Twitter search API to collect the tweets and then
implemented using Tweepy Libra... | [
"# local_id_abusive\n\nThis dataset is for abusive and hate speech detection, using Twitter text containing Javanese and Sundanese words.\n\n\n\n(from the publication source)\n\nThe Indonesian local language dataset collection was conducted using Twitter search API to collect the tweets and then\n\nimplemented usin... | [
"TAGS\n#language-Javanese #language-Sundanese #license-unknown #aspect-based-sentiment-analysis #region-us \n",
"# local_id_abusive\n\nThis dataset is for abusive and hate speech detection, using Twitter text containing Javanese and Sundanese words.\n\n\n\n(from the publication source)\n\nThe Indonesian local lan... | [
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228,
35,
5,
3,
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] | [
"passage: TAGS\n#language-Javanese #language-Sundanese #license-unknown #aspect-based-sentiment-analysis #region-us \n# local_id_abusive\n\nThis dataset is for abusive and hate speech detection, using Twitter text containing Javanese and Sundanese words.\n\n\n\n(from the publication source)\n\nThe Indonesian local ... |
ec428adead6503a36b300d99a557c68196ce1023 |
# su_id_tts
This data set contains high-quality transcribed audio data for Sundanese. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number.
The data set has been man... | SEACrowd/su_id_tts | [
"language:sun",
"text-to-speech",
"region:us"
] | 2023-09-26T10:15:10+00:00 | {"language": ["sun"], "tags": ["text-to-speech"]} | 2023-09-26T11:31:01+00:00 | [] | [
"sun"
] | TAGS
#language-Sundanese #text-to-speech #region-us
|
# su_id_tts
This data set contains high-quality transcribed audio data for Sundanese. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number.
The data set has been man... | [
"# su_id_tts\n\nThis data set contains high-quality transcribed audio data for Sundanese. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepended with a speaker identification number.\n\nThe data set has ... | [
"TAGS\n#language-Sundanese #text-to-speech #region-us \n",
"# su_id_tts\n\nThis data set contains high-quality transcribed audio data for Sundanese. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prepend... | [
19,
111,
35,
7,
3,
16
] | [
"passage: TAGS\n#language-Sundanese #text-to-speech #region-us \n# su_id_tts\n\nThis data set contains high-quality transcribed audio data for Sundanese. The data set consists of wave files, and a TSV file. The file line_index.tsv contains a filename and the transcription of audio in the file. Each filename is prep... |
92e2e0abdde8fa5475e81158017f63124b5a52c5 |
# id_frog_story
Indonesian Frog Storytelling Corpus
Indonesian written and spoken corpus, based on the twenty-eight pictures. (http://compling.hss.ntu.edu.sg/who/david/corpus/pictures.pdf)
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
``... | SEACrowd/id_frog_story | [
"language:ind",
"self-supervised-pretraining",
"region:us"
] | 2023-09-26T10:15:18+00:00 | {"language": ["ind"], "tags": ["self-supervised-pretraining"]} | 2023-09-26T11:31:08+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #self-supervised-pretraining #region-us
|
# id_frog_story
Indonesian Frog Storytelling Corpus
Indonesian written and spoken corpus, based on the twenty-eight pictures. (URL
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
Creative Commons Attribution-ShareAlike 4.0 International (CC... | [
"# id_frog_story\n\nIndonesian Frog Storytelling Corpus\n\nIndonesian written and spoken corpus, based on the twenty-eight pictures. (URL",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
"## License\n\nCreative Commons Attribution-ShareAlike 4... | [
"TAGS\n#language-Indonesian #self-supervised-pretraining #region-us \n",
"# id_frog_story\n\nIndonesian Frog Storytelling Corpus\n\nIndonesian written and spoken corpus, based on the twenty-eight pictures. (URL",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's '... | [
20,
32,
35,
16,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #self-supervised-pretraining #region-us \n# id_frog_story\n\nIndonesian Frog Storytelling Corpus\n\nIndonesian written and spoken corpus, based on the twenty-eight pictures. (URL## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'loa... |
a2eb01df298164639bd21e925a8890cf8890dd6c |
# x_fact
X-FACT: the largest publicly available multilingual dataset for factual verification of naturally existing realworld claims.
## Dataset Usage
Run `pip install nusacrowd` before loading the dataset through HuggingFace's `load_dataset`.
