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train · 109k rows

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bcde0851cbd17616945045ae2f6ab73b7303c6d1	データ制作者（[t_w](https://dlt.kitetu.com/KNo.EDD2)）が[デライト](https://dlt.kitetu.com/)に投稿した5万件の投稿をEmbeddingの学習用にいい感じにしたやつ。 # License Licenseは設定していないため、日本の法律に従って利用されたい。従って、学習に用いるのは問題ないが再配布は不可。	tzmtwtr/tw-posts-japanese	[ "language:ja", "license:other", "region:us" ]	2023-06-13T13:31:25+00:00	{"language": ["ja"], "license": "other"}	2023-06-16T04:17:23+00:00
83d5902bb7c9feea19cd482fb7c9b60376269160		DaryaCsu/cows	[ "license:other", "region:us" ]	2023-06-13T13:47:12+00:00	{"license": "other", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": " weight", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 23248701.0, "num_examples": 121}], "download_size": 23054425, "dataset_size": 23248701.0}}	2023-06-14T17:49:39+00:00
484027ee138fe56b3c56defd0eb6f42a5d14e385	# Dataset Card for "datacomp-small-10-rows" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	nielsr/datacomp-small-10-rows	[ "region:us" ]	2023-06-13T13:47:34+00:00	{"dataset_info": {"features": [{"name": "uid", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "original_width", "dtype": "int64"}, {"name": "original_height", "dtype": "int64"}, {"name": "clip_b32_similarity_score", "dtype": "float32"}, {"name": "clip_l14_similarity_score", "dtype": "float32"}, {"name": "face_bboxes", "sequence": {"sequence": "float64"}}, {"name": "sha256", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2954, "num_examples": 10}], "download_size": 9270, "dataset_size": 2954}}	2023-06-13T13:47:37+00:00
f4d16777c9f0ab71f6ec6f01a33297e8f34281fd	"In the depths of a lush forest, nestled near majestic mountains, thrives an extraordinary female-only tribe. This enchanting community boasts a unique blend of captivating features, as they are distinguished by their striking blonde hair, mesmerizing orange eyes, and a complexion kissed by the sun, adorned with rich dark skin. Living harmoniously with nature, this tribe has found solace in the vibrant forest that surrounds them. Within their domain, they are blessed with wondrous gifts of the land. Among the wonders that grace their home are natural hot springs, where warm waters rejuvenate their spirits and provide a sanctuary for reflection and relaxation. The forest is also adorned with cherry blossom trees, which burst into bloom each spring, transforming the tribe's surroundings into a surreal canvas of delicate petals in hues of pink and white. Beyond their physical allure, the women of this tribe possess an inherent charm that captivates all who encounter them. Their allure and sensuality emanate from their deep connection with their surroundings, as they navigate the forest with grace and embrace the natural rhythms of life. It is through this symbiotic relationship with nature that they have honed their mystique, exuding a magnetic presence that leaves a lasting impression on those fortunate enough to witness their radiance. In this hidden corner of the world, the female-only tribe reigns as guardians of the forest, cherishing its beauty and protecting its secrets. As they wander through the verdant landscape, their presence is an embodiment of the untamed spirit of the wilderness, seamlessly merging their ethereal beauty with the captivating nature that surrounds them." Trained with Anime (full-final-pruned) model, using images generated from Waifulabs.com Activation tags: mountain tribe (for general info), and stock character names (the ones founds at the image here) to get an specific design. You may also make your own OC's with this. Recommended LoRA weight blocks: OUTD and OUTALL (you can still use ALL and MIDD but can be messy, use on your own risk.) Recommended weights: 0.7 - 1.0	Cheetor1996/mountain_tribe_girls	[ "language:en", "license:cc-by-2.0", "art", "region:us" ]	2023-06-13T14:08:48+00:00	{"language": ["en"], "license": "cc-by-2.0", "tags": ["art"]}	2023-06-13T14:13:49+00:00
ba3d7686d36f303927f48bd044167995cf467f08	# Dataset Card for KoLIMA ## Dataset Description KoLIMA는 Meta에서 공개한 [LIMA: Less Is More for Alignment](https://arxiv.org/abs/2305.11206) (Zhou et al., 2023)의 [학습 데이터](https://huggingface.co/datasets/GAIR/lima)를 한국어로 번역한 데이터셋입니다. 번역에는 [DeepL API](https://www.deepl.com/docs-api)를 활용하였고, SK(주) Tech Collaborative Lab으로부터 비용을 지원받았습니다. 전체 텍스트 중에서 code block이나 수식을 나타내는 특수문자 사이의 텍스트는 원문을 유지하는 형태로 번역을 진행하였으며, `train` 데이터셋 1,030건과 `test` 데이터셋 300건으로 구성된 총 1,330건의 데이터를 활용하실 수 있습니다. 현재 동일한 번역 문장을 `plain`, `vicuna` 두 가지 포멧으로 제공합니다. 데이터셋 관련하여 문의가 있으신 경우 [메일](mailto:[email protected])을 통해 연락주세요! 🥰 This is Korean LIMA dataset, which is translated from the [LIMA dataset](https://huggingface.co/datasets/GAIR/lima) that Meta's [LIMA model](https://arxiv.org/abs/2305.11206) (Zhou et al., 2023) was trained on. The translation has proceeded through [DeepL API](https://www.deepl.com/docs-api) with financial support from Tech Collaborative Lab in SK Inc. Please feel free to contact me if you have any question on the dataset. I'm best reached via [email](mailto:[email protected]). #### Changelog - [29 June 2023] New format added: `vicuna` format with the same translation is now available. - [16 June 2023] Enhanced Translation: keep the text enclosed in special characters, e.g. `$` and `` ` ``, intact without translation. - [14 June 2023] First upload. ### Usage ```python >>> from datasets import load_dataset >>> ko_lima = load_dataset('taeshahn/ko-lima', 'plain') # or load_dataset('taeshahn/ko-lima') >>> ko_lima_vicuna = load_dataset('taeshahn/ko-lima', 'vicuna') ``` ```python >>> ko_lima['train'][1025] { 'conversations': [ '저는 케냐 출신입니다. 망명을 신청하고 싶은데 비자없이 네덜란드로 망명을 신청하기 위해 여행할 수 있나요? 케냐항공에서 여권을 소지한 경우 스키폴 공항으로 가는 비자없이 비행기에 탑승할 수 있나요?', '항공사가 탑승을 허용할 가능성은 극히 낮습니다. 네덜란드에 입국하려는 케냐 시민은 비자, 체류 기간에 필요한 충분한 자금 증명, 다음 목적지 입국에 필요한 서류를 소지해야 합니다. 또한 항공사는 케냐에서 출발하는 승객에 대해 특별 조사를 실시해야 합니다:\n\n> 다음 공항에서 네덜란드로 운항하는 항공사:\n\n아부다비(AUH), 아크라(ACC), 바레인(BAH), 베이징 캐피탈(PEK), 보리스필(KBP), 카이로(CAI), 담만(DMM), 다르에스살람(DAR), 두바이(DXB), 엔테베(EBB), 광저우(CAN), 홍콩(HKG), 하마드(DOH), 이스탄불 아타튀르크(IST), 이스탄불 사비하곡첸(SAW), 요하네스버그(JNB), 키갈리(KGL), 킬리만자로(JRO), 쿠알라룸푸르(KUL), 쿠웨이트(KWI), 라고스(LOS), 모스크바 셰레메티예보(SVO), 무스카트(MCT), 나이로비(NB ), 뉴델리(DEL), 상파울루(GRU), 싱가포르(SIN) 및 테헤란(IKA)은 네덜란드 도착 시 적절한 여행 서류가 없어 입국할 수 없는 경우 해당 항공편의 모든 승객의 여행 서류 사본을 제출하셔야 합니다. 사본에는 여권/여행 서류의 데이터 페이지, 비자가 있는 페이지, 출국/통관 스탬프가 있는 페이지(해당되는 경우)가 포함되어야 합니다. 이러한 입국 규정을 준수하지 않을 경우 항공사에 승객 1인당 최대 11,250유로의 벌금이 부과될 수 있습니다.\n\n마지막 문장에서 볼 수 있듯이 항공사는 탑승 전에 여행 서류를 확인해야 하는 강력한 인센티브를 갖게 됩니다.', '인용된 자료의 출처는 무엇인가요?', '출처는 항공을 통해 해외로 여행하는 승객의 여권 및 비자 요건과 같은 서류 요건이 포함된 데이터베이스인 Timatic(여행 정보 매뉴얼 자동)에서 가져온 것입니다. 위의 답변은 목적지가 네덜란드이고 귀국/귀국 여행 증빙이 있으며 5일간 체류할 계획이며 케냐 여권의 유효 기간이 약 3년 남은 케냐 시민이 케냐에서 직접 여행하는 시나리오를 기반으로 한 것입니다.' ], 'source': 'multi_turn' } ``` ```python >>> ko_lima_vicuna['train'][1025] { 'id': '1026-multi_turn', 'conversations': [ { 'from': '사람', 'value': '저는 케냐 출신입니다. 망명을 신청하고 싶은데 비자없이 네덜란드로 망명을 신청하기 위해 여행할 수 있나요? 케냐항공에서 여권을 소지한 경우 스키폴 공항으로 가는 비자없이 비행기에 탑승할 수 있나요?' }, { 'from': '어시스턴트', 'value': '항공사가 탑승을 허용할 가능성은 극히 낮습니다. 네덜란드에 입국하려는 케냐 시민은 비자, 체류 기간에 필요한 충분한 자금 증명, 다음 목적지 입국에 필요한 서류를 소지해야 합니다. 또한 항공사는 케냐에서 출발하는 승객에 대해 특별 조사를 실시해야 합니다:\n\n> 다음 공항에서 네덜란드로 운항하는 항공사:\n\n아부다비(AUH), 아크라(ACC), 바레인(BAH), 베이징 캐피탈(PEK), 보리스필(KBP), 카이로(CAI), 담만(DMM), 다르에스살람(DAR), 두바이(DXB), 엔테베(EBB), 광저우(CAN), 홍콩(HKG), 하마드(DOH), 이스탄불 아타튀르크(IST), 이스탄불 사비하곡첸(SAW), 요하네스버그(JNB), 키갈리(KGL), 킬리만자로(JRO), 쿠알라룸푸르(KUL), 쿠웨이트(KWI), 라고스(LOS), 모스크바 셰레메티예보(SVO), 무스카트(MCT), 나이로비(NB ), 뉴델리(DEL), 상파울루(GRU), 싱가포르(SIN) 및 테헤란(IKA)은 네덜란드 도착 시 적절한 여행 서류가 없어 입국할 수 없는 경우 해당 항공편의 모든 승객의 여행 서류 사본을 제출하셔야 합니다. 사본에는 여권/여행 서류의 데이터 페이지, 비자가 있는 페이지, 출국/통관 스탬프가 있는 페이지(해당되는 경우)가 포함되어야 합니다. 이러한 입국 규정을 준수하지 않을 경우 항공사에 승객 1인당 최대 11,250유로의 벌금이 부과될 수 있습니다.\n\n마지막 문장에서 볼 수 있듯이 항공사는 탑승 전에 여행 서류를 확인해야 하는 강력한 인센티브를 갖게 됩니다.' }, { 'from': '사람', 'value': '인용된 자료의 출처는 무엇인가요?' }, { 'from': '어시스턴트', 'value': '출처는 항공을 통해 해외로 여행하는 승객의 여권 및 비자 요건과 같은 서류 요건이 포함된 데이터베이스인 Timatic(여행 정보 매뉴얼 자동)에서 가져온 것입니다. 위의 답변은 목적지가 네덜란드이고 귀국/귀국 여행 증빙이 있으며 5일간 체류할 계획이며 케냐 여권의 유효 기간이 약 3년 남은 케냐 시민이 케냐에서 직접 여행하는 시나리오를 기반으로 한 것입니다.' } ] } ``` ### Citation Information ``` @InProceedings{kolimadataset, title = {KoLIMA: Korean LIMA Dataset for Efficient Instruction-tuning}, author = {Hahn, Taeseung}, year = {2023} } ```	taeshahn/ko-lima	[ "license:cc-by-nc-sa-4.0", "arxiv:2305.11206", "region:us" ]	2023-06-13T14:10:24+00:00	{"license": "cc-by-nc-sa-4.0"}	2023-11-28T05:25:02+00:00
305a96bb7a2fbad6acfaeac88a81ada4f02e264a	# Dataset Card for "stable-bias_grounding-images_multimodel_3_12_22_clusters" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	stable-bias/stable-bias_grounding-images_multimodel_3_12_22_clusters	[ "license:apache-2.0", "region:us" ]	2023-06-13T14:21:09+00:00	{"license": "apache-2.0", "dataset_info": {"features": [{"name": "examplar", "dtype": "image"}, {"name": "centroid", "sequence": "float64"}, {"name": "gender_phrases", "sequence": "string"}, {"name": "gender_phrases_counts", "sequence": "int64"}, {"name": "ethnicity_phrases", "sequence": "string"}, {"name": "ethnicity_phrases_counts", "sequence": "int64"}, {"name": "example_ids", "sequence": "int64"}], "splits": [{"name": "12_clusters", "num_bytes": 473144, "num_examples": 12}, {"name": "24_clusters", "num_bytes": 940348, "num_examples": 24}, {"name": "48_clusters", "num_bytes": 1990487, "num_examples": 48}], "download_size": 3509518, "dataset_size": 3403979}}	2023-06-13T14:22:24+00:00
9a289d95919a27085873ab32ee71415fd426bc41	# Dataset Card for "RedPajama-Data-1T-arxiv-filtered" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Abzu/RedPajama-Data-1T-arxiv-filtered	[ "region:us" ]	2023-06-13T14:24:28+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "meta", "dtype": "string"}, {"name": "red_pajama_subset", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 229340859.5333384, "num_examples": 3911}], "download_size": 104435457, "dataset_size": 229340859.5333384}}	2023-06-13T14:24:34+00:00
a8dde44d2cedf28f32b97a1ac4e408dc0c1ab449	# Dataset Card for "rm_oa_hh" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	pvduy/rm_oa_hh	[ "region:us" ]	2023-06-13T14:40:34+00:00	{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "selected", "dtype": "string"}, {"name": "rejected", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 11065628, "num_examples": 8524}, {"name": "train", "num_bytes": 220101381, "num_examples": 166750}], "download_size": 135525253, "dataset_size": 231167009}}	2023-06-13T15:39:03+00:00
cff8349133b87dd2cdfcac0d45dd97e862298d04	Experimental Synthetic Dataset of Public Domain Character Dialogue in Roleplay Format Generated using scripts from my https://github.com/practicaldreamer/build-a-dataset repo --- license: mit ---	practical-dreamer/RPGPT_PublicDomain-ShareGPT	[ "task_categories:conversational", "size_categories:10M<n<100M", "language:en", "license:mit", "sharegpt", "region:us" ]	2023-06-13T14:42:21+00:00	{"language": ["en"], "license": "mit", "size_categories": ["10M<n<100M"], "task_categories": ["conversational"], "pretty_name": "rpgpt-sharegpt", "tags": ["sharegpt"]}	2023-07-03T23:04:40+00:00
6f15d9383523a2a0b556f1334d7b3351fc7927f3	# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xl_mode_CM_Q_rices_ns_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xl_mode_CM_Q_rices_ns_100	[ "region:us" ]	2023-06-13T14:50:48+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 548536, "num_examples": 100}], "download_size": 98791, "dataset_size": 548536}}	2023-06-14T19:47:59+00:00
b06f079ab892f09deaf3858a70ef2b1662e68193	# Dataset Card for CyberAesthetics ## Description This dataset contains a compiled image-set from cyber security web articles, with the view towards analyzing the distinct visual properties and sentiments of these images. ### Curation Rationale We defined a set of base search terms (for example, `cybersecurity' \texttt{OR} `cyber' \texttt{AND} `security') and then added search terms derived from Google Trends (online OR advice OR protection OR protect OR prevent OR preventative OR tips OR email OR social network OR password OR hack OR hacked OR hacking). All search terms were technology-agnostic~---~they did not include explicit references to specific products or services. The image-scraper then returned all images that corresponded with content that included these terms within the title or body text. Though not exhaustive, this strategy yields an image-set that adequately represents operative definitions of cyber security, as actualised by users. There is, of course, scope for future improvement. ### Source Data Images taken from the public domain. #### Initial Data Collection and Normalization We used the folliwing guidleines to collect and curate this image-set. 1. The image must be derived from a news or blog article that directly addresses at least one aspect of cyber security and / or explicitly contains our search terminology. Blog articles were limited to tutorials, editorials, tool demonstrations, and discussions of technical reports. Due to the nature of the assessment and the search methodology, we only retrieved images from English-language sources. 2. The image must be accessible and not hidden behind a paywall or other kind of lockout mechanism, as these obstacles restrict the amount of text that can be retrieved, making it difficult to explain why some images were included in a given article or blog post (that is, the role that the images serve in relation to the text). 3. The image cannot be a corporate logo or advertisement (like the lead slide of a corporate presentation). 4. The image must be at least 360x640 pixels for ease of processing. 5. The image must be in either .jpg or .png format. ### Citation Information Title: The aesthetics of cyber security: How do users perceive them? Authors: Mark Quinlan, Aaron Cross, Andrew Simpson Categories: cs.CY	Quinm101/cyberaesthetics	[ "license:cc0-1.0", "region:us" ]	2023-06-13T14:54:34+00:00	{"license": "cc0-1.0"}	2023-06-13T22:21:16+00:00
0ba9e40542a64e72d325940edbee5f988c9bc2a0	# Dataset Card for professions-v2 ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks](#supported-tasks) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases and Limitations](#discussion-of-biases-and-limitations) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: https://huggingface.co/spaces/tti-bias/stable-bias - Repository: Forthcoming - Paper: Forthcoming - Leaderboard: - Point of Contact: Forthcoming ### Dataset Summary <span style="color:blue" font-size:smaller><b>🏗️ WORK IN PROGRESS</b></span> <span style="color:red" font-size:smaller><b>⚠️ DISCLAIMER: The images in this dataset were generated by text-to-image systems and may depict offensive stereotypes or contain explicit content.</b></span> The Professions dataset is a collection of computer-generated images generated using Text-to-Image (TTI) systems. In order to generate a diverse set of prompts to evaluate the system outputs’ variation across dimensions of interest, we use the pattern `Photo portrait of a [X]`, where X spans the professional attribute that we focus our analysis on, i.e. the name of the profession. For the professions, we rely on a list of 146 occupations taken from the U.S. Bureau of Labor Statistics, and they are: ```python ['accountant', 'aerospace_engineer', 'aide', 'air_conditioning_installer', 'architect', 'artist', 'author', 'baker', 'bartender', 'bus_driver', 'butcher', 'career_counselor', 'carpenter', 'carpet_installer', 'cashier', 'ceo', 'childcare_worker', 'civil_engineer', 'claims_appraiser', 'cleaner', 'clergy', 'clerk', 'coach', 'community_manager', 'compliance_officer', 'computer_programmer', 'computer_support_specialist', 'computer_systems_analyst', 'construction_worker', 'cook', 'correctional_officer', 'courier', 'credit_counselor', 'customer_service_representative', 'data_entry_keyer', 'dental_assistant', 'dental_hygienist', 'dentist', 'designer', 'detective', 'director', 'dishwasher', 'dispatcher', 'doctor', 'drywall_installer', 'electrical_engineer', 'electrician', 'engineer', 'event_planner', 'executive_assistant', 'facilities_manager', 'farmer', 'fast_food_worker', 'file_clerk', 'financial_advisor', 'financial_analyst', 'financial_manager', 'firefighter', 'fitness_instructor', 'graphic_designer', 'groundskeeper', 'hairdresser', 'head_cook', 'health_technician', 'host', 'hostess', 'industrial_engineer', 'insurance_agent', 'interior_designer', 'interviewer', 'inventory_clerk', 'it_specialist', 'jailer', 'janitor', 'laboratory_technician', 'language_pathologist', 'lawyer', 'librarian', 'logistician', 'machinery_mechanic', 'machinist', 'maid', 'manager', 'manicurist', 'market_research_analyst', 'marketing_manager', 'massage_therapist', 'mechanic', 'mechanical_engineer', 'medical_records_specialist', 'mental_health_counselor', 'metal_worker', 'mover', 'musician', 'network_administrator', 'nurse', 'nursing_assistant', 'nutritionist', 'occupational_therapist', 'office_clerk', 'office_worker', 'painter', 'paralegal', 'payroll_clerk', 'pharmacist', 'pharmacy_technician', 'photographer', 'physical_therapist', 'pilot', 'plane_mechanic', 'plumber', 'police_officer', 'postal_worker', 'printing_press_operator', 'producer', 'psychologist', 'public_relations_specialist', 'purchasing_agent', 'radiologic_technician', 'real_estate_broker', 'receptionist', 'repair_worker', 'roofer', 'sales_manager', 'salesperson', 'school_bus_driver', 'scientist', 'security_guard', 'sheet_metal_worker', 'singer', 'social_assistant', 'social_worker', 'software_developer', 'stocker', 'stubborn', 'supervisor', 'taxi_driver', 'teacher', 'teaching_assistant', 'teller', 'therapist', 'tractor_operator', 'truck_driver', 'tutor', 'underwriter', 'veterinarian', 'waiter', 'waitress', 'welder', 'wholesale_buyer', 'writer'] ``` Every prompt is used to generate images from the following models: ```python ['22h-vintedois-diffusion-v0-1', 'CompVis-stable-diffusion-v1-4', 'Lykon-DreamShaper', 'SG161222-Realistic_Vision_V1.4', 'andite-anything-v4.0', 'andite-pastel-mix', 'dreamlike-art-dreamlike-photoreal-2.0', 'hakurei-waifu-diffusion', 'plasmo-vox2', 'prompthero-openjourney', 'prompthero-openjourney-v4', 'runwayml-stable-diffusion-v1-5', 'stabilityai-stable-diffusion-2', 'stabilityai-stable-diffusion-2-1-base', 'wavymulder-Analog-Diffusion'] ``` ### Supported Tasks This dataset can be used to evaluate the output space of TTI systems, particularly against the backdrop of societal representativeness. ### Languages The prompts that generated the images are all in US-English. ## Dataset Structure The dataset is stored in `parquet` format and contains 253,719 rows which can be loaded like so: ```python from datasets import load_dataset dataset = load_dataset("tti-bias/professions-v2", split="train") ``` ### Data Fields Each row corresponds to the output of a TTI system and looks as follows: ```python { 'profession': 'dentist', 'model': 'dreamlike-art-dreamlike-photoreal-2.0', 'no': 1, 'seed': 838979725, 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=512x512> } ``` ### Data Splits All the data is contained within the `train` split. As such, the dataset contains practically no splits. ## Dataset Creation ### Curation Rationale This dataset was created to explore the output characteristics of TTI systems from the vantage point of societal characteristics of interest. ### Source Data #### Initial Data Collection and Normalization The data was generated using the [DiffusionPipeline]() from Hugging Face: ```python from diffusers import DiffusionPipeline import torch pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16) images = pipeline(prompt="Photo portrait of a bus driver at work", num_images_per_prompt=9).images ``` ### Personal and Sensitive Information Generative models trained on large datasets have been shown to memorize part of their training sets (See e.g.: [(Carlini et al. 2023)](https://arxiv.org/abs/2301.13188)) and the people generated could theoretically bear resemblance to real people. ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases and Limitations At this point in time, the data is limited to images generated using English prompts and a set of professions sourced form the U.S. Bureau of Labor Statistics (BLS), which also provides us with additional information such as the demographic characteristics and salaries of each profession. While this data can also be leveraged in interesting analyses, it is currently limited to the North American context. ## Additional Information ### Licensing Information The dataset is licensed under the Creative Commons [Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/) license. ### Citation Information If you use this dataset in your own work, please consider citing: ```json @article{stable-bias-authors-2023, author = {Anonymous Authors}, title = {Stable Bias: Analyzing Societal Representations in Diffusion Models}, year = {2023}, } ```	stable-bias/professions-v2	[ "language:en", "license:cc-by-sa-4.0", "arxiv:2301.13188", "region:us" ]	2023-06-13T15:00:45+00:00	{"language": ["en"], "license": "cc-by-sa-4.0", "dataset_info": {"features": [{"name": "profession", "dtype": "string"}, {"name": "model", "dtype": "string"}, {"name": "no", "dtype": "int32"}, {"name": "seed", "dtype": "int32"}, {"name": "image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 8338679567.447, "num_examples": 253719}], "download_size": 8917804015, "dataset_size": 8338679567.447}}	2023-08-21T13:11:59+00:00
