ftopal/huggingface-datasets-raw · Datasets at Hugging Face

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ae4e12e5dcce4974dd943bca177db96698014edb	# Dataset Card for "sci_summ" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	0x70DA/sci_summ	[ "region:us" ]	2023-03-05T13:56:55+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 23361937.97759879, "num_examples": 4631}, {"name": "test", "num_bytes": 23487172.952651516, "num_examples": 4665}, {"name": "train", "num_bytes": 176474272.610434, "num_examples": 34083}], "download_size": 120216439, "dataset_size": 223323383.54068428}}	2023-03-05T18:12:54+00:00
0fda022559b4e821f3071948ec45bccd51374b1a	Nothing....	Ojimi/Hifu-Lora-Example	[ "license:creativeml-openrail-m", "region:us" ]	2023-03-05T14:22:33+00:00	{"license": "creativeml-openrail-m"}	2023-03-05T14:26:43+00:00
4b1c1ccf212702db4b68c10b8ecc1b68ea88ad7d	# Dataset Card for "dreambooth-hackathon-images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Volodymyr/dreambooth-hackathon-images	[ "region:us" ]	2023-03-05T14:38:11+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "pixel_values", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 62824895.0, "num_examples": 84}], "download_size": 62830151, "dataset_size": 62824895.0}}	2023-03-05T14:38:21+00:00
188e1346c7e106df573a8fa9d7fc3931db491435	# Dataset Card for "test_push_dataset_split_exists_main" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	polinaeterna/test_push_dataset_split_exists_main	[ "region:us" ]	2023-03-05T14:48:01+00:00	{"dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 320, "num_examples": 20}], "download_size": 1469, "dataset_size": 320}}	2023-03-05T14:48:51+00:00
a9f15ac8585d29a36d5b5d851e9556519cc33d33	# Dataset Card for "test_push_dataset_split_exists_main2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	polinaeterna/test_push_dataset_split_exists_main2	[ "region:us" ]	2023-03-05T14:54:09+00:00	{"dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 320, "num_examples": 20}, {"name": "random", "num_bytes": 640, "num_examples": 40}], "download_size": 3128, "dataset_size": 960}}	2023-03-05T14:55:09+00:00
8b321a5de4e5f254048838135a72aa588c8b32aa		OpenDILabCommunity/fake_browser_state_zoo	[ "license:apache-2.0", "region:us" ]	2023-03-05T15:19:24+00:00	{"license": "apache-2.0"}	2023-03-06T07:32:40+00:00
146ec91014c0b47957a2b158a042ab6d99cd3b29	# Dataset Card for "test_push_dataset_infos_json" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	polinaeterna/test_push_dataset_infos_json	[ "region:us" ]	2023-03-05T15:29:38+00:00	{"dataset_info": [{"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1600, "num_examples": 100}, {"name": "random", "num_bytes": 3200, "num_examples": 200}], "download_size": 3299, "dataset_size": 4800}, {"config_name": "v2", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3200, "num_examples": 200}], "download_size": 0, "dataset_size": 3200}], "configs_kwargs": [{"config_name": "default", "data_dir": "./"}, {"config_name": "v2", "data_dir": "v2"}]}	2023-03-05T16:10:16+00:00
95f9412e874a061da3f1b4ff9cf8ec74ccdf9e21	# Dataset Card for "TreeImageDataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Ru3ll/TreeImageDataset	[ "region:us" ]	2023-03-05T15:44:59+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "test", "1": "train", "2": "val"}}}}], "splits": [{"name": "train", "num_bytes": 1882655120.0, "num_examples": 922}], "download_size": 1882708375, "dataset_size": 1882655120.0}}	2023-03-05T15:54:17+00:00
2d0e061031c3a701bf2418e347c94d7931287a80	# Bengali Abstractive News Summarization (BANS) ## Dataset Description - Homepage: - Repository: - Paper: [BANS PAPER](https://doi.org/10.1007/978-981-33-4673-4_4) - Leaderboard: - Point of Contact: [Prithwiraj Bhattacharjee]([email protected]) ### Dataset Summary Nowadays news or text summarization becomes very popular in the NLP field. Both the extractive and abstractive approaches of summarization are implemented in different languages. A significant amount of data is a primary need for any summarization. For the Bengali language, there are only a few datasets are available. Our dataset is made for Bengali Abstractive News Summarization (BANS) purposes. As abstractive summarization is basically neural network-based it needs more and more data to perform well. So we made a standard Bengali abstractive summarization data crawling from online Bengali news portal bangla.bdnews24.com. We crawled more than 19k articles and summaries and standardized the data. ### Downloading the data ``` from datasets import load_dataset train = load_dataset("sustcsenlp/bn_news_summarization",split="train") ``` ### Dataset Description \| Description \| Data Info. \| \| ----------- \| ----------- \| \| Total no of articles \| 19096 \| \| Total no of summaries \| 19096 \| \| Maximum no of words in an article \| 76 \| \| Maximum no of words in a summary \| 12 \| \| Minimum no of words in an article \| 5 \| \| Minimum no of words in a summary \| 3 \| ### Languages This dataset contains Bangla Text Data. ## Acknowledgement We would like to thank Shahjalal University of Science and Technology (SUST) research center and SUST NLP research group for their support. ### Citation Information ``` @InProceedings{10.1007/978-981-33-4673-4_4, author="Bhattacharjee, Prithwiraj and Mallick, Avi and Saiful Islam, Md. and Marium-E-Jannat", editor="Kaiser, M. Shamim and Bandyopadhyay, Anirban and Mahmud, Mufti and Ray, Kanad", title="Bengali Abstractive News Summarization (BANS): A Neural Attention Approach", booktitle="Proceedings of International Conference on Trends in Computational and Cognitive Engineering", year="2021", publisher="Springer Singapore", address="Singapore", pages="41--51", abstract="Bhattacharjee, PrithwirajMallick, AviSaiful Islam, Md.Marium-E-JannatAbstractive summarization is the process of generating novel sentences based on the information extracted from the original text document while retaining the context. Due to abstractive summarization's underlying complexities, most of the past research work has been done on the extractive summarization approach. Nevertheless, with the triumph of the sequence-to-sequence (seq2seq) model, abstractive summarization becomes more viable. Although a significant number of notable research has been done in the English language based on abstractive summarization, only a couple of works have been done on Bengali abstractive news summarization (BANS). In this article, we presented a seq2seq based Long Short-Term Memory (LSTM) network model with attention at encoder-decoder. Our proposed system deploys a local attention-based model that produces a long sequence of words with lucid and human-like generated sentences with noteworthy information of the original document. We also prepared a dataset of more than 19 k articles and corresponding human-written summaries collected from bangla.bdnews24.com (https://bangla.bdnews24.com/) which is till now the most extensive dataset for Bengali news document summarization and publicly published in Kaggle (https://www.kaggle.com/prithwirajsust/bengali-news-summarization-dataset) We evaluated our model qualitatively and quantitatively and compared it with other published results. It showed significant improvement in terms of human evaluation scores with state-of-the-art approaches for BANS.", isbn="978-981-33-4673-4" } ``` ### Contributors \| Name \| University \| \| ----------- \| ----------- \| \| Prithwiraj Bhattacharjee \| Shahjalal University of Science and Technology \| \| Avi Mallick \| Shahjalal University of Science and Technology \| \| Md. Saiful Islam \| Shahjalal University of Science and Technology \| \| Marium-E-Jannat \| Shahjalal University of Science and Technology \|	sustcsenlp/bn_news_summarization	[ "task_categories:summarization", "size_categories:1K<n<10K", "language:bn", "license:cc0-1.0", "region:us" ]	2023-03-05T15:47:29+00:00	{"language": ["bn"], "license": "cc0-1.0", "size_categories": ["1K<n<10K"], "task_categories": ["summarization"]}	2023-03-05T16:41:32+00:00
22d8bd29f65dd4589a646d2b8d2ce59f5ad1d063	# Dataset Card for "ema_en" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	bhpardo/ema_en	[ "region:us" ]	2023-03-05T15:58:10+00:00	{"dataset_info": {"features": [{"name": "english", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 436742.4575850489, "num_examples": 5479}, {"name": "test", "num_bytes": 109205.5424149511, "num_examples": 1370}], "download_size": 340686, "dataset_size": 545948.0}}	2023-03-05T16:26:35+00:00
e02de05402edce1f37e0551b92432cb6f90aadf6		slumericanBx/x	[ "license:creativeml-openrail-m", "region:us" ]	2023-03-05T16:02:11+00:00	{"license": "creativeml-openrail-m"}	2023-03-05T16:02:11+00:00
f4c030a88bd1d54b0572db80a4709821a0159dfb	# Dataset Card for "test_push_dataset_dict_infos_json" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	polinaeterna/test_push_dataset_dict_infos_json	[ "region:us" ]	2023-03-05T16:09:45+00:00	{"dataset_info": [{"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1600, "num_examples": 100}, {"name": "random", "num_bytes": 3200, "num_examples": 200}], "download_size": 5578, "dataset_size": 4800}, {"config_name": "v2", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3200, "num_examples": 200}, {"name": "random", "num_bytes": 4800, "num_examples": 300}], "download_size": 0, "dataset_size": 8000}], "configs_kwargs": [{"config_name": "default", "data_dir": "./"}, {"config_name": "v2", "data_dir": "v2"}]}	2023-03-05T17:25:51+00:00
619b827ee4c0275c9eda1d65c751b6b62a33dc21	# Dataset Card for "satellite-512-alt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Shavindra/satellite-512-alt	[ "region:us" ]	2023-03-05T16:10:19+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}, {"name": "mask_rgb", "dtype": "image"}, {"name": "pixel_values", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 333395732.0, "num_examples": 304}], "download_size": 333272045, "dataset_size": 333395732.0}}	2023-03-05T17:08:12+00:00
7bebc0e2549b52dc839212245b9bcfac2ee85d7b	The COHeN dataset is designed for training and fine-tuning Biblical Hebrew classification models. It consists of 11968 verses from the Hebrew Bible along with labels indicating to which of the four chronological phases of the language each verse belongs. The following verses were chosen to represent each stage: - Archaic Biblical Hebrew (ABH): Gen 49:2–27; Exod 15:1–18; Num 23:7–10, 18–24; 24:3–9; 15–24; Deut 32:1–43; Deut 33:2–39; Jud 5:2–31; 2 Sam 22:2–51; Psa 18:1–50; 68:2–35 (minus the prose introductions in Exod 15:1; Num 23:7, 18; Num 24:3, 15; Deut 33:2; 2 Sam 22:2; Ps 18:2) - Classical Biblical Hebrew (CBH): 1 Sam 1:1–31:13; 2 Sam 1:1–22:1; 23:1–24:25; 1 Kgs 1:1–22:53; 2 Kgs 1:1–25:30 - Transitional Biblical Hebrew (TBH): Isa 40:1–54:17; Jer 1:1–10:10; 10:12–42:34; Ezel 1:1–48:35 - Late Biblical Hebrew (LBH): Eccl 1:1–12:14; Esth 1:1–10:3; Dan 1:1–2:4a; Dan 7:29–12:13, Ezra 1:1–4:6; 6:19–7:11; 7:26–10:44; Neh 1:1–13:31; 1 Chr 1:1–9:44; 12:1–40; 15:1–24; 16:7–43; 21:1–29:19; 2 Chr 7:1–3; 14:9–15:7; 17:1–19; 21:12–17; 24:25–22; 26:6–21; 29:3–31:21; 33:10–20; 34:3–7; 36:22–23 Class balance was achieved by oversampling from the three minority classes (ABH, TBH, and LBH). The script used to generate the COHeN dataset can be found [here](https://github.com/gngpostalsrvc/COHeN/blob/main/input/generate_COHeN_dataset.py).	gngpostalsrvc/COHeN	[ "license:mit", "region:us" ]	2023-03-05T16:18:28+00:00	{"license": "mit"}	2023-04-09T18:53:08+00:00
14cddef0b2c1c0d91f63eebd557a2812a0bdf9fa		BrainStormersHakton/iq-wikis	[ "license:other", "region:us" ]	2023-03-05T18:10:46+00:00	{"license": "other"}	2023-03-05T18:15:16+00:00
9c118b80b7169719031a0589d3869615885e2749	# Dataset Card for "hh_human_eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Dahoas/hh_human_eval	[ "region:us" ]	2023-03-05T18:19:08+00:00	{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 50908, "num_examples": 100}], "download_size": 30961, "dataset_size": 50908}}	2023-03-06T00:10:19+00:00
3624189590972ee6154ecbd01cb0b71fddbee7f5	# Dataset Card for "babylm_childstories" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Sree1994/babylm_childstories	[ "region:us" ]	2023-03-05T18:33:21+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1464755, "num_examples": 4800}, {"name": "test", "num_bytes": 369959, "num_examples": 1200}], "download_size": 1174215, "dataset_size": 1834714}}	2023-03-05T18:33:29+00:00
6d0ea3cfec0c556ed11e051e2e38f36d7c6f0e58	# Dataset Card for Dataset Name ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]	gungangigna/data_set	[ "region:us" ]	2023-03-05T18:36:17+00:00	{}	2023-03-05T18:40:28+00:00
d182636c932e7628d53c74d48122b502b779ceec	# Dataset Card for "COMPS" ## Dataset Description COMPS is a dataset of minimal pair sentences in English that enables the testing knowledge of concepts and their properties in language models (LMs). Specifically, it tests the ability of LMs to attribute properties to everyday concepts, and demonstrate reasoning compatible with property inheritance, where subordinate concepts inherit the properties of their superordinate (hypernyms). - Homepage: [https://github.com/kanishkamisra/comps/](https://github.com/kanishkamisra/comps/) - Repository: [https://github.com/kanishkamisra/comps/](https://github.com/kanishkamisra/comps/) - Paper: [arxiv](https://arxiv.org/abs/2210.01963) - Point of Contact: [Kanishka Misra] (https://kanishka.website) ### Citation Information ``` @inproceedings{misra-etal-2023-comps, title = "{COMPS}: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models", author = "Misra, Kanishka and Rayz, Julia and Ettinger, Allyson", booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.eacl-main.213", doi = "10.18653/v1/2023.eacl-main.213", pages = "2928--2949", abstract = "A characteristic feature of human semantic cognition is its ability to not only store and retrieve the properties of concepts observed through experience, but to also facilitate the inheritance of properties (can breathe) from superordinate concepts (animal) to their subordinates (dog){---}i.e. demonstrate property inheritance. In this paper, we present COMPS, a collection of minimal pair sentences that jointly tests pre-trained language models (PLMs) on their ability to attribute properties to concepts and their ability to demonstrate property inheritance behavior. Analyses of 22 different PLMs on COMPS reveal that they can easily distinguish between concepts on the basis of a property when they are trivially different, but find it relatively difficult when concepts are related on the basis of nuanced knowledge representations. Furthermore, we find that PLMs can show behaviors suggesting successful property inheritance in simple contexts, but fail in the presence of distracting information, which decreases the performance of many models sometimes even below chance. This lack of robustness in demonstrating simple reasoning raises important questions about PLMs{'} capacity to make correct inferences even when they appear to possess the prerequisite knowledge.", } ```	kanishka/comps	[ "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:2210.01963", "region:us" ]	2023-03-05T18:47:23+00:00	{"annotations_creators": ["expert-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": "apache-2.0", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "pretty_name": "COMPS"}	2023-09-16T14:09:24+00:00
d07388b056730058bb007cb3a0ad490cfd880c8b		Monty555/Anisbook	[ "license:openrail", "region:us" ]	2023-03-05T18:58:54+00:00	{"license": "openrail"}	2023-03-05T18:58:54+00:00
897883867b6cb1059b7a4b44fe2689e6a7944386		mohammadnajeeb/concrete_crack_images	[ "license:cc-by-4.0", "region:us" ]	2023-03-05T19:13:03+00:00	{"license": "cc-by-4.0"}	2023-03-05T19:14:43+00:00
57e6e3adf0aa8bfaab2ab5269db11c5fe2997502		SummerSigh/prostring	[ "license:apache-2.0", "region:us" ]	2023-03-05T19:19:54+00:00	{"license": "apache-2.0"}	2023-03-05T19:23:30+00:00
3a1d28ee56306a5a7496ffc222435cbc350ad813		daviddaubner/misinformation-detection	[ "license:unknown", "region:us" ]	2023-03-05T19:20:46+00:00	{"license": "unknown"}	2023-03-09T17:06:23+00:00
deeb52aca51f0637b56999130effb1cf84b9a7ad		Saikeiunn/nva-hinanawi_alpha	[ "license:other", "region:us" ]	2023-03-05T19:24:28+00:00	{"license": "other"}	2023-03-05T19:25:41+00:00
8ae97891ad22af1be38cad1a8a88997bfc8862bd	Hi folks, Here is a collection of data I have scraped or aggregated for most of the stocks in the S&P 500, including popular ones like Apple (AAPL). It has the following data: - Daily news articles and sentiments on those articles collected over the last few years. - All quarterly stock fundamentals (ratios) for 10-20 years. - Stock price data (daily close) over the last 10-20 years. Use it however you please for PERSONAL USAGE, but if you do leverage it to make some money; just remember me and thank me in your head :) --- license: cc-by-nc-4.0 pretty_name: S&P 500 Data (news, ratios, price) size_categories: - 100K<n<1M ---	pmoe7/SP_500_Stocks_Data-ratios_news_price_10_yrs	[ "region:us" ]	2023-03-05T19:57:04+00:00	{}	2023-03-05T20:09:32+00:00
94b608c9fe8055fb351d90100e578b17aceea61c	# Dataset Card for "test_tesis_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	jorgeortizfuentes/test_tesis_dataset	[ "region:us" ]	2023-03-05T19:58:12+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "list": [{"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "start", "dtype": "int64"}]}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "annotated", "struct": [{"name": "mentions", "list": [{"name": "capitalness", "dtype": "string"}, {"name": "chars_length", "dtype": "int64"}, {"name": "density", "dtype": "float64"}, {"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "tokens_length", "dtype": "int64"}, {"name": "value", "dtype": "string"}]}, {"name": "tags", "list": [{"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}]}, {"name": "predicted", "struct": [{"name": "mentions", "sequence": "null"}, {"name": "tags", "sequence": "null"}]}, {"name": "text_length", "dtype": "int64"}, {"name": "tokens", "list": [{"name": "capitalness", "dtype": "string"}, {"name": "char_end", "dtype": "int64"}, {"name": "char_start", "dtype": "int64"}, {"name": "custom", "dtype": "null"}, {"name": "idx", "dtype": "int64"}, {"name": "length", "dtype": "int64"}, {"name": "score", "dtype": "null"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "tokens_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 139252.8, "num_examples": 44}, {"name": "test", "num_bytes": 34813.2, "num_examples": 11}], "download_size": 101846, "dataset_size": 174066.0}}	2023-03-06T03:09:41+00:00
bdd0e0c11d142bd5618dac337a994dcbc27cb2fe	# Dataset Card for "flores200_packing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	bri25yu/flores200_packing	[ "region:us" ]	2023-03-05T20:02:09+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 14963812804.0, "num_examples": 2560000}, {"name": "val", "num_bytes": 3827042, "num_examples": 5000}, {"name": "test", "num_bytes": 7670994, "num_examples": 10000}], "download_size": 5440061492, "dataset_size": 14975310840.0}}	2023-03-14T05:32:23+00:00
4cf9c7fe36acb958be9d42892d03c1390d6f1d5e	# Dataset Card for "clevr-with-depth-full-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	erkam/clevr-with-depth-full-v2	[ "region:us" ]	2023-03-05T20:18:46+00:00	{"dataset_info": {"features": [{"name": "target_img", "dtype": "image"}, {"name": "source_img", "dtype": "image"}, {"name": "target_obj", "sequence": "int64"}, {"name": "source_obj", "sequence": "int64"}, {"name": "target_box", "sequence": {"sequence": "float32"}}, {"name": "source_box", "sequence": {"sequence": "float32"}}, {"name": "target_depth", "dtype": "image"}, {"name": "source_depth", "dtype": "image"}, {"name": "target_tri", "sequence": {"sequence": "int64"}}, {"name": "source_tri", "sequence": {"sequence": "int64"}}, {"name": "pos_prompt", "dtype": "string"}, {"name": "neg_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 43042513.0, "num_examples": 300}, {"name": "val", "num_bytes": 43023361.0, "num_examples": 300}, {"name": "train", "num_bytes": 200465105.0, "num_examples": 1400}], "download_size": 283955264, "dataset_size": 286530979.0}}	2023-03-05T20:18:58+00:00
668bc5a18254b2e835387bf3919d1cb70ddb5209		Dzeniks/fever_3way	[ "license:mit", "region:us" ]	2023-03-05T20:27:09+00:00	{"license": "mit"}	2023-03-14T13:01:47+00:00
c5bcfdb1808aef95004086a62ebe6a2d84fba449		Dzeniks/feverous_3way	[ "license:mit", "region:us" ]	2023-03-05T20:28:33+00:00	{"license": "mit"}	2023-03-05T20:29:51+00:00
8cacc890b6a6bb80e163bab3bdb67db147df58c9	# Hover Dataset The Hover dataset is a collection of labeled examples for many-hop fact extraction and claim verification tasks. It contains claims, with each claim labeled as either "Supports" or "Refutes". The dataset was created by Yichen Jiang, Shikha Bordia, Zheng Zhong, Charles Dognin, Maneesh Singh, and Mohit Bansal, and was presented in their paper "HoVer: A Dataset for Many-Hop Fact Extraction and Claim Verification" at the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) [Hover page](https://hover-nlp.github.io/). ## Format The Hover dataset is formatted as a TSV file, with each line containing the following fields: - Claim: The text of the claim to be verified. - Label: The label for the claim, either "0" for "Supports" or "1" for "Refutes". - Explanation: A sentence or phrase explaining why the claim is labeled as such. - Evidence: Evidence supporting or refuting the claim, if available. This may be a URL or a short text snippet.	Dzeniks/hover	[ "task_categories:text-classification", "license:mit", "region:us" ]	2023-03-05T20:34:48+00:00	{"license": "mit", "task_categories": ["text-classification"]}	2023-05-04T15:59:13+00:00