## Citation
```
@inproceedings{gupta2021xfact,
title={{X-FACT: A... | SEACrowd/x_fact | [
"language:ara",
"language:aze",
"language:ben",
"language:deu",
"language:spa",
"language:fas",
"language:fra",
"language:guj",
"language:hin",
"language:ind",
"language:ita",
"language:kat",
"language:mar",
"language:nor",
"language:nld",
"language:pan",
"language:pol",
"language:... | 2023-09-26T10:15:27+00:00 | {"language": ["ara", "aze", "ben", "deu", "spa", "fas", "fra", "guj", "hin", "ind", "ita", "kat", "mar", "nor", "nld", "pan", "pol", "por", "ron", "rus", "sin", "srp", "sqi", "tam", "tur"], "license": "mit", "tags": ["fact-checking"]} | 2023-09-26T11:31:15+00:00 | [] | [
"ara",
"aze",
"ben",
"deu",
"spa",
"fas",
"fra",
"guj",
"hin",
"ind",
"ita",
"kat",
"mar",
"nor",
"nld",
"pan",
"pol",
"por",
"ron",
"rus",
"sin",
"srp",
"sqi",
"tam",
"tur"
] | TAGS
#language-Arabic #language-Azerbaijani #language-Bengali #language-German #language-Spanish #language-Persian #language-French #language-Gujarati #language-Hindi #language-Indonesian #language-Italian #language-Georgian #language-Marathi #language-Norwegian #language-Dutch #language-Panjabi #language-Polish #langu... |
# x_fact
X-FACT: the largest publicly available multilingual dataset for factual verification of naturally existing realworld claims.
## Dataset Usage
Run 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.
## License
MIT
## Homepage
URL
### NusaCatalogue
For easy indexing... | [
"# x_fact\n\nX-FACT: the largest publicly available multilingual dataset for factual verification of naturally existing realworld claims.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loading the dataset through HuggingFace's 'load_dataset'.",
"## License\n\nMIT",
"## Homepage\n\nURL",
"### Nusa... | [
"TAGS\n#language-Arabic #language-Azerbaijani #language-Bengali #language-German #language-Spanish #language-Persian #language-French #language-Gujarati #language-Hindi #language-Indonesian #language-Italian #language-Georgian #language-Marathi #language-Norwegian #language-Dutch #language-Panjabi #language-Polish ... | [
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16
] | [
"passage: TAGS\n#language-Arabic #language-Azerbaijani #language-Bengali #language-German #language-Spanish #language-Persian #language-French #language-Gujarati #language-Hindi #language-Indonesian #language-Italian #language-Georgian #language-Marathi #language-Norwegian #language-Dutch #language-Panjabi #languag... |
10a5c9c80bd0b7d194d47f7c71d5c9990842eea5 |
# postag_su
This dataset contains 3616 lines of Sundanese sentences taken from several online magazines (Mangle, Dewan Dakwah Jabar, and Balebat). Annotated with PoS Labels by several undergraduates of the Sundanese Language Education Study Program (PPBS), UPI Bandung.
## Dataset Usage
Run `pip install nusacrowd` b... | SEACrowd/postag_su | [
"language:sun",
"pos-tagging",
"region:us"
] | 2023-09-26T10:15:31+00:00 | {"language": ["sun"], "tags": ["pos-tagging"]} | 2023-09-26T11:31:19+00:00 | [] | [
"sun"
] | TAGS
#language-Sundanese #pos-tagging #region-us
|
# postag_su
This dataset contains 3616 lines of Sundanese sentences taken from several online magazines (Mangle, Dewan Dakwah Jabar, and Balebat). Annotated with PoS Labels by several undergraduates of the Sundanese Language Education Study Program (PPBS), UPI Bandung.
