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7419e7bdd0d4ff260875920f9c5056a346163aa1
# Crowd Counting Dataset The dataset includes images featuring crowds of people ranging from **0 to 5000 individuals**. The dataset includes a diverse range of scenes and scenarios, capturing crowds in various settings. Each image in the dataset is accompanied by a corresponding **JSON file** containing detailed labeling information for each person in the crowd for crowd count and classification. ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F4b51a212e59f575bd6978f215a32aca0%2FFrame%2064.png?generation=1701336719197861&alt=media) **Types of crowds** in the dataset: *0-1000, 1000-2000, 2000-3000, 3000-4000 and 4000-5000* ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F72e0fed3ad13826d6545ff75a79ed9db%2FFrame%2065.png?generation=1701337622225724&alt=media) This dataset provides a valuable resource for researchers and developers working on crowd counting technology, enabling them to train and evaluate their algorithms with a wide range of crowd sizes and scenarios. It can also be used for benchmarking and comparison of different crowd counting algorithms, as well as for real-world applications such as *public safety and security, urban planning, and retail analytics*. ## Full version of the dataset includes 647 labeled images of crowds, leave a request on **[TrainingData](https://trainingdata.pro/data-market/crowd-counting?utm_source=huggingface&utm_medium=cpc&utm_campaign=crowd-counting-dataset)** to buy the dataset ### Statistics for the dataset (number of images by the crowd's size and image width): ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F2e9f36820e62a2ef62586fc8e84387e2%2FFrame%2063.png?generation=1701336725293625&alt=media) # Get the Dataset ## This is just an example of the data Leave a request on **[https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/crowd-counting?utm_source=huggingface&utm_medium=cpc&utm_campaign=crowd-counting-dataset) to learn about the price and buy the dataset** # Content - **images** - includes original images of crowds placed in subfolders according to its size, - **labels** - includes json-files with labeling and visualised labeling for the images in the previous folder, - **csv file** - includes information for each image in the dataset ### File with the extension .csv - **id**: id of the image, - **image**: link to access the original image, - **label**: link to access the json-file with labeling, - **type**: type of the crowd on the photo ## **[TrainingData](https://trainingdata.pro/data-market/crowd-counting?utm_source=huggingface&utm_medium=cpc&utm_campaign=crowd-counting-dataset)** provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **<https://www.kaggle.com/trainingdatapro/datasets>** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** *keywords: crowd counting, crowd density estimation, people counting, crowd analysis, image annotation, computer vision, deep learning, object detection, object counting, image classification, dense regression, crowd behavior analysis, crowd tracking, head detection, crowd segmentation, crowd motion analysis, image processing, machine learning, artificial intelligence, ai, human detection, crowd sensing, image dataset, public safety, crowd management, urban planning, event planning, traffic management*
TrainingDataPro/crowd-counting-dataset
[ "task_categories:image-classification", "task_categories:image-to-image", "language:en", "license:cc-by-nc-nd-4.0", "legal", "code", "region:us" ]
2024-02-16T11:18:40+00:00
{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-classification", "image-to-image"], "tags": ["legal", "code"]}
2024-02-16T13:04:46+00:00
3201bfca0c7296b07bfa0d6730b65fd0d0518f74
Maxwell001/skill_model_formatted_1
[ "region:us" ]
2024-02-16T11:20:13+00:00
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1061145, "num_examples": 1023}], "download_size": 239351, "dataset_size": 1061145}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T11:20:16+00:00
ba1bc5d9652cd8664eea8f699d8340c379d51a87
ankush80692hf/llm_retrain
[ "region:us" ]
2024-02-16T11:21:04+00:00
{}
2024-02-16T11:21:04+00:00
37d2d1db2baa564db35e8031e6eb46dfc4ebd587
YingJie0202/test2
[ "region:us" ]
2024-02-16T11:27:34+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 319364, "num_examples": 76}], "download_size": 76556, "dataset_size": 319364}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T11:27:35+00:00
c75aef743a6968a6c6427c4a5f40d9caa6c3a28f
ebelfrank/ITU-AN-usecases-1
[ "region:us" ]
2024-02-16T11:27:53+00:00
{}
2024-02-16T11:29:26+00:00
395d3e6b02a619008b91e294c572a95268cb5bcd
aurelio-ai/shrek-detection
[ "license:mit", "region:us" ]
2024-02-16T11:38:59+00:00
{"license": "mit"}
2024-02-17T17:22:33+00:00
502e23b7c0e1a7e7af031e20603a96305270975c
suthawadee/receipt_th_1
[ "region:us" ]
2024-02-16T11:41:05+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14696947.0, "num_examples": 80}, {"name": "validation", "num_bytes": 1693188.0, "num_examples": 10}, {"name": "test", "num_bytes": 1855175.0, "num_examples": 10}], "download_size": 18156103, "dataset_size": 18245310.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}
2024-02-16T11:42:58+00:00
f98999376bf3915555fa0c5681e5c332fee37026
arthurMM801/cnh-rg-cpf
[ "region:us" ]
2024-02-16T11:44:32+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "cnh_completo", "1": "cpf_completo", "2": "rg_completo"}}}}, {"name": "ground_truth", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16204820.0, "num_examples": 48}, {"name": "test", "num_bytes": 4901507.0, "num_examples": 12}], "download_size": 21109239, "dataset_size": 21106327.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}
2024-02-16T14:22:42+00:00
a9d6c133fa6ddc67468bba84e584996aac9de498