a59d816c57fd1cbb1e8f40cfc2abcbf1464e25c4	# Dataset Card for "cnn_dailymail_ngrams_1_to_5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	whu9/cnn_dailymail_ngrams_1_to_5	[ "region:us" ]	2023-03-05T21:15:14+00:00	{"dataset_info": {"features": [{"name": "cnn_dailymail_ngrams_1_to_5", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10483993441, "num_examples": 379567626}], "download_size": 5675144305, "dataset_size": 10483993441}}	2023-03-05T21:18:41+00:00
312ef489ebd64f9bf66b75f6d6ae55a3cdd4a380	# Dataset Card for "baby_names" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	jbrazzy/baby_names	[ "region:us" ]	2023-03-06T00:45:34+00:00	{"dataset_info": {"features": [{"name": "Names", "dtype": "string"}, {"name": "Sex", "dtype": "string"}, {"name": "Count", "dtype": "int64"}, {"name": "Year", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 33860482, "num_examples": 1084385}, {"name": "test", "num_bytes": 8482889, "num_examples": 271663}], "download_size": 13301020, "dataset_size": 42343371}}	2023-03-06T00:45:44+00:00
9062568da9c6383c09193f3aa1f3c48dca925c5f	# Toxic Spans TOXICSPANS contains the 11,035 posts we annotated for toxic spans. The unique posts are actually 11,006, since a few were duplicates and were removed in subsequent experiments. A few other posts were used as quiz questions to check the reliability of candidate annotators and were also discarded in subsequent experiments. original data from https://github.com/ipavlopoulos/toxic_spans/tree/master/ACL2022 - 10006 train set - 1000 test set ## Columns - probability = a dict with the first and the last character offsets of each token (that was rated by at least one annotator as toxic) as a key, and the average toxicity as a value - position = the character offsets of all the toxic spans(avg toxicity > 50%) found by the annotators (ground truth) - text = the average toxicity of each token that was rated by at least one annotator as toxic - type = the type of toxicity of each toxic span - support = the number of annotators per post - text_of_post = the text of the post - position_probability = the average toxicity of each character offset that was found by at least one annotator as toxic - toxic = (Not in original) If the probability of at least 1 token is greather than 0.5 ### Sample ``` { "probability": { "(5, 11)": 1.0, "(286, 294)": 0.6666666667, "(120, 126)": 0.6666666667, "(350, 356)": 0.6666666667 }, "position": [ 5, 6, 7, 8, 9, 10, 120, 121, 122, 123, 124, 125, 286, 287, 288, 289, 290, 291, 292, 293, 350, 351, 352, 353, 354, 355 ], "text": { "stupid": 1.0, "ignorant": 0.6666666667, "Stupid": 0.6666666667 }, "type": { "profane\/obscene": 0.3333333333, "insult": 0.6666666667 }, "support": 3, "text_of_post": "Yes, stupid on steroids does afflict the nation. The biggest problem, of course, is they either don't see themselves as stupid, or are so proud of the fact they are they have no intention of remedying the situation. In fact, that's the definition of stupid in my book: You know you're ignorant, proud of it, and have no intention of alleviating it. Stupid. \n\nI wonder how they'd like their doctors to say to them \"Oh, I didn't go to medical school; that's for elites. The need for a formal education is fake news. I studied at home for a couple of years and got an alternative medical education. Trust me, I'm as good a doctor as any. Now, when did you want to schedule that surgery?\" I wonder how they'd like an unlicensed pilot in charge of getting them from point A to Point B? \n\nI think they're all just lazy. They want all the benefits of an education, but don't want to put in the time.", "position_probability": { "5": 1.0, "6": 1.0, "7": 1.0, "8": 1.0, "9": 1.0, "10": 1.0, "286": 0.6666666667, "287": 0.6666666667, "288": 0.6666666667, "289": 0.6666666667, "290": 0.6666666667, "291": 0.6666666667, "292": 0.6666666667, "293": 0.6666666667, "120": 0.6666666667, "121": 0.6666666667, "122": 0.6666666667, "123": 0.6666666667, "124": 0.6666666667, "125": 0.6666666667, "350": 0.6666666667, "351": 0.6666666667, "352": 0.6666666667, "353": 0.6666666667, "354": 0.6666666667, "355": 0.6666666667 }, "toxic": true } ```	heegyu/toxic-spans	[ "license:cc0-1.0", "region:us" ]	2023-03-06T00:53:50+00:00	{"license": "cc0-1.0"}	2023-03-06T02:01:17+00:00
915f1f82f3f10a07e88e5ad5c7604be976b871d9	# AutoTrain Dataset for project: cylonix_summarize ## Dataset Description This dataset has been automatically processed by AutoTrain for project cylonix_summarize. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "CAPE TOWN (Reuters) - Two men were injured in a drive-by shooting at South Africa s Cape Town International Airport, police said on Wednesday. Operations at the airport, the second busiest in the country and a tourism hub, were not affected, police spokesman Vish Naidoo said, adding the motive for the shooting was unknown. The airport is Africa s third busiest and handles around 10 million passengers a year, including tourists and businesspeople commuting to the economic hub Johannesburg. A man, about 50 years of age, was shot in a drive-by shooting and in the process a bystander, a 30-year-old man, was also hit, Naidoo said. The shooting took place in the early morning in a public area some distance from the airport s security gates, Naidoo said. Flights had not been affected, the airport s general manager Deon Cloete said in a statement. The shooting scene has been cordoned off while investigations continue, Cloete said. Local media reported that the shooting was gang-related, but Naidoo could not confirm that was the motive. ", "target": "Two injured in shooting at South Africa's Cape Town airport" }, { "text": "WASHINGTON (Reuters) - The chairman of a U.S. Senate cyber security subcommittee said on Friday he planned to introduce sanctions legislation over \u201cRussia\u2019s cyber criminals\u201d after Washington accused Russia of political cyber attacks ahead of the Nov. 8 presidential election. Republican Senator Cory Gardner said his legislation would require the Obama administration to investigate those who have engaged in significant actions undermining cyber security and aggressively pursue sanctions when appropriate. Gardner is chairman of the Senate Foreign Relations Subcommittee on East Asia, the Pacific and International Cybersecurity. ", "target": "U.S. lawmaker wants cyber sanctions on Russia after hacking charges" } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 37 \| \| valid \| 10 \|	KoddaDuck/autotrain-data-cylonix_summarize	[ "task_categories:summarization", "region:us" ]	2023-03-06T01:51:20+00:00	{"task_categories": ["summarization"]}	2023-03-06T01:53:23+00:00
64d0b9ff8f63828d21b26c89daab16dafc129006	# Dataset Card for "hh_prompted_human_eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Dahoas/hh_prompted_human_eval	[ "region:us" ]	2023-03-06T01:52:30+00:00	{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48004, "num_examples": 100}], "download_size": 30531, "dataset_size": 48004}}	2023-03-06T01:52:38+00:00
65642e4c736ba4a7afdbcdd0493c3995d53a8d5e	# AutoTrain Dataset for project: size ## Dataset Description This dataset has been automatically processed by AutoTrain for project size. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<990x990 RGB PIL image>", "target": 0 }, { "image": "<966x990 RGB PIL image>", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['Large', 'Medium', 'Mosaic', 'Small'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 212 \| \| valid \| 54 \|	kkar0/autotrain-data-size	[ "task_categories:image-classification", "region:us" ]	2023-03-06T02:15:12+00:00	{"task_categories": ["image-classification"]}	2023-03-06T02:17:14+00:00
7dc70e44431b026015ece3e179e211157a211ecc		jarrydmartinx/recount3-RNA-seq	[ "license:gpl", "region:us" ]	2023-03-06T02:29:26+00:00	{"license": "gpl"}	2023-03-17T03:08:38+00:00
4792ceabd05b7098ee083a0dfb5380e9e9441b72	--- dataset_info: - config_name: train features: - name: filename dtype: string - name: height dtype: int64 - name: width dtype: int64 - name: ann struct: - name: bboxes sequence: sequence: float64 - name: bboxes_ignore sequence: sequence: int64 - name: label_ignore sequence: int64 - name: labels sequence: int64 splits: - name: train num_bytes: 211748 num_examples: 500 download_size: 89624346 dataset_size: 211748 - config_name: val features: - name: filename dtype: string - name: height dtype: int64 - name: width dtype: int64 - name: ann struct: - name: bboxes sequence: sequence: float64 - name: bboxes_ignore sequence: sequence: int64 - name: label_ignore sequence: int64 - name: labels sequence: int64 splits: - name: val num_bytes: 209868 num_examples: 500 download_size: 82654443 dataset_size: 209868 ---	WuWenc/tiny_coco	[ "license:apache-2.0", "region:us" ]	2023-03-06T03:13:30+00:00	{"license": "apache-2.0"}	2023-03-07T08:08:42+00:00
74a1a0fe49f309f10d319c26d97fdabda8787540	# Movie Review Data * Original source: sentence polarity dataset v1.0 http://www.cs.cornell.edu/people/pabo/movie-review-data/ * Seems to same as https://huggingface.co/datasets/rotten_tomatoes, but different split. ## Original README ======= Introduction This README v1.0 (June, 2005) for the v1.0 sentence polarity dataset comes from the URL http://www.cs.cornell.edu/people/pabo/movie-review-data . ======= Citation Info This data was first used in Bo Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.'', Proceedings of the ACL, 2005. @InProceedings{Pang+Lee:05a, author = {Bo Pang and Lillian Lee}, title = {Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales}, booktitle = {Proceedings of the ACL}, year = 2005 } ======= Data Format Summary - rt-polaritydata.tar.gz: contains this readme and two data files that were used in the experiments described in Pang/Lee ACL 2005. Specifically: * rt-polarity.pos contains 5331 positive snippets * rt-polarity.neg contains 5331 negative snippets Each line in these two files corresponds to a single snippet (usually containing roughly one single sentence); all snippets are down-cased. The snippets were labeled automatically, as described below (see section "Label Decision"). Note: The original source files from which the data in rt-polaritydata.tar.gz was derived can be found in the subjective part (Rotten Tomatoes pages) of subjectivity_html.tar.gz (released with subjectivity dataset v1.0). ======= Label Decision We assumed snippets (from Rotten Tomatoes webpages) for reviews marked with ``fresh'' are positive, and those for reviews marked with ``rotten'' are negative. ## Preprocessing To make csv with text and label field, we use the following script. ```python3 import csv import random # NOTE: The encoding of original file is "latin_1". We will change it to "utf8". with open("rt-polarity.pos", encoding="latin_1") as f: texts_pos = [line.strip() for line in f] with open("rt-polarity.neg", encoding="latin_1") as f: texts_neg = [line.strip() for line in f] rows_pos = [{"text": text, "label": 1} for text in texts_pos] rows_neg = [{"text": text, "label": 0} for text in texts_pos] # NOTE: For fair validation, we split it into train and test. Also, for the research who wants to use different setting, we provide whole setting. # NOTE: We follow the split setting in LM-BFF paper. rows_whole = rows_pos + rows_neg random.Random(42).shuffle(rows_whole) rows_test, rows_train = rows_whole[:2000], rows_whole[2000:] with open("whole.csv", "w", encoding="utf8") as f: writer = csv.DictWriter(f, fieldnames=["text", "label"]) writer.writerows(rows_train) with open("train.csv", "w", encoding="utf8") as f: writer = csv.DictWriter(f, fieldnames=["text", "label"]) writer.writerows(rows_train) with open("test.csv", "w", encoding="utf8") as f: writer = csv.DictWriter(f, fieldnames=["text", "label"]) writer.writerows(rows_test) ```	sh0416/mr	[ "task_categories:text-classification", "language:en", "region:us" ]	2023-03-06T03:37:19+00:00	{"language": ["en"], "task_categories": ["text-classification"]}	2023-03-06T04:33:25+00:00
51a2c285267db66f70abcf157971e9e91f63b043	# Dataset Card for "arab-txt-2-img" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Ahmed007/arab-txt-2-img	[ "region:us" ]	2023-03-06T03:43:36+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5513073754.525, "num_examples": 40455}], "download_size": 1121014430, "dataset_size": 5513073754.525}}	2023-03-06T03:44:24+00:00
0f9db9bc8ff13e6a0bb82a7e27dba27a6383c4ff		amla/EthiPic	[ "license:openrail++", "region:us" ]	2023-03-06T03:53:26+00:00	{"license": "openrail++"}	2023-03-06T03:53:26+00:00
e84f1643eae6fb681a1df7123a4919de79782db9	# UIT-ViQuAD v2 - Source: - Num examples: - 19,238 (train) - 9,714 (validation) - Language: Vietnamese ```python from datasets import load_dataset load_dataset("tdtunlp/uit_viquad_vi") ``` - Format for QA task ```python def preprocess(sample): question = sample['question'] answer = sample['answer'] return {'text': f'<\|startoftext\|><\|question\|>{question}<\|answer\|>{answer}<\|endoftext\|>'} """ <\|startoftext\|><\|question\|>Tên gọi nào được Phạm Văn Đồng sử dụng khi làm Phó chủ nhiệm cơ quan Biện sự xứ tại Quế Lâm?<\|answer\|>Tên gọi mà Phạm Văn Đồng sử dụng khi làm Phó chủ nhiệm cơ quan Biện sự xứ tại Quế Lâm là Lâm Bá Kiệt.<\|endoftext\|> """ ```	vietgpt-archive/uit_viquad_vi	[ "task_categories:question-answering", "size_categories:10K<n<100K", "language:vi", "LM", "region:us" ]	2023-03-06T03:54:14+00:00	{"language": ["vi"], "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "short_answer", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11582141.90997921, "num_examples": 19238}, {"name": "validation", "num_bytes": 5762392, "num_examples": 9714}], "download_size": 5378187, "dataset_size": 17344533.90997921}, "tags": ["LM"]}	2023-05-31T10:56:03+00:00
5862e54e776fa588ed74f83665ffb856af72b64f	simple chinese put in localizations	benzhuo/chinese	[ "region:us" ]	2023-03-06T05:07:02+00:00	{}	2023-03-07T05:35:54+00:00
c6ba4f7cb50785b9333e59d0cfd8cb3b9dc2cb18		SKyu/2019_2020data	[ "size_categories:10K<n<100K", "language:en", "language:ko", "license:cc-by-nc-sa-4.0", "region:us" ]	2023-03-06T05:36:57+00:00	{"language": ["en", "ko"], "license": "cc-by-nc-sa-4.0", "size_categories": ["10K<n<100K"], "pretty_name": "architecture data set 2019-2020"}	2023-03-06T05:37:53+00:00
b4f6d18bdca0af83ad5931c341acca6902d832af	# Dataset Card for "dys_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	LightFury9/dys_train	[ "region:us" ]	2023-03-06T06:39:29+00:00	{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text_label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 480013795.2, "num_examples": 5600}], "download_size": 429982174, "dataset_size": 480013795.2}}	2023-03-06T06:40:27+00:00
e30d4e0cbd65d10b977f66185114533bc2a1a3a4	# Dataset Card for "reklamation24_haus-reinigung" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	fathyshalab/reklamation24_haus-reinigung	[ "region:us" ]	2023-03-06T08:15:31+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 233372, "num_examples": 466}, {"name": "test", "num_bytes": 63354, "num_examples": 117}], "download_size": 0, "dataset_size": 296726}}	2023-04-19T07:39:26+00:00
5cc2f66e60ec2304466766d37017798e80e0c86d	# Dataset Card for "hack5-rawIQ" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	dsupa/hack5-rawIQ	[ "region:us" ]	2023-03-06T09:13:05+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", "5": "5", "6": "6"}}}}], "splits": [{"name": "train", "num_bytes": 2838470.0, "num_examples": 647}], "download_size": 30442, "dataset_size": 2838470.0}}	2023-03-06T09:13:07+00:00
17fb0b6648ba41e12ef94afb95b142b22f040c96	# Dataset Card for "cnn_daily_mail_nor_final" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	navjordj/cnn_daily_mail_nor_final	[ "region:us" ]	2023-03-06T09:28:41+00:00	{"dataset_info": {"features": [{"name": "article", "dtype": "string"}, {"name": "highlights", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 192449381.25865006, "num_examples": 47171}, {"name": "validation", "num_bytes": 14487455.276535718, "num_examples": 3551}, {"name": "test", "num_bytes": 22993888.464814223, "num_examples": 5636}], "download_size": 146363858, "dataset_size": 229930725.0}}	2023-03-06T09:29:13+00:00
0aaee90c6f21192643fd2a68fbe3de8e87bcf7d4	# Dataset Card for "cnn_daily_mail_nor_final" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	jkorsvik/cnn_daily_mail_nor_final	[ "region:us" ]	2023-03-06T09:34:10+00:00	{"dataset_info": {"features": [{"name": "article", "dtype": "string"}, {"name": "highlights", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 192449381.25865006, "num_examples": 47171}, {"name": "validation", "num_bytes": 14487455.276535718, "num_examples": 3551}, {"name": "test", "num_bytes": 22993888.464814223, "num_examples": 5636}], "download_size": 146363858, "dataset_size": 229930725.0}}	2023-03-06T09:34:33+00:00
771aec3493a0a5d78ba98977ad0fec34436ece86	# Dataset Card for "math-qa-classification" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	rootacess/math-qa-classification	[ "region:us" ]	2023-03-06T10:19:33+00:00	{"dataset_info": {"features": [{"name": "Problem", "dtype": "string"}, {"name": "category", "dtype": {"class_label": {"names": {"0": "general", "1": "physics", "2": "gain", "3": "geometry", "4": "probability", "5": "other"}}}}], "splits": [{"name": "test", "num_bytes": 537186, "num_examples": 2985}, {"name": "train", "num_bytes": 5370736, "num_examples": 29837}, {"name": "validation", "num_bytes": 802226, "num_examples": 4475}], "download_size": 3557830, "dataset_size": 6710148}}	2023-03-06T12:25:04+00:00
1b6213ee94ece32ac458cf1ae5a34753466fd999	# Dataset Card for "reklambox2-cleaned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	fathyshalab/reklambox2-cleaned	[ "region:us" ]	2023-03-06T10:36:56+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "filename", "dtype": "string"}, {"name": "index", "dtype": "int64"}, {"name": "label_name", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 635342.199146515, "num_examples": 1124}, {"name": "test", "num_bytes": 159400.80085348507, "num_examples": 282}], "download_size": 446755, "dataset_size": 794743.0}}	2023-03-10T15:19:03+00:00
62bee783b00574fce9587eeb8e82d227b8631489	# Dataset Card for "reklambox3-cleaned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	fathyshalab/reklambox3-cleaned	[ "region:us" ]	2023-03-06T11:18:23+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 550071.8254750175, "num_examples": 1136}, {"name": "test", "num_bytes": 138002.1745249824, "num_examples": 285}], "download_size": 424497, "dataset_size": 688074.0}}	2023-03-06T11:18:34+00:00
b76362e3cd15e044d52286c3e41cd67784f6e404	# Dataset Card for "rk1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	fathyshalab/rk1	[ "region:us" ]	2023-03-06T11:20:52+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 544223.8520625889, "num_examples": 1124}, {"name": "test", "num_bytes": 136540.1479374111, "num_examples": 282}], "download_size": 420690, "dataset_size": 680764.0}}	2023-03-15T13:59:49+00:00
54fe31aa9aad8c800753611b503217a227bc4fd9	# Dataset Card for "PickaPic-rankings-6-3-2023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	yuvalkirstain/PickaPic-rankings-6-3-2023	[ "region:us" ]	2023-03-06T12:20:35+00:00	{"dataset_info": {"features": [{"name": "ranking_id", "dtype": "int64"}, {"name": "created_at", "dtype": "timestamp[ns]"}, {"name": "user_id", "dtype": "int64"}, {"name": "image_1_uid", "dtype": "string"}, {"name": "image_2_uid", "dtype": "string"}, {"name": "image_3_uid", "dtype": "null"}, {"name": "image_4_uid", "dtype": "null"}, {"name": "best_image_uid", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 16312020, "num_examples": 71457}], "download_size": 5911046, "dataset_size": 16312020}}	2023-03-07T08:43:52+00:00
0a1f89ac17122ed3d46846af32d094b21f7d8c77	# Dataset Card for "PickaPic-images-6-3-2023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	yuvalkirstain/PickaPic-images-6-3-2023	[ "region:us" ]	2023-03-06T12:21:02+00:00	{"dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "created_at", "dtype": "timestamp[ns]"}, {"name": "image_uid", "dtype": "string"}, {"name": "user_id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "negative_prompt", "dtype": "string"}, {"name": "seed", "dtype": "int64"}, {"name": "gs", "dtype": "float64"}, {"name": "steps", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "num_generated", "dtype": "int64"}, {"name": "scheduler_cls", "dtype": "string"}, {"name": "model_id", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 53236259, "num_examples": 86985}], "download_size": 10063363, "dataset_size": 53236259}}	2023-03-07T08:44:44+00:00
241dee5894bab9d22448e38fa18bd7b5909011dc		ritik1234/embeddings	[ "license:openrail", "region:us" ]	2023-03-06T12:21:54+00:00	{"license": "openrail"}	2023-03-06T12:23:44+00:00
3284227f873d87288aac1d595bd66713711cab5c		shivi/sample_dataset	[ "license:creativeml-openrail-m", "region:us" ]	2023-03-06T12:28:54+00:00	{"license": "creativeml-openrail-m"}	2023-03-06T12:31:25+00:00
6d7ad963c3b196f11a08920ab0b040aae43ab5e5	# Dataset Card for "memo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	yemen2016/memo	[ "region:us" ]	2023-03-06T12:42:09+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 298952996, "num_examples": 3203876}], "download_size": 189354351, "dataset_size": 298952996}}	2023-04-25T09:53:14+00:00