## Dataset Usage
Run 'pip install nusacrowd' b... | [
"# postag_su\n\nThis dataset contains 3616 lines of Sundanese sentences taken from several online magazines (Mangle, Dewan Dakwah Jabar, and Balebat). Annotated with PoS Labels by several undergraduates of the Sundanese Language Education Study Program (PPBS), UPI Bandung.",
"## Dataset Usage\n\nRun 'pip install ... | [
"TAGS\n#language-Sundanese #pos-tagging #region-us \n",
"# postag_su\n\nThis dataset contains 3616 lines of Sundanese sentences taken from several online magazines (Mangle, Dewan Dakwah Jabar, and Balebat). Annotated with PoS Labels by several undergraduates of the Sundanese Language Education Study Program (PPBS... | [
17,
66,
35,
12,
3,
16
] | [
"passage: TAGS\n#language-Sundanese #pos-tagging #region-us \n# postag_su\n\nThis dataset contains 3616 lines of Sundanese sentences taken from several online magazines (Mangle, Dewan Dakwah Jabar, and Balebat). Annotated with PoS Labels by several undergraduates of the Sundanese Language Education Study Program (P... |
285fabea982c3c36c7084cfd2e19f6b97e9799a3 |
# indspeech_newstra_ethnicsr
INDspeech_NEWSTRA_EthnicSR is a collection of graphemically balanced and parallel speech corpora of four major Indonesian ethnic languages: Javanese, Sundanese, Balinese, and Bataks. It was developed in 2013 by the Nara Institute of Science and Technology (NAIST, Japan) [Sakti et al., 201... | SEACrowd/indspeech_newstra_ethnicsr | [
"language:sun",
"language:jav",
"language:btk",
"language:ban",
"speech-recognition",
"region:us"
] | 2023-09-26T10:15:35+00:00 | {"language": ["sun", "jav", "btk", "ban"], "tags": ["speech-recognition"]} | 2023-09-26T11:31:23+00:00 | [] | [
"sun",
"jav",
"btk",
"ban"
] | TAGS
#language-Sundanese #language-Javanese #language-btk #language-Balinese #speech-recognition #region-us
|
# indspeech_newstra_ethnicsr
INDspeech_NEWSTRA_EthnicSR is a collection of graphemically balanced and parallel speech corpora of four major Indonesian ethnic languages: Javanese, Sundanese, Balinese, and Bataks. It was developed in 2013 by the Nara Institute of Science and Technology (NAIST, Japan) [Sakti et al., 201... | [
"# indspeech_newstra_ethnicsr\n\nINDspeech_NEWSTRA_EthnicSR is a collection of graphemically balanced and parallel speech corpora of four major Indonesian ethnic languages: Javanese, Sundanese, Balinese, and Bataks. It was developed in 2013 by the Nara Institute of Science and Technology (NAIST, Japan) [Sakti et al... | [
"TAGS\n#language-Sundanese #language-Javanese #language-btk #language-Balinese #speech-recognition #region-us \n",
"# indspeech_newstra_ethnicsr\n\nINDspeech_NEWSTRA_EthnicSR is a collection of graphemically balanced and parallel speech corpora of four major Indonesian ethnic languages: Javanese, Sundanese, Balin... | [
35,
154,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Sundanese #language-Javanese #language-btk #language-Balinese #speech-recognition #region-us \n# indspeech_newstra_ethnicsr\n\nINDspeech_NEWSTRA_EthnicSR is a collection of graphemically balanced and parallel speech corpora of four major Indonesian ethnic languages: Javanese, Sundanese, Ba... |
211fa0ccf619872d4d897e649c4bfaf45f9f1236 |
# indolem_nerui
NER UI is a Named Entity Recognition dataset that contains 2,125 sentences obtained via an annotation assignment in an NLP course at the University of Indonesia in 2016.
The corpus has three named entity classes: location, organisation, and person with training/dev/test distribution: 1,530/170/42 and... | SEACrowd/indolem_nerui | [
"language:ind",
"license:cc-by-4.0",
"named-entity-recognition",
"arxiv:2011.00677",
"region:us"
] | 2023-09-26T10:15:40+00:00 | {"language": ["ind"], "license": "cc-by-4.0", "tags": ["named-entity-recognition"]} | 2023-09-26T11:31:26+00:00 | [
"2011.00677"
] | [
"ind"
] | TAGS
#language-Indonesian #license-cc-by-4.0 #named-entity-recognition #arxiv-2011.00677 #region-us
|
# indolem_nerui
NER UI is a Named Entity Recognition dataset that contains 2,125 sentences obtained via an annotation assignment in an NLP course at the University of Indonesia in 2016.