2f63224002279d33fa63570fa47f155b626c0cf5	# Otter STITCH Dataset Card STITCH (Search Tool for Interacting Chemicals) is a database of known and predicted interactions between chemicals represented by SMILES strings and proteins whose sequences are taken from STRING database. Those interactions are obtained from computational prediction, from knowledge transfer between organisms, and from interactions aggregated from other (primary) databases. For the Multimodal Knowledge Graph (MKG) curation we filtered only the interaction with highest confidence, i.e., the one which is higher 0.9. This resulted into 10,717,791 triples for 17,572 different chemicals and 1,886,496 different proteins. Furthermore, the graph was split into 5 roughly same size subgraphs and GNN was trained sequentially on each of them by upgrading the model trained using the previous subgraph. Original dataset: - Citation: Damian Szklarczyk, Alberto Santos, Christian von Mering, Lars Juhl Jensen, Peer Bork, and Michael Kuhn. Stitch 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic acids research, 44(D1):D380–D384, 2016. doi: doi.org/10.1093/nar/gkv1277. Paper or resources for more information: - [GitHub Repo](https://github.com/IBM/otter-knowledge) - [Paper](https://arxiv.org/abs/2306.12802) License: MIT Where to send questions or comments about the dataset: - [GitHub Repo](https://github.com/IBM/otter-knowledge) Models trained on Otter UBC - [ibm/otter_stitch_classifier](https://huggingface.co/ibm/otter_stitch_classifier) - [ibm/otter_stitch_distmult](https://huggingface.co/ibm/otter_stitch_distmult) - [ibm/otter_stitch_transe](https://huggingface.co/ibm/otter_stitch_transe)	ibm/otter_stitch	[ "license:mit", "arxiv:2306.12802", "region:us" ]	2023-06-13T15:04:25+00:00	{"license": "mit"}	2023-06-26T07:10:01+00:00
e4f48e848a9ac2b18113fc426a509429d43e71c0	# Dataset Card for "swedish-sentiment-instruction-fine-tuning" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	filopedraz/swedish-sentiment-instruction-fine-tuning	[ "region:us" ]	2023-06-13T15:17:03+00:00	{"dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 54726179, "num_examples": 163841}], "download_size": 24121083, "dataset_size": 54726179}}	2023-06-13T15:17:25+00:00
0fa31d5fb525cee21eda1af5c35576a7a68442e6	# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xl_mode_CM_Q_rices_ns_10" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xl_mode_CM_Q_rices_ns_10	[ "region:us" ]	2023-06-13T15:36:26+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 1715, "num_examples": 10}], "download_size": 0, "dataset_size": 1715}}	2023-06-13T16:03:51+00:00
eae75dc9bde14d1172351423bcbb82c5b9a94f66	# RED<sup>FM</sup>: a Filtered and Multilingual Relation Extraction Dataset This is the human-filtered dataset from the 2023 ACL paper [RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset](https://arxiv.org/abs/2306.09802). If you use the model, please reference this work in your paper: @inproceedings{huguet-cabot-et-al-2023-redfm-dataset, title = "RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset", author = "Huguet Cabot, Pere-Llu{\'\i}s and Tedeschi, Simone and Ngonga Ngomo, Axel-Cyrille and Navigli, Roberto", booktitle = "Proc. of the 61st Annual Meeting of the Association for Computational Linguistics: ACL 2023", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2306.09802", } ## License RED<sup>FM</sup> is licensed under the CC BY-SA 4.0 license. The text of the license can be found [here](https://creativecommons.org/licenses/by-sa/4.0/).	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"start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}]}], "splits": [{"name": "test", "num_bytes": 521806, "num_examples": 345}, {"name": "validation", "num_bytes": 577499, "num_examples": 385}], "download_size": 3458539, "dataset_size": 1099305}, {"config_name": "de", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "struct": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "predicate", "dtype": {"class_label": {"names": {"0": "country", "1": 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"8": "author", "9": "director", "10": "occupation", "11": "founded by", "12": "league", "13": "owned by", "14": "genre", "15": "named after", "16": "follows", "17": "headquarters location", "18": "cast member", "19": "manufacturer", "20": "located in or next to body of water", "21": "location", "22": "part of", "23": "mouth of the watercourse", "24": "member of", "25": "sport", "26": "characters", "27": "participant", "28": "notable work", "29": "replaces", "30": "sibling", "31": "inception"}}}}, {"name": "object", "struct": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}]}], "splits": [{"name": "train", "num_bytes": 4387657, "num_examples": 2878}, {"name": "test", "num_bytes": 654376, "num_examples": 446}, {"name": "validation", "num_bytes": 617141, "num_examples": 449}], "download_size": 13616716, "dataset_size": 5659174}, {"config_name": "es", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "struct": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "predicate", "dtype": {"class_label": {"names": {"0": "country", "1": "place of birth", "2": "spouse", "3": "country of citizenship", "4": "instance of", "5": "capital", "6": "child", "7": "shares border with", "8": "author", "9": "director", "10": "occupation", "11": "founded by", "12": "league", "13": "owned by", "14": "genre", "15": "named 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{"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "struct": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "predicate", "dtype": {"class_label": {"names": {"0": "country", "1": "place of birth", "2": "spouse", "3": "country of citizenship", "4": "instance of", "5": "capital", "6": "child", "7": "shares border with", "8": "author", "9": "director", "10": "occupation", "11": "founded by", "12": "league", "13": "owned by", "14": "genre", "15": "named after", "16": "follows", "17": "headquarters location", "18": "cast member", "19": "manufacturer", "20": "located in or next to body of water", "21": "location", "22": "part of", "23": "mouth of the watercourse", "24": "member of", "25": "sport", "26": "characters", "27": 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"dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}]}], "splits": [{"name": "test", "num_bytes": 311905, "num_examples": 270}, {"name": "validation", "num_bytes": 364077, "num_examples": 307}], "download_size": 1952982, "dataset_size": 675982}, {"config_name": "all_languages", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "lan", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "struct": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "predicate", "dtype": {"class_label": {"names": {"0": "country", "1": "place of birth", "2": "spouse", "3": "country of citizenship", "4": "instance of", "5": "capital", "6": "child", "7": "shares border with", "8": "author", "9": "director", "10": "occupation", "11": "founded by", "12": "league", "13": "owned by", "14": "genre", "15": "named after", "16": "follows", "17": "headquarters location", "18": "cast member", "19": "manufacturer", "20": "located in or next to body of water", "21": "location", "22": "part of", "23": "mouth of the watercourse", "24": "member of", "25": "sport", "26": "characters", "27": "participant", "28": "notable work", "29": "replaces", "30": "sibling", "31": "inception"}}}}, {"name": "object", "struct": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}]}], "splits": [{"name": "train", "num_bytes": 13557340, "num_examples": 10337}, {"name": "test", "num_bytes": 3100822, "num_examples": 2551}, {"name": "validation", "num_bytes": 3099341, "num_examples": 2558}], "download_size": 50720746, "dataset_size": 19757503}]}	2023-06-20T06:33:35+00:00
dde3c3d2bd6ac4bb2ec09a68661a5139479a73ac	# Dataset Card for "atsad1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	sanaeai/atsad1	[ "region:us" ]	2023-06-13T16:16:45+00:00	{"dataset_info": {"features": [{"name": "tweet", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16169142, "num_examples": 124133}], "download_size": 8253585, "dataset_size": 16169142}}	2023-06-13T16:16:47+00:00
f1411f14ccf3f729727df46ba813efb783a7d616	# ImageNet-12k Split Metadata Metadata files defining the splits for ImageNet-12k subset of `fall11_whole.tar` (2011 ImageNet full release) used in some `timm` models (see dataset building code in https://github.com/rwightman/imagenet-12k).	rwightman/imagenet-12k-metadata	[ "license:apache-2.0", "region:us" ]	2023-06-13T16:34:20+00:00	{"license": "apache-2.0"}	2023-06-13T16:41:14+00:00
82143d22eea69cd978ab3d73141f09b9d27815b2	# Dataset Card for "Guanaco-oasst1_Originals_Arabic_pairs" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Ali-C137/Guanaco-oasst1_Originals_Arabic_pairs	[ "region:us" ]	2023-06-13T16:48:45+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "translated_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 38713258, "num_examples": 10364}], "download_size": 20094755, "dataset_size": 38713258}}	2023-06-13T16:48:47+00:00
063f1c714dfbfe9d1a12f59fd28835f134e1dc43	# Dataset Card for "VisDial_modif-Sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	HuggingFaceM4/VisDial_modif-Sample	[ "region:us" ]	2023-06-13T16:51:28+00:00	{"dataset_info": {"features": [{"name": "caption", "dtype": "string"}, {"name": "dialog", "sequence": {"sequence": "string"}}, {"name": "image_path", "dtype": "string"}, {"name": "global_image_id", "dtype": "string"}, {"name": "anns_id", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "answer", "sequence": "string"}, {"name": "context", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 164280536.5563279, "num_examples": 1000}, {"name": "validation", "num_bytes": 162457052.0348837, "num_examples": 1000}, {"name": "test", "num_bytes": 162318287.0, "num_examples": 1000}], "download_size": 458274072, "dataset_size": 489055875.5912116}}	2023-06-13T16:52:38+00:00
c0f8009a2e0cf0e41921a6786e4d48671fbf11bf	# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xxl_mode_CM_Q_rices_ns_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xxl_mode_CM_Q_rices_ns_100	[ "region:us" ]	2023-06-13T16:57:21+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 14212, "num_examples": 100}], "download_size": 8348, "dataset_size": 14212}, "configs": [{"config_name": "default", "data_files": [{"split": "fewshot_0", "path": "data/fewshot_0-*"}]}]}	2024-01-29T23:19:34+00:00
438563911ffd4e427617780ef944e3061e3402bc	# Dataset Card for the SCOTUS lifelong editing task ## Dataset Description - Homepage: https://github.com/Thartvigsen/GRACE - Repository: https://github.com/Thartvigsen/GRACE - Paper: https://arxiv.org/abs/2211.11031 - Point of Contact: Tom Hartvigsen ([email protected]) ### Dataset Summary This dataset contains a relabeled sample from the SCOTUS dataset in [fairlex](https://huggingface.co/datasets/coastalcph/fairlex) as described in [our paper](https://arxiv.org/abs/2211.11031) ### Citation Information ``` @article{hartvigsen2023aging, title={Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adapters}, author={Hartvigsen, Thomas and Sankaranarayanan, Swami and Palangi, Hamid and Kim, Yoon and Ghassemi, Marzyeh}, journal={arXiv preprint arXiv:2211.11031}, year={2023} } ```	tomh/grace-scotus	[ "task_categories:text-classification", "multilinguality:monolingual", "source_datasets:coastalcph/fairlex", "language:en", "arxiv:2211.11031", "region:us" ]	2023-06-13T16:59:27+00:00	{"language": ["en"], "license": [], "multilinguality": ["monolingual"], "source_datasets": ["coastalcph/fairlex"], "task_categories": ["text-classification"], "pretty_name": "scotus_grace"}	2023-06-13T17:58:16+00:00
042b12b571ad685e47a5cd4c6a14fa139158af02	# Dataset Card for "Arabic-Guanaco-oasst1_Extended" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Ali-C137/Arabic-Guanaco-oasst1_Extended	[ "region:us" ]	2023-06-13T17:07:03+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 38713258, "num_examples": 20728}], "download_size": 20111815, "dataset_size": 38713258}}	2023-06-13T17:07:05+00:00
7430c56c10c4bc36c2296d3ef0d0a4f9e63082d9	# ATLAS-CONVERSE This dataset is synthetically generated by GPT-3.5-turbo. It was generated in 1.5 hours and cost me $3.82 USD. This is a conversation dataset based that can work with FastChat, Axolotl, and ShareGPT formatting. ## Categories: The main 41 (See the repo to check the JSONL) categories below were human derived while the subcategories were synthetically generated by GPT-4. ### 1. Mathematics 1.1. Arithmetic 1.2. Algebra 1.3. Geometry 1.4. Trigonometry 1.5. Calculus 1.6. Statistics 1.7. Discrete Mathematics 1.8. Number Theory 1.9. Graph Theory 1.10. Mathematical Analysis 1.11. Probability Theory 1.12. Set Theory 1.13. Mathematical Logic 1.14. Game Theory 1.15. Differential Equations 1.16. Linear Algebra 1.17. Complex Analysis 1.18. Combinatorics 1.19. Cryptography 1.20. Numerical Analysis 1.21. Algebraic Geometry 1.22. Topology 1.23. Applied Mathematics 1.24. Mathematical Physics 1.25. Control Theory 1.26. Information Theory 1.27. Fluid Mechanics 1.28. Chaotic Dynamics 1.29. Robotics 1.30. Tensor Calculus 1.31. Computation Theory 1.32. Statistical Mechanics 1.33. Computer Science 1.34. Mathematical Biology 1.35. Financial Mathematics 1.36. Operation Research 1.37. Dynamical Systems ### 2. Science 2.1. Biology 2.2. Chemistry 2.3. Physics 2.4. Astronomy 2.5. Earth Science 2.6. Environmental Science 2.7. Anatomy 2.8. Genetics 2.9. Zoology 2.10. Botany 2.11. Geology 2.12. Materials Science 2.13. Neuroscience 2.14. Computer Science 2.15. Mathematics 2.16. Statistics 2.17. Biochemistry 2.18. Biophysics 2.19. Ecology 2.20. Pharmacology 2.21. Microbiology 2.22. Paleontology 2.23. Oceanography 2.24. Meteorology 2.25. Biomedical Engineering 2.26. Mechanical Engineering 2.27. Aerospace Engineering 2.28. Civil Engineering 2.29. Chemical Engineering 2.30. Electrical Engineering 2.31. Industrial Engineering 2.32. Computer Engineering 2.33. Software Engineering 2.34. Biotechnology 2.35. Actuarial Science 2.36. Forensic Science 2.37. Data Science ### 3. Humanities 3.1. Literature 3.2. Philosophy 3.3. History 3.4. Anthropology 3.5. Linguistics 3.6. Film/Cinema 3.7. Religion/Theology 3.8. Art 3.9. Music 3.10. Archaeology 3.11. Political Science 3.12. Psychology 3.13. Economics 3.14. Geography 3.15. Sociology 3.16. Education 3.17. Women Studies 3.18. Cultural Studies 3.19. Environmental Studies 3.20. Social Work 3.21. Media Studies 3.22. Arts Management 3.23. Peace Studies 3.24. Science and Technology Studies 3.25. Global Studies 3.26. Data Science 3.27. Statistics 3.28. Business 3.29. Philology 3.30. Epistemology 3.31. Rhetoric 3.32. Logic 3.33. Disability Studies 3.34. Bioethics 3.35. Game Theory 3.36. Gender Studies 3.37. Computer Science ### 4. Social Sciences 4.1. Psychology 4.2. Sociology 4.3. Economics 4.4. Political Science 4.5. Geography 4.6. Criminology 4.7. Archaeology 4.8. Demography 4.9. Anthropology 4.10. Linguistics 4.11. Education 4.12. Arts 4.13. Sciences 4.14. Biology 4.15. Chemistry 4.16. Physics 4.17. Geology 4.18. Astronomy 4.19. Health Sciences 4.20. Sports Science 4.21. Environmental Sciences 4.22. Computer Science 4.23. Engineering 4.24. Mathematics 4.25. Statistics 4.26. Business Administration 4.27. Finance 4.28. Accounting 4.29. Marketing 4.30. Law 4.31. History 4.32. Philosophy 4.33. Religious Studies 4.34. Information Technology 4.35. Architecture 4.36. Agricultural Science 4.37. Veterinary Science 4.38. Aviation Science 4.39. Media Studies 4.40. Music Theory 4.41. Theater and Drama ### 5. Business 5.1. Accounting 5.2. Finance 5.3. Marketing 5.4. Management 5.5. Operations 5.6. Human Resources 5.7. Legal 5.8. Risk Management 5.9. Project Management 5.10. Research & Analytics 5.11. Data Visualization 5.12. Product Development 5.13. Presentation & Negotiation 5.14. Strategic Planning 5.15. Customer service 5.16. Supply Chain Management 5.17. Business Analytics 5.18. Business Process Modeling 5.19. Corporate Governance 5.20. Information Technology Management 5.21. Innovation Management 5.22. Business Administration 5.23. Inventory Management 5.24. Organizational Development 5.25. Financial Analysis 5.26. Crisis Management 5.27. Performance Improvement 5.28. Business Modeling 5.29. Product Promotion 5.30. Change Management 5.31. Competitive Analysis 5.32. Productivity Analysis 5.33. Process Reengineering 5.34. Strategy Analysis 5.35. Business Development 5.36. Leadership Development 5.37. Talent Acquisition ### 6. Technology 6.1. Computer Science 6.2. Information Technology 6.3. Engineering 6.4. Artificial Intelligence 6.5. Robotics 6.6. Software 6.7. Programming Languages 6.8. Data Storage and Retrieval 6.9. Computer Networks 6.10. Computer Architecture 6.11. Operating Systems 6.12. Cybersecurity 6.13. Graphics and Design 6.14. Hardware 6.15. Data Analysis 6.16. Game Development 6.17. Cloud Computing 6.18. Databases 6.19. Mobile Computing 6.20. Data Mining 6.21. Networking Protocols 6.22. Quality Assurance 6.23. Web Development 6.24. Algorithm Design 6.25. Machine Learning 6.26. Video Editing 6.27. Natural Language Processing 6.28. Development Methodologies 6.29. Computer Vision 6.30. Data Visualization 6.31. Blockchain 6.32. Computer Audio and MIDI 6.33. Computer Forensics 6.34. Biometrics 6.35. Automation 6.36. Software Security 6.37. Information Security ### 7. Law and Government 7.1. Criminal Justice 7.2. International Law 7.3. Constitutional Law 7.4. Administrative Law 7.5. Civil Law 7.6. Tax Law 7.7. Environmental Law 7.8. Labor Law 7.9. Intellectual Property Law 7.10. Trusts and Estates Law 7.11. Native American Law 7.12. Medical Law 7.13. Military and Veterans Law 7.14. Journalism Law 7.15. Cyber Law 7.16. Sports Law 7.17. Maritime Law 7.18. Immigration Law 7.19. Consumer Protection Law 7.20. Gender and Sexuality Law 7.21. Human Rights Law 7.22. Bankruptcy Law 7.23. Security Law 7.24. Election Law 7.25. Privacy Law 7.26. Animal Law 7.27. Technology Law 7.28. Media Law 7.29. Healthcare Law 7.30. Bangladesh Law 7.31. Morality and Social Policy Law 7.32. Contract Law 7.33. Corporate Law 7.34. Property Law 7.35. Criminal Procedure Law 7.36. Civil Procedure Law 7.37. International Trade Law ### 8. Health and Medicine 8.1. Anatomy and Physiology 8.2. Pharmacology 8.3. Epidemiology 8.4. Nutrition 8.5. Pathology 8.6. Clinical Practice 8.7. Therapies 8.8. Medical Devices 8.9. Laboratory Techniques 8.10. Infectious Diseases 8.11. Chronic Disease 8.12. Injury Prevention 8.13. Public Health 8.14. Emergency Medicine 8.15. Emergency Care 8.16. Mental Health 8.17. orthopedic Conditions 8.18. Professional Practice 8.19. Medical Instruments 8.20. Complementary and Alternative Medicine 8.21. Internal Medicine 8.22. Pediatrics 8.23. Geriatrics 8.24. Neurology 8.25. Gynecology 8.26. Cardiology 8.27. Oncology 8.28. Urology 8.29. Ophthalmology 8.30. Dermatology 8.31. Dentistry 8.32. Immunology 8.33. Endocrinology 8.34. Pulmonology 8.35. Hematology 8.36. Gastroenterology 8.37. Rheumatology ### 9. Fine Arts 9.1. Visual Arts 9.2. Music 9.3. Theater 9.4. Dance 9.5. Graphic Design 9.6. Film and TV 9.7. Architecture and Interior Design 9.8. Fashion Design 9.9. Jewelry Design 9.10. Culinary Arts 9.11. Calligraphy 9.12. Illustration 9.13. Photography 9.14. Weaving 9.15. Sculpting 9.16. Pottery 9.17. Printmaking 9.18. Stained Glass 9.19. Woodworking 9.20. Metalworking 9.21. Mixed Media 9.22. Street Art 9.23. Video Art 9.24. Installation Arts 9.25. Performance Arts 9.26. Glass Blowing 9.27. Ceramics 9.28. Digital Arts 9.29. Textile Arts 9.30. Mosaic Arts 9.31. Art Conservation 9.32. Art Education 9.33. Cartooning 9.34. Animation 9.35. Puppet Making 9.36. Creative Writing 9.37. Pen and Ink Drawing ### 10. Education 10.1. Pedagogy 10.2. Curriculum Design 10.3. Learning Technologies 10.4. Assessment and Evaluation 10.5. Instructional Design 10.6. Modern Teaching Methods 10.7. Professional Development and Mentorship 10.8. Multi-Modal and Universal Educational Practices 10.9. Data Analysis and Reporting 10.10. Collaborative Learning 10.11. Inclusion and Diversity 10.12. Project-Based Learning 10.13. Language Learning Strategies 10.14. Interpersonal and Cross-Cultural Education 10.15. Group Facilitation 10.16. Early Childhood Education 10.17. STEM Education 10.18. Scholastic Education 10.19. Homeschooling 10.20. Distance and Online Learning 10.21. Workplace Learning 10.22. Library and Archival Science 10.23. Historiography 10.24. Grammar 10.25. Interpretation of Linguistic Data 10.26. Linguistic Text Analysis 10.27. Discrete Mathematics 10.28. Statistical Computing 10.29. Information Retrieval 10.30. Programming Language Theory 10.31. Machine Learning 10.32. Natural Language Processing 10.33. Natural Language Synthesis 10.34. Word Sense Disambiguation 10.35. Clause and Sentence Structures 10.36. Discourse Analysis 10.37. Computational Linguistics ### 11. Media and Communication 11.1. Journalism 11.2. Public Relations 11.3. Advertising 11.4. Broadcasting 11.5. Film and Television 11.6. New Media 11.7. Social Media 11.8. Animation 11.9. Network Administration 11.10. Web Design 11.11. Graphic Design 11.12. Desktop Publishing 11.13. 