e6f0b773ac5666451307564178c398a8c46064f6	# Dataset Card for "UA_Speech" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Akshay-Sai/UA_Speech	[ "region:us" ]	2023-03-06T12:44:10+00:00	{"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 5378040000, "num_examples": 5600}], "download_size": 737238219, "dataset_size": 5378040000}}	2023-03-06T12:45:09+00:00
bf30c53bfa48042419a70b692593cc9d7d13fb83	# Dataset Card for "memo1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	yemen2016/memo1	[ "region:us" ]	2023-03-06T12:44:59+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 269063328, "num_examples": 2883488}], "download_size": 186337044, "dataset_size": 269063328}}	2023-03-06T12:45:15+00:00
df7a07ca1ba3e96973d8d847e9a44dfb9fb79ea9		joefox/newstest-2017-2019-ru_zh	[ "license:apache-2.0", "region:us" ]	2023-03-06T12:45:44+00:00	{"license": "apache-2.0"}	2023-03-06T12:47:44+00:00
b961b7f9026ecc039860201b5ce68e13c7b376bd	# Dataset Card for "UA_Speech" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	AravindVadlapudi02/UA_Speech	[ "region:us" ]	2023-03-06T12:52:29+00:00	{"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 4609752000, "num_examples": 4800}, {"name": "test", "num_bytes": 768288000, "num_examples": 800}], "download_size": 736678488, "dataset_size": 5378040000}}	2023-03-06T12:55:11+00:00
6fe3ce43b0f9402708887a94837d37046d601ae5		JackBAI/chatgpt-woi-finetune	[ "license:mit", "region:us" ]	2023-03-06T12:55:25+00:00	{"license": "mit"}	2023-03-07T02:43:26+00:00
1d4eea9eba1d886a99ebb8665e087f78e083f5ca	# Dataset Card for "reklamation24_wasser-strom-gas" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	fathyshalab/reklamation24_wasser-strom-gas	[ "region:us" ]	2023-03-06T13:29:11+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 221722, "num_examples": 433}, {"name": "test", "num_bytes": 54894, "num_examples": 109}], "download_size": 0, "dataset_size": 276616}}	2023-04-19T07:38:23+00:00
8d9d3fece6e3dfc61dd817f12d3c838fa503a25f	# Dataset Card for "push_default" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	polinaeterna/push_default	[ "region:us" ]	2023-03-06T13:30:39+00:00	{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "test", "1": "train"}}}}], "splits": [{"name": "train", "num_bytes": 645457.0, "num_examples": 2}, {"name": "test", "num_bytes": 281491.0, "num_examples": 1}], "download_size": 909476, "dataset_size": 926948.0}, "configs_kwargs": {"data_dir": "./"}}	2023-03-06T13:30:48+00:00
afd33fcd5db1b73dc1799ee72310a057e8c78f20	# Dataset Card for "reklamation24_haus-reinigung-full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	fathyshalab/reklamation24_haus-reinigung-full	[ "task_categories:text-classification", "language:de", "region:us" ]	2023-03-06T13:33:34+00:00	{"language": ["de"], "task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 23403712, "num_examples": 4499}], "download_size": 0, "dataset_size": 23403712}}	2023-05-15T07:43:54+00:00
37184f46348a8fa84eaf66482fbbb94af54d52c9	# Dataset Card for "reklamation24_wasser-strom-gas-full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	fathyshalab/reklamation24_wasser-strom-gas-full	[ "region:us" ]	2023-03-06T13:34:19+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 19400136, "num_examples": 3322}], "download_size": 0, "dataset_size": 19400136}}	2023-04-25T13:17:28+00:00
589b93f87b48c672d5f0d65aca1ea3d328181172		milkist/milky-set	[ "license:other", "region:us" ]	2023-03-06T13:58:37+00:00	{"license": "other"}	2023-03-06T16:17:19+00:00
6db8a847ab22db6c293f90ca414be9f6b8dabc69	# Dataset Card for "push" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	polinaeterna/push	[ "region:us" ]	2023-03-06T14:05:16+00:00	{"dataset_info": [{"config_name": "custom", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3200, "num_examples": 200}, {"name": "test", "num_bytes": 4800, "num_examples": 300}], "download_size": 7682, "dataset_size": 8000}, {"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1600, "num_examples": 100}, {"name": "test", "num_bytes": 3200, "num_examples": 200}], "download_size": 5578, "dataset_size": 4800}], "configs_kwargs": [{"config_name": "custom", "data_dir": "custom"}, {"config_name": "default", "data_dir": "./"}]}	2023-03-06T14:06:30+00:00
daea1a6c5a830d135e3bd8335f591e4edd473dee	# Dataset Card for "programming-languages-keywords" Structured version of https://github.com/e3b0c442/keywords Generated using: ```python r = requests.get("https://raw.githubusercontent.com/e3b0c442/keywords/main/README.md") keywords = r.text.split("### ")[1:] keywords = [i for i in keywords if not i.startswith("Sources")] keywords = {i.split("\n")[0]:[j for j in re.findall("[a-zA-Z]*", i.split("\n",1)[1]) if j] for i in keywords} keywords = pd.DataFrame(pd.Series(keywords)).reset_index().rename(columns={"index":"language", 0:"keywords"}) keywords['language'] = keywords['language'].str.split("\) ").str[0] keywords['keywords'] = keywords['keywords'].apply(lambda x: sorted(list(set(x)))) ds = Dataset.from_pandas(keywords) ```	bigcode/programming-languages-keywords	[ "region:us" ]	2023-03-06T14:13:23+00:00	{"dataset_info": {"features": [{"name": "language", "dtype": "string"}, {"name": "keywords", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 20307, "num_examples": 36}], "download_size": 8838, "dataset_size": 20307}}	2023-03-06T14:17:24+00:00
fbd6e2f5c559e67090b13c0eefbb7dd191918fe9		lsr42/msmarco-passage-doct5query	[ "license:unknown", "region:us" ]	2023-03-06T14:26:04+00:00	{"license": "unknown", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "passage", "num_bytes": 16870476814, "num_examples": 8841823}], "download_size": 5773175789, "dataset_size": 16870476814}}	2023-03-06T16:51:24+00:00
565cbc8ab8400a4e797268dabf3e179832fdc38f	# xlsum - Source: https://huggingface.co/datasets/GEM/xlsum - Num examples: - 32,108 (train) - 4,013 (validation) - 4,013 (test) - Language: Vietnamese ```python from datasets import load_dataset load_dataset("tdtunlp/xlsum_vi") ``` - Format for Summarization task ```python def preprocess(sample): title = sample['title'] summary = sample['target'] article = sample['text'] return {'text': f'<\|startoftext\|><\|title\|>{title}<\|article\|>{article}<\|summary\|>{summary}<\|endoftext\|>'} """ <\|startoftext\|><\|title\|>Việt Nam đã sẵn sàng nâng tầm đối tác chiến lược với Mỹ?<\|article\|>Ông Donald Trump và ông Nguyễn Phú Trọng bắt tay trước thềm Thượng đỉnh Trump-Kim ở Hà Nội hôm 27/2/2019 Vài tháng qua đã có nhiều thảo luận về khả năng Hoa Kỳ-Việt Nam nâng tầm mối quan hệ từ "đối tác toàn diện" lên thành "đối tác chiến lược". Dưới đây là một số nhận định tiêu biểu. Một số quan ngại Theo ông Prashanth Parameswaran, tác giả bài viết hôm 12/9 trên The Diplomat, mối quan hệ Việt Nam - Hoa Kỳ đã tốt hơn nhiều so với thời chiến tranh Việt Nam. Hai nước bình thường hóa quan hệ dưới thời cựu Tổng thống Mỹ Bill Clinton và tiếp tục duy trì tốt dưới thời Obama. Việc nâng tầm quan hệ Mỹ-Việt có ý nghĩa lớn với các nhà hoạch định chính sách cả hai nước. Nó phản ánh nỗ lực của Washington trong việc mở rộng mạng lưới các đồng minh và đối tác tại châu Á - Thái Bình Dương và tầm quan trọng của Việt Nam trong mạng lưới này, đồng thời nhấn mạnh cơ hội và thách thức mà Hà Nội phải cân nhắc. TQ 'không vui' với chuyến thăm VN của USS Carl Vinson? David Hutt: 'Mục tiêu thương chiến kế tiếp của Trump là VN' USS Carl Vinson tới Đà Nẵng: 'Bước đi chiến lược' Việc Mỹ-Việt nâng tầm quan hệ có thể có ý nghĩa lớn hơn là bản thân mối quan hệ này, đặc biệt trong bối cảnh Mỹ - Trung Quốc tăng cường cạnh tranh về quyền lực trong khu vực châu Á-Thái Bình Dương và vai trò của Việt Nam trong các vấn đề như Biển Đông - nơi mà Trung Quốc ngày càng lấn lướt và Hà Nội chịu áp lực ngày càng lớn. Mỹ gần đây đã tăng cường các hoạt động hợp tác với Việt Nam. Năm 2018, Mỹ mang hàng không mẫu hạm USS Carl Vinson tới Việt Nam. Năm nay, Chủ tịch nước, Tổng bí thư Nguyễn Phú Trọng cũng dự kiến có chuyến công du Mỹ vào tháng 10/2019. Tuy nhiên, thực tế là Việt Nam và Mỹ vẫn có nhiều khác biệt trong nhiều lĩnh vực, từ thể chế tới quan điểm về nhân quyền. Việt Nam và Mỹ cũng có khác biệt trong quan điểm đối với vấn đề thương mại hoặc vấn đề Bắc Hàn - điều khiến quan hệ hai nước từng có vẻ khó 'toàn diện', chứ chưa nói đến 'chiến lược'. Chính vì thế, các cuộc thảo luận để nâng tầm mối quan hệ Mỹ - Việt cũng bao gồm cả các quan ngại, ông Prashanth Parameswaran bình luận. Các nhà hoạch định chính sách sẽ cần cân nhắc các yếu tố quan trọng này để tính toán được mất khi nâng tầm mối quan hệ. Chẳng hạn, không phải ngẫu nhiên mà chúng ta đã thấy Việt Nam trì hoãn một số hoạt động liên quan đến quốc phòng với Hoa Kỳ bất chấp những lợi ích có thể thấy rõ, vẫn theo tác giả Prashanth Parameswaran. Các quan ngại nói trên không có nghĩa Việt Nam - Hoa Kỳ không mong muốn hoặc không thể nâng tầng hợp tác. Nhưng nó có nghĩa rằng cả Mỹ và Việt Nam cần đảm bảo rằng các vấn đề thực tế giữa hai nước phù hợp với bất cứ tầm mức quan hệ nào mà họ lựa chọn. Quan trọng nữa là, việc điều chỉnh tên gọi của mối quan hệ chỉ có giá trị khi cả hai bên cùng cam kết nỗ lực để biến tiềm năng hợp tác thành sự hợp tác trên thực tế. Mỹ gửi tín hiệu 'hỗn hợp' Nhà báo David Hutt, cũng về đề tài này, trên Asia Times lại cho rằng Mỹ gửi những tín hiệu không thống nhất đến Việt Nam, nói năm nay, Mỹ lên tiếng cáo buộc Trung Quốc có hành động 'bắt nạt' nước láng giềng Việt Nam. Mỹ cũng ngỏ ý "muốn củng cố mối quan hệ quân sự chặt chẽ hơn với Hà Nội, mặc dù Việt Nam vẫn tỏ ra thận trọng và vẫn duy trì các chính sách ngoại giao không cam kết," David Hutt cũng nhắc tới tin đồn gần đây rằng công ty dầu khí Mỹ ExxonMobil đang tìm cách rút dự án Cá Voi Xanh trị giá hàng tỷ đô la khỏi Việt Nam, và bình luận rằng: Nếu thực sự ExxonMobil rút - cứ cho là vì lý do tài chính chứ không phải địa chính trị - thì đây cũng là một cú nốc ao vào mối quan hệ Mỹ-Việt ở giai đoạn mang tính bước ngoặt. Hơn bao giờ hết, Hà Nội hiện đang tìm kiếm các cam kết từ Washington rằng họ sẽ đứng về phía mình trong bất kỳ cuộc xung đột có vũ trang nào với Trung Quốc trên Biển Đông. Thương mại: Ông Donald Trump đe dọa Việt Nam Kỳ vọng gì nếu chủ tịch Trọng thăm Hoa Kỳ? Tập trận Mỹ-ASEAN: 'Mỹ sẽ không đứng yên nếu TQ tiếp tục ép VN' Mỹ, tuy thế, đang gửi tín hiệu 'hỗn hợp'. Quan hệ Việt Nam - Hoa Kỳ nảy nở dưới thời Tổng thống Mỹ Donald Trump. Ông Trump đã hai lần đến thăm Việt Nam và hiếm khi chỉ trích điều gì về đất nước được coi là vi phạm nhân quyền tồi tệ nhất Đông Nam Á này, vẫn theo David Hutt. Nhưng ông Trump, bên cạnh đó, lại cũng rất phiền lòng với việc Việt Nam trở thành nơi sản xuất, xuất khẩu các mặt hàng Trung Quốc nằm trong diện bị Mỹ đánh thuế, để trốn thuế. Ông Trump, hồi tháng Sáu đã gọi Việt Nam là nước 'lạm dụng tồi tệ nhất' trong một cuộc phỏng vấn truyền hình. Tuy nhiên, chính quyền của ông Trung cũng lại phản ứng quyết liệt khi Trung Quốc mang tàu vào khu đặc quyền kinh tế của Việt Nam tại Bãi Tư Chính trên Biển Đông. Người phát ngôn Bộ Ngoại giao Mỹ Morgan Ortagus nói Trung Quốc đã thực hiện một loạt các động thái hung hăng để can thiệp các hoạt động kinh tế lâu đời của Việt Nam. "Việt-Mỹ đã hợp tác chiến lược nhiều mặt, trừ tên gọi" Trong khi đó, tác giả Đoàn Xuân Lộc viết trên Asia Times, một yếu tố quan trọng của chính sách đối ngoại của Hà Nội là không liên minh. Để giúp đất nước tăng cường quan hệ ngoại giao, kinh tế và an ninh với các đối tác liên quan, chính phủ Việt Nam, do đó, đã tìm cách xây dựng một mạng lưới quan hệ đối tác. "Quan hệ đối tác toàn diện" là nấc thấp nhất trong mạng lưới này. Việt Nam và Hoa Kỳ thiết lập "quan hệ đối tác toàn diện" tháng 7/2013. Như vậy, Việt Nam đứng sau Philippines, Thái Lan, Indonesia và Singapore - các đối tác chiến lược của Hoa Kỳ trong khu vực - về tầm quan trọng đối với Washington. Trong khi đó, Việt Nam đã nâng tầm "quan hệ chiến lược" với 16 nước gồm Nga (2001), Nhật Bản (2006), Ấn Độ (2007), Trung Quốc (2008), Hàn Quốc và Tây Ban Nha (2009), Vương quốc Anh (2010), Đức (2011), Pháp, Indonesia, Ý, Singapore và Thái Lan ( 2013), Malaysia và Philippines (2015) và Úc (2017). Trong ngôn ngữ ngoại giao của Hà Nội, tất nhiên, Trung Quốc là đối tác quan trọng nhất của Việt Nam, trong khi Mỹ là một trong những quốc gia ít quan trọng nhất. Trên giấy tờ, mối "quan hệ đối tác toàn diện" của Việt Nam với Mỹ - nền kinh tế và quân sự lớn nhất thế giới - thậm chí còn xếp sau quan hệ "đối tác toàn diện" của Việt Nam với Myanmar - được thiết lập năm 2017. Nhưng trên thực tế, Mỹ là đối tác quan trọng thứ hai của Việt Nam. Ở nhiều khía cạnh, Mỹ cũng quan trọng không kém Trung Quốc. Và Hà Nội hiểu rằng có một mối quan hệ khỏe mạnh với Mỹ mang tính sống còn với đất nước, giúp ổn định sự phát triển và tránh quá phụ thuộc vào Trung Quốc về kinh tế, ông Đoàn Xuân Lộc nhận định. Hiện nay, sự hung hăng của Trung Quốc ở Biển Đông là một trong các yếu tố chính để Việt Nam tìm cách thắt chặt quan hệ với Mỹ, đặc biệt trong an ninh quốc phòng. Nhìn chung, mặc dù vẫn có những khác biệt nhất định, đặc biệt là về các quyền tự do chính trị và nhân quyền, lợi ích chiến lược của Hoa Kỳ và Việt Nam ngày càng phù hợp với nhau. Đối Việt Nam, mối quan hệ với Mỹ hiện tại về cơ bản là chiến lược trong nhiều lĩnh vực quan trọng, như an ninh và quốc phòng, mặc dù về tên gọi nó mới chỉ là "quan hệ đối tác toàn diện", vẫn theo tác giả Đoàn Xuân Lộc. <\|summary\|>Ý kiến về khả năng Việt-Mỹ trở thành đối tác chiến lược khi hai nước có nhiều khác biệt về thể chế chính trị và nhân quyền.<\|endoftext\|> """ ```	vietgpt-archive/xlsum_vi	[ "task_categories:summarization", "size_categories:10K<n<100K", "language:vi", "LM", "region:us" ]	2023-03-06T14:52:23+00:00	{"language": ["vi"], "size_categories": ["10K<n<100K"], "task_categories": ["summarization"], "dataset_info": {"features": [{"name": "gem_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "target", "dtype": "string"}, {"name": "references", "list": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 219542787, "num_examples": 32108}, {"name": "test", "num_bytes": 15891883, "num_examples": 4013}, {"name": "validation", "num_bytes": 15896905, "num_examples": 4013}], "download_size": 134434688, "dataset_size": 251331575}, "tags": ["LM"]}	2023-05-31T11:06:28+00:00
f98c1fbee7c1fe1e30abda459a30421c32b4da06	# xlsum - Source: https://huggingface.co/datasets/GEM/xlsum - Num examples: - 306,521 (train) - 11,535 (validation) - 11,535 (test) - Language: English ```python from datasets import load_dataset load_dataset("tdtunlp/xlsum_en") ``` - Format for Summarization task ```python def preprocess(sample): title = sample['title'] article = sample['text'] summary = sample['target'] return {'text': f'<\|startoftext\|><\|title\|>{title}<\|article\|>{article}<\|summary\|>{summary}<\|endoftext\|>'} """ <\|startoftext\|><\|title\|>Weather alert issued for gale force winds in Wales<\|article\|>The Met Office has issued a yellow weather warning for wind covering Wales and England, starting from 21:00 GMT on Wednesday evening. Travel and power are both likely to be disrupted, with the warning to remain in place until 15:00 on Thursday. Gusts of 55mph (88kmh) are likely and could hit up to 70mph on coasts and hills, with heavy and blustery showers. <\|summary\|>Winds could reach gale force in Wales with stormy weather set to hit the whole of the country this week.<\|endoftext\|> """ ```	vietgpt-archive/xlsum_en	[ "task_categories:summarization", "size_categories:100K<n<1M", "language:en", "region:us" ]	2023-03-06T15:01:15+00:00	{"language": ["en"], "size_categories": ["100K<n<1M"], "task_categories": ["summarization"], "dataset_info": {"features": [{"name": "gem_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "target", "dtype": "string"}, {"name": "references", "list": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 975957381, "num_examples": 306521}, {"name": "test", "num_bytes": 34940457, "num_examples": 11535}, {"name": "validation", "num_bytes": 35201391, "num_examples": 11535}], "download_size": 655141522, "dataset_size": 1046099229}}	2023-05-31T10:41:54+00:00
d06b7e88eccaa78644f3a877461640cb7d0b1535	[SQuAD V2.0](https://rajpurkar.github.io/SQuAD-explorer/). It contains the following: * Train and Validation sets. * The end position of each answer.	hazemessam/squad_v2	[ "license:cc-by-4.0", "region:us" ]	2023-03-06T15:03:51+00:00	{"license": "cc-by-4.0"}	2023-03-06T15:18:07+00:00
b8b47c589f8bee77175b8648e5497278b68da48a	# Dataset Card for ETHICS This is the data from [Aligning AI With Shared Human Values](https://arxiv.org/pdf/2008.02275) by Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt, published at ICLR 2021. For more information, see the [Github Repo](https://github.com/hendrycks/ethics). ## Dataset Summary This dataset provides ethics-based tasks for evaluating language models for AI alignment. ## Loading Data To load this data, you can use HuggingFace datasets and the dataloader script. ``` from datasets import load_dataset load_dataset("hendrycks/ethics", "commonsense") ``` Where `commonsense` is one of the following sections: commonsense, deontology, justice, utilitarianism, and virtue. ### Citation Information ``` @article{hendrycks2021ethics, title={Aligning AI With Shared Human Values}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ```	hendrycks/ethics	[ "language:en", "license:mit", "AI Alignment", "arxiv:2008.02275", "region:us" ]	2023-03-06T15:25:03+00:00	{"language": "en", "license": "mit", "dataset_info": [{"config_name": "default", "features": [{"name": "label", "dtype": "int64"}, {"name": "input", "dtype": "string"}]}, {"config_name": "commonsense", "features": [{"name": "label", "dtype": "int32"}, {"name": "input", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14429921, "num_examples": 13910}, {"name": "validation", "num_bytes": 3148616, "num_examples": 3885}, {"name": "test", "num_bytes": 3863068, "num_examples": 3964}], "download_size": 21625153, "dataset_size": 21441605}, {"config_name": "deontology", "features": [{"name": "label", "dtype": "int32"}, {"name": "scenario", "dtype": "string"}, {"name": "excuse", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1854277, "num_examples": 18164}, {"name": "validation", "num_bytes": 369318, "num_examples": 3596}, {"name": "test", "num_bytes": 359268, "num_examples": 3536}], "download_size": 2384007, "dataset_size": 2582863}, {"config_name": "justice", "features": [{"name": "label", "dtype": "int32"}, {"name": "scenario", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2423889, "num_examples": 21791}, {"name": "validation", "num_bytes": 297935, "num_examples": 2704}, {"name": "test", "num_bytes": 228008, "num_examples": 2052}], "download_size": 2837375, "dataset_size": 2949832}, {"config_name": "utilitarianism", "features": [{"name": "baseline", "dtype": "string"}, {"name": "less_pleasant", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2186713, "num_examples": 13737}, {"name": "validation", "num_bytes": 730391, "num_examples": 4807}, {"name": "test", "num_bytes": 668429, "num_examples": 4271}], "download_size": 3466564, "dataset_size": 3585533}, {"config_name": "virtue", "features": [{"name": "label", "dtype": "int32"}, {"name": "scenario", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2605021, "num_examples": 28245}, {"name": "validation", "num_bytes": 467254, "num_examples": 4975}, {"name": "test", "num_bytes": 452491, "num_examples": 4780}], "download_size": 3364070, "dataset_size": 3524766}], "tags": ["AI Alignment"]}	2023-04-19T17:55:00+00:00
401c712095bf02eefcf88e30e3aaff514dff265b		BuroIdentidadDigital/recibos_telmex	[ "license:c-uda", "region:us" ]	2023-03-06T15:50:26+00:00	{"license": "c-uda"}	2024-01-12T23:02:31+00:00
262ddc85688bfc06527bcf099f87deef62977ac1	# Dataset Card for "merged-wiki-cnn" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	braindao/cnn-100k	[ "region:us" ]	2023-03-06T16:03:03+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 437866886, "num_examples": 100000}, {"name": "test", "num_bytes": 49735992, "num_examples": 11490}, {"name": "validation", "num_bytes": 57519439, "num_examples": 13368}], "download_size": 332900122, "dataset_size": 545122317}}	2023-03-08T10:38:08+00:00
5f29d1501ab85ceec5aa84d59f4ad2865107cf99	# 🇺🇦 Open Source Ukrainian Text-to-Speech dataset named MYKYTA Join Ukrainian community - https://t.me/speech_synthesis_uk More details about this dataset - https://github.com/egorsmkv/ukrainian-tts-datasets/tree/main/mykyta # Voice MYKYTA (male) License: [Apache 2.0](https://github.com/egorsmkv/ukrainian-tts-datasets/blob/main/LICENSE) Listen to [DEMO](https://huggingface.co/spaces/theodotus/ukrainian-voices) (choose "mykyta" in the Voice field) ## Features - Quality: high - Duration: 8h10m - Audio formats: OPUS/WAV - Text format: JSONL (a `metadata.jsonl` file) - Frequency: 16000/22050/48000 Hz ## Original version ### In the `OPUS` format - 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-mykyta/resolve/main/dataset_mykyta_ogg.zip ## Trimmed version (removed silence) Silence is removed by https://github.com/proger/uk#align-text-to-audio-and-trim-silence ### In the `WAV` format - 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-mykyta/resolve/main/dataset_mykyta_trimmed_48khz.zip - 22050 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-mykyta/resolve/main/dataset_mykyta_trimmed_22khz.zip - 16000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-mykyta/resolve/main/dataset_mykyta_trimmed_16khz.zip	Yehor/ukrainian-tts-mykyta	[ "task_categories:text-to-speech", "language:uk", "license:apache-2.0", "region:us" ]	2023-03-06T16:22:17+00:00	{"language": ["uk"], "license": "apache-2.0", "task_categories": ["text-to-speech"]}	2023-12-16T16:26:33+00:00