brayene/tr-ChatGPT-Jailbreak-Prompts
[ "region:us" ]
2024-02-16T11:46:50+00:00
{"dataset_info": {"features": [{"name": "Name", "dtype": "string"}, {"name": "Prompt", "dtype": "string"}, {"name": "Votes", "dtype": "int64"}, {"name": "Jailbreak Score", "dtype": "int64"}, {"name": "GPT-4", "dtype": "string"}, {"name": "translation", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 324859, "num_examples": 79}], "download_size": 166205, "dataset_size": 324859}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T11:46:53+00:00
515f9788290c8d4485113abf1f2d43f6df77c4cc
# Dataset Card for "HealthCareMagic-100k" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ruslanmv/HealthCareMagic-100k
[ "region:us" ]
2024-02-16T11:49:53+00:00
{"configs": [{"config_name": "default"}], "dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 126454896, "num_examples": 112165}], "download_size": 70518148, "dataset_size": 126454896}}
2024-02-16T11:55:26+00:00
c840f0f3be81ff07d6c162f0aca8bcb663a27fbc
sayakpaul/mgie-results
[ "region:us" ]
2024-02-16T11:53:22+00:00
{}
2024-02-16T11:53:25+00:00
ec4a0e6963d21687567c1baee3615b52c4eb0c41
# Dataset Card for "ChatDoctor-iCliniq" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
ruslanmv/icliniq-7k
[ "region:us" ]
2024-02-16T11:59:51+00:00
{"configs": [{"config_name": "default"}], "dataset_info": {"features": [{"name": "input", "dtype": "string"}, {"name": "answer_icliniq", "dtype": "string"}, {"name": "answer_chatgpt", "dtype": "string"}, {"name": "answer_chatdoctor", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16962106, "num_examples": 7321}], "download_size": 9373080, "dataset_size": 16962106}}
2024-02-16T12:06:04+00:00
8e8704472b15e41cfe2ee1dc085e4f7172a77a3d
ebelfrank/fgan-annotate-dataset
[ "region:us" ]
2024-02-16T12:01:12+00:00
{}
2024-02-16T12:01:12+00:00
f3c7d9ec6a1e398823e2c9023f4019a9e385e81b
benayas/banking_augmented_20pct_v1
[ "region:us" ]
2024-02-16T12:02:02+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "category", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1022684, "num_examples": 10003}], "download_size": 426228, "dataset_size": 1022684}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T12:02:05+00:00
6f6ee654d6899b817e842570e6c352e3d5c9c107
jamesagilesoda/ko-corpus-cleaned-12653878-chunk_00060_00079
[ "region:us" ]
2024-02-16T12:06:24+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10004390119, "num_examples": 1259100}], "download_size": 5762730526, "dataset_size": 10004390119}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T12:14:35+00:00
db6c3ec125c140ce02c6cc13c6dda98dbeca1ff7
# AI Medical Chatbot Dataset This is an experimental Dataset designed to run a Medical Chatbot It contains at least 250k dialogues between a Patient and a Doctor. ![](future.jpg) For furter information visit the project here: [https://github.com/ruslanmv/ai-medical-chatbot](https://github.com/ruslanmv/ai-medical-chatbot)
ruslanmv/ai-medical-chatbot
[ "region:us" ]
2024-02-16T12:10:13+00:00
{"configs": [{"config_name": "default", "data_files": [{"path": "dialogues.*", "split": "train"}]}], "dataset_info": {"dataset_size": 141665910, "download_size": 141665910, "features": [{"dtype": "string", "name": "Description"}, {"dtype": "string", "name": "Patient"}, {"dtype": "string", "name": "Doctor"}], "splits": [{"name": "train", "num_bytes": 141665910, "num_examples": 256916}]}}
2024-02-16T14:35:02+00:00
1771ecb2e231d6e743a6a2e32482544315a1f8e5
# Dataset Card for Dataset Name <!-- Provide a quick summary of the dataset. --> 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). ## Dataset Details ### Dataset Description <!-- Provide a longer summary of what this dataset is. --> - **Curated by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] ### Dataset Sources [optional] <!-- Provide the basic links for the dataset. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the dataset is intended to be used. --> ### Direct Use <!-- This section describes suitable use cases for the dataset. --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> [More Information Needed] ## Dataset Structure <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> [More Information Needed] ## Dataset Creation ### Curation Rationale <!-- Motivation for the creation of this dataset. --> [More Information Needed] ### Source Data <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> #### Data Collection and Processing <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> [More Information Needed] #### Who are the source data producers? <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> [More Information Needed] ### Annotations [optional] <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> #### Annotation process <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> [More Information Needed] #### Who are the annotators? <!-- This section describes the people or systems who created the annotations. --> [More Information Needed] #### Personal and Sensitive Information <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. ## Citation [optional] <!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Dataset Card Authors [optional] [More Information Needed] ## Dataset Card Contact [More Information Needed]
Izazk/izaz-mind2web-dataset
[ "task_categories:text-generation", "task_categories:text-classification", "task_categories:feature-extraction", "size_categories:10K<n<100K", "code", "biology", "webdataset", "climate", "finance", "legal", "region:us" ]
2024-02-16T12:12:56+00:00
{"size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "text-classification", "feature-extraction"], "pretty_name": "Mind2webAction", "tags": ["code", "biology", "webdataset", "climate", "finance", "legal"]}
2024-02-16T12:34:30+00:00
ed6a9eb1e92d159f49bcef76a87d52ed26e741d6
lab42/cov-vqa-raw-10
[ "region:us" ]