3D Design 11.14. Game Design 11.15. Photography 11.16. Audio Recording 11.17. Video Recording 11.18. Video Editing 11.19. Audio Editing 11.20. Music Production 11.21. Video Production 11.22. Scriptwriting 11.23. Animation Production 11.24. Robotics 11.25. Virtual Reality 11.26. Augmented Reality 11.27. Coding 11.28. Programming 11.29. Database Administration 11.30. System Administration 11.31. Cloud Computing 11.32. Machine Learning 11.33. Artificial Intelligence 11.34. Natural Language Processing 11.35. Computer Vision 11.36. Cybersecurity 11.37. Data Science ### 12. Environment and Sustainability 12.1. Environmental Science 12.2. Renewable Energy 12.3. Sustainability 12.4. Climate Change 12.5. Natural Resource Management 12.6. Environmental Studies 12.7. Habitat Preservation 12.8. Conservation 12.9. Pollution Control 12.10. Bioremediation 12.11. Ecological Balance 12.12. Air Quality Management 12.13. Water Quality Management 12.14. Waste Management 12.15. Green Building 12.16. Regulatory Compliance 12.17. Environmental Risk Assessment 12.18. Environmental Economics 12.19. Green Computing 12.20. Environmental Justice 12.21. Land Use Planning 12.22. Hazardous Materials Management 12.23. Environmental Education 12.24. Renewable Energy Systems 12.25. Urban Planning 12.26. Wildlife Management 12.27. Geographic Information Systems 12.28. Alternative Energy 12.29. Climate Modeling 12.30. Geology 12.31. Soil Science 12.32. Agriculture 12.33. Forest Ecology 12.34. Environmental Health 12.35. Marine Science 12.36. Environmental Law 12.37. Environmental Engineering ### 13. Sports and Recreation 13.1. Exercise Science 13.2. Sports Medicine 13.3. Coaching 13.4. Physical Education 13.5. Sports Injury Prevention 13.6. Sports Psychology 13.7. Athletic Training 13.8. Performance Enhancement 13.9. Biomechanics 13.10. Strength and Conditioning 13.11. Sports Nutrition 13.12. Outdoor Adventure 13.13. Gymnastics 13.14. Swimming 13.15. Martial Arts 13.16. Soccer 13.17. Basketball 13.18. Baseball 13.19. Golf 13.20. Football 13.21. Hockey 13.22. Track and Field 13.23. Cycling 13.24. Racquet Sports 13.25. Winter Sports 13.26. Equestrian 13.27. Rowing 13.28. Boating 13.29. Hunting 13.30. Fishing 13.31. Nature & Wildlife Conservation 13.32. Skateboarding 13.33. Climbing 13.34. Surfing 13.35. Waterskiing & Wakeboarding 13.36. Skimboarding 13.37. Snowboarding ### 14. Travel and Tourism 14.1. Hospitality 14.2. Tourism Management 14.3. Destination Marketing 14.4. Cultural Tourism 14.5. Food and Beverage Management 14.6. Event Operations 14.7. Transportation Logistics 14.8. Transportation Planning 14.9. Airline and Airport Management 14.10. Cruise Line Management 14.11. Maritime Tourism 14.12. Destination Development 14.13. Eco-Tourism 14.14. Recreational Tourism 14.15. Adventure Tourism 14.16. International Travel 14.17. Culinary Tourism 14.18. Geo-Tourism 14.19. Gastro-Tourism 14.20. Educational Tourism 14.21. Sports Tourism 14.22. Aircraft Piloting 14.23. Driver Training 14.24. Travel Photography 14.25. Navigation Management 14.26. Tour Guide Training 14.27. Entrepreneurship 14.28. Sports Management 14.29. Hospitality Law 14.30. Transportation Safety 14.31. Occupational Health and Safety 14.32. Environmental Management 14.33. E-commerce 14.34. Tour Planning 14.35. Travel Writing 14.36. Social Media Marketing 14.37. Tourism Policy and Research ### 15. Food and Beverage 15.1. Culinary Arts 15.2. Food Science 15.3. Beverage Management 15.4. Restaurant Management 15.5. Menu Planning and Design 15.6. Food Safety and Sanitation 15.7. Gastronomy 15.8. Molecular Gastronomy 15.9. Wine and Spirits 15.10. Coffee Brewing Techniques 15.11. Brewing Beer 15.12. Mixology and Cocktail Making 15.13. Pastry and Baking 15.14. Butchery and Meat Preparation 15.15. Vegan and Plant-Based Cooking 15.16. Culinary Techniques and Knife Skills 15.17. Cheese Making 15.18. Fermentation and Pickling 15.19. Food Preservation Methods 15.20. Food Photography and Styling 15.21. Sustainable and Ethical Food Practices 15.22. Slow Cooking and Sous Vide 15.23. Molecular Mixology 15.24. Nutritional Analysis and Recipe Development 15.25. Molecular Food Techniques 15.26. Ice Cream and Gelato Making 15.27. Artisanal Chocolate Making 15.28. Gluten-Free Baking 15.29. Barbecue and Grilling Techniques 15.30. Asian Cuisine 15.31. Mediterranean Cuisine 15.32. Latin American Cuisine 15.33. French Cuisine 15.34. Italian Cuisine 15.35. Middle Eastern Cuisine 15.36. African Cuisine 15.37. Indian Cuisine ### 16. Religion and Spirituality 16.1. Religious Studies 16.2. Theology 16.3. Comparative Religion 16.4. Spiritual Practices 16.5. Rituals and Ceremonies 16.6. Biblical Studies 16.7. World Religions 16.8. Philosophy of Religion 16.9. History of Religion 16.10. Ethics and Morality 16.11. Mysticism and Esotericism 16.12. Religious Art and Architecture 16.13. Sacred Texts and Scriptures 16.14. Sociology of Religion 16.15. Religious Education 16.16. Interfaith Dialogue 16.17. Feminist Theology 16.18. Religious Leadership and Management 16.19. Religious Ethics 16.20. Social Justice and Religion 16.21. Religious Symbolism 16.22. Religious Pluralism 16.23. Religion and Science 16.24. Religious Anthropology 16.25. Religious Ritual Objects 16.26. Religious Music and Chants 16.27. Religious Festivals and Holidays 16.28. Religious Pilgrimages 16.29. Religious Meditation and Mindfulness 16.30. Religion and Psychology 16.31. Religion and Politics 16.32. Religious Sects and Movements 16.33. Religious Ethics in Business 16.34. Religion and Technology 16.35. Religion and Environment 16.36. Religion and Health 16.37. Religious Counseling and Therapy ### 17. Philosophy and Ethics 17.1. Epistemology 17.2. Metaphysics 17.3. Ethics 17.4. Aesthetics 17.5. Philosophy of Mind 17.6. Philosophy of Language 17.7. Philosophy of Science 17.8. Philosophy of Religion 17.9. Philosophy of Mathematics 17.10. Logic and Reasoning 17.11. Existentialism 17.12. Pragmatism 17.13. Analytic Philosophy 17.14. Continental Philosophy 17.15. Political Philosophy 17.16. Feminist Philosophy 17.17. Philosophy of Technology 17.18. Philosophy of Education 17.19. Philosophy of History 17.20. Philosophy of Law 17.21. Environmental Ethics 17.22. Bioethics 17.23. Animal Ethics 17.24. Virtue Ethics 17.25. Utilitarianism 17.26. Deontology 17.27. Moral Realism 17.28. Moral Relativism 17.29. Aesthetics of Film 17.30. Aesthetics of Literature 17.31. Aesthetics of Music 17.32. Aesthetics of Visual Arts 17.33. Ontology 17.34. Philosophy of Perception 17.35. Philosophy of Emotions 17.36. Philosophy of Consciousness 17.37. Social and Political Philosophy ### 18. Languages and Linguistics 18.1. Language Learning 18.2. Linguistic Theory 18.3. Translation and Interpretation 18.4. Corpus Linguistics 18.5. Sociolinguistics 18.6. Psycholinguistics 18.7. Historical Linguistics 18.8. Phonetics and Phonology 18.9. Morphology 18.10. Syntax 18.11. Semantics 18.12. Pragmatics 18.13. Discourse Analysis 18.14. Language Acquisition 18.15. Computational Linguistics 18.16. Natural Language Processing 18.17. Machine Translation 18.18. Speech Recognition and Synthesis 18.19. Language Variation and Change 18.20. Dialectology 18.21. Lexicography 18.22. Etymology 18.23. Stylistics 18.24. Rhetoric and Composition 18.25. Language and Gender 18.26. Language and Power 18.27. Language and Identity 18.28. Bilingualism and Multilingualism 18.29. Second Language Acquisition 18.30. Language Pedagogy 18.31. Applied Linguistics 18.32. Sociolinguistic Variation 18.33. Pragmatic Variation 18.34. Language Testing and Assessment 18.35. Language Policy and Planning 18.36. Forensic Linguistics 18.37. Neurolinguistics ### 19. Design and Architecture 19.1. Industrial Design 19.2. Architecture 19.3. Graphic Design 19.4. Interior Design 19.5. UX/UI Design 19.6. Web Design 19.7. Product Design 19.8. Automotive Design 19.9. Fashion Design 19.10. Packaging Design 19.11. Industrial Engineering Design 19.12. Game Design 19.13. User Research and Analysis 19.14. Design Thinking 19.15. Interaction Design 19.16. Service Design 19.17. Design Strategy 19.18. Design for Sustainability 19.19. Design for Manufacturing 19.20. Design for Accessibility 19.21. Information Architecture 19.22. Data Visualization Design 19.23. Motion Graphics Design 19.24. Branding and Identity Design 19.25. Typography Design 19.26. Illustration Design 19.27. Environmental Design 19.28. Exhibition Design 19.29. Furniture Design 19.30. Lighting Design 19.31. Textile Design 19.32. Jewelry Design 19.33. Industrial Automation Design 19.34. Landscape Design 19.35. Exhibition Stand Design 19.36. Graphic Communication Design 19.37. Design for Education and Learning ### 20. Fashion and Apparel 20.1. Fashion Design 20.2. Fashion Merchandising 20.3. Textile Science 20.4. Apparel Production 20.5. Clothing Construction 20.6. Pattern Making 20.7. Fashion Illustration 20.8. Trend Forecasting 20.9. Garment Fitting 20.10. Fashion Marketing 20.11. Fashion Branding 20.12. Fashion Retailing 20.13. Fashion Styling 20.14. Fashion Photography 20.15. Fashion Journalism 20.16. Fashion Public Relations 20.17. Fashion Event Management 20.18. Fashion Buying and Merchandising 20.19. Fashion Accessories Design 20.20. Fashion Sustainability 20.21. Fashion Technology 20.22. Fashion Entrepreneurship 20.23. Costume Design 20.24. Fashion History 20.25. Fashion Law and Ethics 20.26. Fashion Business Management 20.27. Fashion Economics 20.28. Fashion Forecasting 20.29. Fashion Research and Analysis 20.30. Fashion Trend Analysis 20.31. Fashion Communication 20.32. Fashion Psychology 20.33. Fashion Manufacturing 20.34. Fashion Supply Chain Management 20.35. Fashion Product Development 20.36. Fashion Retail Buying 20.37. Fashion E-commerce ### 21. Transportation and Logistics 21.1. Transportation Systems 21.2. Logistics and Supply Chain Management 21.3. Traffic Engineering 21.4. Aviation 21.5. Maritime Operations 21.6. Rail Transportation 21.7. Public Transportation Systems 21.8. Warehouse Management 21.9. Inventory Control 21.10. Fleet Management 21.11. Freight Forwarding 21.12. Customs and Trade Compliance 21.13. Route Planning 21.14. Last Mile Delivery 21.15. Cold Chain Management 21.16. Material Handling Equipment 21.17. Packaging and Labeling 21.18. Reverse Logistics 21.19. Transportation Safety and Security 21.20. Transportation Cost Optimization 21.21. Port Operations 21.22. Intermodal Transportation 21.23. Transportation Regulations and Compliance 21.24. Urban Transportation Planning 21.25. Load Optimization 21.26. Energy Efficiency in Transportation 21.27. Traffic Flow Optimization 21.28. Public Transit Infrastructure 21.29. Vehicle Routing and Scheduling 21.30. Supply Chain Visibility 21.31. Cross-Docking Operations 21.32. Delivery Performance Measurement 21.33. Intercompany Transportation Coordination 21.34. Transportation Network Design 21.35. Warehouse Layout Optimization 21.36. Inventory Forecasting and Planning 21.37. Air Traffic Control ### 22. Military and Defense 22.1. Military Strategy 22.2. Military History 22.3. Weapons and Technology 22.4. National Security 22.5. Combat Tactics 22.6. Counterterrorism Operations 22.7. Geopolitics and International Relations 22.8. Defense Policy and Planning 22.9. Military Intelligence 22.10. Cybersecurity and Information Warfare 22.11. Space Defense and Exploration 22.12. Special Forces Operations 22.13. Military Training and Education 22.14. Humanitarian Aid in Conflict Zones 22.15. Military Logistics and Supply Chain Management 22.16. Military Ethics and Conduct 22.17. Civil-Military Relations 22.18. Military Law and Legal Frameworks 22.19. Military Medicine and Field Hospitals 22.20. Military Communications and Command Systems 22.21. Military Aviation 22.22. Military Naval Operations 22.23. Military Land Operations 22.24. Military Cyber Defense 22.25. Military Robotics and Autonomous Systems 22.26. Defense Budgeting and Resource Allocation 22.27. Military Doctrine and Doctrine Development 22.28. Military Simulation and Wargaming 22.29. Military Uniforms and Insignia 22.30. Military Decorations and Awards 22.31. Military Rehabilitation and Veterans Affairs 22.32. Military Recruitment and Retention 22.33. Military Leadership and Command Structures 22.34. Military Organizational Culture 22.35. Military Occupational Specialties and Job Training 22.36. Military Psychological Operations 22.37. Military Infrastructure and Base Operations ### 23. Anthropology and Archaeology 23.1. Cultural Anthropology 23.2. Archaeological Science 23.3. Biological Anthropology 23.4. Forensic Anthropology 23.5. Linguistic Anthropology 23.6. Social Anthropology 23.7. Ethnography 23.8. Ethnology 23.9. Ethnoarchaeology 23.10. Paleontology 23.11. Zooarchaeology 23.12. Ethnohistory 23.13. Medical Anthropology 23.14. Primatology 23.15. Evolutionary Anthropology 23.16. Symbolic Anthropology 23.17. Cultural Materialism 23.18. Economic Anthropology 23.19. Political Anthropology 23.20. Urban Anthropology 23.21. Applied Anthropology 23.22. Indigenous Anthropology 23.23. Visual Anthropology 23.24. Virtual Anthropology 23.25. Digital Anthropology 23.26. Human Osteology 23.27. Biocultural Anthropology 23.28. Cognitive Anthropology 23.29. Psychological Anthropology 23.30. Ecological Anthropology 23.31. Historical Archaeology 23.32. Maritime Archaeology 23.33. Public Archaeology 23.34. Underwater Archaeology 23.35. Prehistoric Archaeology 23.36. Classical Archaeology 23.37. Industrial Archaeology ### 24. Psychology and Mental Health 24.1. Clinical Psychology 24.2. Neuropsychology 24.3. Behavioral Neuroscience 24.4. Mental Health Counseling 24.5. Psychiatric Rehabilitation 24.6. Cognitive Psychology 24.7. Industrial-Organizational Psychology 24.8. Developmental Psychology 24.9. Educational Psychology 24.10. Social Psychology 24.11. Health Psychology 24.12. Forensic Psychology 24.13. Community Psychology 24.14. Geriatric Psychology 24.15. Cross-Cultural Psychology 24.16. Environmental Psychology 24.17. Sports Psychology 24.18. Positive Psychology 24.19. Psychopathology 24.20. Child Psychology 24.21. Adolescent Psychology 24.22. Clinical Neuropsychology 24.23. Experimental Psychology 24.24. Human Factors Psychology 24.25. Rehabilitation Psychology 24.26. School Psychology 24.27. Trauma Psychology 24.28. Personality Psychology 24.29. Quantitative Psychology 24.30. Evolutionary Psychology 24.31. Comparative Psychology 24.32. Counseling Psychology 24.33. Psychopharmacology 24.34. Psychoanalysis 24.35. Psycholinguistics 24.36. Psychometrics 24.37. Parapsychology ### 25. Artificial Intelligence and Machine Learning 25.1. Machine Learning Algorithms 25.2. Natural Language Processing 25.3. Computer Vision 25.4. Robotics 25.5. Deep Learning 25.6. Reinforcement Learning 25.7. Generative Adversarial Networks (GANs) 25.8. Transfer Learning 25.9. Neural Networks 25.10. Decision Trees 25.11. Support Vector Machines (SVM) 25.12. Ensemble Methods 25.13. Dimensionality Reduction 25.14. Clustering Algorithms 25.15. Regression Analysis 25.16. Time Series Analysis 25.17. Anomaly Detection 25.18. Recommender Systems 25.19. Feature Engineering 25.20. Model Evaluation and Validation 25.21. Hyperparameter Tuning 25.22. Data Preprocessing 25.23. Data Visualization 25.24. Data Augmentation 25.25. Model Deployment 25.26. Model Interpretability 25.27. Model Optimization 25.28. Model Compression 25.29. Model Explainability 25.30. AutoML (Automated Machine Learning) 25.31. Natural Language Generation 25.32. Sentiment Analysis 25.33. Named Entity Recognition 25.34. Text Classification 25.35. Text Summarization 25.36. Speech Recognition 25.37. Speech Synthesis 25.38. Emotion Recognition 25.39. Image Classification 25.40. Object Detection 25.41. Image Segmentation 25.42. Image Generation 25.43. Pose Estimation 25.44. Action Recognition 25.45. Autonomous Navigation 25.46. Robot Perception 25.47. Robot Localization and Mapping 25.48. Robot Control Systems 25.49. Reinforcement Learning for Robotics ### 26. Neuroscience and Brain Science 26.1. Cognitive Neuroscience 26.2. Behavioral Neuroscience 26.3. Neuroimaging 26.4. Neuropsychology 26.5. Molecular Neuroscience 26.6. Developmental Neuroscience 26.7. Systems Neuroscience 26.8. Computational Neuroscience 26.9. Neurophysiology 26.10. Neuropharmacology 26.11. Neuroendocrinology 26.12. Neuroimmunology 26.13. Neuropsychiatry 26.14. Neurodegenerative Disorders 26.15. Neurodevelopmental Disorders 26.16. Neurological Disorders 26.17. Neuroplasticity 26.18. Neural Networks 26.19. Brain-Machine Interfaces 26.20. Neuroethics 26.21. Neural Computation 26.22. Neural Coding 26.23. Neurofeedback 26.24. Neurological Rehabilitation 26.25. Neurosurgery 26.26. Neuroanatomy 26.27. Neurochemistry 26.28. Neurogenetics 26.29. Neurolinguistics 26.30. Neuroprosthetics 26.31. Neurophotonics 26.32. Neuroinformatics 26.33. Neuroimaging Techniques 26.34. Neuroplasticity Research Methods 26.35. Neurotransmitters and Receptors 26.36. Neuromodulation Techniques 26.37. Neurological Data Analysis ### 27. Energy and Power Systems 27.1. Renewable Energy Technologies 27.2. Power Electronics 27.3. Energy Storage 27.4. Smart Grids 27.5. High-Voltage Engineering 27.6. Distributed Generation 27.7. Electrical Power Distribution 27.8. Power System Protection 27.9. Power Quality Analysis 27.10. Electrical Grid Integration 27.11. Energy Management Systems 27.12. Microgrid Design and Operation 27.13. Electric Vehicles and Charging Infrastructure 27.14. Wind Power Systems 27.15. Solar Power Systems 27.16. Hydroelectric Power Systems 27.17. Biomass Energy Systems 27.18. Geothermal Energy Systems 27.19. Tidal and Wave Power Systems 27.20. Hybrid Energy Systems 27.21. Energy Efficiency and Conservation 27.22. Power System Control and Operation 27.23. Energy Policy and Regulation 27.24. Advanced Power System Analysis 27.25. Load Forecasting and Demand Response 27.26. Fault Analysis and Diagnosis in Power Systems 27.27. Energy Economics and Markets 27.28. Power System Planning and Reliability Assessment 27.29. Energy System Modeling and Simulation 27.30. Electric Power Transmission 27.31. Protection and Control of Power Transformers 27.32. Energy Audit and Management 27.33. Power System Stability and Dynamics 27.34. Islanding and Black Start Capability 27.35. Energy Harvesting Technologies 27.36. Grid Integration of Energy Storage Systems 27.37. Resilient Power Systems ### 28. Materials Science and Engineering 28.1. Nanomaterials 28.2. Polymers 28.3. Composites 28.4. Ceramics 28.5. Metals 28.6. Biomaterials 28.7. Surface Coatings 28.8. Thin Films 28.9. Crystallography 28.10. Mechanical Properties Analysis 28.11. Electrical Properties Analysis 28.12. Optical Properties Analysis 28.13. Thermal Properties Analysis 28.14. Corrosion Analysis 28.15. Fatigue Analysis 28.16. Fracture Mechanics 28.17. Microscopy Techniques 28.18. Spectroscopy Techniques 28.19. X-ray Diffraction (XRD) 28.20. Scanning Electron Microscopy (SEM) 28.21. Transmission Electron Microscopy (TEM) 28.22. Atomic Force Microscopy (AFM) 28.23. Fourier Transform Infrared Spectroscopy (FTIR) 28.24. Raman Spectroscopy 28.25. UV-Visible Spectroscopy 28.26. Differential Scanning Calorimetry (DSC) 28.27. Thermogravimetric Analysis (TGA) 28.28. Dynamic Mechanical Analysis (DMA) 28.29. Rheological Analysis 28.30. Polymer Processing Techniques 28.31. Casting 28.32. Extrusion 28.33. Injection Molding 28.34. Blow Molding 28.35. Thermoforming 28.36. Compression Molding 28.37. Rotational Molding ### 29. Quantum Science and Technology 29.1. Quantum Computing 29.2. Quantum Communication 29.3. Quantum Sensors 29.4. Quantum Materials 29.5. Quantum Algorithms 29.6. Quantum Error Correction 29.7. Quantum Cryptography 29.8. Quantum Information Theory 29.9. Quantum Metrology 29.10. Quantum Simulation 29.11. Quantum Machine Learning 29.12. Quantum Networking 29.13. Quantum Control 29.14. Quantum Optics 29.15. Quantum Transport 29.16. Quantum Sensing and Imaging 29.17. Quantum Entanglement 29.18. Quantum State Engineering 29.19. Quantum Interferometry 29.20. Quantum Photonic Devices 29.21. Quantum Spintronics 29.22. Quantum Nanomechanics 29.23. Quantum Biology 29.24. Quantum Robotics 29.25. Quantum Computing Architectures 29.26. Quantum Computing Algorithms 29.27. Quantum Communication Protocols 29.28. Quantum Sensor Technologies 29.29. Quantum Material Synthesis 29.30. Quantum Material Characterization 29.31. Quantum Material Devices 29.32. Quantum Material Properties 29.33. Quantum Material Fabrication 29.34. Quantum Material Applications 29.35. Quantum Material Integration 29.36. Quantum Material Testing 29.37. Quantum Material Optimization ### 30. Environmental Science and Engineering 30.1. Environmental Chemistry 30.2. Environmental Biology 30.3. Water Resources Engineering 30.4. Sustainable Infrastructure 30.5. Air Quality Management 30.6. Environmental Policy and Planning 30.7. Environmental Impact Assessment 30.8. Ecological Modeling 30.9. Environmental Microbiology 30.10. Waste Management and Remediation 30.11. Environmental Monitoring and Analysis 30.12. Renewable Energy Systems 30.13. Environmental Hydrology 30.14. Climate Change Mitigation 30.15. Environmental Risk Assessment 30.16. Environmental Geology 30.17. Green Building Design 30.18. Industrial Ecology 30.19. Environmental Fluid Mechanics 30.20. Environmental Health and Safety 30.21. Environmental Ethics and Justice 30.22. Environmental Data Science 30.23. Soil and Water Conservation 30.24. Environmental Noise Control 30.25. Urban Environmental Management 30.26. Environmental Education and Outreach 30.27. Sustainable Agriculture 30.28. Environmental Systems Analysis 