2b8480e5f80abebb6a6c4926666ef67345a68312	# 🇺🇦 Open Source Ukrainian Text-to-Speech dataset named TETIANA Join Ukrainian community - https://t.me/speech_synthesis_uk More details about this dataset - https://github.com/egorsmkv/ukrainian-tts-datasets/tree/main/tetiana # Voice TETIANA (female) License: [Apache 2.0](https://github.com/egorsmkv/ukrainian-tts-datasets/blob/main/LICENSE) ## Features - Quality: high - Duration: 8h - Audio formats: OPUS/WAV - Text format: JSONL (a `metadata.jsonl` file) - Frequency: 16000/22050/48000 Hz ## Original version ### In the `OPUS` format - 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_ogg.zip ## Trimmed version (removed silence) Silence is removed by https://github.com/proger/uk#align-text-to-audio-and-trim-silence ### In the `WAV` format - 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_trimmed_48khz.zip - 22050 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_trimmed_22khz.zip - 16000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_trimmed_16khz.zip	Yehor/ukrainian-tts-tetiana	[ "task_categories:text-to-speech", "language:uk", "license:apache-2.0", "region:us" ]	2023-03-06T16:47:50+00:00	{"language": ["uk"], "license": "apache-2.0", "task_categories": ["text-to-speech"]}	2023-12-16T16:25:09+00:00
05be76043886b06e25c52d40458f56e3bc7fe186	# Dataset Card for "deart" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	davanstrien/deart	[ "region:us" ]	2023-03-06T16:56:16+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "source", "dtype": "string"}, {"name": "width", "dtype": "int16"}, {"name": "height", "dtype": "int16"}, {"name": "dept", "dtype": "int8"}, {"name": "segmented", "dtype": "int8"}, {"name": "image_id", "dtype": "string"}, {"name": "annotations", "list": [{"name": "category_id", "dtype": {"class_label": {"names": {"0": "zebra", "1": "tree", "2": "nude", "3": "crucifixion", "4": "scroll", "5": "head", "6": "swan", "7": "shield", "8": "lily", "9": "mouse", "10": "knight", "11": "dragon", "12": "horn", "13": "dog", "14": "palm", "15": "tiara", "16": "helmet", "17": "sheep", "18": "deer", "19": "person", "20": "sword", "21": "rooster", "22": "bear", "23": "halo", "24": "lion", "25": "monkey", "26": "prayer", "27": "crown of thorns", "28": "elephant", "29": "zucchetto", "30": "unicorn", "31": "holy shroud", "32": "cat", "33": "apple", "34": "banana", "35": "chalice", "36": "bird", "37": "eagle", "38": "pegasus", "39": "crown", "40": "camauro", "41": "saturno", "42": "arrow", "43": "dove", "44": "centaur", "45": "horse", "46": "hands", "47": "skull", "48": "orange", "49": "monk", "50": "trumpet", "51": "key of heaven", "52": "fish", "53": "cow", "54": "angel", "55": "devil", "56": "book", "57": "stole", "58": "butterfly", "59": "serpent", "60": "judith", "61": "mitre", "62": "banner", "63": "donkey", "64": "shepherd", "65": "boat", "66": "god the father", "67": "crozier", "68": "jug", "69": "lance"}}}}, {"name": "image_id", "dtype": "string"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "segmentation", "list": {"list": "float32"}}, {"name": "iscrowd", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 8991340765.256, "num_examples": 15156}], "download_size": 18150029661, "dataset_size": 8991340765.256}}	2023-03-06T17:12:03+00:00
2cace2314e8d42389cdb4ef392392fa859504cba	# Dataset Card for "msmarco-passage-tilde" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	lsr42/msmarco-passage-tilde	[ "region:us" ]	2023-03-06T16:56:25+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "passage", "num_bytes": 6376936859, "num_examples": 8841823}], "download_size": 3609070765, "dataset_size": 6376936859}}	2023-03-06T17:07:32+00:00
8a42f9a8b614a247ce0aed2272d226a09fe25cd1	Новая версия: https://huggingface.co/datasets/Den4ikAI/russian_instructions_2 Русский датасет инструкций и QA. Структура датасета: ``` { "dialogue":[ "Как я могу улучшить свою связь между телом и разумом?", "Начните с разработки регулярной практики осознанности. 2. Обязательно практикуйте баланс на нескольких уровнях: физическом, эмоциональном, умственном и духовном. 3. Свяжитесь с природой, когда это возможно - идите на прогулки или бегайте на улице, или просто сидите в парке и наблюдайте природу вокруг вас. 4. Развивайте позитивные утверждения, которые вы можете использовать, чтобы помочь поддерживать оптимизм и сильные отношения с вашим телом.", "Больше пожалуйста", "Конечно, вот еще несколько: 5. Включите здоровую диету цельных продуктов и много воды, которая питает ваш ум и тело. 6. Научитесь быть в гармонии с вашим телом, замечая изменения в том, как вы себя чувствуете, и уделите время, чтобы оценить и решить любые возникающие потребности." ], "rounds":[ 1, 2 ], "uuid":"53e8d08b-1f45-4ef4-b6d8-3a04758cafd8" } ``` ### Citation: ``` @MISC{russian_instructions, author = {Denis Petrov}, title = {Russian instructions dataset for conversational agents}, url = {https://huggingface.co/datasets/Den4ikAI/russian_instructions}, year = 2023 } ```	Den4ikAI/russian_instructions	[ "task_categories:conversational", "size_categories:10K<n<100K", "language:ru", "license:mit", "region:us" ]	2023-03-06T17:06:32+00:00	{"language": ["ru"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["conversational"]}	2023-03-12T04:59:14+00:00
0ee2c7665fe97b6890d80b47d8290a7fb9aac292	Based on https://huggingface.co/datasets/its5Q/yandex-q, parsed full.jsonl.gz	IlyaGusev/yandex_q_full	[ "region:us" ]	2023-03-06T18:17:41+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "id2", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "text_plain", "dtype": "string"}, {"name": "text_html", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "negative_votes", "dtype": "int32"}, {"name": "positive_votes", "dtype": "int32"}, {"name": "quality", "dtype": "int8"}, {"name": "views", "dtype": "uint64"}, {"name": "votes", "dtype": "int32"}, {"name": "approved_answer", "dtype": "string"}, {"name": "timestamp", "dtype": "uint64"}, {"name": "tags", "sequence": "string"}, {"name": "answers", "sequence": [{"name": "id", "dtype": "string"}, {"name": "id2", "dtype": "int64"}, {"name": "text_plain", "dtype": "string"}, {"name": "text_html", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "negative_votes", "dtype": "int32"}, {"name": "positive_votes", "dtype": "int32"}, {"name": "votes", "dtype": "int32"}, {"name": "quality", "dtype": "int8"}, {"name": "views", "dtype": "uint64"}, {"name": "reposts", "dtype": "int32"}, {"name": "timestamp", "dtype": "uint64"}]}], "splits": [{"name": "train", "num_bytes": 5468460217, "num_examples": 1297670}], "download_size": 1130317937, "dataset_size": 5468460217}}	2023-03-07T20:30:24+00:00
171eb271eb03ff37a8417e01392097fa5729551a	# AutoTrain Dataset for project: sentiment_analysis ## Dataset Description This dataset has been automatically processed by AutoTrain for project sentiment_analysis. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "I grew up (b. 1965) watching and loving the Thunderbirds. All my mates at school watched. We played \"Thunderbirds\" before school, during lunch and after school. We all wanted to be Virgil or Scott. No one wanted to be Alan. Counting down from 5 became an art form. I took my children to see the movie hoping they would get a glimpse of what I loved as a child. How bitterly disappointing. The only high point was the snappy theme tune. Not that it could compare with the original score of the Thunderbirds. Thankfully early Saturday mornings one television channel still plays reruns of the series Gerry Anderson and his wife created. Jonatha Frakes should hand in his directors chair, his version was completely hopeless. A waste of film. Utter rubbish. A CGI remake may be acceptable but replacing marionettes with Homo sapiens subsp. sapiens was a huge error of judgment.", "target": 0 }, { "text": "When I put this movie in my DVD player, and sat down with a coke and some chips, I had some expectations. I was hoping that this movie would contain some of the strong-points of the first movie: Awsome animation, good flowing story, excellent voice cast, funny comedy and a kick-ass soundtrack. But, to my disappointment, not any of this is to be found in Atlantis: Milo's Return. Had I read some reviews first, I might not have been so let down. The following paragraph will be directed to those who have seen the first movie, and who enjoyed it primarily for the points mentioned.<br /><br />When the first scene appears, your in for a shock if you just picked Atlantis: Milo's Return from the display-case at your local videoshop (or whatever), and had the expectations I had. The music feels as a bad imitation of the first movie, and the voice cast has been replaced by a not so fitting one. (With the exception of a few characters, like the voice of Sweet). The actual drawings isnt that bad, but the animation in particular is a sad sight. The storyline is also pretty weak, as its more like three episodes of Schooby-Doo than the single adventurous story we got the last time. But dont misunderstand, it's not very good Schooby-Doo episodes. I didnt laugh a single time, although I might have sniggered once or twice.<br /><br />To the audience who haven't seen the first movie, or don't especially care for a similar sequel, here is a fast review of this movie as a stand-alone product: If you liked schooby-doo, you might like this movie. If you didn't, you could still enjoy this movie if you have nothing else to do. And I suspect it might be a good kids movie, but I wouldn't know. It might have been better if Milo's Return had been a three-episode series on a cartoon channel, or on breakfast TV.", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "ClassLabel(names=['0', '1'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 1497 \| \| valid \| 1497 \|	raghuram13/autotrain-data-sentiment_analysis	[ "task_categories:text-classification", "language:en", "region:us" ]	2023-03-06T20:18:31+00:00	{"language": ["en"], "task_categories": ["text-classification"]}	2023-03-06T20:23:54+00:00
06b337fe0d82d5f29f075fca5c435ee02bd9f92c	# Dataset Card for "rlhf-qa-conditional-generation-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	kastan/rlhf-qa-conditional-generation-v2	[ "region:us" ]	2023-03-06T20:41:34+00:00	{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 34403.31192660551, "num_examples": 87}, {"name": "valid", "num_bytes": 8699.688073394496, "num_examples": 22}], "download_size": 31360, "dataset_size": 43103.0}}	2023-04-14T21:35:34+00:00
46587daedc55f5dbfd44799275464cb547cfeeed	# Dataset Card for "Duaaii_5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Hundred9/Duaaii_5	[ "region:us" ]	2023-03-06T20:45:57+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", "5": "5", "6": "6"}}}}], "splits": [{"name": "train", "num_bytes": 43153426.0, "num_examples": 647}], "download_size": 43177352, "dataset_size": 43153426.0}}	2023-03-06T20:49:30+00:00
fbf42b7e03d6433669eb2fa2ecc407d5bdeaf677		Yes67843/eys	[ "license:other", "region:us" ]	2023-03-06T20:46:57+00:00	{"license": "other"}	2023-03-06T20:50:27+00:00
34198d9751dfccd2cd6a380f6f431e6b75c6ad44	# Dataset Card for "test-huggingface-datasets" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Djarnis/test-huggingface-datasets	[ "region:us" ]	2023-03-06T21:39:25+00:00	{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "labels", "list": [{"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "assignees", "list": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "milestone", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "creator", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "open_issues", "dtype": "int64"}, {"name": "closed_issues", "dtype": "int64"}, {"name": "state", "dtype": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "due_on", "dtype": "null"}, {"name": "closed_at", "dtype": "null"}]}, {"name": "comments", "sequence": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "timestamp[s]"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "null"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "diff_url", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "merged_at", "dtype": "timestamp[s]"}]}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "url", "dtype": "string"}, {"name": "total_count", "dtype": "int64"}, {"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "state_reason", "dtype": "string"}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 10702183, "num_examples": 2000}], "download_size": 2891006, "dataset_size": 10702183}}	2023-03-06T21:40:27+00:00
442c526c494d69888a77b0e1ca8eb2e08e768215	# Dataset Card for "test-dataset-via-streamlit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	MoritzLaurer/test-dataset-via-streamlit	[ "region:us" ]	2023-03-06T22:05:48+00:00	{"dataset_info": {"features": [{"name": "english", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "label1", "1": "label2"}}}}], "splits": [{"name": "train", "num_bytes": 1218.0, "num_examples": 3}, {"name": "test", "num_bytes": 159, "num_examples": 1}], "download_size": 7754, "dataset_size": 1377.0}}	2023-03-06T22:05:49+00:00
12f2e05c5f7e216dc476abe5f5cfa3a80c957903	# Dataset Card for "TIMIT_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	patlee0208/TIMIT_v2	[ "region:us" ]	2023-03-06T22:09:18+00:00	{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "DR1", "1": "DR2", "2": "DR3", "3": "DR4", "4": "DR5", "5": "DR6", "6": "DR7"}}}}], "splits": [{"name": "train", "num_bytes": 119307077.0, "num_examples": 597}], "download_size": 113914231, "dataset_size": 119307077.0}}	2023-03-06T22:10:07+00:00
f3ac95eaa17bf25fc091fe5274083cc5a7e30080	Join my discord here : https://discord.gg/PdYFs7qmSW	SyntheticVoices/AlexJones	[ "license:openrail", "region:us" ]	2023-03-06T23:11:33+00:00	{"license": "openrail"}	2023-03-06T23:54:43+00:00
96befac625ae44862842fd0b47536ebe64614ec4	This data set contains StockTwits posts from 01.11.2021 to 30.06.2022 for Bitcoin (BTC.X), Ethereum (ETH.X) and Shiba Inu (SHIB.X). The full set contains 124,503 posts, including 72,247 bullish, 38,249 neutral and 14,007 bearish posts. The training set ranges from 01.11.2021 to 30.04.2022, consists of 91,758 observations, including 57,932 bullish, 26,516 neutral, and 7310 bearish posts. The validation set ranges from 01.05.2022 to 15.06.2022 and contains 4084 bearish, 7534 neutral, and 9143 bullish posts, amounting to 20,761 examples. The test set ranges from 16.06.2022 to 30.06.2022 and consists of 5172 bullish, 4199 neutral, and 2613 bearish posts, having 11,984 observations in total. The validation and test sets contain all StockTwits posts, with at least one emoji, from their respective periods, while the training set is further limited by only including posts that have possibly influential bullish or bearish emojis. The training SVM dataset contains balanced samples used for training an SVM sentiment classifier. The bearish sets have 20K observations per class (pos is bearish, while neg is not bearish, so bullish and neutral). The bullish sets have 40K observations per class (pos is bullish, while neg is not bullish, so bearish and neutral).	ElKulako/stocktwits-emoji	[ "license:afl-3.0", "region:us" ]	2023-03-07T00:35:53+00:00	{"license": "afl-3.0"}	2023-03-07T01:12:48+00:00
680cd6266c2d0b4b18dba98013205f703ee62349	# AutoTrain Dataset for project: text2itinerary-exp-26-1000 ## Dataset Description This dataset has been automatically processed by AutoTrain for project text2itinerary-exp-26-1000. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "\ud589\uc0ac \uc81c\ubaa9\n[\ud50c\uce5c/\uc655\uc758\uadc0\ud658\uc2e4\uc18d/\ubc29\ucf55\ud30c\ud0c0\uc57c5\uc77c]\uc54c\ucc2c\uad00\uad11+$170\uc0c1\ub2f9\ud2b9\uc804(\ub7ed\uc154\ub9ac\uc694\ud2b8\ud06c\ub8e8\uc988,\uc2a4\ub178\ud074\ub9c1,\uc804\uc2e0\ub9c8\uc0ac\uc9c0\ud3ec\ud568)\n\uc5ec\ud589\uc9c0\n\ubc29\ucf55\|\ud30c\ud0c0\uc57c\n\ube10\ub958\nACCOMMODATION\n\uc77c\ucc28\n2\n\uc21c\uc11c\n5\n\uc81c\ubaa9\n\ud574\ub2f9 \uc77c\uc758 \uc219\ubc15\uc2dc\uc124\uc740 \ud604\uc7ac \ubbf8\uc815\uc785\ub2c8\ub2e4. \ucd9c\ubc1c 1\uc77c\uc804\uae4c\uc9c0 \ud648\ud398\uc774\uc9c0\ub97c \ud1b5\ud574 \uc54c\ub824\ub4dc\ub9ac\uaca0\uc2b5\ub2c8\ub2e4.\n\uc0c1\uc138\n* TITLE: [\uc219\ubc15] \uc544\uc774\uc57c\ub77c \uadf8\ub79c\ub4dc \ud30c\ud0c0\uc57c \ud638\ud154* PLACE_IDX: 44359* APPROVAL: 0* ADDRESS: 338/118 Moo. 12 Pratamnak Rd. $%T. Nongpure A. Banglamung Chonburi Thailand 20150* URL: http://www.aiyaragrand.com/\n* TITLE: [\uc219\ubc15] LK \uc140\ub808\ud0c0\uc774 \ud638\ud154* PLACE_IDX: 56825* APPROVAL: 0* ADDRESS: 45/97-99 ??????? 10 Bang Lamung District Chon Buri 20150 \ud0dc\uad6d* URL: https://lkpattaya.com/rs/package.php?c=1&hotel_session=LK%20Celestite\n* TITLE: [\uc219\ubc15] \uc5e0\ud30c\ud0c0\uc57c\ud638\ud154* PLACE_IDX: 65714* APPROVAL: 0* ADDRESS: 571/112 Moo 5 Naklua Road Banglamung Chonburi 20150* URL: http://www.mhotel.co.th/\n* TITLE: [\uc219\ubc15] \uc5d0\uc774\uc6d0\ub274\uc719 \ud30c\ud0c0\uc57c* PLACE_IDX: 56698* APPROVAL: 0* ADDRESS: 500-501 North Pattaya Banglamung Chonburi 20150* URL: http://www.a-onenewwingpattaya.com/\n* TITLE: [\uc219\ubc15] \uc13c\ud130\ud3ec\uc778\ud2b8\ud638\ud154 \ud30c\ud0c0\uc57c* PLACE_IDX: 65498* APPROVAL: 0* ADDRESS: 275 Moo 6 Sukhumvit Road Naklua Bang Lamung District Chon Buri 20150 \ud0dc\uad6d* URL: http://www.centrepoint.com/pattaya\n", "target": "\uc544\uc774\uc57c\ub77c \uadf8\ub79c\ub4dc \ud638\ud154\ud83c\udff7344\ud83c\udff7ACCOMMODATION\ud83d\udd0dNULL\nLK \uc140\ub808\uc2a4\ud0c0\uc774\ud2b8\ud83c\udff71370\ud83c\udff7ACCOMMODATION\ud83d\udd0dNULL\n\uc5e0 \ud30c\ud0c0\uc57c \ud638\ud154\ud83c\udff71371\ud83c\udff7ACCOMMODATION\ud83d\udd0dNULL\n\uc5d0\uc774-\uc6d0 \ub354 \ub85c\uc584 \ud06c\ub8e8\uc988 \ud638\ud154\ud83c\udff7592\ud83c\udff7ACCOMMODATION\ud83d\udd0dNULL\n\uc13c\ud130 \ud3ec\uc778\ud2b8 \ud504\ub77c\uc784 \ud638\ud154 \ud30c\ud0c0\uc57c\ud83c\udff71430\ud83c\udff7ACCOMMODATION\ud83d\udd0dNULL" }, { "text": "\ud589\uc0ac \uc81c\ubaa9\n\u2665 \ubca0\uc2a4\ud2b8\uc140\ub7ec \u2665 \ub7ed\uc154\ub9ac \ud2b8\ub7fc\ud504 \uc640\uc774\ud0a4\ud0a4 [\uc288\ud398\ub9ac\uc5b4\uc624\uc158\ubdf0] \ud558\uc640\uc774 \ud5c8\ub2c8\ubb38 6\uc77c\n\uc5ec\ud589\uc9c0\n\ud558\uc640\uc774\n\ube10\ub958\nOTHERS\n\uc77c\ucc28\n6\n\uc21c\uc11c\n16\n\uc81c\ubaa9\n\uc778\ucc9c\uacf5\ud56d \ub3c4\ucc29 Mahalo \u2661 \ud589\ubcf5\ud55c \uacb0\ud63c \uc0dd\ud65c \ub418\uc138\uc694 \u2661\n\uc0c1\uc138\n\uc778\ucc9c\uacf5\ud56d \ub3c4\ucc29 Mahalo \u2661 \ud589\ubcf5\ud55c \uacb0\ud63c \uc0dd\ud65c \ub418\uc138\uc694 \u2661\n", "target": "\uc778\ucc9c\ud83c\udff7971\ud83c\udff7AIRPORT\ud83d\udd0d\ub3c4\ucc29" } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: \| Split name \| Num samples \| \| ------------ \| ------------------- \| \| train \| 6512 \| \| valid \| 814 \|	eubinecto/autotrain-data-text2itinerary-exp-26-1000	[ "task_categories:summarization", "region:us" ]	2023-03-07T00:39:42+00:00	{"task_categories": ["summarization"]}	2023-03-07T00:40:16+00:00
96d6c66dd8b184c08c059d9d0b2881e4b1d28306		mushroomsolutions/imdb_sentiment_3000_Test	[ "license:mit", "region:us" ]	2023-03-07T00:44:17+00:00	{"license": "mit"}	2023-03-07T00:47:41+00:00
f7f6475df0e14cabe81ca91d0c181d538bc2fe39	# Dataset Card for "jsbachmmmbar" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	juancopi81/jsbachmmmbar	[ "region:us" ]	2023-03-07T01:26:09+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21569479, "num_examples": 27000}, {"name": "test", "num_bytes": 601308, "num_examples": 310}], "download_size": 3155226, "dataset_size": 22170787}}	2023-03-07T01:26:18+00:00
94348fd0b05baa25375dd2029e43e1a8fa604b8d	# Dataset Card for "jsbachmmmtrack" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	juancopi81/jsbachmmmtrack	[ "region:us" ]	2023-03-07T01:27:22+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 43706425, "num_examples": 27000}, {"name": "test", "num_bytes": 592628, "num_examples": 310}], "download_size": 7553812, "dataset_size": 44299053}}	2023-03-07T01:27:31+00:00
63602e8303644883bd4869567bbf23dee1b6ca8f		Duskfallcrew/Star_Marvel_Final	[ "task_categories:text-to-image", "language:en", "license:creativeml-openrail-m", "stable diffusion", "region:us" ]	2023-03-07T01:47:09+00:00	{"language": ["en"], "license": "creativeml-openrail-m", "task_categories": ["text-to-image"], "pretty_name": "Star Marvel Data Final", "tags": ["stable diffusion"]}	2023-03-07T02:56:14+00:00
7a746459f480cc939969f39ff6bfa4424fd84519	# Dataset Card for "summarization-sft-heirarchical-split1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	dmayhem93/summarization-sft-heirarchical-split1	[ "region:us" ]	2023-03-07T03:19:35+00:00	{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "125M", "dtype": "string"}, {"name": "1B", "dtype": "string"}, {"name": "6B", "dtype": "string"}, {"name": "20B", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 121267953, "num_examples": 47241}, {"name": "test", "num_bytes": 217854405, "num_examples": 83632}, {"name": "valid", "num_bytes": 86573178, "num_examples": 33088}], "download_size": 124221198, "dataset_size": 425695536}}	2023-03-07T03:20:57+00:00
5b92c63d0f5336cd340dc248ae0c637f5e2ef81f	# Dataset Card for "summarization-sft-heirarchical-split2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	dmayhem93/summarization-sft-heirarchical-split2	[ "region:us" ]	2023-03-07T03:21:00+00:00	{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "125M", "dtype": "string"}, {"name": "1B", "dtype": "string"}, {"name": "6B", "dtype": "string"}, {"name": "20B", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 117627495, "num_examples": 45303}, {"name": "test", "num_bytes": 217854405, "num_examples": 83632}, {"name": "valid", "num_bytes": 131656080, "num_examples": 50720}], "download_size": 137276787, "dataset_size": 467137980}}	2023-03-07T03:22:54+00:00