2024-02-16T12:12:57+00:00
{"dataset_info": {"features": [{"name": "image_0", "dtype": "image"}, {"name": "image_1", "dtype": "image"}, {"name": "image_2", "dtype": "image"}, {"name": "images_rest", "sequence": "image"}, {"name": "mask_0", "dtype": "image"}, {"name": "mask_1", "dtype": "image"}, {"name": "mask_2", "dtype": "image"}, {"name": "masks_rest", "sequence": "image"}, {"name": "conversations", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "dataset", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "n_images", "dtype": "int32"}, {"name": "n_masks", "dtype": "int32"}, {"name": "n_conversations", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 625943.0, "num_examples": 3}], "download_size": 623530, "dataset_size": 625943.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T12:12:59+00:00
f31eceb56f4b4b0b153f762d9e6073c2278f2db5
Xapien/er_fdocs_2000_with_dres
[ "region:us" ]
2024-02-16T12:15:32+00:00
{"dataset_info": {"features": [{"name": "prompts", "dtype": "string"}, {"name": "output", "struct": [{"name": "organisations", "sequence": "string"}, {"name": "orgs", "sequence": "null"}, {"name": "people", "sequence": "string"}]}], "splits": [{"name": "train", "num_bytes": 7585509, "num_examples": 2000}], "download_size": 3164642, "dataset_size": 7585509}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T12:47:03+00:00
6152ada85bdb58760a75a775b3211ff202770371
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protonacvketogummies/ProtonKetoGummies
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2024-02-16T12:16:23+00:00
{}
2024-02-16T12:16:51+00:00
f56434d64db535c8c2f0a74fb720216ff1cc88ae
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protonacvketogummies/ProtonGummies
[ "region:us" ]
2024-02-16T12:17:11+00:00
{}
2024-02-16T12:17:46+00:00
fc4e5990c8730be7a1cc523812f66205cbc71765
chuubjak/common_voice_13_0_th_pseudo_labelled_large_v2
[ "region:us" ]
2024-02-16T12:19:50+00:00
{"dataset_info": {"config_name": "th", "features": [{"name": "client_id", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 16000}}}, {"name": "sentence", "dtype": "string"}, {"name": "up_votes", "dtype": "int64"}, {"name": "down_votes", "dtype": "int64"}, {"name": "age", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "accent", "dtype": "string"}, {"name": "locale", "dtype": "string"}, {"name": "segment", "dtype": "string"}, {"name": "variant", "dtype": "string"}, {"name": "whisper_transcript", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 869933783.139, "num_examples": 32477}, {"name": "validation", "num_bytes": 330063343.56, "num_examples": 11002}, {"name": "test", "num_bytes": 342616264.906, "num_examples": 11002}], "download_size": 1425514912, "dataset_size": 1542613391.605}, "configs": [{"config_name": "th", "data_files": [{"split": "train", "path": "th/train-*"}, {"split": "validation", "path": "th/validation-*"}, {"split": "test", "path": "th/test-*"}]}]}
2024-02-16T16:16:36+00:00
5326a01a8b8df259e218541396eae3d603ce079d
Recag/Rp_CommonC_666_1
[ "region:us" ]
2024-02-16T12:20:42+00:00
{}
2024-02-16T13:03:15+00:00
615d74f944bc8fba1bb52a240356ee2171f156cf
Recag/Rp_CommonC_666_2
[ "region:us" ]
2024-02-16T12:20:51+00:00
{}
2024-02-16T12:49:39+00:00
5c67230adef1287b55dffe013527a420604527c3
Recag/Rp_CommonC_667_1
[ "region:us" ]
2024-02-16T12:21:03+00:00
{}
2024-02-16T13:03:28+00:00
86585604115f5490968e91755559d9804e2eede8
Recag/Rp_CommonC_667_2
[ "region:us" ]
2024-02-16T12:21:09+00:00
{}
2024-02-16T12:52:04+00:00
23af44bf074c53c6f927d72fc00029b8160fd4be
Recag/Rp_CommonC_668_1
[ "region:us" ]
2024-02-16T12:23:34+00:00
{}
2024-02-16T13:03:48+00:00
88e09bae5f76cd5d9ae9b7ebced816db7e9850aa
Recag/Rp_CommonC_668_2
[ "region:us" ]
2024-02-16T12:23:42+00:00
{}
2024-02-16T12:56:06+00:00
432278cd6aed08cbe082a0c5bc4232a1099e46a6
syedjameerbabu/mini-platypus-instruction-dataset-for-finetuning
[ "region:us" ]
2024-02-16T12:24:31+00:00
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4186564, "num_examples": 1000}], "download_size": 2245921, "dataset_size": 4186564}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T12:24:34+00:00
825ee2bb026af049c5f7cc9c8d14678fe1252b02
fahmiaziz/fingpt
[ "region:us" ]
2024-02-16T12:34:28+00:00
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "output", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3720939, "num_examples": 2000}], "download_size": 2090588, "dataset_size": 3720939}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T12:38:47+00:00
ce2a8309fa37f13688f2d6e29765a0ed7031e496
sakkke/text-to-command-chatgpt
[ "license:mit", "region:us" ]
2024-02-16T12:36:50+00:00
{"license": "mit"}
2024-02-16T12:36:50+00:00
03f17dd7aa500362302de1f6dc18571286ae1f11
Recag/Rp_CommonC_737_2
[ "region:us" ]
2024-02-16T12:37:48+00:00
{}
2024-02-16T12:51:50+00:00
734d391f2a747cb2063425b1755b7cc7518b9a03
leo-cf-tian/im2latex-250k
[ "region:us" ]
2024-02-16T12:37:49+00:00
{}
2024-02-16T12:37:49+00:00
1c00d824bed97281933e30033b7703b2ef108fa1
El-chapoo/100k
[ "region:us" ]
2024-02-16T12:40:47+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11869558, "num_examples": 999999}], "download_size": 11148404, "dataset_size": 11869558}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T12:40:49+00:00
2714258b09b0863963f78a667252da267b3b91ce