The corpus has three named entity classes: location, organisation, and person with training/dev/test distribution: 1,530/170/42 and... | [
"# indolem_nerui\n\nNER UI is a Named Entity Recognition dataset that contains 2,125 sentences obtained via an annotation assignment in an NLP course at the University of Indonesia in 2016.\n\nThe corpus has three named entity classes: location, organisation, and person with training/dev/test distribution: 1,530/17... | [
"TAGS\n#language-Indonesian #license-cc-by-4.0 #named-entity-recognition #arxiv-2011.00677 #region-us \n",
"# indolem_nerui\n\nNER UI is a Named Entity Recognition dataset that contains 2,125 sentences obtained via an annotation assignment in an NLP course at the University of Indonesia in 2016.\n\nThe corpus has... | [
38,
86,
35,
6,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-cc-by-4.0 #named-entity-recognition #arxiv-2011.00677 #region-us \n# indolem_nerui\n\nNER UI is a Named Entity Recognition dataset that contains 2,125 sentences obtained via an annotation assignment in an NLP course at the University of Indonesia in 2016.\n\nThe corpus ... |
a4e217dea0f85d57a4a950797cc90aca2653f0b5 |
# idk_mrc
I(n)dontKnow-MRC (IDK-MRC) is an Indonesian Machine Reading Comprehension dataset that covers
answerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA,
the new unanswerable question in IDK-MRC is generated using a question generation model and human-wr... | SEACrowd/idk_mrc | [
"language:ind",
"question-answering",
"arxiv:2210.13778",
"region:us"
] | 2023-09-26T10:15:43+00:00 | {"language": ["ind"], "tags": ["question-answering"]} | 2023-09-26T11:31:30+00:00 | [
"2210.13778"
] | [
"ind"
] | TAGS
#language-Indonesian #question-answering #arxiv-2210.13778 #region-us
|
# idk_mrc
I(n)dontKnow-MRC (IDK-MRC) is an Indonesian Machine Reading Comprehension dataset that covers
answerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA,
the new unanswerable question in IDK-MRC is generated using a question generation model and human-wr... | [
"# idk_mrc\n\nI(n)dontKnow-MRC (IDK-MRC) is an Indonesian Machine Reading Comprehension dataset that covers\n\nanswerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA,\n\nthe new unanswerable question in IDK-MRC is generated using a question generation model an... | [
"TAGS\n#language-Indonesian #question-answering #arxiv-2210.13778 #region-us \n",
"# idk_mrc\n\nI(n)dontKnow-MRC (IDK-MRC) is an Indonesian Machine Reading Comprehension dataset that covers\n\nanswerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA,\n\nthe ne... | [
26,
251,
35,
8,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #question-answering #arxiv-2210.13778 #region-us \n# idk_mrc\n\nI(n)dontKnow-MRC (IDK-MRC) is an Indonesian Machine Reading Comprehension dataset that covers\n\nanswerable and unanswerable questions. Based on the combination of the existing answerable questions in TyDiQA,\n\nthe... |
d3003c5d50a2269c994c6db283330e680ee6536a |
# tydiqa_id
TyDiQA dataset is collected from Wikipedia articles with human-annotated question and answer pairs covering 11 languages.
The question-answer pairs are collected for each language without using translation services.
IndoNLG uses the Indonesian data from the secondary Gold passage task of the original T... | SEACrowd/tydiqa_id | [
"language:ind",
"question-answering",
"region:us"
] | 2023-09-26T10:15:48+00:00 | {"language": ["ind"], "tags": ["question-answering"]} | 2023-09-26T11:31:34+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #question-answering #region-us
|
# tydiqa_id
TyDiQA dataset is collected from Wikipedia articles with human-annotated question and answer pairs covering 11 languages.
The question-answer pairs are collected for each language without using translation services.
IndoNLG uses the Indonesian data from the secondary Gold passage task of the original T... | [
"# tydiqa_id\n\nTyDiQA dataset is collected from Wikipedia articles with human-annotated question and answer pairs covering 11 languages. \n\nThe question-answer pairs are collected for each language without using translation services.\n\nIndoNLG uses the Indonesian data from the secondary Gold passage task of the ... | [
"TAGS\n#language-Indonesian #question-answering #region-us \n",
"# tydiqa_id\n\nTyDiQA dataset is collected from Wikipedia articles with human-annotated question and answer pairs covering 11 languages. \n\nThe question-answer pairs are collected for each language without using translation services.\n\nIndoNLG use... | [
17,
94,
35,
10,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #question-answering #region-us \n# tydiqa_id\n\nTyDiQA dataset is collected from Wikipedia articles with human-annotated question and answer pairs covering 11 languages. \n\nThe question-answer pairs are collected for each language without using translation services.\n\nIndoNLG ... |
c0b35be522278e418087ccba7878fb57c9ffcc1f |
# korpus_nusantara
This parallel corpus was collected from several studies, assignments, and thesis of
students of the Informatics Study Program, Tanjungpura University. Some of the corpus
are used in the translation machine from Indonesian to local languages http://nustor.untan.ac.id/cammane/.