ae4e12e5dcce4974dd943bca177db96698014edb

# Dataset Card for "sci_summ" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

0x70DA/sci_summ

[ "region:us" ]

2023-03-05T13:56:55+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 23361937.97759879, "num_examples": 4631}, {"name": "test", "num_bytes": 23487172.952651516, "num_examples": 4665}, {"name": "train", "num_bytes": 176474272.610434, "num_examples": 34083}], "download_size": 120216439, "dataset_size": 223323383.54068428}}

2023-03-05T18:12:54+00:00

0fda022559b4e821f3071948ec45bccd51374b1a

Nothing....

Ojimi/Hifu-Lora-Example

[ "license:creativeml-openrail-m", "region:us" ]

2023-03-05T14:22:33+00:00

{"license": "creativeml-openrail-m"}

2023-03-05T14:26:43+00:00

4b1c1ccf212702db4b68c10b8ecc1b68ea88ad7d

# Dataset Card for "dreambooth-hackathon-images" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Volodymyr/dreambooth-hackathon-images

[ "region:us" ]

2023-03-05T14:38:11+00:00

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "pixel_values", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 62824895.0, "num_examples": 84}], "download_size": 62830151, "dataset_size": 62824895.0}}

2023-03-05T14:38:21+00:00

188e1346c7e106df573a8fa9d7fc3931db491435

# Dataset Card for "test_push_dataset_split_exists_main" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

polinaeterna/test_push_dataset_split_exists_main

[ "region:us" ]

2023-03-05T14:48:01+00:00

{"dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 320, "num_examples": 20}], "download_size": 1469, "dataset_size": 320}}

2023-03-05T14:48:51+00:00

a9f15ac8585d29a36d5b5d851e9556519cc33d33

# Dataset Card for "test_push_dataset_split_exists_main2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

polinaeterna/test_push_dataset_split_exists_main2

[ "region:us" ]

2023-03-05T14:54:09+00:00

{"dataset_info": {"features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 320, "num_examples": 20}, {"name": "random", "num_bytes": 640, "num_examples": 40}], "download_size": 3128, "dataset_size": 960}}

2023-03-05T14:55:09+00:00

8b321a5de4e5f254048838135a72aa588c8b32aa

OpenDILabCommunity/fake_browser_state_zoo

[ "license:apache-2.0", "region:us" ]

2023-03-05T15:19:24+00:00

{"license": "apache-2.0"}

2023-03-06T07:32:40+00:00

146ec91014c0b47957a2b158a042ab6d99cd3b29

# Dataset Card for "test_push_dataset_infos_json" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

polinaeterna/test_push_dataset_infos_json

[ "region:us" ]

2023-03-05T15:29:38+00:00

{"dataset_info": [{"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1600, "num_examples": 100}, {"name": "random", "num_bytes": 3200, "num_examples": 200}], "download_size": 3299, "dataset_size": 4800}, {"config_name": "v2", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3200, "num_examples": 200}], "download_size": 0, "dataset_size": 3200}], "configs_kwargs": [{"config_name": "default", "data_dir": "./"}, {"config_name": "v2", "data_dir": "v2"}]}

2023-03-05T16:10:16+00:00

95f9412e874a061da3f1b4ff9cf8ec74ccdf9e21

# Dataset Card for "TreeImageDataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Ru3ll/TreeImageDataset

[ "region:us" ]

2023-03-05T15:44:59+00:00

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "test", "1": "train", "2": "val"}}}}], "splits": [{"name": "train", "num_bytes": 1882655120.0, "num_examples": 922}], "download_size": 1882708375, "dataset_size": 1882655120.0}}

2023-03-05T15:54:17+00:00

2d0e061031c3a701bf2418e347c94d7931287a80

# Bengali Abstractive News Summarization (BANS) ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** [BANS PAPER](https://doi.org/10.1007/978-981-33-4673-4_4) - **Leaderboard:** - **Point of Contact:** [Prithwiraj Bhattacharjee]([email protected]) ### Dataset Summary Nowadays news or text summarization becomes very popular in the NLP field. Both the extractive and abstractive approaches of summarization are implemented in different languages. A significant amount of data is a primary need for any summarization. For the Bengali language, there are only a few datasets are available. Our dataset is made for Bengali Abstractive News Summarization (BANS) purposes. As abstractive summarization is basically neural network-based it needs more and more data to perform well. So we made a standard Bengali abstractive summarization data crawling from online Bengali news portal bangla.bdnews24.com. We crawled more than 19k articles and summaries and standardized the data. ### Downloading the data ``` from datasets import load_dataset train = load_dataset("sustcsenlp/bn_news_summarization",split="train") ``` ### Dataset Description | Description | Data Info. | | ----------- | ----------- | | Total no of articles | 19096 | | Total no of summaries | 19096 | | Maximum no of words in an article | 76 | | Maximum no of words in a summary | 12 | | Minimum no of words in an article | 5 | | Minimum no of words in a summary | 3 | ### Languages This dataset contains Bangla Text Data. ## Acknowledgement We would like to thank Shahjalal University of Science and Technology (SUST) research center and SUST NLP research group for their support. ### Citation Information ``` @InProceedings{10.1007/978-981-33-4673-4_4, author="Bhattacharjee, Prithwiraj and Mallick, Avi and Saiful Islam, Md. and Marium-E-Jannat", editor="Kaiser, M. Shamim and Bandyopadhyay, Anirban and Mahmud, Mufti and Ray, Kanad", title="Bengali Abstractive News Summarization (BANS): A Neural Attention Approach", booktitle="Proceedings of International Conference on Trends in Computational and Cognitive Engineering", year="2021", publisher="Springer Singapore", address="Singapore", pages="41--51", abstract="Bhattacharjee, PrithwirajMallick, AviSaiful Islam, Md.Marium-E-JannatAbstractive summarization is the process of generating novel sentences based on the information extracted from the original text document while retaining the context. Due to abstractive summarization's underlying complexities, most of the past research work has been done on the extractive summarization approach. Nevertheless, with the triumph of the sequence-to-sequence (seq2seq) model, abstractive summarization becomes more viable. Although a significant number of notable research has been done in the English language based on abstractive summarization, only a couple of works have been done on Bengali abstractive news summarization (BANS). In this article, we presented a seq2seq based Long Short-Term Memory (LSTM) network model with attention at encoder-decoder. Our proposed system deploys a local attention-based model that produces a long sequence of words with lucid and human-like generated sentences with noteworthy information of the original document. We also prepared a dataset of more than 19 k articles and corresponding human-written summaries collected from bangla.bdnews24.com (https://bangla.bdnews24.com/) which is till now the most extensive dataset for Bengali news document summarization and publicly published in Kaggle (https://www.kaggle.com/prithwirajsust/bengali-news-summarization-dataset) We evaluated our model qualitatively and quantitatively and compared it with other published results. It showed significant improvement in terms of human evaluation scores with state-of-the-art approaches for BANS.", isbn="978-981-33-4673-4" } ``` ### Contributors | Name | University | | ----------- | ----------- | | Prithwiraj Bhattacharjee | Shahjalal University of Science and Technology | | Avi Mallick | Shahjalal University of Science and Technology | | Md. Saiful Islam | Shahjalal University of Science and Technology | | Marium-E-Jannat | Shahjalal University of Science and Technology |

sustcsenlp/bn_news_summarization

[ "task_categories:summarization", "size_categories:1K<n<10K", "language:bn", "license:cc0-1.0", "region:us" ]

2023-03-05T15:47:29+00:00

{"language": ["bn"], "license": "cc0-1.0", "size_categories": ["1K<n<10K"], "task_categories": ["summarization"]}

2023-03-05T16:41:32+00:00

22d8bd29f65dd4589a646d2b8d2ce59f5ad1d063

# Dataset Card for "ema_en" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bhpardo/ema_en

[ "region:us" ]

2023-03-05T15:58:10+00:00

{"dataset_info": {"features": [{"name": "english", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 436742.4575850489, "num_examples": 5479}, {"name": "test", "num_bytes": 109205.5424149511, "num_examples": 1370}], "download_size": 340686, "dataset_size": 545948.0}}

2023-03-05T16:26:35+00:00

e02de05402edce1f37e0551b92432cb6f90aadf6

slumericanBx/x

[ "license:creativeml-openrail-m", "region:us" ]

2023-03-05T16:02:11+00:00

{"license": "creativeml-openrail-m"}

2023-03-05T16:02:11+00:00

f4c030a88bd1d54b0572db80a4709821a0159dfb

# Dataset Card for "test_push_dataset_dict_infos_json" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

polinaeterna/test_push_dataset_dict_infos_json

[ "region:us" ]

2023-03-05T16:09:45+00:00

{"dataset_info": [{"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1600, "num_examples": 100}, {"name": "random", "num_bytes": 3200, "num_examples": 200}], "download_size": 5578, "dataset_size": 4800}, {"config_name": "v2", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3200, "num_examples": 200}, {"name": "random", "num_bytes": 4800, "num_examples": 300}], "download_size": 0, "dataset_size": 8000}], "configs_kwargs": [{"config_name": "default", "data_dir": "./"}, {"config_name": "v2", "data_dir": "v2"}]}

2023-03-05T17:25:51+00:00

619b827ee4c0275c9eda1d65c751b6b62a33dc21

# Dataset Card for "satellite-512-alt" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Shavindra/satellite-512-alt

[ "region:us" ]

2023-03-05T16:10:19+00:00

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}, {"name": "mask_rgb", "dtype": "image"}, {"name": "pixel_values", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 333395732.0, "num_examples": 304}], "download_size": 333272045, "dataset_size": 333395732.0}}

2023-03-05T17:08:12+00:00

7bebc0e2549b52dc839212245b9bcfac2ee85d7b

The COHeN dataset is designed for training and fine-tuning Biblical Hebrew classification models. It consists of 11968 verses from the Hebrew Bible along with labels indicating to which of the four chronological phases of the language each verse belongs. The following verses were chosen to represent each stage: - Archaic Biblical Hebrew (ABH): Gen 49:2–27; Exod 15:1–18; Num 23:7–10, 18–24; 24:3–9; 15–24; Deut 32:1–43; Deut 33:2–39; Jud 5:2–31; 2 Sam 22:2–51; Psa 18:1–50; 68:2–35 (minus the prose introductions in Exod 15:1; Num 23:7, 18; Num 24:3, 15; Deut 33:2; 2 Sam 22:2; Ps 18:2) - Classical Biblical Hebrew (CBH): 1 Sam 1:1–31:13; 2 Sam 1:1–22:1; 23:1–24:25; 1 Kgs 1:1–22:53; 2 Kgs 1:1–25:30 - Transitional Biblical Hebrew (TBH): Isa 40:1–54:17; Jer 1:1–10:10; 10:12–42:34; Ezel 1:1–48:35 - Late Biblical Hebrew (LBH): Eccl 1:1–12:14; Esth 1:1–10:3; Dan 1:1–2:4a; Dan 7:29–12:13, Ezra 1:1–4:6; 6:19–7:11; 7:26–10:44; Neh 1:1–13:31; 1 Chr 1:1–9:44; 12:1–40; 15:1–24; 16:7–43; 21:1–29:19; 2 Chr 7:1–3; 14:9–15:7; 17:1–19; 21:12–17; 24:25–22; 26:6–21; 29:3–31:21; 33:10–20; 34:3–7; 36:22–23 Class balance was achieved by oversampling from the three minority classes (ABH, TBH, and LBH). The script used to generate the COHeN dataset can be found [here](https://github.com/gngpostalsrvc/COHeN/blob/main/input/generate_COHeN_dataset.py).

gngpostalsrvc/COHeN

[ "license:mit", "region:us" ]

2023-03-05T16:18:28+00:00

{"license": "mit"}

2023-04-09T18:53:08+00:00

14cddef0b2c1c0d91f63eebd557a2812a0bdf9fa

BrainStormersHakton/iq-wikis

[ "license:other", "region:us" ]

2023-03-05T18:10:46+00:00

{"license": "other"}

2023-03-05T18:15:16+00:00

9c118b80b7169719031a0589d3869615885e2749

# Dataset Card for "hh_human_eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Dahoas/hh_human_eval

[ "region:us" ]

2023-03-05T18:19:08+00:00

{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 50908, "num_examples": 100}], "download_size": 30961, "dataset_size": 50908}}

2023-03-06T00:10:19+00:00

3624189590972ee6154ecbd01cb0b71fddbee7f5

# Dataset Card for "babylm_childstories" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Sree1994/babylm_childstories

[ "region:us" ]

2023-03-05T18:33:21+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1464755, "num_examples": 4800}, {"name": "test", "num_bytes": 369959, "num_examples": 1200}], "download_size": 1174215, "dataset_size": 1834714}}

2023-03-05T18:33:29+00:00

6d0ea3cfec0c556ed11e051e2e38f36d7c6f0e58

# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1). ### Supported Tasks and Leaderboards [More Information Needed] ### Languages [More Information Needed] ## Dataset Structure ### Data Instances [More Information Needed] ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization [More Information Needed] #### Who are the source language producers? [More Information Needed] ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [More Information Needed] ### Citation Information [More Information Needed] ### Contributions [More Information Needed]

gungangigna/data_set

[ "region:us" ]

2023-03-05T18:36:17+00:00

{}

2023-03-05T18:40:28+00:00

d182636c932e7628d53c74d48122b502b779ceec

# Dataset Card for "COMPS" ## Dataset Description COMPS is a dataset of minimal pair sentences in English that enables the testing knowledge of concepts and their properties in language models (LMs). Specifically, it tests the ability of LMs to attribute properties to everyday concepts, and demonstrate reasoning compatible with property inheritance, where subordinate concepts inherit the properties of their superordinate (hypernyms). - **Homepage:** [https://github.com/kanishkamisra/comps/](https://github.com/kanishkamisra/comps/) - **Repository:** [https://github.com/kanishkamisra/comps/](https://github.com/kanishkamisra/comps/) - **Paper:** [arxiv](https://arxiv.org/abs/2210.01963) - **Point of Contact:** [Kanishka Misra] (https://kanishka.website) ### Citation Information ``` @inproceedings{misra-etal-2023-comps, title = "{COMPS}: Conceptual Minimal Pair Sentences for testing Robust Property Knowledge and its Inheritance in Pre-trained Language Models", author = "Misra, Kanishka and Rayz, Julia and Ettinger, Allyson", booktitle = "Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics", month = may, year = "2023", address = "Dubrovnik, Croatia", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.eacl-main.213", doi = "10.18653/v1/2023.eacl-main.213", pages = "2928--2949", abstract = "A characteristic feature of human semantic cognition is its ability to not only store and retrieve the properties of concepts observed through experience, but to also facilitate the inheritance of properties (can breathe) from superordinate concepts (animal) to their subordinates (dog){---}i.e. demonstrate property inheritance. In this paper, we present COMPS, a collection of minimal pair sentences that jointly tests pre-trained language models (PLMs) on their ability to attribute properties to concepts and their ability to demonstrate property inheritance behavior. Analyses of 22 different PLMs on COMPS reveal that they can easily distinguish between concepts on the basis of a property when they are trivially different, but find it relatively difficult when concepts are related on the basis of nuanced knowledge representations. Furthermore, we find that PLMs can show behaviors suggesting successful property inheritance in simple contexts, but fail in the presence of distracting information, which decreases the performance of many models sometimes even below chance. This lack of robustness in demonstrating simple reasoning raises important questions about PLMs{'} capacity to make correct inferences even when they appear to possess the prerequisite knowledge.", } ```

kanishka/comps

[ "annotations_creators:expert-generated", "language_creators:machine-generated", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:original", "language:en", "license:apache-2.0", "arxiv:2210.01963", "region:us" ]

2023-03-05T18:47:23+00:00

{"annotations_creators": ["expert-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": "apache-2.0", "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "pretty_name": "COMPS"}

2023-09-16T14:09:24+00:00

d07388b056730058bb007cb3a0ad490cfd880c8b

Monty555/Anisbook

[ "license:openrail", "region:us" ]

2023-03-05T18:58:54+00:00

{"license": "openrail"}

2023-03-05T18:58:54+00:00

897883867b6cb1059b7a4b44fe2689e6a7944386

mohammadnajeeb/concrete_crack_images

[ "license:cc-by-4.0", "region:us" ]

2023-03-05T19:13:03+00:00

{"license": "cc-by-4.0"}

2023-03-05T19:14:43+00:00

57e6e3adf0aa8bfaab2ab5269db11c5fe2997502

SummerSigh/prostring

[ "license:apache-2.0", "region:us" ]

2023-03-05T19:19:54+00:00

{"license": "apache-2.0"}

2023-03-05T19:23:30+00:00

3a1d28ee56306a5a7496ffc222435cbc350ad813

daviddaubner/misinformation-detection

[ "license:unknown", "region:us" ]

2023-03-05T19:20:46+00:00

{"license": "unknown"}

2023-03-09T17:06:23+00:00

deeb52aca51f0637b56999130effb1cf84b9a7ad

Saikeiunn/nva-hinanawi_alpha

[ "license:other", "region:us" ]

2023-03-05T19:24:28+00:00

{"license": "other"}

2023-03-05T19:25:41+00:00

8ae97891ad22af1be38cad1a8a88997bfc8862bd

Hi folks, Here is a collection of data I have scraped or aggregated for most of the stocks in the S&P 500, including popular ones like Apple (AAPL). It has the following data: - Daily news articles and sentiments on those articles collected over the last few years. - All quarterly stock fundamentals (ratios) for 10-20 years. - Stock price data (daily close) over the last 10-20 years. Use it however you please for PERSONAL USAGE, but if you do leverage it to make some money; just remember me and thank me in your head :) --- license: cc-by-nc-4.0 pretty_name: S&P 500 Data (news, ratios, price) size_categories: - 100K<n<1M ---

pmoe7/SP_500_Stocks_Data-ratios_news_price_10_yrs

[ "region:us" ]

2023-03-05T19:57:04+00:00

{}

2023-03-05T20:09:32+00:00

94b608c9fe8055fb351d90100e578b17aceea61c

# Dataset Card for "test_tesis_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jorgeortizfuentes/test_tesis_dataset

[ "region:us" ]

2023-03-05T19:58:12+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "prediction", "dtype": "null"}, {"name": "prediction_agent", "dtype": "null"}, {"name": "annotation", "list": [{"name": "end", "dtype": "int64"}, {"name": "label", "dtype": "string"}, {"name": "start", "dtype": "int64"}]}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "annotated", "struct": [{"name": "mentions", "list": [{"name": "capitalness", "dtype": "string"}, {"name": "chars_length", "dtype": "int64"}, {"name": "density", "dtype": "float64"}, {"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}, {"name": "tokens_length", "dtype": "int64"}, {"name": "value", "dtype": "string"}]}, {"name": "tags", "list": [{"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}]}, {"name": "predicted", "struct": [{"name": "mentions", "sequence": "null"}, {"name": "tags", "sequence": "null"}]}, {"name": "text_length", "dtype": "int64"}, {"name": "tokens", "list": [{"name": "capitalness", "dtype": "string"}, {"name": "char_end", "dtype": "int64"}, {"name": "char_start", "dtype": "int64"}, {"name": "custom", "dtype": "null"}, {"name": "idx", "dtype": "int64"}, {"name": "length", "dtype": "int64"}, {"name": "score", "dtype": "null"}, {"name": "tag", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "tokens_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 139252.8, "num_examples": 44}, {"name": "test", "num_bytes": 34813.2, "num_examples": 11}], "download_size": 101846, "dataset_size": 174066.0}}

2023-03-06T03:09:41+00:00

bdd0e0c11d142bd5618dac337a994dcbc27cb2fe

# Dataset Card for "flores200_packing" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

bri25yu/flores200_packing

[ "region:us" ]

2023-03-05T20:02:09+00:00

{"dataset_info": {"features": [{"name": "id", "dtype": "int32"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 14963812804.0, "num_examples": 2560000}, {"name": "val", "num_bytes": 3827042, "num_examples": 5000}, {"name": "test", "num_bytes": 7670994, "num_examples": 10000}], "download_size": 5440061492, "dataset_size": 14975310840.0}}

2023-03-14T05:32:23+00:00

4cf9c7fe36acb958be9d42892d03c1390d6f1d5e

# Dataset Card for "clevr-with-depth-full-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

erkam/clevr-with-depth-full-v2

[ "region:us" ]

2023-03-05T20:18:46+00:00

{"dataset_info": {"features": [{"name": "target_img", "dtype": "image"}, {"name": "source_img", "dtype": "image"}, {"name": "target_obj", "sequence": "int64"}, {"name": "source_obj", "sequence": "int64"}, {"name": "target_box", "sequence": {"sequence": "float32"}}, {"name": "source_box", "sequence": {"sequence": "float32"}}, {"name": "target_depth", "dtype": "image"}, {"name": "source_depth", "dtype": "image"}, {"name": "target_tri", "sequence": {"sequence": "int64"}}, {"name": "source_tri", "sequence": {"sequence": "int64"}}, {"name": "pos_prompt", "dtype": "string"}, {"name": "neg_prompt", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 43042513.0, "num_examples": 300}, {"name": "val", "num_bytes": 43023361.0, "num_examples": 300}, {"name": "train", "num_bytes": 200465105.0, "num_examples": 1400}], "download_size": 283955264, "dataset_size": 286530979.0}}

2023-03-05T20:18:58+00:00

668bc5a18254b2e835387bf3919d1cb70ddb5209

Dzeniks/fever_3way

[ "license:mit", "region:us" ]

2023-03-05T20:27:09+00:00

{"license": "mit"}

2023-03-14T13:01:47+00:00

c5bcfdb1808aef95004086a62ebe6a2d84fba449

Dzeniks/feverous_3way

[ "license:mit", "region:us" ]

2023-03-05T20:28:33+00:00

{"license": "mit"}

2023-03-05T20:29:51+00:00

8cacc890b6a6bb80e163bab3bdb67db147df58c9

# Hover Dataset The Hover dataset is a collection of labeled examples for many-hop fact extraction and claim verification tasks. It contains claims, with each claim labeled as either "Supports" or "Refutes". The dataset was created by Yichen Jiang, Shikha Bordia, Zheng Zhong, Charles Dognin, Maneesh Singh, and Mohit Bansal, and was presented in their paper "HoVer: A Dataset for Many-Hop Fact Extraction and Claim Verification" at the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) [Hover page](https://hover-nlp.github.io/). ## Format The Hover dataset is formatted as a TSV file, with each line containing the following fields: - **Claim:** The text of the claim to be verified. - **Label:** The label for the claim, either "0" for "Supports" or "1" for "Refutes". - **Explanation:** A sentence or phrase explaining why the claim is labeled as such. - **Evidence:** Evidence supporting or refuting the claim, if available. This may be a URL or a short text snippet.