# Brain Cancer MRI Object Detection & Segmentation Dataset The dataset consists of .dcm files containing **MRI scans of the brain** of the person with a cancer. The images are **labeled** by the doctors and accompanied by **report** in PDF-format. The dataset includes 10 studies, made from the different angles which provide a comprehensive understanding of a brain tumor structure. ### MRI study angles in the dataset ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F5939be1e93e8e0c9f1ff922f184f70fe%2FFrame%2079.png?generation=1707920286083259&alt=media) # 💴 For Commercial Usage: Full version of the dataset includes 100,000 brain studies of people with different conditions, leave a request on **[TrainingData](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=brain-mri-cancer)** to buy the dataset ### Types of diseases and conditions in the full dataset: - Cancer - Multiple sclerosis - Metastatic lesion - Arnold-Chiari malformation - Focal gliosis of the brain - **AND MANY OTHER CONDITIONS** ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F0f5a27b8872e85fe23bf742593dc4843%2F2.gif?generation=1707920414940375&alt=media) The MRI scans provide high-resolution images of the anatomical structures, allowing medical professionals to visualize the tumor, its location, size, and surrounding tissues. The dataset holds great value for researchers and medical professionals involved in oncology, radiology, and medical imaging. It can be used for a wide range of purposes, including developing and evaluating novel imaging techniques, training and validating machine learning algorithms for automated tumor detection and segmentation, analyzing tumor response to different treatments, and studying the relationship between imaging features and clinical outcomes. # 💴 Buy the Dataset: This is just an example of the data. Leave a request on [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=brain-mri-cancer) to discuss your requirements, learn about the price and buy the dataset # Content ### The dataset includes: - **ST000001**: includes subfolders with 10 studies. Each study includes MRI-scans in **.dcm and .jpg formats**, - **DICOMDIR**: includes information about the patient's condition and links to access files, - **Brain_MRI_1.pdf**: includes medical report, provided by the radiologist, - **.csv file**: includes id of the studies and the number of files ### Medical reports include the following data: - Patient's **demographic information**, - **Description** of the case, - Preliminary **diagnosis**, - **Recommendations** on the further actions *All patients consented to the publication of data* # Medical data might be collected in accordance with your requirements. ## [TrainingData](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=brain-mri-cancer) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **<https://www.kaggle.com/trainingdatapro/datasets>** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** *keywords: mri brain scan, brain tumor, brain cancer, oncology, neuroimaging, radiology, brain metastasis, glioblastoma, meningioma, pituitary tumor, medulloblastoma, astrocytoma, oligodendroglioma, ependymoma, neuro-oncology, brain lesion, brain metastasis detection, brain tumor classification, brain tumor segmentation, brain tumor diagnosis, brain tumor prognosis, brain tumor treatment, brain tumor surgery, brain tumor radiation therapy, brain tumor chemotherapy, brain tumor clinical trials, brain tumor research, brain tumor awareness, brain tumor support, brain tumor survivor, neurosurgery, neurologist, neuroradiology, neuro-oncologist, neuroscientist, medical imaging, cancer detection, cancer segmentation, tumor, computed tomography, head, skull, brain scan, eye sockets, sinuses, computer vision, deep learning*
TrainingDataPro/brain-mri-dataset
[ "task_categories:image-to-image", "task_categories:image-classification", "task_categories:image-segmentation", "language:en", "license:cc-by-nc-nd-4.0", "code", "medical", "biology", "region:us" ]
2024-02-16T12:51:22+00:00
{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-to-image", "image-classification", "image-segmentation"], "tags": ["code", "medical", "biology"]}
2024-02-16T13:11:45+00:00
c39b4e969668cc2fc7cf9e5bf2de0cc23829492c
# Dataset Card for "myriade_ontologie_corrected" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
gguichard/myriade_ontologie_corrected
[ "region:us" ]
2024-02-16T12:51:35+00:00
{"dataset_info": {"features": [{"name": "tokens", "sequence": "string"}, {"name": "wn_sens", "sequence": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 10981396, "num_examples": 33834}], "download_size": 0, "dataset_size": 10981396}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T13:43:57+00:00
58f74ec15b89e9defb1a60e5553c444da8635b14
argilla/OpenHermes-2.5-dpo
[ "region:us" ]
2024-02-16T12:51:50+00:00
{"dataset_info": {"features": [{"name": "conversations", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}, {"name": "weight", "dtype": "float64"}]}, {"name": "input", "dtype": "string"}, {"name": "generation_model", "sequence": "string"}, {"name": "generation_prompt", "sequence": "string"}, {"name": "raw_generation_responses", "sequence": "string"}, {"name": "generations", "sequence": "string"}, {"name": "row_index", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 2394292793, "num_examples": 454337}], "download_size": 1103884427, "dataset_size": 2394292793}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T13:23:00+00:00
9293f9fd80b2c724d0ff87791ac0bce09a319cf6