This corpus can ... | SEACrowd/korpus_nusantara | [
"language:ind",
"language:jav",
"language:xdy",
"language:bug",
"language:sun",
"language:mad",
"language:bjn",
"language:bbc",
"language:msa",
"language:min",
"license:unknown",
"machine-translation",
"region:us"
] | 2023-09-26T10:15:53+00:00 | {"language": ["ind", "jav", "xdy", "bug", "sun", "mad", "bjn", "bbc", "msa", "min"], "license": "unknown", "tags": ["machine-translation"]} | 2023-09-26T11:31:37+00:00 | [] | [
"ind",
"jav",
"xdy",
"bug",
"sun",
"mad",
"bjn",
"bbc",
"msa",
"min"
] | TAGS
#language-Indonesian #language-Javanese #language-Malayic Dayak #language-Buginese #language-Sundanese #language-Madurese #language-Banjar #language-Batak Toba #language-Malay (macrolanguage) #language-Minangkabau #license-unknown #machine-translation #region-us
|
# korpus_nusantara
This parallel corpus was collected from several studies, assignments, and thesis of
students of the Informatics Study Program, Tanjungpura University. Some of the corpus
are used in the translation machine from Indonesian to local languages URL
This corpus can be used freely for research purp... | [
"# korpus_nusantara\n\nThis parallel corpus was collected from several studies, assignments, and thesis of \n\nstudents of the Informatics Study Program, Tanjungpura University. Some of the corpus \n\nare used in the translation machine from Indonesian to local languages URL \n\nThis corpus can be used freely for r... | [
"TAGS\n#language-Indonesian #language-Javanese #language-Malayic Dayak #language-Buginese #language-Sundanese #language-Madurese #language-Banjar #language-Batak Toba #language-Malay (macrolanguage) #language-Minangkabau #license-unknown #machine-translation #region-us \n",
"# korpus_nusantara\n\nThis parallel co... | [
82,
180,
35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #language-Javanese #language-Malayic Dayak #language-Buginese #language-Sundanese #language-Madurese #language-Banjar #language-Batak Toba #language-Malay (macrolanguage) #language-Minangkabau #license-unknown #machine-translation #region-us \n# korpus_nusantara\n\nThis parallel... |
257ae3a34f9c7d75de7233a0897581715f83ad2b |
# paracotta_id
ParaCotta is a synthetic parallel paraphrase corpus across 17 languages: Arabic, Catalan, Czech, German, English, Spanish, Estonian, French, Hindi, Indonesian, Italian, Dutch, Ro- manian, Russian, Swedish, Vietnamese, and Chinese.
## Dataset Usage
Run `pip install nusacrowd` before loading the datase... | SEACrowd/paracotta_id | [
"language:ind",
"license:unknown",
"paraphrasing",
"region:us"
] | 2023-09-26T10:15:56+00:00 | {"language": ["ind"], "license": "unknown", "tags": ["paraphrasing"]} | 2023-09-26T11:31:40+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #license-unknown #paraphrasing #region-us
|
# paracotta_id
ParaCotta is a synthetic parallel paraphrase corpus across 17 languages: Arabic, Catalan, Czech, German, English, Spanish, Estonian, French, Hindi, Indonesian, Italian, Dutch, Ro- manian, Russian, Swedish, Vietnamese, and Chinese.