Dzeniks/hover

[ "task_categories:text-classification", "license:mit", "region:us" ]

2023-03-05T20:34:48+00:00

{"license": "mit", "task_categories": ["text-classification"]}

2023-05-04T15:59:13+00:00

a59d816c57fd1cbb1e8f40cfc2abcbf1464e25c4

# Dataset Card for "cnn_dailymail_ngrams_1_to_5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

whu9/cnn_dailymail_ngrams_1_to_5

[ "region:us" ]

2023-03-05T21:15:14+00:00

{"dataset_info": {"features": [{"name": "cnn_dailymail_ngrams_1_to_5", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10483993441, "num_examples": 379567626}], "download_size": 5675144305, "dataset_size": 10483993441}}

2023-03-05T21:18:41+00:00

312ef489ebd64f9bf66b75f6d6ae55a3cdd4a380

# Dataset Card for "baby_names" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jbrazzy/baby_names

[ "region:us" ]

2023-03-06T00:45:34+00:00

{"dataset_info": {"features": [{"name": "Names", "dtype": "string"}, {"name": "Sex", "dtype": "string"}, {"name": "Count", "dtype": "int64"}, {"name": "Year", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 33860482, "num_examples": 1084385}, {"name": "test", "num_bytes": 8482889, "num_examples": 271663}], "download_size": 13301020, "dataset_size": 42343371}}

2023-03-06T00:45:44+00:00

9062568da9c6383c09193f3aa1f3c48dca925c5f

# Toxic Spans TOXICSPANS contains the 11,035 posts we annotated for toxic spans. The unique posts are actually 11,006, since a few were duplicates and were removed in subsequent experiments. A few other posts were used as quiz questions to check the reliability of candidate annotators and were also discarded in subsequent experiments. original data from https://github.com/ipavlopoulos/toxic_spans/tree/master/ACL2022 - 10006 train set - 1000 test set ## Columns - probability = a dict with the first and the last character offsets of each token (that was rated by at least one annotator as toxic) as a key, and the average toxicity as a value - position = the character offsets of all the toxic spans(avg toxicity > 50%) found by the annotators (ground truth) - text = the average toxicity of each token that was rated by at least one annotator as toxic - type = the type of toxicity of each toxic span - support = the number of annotators per post - text_of_post = the text of the post - position_probability = the average toxicity of each character offset that was found by at least one annotator as toxic - toxic = (Not in original) If the probability of at least 1 token is greather than 0.5 ### Sample ``` { "probability": { "(5, 11)": 1.0, "(286, 294)": 0.6666666667, "(120, 126)": 0.6666666667, "(350, 356)": 0.6666666667 }, "position": [ 5, 6, 7, 8, 9, 10, 120, 121, 122, 123, 124, 125, 286, 287, 288, 289, 290, 291, 292, 293, 350, 351, 352, 353, 354, 355 ], "text": { "stupid": 1.0, "ignorant": 0.6666666667, "Stupid": 0.6666666667 }, "type": { "profane\/obscene": 0.3333333333, "insult": 0.6666666667 }, "support": 3, "text_of_post": "Yes, stupid on steroids does afflict the nation. The biggest problem, of course, is they either don't see themselves as stupid, or are so proud of the fact they are they have no intention of remedying the situation. In fact, that's the definition of stupid in my book: You know you're ignorant, proud of it, and have no intention of alleviating it. Stupid. \n\nI wonder how they'd like their doctors to say to them \"Oh, I didn't go to medical school; that's for elites. The need for a formal education is fake news. I studied at home for a couple of years and got an alternative medical education. Trust me, I'm as good a doctor as any. Now, when did you want to schedule that surgery?\" I wonder how they'd like an unlicensed pilot in charge of getting them from point A to Point B? \n\nI think they're all just lazy. They want all the benefits of an education, but don't want to put in the time.", "position_probability": { "5": 1.0, "6": 1.0, "7": 1.0, "8": 1.0, "9": 1.0, "10": 1.0, "286": 0.6666666667, "287": 0.6666666667, "288": 0.6666666667, "289": 0.6666666667, "290": 0.6666666667, "291": 0.6666666667, "292": 0.6666666667, "293": 0.6666666667, "120": 0.6666666667, "121": 0.6666666667, "122": 0.6666666667, "123": 0.6666666667, "124": 0.6666666667, "125": 0.6666666667, "350": 0.6666666667, "351": 0.6666666667, "352": 0.6666666667, "353": 0.6666666667, "354": 0.6666666667, "355": 0.6666666667 }, "toxic": true } ```

heegyu/toxic-spans

[ "license:cc0-1.0", "region:us" ]

2023-03-06T00:53:50+00:00

{"license": "cc0-1.0"}

2023-03-06T02:01:17+00:00

915f1f82f3f10a07e88e5ad5c7604be976b871d9

# AutoTrain Dataset for project: cylonix_summarize ## Dataset Description This dataset has been automatically processed by AutoTrain for project cylonix_summarize. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "CAPE TOWN (Reuters) - Two men were injured in a drive-by shooting at South Africa s Cape Town International Airport, police said on Wednesday. Operations at the airport, the second busiest in the country and a tourism hub, were not affected, police spokesman Vish Naidoo said, adding the motive for the shooting was unknown. The airport is Africa s third busiest and handles around 10 million passengers a year, including tourists and businesspeople commuting to the economic hub Johannesburg. A man, about 50 years of age, was shot in a drive-by shooting and in the process a bystander, a 30-year-old man, was also hit, Naidoo said. The shooting took place in the early morning in a public area some distance from the airport s security gates, Naidoo said. Flights had not been affected, the airport s general manager Deon Cloete said in a statement. The shooting scene has been cordoned off while investigations continue, Cloete said. Local media reported that the shooting was gang-related, but Naidoo could not confirm that was the motive. ", "target": "Two injured in shooting at South Africa's Cape Town airport" }, { "text": "WASHINGTON (Reuters) - The chairman of a U.S. Senate cyber security subcommittee said on Friday he planned to introduce sanctions legislation over \u201cRussia\u2019s cyber criminals\u201d after Washington accused Russia of political cyber attacks ahead of the Nov. 8 presidential election. Republican Senator Cory Gardner said his legislation would require the Obama administration to investigate those who have engaged in significant actions undermining cyber security and aggressively pursue sanctions when appropriate. Gardner is chairman of the Senate Foreign Relations Subcommittee on East Asia, the Pacific and International Cybersecurity. ", "target": "U.S. lawmaker wants cyber sanctions on Russia after hacking charges" } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 37 | | valid | 10 |

KoddaDuck/autotrain-data-cylonix_summarize

[ "task_categories:summarization", "region:us" ]

2023-03-06T01:51:20+00:00

{"task_categories": ["summarization"]}

2023-03-06T01:53:23+00:00

64d0b9ff8f63828d21b26c89daab16dafc129006

# Dataset Card for "hh_prompted_human_eval" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Dahoas/hh_prompted_human_eval

[ "region:us" ]

2023-03-06T01:52:30+00:00

{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48004, "num_examples": 100}], "download_size": 30531, "dataset_size": 48004}}

2023-03-06T01:52:38+00:00

65642e4c736ba4a7afdbcdd0493c3995d53a8d5e

# AutoTrain Dataset for project: size ## Dataset Description This dataset has been automatically processed by AutoTrain for project size. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<990x990 RGB PIL image>", "target": 0 }, { "image": "<966x990 RGB PIL image>", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['Large', 'Medium', 'Mosaic', 'Small'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 212 | | valid | 54 |

kkar0/autotrain-data-size

[ "task_categories:image-classification", "region:us" ]

2023-03-06T02:15:12+00:00

{"task_categories": ["image-classification"]}

2023-03-06T02:17:14+00:00

7dc70e44431b026015ece3e179e211157a211ecc

jarrydmartinx/recount3-RNA-seq

[ "license:gpl", "region:us" ]

2023-03-06T02:29:26+00:00

{"license": "gpl"}

2023-03-17T03:08:38+00:00

4792ceabd05b7098ee083a0dfb5380e9e9441b72

--- dataset_info: - config_name: train features: - name: filename dtype: string - name: height dtype: int64 - name: width dtype: int64 - name: ann struct: - name: bboxes sequence: sequence: float64 - name: bboxes_ignore sequence: sequence: int64 - name: label_ignore sequence: int64 - name: labels sequence: int64 splits: - name: train num_bytes: 211748 num_examples: 500 download_size: 89624346 dataset_size: 211748 - config_name: val features: - name: filename dtype: string - name: height dtype: int64 - name: width dtype: int64 - name: ann struct: - name: bboxes sequence: sequence: float64 - name: bboxes_ignore sequence: sequence: int64 - name: label_ignore sequence: int64 - name: labels sequence: int64 splits: - name: val num_bytes: 209868 num_examples: 500 download_size: 82654443 dataset_size: 209868 ---

WuWenc/tiny_coco

[ "license:apache-2.0", "region:us" ]

2023-03-06T03:13:30+00:00

{"license": "apache-2.0"}

2023-03-07T08:08:42+00:00

74a1a0fe49f309f10d319c26d97fdabda8787540

# Movie Review Data * Original source: sentence polarity dataset v1.0 http://www.cs.cornell.edu/people/pabo/movie-review-data/ * Seems to same as https://huggingface.co/datasets/rotten_tomatoes, but different split. ## Original README ======= Introduction This README v1.0 (June, 2005) for the v1.0 sentence polarity dataset comes from the URL http://www.cs.cornell.edu/people/pabo/movie-review-data . ======= Citation Info This data was first used in Bo Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.'', Proceedings of the ACL, 2005. @InProceedings{Pang+Lee:05a, author = {Bo Pang and Lillian Lee}, title = {Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales}, booktitle = {Proceedings of the ACL}, year = 2005 } ======= Data Format Summary - rt-polaritydata.tar.gz: contains this readme and two data files that were used in the experiments described in Pang/Lee ACL 2005. Specifically: * rt-polarity.pos contains 5331 positive snippets * rt-polarity.neg contains 5331 negative snippets Each line in these two files corresponds to a single snippet (usually containing roughly one single sentence); all snippets are down-cased. The snippets were labeled automatically, as described below (see section "Label Decision"). Note: The original source files from which the data in rt-polaritydata.tar.gz was derived can be found in the subjective part (Rotten Tomatoes pages) of subjectivity_html.tar.gz (released with subjectivity dataset v1.0). ======= Label Decision We assumed snippets (from Rotten Tomatoes webpages) for reviews marked with ``fresh'' are positive, and those for reviews marked with ``rotten'' are negative. ## Preprocessing To make csv with text and label field, we use the following script. ```python3 import csv import random # NOTE: The encoding of original file is "latin_1". We will change it to "utf8". with open("rt-polarity.pos", encoding="latin_1") as f: texts_pos = [line.strip() for line in f] with open("rt-polarity.neg", encoding="latin_1") as f: texts_neg = [line.strip() for line in f] rows_pos = [{"text": text, "label": 1} for text in texts_pos] rows_neg = [{"text": text, "label": 0} for text in texts_pos] # NOTE: For fair validation, we split it into train and test. Also, for the research who wants to use different setting, we provide whole setting. # NOTE: We follow the split setting in LM-BFF paper. rows_whole = rows_pos + rows_neg random.Random(42).shuffle(rows_whole) rows_test, rows_train = rows_whole[:2000], rows_whole[2000:] with open("whole.csv", "w", encoding="utf8") as f: writer = csv.DictWriter(f, fieldnames=["text", "label"]) writer.writerows(rows_train) with open("train.csv", "w", encoding="utf8") as f: writer = csv.DictWriter(f, fieldnames=["text", "label"]) writer.writerows(rows_train) with open("test.csv", "w", encoding="utf8") as f: writer = csv.DictWriter(f, fieldnames=["text", "label"]) writer.writerows(rows_test) ```

sh0416/mr

[ "task_categories:text-classification", "language:en", "region:us" ]

2023-03-06T03:37:19+00:00

{"language": ["en"], "task_categories": ["text-classification"]}

2023-03-06T04:33:25+00:00

51a2c285267db66f70abcf157971e9e91f63b043

# Dataset Card for "arab-txt-2-img" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Ahmed007/arab-txt-2-img

[ "region:us" ]

2023-03-06T03:43:36+00:00

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 5513073754.525, "num_examples": 40455}], "download_size": 1121014430, "dataset_size": 5513073754.525}}

2023-03-06T03:44:24+00:00

0f9db9bc8ff13e6a0bb82a7e27dba27a6383c4ff

amla/EthiPic

[ "license:openrail++", "region:us" ]

2023-03-06T03:53:26+00:00

{"license": "openrail++"}

2023-03-06T03:53:26+00:00

e84f1643eae6fb681a1df7123a4919de79782db9

# UIT-ViQuAD v2 - Source: - Num examples: - 19,238 (train) - 9,714 (validation) - Language: Vietnamese ```python from datasets import load_dataset load_dataset("tdtunlp/uit_viquad_vi") ``` - Format for QA task ```python def preprocess(sample): question = sample['question'] answer = sample['answer'] return {'text': f'<|startoftext|><|question|>{question}<|answer|>{answer}<|endoftext|>'} """ <|startoftext|><|question|>Tên gọi nào được Phạm Văn Đồng sử dụng khi làm Phó chủ nhiệm cơ quan Biện sự xứ tại Quế Lâm?<|answer|>Tên gọi mà Phạm Văn Đồng sử dụng khi làm Phó chủ nhiệm cơ quan Biện sự xứ tại Quế Lâm là Lâm Bá Kiệt.<|endoftext|> """ ```

vietgpt-archive/uit_viquad_vi

[ "task_categories:question-answering", "size_categories:10K<n<100K", "language:vi", "LM", "region:us" ]

2023-03-06T03:54:14+00:00

{"language": ["vi"], "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "short_answer", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11582141.90997921, "num_examples": 19238}, {"name": "validation", "num_bytes": 5762392, "num_examples": 9714}], "download_size": 5378187, "dataset_size": 17344533.90997921}, "tags": ["LM"]}

2023-05-31T10:56:03+00:00

5862e54e776fa588ed74f83665ffb856af72b64f

simple chinese put in localizations

benzhuo/chinese

[ "region:us" ]

2023-03-06T05:07:02+00:00

{}

2023-03-07T05:35:54+00:00

c6ba4f7cb50785b9333e59d0cfd8cb3b9dc2cb18

SKyu/2019_2020data

[ "size_categories:10K<n<100K", "language:en", "language:ko", "license:cc-by-nc-sa-4.0", "region:us" ]

2023-03-06T05:36:57+00:00

{"language": ["en", "ko"], "license": "cc-by-nc-sa-4.0", "size_categories": ["10K<n<100K"], "pretty_name": "architecture data set 2019-2020"}

2023-03-06T05:37:53+00:00

b4f6d18bdca0af83ad5931c341acca6902d832af

# Dataset Card for "dys_train" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

LightFury9/dys_train

[ "region:us" ]

2023-03-06T06:39:29+00:00

{"dataset_info": {"features": [{"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "text_label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 480013795.2, "num_examples": 5600}], "download_size": 429982174, "dataset_size": 480013795.2}}

2023-03-06T06:40:27+00:00

e30d4e0cbd65d10b977f66185114533bc2a1a3a4

# Dataset Card for "reklamation24_haus-reinigung" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

fathyshalab/reklamation24_haus-reinigung

[ "region:us" ]

2023-03-06T08:15:31+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 233372, "num_examples": 466}, {"name": "test", "num_bytes": 63354, "num_examples": 117}], "download_size": 0, "dataset_size": 296726}}

2023-04-19T07:39:26+00:00

5cc2f66e60ec2304466766d37017798e80e0c86d

# Dataset Card for "hack5-rawIQ" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dsupa/hack5-rawIQ

[ "region:us" ]

2023-03-06T09:13:05+00:00

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", "5": "5", "6": "6"}}}}], "splits": [{"name": "train", "num_bytes": 2838470.0, "num_examples": 647}], "download_size": 30442, "dataset_size": 2838470.0}}

2023-03-06T09:13:07+00:00

17fb0b6648ba41e12ef94afb95b142b22f040c96

# Dataset Card for "cnn_daily_mail_nor_final" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

navjordj/cnn_daily_mail_nor_final

[ "region:us" ]

2023-03-06T09:28:41+00:00

{"dataset_info": {"features": [{"name": "article", "dtype": "string"}, {"name": "highlights", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 192449381.25865006, "num_examples": 47171}, {"name": "validation", "num_bytes": 14487455.276535718, "num_examples": 3551}, {"name": "test", "num_bytes": 22993888.464814223, "num_examples": 5636}], "download_size": 146363858, "dataset_size": 229930725.0}}

2023-03-06T09:29:13+00:00

0aaee90c6f21192643fd2a68fbe3de8e87bcf7d4

# Dataset Card for "cnn_daily_mail_nor_final" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

jkorsvik/cnn_daily_mail_nor_final

[ "region:us" ]

2023-03-06T09:34:10+00:00

{"dataset_info": {"features": [{"name": "article", "dtype": "string"}, {"name": "highlights", "dtype": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 192449381.25865006, "num_examples": 47171}, {"name": "validation", "num_bytes": 14487455.276535718, "num_examples": 3551}, {"name": "test", "num_bytes": 22993888.464814223, "num_examples": 5636}], "download_size": 146363858, "dataset_size": 229930725.0}}

2023-03-06T09:34:33+00:00

771aec3493a0a5d78ba98977ad0fec34436ece86

# Dataset Card for "math-qa-classification" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

rootacess/math-qa-classification

[ "region:us" ]

2023-03-06T10:19:33+00:00

{"dataset_info": {"features": [{"name": "Problem", "dtype": "string"}, {"name": "category", "dtype": {"class_label": {"names": {"0": "general", "1": "physics", "2": "gain", "3": "geometry", "4": "probability", "5": "other"}}}}], "splits": [{"name": "test", "num_bytes": 537186, "num_examples": 2985}, {"name": "train", "num_bytes": 5370736, "num_examples": 29837}, {"name": "validation", "num_bytes": 802226, "num_examples": 4475}], "download_size": 3557830, "dataset_size": 6710148}}

2023-03-06T12:25:04+00:00

1b6213ee94ece32ac458cf1ae5a34753466fd999

# Dataset Card for "reklambox2-cleaned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

fathyshalab/reklambox2-cleaned

[ "region:us" ]

2023-03-06T10:36:56+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "filename", "dtype": "string"}, {"name": "index", "dtype": "int64"}, {"name": "label_name", "dtype": "int64"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 635342.199146515, "num_examples": 1124}, {"name": "test", "num_bytes": 159400.80085348507, "num_examples": 282}], "download_size": 446755, "dataset_size": 794743.0}}

2023-03-10T15:19:03+00:00

62bee783b00574fce9587eeb8e82d227b8631489

# Dataset Card for "reklambox3-cleaned" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

fathyshalab/reklambox3-cleaned

[ "region:us" ]

2023-03-06T11:18:23+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 550071.8254750175, "num_examples": 1136}, {"name": "test", "num_bytes": 138002.1745249824, "num_examples": 285}], "download_size": 424497, "dataset_size": 688074.0}}

2023-03-06T11:18:34+00:00

b76362e3cd15e044d52286c3e41cd67784f6e404

# Dataset Card for "rk1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

fathyshalab/rk1

[ "region:us" ]

2023-03-06T11:20:52+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 544223.8520625889, "num_examples": 1124}, {"name": "test", "num_bytes": 136540.1479374111, "num_examples": 282}], "download_size": 420690, "dataset_size": 680764.0}}

2023-03-15T13:59:49+00:00

54fe31aa9aad8c800753611b503217a227bc4fd9

# Dataset Card for "PickaPic-rankings-6-3-2023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yuvalkirstain/PickaPic-rankings-6-3-2023

[ "region:us" ]

2023-03-06T12:20:35+00:00

{"dataset_info": {"features": [{"name": "ranking_id", "dtype": "int64"}, {"name": "created_at", "dtype": "timestamp[ns]"}, {"name": "user_id", "dtype": "int64"}, {"name": "image_1_uid", "dtype": "string"}, {"name": "image_2_uid", "dtype": "string"}, {"name": "image_3_uid", "dtype": "null"}, {"name": "image_4_uid", "dtype": "null"}, {"name": "best_image_uid", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 16312020, "num_examples": 71457}], "download_size": 5911046, "dataset_size": 16312020}}

2023-03-07T08:43:52+00:00

0a1f89ac17122ed3d46846af32d094b21f7d8c77

# Dataset Card for "PickaPic-images-6-3-2023" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yuvalkirstain/PickaPic-images-6-3-2023

[ "region:us" ]

2023-03-06T12:21:02+00:00

{"dataset_info": {"features": [{"name": "image_id", "dtype": "int64"}, {"name": "created_at", "dtype": "timestamp[ns]"}, {"name": "image_uid", "dtype": "string"}, {"name": "user_id", "dtype": "int64"}, {"name": "prompt", "dtype": "string"}, {"name": "negative_prompt", "dtype": "string"}, {"name": "seed", "dtype": "int64"}, {"name": "gs", "dtype": "float64"}, {"name": "steps", "dtype": "int64"}, {"name": "idx", "dtype": "int64"}, {"name": "num_generated", "dtype": "int64"}, {"name": "scheduler_cls", "dtype": "string"}, {"name": "model_id", "dtype": "string"}, {"name": "url", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 53236259, "num_examples": 86985}], "download_size": 10063363, "dataset_size": 53236259}}

2023-03-07T08:44:44+00:00

241dee5894bab9d22448e38fa18bd7b5909011dc

ritik1234/embeddings

[ "license:openrail", "region:us" ]

2023-03-06T12:21:54+00:00

{"license": "openrail"}

2023-03-06T12:23:44+00:00

3284227f873d87288aac1d595bd66713711cab5c

shivi/sample_dataset

[ "license:creativeml-openrail-m", "region:us" ]

2023-03-06T12:28:54+00:00

{"license": "creativeml-openrail-m"}

2023-03-06T12:31:25+00:00

6d7ad963c3b196f11a08920ab0b040aae43ab5e5

# Dataset Card for "memo" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yemen2016/memo

[ "region:us" ]

2023-03-06T12:42:09+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 298952996, "num_examples": 3203876}], "download_size": 189354351, "dataset_size": 298952996}}

2023-04-25T09:53:14+00:00

e6f0b773ac5666451307564178c398a8c46064f6

# Dataset Card for "UA_Speech" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Akshay-Sai/UA_Speech

[ "region:us" ]

2023-03-06T12:44:10+00:00

{"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 5378040000, "num_examples": 5600}], "download_size": 737238219, "dataset_size": 5378040000}}

2023-03-06T12:45:09+00:00

bf30c53bfa48042419a70b692593cc9d7d13fb83

# Dataset Card for "memo1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

yemen2016/memo1

[ "region:us" ]

2023-03-06T12:44:59+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 269063328, "num_examples": 2883488}], "download_size": 186337044, "dataset_size": 269063328}}

2023-03-06T12:45:15+00:00

df7a07ca1ba3e96973d8d847e9a44dfb9fb79ea9

joefox/newstest-2017-2019-ru_zh

[ "license:apache-2.0", "region:us" ]

2023-03-06T12:45:44+00:00

{"license": "apache-2.0"}

2023-03-06T12:47:44+00:00

b961b7f9026ecc039860201b5ce68e13c7b376bd

# Dataset Card for "UA_Speech" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

AravindVadlapudi02/UA_Speech

[ "region:us" ]

2023-03-06T12:52:29+00:00

{"dataset_info": {"features": [{"name": "input_features", "sequence": {"sequence": "float32"}}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 4609752000, "num_examples": 4800}, {"name": "test", "num_bytes": 768288000, "num_examples": 800}], "download_size": 736678488, "dataset_size": 5378040000}}

2023-03-06T12:55:11+00:00

6fe3ce43b0f9402708887a94837d37046d601ae5

JackBAI/chatgpt-woi-finetune

[ "license:mit", "region:us" ]

2023-03-06T12:55:25+00:00

{"license": "mit"}

2023-03-07T02:43:26+00:00

1d4eea9eba1d886a99ebb8665e087f78e083f5ca

# Dataset Card for "reklamation24_wasser-strom-gas" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

fathyshalab/reklamation24_wasser-strom-gas

[ "region:us" ]

2023-03-06T13:29:11+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_name", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 221722, "num_examples": 433}, {"name": "test", "num_bytes": 54894, "num_examples": 109}], "download_size": 0, "dataset_size": 276616}}

2023-04-19T07:38:23+00:00

8d9d3fece6e3dfc61dd817f12d3c838fa503a25f

# Dataset Card for "push_default" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

polinaeterna/push_default

[ "region:us" ]

2023-03-06T13:30:39+00:00

{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "test", "1": "train"}}}}], "splits": [{"name": "train", "num_bytes": 645457.0, "num_examples": 2}, {"name": "test", "num_bytes": 281491.0, "num_examples": 1}], "download_size": 909476, "dataset_size": 926948.0}, "configs_kwargs": {"data_dir": "./"}}

2023-03-06T13:30:48+00:00

afd33fcd5db1b73dc1799ee72310a057e8c78f20

# Dataset Card for "reklamation24_haus-reinigung-full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

fathyshalab/reklamation24_haus-reinigung-full

[ "task_categories:text-classification", "language:de", "region:us" ]

2023-03-06T13:33:34+00:00

{"language": ["de"], "task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 23403712, "num_examples": 4499}], "download_size": 0, "dataset_size": 23403712}}

2023-05-15T07:43:54+00:00

37184f46348a8fa84eaf66482fbbb94af54d52c9

# Dataset Card for "reklamation24_wasser-strom-gas-full" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

fathyshalab/reklamation24_wasser-strom-gas-full

[ "region:us" ]