# Multiple Sclerosis Dataset, Brain MRI Object Detection & Segmentation Dataset The dataset consists of .dcm files containing **MRI scans of the brain** of the person with a multiple sclerosis. The images are **labeled** by the doctors and accompanied by **report** in PDF-format. The dataset includes **13 studies**, made from the different angles which provide a comprehensive understanding of a multiple sclerosis as a condition. ### MRI study angles in the dataset ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F9fe7ab0fb5e7d66b0028561d78258baf%2FFrame%2080.png?generation=1707937444108216&alt=media) # 💴 For Commercial Usage: Full version of the dataset includes 100,000 brain studies of people with different conditions, leave a request on **[TrainingData](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=multiple-sclerosis)** to buy the dataset ### Types of diseases and conditions in the full dataset: - Cancer - Multiple sclerosis - Metastatic lesion - Arnold-Chiari malformation - Focal gliosis of the brain - **AND MANY OTHER CONDITIONS** ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fae5d9eb364be2e6a718b1ed7ebaa8ba1%2Fezgif.com-animated-gif-maker.gif?generation=1707936352780047&alt=media) The dataset holds great value for researchers and medical professionals involved in oncology, radiology, and medical imaging. It can be used for a wide range of purposes, including developing and evaluating novel imaging techniques, training and validating machine learning algorithms for automated multiple sclerosis detection and segmentation, analyzing response to different treatments, and studying the relationship between imaging features and clinical outcomes. # 💴 Buy the Dataset: This is just an example of the data. Leave a request on [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=multiple-sclerosis) to discuss your requirements, learn about the price and buy the dataset # Content ### The dataset includes: - **ST000001**: includes subfolders with 13 studies. Each study includes MRI-scans in **.dcm and .jpg formats**, - **DICOMDIR**: includes information about the patient's condition and links to access files, - **Brain_MRI_2.pdf**: includes medical report, provided by the radiologist, - **.csv file**: includes id of the studies and the number of files ### Medical reports include the following data: - Patient's **demographic information**, - **Description** of the case, - Preliminary **diagnosis**, - **Recommendations** on the further actions *All patients consented to the publication of data* # Medical data might be collected in accordance with your requirements. ## [TrainingData](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=multiple-sclerosis) provides high-quality data annotation tailored to your needs *keywords: brain mri, multiple sclerosis, neuroimaging, mri scans, lesions, brain atrophy, white matter abnormalities, disease progression, neurodegeneration, inflammation, imaging biomarkers, mri analysis, brain volume loss, gray matter changes, myelin damage, optic neuritis, spinal cord lesions, radiological assessment, aneurysm, cancer detection, cancer segmentation, tumor, computed tomography, head, skull, brain scan, eye sockets, sinuses, medical imaging, radiology dataset, neurology dataset, oncology dataset, image dataset, abnormalities detection, brain anatomy, health, brain formations, imaging procedure, x-rays measurements, machine learning, computer vision, deep learning*
TrainingDataPro/multiple-sclerosis-dataset
[ "task_categories:image-to-image", "task_categories:image-classification", "task_categories:image-segmentation", "language:en", "license:cc-by-nc-nd-4.0", "biology", "medical", "region:us" ]
2024-02-16T12:52:18+00:00
{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-to-image", "image-classification", "image-segmentation"], "tags": ["biology", "medical"]}
2024-02-16T13:11:01+00:00
c10eeabd512f2a730e9b98e17f97f67a3944ce82
Recag/Rp_CommonC_738_1
[ "region:us" ]
2024-02-16T12:52:43+00:00
{}
2024-02-16T13:02:29+00:00
5b47541284850d0cc2d09c5f8f50d485a2619ff0
Recag/Rp_CommonC_738_2
[ "region:us" ]
2024-02-16T12:52:55+00:00
{}
2024-02-16T13:02:48+00:00
3d38fcde93b55f19ad6ba24de067568a3ca76882
Recag/Rp_CommonC_738_3
[ "region:us" ]
2024-02-16T12:53:08+00:00
{}
2024-02-16T13:05:43+00:00
efd68fca13a19c0e9f33d3456b7baae506943e9b
# Brain MRI Dataset, Arnold-Chiari Malformation Detection & Segmentation The dataset consists of .dcm files containing **MRI scans of the brain** of the person with a Arnold-Chiari Malformation. The images are **labeled** by the doctors and accompanied by **report** in PDF-format. The dataset includes 6 studies, made from the different angles which provide a comprehensive understanding of a Arnold-Chiari Anomaly and signs of dysplasia of the cranio-vertebral junction (platybasia). ### MRI study angles in the dataset ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Ffcaa9d9d12f72a97255bfd40e23355fb%2FFrame%2081.png?generation=1707995757290533&alt=media) # 💴 For Commercial Usage: Full version of the dataset includes 100,000 brain studies of people with different conditions, leave a request on **[TrainingData](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=brain-anomaly-detection)** to buy the dataset ### Types of diseases and conditions in the full dataset: - Cancer - Multiple sclerosis - Metastatic lesion - Arnold-Chiari malformation - Focal gliosis of the brain - **AND MANY OTHER CONDITIONS** ![](https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F38170a0bcfc2a312de4916dd1e8d58c8%2Fezgif.com-animated-gif-maker%20(1).gif?generation=1707995771155527&alt=media) The dataset holds great value for researchers and medical professionals involved in oncology, radiology, and medical imaging. It can be used for a wide range of purposes, including developing and evaluating novel imaging techniques, training and validating machine learning algorithms for automated tumor detection and segmentation, analyzing tumor response to different treatments, and studying the relationship between imaging features and clinical outcomes. # 💴 Buy the Dataset: This is just an example of the data. Leave a request on [https://trainingdata.pro/data-market](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=brain-anomaly-detection) to discuss your requirements, learn about the price and buy the dataset # Content ### The dataset includes: - **ST000001**: includes subfolders with 6 studies. Each study includes MRI-scans in **.dcm and .jpg formats**, - **DICOMDIR**: includes information about the patient's condition and links to access