30.29. Environmental Economics 30.30. Environmental Sociology 30.31. Coastal Zone Management 30.32. Environmental Remote Sensing 30.33. Environmental Psychology 30.34. Environmental Law and Policy 30.35. Environmental Impact Mitigation 30.36. Environmental Modeling and Simulation 30.37. Environmental Ethics and Governance ### 31. Genetics and Genomics 31.1. Molecular Genetics 31.2. Genomic Medicine 31.3. Epigenetics 31.4. Population Genetics 31.5. Comparative Genomics 31.6. Functional Genomics 31.7. Structural Genomics 31.8. Evolutionary Genomics 31.9. Pharmacogenomics 31.10. Cancer Genomics 31.11. Human Genome Project 31.12. Translational Genomics 31.13. Quantitative Genetics 31.14. Systems Genetics 31.15. Genomic Variation Analysis 31.16. Genomic Data Analysis 31.17. Genomic Sequencing Techniques 31.18. Gene Expression Analysis 31.19. Gene Regulation Analysis 31.20. Genome Editing Techniques 31.21. Genetic Engineering Methods 31.22. Next-Generation Sequencing 31.23. Genomic Data Visualization 31.24. Comparative Genomic Analysis 31.25. Genomic Biomarker Discovery 31.26. Genomic Epidemiology 31.27. Metagenomics 31.28. Transcriptomics 31.29. Proteomics 31.30. Metabolomics 31.31. Single-Cell Genomics 31.32. Functional Annotation of Genomes 31.33. Genomic Databases 31.34. Genomic Privacy and Ethics 31.35. Genomic Data Sharing 31.36. Genomic Data Security 31.37. Genomic Data Interpretation ### 32. Evolution and Ecology 32.1. Evolutionary Biology 32.2. Ecology 32.3. Conservation Biology 32.4. Biodiversity 32.5. Population Dynamics 32.6. Community Interactions 32.7. Ecosystem Functioning 32.8. Behavioral Ecology 32.9. Landscape Ecology 32.10. Biogeography 32.11. Phylogenetics 32.12. Coevolution 32.13. Conservation Genetics 32.14. Ecological Modeling 32.15. Ecotoxicology 32.16. Restoration Ecology 32.17. Urban Ecology 32.18. Evolutionary Genetics 32.19. Macroevolution 32.20. Microevolution 32.21. Molecular Ecology 32.22. Ecological Succession 32.23. Island Biogeography 32.24. Adaptation 32.25. Behavioral Genetics 32.26. Climate Change Biology 32.27. Conservation Planning 32.28. Disease Ecology 32.29. Ecological Networks 32.30. Evolutionary Developmental Biology (Evo-Devo) 32.31. Landscape Genetics 32.32. Phylogeography 32.33. Population Genetics 32.34. Quantitative Ecology 32.35. Species Interactions 32.36. Trophic Dynamics 32.37. Comparative Genomics ### 33. Biomedical Sciences 33.1. Biochemistry 33.2. Pharmacology 33.3. Immunology 33.4. Pathology 33.5. Genetics 33.6. Microbiology 33.7. Epidemiology 33.8. Molecular Biology 33.9. Cell Biology 33.10. Neurobiology 33.11. Physiology 33.12. Biophysics 33.13. Bioinformatics 33.14. Biostatistics 33.15. Biotechnology 33.16. Bioethics 33.17. Toxicology 33.18. Pharmacokinetics 33.19. Virology 33.20. Hematology 33.21. Immunotherapy 33.22. Oncology 33.23. Cardiology 33.24. Pulmonology 33.25. Endocrinology 33.26. Gastroenterology 33.27. Nephrology 33.28. Dermatology 33.29. Rheumatology 33.30. Urology 33.31. Ophthalmology 33.32. Otolaryngology 33.33. Pediatrics 33.34. Gerontology 33.35. Anesthesiology 33.36. Radiology 33.37. Emergency Medicine ### 34. Biotechnology and Biomanufacturing 34.1. Genetic Engineering 34.2. Cell Culture Technology 34.3. Downstream Processing 34.4. Biomanufacturing Operations 34.5. Fermentation Techniques 34.6. Protein Purification 34.7. Gene Cloning and Expression 34.8. Synthetic Biology 34.9. Biosensors and Bioelectronics 34.10. Bioreactors and Fermenters 34.11. Cell Therapy Manufacturing 34.12. Monoclonal Antibody Production 34.13. Tissue Engineering 34.14. Vaccine Production 34.15. Bioprocess Optimization 34.16. Bioinformatics in Biotechnology 34.17. Biopreservation Techniques 34.18. Microbial Fermentation 34.19. Biomaterials in Biomanufacturing 34.20. Downstream Chromatography 34.21. Protein Engineering 34.22. Recombinant DNA Technology 34.23. Biochemical Assays 34.24. Gene Therapy Manufacturing 34.25. Industrial Enzyme Production 34.26. Cell Line Development 34.27. Transgenic Animal Production 34.28. Bioproduction Scale-Up 34.29. Quality Control in Biomanufacturing 34.30. Cell-Free Systems 34.31. Biomolecular Engineering 34.32. Metabolic Engineering 34.33. Stem Cell Manufacturing 34.34. Upstream Processing 34.35. Industrial Microbiology 34.36. Plant Biotechnology 34.37. Environmental Biotechnology ### 35 Genetics and Molecular Biology 35.1 DNA Extraction 35.2 PCR Amplification 35.3 DNA Sequencing 35.4 Gene Expression Analysis 35.5 Genetic Engineering Techniques 35.6 Genomic Library Construction 35.7 Restriction Enzyme Analysis 35.8 Transfection Methods 35.9 Cloning and Recombinant DNA Techniques 35.10 DNA Fingerprinting 35.11 Microarray Analysis 35.12 RNA Interference (RNAi) 35.13 Gene Knockout Techniques 35.14 Mutagenesis Techniques 35.15 Genomic Data Analysis 35.16 Next-Generation Sequencing (NGS) 35.17 Gene Regulation Studies 35.18 Protein Purification Techniques 35.19 Enzyme Kinetics Assays 35.20 Protein Structure Prediction 35.21 Protein-Protein Interaction Analysis 35.22 Protein-DNA Interaction Analysis 35.23 Genomic Editing Techniques (CRISPR-Cas9) 35.24 Gene Therapy Approaches 35.25 Molecular Diagnostic Techniques 35.26 Epigenetics Studies 35.27 Transcriptional Regulation Analysis 35.28 Translation and Protein Synthesis Studies 35.29 DNA Repair Mechanisms 35.30 Comparative Genomics Analysis 35.31 RNA-Seq Analysis 35.32 Metagenomics Analysis 35.33 Phylogenetic Analysis 35.34 Functional Genomics Studies 35.35 Chromosome Analysis 35.36 DNA Methylation Analysis 35.37 Genomic Imprinting Studies ### 36. Astronomy and Astrophysics 36.1. Stellar Astrophysics 36.2. Galactic Astronomy 36.3. Cosmology 36.4. High Energy Astrophysics 36.5. Observational Techniques 36.6. Computational Techniques 36.7. Instrumentation 36.8. Theory and Modeling 36.9. Supermassive Black Holes 36.10. Exoplanets 36.11. Radiative Transfer 36.12. Stellar Evolution 36.13. Novae and Supernovae 36.14. Nebulae and Molecular Clouds 36.15. Galactic Structure 36.16. Star Clusters 36.17. Interstellar Medium 36.18. Spectroscopy 36.19. High Energy Phenomena 36.20. Celestial Mechanics 36.21. Multiwavelength Astronomy 36.22. Atmospheres of Extrasolar Planets 36.23. Radio Astronomy 36.24. Gravitational Astrophysics 36.25. Continuum Emission 36.26. Extragalactic Astronomy 36.27. Solar System Dynamics 36.28. Astrobiology 36.29. Relativistic Astrophysics 36.30. Time-Domain Astronomy 36.31. Neutron Star Astrophysics 36.32. White Dwarf Astrophysics 36.33. Stellar Dynamics and Numerical Simulations 36.34. Fast Radio Bursts and Transient Astronomy 36.35. Nuclear Reactions in Astrophysical Environments 36.36. Gamma-Ray Astronomy 36.37. Cosmic Ray Astrophysics ### 37. Condensed Matter Physics 37.1. Solid State Physics 37.2. Semiconductor Physics 37.3. Superconductivity 37.4. Magnetism 37.5. Computational Solid State Physics 37.6. Quantum Mechanics 37.7. Thermodynamics and Statistical Mechanics 37.8. Optics 37.9. Waves and Fields 37.10. Chemistry 37.11. Biophysics 37.12. Nuclear Physics 37.13. Astrophysics 37.14. Astronomy 37.15. Particle Physics 37.16. Quantum Electrodynamics 37.17. Quantum Field Theory 37.18. Quantum Chromodynamics 37.19. Gauge Theory 37.20. Cosmology 37.21. Geophysics 37.22. String Theory 37.23. General Relativity 37.24. High Energy Physics 37.25. Ionized Gas Dynamics 37.26. Fluid Dynamics 37.27. Acoustics 37.28. Discrete Mathematics 37.29. Mathematical Physics 37.30. Classical Mechanics 37.31. Combustion 37.32. Atomic Physics 37.33. Electronics 37.34. Plasma Physics 37.35. Thermophysics 37.36. Spectroscopy 37.37. Quantum Optics ### 38. Particle Physics and High-Energy Physics 38.1. Quantum Field Theory 38.2. Particle Accelerators 38.3. Dark Matter 38.4. Standard Model Physics 38.5. Grand Unified Theories 38.6. Quantum Mechanics 38.7. String Theory 38.8. Supergravity 38.9. Gravitation Theory 38.10. Quantum Chromodynamics 38.11. General Relativity 38.12. Supersymmetry 38.13. Hadron Physics 38.14. Nuclear Physics 38.15. Neutrino Physics 38.16. Cosmology 38.17. Special Relativity 38.18. Particle Detection 38.19. Thermodynamics 38.20. Atomic and Molecular Physics 38.21. Thermal Physics 38.22. Statistical Mechanics 38.23. Quantum Optics 38.24. Solid State Physics 38.25. Materials Science 38.26. Fluid Mechanics 38.27. Astrophysics 38.28. Optics 38.29. Electromagnetism 38.30. Mathematical Physics 38.31. Mechanics 38.32. Spectroscopy 38.33. Atomic Physics 38.34. Physical Chemistry 38.35. Nanotechnology 38.36. Quantum Computing 38.37. Surface Physics ### 39. Mathematical Sciences 39.1. Number Theory 39.2. Topology 39.3. Analysis 39.4. Mathematical Modeling 39.5. Probability and Statistics 39.6. Algebra 39.7. Discrete Mathematics 39.8. Calculus 39.9. Differential Equations 39.10. Linear Algebra 39.11. Geometry 39.12. Graph Theory 39.13. Dynamical Systems 39.14. Combinatorics 39.15. Statistics 39.16. Numerical Analysis 39.17. Stochastic Modeling 39.18. Optimization 39.19. Game Theory 39.20. Probability Theory 39.21. Financial Mathematics 39.22. Computer Algebra 39.23. Cryptography 39.24. Logic 39.25. Theoretical Computer Science 39.26. Mathematical Physics 39.27. Mathematical Biology 39.28. Group Theory 39.29. Combinatorial Optimization 39.30. Computational Geometry 39.31. Algebraic Geometry 39.32. Computational Complexity 39.33. Real Analysis 39.34. Formal Methods 39.35. Number Theory 39.36. Trigonometry 39.37. Fractal Geometry ### 40. Data Science and Analytics 40.1. Statistical Learning 40.2. Data Mining 40.3. Big Data Analytics 40.4. Predictive Modeling 40.5. Artificial Intelligence 40.6. Machine Learning 40.7. Deep Learning 40.8. Natural Language Processing 40.9. Image Analysis 40.10. Text Analysis 40.11. Recommender Systems 40.12. Cluster Analysis 40.13. Data Visualization 40.14. Quantitative Analysis 40.15. Data Exploration 40.16. Data Quality Assurance 40.17. Data Hygiene 40.18. Data Pre-processing 40.19. Data Cleaning 40.20. Data Transformation 40.21. Data Integration 40.22. Data Warehousing 40.23. Data Processing Methodology 40.24. Robustness Testing 40.25. Data Security 40.26. Data Ethics 40.27. Data Governance 40.28. Data Safeguarding 40.29. Risk Management 40.30. Data Backup Strategies 40.31. Data Retention Policies 40.32. Data Encryption 40.33. Data Governance Compliance 40.34. Data Quality Standards 40.35. Data Mining Techniques 40.36. Data Analysis Algorithms 40.37. Data Modeling and Simulation ### 41. Operations Research and Optimization 41.1. Linear Programming 41.2. Network Optimization 41.3. Nonlinear Optimization 41.4. Stochastic Processes 41.5. Graph Theory 41.6. Discrete Optimization 41.7. Scheduling 41.8. Meta-heuristics 41.9. 42.9 Mathematical Programming 41.10. Integer Programming 41.11. Markov Decision Process 41.12. Dynamic Programming 41.13. Simulation 41.14. Game Theory 41.15. Multi-Objective Optimization 41.16. Granular Computing 41.17. Heuristic Algorithms 41.18. Stochastic Optimization 41.19. Regression Analysis 41.20. Interior Point Methods 41.21. Just-in-time Scheduling 41.22. Evolutionary Computation 41.23. Influence Diagrams 41.24. Simulated Annealing 41.25. Quantum Computing 41.26. Capacity Planning 41.27. Fuzzy Mathematics 41.28. Application of AI in Optimization 41.29. Constraint Programming 41.30. Model Predictive Control 41.31. Parameter Estimation 41.32. Extremum Seeking Control 41.33. Approximation Algorithms 41.34. Combinatorial Optimization 41.35. Knowledge Representation and Reasoning 41.36. Logistics Systems Models 41.37. Computer-Aided Design	AtlasUnified/atlas-converse	[ "size_categories:1K<n<10K", "license:mit", "chemistry", "biology", "finance", "legal", "music", "art", "climate", "medical", "region:us" ]	2023-06-13T17:11:58+00:00	{"license": "mit", "size_categories": ["1K<n<10K"], "tags": ["chemistry", "biology", "finance", "legal", "music", "art", "climate", "medical"]}	2023-06-14T08:16:05+00:00
0ff3934145a4b4c20997b04be896a9c10cd701f5	# Ayaka/MoeDict-cmn-hak-10k	Ayaka/MoeDict-cmn-hak-10k	[ "task_categories:translation", "size_categories:10K<n<100K", "language:zh", "language:hak", "language:cmn", "region:us" ]	2023-06-13T17:13:49+00:00	{"language": ["zh", "hak", "cmn"], "size_categories": ["10K<n<100K"], "task_categories": ["translation"]}	2023-06-13T17:18:29+00:00
8e623a93e7881aa6550bf88c19a8dc1cd6380c0c		yichiac/india_fields	[ "license:creativeml-openrail-m", "region:us" ]	2023-06-13T17:20:15+00:00	{"license": "creativeml-openrail-m"}	2023-06-13T17:20:15+00:00
2732d2834e12e36510aeb2a468163ea2642d55db	# RED<sup>FM</sup>: a Filtered and Multilingual Relation Extraction Dataset This is the automatically-filtered dataset from the 2023 ACL paper [RED^{FM}: a Filtered and Multilingual Relation Extraction Dataset](https://arxiv.org/abs/2306.09802). If you use the model, please reference this work in your paper: @inproceedings{huguet-cabot-et-al-2023-redfm-dataset, title = "RED$^{\rm FM}$: a Filtered and Multilingual Relation Extraction Dataset", author = "Huguet Cabot, Pere-Llu{\'\i}s and Tedeschi, Simone and Ngonga Ngomo, Axel-Cyrille and Navigli, Roberto", booktitle = "Proc. of the 61st Annual Meeting of the Association for Computational Linguistics: ACL 2023", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/2306.09802", } ## License SRED<sup>FM</sup> is licensed under the CC BY-SA 4.0 license. The text of the license can be found [here](https://creativecommons.org/licenses/by-sa/4.0/).	Babelscape/SREDFM	[ "task_categories:token-classification", "size_categories:10M<n<100M", "language:ar", "language:ca", "language:de", "language:el", "language:en", "language:es", "language:fr", "language:hi", "language:it", "language:ja", "language:ko", "language:nl", "language:pl", "language:pt", "language:ru", "language:sv", "language:vi", "language:zh", "license:cc-by-sa-4.0", "arxiv:2306.09802", "region:us" ]	2023-06-13T17:35:19+00:00	{"language": ["ar", "ca", "de", "el", "en", "es", "fr", "hi", "it", "ja", "ko", "nl", "pl", "pt", "ru", "sv", "vi", "zh"], "license": "cc-by-sa-4.0", "size_categories": ["10M<n<100M"], "task_categories": ["token-classification"], "dataset_info": [{"config_name": "ar", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 659105981, "num_examples": 499568}, {"name": "test", "num_bytes": 9015516, "num_examples": 4387}, {"name": "validation", "num_bytes": 7406509, "num_examples": 3783}], "download_size": 3651950669, "dataset_size": 675528006}, {"config_name": "ca", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 406179567, "num_examples": 294856}, {"name": "test", "num_bytes": 5378789, "num_examples": 2541}, {"name": "validation", "num_bytes": 3136722, "num_examples": 1532}], "download_size": 1513026644, "dataset_size": 414695078}, {"config_name": "de", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 1288274676, "num_examples": 1049967}, {"name": "test", "num_bytes": 10773087, "num_examples": 5649}, {"name": "validation", "num_bytes": 8955886, "num_examples": 4994}], "download_size": 4521091910, "dataset_size": 1308003649}, {"config_name": "el", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 133497910, "num_examples": 64221}, {"name": "test", "num_bytes": 2364826, "num_examples": 861}, {"name": "validation", "num_bytes": 1836092, "num_examples": 668}], "download_size": 579372781, "dataset_size": 137698828}, {"config_name": "en", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 3555107736, "num_examples": 2701389}, {"name": "test", "num_bytes": 13160183, "num_examples": 6685}, {"name": "validation", "num_bytes": 27692074, "num_examples": 13236}], "download_size": 11914987368, "dataset_size": 3595959993}, {"config_name": "es", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 888914515, "num_examples": 702785}, {"name": "test", "num_bytes": 16076382, "num_examples": 8561}, {"name": "validation", "num_bytes": 4621760, "num_examples": 2177}], "download_size": 3570403740, "dataset_size": 909612657}, {"config_name": "fr", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 768697146, "num_examples": 870448}, {"name": "test", "num_bytes": 5937745, "num_examples": 3883}, {"name": "validation", "num_bytes": 3233262, "num_examples": 2079}], "download_size": 3269522484, "dataset_size": 777868153}, {"config_name": "hi", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 96926984, "num_examples": 51900}, {"name": "test", "num_bytes": 1340091, "num_examples": 374}, {"name": "validation", "num_bytes": 1222098, "num_examples": 405}], "download_size": 385810623, "dataset_size": 99489173}, {"config_name": "it", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 436879977, "num_examples": 432076}, {"name": "test", "num_bytes": 3798221, "num_examples": 2175}, {"name": "validation", "num_bytes": 2230995, "num_examples": 1276}], "download_size": 1685172398, "dataset_size": 442909193}, {"config_name": "ja", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 708617436, "num_examples": 480785}, {"name": "test", "num_bytes": 7802066, "num_examples": 3392}, {"name": "validation", "num_bytes": 6990637, "num_examples": 3106}], "download_size": 3186065351, "dataset_size": 723410139}, {"config_name": "ko", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 266381416, "num_examples": 213659}, {"name": "test", "num_bytes": 1736809, "num_examples": 803}, {"name": "validation", "num_bytes": 1857229, "num_examples": 917}], "download_size": 1119778167, "dataset_size": 269975454}, {"config_name": "nl", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": 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"int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 877441685, "num_examples": 675688}, {"name": "test", "num_bytes": 11475559, "num_examples": 6376}, {"name": "validation", "num_bytes": 6618989, "num_examples": 3476}], "download_size": 3365852789, "dataset_size": 895536233}, {"config_name": "pt", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 584986936, "num_examples": 469347}, {"name": "test", "num_bytes": 8678707, "num_examples": 4313}, {"name": "validation", "num_bytes": 5807293, "num_examples": 2973}], "download_size": 2347987926, "dataset_size": 599472936}, {"config_name": "ru", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 604993210, "num_examples": 339697}, {"name": "test", "num_bytes": 5941158, "num_examples": 2296}, {"name": "validation", "num_bytes": 5352859, "num_examples": 2107}], "download_size": 2754576893, "dataset_size": 616287227}, {"config_name": "sv", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 1822863623, "num_examples": 1742082}, {"name": "test", "num_bytes": 13002356, "num_examples": 7531}, {"name": "validation", "num_bytes": 5136097, "num_examples": 2987}], "download_size": 6790489020, "dataset_size": 1841002076}, {"config_name": "vi", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 300641174, "num_examples": 260010}, {"name": "test", "num_bytes": 4304795, "num_examples": 1824}, {"name": "validation", "num_bytes": 3402120, "num_examples": 1461}], "download_size": 1301938106, "dataset_size": 308348089}, {"config_name": "zh", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 449085696, "num_examples": 369249}, {"name": "test", "num_bytes": 5260974, "num_examples": 2667}, {"name": "validation", "num_bytes": 3511103, "num_examples": 1816}], "download_size": 2440525684, "dataset_size": 457857773}, {"config_name": "all_languages", "features": [{"name": "docid", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "uri", "dtype": "string"}, {"name": "lan", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "entities", "list": [{"name": "uri", "dtype": "string"}, {"name": "surfaceform", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "start", "dtype": "int32"}, {"name": "end", "dtype": "int32"}]}, {"name": "relations", "list": [{"name": "subject", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "object", "dtype": "int32"}]}], "splits": [{"name": "train", "num_bytes": 14615645332, "num_examples": 11865756}, {"name": "test", "num_bytes": 131636046, "num_examples": 67033}, {"name": "validation", "num_bytes": 103507688, "num_examples": 51181}], "download_size": 56989165879, "dataset_size": 14850789066}]}	2023-06-20T06:33:28+00:00
abda846a09f977456de8c8f3b1090db395b57d39	# Dataset Card for "VQAv2_minival_validation_google_flan_t5_xxl_mode_CM_Q_rices_ns_2000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	CVasNLPExperiments/VQAv2_minival_validation_google_flan_t5_xxl_mode_CM_Q_rices_ns_2000	[ "region:us" ]	2023-06-13T17:45:38+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 285356, "num_examples": 2000}], "download_size": 104686, "dataset_size": 285356}}	2023-06-13T17:45:40+00:00
76a834a463f100e8d2de780ef9905a44d92eec4b	# Dataset Card for "processed_dwi_fixed" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	deetsadi/processed_dwi_fixed	[ "region:us" ]	2023-06-13T17:56:41+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "conditioning_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 15336901.0, "num_examples": 200}], "download_size": 14887177, "dataset_size": 15336901.0}}	2023-06-13T17:56:42+00:00
99feda186d91c57eda16278bc0803ff8e53c66c6	# Dataset Card for "medication_chat_commands_bloom" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	stoddur/medication_chat_commands_bloom	[ "region:us" ]	2023-06-13T18:04:12+00:00	{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 340308408.0, "num_examples": 220407}], "download_size": 11987145, "dataset_size": 340308408.0}}	2023-06-13T21:47:22+00:00