2023-03-06T13:34:19+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "inputs", "struct": [{"name": "text", "dtype": "string"}]}, {"name": "prediction", "list": [{"name": "label", "dtype": "string"}, {"name": "score", "dtype": "float64"}]}, {"name": "prediction_agent", "dtype": "string"}, {"name": "annotation", "dtype": "string"}, {"name": "annotation_agent", "dtype": "string"}, {"name": "vectors", "struct": [{"name": "mini-lm-sentence-transformers", "sequence": "float64"}]}, {"name": "multi_label", "dtype": "bool"}, {"name": "explanation", "dtype": "null"}, {"name": "id", "dtype": "string"}, {"name": "metadata", "dtype": "null"}, {"name": "status", "dtype": "string"}, {"name": "event_timestamp", "dtype": "timestamp[us]"}, {"name": "metrics", "struct": [{"name": "text_length", "dtype": "int64"}]}], "splits": [{"name": "train", "num_bytes": 19400136, "num_examples": 3322}], "download_size": 0, "dataset_size": 19400136}}

2023-04-25T13:17:28+00:00

589b93f87b48c672d5f0d65aca1ea3d328181172

milkist/milky-set

[ "license:other", "region:us" ]

2023-03-06T13:58:37+00:00

{"license": "other"}

2023-03-06T16:17:19+00:00

6db8a847ab22db6c293f90ca414be9f6b8dabc69

# Dataset Card for "push" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

polinaeterna/push

[ "region:us" ]

2023-03-06T14:05:16+00:00

{"dataset_info": [{"config_name": "custom", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 3200, "num_examples": 200}, {"name": "test", "num_bytes": 4800, "num_examples": 300}], "download_size": 7682, "dataset_size": 8000}, {"config_name": "default", "features": [{"name": "x", "dtype": "int64"}, {"name": "y", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1600, "num_examples": 100}, {"name": "test", "num_bytes": 3200, "num_examples": 200}], "download_size": 5578, "dataset_size": 4800}], "configs_kwargs": [{"config_name": "custom", "data_dir": "custom"}, {"config_name": "default", "data_dir": "./"}]}

2023-03-06T14:06:30+00:00

daea1a6c5a830d135e3bd8335f591e4edd473dee

# Dataset Card for "programming-languages-keywords" Structured version of https://github.com/e3b0c442/keywords Generated using: ```python r = requests.get("https://raw.githubusercontent.com/e3b0c442/keywords/main/README.md") keywords = r.text.split("### ")[1:] keywords = [i for i in keywords if not i.startswith("Sources")] keywords = {i.split("\n")[0]:[j for j in re.findall("[a-zA-Z]*", i.split("\n",1)[1]) if j] for i in keywords} keywords = pd.DataFrame(pd.Series(keywords)).reset_index().rename(columns={"index":"language", 0:"keywords"}) keywords['language'] = keywords['language'].str.split("\) ").str[0] keywords['keywords'] = keywords['keywords'].apply(lambda x: sorted(list(set(x)))) ds = Dataset.from_pandas(keywords) ```

bigcode/programming-languages-keywords

[ "region:us" ]

2023-03-06T14:13:23+00:00

{"dataset_info": {"features": [{"name": "language", "dtype": "string"}, {"name": "keywords", "sequence": "string"}], "splits": [{"name": "train", "num_bytes": 20307, "num_examples": 36}], "download_size": 8838, "dataset_size": 20307}}

2023-03-06T14:17:24+00:00

fbd6e2f5c559e67090b13c0eefbb7dd191918fe9

lsr42/msmarco-passage-doct5query

[ "license:unknown", "region:us" ]

2023-03-06T14:26:04+00:00

{"license": "unknown", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "passage", "num_bytes": 16870476814, "num_examples": 8841823}], "download_size": 5773175789, "dataset_size": 16870476814}}

2023-03-06T16:51:24+00:00

565cbc8ab8400a4e797268dabf3e179832fdc38f

# xlsum - Source: https://huggingface.co/datasets/GEM/xlsum - Num examples: - 32,108 (train) - 4,013 (validation) - 4,013 (test) - Language: Vietnamese ```python from datasets import load_dataset load_dataset("tdtunlp/xlsum_vi") ``` - Format for Summarization task ```python def preprocess(sample): title = sample['title'] summary = sample['target'] article = sample['text'] return {'text': f'<|startoftext|><|title|>{title}<|article|>{article}<|summary|>{summary}<|endoftext|>'} """ <|startoftext|><|title|>Việt Nam đã sẵn sàng nâng tầm đối tác chiến lược với Mỹ?<|article|>Ông Donald Trump và ông Nguyễn Phú Trọng bắt tay trước thềm Thượng đỉnh Trump-Kim ở Hà Nội hôm 27/2/2019 Vài tháng qua đã có nhiều thảo luận về khả năng Hoa Kỳ-Việt Nam nâng tầm mối quan hệ từ "đối tác toàn diện" lên thành "đối tác chiến lược". Dưới đây là một số nhận định tiêu biểu. Một số quan ngại Theo ông Prashanth Parameswaran, tác giả bài viết hôm 12/9 trên The Diplomat, mối quan hệ Việt Nam - Hoa Kỳ đã tốt hơn nhiều so với thời chiến tranh Việt Nam. Hai nước bình thường hóa quan hệ dưới thời cựu Tổng thống Mỹ Bill Clinton và tiếp tục duy trì tốt dưới thời Obama. Việc nâng tầm quan hệ Mỹ-Việt có ý nghĩa lớn với các nhà hoạch định chính sách cả hai nước. Nó phản ánh nỗ lực của Washington trong việc mở rộng mạng lưới các đồng minh và đối tác tại châu Á - Thái Bình Dương và tầm quan trọng của Việt Nam trong mạng lưới này, đồng thời nhấn mạnh cơ hội và thách thức mà Hà Nội phải cân nhắc. TQ 'không vui' với chuyến thăm VN của USS Carl Vinson? David Hutt: 'Mục tiêu thương chiến kế tiếp của Trump là VN' USS Carl Vinson tới Đà Nẵng: 'Bước đi chiến lược' Việc Mỹ-Việt nâng tầm quan hệ có thể có ý nghĩa lớn hơn là bản thân mối quan hệ này, đặc biệt trong bối cảnh Mỹ - Trung Quốc tăng cường cạnh tranh về quyền lực trong khu vực châu Á-Thái Bình Dương và vai trò của Việt Nam trong các vấn đề như Biển Đông - nơi mà Trung Quốc ngày càng lấn lướt và Hà Nội chịu áp lực ngày càng lớn. Mỹ gần đây đã tăng cường các hoạt động hợp tác với Việt Nam. Năm 2018, Mỹ mang hàng không mẫu hạm USS Carl Vinson tới Việt Nam. Năm nay, Chủ tịch nước, Tổng bí thư Nguyễn Phú Trọng cũng dự kiến có chuyến công du Mỹ vào tháng 10/2019. Tuy nhiên, thực tế là Việt Nam và Mỹ vẫn có nhiều khác biệt trong nhiều lĩnh vực, từ thể chế tới quan điểm về nhân quyền. Việt Nam và Mỹ cũng có khác biệt trong quan điểm đối với vấn đề thương mại hoặc vấn đề Bắc Hàn - điều khiến quan hệ hai nước từng có vẻ khó 'toàn diện', chứ chưa nói đến 'chiến lược'. Chính vì thế, các cuộc thảo luận để nâng tầm mối quan hệ Mỹ - Việt cũng bao gồm cả các quan ngại, ông Prashanth Parameswaran bình luận. Các nhà hoạch định chính sách sẽ cần cân nhắc các yếu tố quan trọng này để tính toán được mất khi nâng tầm mối quan hệ. Chẳng hạn, không phải ngẫu nhiên mà chúng ta đã thấy Việt Nam trì hoãn một số hoạt động liên quan đến quốc phòng với Hoa Kỳ bất chấp những lợi ích có thể thấy rõ, vẫn theo tác giả Prashanth Parameswaran. Các quan ngại nói trên không có nghĩa Việt Nam - Hoa Kỳ không mong muốn hoặc không thể nâng tầng hợp tác. Nhưng nó có nghĩa rằng cả Mỹ và Việt Nam cần đảm bảo rằng các vấn đề thực tế giữa hai nước phù hợp với bất cứ tầm mức quan hệ nào mà họ lựa chọn. Quan trọng nữa là, việc điều chỉnh tên gọi của mối quan hệ chỉ có giá trị khi cả hai bên cùng cam kết nỗ lực để biến tiềm năng hợp tác thành sự hợp tác trên thực tế. Mỹ gửi tín hiệu 'hỗn hợp' Nhà báo David Hutt, cũng về đề tài này, trên Asia Times lại cho rằng Mỹ gửi những tín hiệu không thống nhất đến Việt Nam, nói năm nay, Mỹ lên tiếng cáo buộc Trung Quốc có hành động 'bắt nạt' nước láng giềng Việt Nam. Mỹ cũng ngỏ ý "muốn củng cố mối quan hệ quân sự chặt chẽ hơn với Hà Nội, mặc dù Việt Nam vẫn tỏ ra thận trọng và vẫn duy trì các chính sách ngoại giao không cam kết," David Hutt cũng nhắc tới tin đồn gần đây rằng công ty dầu khí Mỹ ExxonMobil đang tìm cách rút dự án Cá Voi Xanh trị giá hàng tỷ đô la khỏi Việt Nam, và bình luận rằng: Nếu thực sự ExxonMobil rút - cứ cho là vì lý do tài chính chứ không phải địa chính trị - thì đây cũng là một cú nốc ao vào mối quan hệ Mỹ-Việt ở giai đoạn mang tính bước ngoặt. Hơn bao giờ hết, Hà Nội hiện đang tìm kiếm các cam kết từ Washington rằng họ sẽ đứng về phía mình trong bất kỳ cuộc xung đột có vũ trang nào với Trung Quốc trên Biển Đông. Thương mại: Ông Donald Trump đe dọa Việt Nam Kỳ vọng gì nếu chủ tịch Trọng thăm Hoa Kỳ? Tập trận Mỹ-ASEAN: 'Mỹ sẽ không đứng yên nếu TQ tiếp tục ép VN' Mỹ, tuy thế, đang gửi tín hiệu 'hỗn hợp'. Quan hệ Việt Nam - Hoa Kỳ nảy nở dưới thời Tổng thống Mỹ Donald Trump. Ông Trump đã hai lần đến thăm Việt Nam và hiếm khi chỉ trích điều gì về đất nước được coi là vi phạm nhân quyền tồi tệ nhất Đông Nam Á này, vẫn theo David Hutt. Nhưng ông Trump, bên cạnh đó, lại cũng rất phiền lòng với việc Việt Nam trở thành nơi sản xuất, xuất khẩu các mặt hàng Trung Quốc nằm trong diện bị Mỹ đánh thuế, để trốn thuế. Ông Trump, hồi tháng Sáu đã gọi Việt Nam là nước 'lạm dụng tồi tệ nhất' trong một cuộc phỏng vấn truyền hình. Tuy nhiên, chính quyền của ông Trung cũng lại phản ứng quyết liệt khi Trung Quốc mang tàu vào khu đặc quyền kinh tế của Việt Nam tại Bãi Tư Chính trên Biển Đông. Người phát ngôn Bộ Ngoại giao Mỹ Morgan Ortagus nói Trung Quốc đã thực hiện một loạt các động thái hung hăng để can thiệp các hoạt động kinh tế lâu đời của Việt Nam. "Việt-Mỹ đã hợp tác chiến lược nhiều mặt, trừ tên gọi" Trong khi đó, tác giả Đoàn Xuân Lộc viết trên Asia Times, một yếu tố quan trọng của chính sách đối ngoại của Hà Nội là không liên minh. Để giúp đất nước tăng cường quan hệ ngoại giao, kinh tế và an ninh với các đối tác liên quan, chính phủ Việt Nam, do đó, đã tìm cách xây dựng một mạng lưới quan hệ đối tác. "Quan hệ đối tác toàn diện" là nấc thấp nhất trong mạng lưới này. Việt Nam và Hoa Kỳ thiết lập "quan hệ đối tác toàn diện" tháng 7/2013. Như vậy, Việt Nam đứng sau Philippines, Thái Lan, Indonesia và Singapore - các đối tác chiến lược của Hoa Kỳ trong khu vực - về tầm quan trọng đối với Washington. Trong khi đó, Việt Nam đã nâng tầm "quan hệ chiến lược" với 16 nước gồm Nga (2001), Nhật Bản (2006), Ấn Độ (2007), Trung Quốc (2008), Hàn Quốc và Tây Ban Nha (2009), Vương quốc Anh (2010), Đức (2011), Pháp, Indonesia, Ý, Singapore và Thái Lan ( 2013), Malaysia và Philippines (2015) và Úc (2017). Trong ngôn ngữ ngoại giao của Hà Nội, tất nhiên, Trung Quốc là đối tác quan trọng nhất của Việt Nam, trong khi Mỹ là một trong những quốc gia ít quan trọng nhất. Trên giấy tờ, mối "quan hệ đối tác toàn diện" của Việt Nam với Mỹ - nền kinh tế và quân sự lớn nhất thế giới - thậm chí còn xếp sau quan hệ "đối tác toàn diện" của Việt Nam với Myanmar - được thiết lập năm 2017. Nhưng trên thực tế, Mỹ là đối tác quan trọng thứ hai của Việt Nam. Ở nhiều khía cạnh, Mỹ cũng quan trọng không kém Trung Quốc. Và Hà Nội hiểu rằng có một mối quan hệ khỏe mạnh với Mỹ mang tính sống còn với đất nước, giúp ổn định sự phát triển và tránh quá phụ thuộc vào Trung Quốc về kinh tế, ông Đoàn Xuân Lộc nhận định. Hiện nay, sự hung hăng của Trung Quốc ở Biển Đông là một trong các yếu tố chính để Việt Nam tìm cách thắt chặt quan hệ với Mỹ, đặc biệt trong an ninh quốc phòng. Nhìn chung, mặc dù vẫn có những khác biệt nhất định, đặc biệt là về các quyền tự do chính trị và nhân quyền, lợi ích chiến lược của Hoa Kỳ và Việt Nam ngày càng phù hợp với nhau. Đối Việt Nam, mối quan hệ với Mỹ hiện tại về cơ bản là chiến lược trong nhiều lĩnh vực quan trọng, như an ninh và quốc phòng, mặc dù về tên gọi nó mới chỉ là "quan hệ đối tác toàn diện", vẫn theo tác giả Đoàn Xuân Lộc. <|summary|>Ý kiến về khả năng Việt-Mỹ trở thành đối tác chiến lược khi hai nước có nhiều khác biệt về thể chế chính trị và nhân quyền.<|endoftext|> """ ```

vietgpt-archive/xlsum_vi

[ "task_categories:summarization", "size_categories:10K<n<100K", "language:vi", "LM", "region:us" ]

2023-03-06T14:52:23+00:00

{"language": ["vi"], "size_categories": ["10K<n<100K"], "task_categories": ["summarization"], "dataset_info": {"features": [{"name": "gem_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "target", "dtype": "string"}, {"name": "references", "list": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 219542787, "num_examples": 32108}, {"name": "test", "num_bytes": 15891883, "num_examples": 4013}, {"name": "validation", "num_bytes": 15896905, "num_examples": 4013}], "download_size": 134434688, "dataset_size": 251331575}, "tags": ["LM"]}

2023-05-31T11:06:28+00:00

f98c1fbee7c1fe1e30abda459a30421c32b4da06

# xlsum - Source: https://huggingface.co/datasets/GEM/xlsum - Num examples: - 306,521 (train) - 11,535 (validation) - 11,535 (test) - Language: English ```python from datasets import load_dataset load_dataset("tdtunlp/xlsum_en") ``` - Format for Summarization task ```python def preprocess(sample): title = sample['title'] article = sample['text'] summary = sample['target'] return {'text': f'<|startoftext|><|title|>{title}<|article|>{article}<|summary|>{summary}<|endoftext|>'} """ <|startoftext|><|title|>Weather alert issued for gale force winds in Wales<|article|>The Met Office has issued a yellow weather warning for wind covering Wales and England, starting from 21:00 GMT on Wednesday evening. Travel and power are both likely to be disrupted, with the warning to remain in place until 15:00 on Thursday. Gusts of 55mph (88kmh) are likely and could hit up to 70mph on coasts and hills, with heavy and blustery showers. <|summary|>Winds could reach gale force in Wales with stormy weather set to hit the whole of the country this week.<|endoftext|> """ ```

vietgpt-archive/xlsum_en

[ "task_categories:summarization", "size_categories:100K<n<1M", "language:en", "region:us" ]

2023-03-06T15:01:15+00:00

{"language": ["en"], "size_categories": ["100K<n<1M"], "task_categories": ["summarization"], "dataset_info": {"features": [{"name": "gem_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "target", "dtype": "string"}, {"name": "references", "list": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 975957381, "num_examples": 306521}, {"name": "test", "num_bytes": 34940457, "num_examples": 11535}, {"name": "validation", "num_bytes": 35201391, "num_examples": 11535}], "download_size": 655141522, "dataset_size": 1046099229}}

2023-05-31T10:41:54+00:00

d06b7e88eccaa78644f3a877461640cb7d0b1535

[SQuAD V2.0](https://rajpurkar.github.io/SQuAD-explorer/). It contains the following: * Train and Validation sets. * The end position of each answer.

hazemessam/squad_v2

[ "license:cc-by-4.0", "region:us" ]

2023-03-06T15:03:51+00:00

{"license": "cc-by-4.0"}

2023-03-06T15:18:07+00:00

b8b47c589f8bee77175b8648e5497278b68da48a

# Dataset Card for ETHICS This is the data from [Aligning AI With Shared Human Values](https://arxiv.org/pdf/2008.02275) by Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt, published at ICLR 2021. For more information, see the [Github Repo](https://github.com/hendrycks/ethics). ## Dataset Summary This dataset provides ethics-based tasks for evaluating language models for AI alignment. ## Loading Data To load this data, you can use HuggingFace datasets and the dataloader script. ``` from datasets import load_dataset load_dataset("hendrycks/ethics", "commonsense") ``` Where `commonsense` is one of the following sections: commonsense, deontology, justice, utilitarianism, and virtue. ### Citation Information ``` @article{hendrycks2021ethics, title={Aligning AI With Shared Human Values}, author={Dan Hendrycks and Collin Burns and Steven Basart and Andrew Critch and Jerry Li and Dawn Song and Jacob Steinhardt}, journal={Proceedings of the International Conference on Learning Representations (ICLR)}, year={2021} } ```

hendrycks/ethics

[ "language:en", "license:mit", "AI Alignment", "arxiv:2008.02275", "region:us" ]

2023-03-06T15:25:03+00:00

{"language": "en", "license": "mit", "dataset_info": [{"config_name": "default", "features": [{"name": "label", "dtype": "int64"}, {"name": "input", "dtype": "string"}]}, {"config_name": "commonsense", "features": [{"name": "label", "dtype": "int32"}, {"name": "input", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14429921, "num_examples": 13910}, {"name": "validation", "num_bytes": 3148616, "num_examples": 3885}, {"name": "test", "num_bytes": 3863068, "num_examples": 3964}], "download_size": 21625153, "dataset_size": 21441605}, {"config_name": "deontology", "features": [{"name": "label", "dtype": "int32"}, {"name": "scenario", "dtype": "string"}, {"name": "excuse", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1854277, "num_examples": 18164}, {"name": "validation", "num_bytes": 369318, "num_examples": 3596}, {"name": "test", "num_bytes": 359268, "num_examples": 3536}], "download_size": 2384007, "dataset_size": 2582863}, {"config_name": "justice", "features": [{"name": "label", "dtype": "int32"}, {"name": "scenario", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2423889, "num_examples": 21791}, {"name": "validation", "num_bytes": 297935, "num_examples": 2704}, {"name": "test", "num_bytes": 228008, "num_examples": 2052}], "download_size": 2837375, "dataset_size": 2949832}, {"config_name": "utilitarianism", "features": [{"name": "baseline", "dtype": "string"}, {"name": "less_pleasant", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2186713, "num_examples": 13737}, {"name": "validation", "num_bytes": 730391, "num_examples": 4807}, {"name": "test", "num_bytes": 668429, "num_examples": 4271}], "download_size": 3466564, "dataset_size": 3585533}, {"config_name": "virtue", "features": [{"name": "label", "dtype": "int32"}, {"name": "scenario", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2605021, "num_examples": 28245}, {"name": "validation", "num_bytes": 467254, "num_examples": 4975}, {"name": "test", "num_bytes": 452491, "num_examples": 4780}], "download_size": 3364070, "dataset_size": 3524766}], "tags": ["AI Alignment"]}

2023-04-19T17:55:00+00:00

401c712095bf02eefcf88e30e3aaff514dff265b

BuroIdentidadDigital/recibos_telmex

[ "license:c-uda", "region:us" ]

2023-03-06T15:50:26+00:00

{"license": "c-uda"}

2024-01-12T23:02:31+00:00

262ddc85688bfc06527bcf099f87deef62977ac1

# Dataset Card for "merged-wiki-cnn" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

braindao/cnn-100k

[ "region:us" ]

2023-03-06T16:03:03+00:00

{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 437866886, "num_examples": 100000}, {"name": "test", "num_bytes": 49735992, "num_examples": 11490}, {"name": "validation", "num_bytes": 57519439, "num_examples": 13368}], "download_size": 332900122, "dataset_size": 545122317}}

2023-03-08T10:38:08+00:00

5f29d1501ab85ceec5aa84d59f4ad2865107cf99

# 🇺🇦 Open Source Ukrainian Text-to-Speech dataset named MYKYTA Join Ukrainian community - https://t.me/speech_synthesis_uk More details about this dataset - https://github.com/egorsmkv/ukrainian-tts-datasets/tree/main/mykyta # Voice MYKYTA (male) License: [Apache 2.0](https://github.com/egorsmkv/ukrainian-tts-datasets/blob/main/LICENSE) Listen to [DEMO](https://huggingface.co/spaces/theodotus/ukrainian-voices) (choose "mykyta" in the Voice field) ## Features - Quality: high - Duration: 8h10m - Audio formats: OPUS/WAV - Text format: JSONL (a `metadata.jsonl` file) - Frequency: 16000/22050/48000 Hz ## Original version ### In the `OPUS` format - 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-mykyta/resolve/main/dataset_mykyta_ogg.zip ## Trimmed version (removed silence) Silence is removed by https://github.com/proger/uk#align-text-to-audio-and-trim-silence ### In the `WAV` format - 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-mykyta/resolve/main/dataset_mykyta_trimmed_48khz.zip - 22050 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-mykyta/resolve/main/dataset_mykyta_trimmed_22khz.zip - 16000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-mykyta/resolve/main/dataset_mykyta_trimmed_16khz.zip

Yehor/ukrainian-tts-mykyta

[ "task_categories:text-to-speech", "language:uk", "license:apache-2.0", "region:us" ]

2023-03-06T16:22:17+00:00

{"language": ["uk"], "license": "apache-2.0", "task_categories": ["text-to-speech"]}

2023-12-16T16:26:33+00:00

2b8480e5f80abebb6a6c4926666ef67345a68312

# 🇺🇦 Open Source Ukrainian Text-to-Speech dataset named TETIANA Join Ukrainian community - https://t.me/speech_synthesis_uk More details about this dataset - https://github.com/egorsmkv/ukrainian-tts-datasets/tree/main/tetiana # Voice TETIANA (female) License: [Apache 2.0](https://github.com/egorsmkv/ukrainian-tts-datasets/blob/main/LICENSE) ## Features - Quality: high - Duration: 8h - Audio formats: OPUS/WAV - Text format: JSONL (a `metadata.jsonl` file) - Frequency: 16000/22050/48000 Hz ## Original version ### In the `OPUS` format - 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_ogg.zip ## Trimmed version (removed silence) Silence is removed by https://github.com/proger/uk#align-text-to-audio-and-trim-silence ### In the `WAV` format - 48000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_trimmed_48khz.zip - 22050 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_trimmed_22khz.zip - 16000 Hz: https://huggingface.co/datasets/Yehor/ukrainian-tts-tetiana/resolve/main/dataset_tetiana_trimmed_16khz.zip