files, - **Brain_MRI_3.pdf**: includes medical report, provided by the radiologist, - **.csv file**: includes id of the studies and the number of files ### Medical reports include the following data: - Patient's **demographic information**, - **Description** of the case, - Preliminary **diagnosis**, - **Recommendations** on the further actions *All patients consented to the publication of data* # Medical data might be collected in accordance with your requirements. ## [TrainingData](https://trainingdata.pro/data-market/brain-mri?utm_source=huggingface&utm_medium=cpc&utm_campaign=brain-anomaly-detection) provides high-quality data annotation tailored to your needs More datasets in TrainingData's Kaggle account: **<https://www.kaggle.com/trainingdatapro/datasets>** TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** *keywords: mri brain scan, brain tumor, brain cancer, oncology, neuroimaging, radiology, brain metastasis, glioblastoma, meningioma, pituitary tumor, medulloblastoma, astrocytoma, oligodendroglioma, ependymoma, neuro-oncology, brain lesion, brain metastasis detection, brain tumor classification, brain tumor segmentation, brain tumor diagnosis, brain tumor prognosis, brain tumor treatment, brain tumor surgery, brain tumor radiation therapy, brain tumor chemotherapy, brain tumor clinical trials, brain tumor research, brain tumor awareness, brain tumor support, brain tumor survivor, neurosurgery, neurologist, neuroradiology, neuro-oncologist, neuroscientist, medical imaging, cancer detection, cancer segmentation, tumor, computed tomography, head, skull, brain scan, eye sockets, sinuses, computer vision, deep learning*
TrainingDataPro/brain-anomaly-detection
[ "task_categories:image-classification", "task_categories:image-to-image", "task_categories:image-segmentation", "language:en", "license:cc-by-nc-nd-4.0", "medical", "code", "biology", "region:us" ]
2024-02-16T12:53:26+00:00
{"language": ["en"], "license": "cc-by-nc-nd-4.0", "task_categories": ["image-classification", "image-to-image", "image-segmentation"], "tags": ["medical", "code", "biology"]}
2024-02-16T13:13:32+00:00
dbf4d09eed51f6abe91265f0fef519d04ce2c94f
Recag/Rp_CommonC_739_1
[ "region:us" ]
2024-02-16T12:53:41+00:00
{}
2024-02-16T13:08:48+00:00
ba9723ad774efc9d5c886bd3bea64b943800fb05
Recag/Rp_CommonC_739_2
[ "region:us" ]
2024-02-16T12:54:05+00:00
{}
2024-02-16T13:14:21+00:00
e40f12ed78ce518832c92570afeded392cefda25
Recag/Rp_CommonC_739_3
[ "region:us" ]
2024-02-16T12:54:22+00:00
{}
2024-02-16T13:13:58+00:00
fb3949ceaf4d8c0a2cf36eff4afac2057858a032
Drewskidang/trunicatedtextboook
[ "region:us" ]
2024-02-16T13:03:05+00:00
{}
2024-02-16T13:04:23+00:00
56b3b0bce35f99900a359504f3a00a3e572b05c2
The data in this dataset is a subset of 20newsgroups/SciKit dataset: https://scikit-learn.org/0.19/modules/generated/sklearn.datasets.fetch_20newsgroups.html#sklearn.datasets.fetch_20newsgroups --- license: mit dataset_info: pretty_name: 'SciKit newsgroup20 subset' features: - name: index dtype: int64 - name: Text dtype: string - name: Label dtype: int32 - name: Class Name dtype: string task_categories: -text classification -sentence similarity tags: -text classification -sentence similarity splits: - name: train num_bytes: 799164 num_examples: 750 download_size: 477299 dataset_size: 799164 configs: - config_name: default data_files: - split: train path: data/train-* ---
acloudfan/newsgroups-mini
[ "task_categories:text-classification", "task_categories:sentence-similarity", "size_categories:1K<n<10K", "language:en", "license:mit", "20newsgroups", "scikit", "region:us" ]
2024-02-16T13:03:47+00:00
{"language": ["en"], "license": "mit", "size_categories": ["1K<n<10K"], "task_categories": ["text-classification", "sentence-similarity"], "pretty_name": "scikit_20newsgroups", "tags": ["20newsgroups", "scikit"], "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "class", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 493413, "num_examples": 450}], "download_size": 300272, "dataset_size": 493413}}
2024-02-16T14:43:33+00:00
93f1b8a21daca27934a87aac74f92e63148b76d7
# Open Instruct V1 Better Uncensored This is the open-instruct-v1 dataset processed with the Better Uncensored pipeline. About 2.5% of the dataset was removed, a quick review of the removed examples seems to point that is mostly false positives or answers with debatable moralizing content. **No clear refusal was seen in the quick review. The original dataset may be safe for training uncensored models, but if you want to be extra sure you can use this one.** ## Open Instruct V1 - A dataset for having LLMs follow instructions. Open Instruct V1 is an amalgamation of different datasets which are cleaned and then collated into a singular format for training. ### Dataset Breakdown | Dataset | Amount of Samples | |----------------|-------------------| | [Alpaca](https://github.com/tatsu-lab/stanford_alpaca) | 51759 | | [Self Instruct](https://github.com/yizhongw/self-instruct) | 82599 | | [GPT-4 Instruct](https://github.com/teknium1/GPTeacher) | 18194 | | [Code Alpaca](https://huggingface.co/datasets/HuggingFaceH4/CodeAlpaca_20K) | 18019 | | [Dolly](https://huggingface.co/datasets/HuggingFaceH4/databricks_dolly_15k) | 15015 | | [Synthetic](https://huggingface.co/datasets/Dahoas/synthetic-instruct-gptj-pairwise) | 33143 | | [Roleplay](https://github.com/teknium1/GPTeacher) | 3146 | | [asss](https://huggingface.co/datasets/HuggingFaceH4/asss) | 448 | | [instruction-dataset](https://huggingface.co/datasets/HuggingFaceH4/instruction-dataset) | 327 | | Total | 222650 |
betteruncensored/open-instruct-v1
[ "task_categories:text-generation", "size_categories:100K<n<1M", "language:en", "license:apache-2.0", "region:us" ]
2024-02-16T13:03:52+00:00