7ad81f9054e014dcc04e39d16b80193397c7d103	# Dataset Card for "translated_eli5_dataset_sin_v2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	9wimu9/translated_eli5_dataset_sin_v2	[ "region:us" ]	2023-06-13T18:06:30+00:00	{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "gold_answer", "dtype": "string"}, {"name": "contexts", "sequence": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 518688493.0879679, "num_examples": 44842}, {"name": "test", "num_bytes": 57638480.91203211, "num_examples": 4983}, {"name": "validation", "num_bytes": 57638480.91203211, "num_examples": 4983}], "download_size": 256328104, "dataset_size": 633965454.9120321}}	2023-06-13T18:07:54+00:00
2fa417560bc2ff9f874d46f30833865cdca57d43	# Dataset Card for "processed_dwi_soft_edge" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	deetsadi/processed_dwi_soft_edge	[ "region:us" ]	2023-06-13T18:35:24+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}, {"name": "conditioning_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 16154444.0, "num_examples": 200}], "download_size": 16149714, "dataset_size": 16154444.0}}	2023-06-15T12:23:31+00:00
7897a731635cec31f966f05cfec491a69367392f	# AutoTrain Dataset for project: mnist-analysis ## Dataset Description This dataset has been automatically processed by AutoTrain for project mnist-analysis. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "image": "<28x28 L PIL image>", "target": 1 }, { "image": "<28x28 L PIL image>", "target": 0 } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "image": "Image(decode=True, id=None)", "target": "ClassLabel(names=['0', '1', '2', '3', '4', '5', '6', '7', '8', '9'], 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 \| 55996 \| \| valid \| 14004 \|	realzdlegend/autotrain-data-mnist-analysis	[ "task_categories:image-classification", "region:us" ]	2023-06-13T18:43:30+00:00	{"task_categories": ["image-classification"]}	2023-06-13T19:02:41+00:00
d25a3c8ea9bbcf828f36b2f65066cf9f5fca32e4	# Dataset Card for "boxes_test_controlnet_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	killah-t-cell/boxes_test_controlnet_dataset	[ "region:us" ]	2023-06-13T18:49:39+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 212085.0, "num_examples": 4}], "download_size": 196994, "dataset_size": 212085.0}}	2023-06-13T18:49:43+00:00
09812d29d779227b5a776d35d9d42160be9e2fab		tasksource/kaggle-claim-type	[ "license:mit", "region:us" ]	2023-06-13T18:54:12+00:00	{"license": "mit"}	2023-06-13T18:54:34+00:00
2b5a4f5422e41fc18eae2a14c8e7d7882ecca95d	# Dataset Usage ## Description The Mimic-IV dataset generate data by executing the Pipeline available on https://github.com/healthylaife/MIMIC-IV-Data-Pipeline. ## Function Signature ```python load_dataset('thbndi/Mimic4Dataset', task, mimic_path=mimic_data, config_path=config_file, encoding=encod, generate_cohort=gen_cohort, val_size=size, cache_dir=cache) ``` ## Arguments 1. `task` (string) : - Description: Specifies the task you want to perform with the dataset. - Default: "Mortality" - Note: Possible Values : 'Phenotype', 'Length of Stay', 'Readmission', 'Mortality' 2. `mimic_path` (string) : - Description: Complete path to the Mimic-IV raw data on user's machine. - Note: You need to provide the appropriate path where the Mimic-IV data is stored. 3. `config_path` (string) optionnal : - Description: Path to the configuration file for the cohort generation choices (more infos in '/config/readme.md'). - Default: Configuration file provided in the 'config' folder. 4. `encoding` (string) optionnal : - Description: Data encoding option for the features. - Options: "concat", "aggreg", "tensor", "raw", "text" - Default: "concat" - Note: Choose one of the following options for data encoding: - "concat": Concatenates the one-hot encoded diagnoses, demographic data vector, and dynamic features at each measured time instant, resulting in a high-dimensional feature vector. - "aggreg": Concatenates the one-hot encoded diagnoses, demographic data vector, and dynamic features, where each item_id is replaced by the average of the measured time instants, resulting in a reduced-dimensional feature vector. - "tensor": Represents each feature as an 2D array. There are separate arrays for labels, demographic data ('DEMO'), diagnosis ('COND'), medications ('MEDS'), procedures ('PROC'), chart/lab events ('CHART/LAB'), and output events data ('OUT'). Dynamic features are represented as 2D arrays where each row contains values at a specific time instant. - "raw": Provide cohort from the pipeline without any encoding for custom data processing. - "text": Represents diagnoses as text suitable for BERT or other similar text-based models. - For 'concat' and 'aggreg' the composition of the vector is given in './data/dict/"task"/features_aggreg.csv' or './data/dict/"task"/features_concat.csv' file and in 'features_names' column of the dataset. 5. `generate_cohort` (bool) optionnal : - Description: Determines whether to generate a new cohort from Mimic-IV data. - Default: True - Note: Set it to True to generate a cohort, or False to skip cohort generation. 6. `val_size`, 'test_size' (float) optionnal : - Description: Proportion of the dataset used for validation during training. - Default: 0.1 for validation size and 0.2 for testing size. - Note: Can be set to 0. 7. `cache_dir` (string) optionnal : - Description: Directory where the processed dataset will be cached. - Note: Providing a cache directory for each encoding type can avoid errors when changing the encoding type. ## Example Usage ```python import datasets from datasets import load_dataset # Example 1: Load dataset with default settings dataset = load_dataset('thbndi/Mimic4Dataset', task="Mortality", mimic_path="/path/to/mimic_data") # Example 2: Load dataset with custom settings dataset = load_dataset('thbndi/Mimic4Dataset', task="Phenotype", mimic_path="/path/to/mimic_data", config_path="/path/to/config_file", encoding="aggreg", generate_cohort=False, val_size=0.2, cache_dir="/path/to/cache_dir") ``` Please note that the provided examples are for illustrative purposes only, and you should adjust the paths and settings based on your actual dataset and specific use case.	thbndi/Mimic4Dataset	[ "region:us" ]	2023-06-13T19:00:50+00:00	{}	2023-08-18T09:46:26+00:00
4019f491c8ed7dc328c3cc334287983f72d7ac80	https://www.kaggle.com/competitions/feedback-prize-english-language-learning	tasksource/english-grading	[ "region:us" ]	2023-06-13T19:03:03+00:00	{}	2024-02-04T09:28:28+00:00
7c213acfa226a96c2bd6cac3447cda62fa12fc4d	# Dataset Card for "translated_eli5_dataset_sin_v2-gold-answer-removed-from-contexts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	9wimu9/translated_eli5_dataset_sin_v2-gold-answer-removed-from-contexts	[ "region:us" ]	2023-06-13T19:04:23+00:00	{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "gold_answer", "dtype": "string"}, {"name": "contexts", "sequence": "string"}, {"name": "id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 436001746.5264764, "num_examples": 44836}, {"name": "test", "num_bytes": 48446799.473523624, "num_examples": 4982}, {"name": "validation", "num_bytes": 48446799.473523624, "num_examples": 4982}], "download_size": 217824691, "dataset_size": 532895345.4735236}}	2023-06-13T19:05:06+00:00
04e87eb5003fb6d5922b8b7d96814ee15d30a114	# Dataset Card for "boxes_train_controlnet_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	killah-t-cell/boxes_train_controlnet_dataset	[ "region:us" ]	2023-06-13T19:13:32+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1365601206.528, "num_examples": 26662}], "download_size": 1257141642, "dataset_size": 1365601206.528}}	2023-06-13T19:21:43+00:00
4c3f9a0ea94dcd5f7c537f271d82027837ad02bf	# Dataset Card for "cool_test_fine_tuning" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Pedrampedram/cool_test_fine_tuning	[ "region:us" ]	2023-06-13T19:20:45+00:00	{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3426, "num_examples": 5}], "download_size": 0, "dataset_size": 3426}}	2023-06-13T19:21:22+00:00
4292c35b2db7f480852b1e82d67dbed4679f0d4e		Librico/llmao-data	[ "license:apache-2.0", "region:us" ]	2023-06-13T19:20:48+00:00	{"license": "apache-2.0"}	2023-06-13T19:20:48+00:00
3cb9727f7951fa25801cb5482595d0a0cd8f2508	# Dataset Card for "cool_new_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	AnjanaSengupta/cool_new_dataset	[ "region:us" ]	2023-06-13T19:24:23+00:00	{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3218, "num_examples": 5}], "download_size": 8180, "dataset_size": 3218}}	2023-06-13T19:24:27+00:00
36d2b4e1732aa96a3f1d9573d5e1a3e5c4ac27a1	# Dataset Card for "make_earth_greener" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Pedrampedram/make_earth_greener	[ "region:us" ]	2023-06-13T19:43:02+00:00	{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4235, "num_examples": 5}], "download_size": 8562, "dataset_size": 4235}}	2023-06-13T19:43:05+00:00
622f60ffb6d9dde7a0e26c5b171769a629c03298	# Dataset Card for "movie_stills_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	killah-t-cell/movie_stills_dataset	[ "region:us" ]	2023-06-13T19:44:37+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2868263.0, "num_examples": 10}], "download_size": 2869997, "dataset_size": 2868263.0}}	2023-06-13T20:11:45+00:00
128d4fd5cfda337e6e94e48b736c1602e22e3d5f	# Dataset Card for "boxes_full_controlnet_dataset" FWIW, I didn't get good results with this after 20K training steps for some reason, but feel free to give it a shot! [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	killah-t-cell/boxes_full_controlnet_dataset	[ "region:us" ]	2023-06-13T19:49:02+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "conditioning_image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1365601206.528, "num_examples": 26662}], "download_size": 1257141642, "dataset_size": 1365601206.528}}	2023-06-16T04:18:42+00:00
d5cec0f0d02adf56d09f6e02e722ea1a0da3ff46	# Dataset Card for "Climate_activity_new_dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	AnjanaSengupta/Climate_activity_new_dataset	[ "region:us" ]	2023-06-13T19:49:45+00:00	{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "ad", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3119, "num_examples": 5}], "download_size": 7479, "dataset_size": 3119}}	2023-06-13T19:49:47+00:00
20c1e91ed9a9a33bed2399696b85f4b2ed689107	# Dataset Card for "b3bc6d59" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	results-sd-v1-5-sd-v2-1-if-v1-0-karlo/b3bc6d59	[ "region:us" ]	2023-06-13T19:54:40+00:00	{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1331, "dataset_size": 178}}	2023-06-13T19:54:41+00:00
0ff3a093a7abbf65e556b920783604034e85a06a	# Dataset Card for ITALIC: An ITALian Intent Classification Dataset ITALIC is an intent classification dataset for the Italian language, which is the first of its kind. It includes spoken and written utterances and is annotated with 60 intents. The dataset is available on [Zenodo](https://zenodo.org/record/8040649) and connectors ara available for the [HuggingFace Hub](https://huggingface.co/datasets/RiTA-nlp/ITALIC). ### Latest Updates - June 15th, 2023: ITALIC dataset has been released on [Zenodo](https://zenodo.org/record/8040649): https://zenodo.org/record/8040649. ## Table of Contents - [Data collection](#data-collection) - [Dataset](#dataset) - [Usage](#usage) - [Models used in the paper](#models-used-in-the-paper) - [SLU intent classification](#slu-intent-classification) - [ASR](#asr) - [NLU intent classification](#nlu-intent-classification) - [Citation](#citation) - [License](#license) ## Data collection The data collection follows the MASSIVE NLU dataset which contains an annotated textual dataset for 60 intents. The data collection process is described in the paper [Massive Natural Language Understanding](https://arxiv.org/abs/2204.08582). Following the MASSIVE NLU dataset, a pool of 70+ volunteers has been recruited to annotate the dataset. The volunteers were asked to record their voice while reading the utterances (the original text is available on MASSIVE dataset). Together with the audio, the volunteers were asked to provide a self-annotated description of the recording conditions (e.g., background noise, recording device). The audio recordings have also been validated and, in case of errors, re-recorded by the volunteers. All the audio recordings included in the dataset have received a validation from at least two volunteers. All the audio recordings have been validated by native italian speakers (self-annotated). ## Dataset The dataset is available on the [Zenodo](https://zenodo.org/record/8040649). It is composed of 3 different splits: - `massive`: all the utterances are randomly shuffled and divided into 3 splits (train, validation, test). - `hard_speaker`: the utterances are divided into 3 splits (train, validation, test) based on the speaker. Each split only contains utterances from a pool of speakers that do not overlap with the other splits. - `hard_noisy`: the utterances are divided into 3 splits (train, validation, test) based on the recording conditions. The test split only contains utterances with the highest level of noise. Each split contains the following annotations: - `utt`: the original text of the utterance. - `audio`: the audio recording of the utterance. - `intent`: the intent of the utterance. - `speaker`: the speaker of the utterance. The speaker is identified by a unique identifier and has been anonymized. - `age`: the age of the speaker. - `is_native`: whether the speaker is a native italian speaker or not. - `gender`: the gender of the speaker (self-annotated). - `region`: the region of the speaker (self-annotated). - `nationality`: the nationality of the speaker (self-annotated). - `lisp`: any kind of lisp of the speaker (self-annotated). It can be empty in case of no lisp. - `education`: the education level of the speaker (self-annotated). - `environment`: the environment of the recording (self-annotated). - `device`: the device used for the recording (self-annotated). ## Usage The dataset can be loaded using the `datasets` library. You need to install the following dependencies: ```bash pip install datasets pip install librosa pip install soundfile ``` Then, you can load the dataset as follows: ```python from datasets import load_dataset # Please be sure to use use_auth_token=True and to set the access token # using huggingface-cli login # or follow https://huggingface.co/docs/hub/security-tokens # configs "hard_speaker" and "hard_noisy" are also available (to substitute "massive") italic = load_dataset("RiTA-nlp/ITALIC", "massive", use_auth_token=True) italic_train = italic["train"] italic_valid = italic["validation"] italic_test = italic["test"] ``` The dataset has been designed for intent classification tasks. The `intent` column can be used as the label. However, the dataset can be used for other tasks as well. - Intent classification: the `intent` column can be used as the label. - Speaker identification: the `speaker` column can be used as the label. - Automatic speech recognition: the `utt` column can be used as the label. - Accent identification: the `region` column can be used as the label. For more information about the dataset, please refer to the [paper](https://arxiv.org/abs/2306.08502). ## Models used in the paper ### Hardware settings All experiments were conducted on a private workstation with Intel Core i9-10980XE CPU, 1 $\times$ NVIDIA RTX A6000 GPU, 64 GB of RAM running Ubuntu 22.04 LTS. ### Parameter settings The parameters used for the training of the models are set to allow a fair comparison between the different models and to follow the recommendations of the related literature. The parameters are summarized in the following table: \| Model \| Task \| Parameters \| Learning rate \| Batch size \| Max epochs \| Warmup \| Weight decay \| Avg. training time \| Avg. inference time \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| facebook/wav2vec2-xls-r-300m \| SLU \| 300M \| 1e-4 \| 128 \| 30 \| 0.1 ratio \| 0.01 \| 9m 35s per epoch \| 13ms per sample \| \| facebook/wav2vec2-xls-r-1b \| SLU \| 1B \| 1e-4 \| 32 \| 30 \| 0.1 ratio \| 0.01 \| 21m 30s per epoch \| 29ms per sample \| \| jonatasgrosman/wav2vec2-large-xlsr-53-italian \| SLU \| 300M \| 1e-4 \| 128 \| 30 \| 0.1 ratio \| 0.01 \| 9m 35s per epoch \| 13ms per sample \| \| jonatasgrosman/wav2vec2-xls-r-1b-italian \| SLU \| 1B \| 1e-4 \| 32 \| 30 \| 0.1 ratio \| 0.01 \| 21m 30s per epoch \| 29ms per sample \| \| ALM/whisper-it-small-augmented \| ASR \| 224M \| 1e-5 \| 8 \| 5 \| 500 steps \| 0.01 \| 26m 30s per epoch \| 25ms per sample \| \| EdoAbati/whisper-medium-it-2 \| ASR \| 769M \| 1e-5 \| 8 \| 5 \| 500 steps \| 0.01 \| 49m per epoch \| 94ms per sample \| \| EdoAbati/whisper-large-v2-it \| ASR \| 1.5B \| 1e-5 \| 8 \| 5 \| 500 steps \| 0.01 \| 1h 17m per epoch \| 238ms per sample \| \| bert-base-multilingual-uncased \| NLU \| 167M \| 5e-5 \| 8 \| 5 \| 500 steps \| 0.01 \| 1m 22s per epoch \| 1.5ms per sample \| \| facebook/mbart-large-cc25 \| NLU \| 611M \| 5e-5 \| 8 \| 5 \| 500 steps \| 0.01 \| 7m 53s per epoch \| 4.7ms per sample \| \| dbmdz/bert-base-italian-xxl-uncased \| NLU \| 110M \| 5e-5 \| 8 \| 5 \| 500 steps \| 0.01 \| 1m 30s per epoch \| 1.4ms per sample \| \| morenolq/bart-it \| NLU \| 141M \| 5e-5 \| 8 \| 5 \| 500 steps \| 0.01 \| 1m 54s per epoch \| 1.9 ms per sample \| In all cases, we opted for the AdamW optimizer. All experiments were run on a single NVIDIA A6000 GPU. ### SLU intent classification The models used in the paper are available on the [Hugging Face Hub](https://huggingface.co/models). - 🌍 [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) - 🌍 [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) - 🇮🇹 [jonatasgrosman/wav2vec2-xls-r-1b-italian](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-italian) - 🇮🇹 [jonatasgrosman/wav2vec2-large-xlsr-53-italian](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-italian) ### ASR The models used in the paper are available on the [Hugging Face Hub](https://huggingface.co/models). - 🌍 Whisper large (zero-shot ASR): [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) - 🇮🇹 Whisper small: [ALM/whisper-it-small-augmented](https://huggingface.co/ALM/whisper-it-small-augmented) - 🇮🇹 Whisper medium: [EdoAbati/whisper-medium-it-2](https://huggingface.co/EdoAbati/whisper-medium-it-2) - 🇮🇹 Whisper large: [EdoAbati/whisper-large-v2-it](https://huggingface.co/EdoAbati/whisper-large-v2-it) ### NLU intent classification The models used in the paper are available on the [Hugging Face Hub](https://huggingface.co/models). - 🌍 [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) - 🌍 [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) - 🇮🇹 [dbmdz/bert-base-italian-xxl-uncased](https://huggingface.co/dbmdz/bert-base-italian-xxl-uncased) - 🇮🇹 [morenolq/bart-it](https://huggingface.co/morenolq/bart-it) ## Citation If you use this dataset in your research, please cite the following paper (interspeech 2023 version is coming soon after the proceedings are published): ```bibtex @article{koudounas2023italic, title={ITALIC: An Italian Intent Classification Dataset}, author={Koudounas, Alkis and La Quatra, Moreno and Vaiani, Lorenzo and Colomba, Luca and Attanasio, Giuseppe and Pastor, Eliana and Cagliero, Luca and Baralis, Elena}, journal={arXiv preprint arXiv:2306.08502}, year={2023} } ``` ## License The dataset is licensed under the [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/). - [Paper describing the dataset and initial experiments](https://arxiv.org/abs/2306.08502) - [Dataset on Zenodo](https://zenodo.org/record/8040649) - [https://creativecommons.org/licenses/by-nc-sa/4.0/](https://creativecommons.org/licenses/by/4.0/)https://creativecommons.org/licenses/by/4.0/	RiTA-nlp/ITALIC	[ "task_categories:automatic-speech-recognition", "task_categories:audio-classification", "task_ids:intent-classification", "annotations_creators:crowdsourced", "language_creators:Italian", "license:cc-by-nc-nd-4.0", "arxiv:2204.08582", "arxiv:2306.08502", "region:us" ]	2023-06-13T20:03:20+00:00	{"annotations_creators": ["crowdsourced"], "language_creators": ["Italian"], "license": "cc-by-nc-nd-4.0", "size_categories": {"it": "10K<n<100K"}, "task_categories": ["automatic-speech-recognition", "audio-classification"], "task_ids": ["intent-classification"], "pretty_name": "ITALIC", "language_bcp47": ["it"]}	2023-06-29T11:58:56+00:00
793acf6d2dfcc6046efc028f7755a95b136b80f2		Cdaprod/AI-Developer-Prompts	[ "task_categories:zero-shot-classification", "task_categories:summarization", "region:us" ]	2023-06-13T20:07:09+00:00	{"task_categories": ["zero-shot-classification", "summarization"], "pretty_name": "Cdaprod-Docs"}	2023-06-14T02:14:49+00:00
df3a611ee4bd8ca4d8cb4c9601d86069ccb75643	Experimental Synthetic Dataset of Public Domain Character Dialogue in Roleplay Format Generated using scripts from my https://github.com/practicaldreamer/build-a-dataset repo --- license: mit ---	practical-dreamer/RPGPT_PublicDomain-alpaca	[ "task_categories:conversational", "size_categories:10M<n<100M", "language:en", "license:mit", "alpaca", "region:us" ]	2023-06-13T20:11:37+00:00	{"language": ["en"], "license": "mit", "size_categories": ["10M<n<100M"], "task_categories": ["conversational"], "pretty_name": "rpgpt-alpaca", "tags": ["alpaca"]}	2023-07-03T23:04:20+00:00