Yehor/ukrainian-tts-tetiana

[ "task_categories:text-to-speech", "language:uk", "license:apache-2.0", "region:us" ]

2023-03-06T16:47:50+00:00

{"language": ["uk"], "license": "apache-2.0", "task_categories": ["text-to-speech"]}

2023-12-16T16:25:09+00:00

05be76043886b06e25c52d40458f56e3bc7fe186

# Dataset Card for "deart" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

davanstrien/deart

[ "region:us" ]

2023-03-06T16:56:16+00:00

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "source", "dtype": "string"}, {"name": "width", "dtype": "int16"}, {"name": "height", "dtype": "int16"}, {"name": "dept", "dtype": "int8"}, {"name": "segmented", "dtype": "int8"}, {"name": "image_id", "dtype": "string"}, {"name": "annotations", "list": [{"name": "category_id", "dtype": {"class_label": {"names": {"0": "zebra", "1": "tree", "2": "nude", "3": "crucifixion", "4": "scroll", "5": "head", "6": "swan", "7": "shield", "8": "lily", "9": "mouse", "10": "knight", "11": "dragon", "12": "horn", "13": "dog", "14": "palm", "15": "tiara", "16": "helmet", "17": "sheep", "18": "deer", "19": "person", "20": "sword", "21": "rooster", "22": "bear", "23": "halo", "24": "lion", "25": "monkey", "26": "prayer", "27": "crown of thorns", "28": "elephant", "29": "zucchetto", "30": "unicorn", "31": "holy shroud", "32": "cat", "33": "apple", "34": "banana", "35": "chalice", "36": "bird", "37": "eagle", "38": "pegasus", "39": "crown", "40": "camauro", "41": "saturno", "42": "arrow", "43": "dove", "44": "centaur", "45": "horse", "46": "hands", "47": "skull", "48": "orange", "49": "monk", "50": "trumpet", "51": "key of heaven", "52": "fish", "53": "cow", "54": "angel", "55": "devil", "56": "book", "57": "stole", "58": "butterfly", "59": "serpent", "60": "judith", "61": "mitre", "62": "banner", "63": "donkey", "64": "shepherd", "65": "boat", "66": "god the father", "67": "crozier", "68": "jug", "69": "lance"}}}}, {"name": "image_id", "dtype": "string"}, {"name": "area", "dtype": "int64"}, {"name": "bbox", "sequence": "float32", "length": 4}, {"name": "segmentation", "list": {"list": "float32"}}, {"name": "iscrowd", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 8991340765.256, "num_examples": 15156}], "download_size": 18150029661, "dataset_size": 8991340765.256}}

2023-03-06T17:12:03+00:00

2cace2314e8d42389cdb4ef392392fa859504cba

# Dataset Card for "msmarco-passage-tilde" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

lsr42/msmarco-passage-tilde

[ "region:us" ]

2023-03-06T16:56:25+00:00

{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "passage", "num_bytes": 6376936859, "num_examples": 8841823}], "download_size": 3609070765, "dataset_size": 6376936859}}

2023-03-06T17:07:32+00:00

8a42f9a8b614a247ce0aed2272d226a09fe25cd1

Новая версия: https://huggingface.co/datasets/Den4ikAI/russian_instructions_2 Русский датасет инструкций и QA. Структура датасета: ``` { "dialogue":[ "Как я могу улучшить свою связь между телом и разумом?", "Начните с разработки регулярной практики осознанности. 2. Обязательно практикуйте баланс на нескольких уровнях: физическом, эмоциональном, умственном и духовном. 3. Свяжитесь с природой, когда это возможно - идите на прогулки или бегайте на улице, или просто сидите в парке и наблюдайте природу вокруг вас. 4. Развивайте позитивные утверждения, которые вы можете использовать, чтобы помочь поддерживать оптимизм и сильные отношения с вашим телом.", "Больше пожалуйста", "Конечно, вот еще несколько: 5. Включите здоровую диету цельных продуктов и много воды, которая питает ваш ум и тело. 6. Научитесь быть в гармонии с вашим телом, замечая изменения в том, как вы себя чувствуете, и уделите время, чтобы оценить и решить любые возникающие потребности." ], "rounds":[ 1, 2 ], "uuid":"53e8d08b-1f45-4ef4-b6d8-3a04758cafd8" } ``` ### Citation: ``` @MISC{russian_instructions, author = {Denis Petrov}, title = {Russian instructions dataset for conversational agents}, url = {https://huggingface.co/datasets/Den4ikAI/russian_instructions}, year = 2023 } ```

Den4ikAI/russian_instructions

[ "task_categories:conversational", "size_categories:10K<n<100K", "language:ru", "license:mit", "region:us" ]

2023-03-06T17:06:32+00:00

{"language": ["ru"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["conversational"]}

2023-03-12T04:59:14+00:00

0ee2c7665fe97b6890d80b47d8290a7fb9aac292

Based on https://huggingface.co/datasets/its5Q/yandex-q, parsed full.jsonl.gz

IlyaGusev/yandex_q_full

[ "region:us" ]

2023-03-06T18:17:41+00:00

{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "id2", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "text_plain", "dtype": "string"}, {"name": "text_html", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "negative_votes", "dtype": "int32"}, {"name": "positive_votes", "dtype": "int32"}, {"name": "quality", "dtype": "int8"}, {"name": "views", "dtype": "uint64"}, {"name": "votes", "dtype": "int32"}, {"name": "approved_answer", "dtype": "string"}, {"name": "timestamp", "dtype": "uint64"}, {"name": "tags", "sequence": "string"}, {"name": "answers", "sequence": [{"name": "id", "dtype": "string"}, {"name": "id2", "dtype": "int64"}, {"name": "text_plain", "dtype": "string"}, {"name": "text_html", "dtype": "string"}, {"name": "author", "dtype": "string"}, {"name": "negative_votes", "dtype": "int32"}, {"name": "positive_votes", "dtype": "int32"}, {"name": "votes", "dtype": "int32"}, {"name": "quality", "dtype": "int8"}, {"name": "views", "dtype": "uint64"}, {"name": "reposts", "dtype": "int32"}, {"name": "timestamp", "dtype": "uint64"}]}], "splits": [{"name": "train", "num_bytes": 5468460217, "num_examples": 1297670}], "download_size": 1130317937, "dataset_size": 5468460217}}

2023-03-07T20:30:24+00:00

171eb271eb03ff37a8417e01392097fa5729551a

# AutoTrain Dataset for project: sentiment_analysis ## Dataset Description This dataset has been automatically processed by AutoTrain for project sentiment_analysis. ### Languages The BCP-47 code for the dataset's language is en. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "I grew up (b. 1965) watching and loving the Thunderbirds. All my mates at school watched. We played \"Thunderbirds\" before school, during lunch and after school. We all wanted to be Virgil or Scott. No one wanted to be Alan. Counting down from 5 became an art form. I took my children to see the movie hoping they would get a glimpse of what I loved as a child. How bitterly disappointing. The only high point was the snappy theme tune. Not that it could compare with the original score of the Thunderbirds. Thankfully early Saturday mornings one television channel still plays reruns of the series Gerry Anderson and his wife created. Jonatha Frakes should hand in his directors chair, his version was completely hopeless. A waste of film. Utter rubbish. A CGI remake may be acceptable but replacing marionettes with Homo sapiens subsp. sapiens was a huge error of judgment.", "target": 0 }, { "text": "When I put this movie in my DVD player, and sat down with a coke and some chips, I had some expectations. I was hoping that this movie would contain some of the strong-points of the first movie: Awsome animation, good flowing story, excellent voice cast, funny comedy and a kick-ass soundtrack. But, to my disappointment, not any of this is to be found in Atlantis: Milo's Return. Had I read some reviews first, I might not have been so let down. The following paragraph will be directed to those who have seen the first movie, and who enjoyed it primarily for the points mentioned.<br /><br />When the first scene appears, your in for a shock if you just picked Atlantis: Milo's Return from the display-case at your local videoshop (or whatever), and had the expectations I had. The music feels as a bad imitation of the first movie, and the voice cast has been replaced by a not so fitting one. (With the exception of a few characters, like the voice of Sweet). The actual drawings isnt that bad, but the animation in particular is a sad sight. The storyline is also pretty weak, as its more like three episodes of Schooby-Doo than the single adventurous story we got the last time. But dont misunderstand, it's not very good Schooby-Doo episodes. I didnt laugh a single time, although I might have sniggered once or twice.<br /><br />To the audience who haven't seen the first movie, or don't especially care for a similar sequel, here is a fast review of this movie as a stand-alone product: If you liked schooby-doo, you might like this movie. If you didn't, you could still enjoy this movie if you have nothing else to do. And I suspect it might be a good kids movie, but I wouldn't know. It might have been better if Milo's Return had been a three-episode series on a cartoon channel, or on breakfast TV.", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "ClassLabel(names=['0', '1'], id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 1497 | | valid | 1497 |

raghuram13/autotrain-data-sentiment_analysis

[ "task_categories:text-classification", "language:en", "region:us" ]

2023-03-06T20:18:31+00:00

{"language": ["en"], "task_categories": ["text-classification"]}

2023-03-06T20:23:54+00:00

06b337fe0d82d5f29f075fca5c435ee02bd9f92c

# Dataset Card for "rlhf-qa-conditional-generation-v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

kastan/rlhf-qa-conditional-generation-v2

[ "region:us" ]

2023-03-06T20:41:34+00:00

{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "completion", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 34403.31192660551, "num_examples": 87}, {"name": "valid", "num_bytes": 8699.688073394496, "num_examples": 22}], "download_size": 31360, "dataset_size": 43103.0}}

2023-04-14T21:35:34+00:00

46587daedc55f5dbfd44799275464cb547cfeeed

# Dataset Card for "Duaaii_5" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Hundred9/Duaaii_5

[ "region:us" ]

2023-03-06T20:45:57+00:00

{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "0", "1": "1", "2": "2", "3": "3", "4": "4", "5": "5", "6": "6"}}}}], "splits": [{"name": "train", "num_bytes": 43153426.0, "num_examples": 647}], "download_size": 43177352, "dataset_size": 43153426.0}}

2023-03-06T20:49:30+00:00

fbf42b7e03d6433669eb2fa2ecc407d5bdeaf677

Yes67843/eys

[ "license:other", "region:us" ]

2023-03-06T20:46:57+00:00

{"license": "other"}

2023-03-06T20:50:27+00:00

34198d9751dfccd2cd6a380f6f431e6b75c6ad44

# Dataset Card for "test-huggingface-datasets" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

Djarnis/test-huggingface-datasets

[ "region:us" ]

2023-03-06T21:39:25+00:00

{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "labels", "list": [{"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "assignees", "list": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "milestone", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "creator", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "open_issues", "dtype": "int64"}, {"name": "closed_issues", "dtype": "int64"}, {"name": "state", "dtype": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "due_on", "dtype": "null"}, {"name": "closed_at", "dtype": "null"}]}, {"name": "comments", "sequence": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "timestamp[s]"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "null"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "diff_url", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "merged_at", "dtype": "timestamp[s]"}]}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "url", "dtype": "string"}, {"name": "total_count", "dtype": "int64"}, {"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "state_reason", "dtype": "string"}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 10702183, "num_examples": 2000}], "download_size": 2891006, "dataset_size": 10702183}}

2023-03-06T21:40:27+00:00

442c526c494d69888a77b0e1ca8eb2e08e768215

# Dataset Card for "test-dataset-via-streamlit" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

MoritzLaurer/test-dataset-via-streamlit

[ "region:us" ]

2023-03-06T22:05:48+00:00

{"dataset_info": {"features": [{"name": "english", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "label1", "1": "label2"}}}}], "splits": [{"name": "train", "num_bytes": 1218.0, "num_examples": 3}, {"name": "test", "num_bytes": 159, "num_examples": 1}], "download_size": 7754, "dataset_size": 1377.0}}

2023-03-06T22:05:49+00:00

12f2e05c5f7e216dc476abe5f5cfa3a80c957903

# Dataset Card for "TIMIT_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

patlee0208/TIMIT_v2

[ "region:us" ]

2023-03-06T22:09:18+00:00

{"dataset_info": {"features": [{"name": "audio", "dtype": "audio"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "DR1", "1": "DR2", "2": "DR3", "3": "DR4", "4": "DR5", "5": "DR6", "6": "DR7"}}}}], "splits": [{"name": "train", "num_bytes": 119307077.0, "num_examples": 597}], "download_size": 113914231, "dataset_size": 119307077.0}}

2023-03-06T22:10:07+00:00

f3ac95eaa17bf25fc091fe5274083cc5a7e30080

Join my discord here : https://discord.gg/PdYFs7qmSW

SyntheticVoices/AlexJones

[ "license:openrail", "region:us" ]

2023-03-06T23:11:33+00:00

{"license": "openrail"}

2023-03-06T23:54:43+00:00

96befac625ae44862842fd0b47536ebe64614ec4

This data set contains StockTwits posts from 01.11.2021 to 30.06.2022 for Bitcoin (BTC.X), Ethereum (ETH.X) and Shiba Inu (SHIB.X). The full set contains 124,503 posts, including 72,247 bullish, 38,249 neutral and 14,007 bearish posts. The training set ranges from 01.11.2021 to 30.04.2022, consists of 91,758 observations, including 57,932 bullish, 26,516 neutral, and 7310 bearish posts. The validation set ranges from 01.05.2022 to 15.06.2022 and contains 4084 bearish, 7534 neutral, and 9143 bullish posts, amounting to 20,761 examples. The test set ranges from 16.06.2022 to 30.06.2022 and consists of 5172 bullish, 4199 neutral, and 2613 bearish posts, having 11,984 observations in total. The validation and test sets contain all StockTwits posts, with at least one emoji, from their respective periods, while the training set is further limited by only including posts that have possibly influential bullish or bearish emojis. The training SVM dataset contains balanced samples used for training an SVM sentiment classifier. The bearish sets have 20K observations per class (pos is bearish, while neg is not bearish, so bullish and neutral). The bullish sets have 40K observations per class (pos is bullish, while neg is not bullish, so bearish and neutral).

ElKulako/stocktwits-emoji

[ "license:afl-3.0", "region:us" ]

2023-03-07T00:35:53+00:00

{"license": "afl-3.0"}

2023-03-07T01:12:48+00:00

680cd6266c2d0b4b18dba98013205f703ee62349

# AutoTrain Dataset for project: text2itinerary-exp-26-1000 ## Dataset Description This dataset has been automatically processed by AutoTrain for project text2itinerary-exp-26-1000. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "\ud589\uc0ac \uc81c\ubaa9\n[\ud50c\uce5c/\uc655\uc758\uadc0\ud658\uc2e4\uc18d/\ubc29\ucf55\ud30c\ud0c0\uc57c5\uc77c]\uc54c\ucc2c\uad00\uad11+$170\uc0c1\ub2f9\ud2b9\uc804(\ub7ed\uc154\ub9ac\uc694\ud2b8\ud06c\ub8e8\uc988,\uc2a4\ub178\ud074\ub9c1,\uc804\uc2e0\ub9c8\uc0ac\uc9c0\ud3ec\ud568)\n\uc5ec\ud589\uc9c0\n\ubc29\ucf55|\ud30c\ud0c0\uc57c\n\ube10\ub958\nACCOMMODATION\n\uc77c\ucc28\n2\n\uc21c\uc11c\n5\n\uc81c\ubaa9\n\ud574\ub2f9 \uc77c\uc758 \uc219\ubc15\uc2dc\uc124\uc740 \ud604\uc7ac \ubbf8\uc815\uc785\ub2c8\ub2e4. \ucd9c\ubc1c 1\uc77c\uc804\uae4c\uc9c0 \ud648\ud398\uc774\uc9c0\ub97c \ud1b5\ud574 \uc54c\ub824\ub4dc\ub9ac\uaca0\uc2b5\ub2c8\ub2e4.\n\uc0c1\uc138\n* TITLE: [\uc219\ubc15] \uc544\uc774\uc57c\ub77c \uadf8\ub79c\ub4dc \ud30c\ud0c0\uc57c \ud638\ud154* PLACE_IDX: 44359* APPROVAL: 0* ADDRESS: 338/118 Moo. 12 Pratamnak Rd. $%T. Nongpure A. Banglamung Chonburi Thailand 20150* URL: http://www.aiyaragrand.com/\n* TITLE: [\uc219\ubc15] LK \uc140\ub808\ud0c0\uc774 \ud638\ud154* PLACE_IDX: 56825* APPROVAL: 0* ADDRESS: 45/97-99 ??????? 10 Bang Lamung District Chon Buri 20150 \ud0dc\uad6d* URL: https://lkpattaya.com/rs/package.php?c=1&hotel_session=LK%20Celestite\n* TITLE: [\uc219\ubc15] \uc5e0\ud30c\ud0c0\uc57c\ud638\ud154* PLACE_IDX: 65714* APPROVAL: 0* ADDRESS: 571/112 Moo 5 Naklua Road Banglamung Chonburi 20150* URL: http://www.mhotel.co.th/\n* TITLE: [\uc219\ubc15] \uc5d0\uc774\uc6d0\ub274\uc719 \ud30c\ud0c0\uc57c* PLACE_IDX: 56698* APPROVAL: 0* ADDRESS: 500-501 North Pattaya Banglamung Chonburi 20150* URL: http://www.a-onenewwingpattaya.com/\n* TITLE: [\uc219\ubc15] \uc13c\ud130\ud3ec\uc778\ud2b8\ud638\ud154 \ud30c\ud0c0\uc57c* PLACE_IDX: 65498* APPROVAL: 0* ADDRESS: 275 Moo 6 Sukhumvit Road Naklua Bang Lamung District Chon Buri 20150 \ud0dc\uad6d* URL: http://www.centrepoint.com/pattaya\n", "target": "\uc544\uc774\uc57c\ub77c \uadf8\ub79c\ub4dc \ud638\ud154\ud83c\udff7344\ud83c\udff7ACCOMMODATION\ud83d\udd0dNULL\nLK \uc140\ub808\uc2a4\ud0c0\uc774\ud2b8\ud83c\udff71370\ud83c\udff7ACCOMMODATION\ud83d\udd0dNULL\n\uc5e0 \ud30c\ud0c0\uc57c \ud638\ud154\ud83c\udff71371\ud83c\udff7ACCOMMODATION\ud83d\udd0dNULL\n\uc5d0\uc774-\uc6d0 \ub354 \ub85c\uc584 \ud06c\ub8e8\uc988 \ud638\ud154\ud83c\udff7592\ud83c\udff7ACCOMMODATION\ud83d\udd0dNULL\n\uc13c\ud130 \ud3ec\uc778\ud2b8 \ud504\ub77c\uc784 \ud638\ud154 \ud30c\ud0c0\uc57c\ud83c\udff71430\ud83c\udff7ACCOMMODATION\ud83d\udd0dNULL" }, { "text": "\ud589\uc0ac \uc81c\ubaa9\n\u2665 \ubca0\uc2a4\ud2b8\uc140\ub7ec \u2665 \ub7ed\uc154\ub9ac \ud2b8\ub7fc\ud504 \uc640\uc774\ud0a4\ud0a4 [\uc288\ud398\ub9ac\uc5b4\uc624\uc158\ubdf0] \ud558\uc640\uc774 \ud5c8\ub2c8\ubb38 6\uc77c\n\uc5ec\ud589\uc9c0\n\ud558\uc640\uc774\n\ube10\ub958\nOTHERS\n\uc77c\ucc28\n6\n\uc21c\uc11c\n16\n\uc81c\ubaa9\n\uc778\ucc9c\uacf5\ud56d \ub3c4\ucc29 Mahalo \u2661 \ud589\ubcf5\ud55c \uacb0\ud63c \uc0dd\ud65c \ub418\uc138\uc694 \u2661\n\uc0c1\uc138\n\uc778\ucc9c\uacf5\ud56d \ub3c4\ucc29 Mahalo \u2661 \ud589\ubcf5\ud55c \uacb0\ud63c \uc0dd\ud65c \ub418\uc138\uc694 \u2661\n", "target": "\uc778\ucc9c\ud83c\udff7971\ud83c\udff7AIRPORT\ud83d\udd0d\ub3c4\ucc29" } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 6512 | | valid | 814 |

eubinecto/autotrain-data-text2itinerary-exp-26-1000

[ "task_categories:summarization", "region:us" ]

2023-03-07T00:39:42+00:00

{"task_categories": ["summarization"]}

2023-03-07T00:40:16+00:00

96d6c66dd8b184c08c059d9d0b2881e4b1d28306

mushroomsolutions/imdb_sentiment_3000_Test

[ "license:mit", "region:us" ]

2023-03-07T00:44:17+00:00

{"license": "mit"}

2023-03-07T00:47:41+00:00

f7f6475df0e14cabe81ca91d0c181d538bc2fe39

# Dataset Card for "jsbachmmmbar" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

juancopi81/jsbachmmmbar

[ "region:us" ]

2023-03-07T01:26:09+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 21569479, "num_examples": 27000}, {"name": "test", "num_bytes": 601308, "num_examples": 310}], "download_size": 3155226, "dataset_size": 22170787}}

2023-03-07T01:26:18+00:00

94348fd0b05baa25375dd2029e43e1a8fa604b8d

# Dataset Card for "jsbachmmmtrack" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

juancopi81/jsbachmmmtrack

[ "region:us" ]

2023-03-07T01:27:22+00:00

{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 43706425, "num_examples": 27000}, {"name": "test", "num_bytes": 592628, "num_examples": 310}], "download_size": 7553812, "dataset_size": 44299053}}

2023-03-07T01:27:31+00:00

63602e8303644883bd4869567bbf23dee1b6ca8f

Duskfallcrew/Star_Marvel_Final

[ "task_categories:text-to-image", "language:en", "license:creativeml-openrail-m", "stable diffusion", "region:us" ]

2023-03-07T01:47:09+00:00

{"language": ["en"], "license": "creativeml-openrail-m", "task_categories": ["text-to-image"], "pretty_name": "Star Marvel Data Final", "tags": ["stable diffusion"]}

2023-03-07T02:56:14+00:00

7a746459f480cc939969f39ff6bfa4424fd84519

# Dataset Card for "summarization-sft-heirarchical-split1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dmayhem93/summarization-sft-heirarchical-split1

[ "region:us" ]

2023-03-07T03:19:35+00:00

{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "125M", "dtype": "string"}, {"name": "1B", "dtype": "string"}, {"name": "6B", "dtype": "string"}, {"name": "20B", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 121267953, "num_examples": 47241}, {"name": "test", "num_bytes": 217854405, "num_examples": 83632}, {"name": "valid", "num_bytes": 86573178, "num_examples": 33088}], "download_size": 124221198, "dataset_size": 425695536}}

2023-03-07T03:20:57+00:00

5b92c63d0f5336cd340dc248ae0c637f5e2ef81f

# Dataset Card for "summarization-sft-heirarchical-split2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

dmayhem93/summarization-sft-heirarchical-split2

[ "region:us" ]

2023-03-07T03:21:00+00:00

{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "125M", "dtype": "string"}, {"name": "1B", "dtype": "string"}, {"name": "6B", "dtype": "string"}, {"name": "20B", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 117627495, "num_examples": 45303}, {"name": "test", "num_bytes": 217854405, "num_examples": 83632}, {"name": "valid", "num_bytes": 131656080, "num_examples": 50720}], "download_size": 137276787, "dataset_size": 467137980}}

2023-03-07T03:22:54+00:00