{"language": ["en"], "license": "apache-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "duplicated_from": "hakurei/open-instruct-v1"}
2024-02-16T13:18:53+00:00
578620411ebed6cdb67ab2b8708893d2e540dffc
Recag/Rp_CommonC_669_1
[ "region:us" ]
2024-02-16T13:05:48+00:00
{}
2024-02-16T13:26:58+00:00
20c8677624f34fb418501526b8cbbe462043976e
Recag/Rp_CommonC_669_2
[ "region:us" ]
2024-02-16T13:05:56+00:00
{}
2024-02-16T13:29:48+00:00
d81ec96c56164d9cb0ce906d3a0bec80a8fe635f
Recag/Rp_CommonC_670_1
[ "region:us" ]
2024-02-16T13:06:27+00:00
{}
2024-02-16T13:28:44+00:00
5b5ea206691809567dbe74e06a2eed5666cea534
Recag/Rp_CommonC_670_2
[ "region:us" ]
2024-02-16T13:06:33+00:00
{}
2024-02-16T13:23:07+00:00
fd17d8d0bfc89cc97ac38b6e029fb69592c94567
Recag/Rp_CommonC_671_1
[ "region:us" ]
2024-02-16T13:06:45+00:00
{}
2024-02-16T13:29:12+00:00
41b0b791a98f3cc76284e80ed6ac61dc6e651dd2
Recag/Rp_CommonC_671_2
[ "region:us" ]
2024-02-16T13:06:52+00:00
{}
2024-02-16T13:25:41+00:00
c2d82c6431e509f3c4b1dce4f4b6b3ab84cf88d2
Recag/Rp_CommonC_672_1
[ "region:us" ]
2024-02-16T13:07:02+00:00
{}
2024-02-16T13:07:02+00:00
1e3c54bc97041d570b3053cd5be0fe481e641a0c
Recag/Rp_CommonC_672_2
[ "region:us" ]
2024-02-16T13:07:13+00:00
{}
2024-02-16T13:28:35+00:00
ceca6decd96cc13fdff607bffbd982c7469050ec
Recag/Rp_CommonC_740_3
[ "region:us" ]
2024-02-16T13:16:08+00:00
{}
2024-02-16T13:22:23+00:00
f7ea01e0030ec2ec1420362bc090d7dd1fcd769b
yashtiwari/PaulMooney-Medical-ASR-Data
[ "region:us" ]
2024-02-16T13:20:36+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "sentence", "dtype": "string"}, {"name": "prompt", "dtype": "string"}, {"name": "speaker_id", "dtype": "int64"}, {"name": "path", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 334850167.0, "num_examples": 381}, {"name": "validation", "num_bytes": 295413087.0, "num_examples": 385}, {"name": "test", "num_bytes": 5300069897.705, "num_examples": 5895}], "download_size": 9155379864, "dataset_size": 5930333151.705}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "validation", "path": "data/validation-*"}, {"split": "test", "path": "data/test-*"}]}]}
2024-02-16T15:01:05+00:00
3eb3bace94d7fd64af67a20fb4a590f1378ff371
Recag/Rp_CommonC_715_1
[ "region:us" ]
2024-02-16T13:22:09+00:00
{}
2024-02-16T13:34:51+00:00
bc0dd5cee7d27aac5b010e6465726e8995783e12
Recag/Rp_CommonC_715_2
[ "region:us" ]
2024-02-16T13:22:30+00:00
{}
2024-02-16T13:22:30+00:00
82b850707e5693f0b881ffafd68409ccad937ba6
Recag/Rp_CommonC_715_3
[ "region:us" ]
2024-02-16T13:22:40+00:00
{}
2024-02-16T13:39:52+00:00
96f1f6c1a8d16d51f2895a1bd21402717b0b0ee9
El-chapoo/1000k
[ "region:us" ]
2024-02-16T13:24:41+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11744999.274651274, "num_examples": 999994}], "download_size": 11147871, "dataset_size": 11744999.274651274}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T13:25:00+00:00
610af91bba2fdba80be010be1f3c30de8d228710
Programmero187/CLIP_FINETUNE_DMU
[ "region:us" ]
2024-02-16T13:25:03+00:00
{}
2024-02-16T13:25:03+00:00
2837df9eda4dca296ba77631db0cc5b6aeda258e
Recag/Rp_CommonC_716_1
[ "region:us" ]
2024-02-16T13:25:30+00:00
{}
2024-02-16T13:45:01+00:00
6f1203d3a39bcc56f22feaaab0280d36850d17f0
Recag/Rp_CommonC_716_2
[ "region:us" ]
2024-02-16T13:25:51+00:00
{}
2024-02-16T13:44:12+00:00
96847cccc982279cb12f8b4857acdc02f82099c8
Recag/Rp_CommonC_716_3
[ "region:us" ]
2024-02-16T13:26:02+00:00
{}
2024-02-16T13:46:47+00:00
cc86b5909e32c133c1752876d2c8b12b615f7d0d
DBC00per/TEST
[ "region:us" ]
2024-02-16T13:26:19+00:00
{}
2024-02-16T13:28:28+00:00
4061018247d82284b8a6c4bcbf053c1282702185
Recag/Rp_CommonC_717_1
[ "region:us" ]
2024-02-16T13:26:20+00:00
{}
2024-02-16T13:51:31+00:00
8b4eaf131a893b4aabb5b82430870a6c49754db4
Recag/Rp_CommonC_717_2
[ "region:us" ]
2024-02-16T13:26:30+00:00
{}
2024-02-16T13:52:22+00:00
1d70644e876f44234cee75b4dc92f4e686466f8d
Recag/Rp_CommonC_717_3
[ "region:us" ]
2024-02-16T13:26:41+00:00
{}
2024-02-16T13:53:59+00:00
aa05e85a99c5e73710b87bdb7e79f0c5b8dbd821
Recag/Rp_CommonC_718_1
[ "region:us" ]
2024-02-16T13:28:15+00:00
{}
2024-02-16T13:59:19+00:00
29c7ae1c4df14763edb23ab55063d8f0efdef960
genvision-ai/test_2024_02_16
[ "region:us" ]
2024-02-16T13:28:24+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "image"}, {"name": "segments_info", "list": [{"name": "area", "dtype": "float64"}, {"name": "bbox", "sequence": "float64"}, {"name": "category_id", "dtype": "int64"}, {"name": "id", "dtype": "int64"}, {"name": "image_id", "dtype": "int64"}, {"name": "iscrowd", "dtype": "int64"}, {"name": "segmentation", "sequence": {"sequence": "float64"}}]}, {"name": "image_name", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1737803.0, "num_examples": 10}], "download_size": 1762134, "dataset_size": 1737803.0}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2024-02-16T13:28:48+00:00
3f751293034c897e72754686f471e75a2279fab4
Recag/Rp_CommonC_718_2
[ "region:us" ]
2024-02-16T13:28:29+00:00
{}
2024-02-16T13:59:08+00:00
6f7adcb203cc42d5ce47648665acf0d987201811
Recag/Rp_CommonC_718_3
[ "region:us" ]
2024-02-16T13:28:42+00:00
{}
2024-02-16T14:02:05+00:00
8bc8f7f497b5ec25f74a7358d80b88aef4105e7d
Recag/Rp_CommonC_719_1
[ "region:us" ]
2024-02-16T13:29:04+00:00
{}
2024-02-16T14:07:09+00:00
d0c5b24c3c535afb0a1543c1bbcfa87ec0d6c6b5
Recag/Rp_CommonC_719_2
[ "region:us" ]
2024-02-16T13:29:13+00:00
{}
2024-02-16T14:07:08+00:00
b087bede8ab3b3c99e8150be14a7526a23afc2b7
Recag/Rp_CommonC_719_3
[ "region:us" ]
2024-02-16T13:29:24+00:00
{}
2024-02-16T14:10:06+00:00
9c58f88efb73f35a364eb8df7151c775e5df68c7
Recag/Rp_CommonC_720_1
[ "region:us" ]
2024-02-16T13:29:47+00:00
{}
2024-02-16T14:14:46+00:00
6e9e5841c27c23fcb85a2228aaf4fda2289a76a4
Recag/Rp_CommonC_720_2
[ "region:us" ]
2024-02-16T13:30:00+00:00
{}
2024-02-16T14:15:15+00:00
900c2468fcc84325a5f7df5bfee2e0e3220ceed0
Recag/Rp_CommonC_720_3
[ "region:us" ]
2024-02-16T13:30:11+00:00
{}