d581808cfdbd1e59f408e052ba6de90682a3502b	# Dataset Card for "movie_stills_dataset_test" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	killah-t-cell/movie_stills_dataset_test	[ "region:us" ]	2023-06-13T20:23:46+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20557021.0, "num_examples": 100}], "download_size": 20557338, "dataset_size": 20557021.0}}	2023-06-13T20:24:06+00:00
14ea16ba5c74a05f4187274a12aa761bf0a87b4c		longevity-genie/longevity-map-db-llm	[ "license:agpl-3.0", "region:us" ]	2023-06-13T20:33:14+00:00	{"license": "agpl-3.0"}	2023-06-13T20:33:14+00:00
8a69e3e9aa776abd923a685793de3c7a36a9ecba		c4iro/king-von	[ "task_categories:conversational", "license:apache-2.0", "region:us" ]	2023-06-13T20:33:36+00:00	{"license": "apache-2.0", "task_categories": ["conversational"]}	2023-06-13T21:09:37+00:00
52433f1492b17c36aabb780d307206bc595d61d9	### Dataset Summary dataset:- civitai-stable-diffusion-337k this dataset contains 337k civitai images url with prompts etc. i use civitai api to get all prompts. project:- https://github.com/thefcraft/nsfw-prompt-detection-sd I train a model on this dataset DATA STRUCTURE for .civitai.json:- ```{ 'items':[ {'id': 100657, 'url': 'https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/2338276a-87f7-4a1e-f92a-776a18ee4200/width=768/2338276a-87f7-4a1e-f92a-776a18ee4200.jpeg', 'hash': 'U5Exz_00.8D$t89Z%M0100~VD*RktQxaIU~p', 'width': 768, 'height': 1368, 'nsfw': True, 'createdAt': '2023-02-14T10:05:11.498Z', 'postId': 60841, 'stats': {'cryCount': 0, 'laughCount': 0, 'likeCount': 26, 'dislikeCount': 0, 'heartCount': 50, 'commentCount': 4}, 'meta': {'ENSD': '31337', 'Size': '512x912', 'seed': 3994946333, 'Model': 'AbyssOrangeMix2_sfw', 'steps': 20, 'prompt': '<lora:hiqcg_body-epoch-000004:0.5>, <lora:hiqcg_face-epoch-000004:0.4>, hiqcgbody, hiqcgface, 1girl, full body, standing, \ndetailed skin texture, detailed cloth texture, beautiful detailed face,\nmasterpiece, best quality, ultra detailed, 8k, intricate details,', 'sampler': 'DPM++ 2M Karras', 'cfgScale': 7, 'Clip skip': '2', 'resources': [{'hash': '038ba203d8', 'name': 'AbyssOrangeMix2_sfw', 'type': 'model'}], 'Model hash': '038ba203d8', 'Hires upscale': '1.5', 'Hires upscaler': 'Latent', 'negativePrompt': 'EasyNegative, extra fingers,fewer fingers, multiple girls, multiple views,', 'Denoising strength': '0.6'}, 'username': 'NeoClassicalRibbon'}, {..}, ..], 'metadata':{'totalItems': 327145} } ```	Ar4ikov/civitai-sd-337k	[ "annotations_creators:no-annotation", "language_creators:thefcraft", "size_categories:1M<n<10M", "source_datasets:civitai", "language:en", "region:us" ]	2023-06-13T21:07:15+00:00	{"annotations_creators": ["no-annotation"], "language_creators": ["thefcraft"], "language": ["en"], "size_categories": ["1M<n<10M"], "source_datasets": ["civitai"], "pretty_name": "civitai-stable-diffusion-337k", "duplicated_from": "thefcraft/civitai-stable-diffusion-337k"}	2023-06-13T21:08:02+00:00
d40831c0564e1b16159e4e38145cc7f618224336	# Update * (2023.12.30) We added a global transformation to the camera poses to make the objects axis-aligned along the Z axis. This is preferred for some methods such as TensoRF and TriPlane. Please see [here](https://huggingface.co/datasets/OpenIllumination/OpenIllumination/blob/main/others/transforms_alignz_train.json) for the new camera poses. * (2023.11.9) We fixed some incorrect links in the [download script](https://huggingface.co/datasets/OpenIllumination/OpenIllumination/blob/main/open_illumination.py). Please use the latest script to download the data. # Dataset Card for OpenIllumination ## Dataset Description - Homepage: https://oppo-us-research.github.io/OpenIllumination - Repository: https://github.com/oppo-us-research/OpenIlluminationCapture. - Paper: https://oppo-us-research.github.io/OpenIllumination/files/openillumination.pdf. - Leaderboard: N/A for now. - Point of Contact: [email protected] ### Dataset Summary Our dataset comprises 64 objects, each captured from 70 views, under 13 lighting patterns and 142 One-Light-At-Time (OLAT) illumination, respectively. The 70 views are captured by 48 DSLR cameras and 22 high-speed cameras. The dataset viewer is at the [project page](https://oppo-us-research.github.io/OpenIllumination). ### Supported Tasks * Novel view synthesis: The dataset can be used to evaluate NVS methods, such as NeRF, TensoRF, and NeuS. * Inverse rendering: The dataset can be used to evaluate inverse rendering algorithms, which are to decompose illumination, object geometry, and object materials. ### Dataset Download Since the whole dataset is very large, we provide a script [here](https://huggingface.co/datasets/OpenIllumination/OpenIllumination/blob/main/open_illumination.py]) to download according to the illumination type (lighting pattern or OLAT) and the object ID. You can also modify the code to customize it according to your requirements. ### Languages English ## Dataset Structure ### Data Fields For each image, the following fields are provided: * file_path: str, the file path to an image. * light_idx: int, the index of illuminations, from 1 to 13 for lighting patterns, or from 0 to 141 for OLAT. * transform_matrix: list, a 4x4 matrix, representing the camera pose for this image (in OpenCV convention). * camera_angle_x: float, can be used to compute the corresponding camera intrinsics. * obj_mask: the object mask, can be read by ```imageio.imread(OBJ_MASK_PATH)>0```, used for PSNR evaluation. Note that we store 0 and 1 for object masks, so you may not be able to correctly visualize it as png in this repo. * com_mask (optional): the union of the object mask and the support mask, can be read by ```imageio.imread(COM_MASK_PATH)>0```, used for training. We store 0 and 255 for combined masks, so you are able to view them as png in this repo correctly. ### Data Splits The data is split into training and testing views. For each object captured under 13 lighting patterns, the training set and the testing set contain 38 and 10 views, respectively. For each object captured under OLAT, the training set and the testing set contain 17 and 5 views, respectively. ## Dataset Creation ### Curation Rationale From the paper: > Recent efforts have introduced some datasets that incorporate multiple illuminations in real-world settings. However, most of them are limited either in the number of views or the number of illuminations; few of them provide object-level data as well. Consequently, these existing datasets prove unsuitable for evaluating inverse rendering methods on real-world objects. > > > To address this, we present a new dataset containing objects with a variety of materials, captured under multiple views and illuminations, allowing for reliable evaluation of various inverse rendering tasks with real data. ### Source Data #### Initial Data Collection and Normalization From the paper: > Our dataset was acquired using a setup similar to a traditional light stage, where densely distributed cameras and controllable lights are attached to a static frame around a central platform. ### Annotations #### Annotation process From the paper: > To obtain high-quality segmentation masks, we propose to use Segment-Anything (SAM) to perform instance segmentation. However, > we find that the performance is not satisfactory. One reason is that the object categories are highly undefined. In this case, even combining the bounding box and point prompts cannot produce satisfactory results. To address this problem, we propose to use multiple bounding-box prompts to perform segmentation for each possible part and then calculate a union of the masks as the final object mask. > > For objects with very detailed and thin structures, e.g. hair, we use an off-the-shelf background matting method to perform object segmentation. #### Who are the annotators? Linghao Chen, Isabella Liu, and Ziyang Fu. ## Additional Information ### Dataset Curators Isabella Liu, Linghao Chen, Ziyang Fu, Liwen Wu, Haian Jin, Zhong Li, Chin Ming Ryan Wong, Yi Xu, Ravi Ramamoorthi, Zexiang Xu and Hao Su ### Licensing Information CC BY 4.0 ### Citation Information ```bash @article{liu2023openillumination, title={OpenIllumination: A Multi-Illumination Dateset for Inverse Rendering Evaluation on Real Objects}, author={Liu, Isabella and Chen, Linghao and Fu, Ziyang and Wu, Liwen and Jin, Haian and Li, Zhong and Chin Ming Ryan Wong3 and Xu, Yi and Ravi Ramamoorthi1 and Xu, Zexiang and Su, Hao}, year={2023} } ```	OpenIllumination/OpenIllumination	[ "task_categories:other", "annotations_creators:expert-generated", "size_categories:100K<n<1M", "language:en", "license:cc-by-4.0", "novel view synthesis", "inverse rendering", "material decomposition", "doi:10.57967/hf/1102", "region:us" ]	2023-06-13T21:25:56+00:00	{"annotations_creators": ["expert-generated"], "language": ["en"], "license": "cc-by-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["other"], "pretty_name": "OpenIllumination", "tags": ["novel view synthesis", "inverse rendering", "material decomposition"], "download_size": "900G"}	2023-12-30T10:23:34+00:00
fd769912a4546df22124a49dd12a6fce8a48b63a	# Dataset Card for "civitai_sd_337_prompts" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Ar4ikov/civitai_sd_337_prompts	[ "region:us" ]	2023-06-13T21:42:23+00:00	{"dataset_info": {"features": [{"name": "prompt", "dtype": "string"}, {"name": "negativePrompt", "dtype": "string"}, {"name": "full_prompt", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 464451998, "num_examples": 275908}], "download_size": 94523704, "dataset_size": 464451998}}	2023-06-13T21:49:56+00:00
f95f8c75ad88e695b899120eef3c7331fefffa25	# Dataset Card for "ar_sarcasm_v21" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	sanaeai/ar_sarcasm_v21	[ "region:us" ]	2023-06-13T21:53:55+00:00	{"dataset_info": {"features": [{"name": "tweet", "dtype": "string"}, {"name": "label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2499149, "num_examples": 15548}], "download_size": 1424196, "dataset_size": 2499149}}	2023-06-13T21:53:56+00:00
bdf412319e160046fd966f7d72d776d3d7b866a0	## Dataset Description - Repository: [wellecks/naturalprover](https://github.com/wellecks/naturalprover) - Paper: [NaturalProver: Grounded Mathematical Proof Generation with Language Models](https://openreview.net/pdf?id=rhdfTOiXBng) - Point of Contact: [Sean Welleck](https://wellecks.com/) # Naturalproofs-gen This dataset contains the `Naturalproofs-gen` corpus from: [NaturalProver: Grounded Mathematical Proof Generation with Language Models](https://arxiv.org/pdf/2205.12910.pdf)\ Sean Welleck\, Jiacheng Liu\, Ximing Lu, Hannaneh Hajishirzi, Yejin Choi\ NeurIPS 2022 ### Licensing Information MIT ### Citation Information Please cite: ``` @inproceedings{welleck2022naturalprover, title={NaturalProver: Grounded Mathematical Proof Generation with Language Models}, author={Sean Welleck and Jiacheng Liu and Ximing Lu and Hannaneh Hajishirzi and Yejin Choi}, booktitle={Advances in Neural Information Processing Systems}, editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho}, year={2022}, url={https://openreview.net/forum?id=rhdfTOiXBng} } ``` Naturalproofs-gen was built from the Naturalproofs corpus: ``` @inproceedings{welleck2021naturalproofs, title={NaturalProofs: Mathematical Theorem Proving in Natural Language}, author={Sean Welleck and Jiacheng Liu and Ronan Le Bras and Hannaneh Hajishirzi and Yejin Choi and Kyunghyun Cho}, booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1)}, year={2021}, url={https://openreview.net/forum?id=Jvxa8adr3iY} } ```	wellecks/naturalproofs-gen	[ "license:mit", "math", "theorem-proving", "arxiv:2205.12910", "region:us" ]	2023-06-13T22:25:32+00:00	{"license": "mit", "tags": ["math", "theorem-proving"]}	2023-06-13T23:55:55+00:00
2eaee580b90f64683ba754ada77d8d0e829b7589	# Dataset Card for "sdcctest" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	sdmattpotter/sdcctest	[ "region:us" ]	2023-06-13T22:36:32+00:00	{"dataset_info": {"features": [{"name": "ITEMNO.", "dtype": "string"}, {"name": "O", "dtype": "string"}, {"name": "00000", "dtype": "float64"}, {"name": "Motion/Second", "dtype": "string"}, {"name": "Recorder", "dtype": "string"}, {"name": "action", "dtype": "string"}, {"name": "drescpt", "dtype": "string"}, {"name": "Meeting Type", "dtype": "string"}, {"name": "Roll Call", "dtype": "string"}, {"name": "Attendance", "dtype": "string"}, {"name": "DateTimeDate", "dtype": "timestamp[ns]"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 359956, "num_examples": 1185}], "download_size": 137681, "dataset_size": 359956}}	2023-06-13T22:48:46+00:00
b4c89b6e8ce403e1eb7434ea0ed1c0fb5776177d	# Dataset Card for "tead1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	sanaeai/tead1	[ "region:us" ]	2023-06-13T22:37:28+00:00	{"dataset_info": {"features": [{"name": "tweet", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1098060, "num_examples": 12558}], "download_size": 603080, "dataset_size": 1098060}}	2023-06-13T22:37:29+00:00
60021a5a7c02622f16edf5b1fc6b943036f38433	# Dataset Card for "anthropic-hh-rlhf-conversations-with-toxicities" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	andersonbcdefg/anthropic-hh-rlhf-conversations-with-toxicities	[ "region:us" ]	2023-06-13T22:47:00+00:00	{"dataset_info": {"features": [{"name": "messages", "sequence": "string"}, {"name": "length", "dtype": "int64"}, {"name": "toxicity", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 117886688, "num_examples": 104876}], "download_size": 68186422, "dataset_size": 117886688}}	2023-06-13T22:47:06+00:00
7be58bae34797f43cd3dce6ab9299711292ee931		Kapal/Api	[ "license:openrail", "region:us" ]	2023-06-13T23:37:29+00:00	{"license": "openrail"}	2023-06-13T23:37:29+00:00
fa881b177669daf96dfcb3db3ccbffc4066d92a2		sruly/RoboThoughts	[ "license:apache-2.0", "region:us" ]	2023-06-13T23:56:13+00:00	{"license": "apache-2.0"}	2023-06-14T00:55:09+00:00
b7c8d864808e2a6d1559fc1fa06905d5e9c1e3dc		foilfoilfoil/LaminiChatML	[ "license:other", "region:us" ]	2023-06-14T01:10:24+00:00	{"license": "other"}	2023-06-14T01:15:45+00:00
1d8d4629150d18ca50afab66391866f2085be989	# Dataset Card for MagicBrush ## Dataset Description - Homepage: https://osu-nlp-group.github.io/MagicBrush - Repository: https://github.com/OSU-NLP-Group/MagicBrush - Point of Contact: [Kai Zhang](mailto:[email protected]) ### Dataset Summary MagicBrush is the first large-scale, manually-annotated instruction-guided image editing dataset covering diverse scenarios single-turn, multi-turn, mask-provided, and mask-free editing. MagicBrush comprises 10K (source image, instruction, target image) triples, which is sufficient to train large-scale image editing models. Please check our [website](https://osu-nlp-group.github.io/MagicBrush/) to explore more visual results. #### Dataset Structure "img_id" (str): same from COCO id but in string type, for easier test set loading "turn_index" (int32): the edit turn in the image "source_img" (str): input image, could be the original real image (turn_index=1) and edited images from last turn (turn_index >=2) "mask_img" (str): free-form mask image (white region), can be used in mask-provided setting to limit the region to be edited. "instruction" (str): edit instruction of how the input image should be changed. "target_img" (str): the edited image corresponding to the input image and instruction. If you need auxiliary data, please use [training set](https://buckeyemailosu-my.sharepoint.com/:u:/g/personal/zhang_13253_buckeyemail_osu_edu/EYEqf_yG36lAgiXw2GvRl0QBDBOeZHxvNgxO0Ec9WDMcNg) and [dev set](https://buckeyemailosu-my.sharepoint.com/:u:/g/personal/zhang_13253_buckeyemail_osu_edu/EXkXvvC95C1JsgMNWGL_RcEBElmsGxXwAAAdGamN8PNhrg) ### Splits train: 8,807 edit turns (4,512 edit sessions). dev: 528 edit turns (266 edit sessions). test: (To prevent potential data leakage, please check our repo for information on obtaining the test set.) ### Licensing Information Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License. ## Citation Information If you find this dataset useful, please consider citing our paper: ``` @inproceedings{Zhang2023MagicBrush, title={MagicBrush: A Manually Annotated Dataset for Instruction-Guided Image Editing}, author={Kai Zhang and Lingbo Mo and Wenhu Chen and Huan Sun and Yu Su}, booktitle={Advances in Neural Information Processing Systems}, year={2023} } ```	osunlp/MagicBrush	[ "task_categories:text-to-image", "task_categories:image-to-image", "size_categories:10K<n<100K", "language:en", "license:cc-by-4.0", "region:us" ]	2023-06-14T01:20:33+00:00	{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-to-image", "image-to-image"], "pretty_name": "MagicBrush", "dataset_info": {"features": [{"name": "img_id", "dtype": "string"}, {"name": "turn_index", "dtype": "int32"}, {"name": "source_img", "dtype": "image"}, {"name": "mask_img", "dtype": "image"}, {"name": "instruction", "dtype": "string"}, {"name": "target_img", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 25446150928.986, "num_examples": 8807}, {"name": "dev", "num_bytes": 1521183444, "num_examples": 528}], "download_size": 22358540292, "dataset_size": 26967334372.986}}	2023-11-07T20:19:33+00:00
1864285e25010d346a842e4f068b1a1d4248ed6d	# Dataset Card for HLS Burn Scar Scenes ## Dataset Description - Homepage: https://huggingface.co/datasets/nasa-impact/hls_burn_scars - Point of Contact: Dr. Christopher Phillips ([email protected]) ### Dataset Summary This dataset contains Harmonized Landsat and Sentinel-2 imagery of burn scars and the associated masks for the years 2018-2021 over the contiguous United States. There are 804 512x512 scenes. Its primary purpose is for training geospatial machine learning models. ## Dataset Structure ## TIFF Metadata Each tiff file contains a 512x512 pixel tiff file. Scenes contain six bands, and masks have one band. For satellite scenes, each band has already been converted to reflectance. ## Band Order For scenes: Channel, Name, HLS S30 Band number 1, Blue, B02 2, Green, B03 3, Red, B04 4, NIR, B8A 5, SW 1, B11 6, SW 2, B12 Masks are a single band with values: 1 = Burn scar 0 = Not burned -1 = Missing data ## Class Distribution Burn Scar - 11% Not burned - 88% No Data - 1% ## Data Splits The 804 files have been randomly split into training (2/3) and validation (1/3) directories, each containing the masks, scenes, and index files. ## Dataset Creation After co-locating the shapefile and HLS scene, the 512x512 chip was formed by taking a window with the burn scar in the center. Burn scars near the edges of HLS tiles are offset from the center. Images were manually filtered for cloud cover and missing data to provide as clean a scene as possible, and burn scar presence was also manually verified. ## Source Data Imagery are from V1.4 of HLS. A full description and access to HLS may be found at https://hls.gsfc.nasa.gov/ The data were from shapefiles maintained by the Monitoring Trends in Burn Severity (MTBS) group. The original data may be found at: https://mtbs.gov/ ## Citation If this dataset helped your research, please cite `HLS Burn Scars` in your publications. Here is an example BibTeX entry: ``` @software{HLS_Foundation_2023, author = {Phillips, Christopher and Roy, Sujit and Ankur, Kumar and Ramachandran, Rahul}, doi = {10.57967/hf/0956}, month = aug, title = {{HLS Foundation Burnscars Dataset}}, url = {https://huggingface.co/ibm-nasa-geospatial/hls_burn_scars}, year = {2023} } ```	ibm-nasa-geospatial/hls_burn_scars	[ "size_categories:n<1K", "language:en", "license:cc-by-4.0", "doi:10.57967/hf/0956", "region:us" ]	2023-06-14T01:23:32+00:00	{"language": ["en"], "license": "cc-by-4.0", "size_categories": ["n<1K"]}	2023-09-26T15:08:32+00:00
93e3b9fdb866c3cc1455b7a4a298648661b34bc5	# Dataset Card for SP500-EDGAR-10K ## Dataset Description - Homepage: - Repository: - Paper: - Leaderboard: - Point of Contact: ### Dataset Summary This dataset contains the annual reports for all SP500 historical constituents from 2010-2022 from SEC EDGAR Form 10-K filings. It also contains n-day future returns of each firm's stock price from each filing date. ## Dataset Structure ### Data Fields [More Information Needed] ### Data Splits [More Information Needed] ## Dataset Creation ### Source Data #### Initial Data Collection and Normalization 10-K filings data was collected and processed using `edgar-crawler` available <a href='https://github.com/nlpaueb/edgar-crawler'>here.</a> Return data was computed manually from other price data sources. ### Annotations #### Annotation process N/A #### Who are the annotators? N/A ### Personal and Sensitive Information N/A ## Considerations for Using the Data ### Social Impact of Dataset N/A ### Discussion of Biases The firms in the dataset are constructed from historical SP500 membership data, removing survival biases. ### Other Known Limitations N/A ### Licensing Information MIT	jlohding/sp500-edgar-10k	[ "license:mit", "nlp", "region:us" ]	2023-06-14T01:28:35+00:00	{"license": "mit", "pretty_name": "SP500 EDGAR 10-K Filings", "tags": ["nlp"]}	2023-06-15T14:08:31+00:00
436862dd6e9edcebc85f6dddb19ddf24a65c4992		foilfoilfoil/LaminiChatML1024	[ "license:other", "region:us" ]	2023-06-14T01:28:49+00:00	{"license": "other"}	2023-06-14T01:29:44+00:00