## Dataset Usage
Run 'pip install nusacrowd' before loading the datase... | [
"# paracotta_id\n\nParaCotta is a synthetic parallel paraphrase corpus across 17 languages: Arabic, Catalan, Czech, German, English, Spanish, Estonian, French, Hindi, Indonesian, Italian, Dutch, Ro- manian, Russian, Swedish, Vietnamese, and Chinese.",
"## Dataset Usage\n\nRun 'pip install nusacrowd' before loadin... | [
"TAGS\n#language-Indonesian #license-unknown #paraphrasing #region-us \n",
"# paracotta_id\n\nParaCotta is a synthetic parallel paraphrase corpus across 17 languages: Arabic, Catalan, Czech, German, English, Spanish, Estonian, French, Hindi, Indonesian, Italian, Dutch, Ro- manian, Russian, Swedish, Vietnamese, an... | [
22,
65,
35,
5,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #license-unknown #paraphrasing #region-us \n# paracotta_id\n\nParaCotta is a synthetic parallel paraphrase corpus across 17 languages: Arabic, Catalan, Czech, German, English, Spanish, Estonian, French, Hindi, Indonesian, Italian, Dutch, Ro- manian, Russian, Swedish, Vietnamese,... |
c868f9437d1d82eaeb99a73d6e39d2d880fa6d01 |
# id_am2ico
In this work, we present AM2iCo, a wide-coverage and carefully designed cross-lingual and multilingual evaluation set;
it aims to assess the ability of state-of-the-art representation models to reason over cross-lingual
lexical-level concept alignment in context for 14 language pairs.
This dataset ... | SEACrowd/id_am2ico | [
"language:ind",
"language:eng",
"concept-alignment-classification",
"region:us"
] | 2023-09-26T10:15:59+00:00 | {"language": ["ind", "eng"], "tags": ["concept-alignment-classification"]} | 2023-09-26T11:31:44+00:00 | [] | [
"ind",
"eng"
] | TAGS
#language-Indonesian #language-English #concept-alignment-classification #region-us
|
# id_am2ico
In this work, we present AM2iCo, a wide-coverage and carefully designed cross-lingual and multilingual evaluation set;
it aims to assess the ability of state-of-the-art representation models to reason over cross-lingual
lexical-level concept alignment in context for 14 language pairs.
This dataset ... | [
"# id_am2ico\n\nIn this work, we present AM2iCo, a wide-coverage and carefully designed cross-lingual and multilingual evaluation set;\n\nit aims to assess the ability of state-of-the-art representation models to reason over cross-lingual \n\nlexical-level concept alignment in context for 14 language pairs. \n\n\n\... | [
"TAGS\n#language-Indonesian #language-English #concept-alignment-classification #region-us \n",
"# id_am2ico\n\nIn this work, we present AM2iCo, a wide-coverage and carefully designed cross-lingual and multilingual evaluation set;\n\nit aims to assess the ability of state-of-the-art representation models to reaso... | [
24,
88,
35,
6,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #language-English #concept-alignment-classification #region-us \n# id_am2ico\n\nIn this work, we present AM2iCo, a wide-coverage and carefully designed cross-lingual and multilingual evaluation set;\n\nit aims to assess the ability of state-of-the-art representation models to re... |
657653b806d4dbb1cf2720b64101658a90747738 |
# casa
CASA: An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms (Ilmania et al., 2018).
The dataset covers six aspects of car quality.
We define the task to be a multi-label classification task,
where each label repr... | SEACrowd/casa | [
"language:ind",
"aspect-based-sentiment-analysis",
"region:us"
] | 2023-09-26T10:16:04+00:00 | {"language": ["ind"], "tags": ["aspect-based-sentiment-analysis"]} | 2023-09-26T11:31:48+00:00 | [] | [
"ind"
] | TAGS
#language-Indonesian #aspect-based-sentiment-analysis #region-us
|
# casa
CASA: An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms (Ilmania et al., 2018).
The dataset covers six aspects of car quality.
We define the task to be a multi-label classification task,
where each label repr... | [
"# casa\n\nCASA: An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms (Ilmania et al., 2018).\n\nThe dataset covers six aspects of car quality.\n\nWe define the task to be a multi-label classification task,\n\nwhere eac... | [
"TAGS\n#language-Indonesian #aspect-based-sentiment-analysis #region-us \n",
"# casa\n\nCASA: An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms (Ilmania et al., 2018).\n\nThe dataset covers six aspects of car quali... | [
21,
87,
35,
8,
3,
16
] | [
"passage: TAGS\n#language-Indonesian #aspect-based-sentiment-analysis #region-us \n# casa\n\nCASA: An aspect-based sentiment analysis dataset consisting of around a thousand car reviews collected from multiple Indonesian online automobile platforms (Ilmania et al., 2018).\n\nThe dataset covers six aspects of car qu... |
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