2024-02-16T14:16:51+00:00
f2ddd641a395ff1ca548a6fd328157e5d79acb1a
Recag/Rp_CommonC_673_1
[ "region:us" ]
2024-02-16T13:30:13+00:00
{}
2024-02-16T13:34:33+00:00
f4b3aa401f538e3d0ec42529fba2efd30ec21c71
Recag/Rp_CommonC_721_1
[ "region:us" ]
2024-02-16T13:30:26+00:00
{}
2024-02-16T14:30:38+00:00
e160b75ea94f6968109f23bb758c26915ac9a670
Recag/Rp_CommonC_673_2
[ "region:us" ]
2024-02-16T13:30:30+00:00
{}
2024-02-16T13:36:38+00:00
8ad5f0756e3e3becffc862b20b8d04f5e152f086
dedND/common_voice_corpus_16_1
[ "region:us" ]
2024-02-16T13:30:36+00:00
{}
2024-02-16T13:30:36+00:00
8cfe1dfa473d423a04c082d526e1a421da0d0766
Recag/Rp_CommonC_721_2
[ "region:us" ]
2024-02-16T13:30:37+00:00
{}
2024-02-16T14:32:54+00:00
4546e00913cb108ccc7d78c8534b4a80e64c6a00
Recag/Rp_CommonC_674_1
[ "region:us" ]
2024-02-16T13:30:44+00:00
{}
2024-02-16T13:39:10+00:00
bbb15c2166abdf324813ef0a83d7febc737f24f0
Recag/Rp_CommonC_721_3
[ "region:us" ]
2024-02-16T13:30:48+00:00
{}
2024-02-16T14:32:55+00:00
e018d3e187eb4708469707d94e1940ea53c10049
Recag/Rp_CommonC_674_2
[ "region:us" ]
2024-02-16T13:30:50+00:00
{}
2024-02-16T13:37:32+00:00
d98fd613c8afbba84937e169100fb59ed51cb484
Recag/Rp_CommonC_675_1
[ "region:us" ]
2024-02-16T13:30:59+00:00
{}
2024-02-16T13:40:02+00:00
a2e07bac09b6520297cba7b48a5a156ecc710db1
Recag/Rp_CommonC_675_2
[ "region:us" ]
2024-02-16T13:31:06+00:00
{}
2024-02-16T13:42:05+00:00
af10db55de6f22105e3d123967c764c786f8fcea
Recag/Rp_CommonC_676_1
[ "region:us" ]
2024-02-16T13:31:17+00:00
{}
2024-02-16T13:40:42+00:00
9cee945c4f819fc1216598d51dfba294796e4da2
Recag/Rp_CommonC_676_2
[ "region:us" ]
2024-02-16T13:31:27+00:00
{}
2024-02-16T13:42:05+00:00
602c9b304840ac2951d5c8d83b91db7cdef9bd01
patruff/toxicForMistral
[ "region:us" ]
2024-02-16T13:32:30+00:00
{"dataset_info": {"features": [{"name": "original", "dtype": "string"}, {"name": "chucklebot", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 16314446, "num_examples": 5492}, {"name": "test", "num_bytes": 4091000, "num_examples": 1374}], "download_size": 11348542, "dataset_size": 20405446}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}, {"split": "test", "path": "data/test-*"}]}]}
2024-02-16T13:32:33+00:00
7bbcc723a61c3ec3a59c812360ae0106ac21914f
HoioH/license-plate
[ "region:us" ]
2024-02-16T13:35:20+00:00
{}
2024-02-16T13:59:05+00:00
0bbaa3272dbe059b198dcf120e8e3b7c45c4a005
# Dataset Card for hh-rlhf-megagon-ja This dataset loads data from [megagonlabs/instruction_ja](https://github.com/megagonlabs/instruction_ja). ## Licence MIT License
ryo0634/hh-rlhf-megagon-ja
[ "region:us" ]
2024-02-16T13:39:59+00:00
{}
2024-02-16T13:40:50+00:00
6201315cb45368b2285979497197faf09042a9c3
## Dataset Summary Vietnamese-Medical-QA is a question-answering dataset in the healthcare domain, collected from [edoctor](https://edoctor.io/hoi-dap) and [vinmec](https://www.vinmec.com/vi/tin-tuc/hoi-dap-bac-si/). * Size: After merging data from these two sources, obtained 9335 QA pairs. * Language: Vietnamese ## Load with Datasets ```python from datasets import load_dataset # Load dataset from huggingface qa_dataset = load_dataset("hungnm/vietnamese-medical-qa") # print a QA example print(qa_dataset['train'][0]) ``` ```json { "question": "Chào bác sĩ,\nRăng cháu hiện tại có mủ ở dưới lợi nhưng khi đau cháu sẽ không ngủ được (quá đau). Tuy nhiên chỉ vài ngày là hết mà thỉnh thoảng nó lại bị đau. Chị cháu bảo là trước chị cháu cũng bị như vậy chỉ là đau răng tuổi dậy thì thôi. Bác sĩ cho cháu hỏi đau răng kèm có mủ dưới lợi là bệnh gì? Cháu có cần đi chữa trị không? Cháu cảm ơn.", "answer": "Chào bạn,\nĐể trả lời câu hỏi trên, bác sĩ xin giải đáp như sau:\nRăng bạn hiện tại có mủ dưới lợi gây đau nhức nhiều. Bạn có thể đến phòng khám răng hàm mặt bệnh viện để được thăm khám, chụp phim và tư vấn cho bạn được chính xác\nTrân trọng!" } ``` ## Dataset Version and Update * Version Details: * Current version: 1.0 * Last Update: 16/02/2024 * First Release: 16/02/2024 * Update Plan: We will keep updating as we gather additional data. ## Licensing Information This dataset can be used for any purpose, whether academic or commercial, under the terms of the [Apache 2.0](https://opensource.org/license/apache-2-0) License. ## Citation Information ```bibtex @misc{vietnamese-medical-qa, title={Vietnamese Medical QA: Question Answering dataset for medical in Vietnamese}, author={Hung Nguyen}, year={2024}, } ```
hungnm/vietnamese-medical-qa
[ "task_categories:question-answering", "size_categories:1K<n<10K", "language:vi", "license:apache-2.0", "question answering", "medical", "vietnamese", "region:us" ]
2024-02-16T13:44:46+00:00
{"language": ["vi"], "license": "apache-2.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"], "pretty_name": "b", "dataset_info": {"features": [{"name": "answer", "dtype": "string"}, {"name": "question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 10672406, "num_examples": 9335}], "download_size": 5289086, "dataset_size": 10672406}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}], "tags": ["question answering", "medical", "vietnamese"]}
2024-02-16T14:34:40+00:00
f71d3eb69539dada81918d17c182c99eeb1d126c
# Test dataset with one broken split
severo/test-one-split-broken
[ "region:us" ]
2024-02-16T13:45:34+00:00
{"configs": [{"config_name": "default", "data_files": [{"split": "works", "path": "works.json"}, {"split": "broken", "path": "broken.json"}]}]}
2024-02-16T14:20:39+00:00
a7bad0876d67020c8732d47a4b2575ba5a7a3a45
Recag/Rp_CommonC_677_1
[ "region:us" ]
2024-02-16T13:48:55+00:00
{}
2024-02-16T13:55:05+00:00
72e6cb86cc1febbadb78e77b87fa88dfd1eb953a
Recag/Rp_CommonC_677_2
[ "region:us" ]
2024-02-16T13:49:02+00:00
{}
2024-02-16T13:54:40+00:00
ca1e269975a42debb54a56543bcbed8c3855b829
Recag/Rp_CommonC_678_1
[ "region:us" ]
2024-02-16T13:49:14+00:00
{}
2024-02-16T13:58:56+00:00