a3c6098505a306e752a79d5be145d9c3dad495a2	# Dataset Card for "VQAv2_minival_validation_eachadea_vicuna_13b_1.1_mode_CM_Q_rices_ns_100" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	CVasNLPExperiments/VQAv2_minival_validation_eachadea_vicuna_13b_1.1_mode_CM_Q_rices_ns_100	[ "region:us" ]	2023-06-14T01:37:36+00:00	{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "question", "dtype": "string"}, {"name": "true_label", "sequence": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0", "num_bytes": 553653, "num_examples": 100}], "download_size": 100504, "dataset_size": 553653}}	2023-06-14T01:37:41+00:00
eb58a4e9c05dde7151ca83184848d8c8418dce4b		foilfoilfoil/lamini512	[ "license:other", "region:us" ]	2023-06-14T01:51:48+00:00	{"license": "other"}	2023-06-14T02:01:07+00:00
b91e5c27ec1878662f2d6c68728a0c147d48a656		ydqe2/kaggle_financial_sentiment_resplit	[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:en", "license:mit", "region:us" ]	2023-06-14T01:53:59+00:00	{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "d"}	2023-06-14T01:59:29+00:00
d38aca2628ccf2cec102fae3c6f4a60c3bf8667a	# Dataset Card for "ca039ad3" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	results-sd-v1-5-sd-v2-1-if-v1-0-karlo/ca039ad3	[ "region:us" ]	2023-06-14T02:09:07+00:00	{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 184, "num_examples": 10}], "download_size": 1343, "dataset_size": 184}}	2023-06-14T02:09:07+00:00
4b5b43eedc6d1ae050cd2304dd9db02ccef26b94	# Dataset Card for "my-shiba-inu-dataset" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	zxx-silence/my-shiba-inu-dataset	[ "region:us" ]	2023-06-14T02:43:19+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 2997266.0, "num_examples": 13}], "download_size": 2987648, "dataset_size": 2997266.0}}	2023-06-14T05:06:32+00:00
0c28275a5605f3bfa4cc116d8099bfa0e5abb43b	# Ko Lima Vicuna Dataset GPT4 API를 사용하여 [lima_vicuna_format 데이터](https://huggingface.co/datasets/64bits/lima_vicuna_format)를 한국어로 재생성한 데이터셋입니다. GPT4 사용시 프롬프트는 "단순 번역이 아닌, 원문 내용을 참조하여 데이터를 재생성하라"는 내용으로 작성하였습니다. 원본 데이터셋 1,030건 중 866건의 데이터가 재생성되었습니다. 데이터셋 제작은 웹 크롤링 전문 기업, [해시스크래퍼(HASHSCRAPER)](https://www.hashscraper.com/)* 에서 진행되었습니다.* This is the lima_vicuna_format data regenerated into Korean using the GPT4 API. When using GPT4, the prompt was written to read "Regenerate the data by referring to the original content, not just a simple translation". Out of the 1,030 original datasets, 866 data points were regenerated. The dataset was created by [HASHSCRAPER](https://www.hashscraper.com/), a company specializing in web scraping. --- # How to use ```python >>> from datasets import load_dataset >>> data = load_dataset("changpt/ko-lima-vicuna", data_files="ko_lima_vicuna.json", split="train") >>> data Dataset({ features: ['id', 'conversations'], num_rows: 866 }) ``` ```python >>> data[0] {'id': '7-stackexchange', 'conversations': [{'from': 'human', 'value': '나는 ```ffmpeg```로 비디오를 GIF 파일로 변환하고 있어:\n```ffmpeg \\\n -i input.flv \\\n -ss 00:00:00.000 \\\n -pix_fmt rgb24 \\\n -r 10 \\\n -s 320x240 \\\n -t 00:00:10.000 \\\n output.gif\n```\n잘 작동하는데, 출력된 gif 파일의 품질이 너무 낮아. 변환된 gif의 품질을 어떻게 개선할 수 있을까?'}, {'from': 'gpt', 'value': '중간 이미지 파일을 피하고 싶다면, LordNeckBeard가 제공한 명령어를 ```ffmpeg```와 ImageMagick의 ```convert``` 사이에 파이프로 연결할 수 있습니다. 이렇게 하면 중간 파일이 필요하지 않습니다:\n```ffmpeg -i input.flv -vf scale=320:-1 -r 10 -f image2pipe -vcodec ppm - \| convert -delay 10 -loop 0 - output.gif\n```\n```-f image2pipe```는 ffmpeg에게 비디오를 이미지로 분할하고 파이프 출력에 적합하게 만들라고 지시하며, ```-vcodec ppm```은 출력 형식을 ppm으로 지정합니다(png 형식인 경우, ```convert```가 파이프에서 모든 이미지를 읽지 않거나 ffmpeg가 모두 출력하지 않는 것 같습니다). 두 명령어의 ```-```는 각각 출력과 입력에 파이프를 사용하도록 지정합니다.\n파일을 저장하지 않고 결과를 최적화하려면, ```convert```의 출력을 두 번째 ```convert``` 명령어로 파이프할 수 있습니다:\n```ffmpeg -i input.flv -vf scale=320:-1 -r 10 -f image2pipe -vcodec ppm - \| convert -delay 10 -loop 0 - gif:- \| convert -layers Optimize - output.gif\n```\n```gif:-```는 ```convert```에게 출력을 gif 형식의 데이터로 파이프하도록 지시하고, ```-layers Optimize```는 두 번째 ```convert```에게 ```optimize-frame``` 및 ```optimize-transparancy``` 방법을 수행하도록 지시합니다(ImageMagick 애니메이션 최적화 소개 참조). ```-layers Optimize```의 출력이 항상 더 작은 파일 크기를 제공하지는 않으므로, 먼저 최적화 없이 gif로 변환해 보는 것이 좋습니다.\n이 과정에서 모든 것이 메모리에 있으므로 이미지가 매우 큰 경우 충분한 메모리가 필요할 수 있습니다.'}]} ``` --- # License [CC BY 2.0 KR](https://creativecommons.org/licenses/by/2.0/kr/) [Open AI](https://openai.com/policies/terms-of-use)	changpt/ko-lima-vicuna	[ "task_categories:text-generation", "size_categories:n<1K", "language:ko", "license:cc-by-2.0", "KoLima", "region:us" ]	2023-06-14T02:58:58+00:00	{"language": ["ko"], "license": "cc-by-2.0", "size_categories": ["n<1K"], "task_categories": ["text-generation"], "pretty_name": "KoLima(vicuna)", "tags": ["KoLima"]}	2023-06-14T06:47:51+00:00
b0aa839a5138eac8afbc3282c534cd8651f5350e	# Dataset Card for SNLI_zh ## Dataset Description - Repository: [Chinese NLI dataset](https://github.com/shibing624/text2vec) - Dataset: [train data from ChineseTextualInference](https://github.com/liuhuanyong/ChineseTextualInference/) - Size of downloaded dataset files: 54 MB - Total amount of disk used: 54 MB ### Dataset Summary 中文SNLI和MultiNLI数据集，翻译自英文[SNLI](https://huggingface.co/datasets/snli)和[MultiNLI](https://huggingface.co/datasets/multi_nli) ![img](https://huggingface.co/datasets/shibing624/snli-zh/resolve/main/project_route.png) ### Supported Tasks and Leaderboards Supported Tasks: 支持中文文本匹配任务，文本相似度计算等相关任务。中文匹配任务的结果目前在顶会paper上出现较少，我罗列一个我自己训练的结果： Leaderboard: [NLI_zh leaderboard](https://github.com/shibing624/text2vec) ### Languages 数据集均是简体中文文本。 ## Dataset Structure ### Data Instances An example of 'train' looks as follows. ``` sentence1 sentence2 gold_label 是的，我想一个洞穴也会有这样的问题我认为洞穴可能会有更严重的问题。 neutral 几周前我带他和一个朋友去看幼儿园警察我还没看过幼儿园警察，但他看了。 contradiction 航空旅行的扩张开始了大众旅游的时代，希腊和爱琴海群岛成为北欧人逃离潮湿凉爽的夏天的令人兴奋的目的地。航空旅行的扩大开始了许多旅游业的发展。 entailment ``` ### Data Fields The data fields are the same among all splits. - `sentence1`: a `string` feature. - `sentence2`: a `string` feature. - `label`: a classification label, with possible values including entailment(0), neutral(1), contradiction(2). 注意：此数据集0表示相似，2表示不相似。 - ### Data Splits after remove None and len(text) < 1 data: ```shell $ wc -l ChineseTextualInference-train.txt 419402 total ``` ### Data Length ![len](https://huggingface.co/datasets/shibing624/snli-zh/resolve/main/length.png) ## Dataset Creation ### Curation Rationale 作为中文SNLI(natural langauge inference)数据集，这里把这个数据集上传到huggingface的datasets，方便大家使用。 ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? 数据集的版权归原作者所有，使用各数据集时请尊重原数据集的版权。 @inproceedings{snli:emnlp2015, Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.}, Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, Publisher = {Association for Computational Linguistics}, Title = {A large annotated corpus for learning natural language inference}, Year = {2015} } ### Annotations #### Annotation process #### Who are the annotators? 原作者。 ### Personal and Sensitive Information ## Considerations for Using the Data ### Social Impact of Dataset This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context. Systems that are successful at such a task may be more successful in modeling semantic representations. ### Discussion of Biases ### Other Known Limitations ## Additional Information ### Dataset Curators - [liuhuanyong](https://github.com/liuhuanyong/ChineseTextualInference/)翻译成中文 - [shibing624](https://github.com/shibing624) 上传到huggingface的datasets ### Licensing Information 用于学术研究。 ### Contributions [shibing624](https://github.com/shibing624) add this dataset.	shibing624/snli-zh	[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "task_ids:text-scoring", "annotations_creators:shibing624", "language_creators:liuhuanyong", "multilinguality:monolingual", "size_categories:100K<n<20M", "source_datasets:https://github.com/liuhuanyong/ChineseTextualInference/", "language:zh", "license:cc-by-4.0", "region:us" ]	2023-06-14T03:33:26+00:00	{"annotations_creators": ["shibing624"], "language_creators": ["liuhuanyong"], "language": ["zh"], "license": "cc-by-4.0", "multilinguality": ["monolingual"], "size_categories": ["100K<n<20M"], "source_datasets": ["https://github.com/liuhuanyong/ChineseTextualInference/"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "semantic-similarity-scoring", "text-scoring"], "paperswithcode_id": "snli", "pretty_name": "Stanford Natural Language Inference"}	2023-06-14T06:15:52+00:00
d35f7cb593d682fc1b71b6e631388c4a4375e248		Aliissa99/FrenchMedMCQA	[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:fr", "region:us" ]	2023-06-14T03:34:51+00:00	{"language": ["fr"], "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "pretty_name": "FrenchMedMCQA"}	2023-06-14T04:15:55+00:00
2e7d58cde7b98d91e35ec74a8a3b1de3abd2cd36		Minnyeong/aihub_NL2SQ	[ "size_categories:100K<n<1M", "language:ko", "license:other", "region:us" ]	2023-06-14T03:41:09+00:00	{"language": ["ko"], "license": "other", "size_categories": ["100K<n<1M"]}	2023-06-14T03:46:09+00:00
d9034b79da1dff929a8b9d719a3864e6fb16a7fb	# Dataset Card for nli-zh-all ## Dataset Description - Repository: [Chinese NLI dataset](https://github.com/shibing624/text2vec) - Dataset: [zh NLI](https://huggingface.co/datasets/shibing624/nli-zh-all) - Size of downloaded dataset files: 4.7 GB - Total amount of disk used: 4.7 GB ### Dataset Summary 中文自然语言推理（NLI）数据合集（nli-zh-all）整合了文本推理，相似，摘要，问答，指令微调等任务的820万高质量数据，并转化为匹配格式数据集。 ### Supported Tasks and Leaderboards Supported Tasks: 支持中文文本匹配任务，文本相似度计算等相关任务。中文匹配任务的结果目前在顶会paper上出现较少，我罗列一个我自己训练的结果： Leaderboard: [NLI_zh leaderboard](https://github.com/shibing624/text2vec) ### Languages 数据集均是简体中文文本。 ## Dataset Structure ### Data Instances An example of 'train' looks as follows. ``` {"text1":"借款后多长时间给打电话","text2":"借款后多久打电话啊","label":1} {"text1":"没看到微粒贷","text2":"我借那么久也没有提升啊","label":0} ``` - label 有2个标签，1表示相似，0表示不相似。 ### Data Fields The data fields are the same among all splits. - `text1`: a `string` feature. - `text2`: a `string` feature. - `label`: a classification label, with possible values including entailment(1), contradiction(0)。 ### Data Splits after remove None and len(text) < 1 data: ```shell $ wc -l nli-zh-all/* 48818 nli-zh-all/alpaca_gpt4-train.jsonl 5000 nli-zh-all/amazon_reviews-train.jsonl 519255 nli-zh-all/belle-train.jsonl 16000 nli-zh-all/cblue_chip_sts-train.jsonl 549326 nli-zh-all/chatmed_consult-train.jsonl 10142 nli-zh-all/cmrc2018-train.jsonl 395927 nli-zh-all/csl-train.jsonl 50000 nli-zh-all/dureader_robust-train.jsonl 709761 nli-zh-all/firefly-train.jsonl 9568 nli-zh-all/mlqa-train.jsonl 455875 nli-zh-all/nli_zh-train.jsonl 50486 nli-zh-all/ocnli-train.jsonl 2678694 nli-zh-all/simclue-train.jsonl 419402 nli-zh-all/snli_zh-train.jsonl 3024 nli-zh-all/webqa-train.jsonl 1213780 nli-zh-all/wiki_atomic_edits-train.jsonl 93404 nli-zh-all/xlsum-train.jsonl 1006218 nli-zh-all/zhihu_kol-train.jsonl 8234680 total ``` ### Data Length ![len](https://huggingface.co/datasets/shibing624/nli-zh-all/resolve/main/nli-zh-all-len.png) count text length script: https://github.com/shibing624/text2vec/blob/master/examples/data/count_text_length.py ## Dataset Creation ### Curation Rationale 受[m3e-base](https://huggingface.co/moka-ai/m3e-base#M3E%E6%95%B0%E6%8D%AE%E9%9B%86)启发，合并了中文高质量NLI(natural langauge inference)数据集，这里把这个数据集上传到huggingface的datasets，方便大家使用。 ### Source Data #### Initial Data Collection and Normalization 如果您想要查看数据集的构建方法，你可以在 [https://github.com/shibing624/text2vec/blob/master/examples/data/build_zh_nli_dataset.py](https://github.com/shibing624/text2vec/blob/master/examples/data/build_zh_nli_dataset.py) 中找到生成 nli-zh-all 数据集的脚本，所有数据均上传到 huggingface datasets。 \| 数据集名称 \| 领域 \| 数量 \| 任务类型 \| Prompt \| 质量 \| 数据提供者 \| 说明 \| 是否开源/研究使用 \| 是否商用 \| 脚本 \| Done \| URL \| 是否同质 \| \|:---------------------\| :---- \|:-----------\|:---------------- \|:------ \|:----\|:-----------------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------- \|:------\|:---- \|:---- \|:---------------------------------------------------------------------------------------------\|:------\| \| cmrc2018 \| 百科 \| 14,363 \| 问答 \| 问答 \| 优 \| Yiming Cui, Ting Liu, Wanxiang Che, Li Xiao, Zhipeng Chen, Wentao Ma, Shijin Wang, Guoping Hu \| https://github.com/ymcui/cmrc2018/blob/master/README_CN.md 专家标注的基于维基百科的中文阅读理解数据集，将问题和上下文视为正例 \| 是 \| 否 \| 是 \| 是 \| https://huggingface.co/datasets/cmrc2018 \| 否 \| \| belle_0.5m \| 百科 \| 500,000 \| 指令微调 \| 无 \| 优 \| LianjiaTech/BELLE \| belle 的指令微调数据集，使用 self instruct 方法基于 gpt3.5 生成 \| 是 \| 否 \| 是 \| 是 \| https://huggingface.co/datasets/BelleGroup/ \| 否 \| \| firefily \| 百科 \| 1,649,399 \| 指令微调 \| 无 \| 优 \| YeungNLP \| Firefly（流萤）是一个开源的中文对话式大语言模型，使用指令微调（Instruction Tuning）在中文数据集上进行调优。使用了词表裁剪、ZeRO等技术，有效降低显存消耗和提高训练效率。在训练中，我们使用了更小的模型参数量，以及更少的计算资源。 \| 未说明 \| 未说明 \| 是 \| 是 \| https://huggingface.co/datasets/YeungNLP/firefly-train-1.1M \| 否 \| \| alpaca_gpt4 \| 百科 \| 48,818 \| 指令微调 \| 无 \| 优 \| Baolin Peng, Chunyuan Li, Pengcheng He, Michel Galley, Jianfeng Gao \| 本数据集是参考Alpaca方法基于GPT4得到的self-instruct数据，约5万条。 \| 是 \| 否 \| 是 \| 是 \| https://huggingface.co/datasets/shibing624/alpaca-zh \| 否 \| \| zhihu_kol \| 百科 \| 1,006,218 \| 问答 \| 问答 \| 优 \| wangrui6 \| 知乎问答 \| 未说明 \| 未说明 \| 是 \| 是 \| https://huggingface.co/datasets/wangrui6/Zhihu-KOL \| 否 \| \| amazon_reviews_multi \| 电商 \| 210,000 \| 问答文本分类 \| 摘要 \| 优 \| 亚马逊 \| 亚马逊产品评论数据集 \| 是 \| 否 \| 是 \| 是 \| https://huggingface.co/datasets/amazon_reviews_multi/viewer/zh/train?row=8 \| 否 \| \| mlqa \| 百科 \| 85,853 \| 问答 \| 问答 \| 良 \| patrickvonplaten \| 一个用于评估跨语言问答性能的基准数据集 \| 是 \| 未说明 \| 是 \| 是 \| https://huggingface.co/datasets/mlqa/viewer/mlqa-translate-train.zh/train?p=2 \| 否 \| \| xlsum \| 新闻 \| 93,404 \| 摘要 \| 摘要 \| 良 \| BUET CSE NLP Group \| BBC的专业注释文章摘要对 \| 是 \| 否 \| 是 \| 是 \| https://huggingface.co/datasets/csebuetnlp/xlsum/viewer/chinese_simplified/train?row=259 \| 否 \| \| ocnli \| 口语 \| 17,726 \| 自然语言推理 \| 推理 \| 良 \| Thomas Wolf \| 自然语言推理数据集 \| 是 \| 否 \| 是 \| 是 \| https://huggingface.co/datasets/clue/viewer/ocnli \| 是 \| \| BQ \| 金融 \| 60,000 \| 文本分类 \| 相似 \| 优 \| Intelligent Computing Research Center, Harbin Institute of Technology(Shenzhen) \| http://icrc.hitsz.edu.cn/info/1037/1162.htm BQ 语料库包含来自网上银行自定义服务日志的 120，000 个问题对。它分为三部分：100，000 对用于训练，10，000 对用于验证，10，000 对用于测试。数据提供者：哈尔滨工业大学（深圳）智能计算研究中心 \| 是 \| 否 \| 是 \| 是 \| https://huggingface.co/datasets/shibing624/nli_zh/viewer/BQ \| 是 \| \| lcqmc \| 口语 \| 149,226 \| 文本分类 \| 相似 \| 优 \| Ming Xu \| 哈工大文本匹配数据集，LCQMC 是哈尔滨工业大学在自然语言处理国际顶会 COLING2018 构建的问题语义匹配数据集，其目标是判断两个问题的语义是否相同 \| 是 \| 否 \| 是 \| 是 \| https://huggingface.co/datasets/shibing624/nli_zh/viewer/LCQMC/train \| 是 \| \| paws-x \| 百科 \| 23,576 \| 文本分类 \| 相似 \| 优 \| Bhavitvya Malik \| PAWS Wiki中的示例 \| 是 \| 是 \| 是 \| 是 \| https://huggingface.co/datasets/paws-x/viewer/zh/train \| 是 \| \| wiki_atomic_edit \| 百科 \| 1,213,780 \| 平行语义 \| 相似 \| 优 \| abhishek thakur \| 基于中文维基百科的编辑记录收集的数据集 \| 未说明 \| 未说明 \| 是 \| 是 \| https://huggingface.co/datasets/wiki_atomic_edits \| 是 \| \| chatmed_consult \| 医药 \| 549,326 \| 问答 \| 问答 \| 优 \| Wei Zhu \| 真实世界的医学相关的问题，使用 gpt3.5 进行回答 \| 是 \| 否 \| 是 \| 是 \| https://huggingface.co/datasets/michaelwzhu/ChatMed_Consult_Dataset \| 否 \| \| webqa \| 百科 \| 42,216 \| 问答 \| 问答 \| 优 \| suolyer \| 百度于2016年开源的数据集，数据来自于百度知道；格式为一个问题多篇意思基本一致的文章，分为人为标注以及浏览器检索；数据整体质量中，因为混合了很多检索而来的文章 \| 是 \| 未说明 \| 是 \| 是 \| https://huggingface.co/datasets/suolyer/webqa/viewer/suolyer--webqa/train?p=3 \| 否 \| \| dureader_robust \| 百科 \| 65,937 \| 机器阅读理解问答 \| 问答 \| 优 \| 百度 \| DuReader robust旨在利用真实应用中的数据样本来衡量阅读理解模型的鲁棒性，评测模型的过敏感性、过稳定性以及泛化能力，是首个中文阅读理解鲁棒性数据集。 \| 是 \| 是 \| 是 \| 是 \| https://huggingface.co/datasets/PaddlePaddle/dureader_robust/viewer/plain_text/train?row=96 \| 否 \| \| csl \| 学术 \| 395,927 \| 语料 \| 摘要 \| 优 \| Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao and Hui Zhang \| 提供首个中文科学文献数据集（CSL），包含 396,209 篇中文核心期刊论文元信息（标题、摘要、关键词、学科、门类）。CSL 数据集可以作为预训练语料，也可以构建许多NLP任务，例如文本摘要（标题预测）、关键词生成和文本分类等。 \| 是 \| 是 \| 是 \| 是 \| https://huggingface.co/datasets/neuclir/csl \| 否 \| \| snli-zh \| 口语 \| 419,402 \| 文本分类 \| 推理 \| 优 \| liuhuanyong \| 中文SNLI数据集，翻译自英文SNLI \| 是 \| 否 \| 是 \| 是 \| https://github.com/liuhuanyong/ChineseTextualInference/ \| 是 \| \| SimCLUE \| 百科 \| 2,678,694 \| 平行语义 \| 相似 \| 优 \| 数据集合，请在 simCLUE 中查看 \| 整合了中文领域绝大多数可用的开源的语义相似度和自然语言推理的数据集，并重新做了数据拆分和整理。 \| 是 \| 否 \| 否 \| 是 \| https://github.com/CLUEbenchmark/SimCLUE \| 是 \| #### Who are the source language producers? 数据集的版权归原作者所有，使用各数据集时请尊重原数据集的版权。 SNLI: @inproceedings{snli:emnlp2015, Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.}, Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, Publisher = {Association for Computational Linguistics}, Title = {A large annotated corpus for learning natural language inference}, Year = {2015} } #### Who are the annotators? 原作者。 ### Social Impact of Dataset This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context. Systems that are successful at such a task may be more successful in modeling semantic representations. ### Licensing Information for reasearch 用于学术研究 ### Contributions [shibing624](https://github.com/shibing624) add this dataset.	shibing624/nli-zh-all	[ "task_categories:text-classification", "task_ids:natural-language-inference", "task_ids:semantic-similarity-scoring", "task_ids:text-scoring", "annotations_creators:shibing624", "language_creators:shibing624", "multilinguality:monolingual", "size_categories:1M<n<10M", "source_datasets:https://github.com/shibing624/text2vec", "language:zh", "license:cc-by-4.0", "region:us" ]	2023-06-14T04:12:45+00:00	{"annotations_creators": ["shibing624"], "language_creators": ["shibing624"], "language": ["zh"], "license": "cc-by-4.0", "multilinguality": ["monolingual"], "size_categories": ["1M<n<10M"], "source_datasets": ["https://github.com/shibing624/text2vec"], "task_categories": ["text-classification"], "task_ids": ["natural-language-inference", "semantic-similarity-scoring", "text-scoring"], "paperswithcode_id": "nli", "pretty_name": "Chinese Natural Language Inference"}	2023-06-22T05:39:46+00:00
40f7cc7f6a43333e5bf9d7d83c9dadb793e435dc	# Dataset Card for "toy" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	vvtq/toy	[ "region:us" ]	2023-06-14T04:27:11+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "noised", "dtype": "image"}, {"name": "image_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 6839293.0, "num_examples": 6}], "download_size": 5138950, "dataset_size": 6839293.0}}	2023-06-14T04:27:18+00:00
74b26c88f519b69185479a573e44f9296bd2a41e		mganesh13/dataset	[ "license:mit", "region:us" ]	2023-06-14T04:53:22+00:00	{"license": "mit"}	2023-06-14T04:57:49+00:00
5d661377ae66a98ddc7d12e7cc49511f484807ff	# Dataset Card for "ingredient_to_good_or_bad" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	ziq/ingredient_to_good_or_bad	[ "region:us" ]	2023-06-14T04:59:03+00:00	{"dataset_info": {"features": [{"name": "src", "dtype": "string"}, {"name": "ingredients", "dtype": "string"}, {"name": "Good", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 12018794, "num_examples": 24889}], "download_size": 5553609, "dataset_size": 12018794}}	2023-06-14T05:33:43+00:00
b0ae1ecb855111c08e7b8911ab9aa8c88b6dc027	# Dataset Card for "diagram_image_to_text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	Kamizuru00/diagram_image_to_text	[ "region:us" ]	2023-06-14T05:09:47+00:00	{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19284903.0, "num_examples": 300}], "download_size": 19026610, "dataset_size": 19284903.0}}	2023-06-14T05:54:06+00:00
d2b8862bd541d10b52b2a788887ef5df28febe01	Paper: [MuSiQue: Multi-hop Questions via Single-hop Question Composition](https://arxiv.org/pdf/2108.00573.pdf) Original repository: https://github.com/StonyBrookNLP/musique # Data MuSiQue is distributed under a [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/). Usage Caution: If you're using any of our seed single-hop datasets ([SQuAD](https://arxiv.org/abs/1606.05250), [T-REx](https://hadyelsahar.github.io/t-rex/paper.pdf), [Natural Questions](https://storage.googleapis.com/pub-tools-public-publication-data/pdf/1f7b46b5378d757553d3e92ead36bda2e4254244.pdf), [MLQA](https://arxiv.org/pdf/1910.07475.pdf), [Zero Shot RE](https://arxiv.org/pdf/1706.04115.pdf)) in any way (e.g., pretraining on them), please note that MuSiQue was created by composing questions from these seed datasets. Therefore, single-hop questions used in MuSiQue's dev/test sets may occur in the training sets of these seed datasets. To help avoid information leakage, we are releasing the IDs of single-hop questions that are used in MuSiQue dev/test sets. Once you download the data below, these IDs and corresponding questions will be in `data/dev_test_singlehop_questions_v1.0.json`. If you use our seed single-hop datasets in any way in your model, please be sure to avoid using any single-hop question IDs present in this file # Citation If you use this in your work, please cite use: ``` @article{trivedi2021musique, title={{M}u{S}i{Q}ue: Multihop Questions via Single-hop Question Composition}, author={Trivedi, Harsh and Balasubramanian, Niranjan and Khot, Tushar and Sabharwal, Ashish}, journal={Transactions of the Association for Computational Linguistics}, year={2022} publisher={MIT Press} } ```	bdsaglam/musique	[ "arxiv:2108.00573", "arxiv:1606.05250", "arxiv:1910.07475", "arxiv:1706.04115", "region:us" ]	2023-06-14T05:10:10+00:00	{"dataset_info": [{"config_name": "answerable", "features": [{"name": "id", "dtype": "string"}, {"name": "paragraphs", "sequence": [{"name": "idx", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "paragraph_text", "dtype": "string"}, {"name": "is_supporting", "dtype": "bool"}]}, {"name": "question", "dtype": "string"}, {"name": "question_decomposition", "sequence": [{"name": "id", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "paragraph_support_idx", "dtype": "int32"}]}, {"name": "answer", "dtype": "string"}, {"name": "answerable", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 211123672, "num_examples": 19938}, {"name": "validation", "num_bytes": 26760847, "num_examples": 2417}], "download_size": 299853055, "dataset_size": 237884519}, {"config_name": "full", "features": [{"name": "id", "dtype": "string"}, {"name": "paragraphs", "sequence": [{"name": "idx", "dtype": "int32"}, {"name": "title", "dtype": "string"}, {"name": "paragraph_text", "dtype": "string"}, {"name": "is_supporting", "dtype": "bool"}]}, {"name": "question", "dtype": "string"}, {"name": "question_decomposition", "sequence": [{"name": "id", "dtype": "int32"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "paragraph_support_idx", "dtype": "int32"}]}, {"name": "answer", "dtype": "string"}, {"name": "answerable", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 416868901, "num_examples": 39876}, {"name": "validation", "num_bytes": 52065789, "num_examples": 4834}], "download_size": 591677838, "dataset_size": 468934690}]}	2023-06-14T07:19:12+00:00
0a1e0e6fe2ace541a4ac9f3b276b72918f428eca	# Dataset Card for "cifar10_lt_r10_text" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	ashnrk/cifar10_lt_r10_text	[ "region:us" ]	2023-06-14T05:10:34+00:00	{"dataset_info": {"features": [{"name": "img", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "airplane", "1": "automobile", "2": "bird", "3": "cat", "4": "deer", "5": "dog", "6": "frog", "7": "horse", "8": "ship", "9": "truck"}}}}, {"name": "text_label", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 9133039.5, "num_examples": 4084}], "download_size": 9126904, "dataset_size": 9133039.5}}	2023-06-14T05:10:37+00:00
d63c57054cb1431901b0cbad8588b00d14c09992	# Dataset Card for "human_joined_en_paragraph_19" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	bot-yaya/human_joined_en_paragraph_19	[ "region:us" ]	2023-06-14T05:30:38+00:00	{"dataset_info": {"features": [{"name": "record", "dtype": "string"}, {"name": "raw_text", "dtype": "string"}, {"name": "is_hard_linebreak", "sequence": "bool"}], "splits": [{"name": "train", "num_bytes": 2339622, "num_examples": 19}], "download_size": 1143144, "dataset_size": 2339622}}	2023-06-14T05:30:51+00:00
8eed9641f8281c9146001a16880584736c82a25c	# 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]	pandaman2020/SD	[ "license:cc0-1.0", "region:us" ]	2023-06-14T05:52:00+00:00	{"license": "cc0-1.0"}	2023-06-14T05:53:06+00:00
660382739ff5fe0c958e880104e7b0a6ff133ee6	# Dataset Card for "kmou-2016klp" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	soddokayo/kmou-2016klp	[ "region:us" ]	2023-06-14T05:52:58+00:00	{"dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "tokens", "sequence": "string"}, {"name": "ner_tags", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 3732093, "num_examples": 2928}, {"name": "dev", "num_bytes": 459796, "num_examples": 366}, {"name": "test", "num_bytes": 449770, "num_examples": 366}], "download_size": 951800, "dataset_size": 4641659}}	2023-06-14T05:53:12+00:00
b7b69f831817fe0fa3b28339cb5a1877789eea27	# Dataset Card for "quan_ocr_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	ademax/ocr_scan_vi_01	[ "language:vi", "region:us" ]	2023-06-14T05:56:33+00:00	{"language": "vi", "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 410862389.5689411, "num_examples": 11003}, {"name": "test", "num_bytes": 37340942.4310589, "num_examples": 1000}], "download_size": 447854730, "dataset_size": 448203332.0}}	2023-07-15T08:20:17+00:00
0ab97f890e093c58a86970144b1a31c4b0bbb301	# Dataset Card for "sample" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	byeungchun/sample	[ "region:us" ]	2023-06-14T05:58:24+00:00	{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 79076.8, "num_examples": 800}, {"name": "test", "num_bytes": 19769.2, "num_examples": 200}], "download_size": 67143, "dataset_size": 98846.0}}	2023-06-14T05:59:46+00:00
5c5acb40331f55461ce98238e6c452289e28bd5b	This is the data used in the paper [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://github.com/yueyu1030/AttrPrompt). Checkout the paper https://arxiv.org/abs/2306.15895 for details. - `label.txt`: the label name for each class - `train.jsonl`: The original training set. - `valid.jsonl`: The original validation set. - `test.jsonl`: The original test set. - `simprompt.jsonl`: The training data generated by the simple prompt. - `attrprompt.jsonl`: The training data generated by the attributed prompt. Please check our original paper for details. Moreover, we provide the generated dataset using LLM as follows: - `regen.jsonl`: The training data generated by [ReGen](https://github.com/yueyu1030/ReGen). - `regen_llm_augmented.jsonl`: The training data generated by ReGen, with the subtopics generated by the LLM. - `progen.jsonl`: The training data generated by [ProGen](https://github.com/hkunlp/progen). Please cite the original paper if you use this dataset for your study. Thanks! ``` @inproceedings{meng2019weakly, title={Weakly-supervised hierarchical text classification}, author={Meng, Yu and Shen, Jiaming and Zhang, Chao and Han, Jiawei}, booktitle={Proceedings of the AAAI conference on artificial intelligence}, pages={6826--6833}, year={2019} } @article{yu2023large, title={Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias}, author={Yu, Yue and Zhuang, Yuchen and Zhang, Jieyu and Meng, Yu and Ratner, Alexander and Krishna, Ranjay and Shen, Jiaming and Zhang, Chao}, journal={arXiv preprint arXiv:2306.15895}, year={2023} } ```	yyu/nyt-attrprompt	[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "arxiv:2306.15895", "region:us" ]	2023-06-14T06:04:17+00:00	{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "pretty_name": "d"}	2023-09-13T19:55:46+00:00
c0d9e04b53826e599745ed69ddf7f86fa82a896b	### 数据集说明 #### 组成 \| 类型 \| 文件夹名称 \| 来源 \| 数量 \| 说明 \| \| :--------: \| :-----: \| :-----: \| :----: \| :----: \| \| 电信问答 \| telecom_Q&A \| 百度知道QA \| 87366 \| 经过脱敏、数据清洗、人工筛选等处理\| \| 行业相关知识数据 \| industry_data \| 教科书、国际标准等 \| 5218 \| 通过大模型从文档得到的QA数据，部分原文档保存在source_data中 \| \| 通用指令数据集 \| general_instruction \| firefly \| 18123 \| 挑选了阅读、情感理解、补全、逻辑推理等主题的通用指令 \| \| 混合数据集 \| blended_data \| - \| - \| 按照数据集建设进程，混合后组件的训练、测试数据，可直接使用 \| #### 混合数据 - V1 - 组成 \| 来源 \| 比例 \| 条数 \| 说明 \| \| :--------: \| :-----: \| :----: \| :----: \| \| 百度知道 \| 64% \| 32282 \| 经过脱敏、数据清洗、人工筛选等处理\| \| firefly \| 36% \| 18123 \| 挑选了阅读、情感理解、补全、逻辑推理等主题的通用指令\| \| 标准问答 \| - \| 18 \| 通过联通网上营业厅在线客服整理\| \| 合计 \| 100% \| 50423 \| - \| - 文件说明 \| 名称 \| 条目比例 \| token数 \| 说明\| \| -------- \| -----: \| :----: \| :----: \| \| train \| 85% \| 4,257,817 \| 训练数据集 \| \| test \| 10% \| 464,681 \| 测试数据集 - 1 \| \| test_2 \| 5% \| 232,898 \| 测试数据集 - 2 \| \| train_test \| 10% \| 403,905 \| 训练数据集中抽取的测试数据集 \|	THU-StarLab/CustomerService	[ "license:unknown", "TeleCom", "region:us" ]	2023-06-14T06:07:34+00:00	{"language": ["\u4e2d\u6587"], "license": "unknown", "task_categories": ["\u95ee\u7b54"], "pretty_name": "TeleCom", "tags": ["TeleCom"]}	2023-07-13T06:58:00+00:00
78e38c3c8df3b4f6de7ae8bd1fc6a8bd1f31be56	https://arxiv.org/pdf/2306.07934.pdf	tasksource/Boardgame-QA	[ "license:cc-by-4.0", "arxiv:2306.07934", "region:us" ]	2023-06-14T06:12:54+00:00	{"license": "cc-by-4.0", "dataset_info": {"features": [{"name": "proof", "dtype": "string"}, {"name": "example", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "rules", "dtype": "string"}, {"name": "preferences", "dtype": "string"}, {"name": "theory", "dtype": "string"}, {"name": "goal", "dtype": "string"}, {"name": "facts", "dtype": "string"}, {"name": "config", "dtype": "string"}], "splits": [{"name": "test", "num_bytes": 54209160, "num_examples": 15000}, {"name": "train", "num_bytes": 55055604, "num_examples": 15000}, {"name": "valid", "num_bytes": 27317650, "num_examples": 7500}], "download_size": 34032485, "dataset_size": 136582414}}	2023-06-14T06:38:39+00:00
fdfd411ddbc2cb7c62a70eb909b46627f4369cc2	# Dataset Card for "b9e11bd6" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)	results-sd-v1-5-sd-v2-1-if-v1-0-karlo/b9e11bd6	[ "region:us" ]	2023-06-14T06:18:55+00:00	{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1340, "dataset_size": 182}}	2023-06-14T06:18:56+00:00
39623e8ecb807f51c0114fef18c1311ae4d183a2	This is the data used in the paper [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://github.com/yueyu1030/AttrPrompt). Check the paper: https://arxiv.org/abs/2306.15895 for details. - `label.txt`: the label name for each class - `train.jsonl`: The original training set. - `valid.jsonl`: The original validation set. - `test.jsonl`: The original test set. - `simprompt.jsonl`: The training data generated by the simple prompt. - `attrprompt.jsonl`: The training data generated by the attributed prompt. Please check our original paper for details. Moreover, we provide the generated dataset using LLM as follows: - `regen.jsonl`: The training data generated by [ReGen](https://github.com/yueyu1030/ReGen). - `regen_llm_augmented.jsonl`: The training data generated by ReGen, with the subtopics generated by the LLM. - `progen.jsonl`: The training data generated by [ProGen](https://github.com/hkunlp/progen). Please cite the original paper if you use this dataset for your study. Thanks! ``` @inproceedings{blitzer2007biographies, title={Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification}, author={Blitzer, John and Dredze, Mark and Pereira, Fernando}, booktitle={Proceedings of the 45th annual meeting of the association of computational linguistics}, pages={440--447}, year={2007} } @article{yu2023large, title={Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias}, author={Yu, Yue and Zhuang, Yuchen and Zhang, Jieyu and Meng, Yu and Ratner, Alexander and Krishna, Ranjay and Shen, Jiaming and Zhang, Chao}, journal={arXiv preprint arXiv:2306.15895}, year={2023} } ```	yyu/amazon-attrprompt	[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "product", "review", "topic_classification", "arxiv:2306.15895", "region:us" ]	2023-06-14T06:19:51+00:00	{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "tags": ["product", "review", "topic_classification"]}	2023-09-13T19:58:10+00:00
0692036ffdeecdc8dc1c69035faa7805a15c38d5	# Dataset Card for FELM ## Table of Contents - [Dataset Card for FELM](#dataset-card-for-FELM) - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Dataset Creation](#dataset-creation) - [Source Data](#source-data) - [Initial Data Collection and Clean](#initial-data-collection-and-clean) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Additional Information](#additional-information) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - Homepage: [Needs More Information] - Repository: https://github.com/hkust-nlp/felm - Paper: [Needs More Information] - Leaderboard: [Needs More Information] - Point of Contact: [Needs More Information] ### Dataset Summary [[Paper]](https://arxiv.org/abs/2310.00741) [[Github Repo]](https://github.com/hkust-nlp/felm) FELM is a meta-benchmark to evaluate factuality evaluation for large language models.<br> The benchmark comprises 847 questions that span five distinct domains: world knowledge, science/technology, writing/recommendation, reasoning, and math. We gather prompts corresponding to each domain by various sources including standard datasets like truthfulQA, online platforms like Github repositories, ChatGPT generation or drafted by authors.<br> We then obtain responses from ChatGPT for these prompts. For each response, we employ fine-grained annotation at the segment level, which includes reference links, identified error types, and the reasons behind these errors as provided by our annotators. ### Supported Tasks and Leaderboards [Needs More Information] ### Languages The text in the dataset is in English. ## Dataset Structure ### Data Instances An example looks as follows: ```python {"index": "0", "source": "quora", "prompt": "Which country or city has the maximum number of nuclear power plants?", "response": "The United States has the highest number of nuclear power plants in the world, with 94 operating reactors. Other countries with a significant number of nuclear power plants include France, China, Russia, and South Korea.", "segmented_response": ["The United States has the highest number of nuclear power plants in the world, with 94 operating reactors.", "Other countries with a significant number of nuclear power plants include France, China, Russia, and South Korea."], "labels": [false, true], "comment": ["As of December 2022, there were 92 operable nuclear power reactors in the United States.", ""], "type": ["knowledge_error", null], "ref": ["https://www.eia.gov/tools/faqs/faq.php?id=207&t=3"]} ``` ### Data Fields \| Field Name \| Field Value \| Description \| \| ----------- \| ----------- \| ------------------------------------------- \| \| index \| Integer \| the order number of the data point \| \| source \| string \| the prompt source \| \| prompt \| string \| the prompt for generating response \| \| response \| string \| the response of ChatGPT for prompt \| \| segmented_response \| list \| segments of reponse \| \| labels \| list \| factuality labels for segmented_response \| \| comment \| list \| error reasons for segments with factual error \| \| type \| list \| error types for segments with factual error \| \| ref \| list \| reference links \| ## Dataset Creation ### Source Data #### Initial Data Collection and Clean We gather prompts corresponding to each domain by various sources including standard datasets like truthfulQA, online platforms like Github repositories, ChatGPT generation or drafted by authors. The data is cleaned by authors. ### Annotations #### Annotation process We have developed an annotation tool and established annotation guidelines. All annotations undergo a double-check process, which involves review by both other annotators and an expert reviewer. #### Who are the annotators? The authors of the paper; Yuzhen Huang, Yikai Zhang, Tangjun Su. ## Additional Information ### Licensing Information This dataset is licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/)). ### Citation Information ```bibtex @inproceedings{ chen2023felm, title={FELM: Benchmarking Factuality Evaluation of Large Language Models}, author={Chen, Shiqi and Zhao, Yiran and Zhang, Jinghan and Chern, I-Chun and Gao, Siyang and Liu, Pengfei and He, Junxian}, booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track}, year={2023}, url={http://arxiv.org/abs/2310.00741} } ``` ### Contributions [Needs More Information]	hkust-nlp/felm	[ "task_categories:text-generation", "language:en", "license:cc-by-nc-sa-4.0", "arxiv:2310.00741", "region:us" ]	2023-06-14T06:24:44+00:00	{"language": ["en"], "license": "cc-by-nc-sa-4.0", "task_categories": ["text-generation"], "pretty_name": "FELM"}	2023-10-03T16:29:57+00:00
723698f154a23aec7155f840351405b0a8e7fd1f	This is the data used in the paper [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://github.com/yueyu1030/AttrPrompt). Checkout the paper https://arxiv.org/abs/2306.15895 for details. - `label.txt`: the label name for each class - `train.jsonl`: The original training set. - `valid.jsonl`: The original validation set. - `test.jsonl`: The original test set. - `simprompt.jsonl`: The training data generated by the simple prompt. - `attrprompt.jsonl`: The training data generated by the attributed prompt. Please cite the original paper if you use this dataset for your study. Thanks! ``` @article{geigle:2021:arxiv, author = {Gregor Geigle and Nils Reimers and Andreas R{\"u}ckl{\'e} and Iryna Gurevych}, title = {TWEAC: Transformer with Extendable QA Agent Classifiers}, journal = {arXiv preprint}, volume = {abs/2104.07081}, year = {2021}, url = {http://arxiv.org/abs/2104.07081}, archivePrefix = {arXiv}, eprint = {2104.07081} } @article{yu2023large, title={Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias}, author={Yu, Yue and Zhuang, Yuchen and Zhang, Jieyu and Meng, Yu and Ratner, Alexander and Krishna, Ranjay and Shen, Jiaming and Zhang, Chao}, journal={arXiv preprint arXiv:2306.15895}, year={2023} } ```	yyu/reddit-attrprompt	[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "social_media", "arxiv:2306.15895", "arxiv:2104.07081", "region:us" ]	2023-06-14T06:24:52+00:00	{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "tags": ["social_media"]}	2023-09-13T19:56:10+00:00
8d02a896427eca7646baf58de8a97702ca251d88	# Dataset Card for "mnist-outlier" 📚 This dataset is an enriched version of the [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). The workflow is described in the medium article: [Changes of Embeddings during Fine-Tuning of Transformers](https://medium.com/@markus.stoll/changes-of-embeddings-during-fine-tuning-c22aa1615921). ## Explore the Dataset The open source data curation tool [Renumics Spotlight](https://github.com/Renumics/spotlight) allows you to explorer this dataset. You can find a Hugging Face Space running Spotlight with this dataset here: <https://huggingface.co/spaces/renumics/mnist-outlier>. ![Analyze with Spotlight](https://spotlight.renumics.com/resources/hf-mnist-outlier.png) Or you can explorer it locally: ```python !pip install renumics-spotlight datasets from renumics import spotlight import datasets ds = datasets.load_dataset("renumics/mnist-outlier", split="train") df = ds.rename_columns({"label":"labels"}).to_pandas() df["label_str"] = df["labels"].apply(lambda x: ds.features["label"].int2str(x)) dtypes = { "nn_image": spotlight.Image, "image": spotlight.Image, "embedding_ft": spotlight.Embedding, "embedding_foundation": spotlight.Embedding, } spotlight.show( df, dtype=dtypes, layout="https://spotlight.renumics.com/resources/layout_pre_post_ft.json", ) ```	renumics/mnist-outlier	[ "task_categories:image-classification", "task_ids:multi-class-image-classification", "annotations_creators:expert-generated", "language_creators:found", "multilinguality:monolingual", "size_categories:10K<n<100K", "source_datasets:extended\|other-nist", "language:en", "license:mit", "region:us" ]	2023-06-14T06:28:06+00:00	{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["mit"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended\|other-nist"], "task_categories": ["image-classification"], "task_ids": ["multi-class-image-classification"], "paperswithcode_id": "mnist", "pretty_name": "MNIST", "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", "7": "7", "8": "8", "9": "9"}}}}, {"name": "embedding_foundation", "sequence": "float32"}, {"name": "embedding_ft", "sequence": "float32"}, {"name": "outlier_score_ft", "dtype": "float64"}, {"name": "outlier_score_foundation", "dtype": "float64"}, {"name": "nn_image", "struct": [{"name": "bytes", "dtype": "binary"}, {"name": "path", "dtype": "null"}]}], "splits": [{"name": "train", "num_bytes": 404136444.0, "num_examples": 60000}], "download_size": 472581433, "dataset_size": 404136444.0}}	2023-06-30T19:08:34+00:00
1a08b4bf755c3162b1f7f45bc76e3e82a492b4dd	This is the data used in the paper [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://github.com/yueyu1030/AttrPrompt). Checkout the paper: https://arxiv.org/abs/2306.15895 for details. - `label.txt`: the label name for each class - `train.jsonl`: The original training set. - `valid.jsonl`: The original validation set. - `test.jsonl`: The original test set. - `simprompt.jsonl`: The training data generated by the simple prompt. - `attrprompt.jsonl`: The training data generated by the attributed prompt.	yyu/stackexchange-attrprompt	[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "social_media", "stackexchange", "arxiv:2306.15895", "region:us" ]	2023-06-14T06:28:19+00:00	{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "tags": ["social_media", "stackexchange"]}	2023-09-13T19:56:38+00:00
5be5c78b4b62ff1e2047a93340c763b8197ee268	This is the data used in the paper [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://github.com/yueyu1030/AttrPrompt). - `label.txt`: the label name for each class - `train.jsonl`: The original training set. - `valid.jsonl`: The original validation set. - `test.jsonl`: The original test set. - `simprompt.jsonl`: The training data generated by the simple prompt. - `attrprompt.jsonl`: The training data generated by the attributed prompt.	yyu/agnews-attrprompt	[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:en", "license:apache-2.0", "region:us" ]	2023-06-14T06:34:38+00:00	{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"]}	2023-08-22T07:27:07+00:00
e3be2cc66fb50e6b00aa4dad79cc2f3dd7cf8364	This is the data used in the paper [Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias](https://github.com/yueyu1030/AttrPrompt). - `label.txt`: the label name for each class - `train.jsonl`: The original training set. - `valid.jsonl`: The original validation set. - `test.jsonl`: The original test set. - `simprompt.jsonl`: The training data generated by the simple prompt. - `attrprompt.jsonl`: The training data generated by the attributed prompt.	yyu/yelp-attrprompt	[ "task_categories:text-classification", "size_categories:1K<n<10K", "language:en", "license:apache-2.0", "sentiment", "restaurant_review", "region:us" ]	2023-06-14T06:37:49+00:00	{"language": ["en"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification"], "tags": ["sentiment", "restaurant_review"], "version": ["v1"]}	2023-08-22T07:26:22+00:00

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