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25c812860033246973b5cfe04626d7d2f6811b96
# Dataset Card for "code-search-net-php" ## Dataset Description - **Homepage:** None - **Repository:** https://huggingface.co/datasets/Nan-Do/code-search-net-go - **Paper:** None - **Leaderboard:** None - **Point of Contact:** [@Nan-Do](https://github.com/Nan-Do) ### Dataset Summary This dataset is the Php portion of the CodeSarchNet annotated with a summary column. The code-search-net dataset includes open source functions that include comments found at GitHub. The summary is a short description of what the function does. ### Languages The dataset's comments are in English and the functions are coded in Php ### Data Splits Train, test, validation labels are included in the dataset as a column. ## Dataset Creation May of 2023 ### Curation Rationale This dataset can be used to generate instructional (or many other interesting) datasets that are useful to train LLMs ### Source Data The CodeSearchNet dataset can be found at https://www.kaggle.com/datasets/omduggineni/codesearchnet ### Annotations This datasets include a summary column including a short description of the function. #### Annotation process The annotation procedure was done using [Salesforce](https://huggingface.co/Salesforce) T5 summarization models. A sample notebook of the process can be found at https://github.com/Nan-Do/OpenAssistantInstructionResponsePython The annontations have been cleaned to make sure there are no repetitions and/or meaningless summaries. (some may still be present in the dataset) ### Licensing Information Apache 2.0
Nan-Do/code-search-net-php
[ "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:summarization", "language:en", "license:apache-2.0", "code", "php", "CodeSearchNet", "summary", "region:us" ]
2023-05-14T12:57:10+00:00
{"language": ["en"], "license": "apache-2.0", "task_categories": ["text-generation", "text2text-generation", "summarization"], "pretty_name": "Php CodeSearchNet with Summaries", "dataset_info": {"features": [{"name": "repo", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "func_name", "dtype": "string"}, {"name": "original_string", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "code_tokens", "sequence": "string"}, {"name": "docstring", "dtype": "string"}, {"name": "docstring_tokens", "sequence": "string"}, {"name": "sha", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1735380857, "num_examples": 577190}], "download_size": 526417871, "dataset_size": 1735380857}, "tags": ["code", "php", "CodeSearchNet", "summary"]}
2023-05-15T00:01:40+00:00
ce9b8e062e9dc0ce6df29337bb9e0b610744ef63
https://github.com/dwslab/StArCon ``` @inproceedings{kobbe-etal-2020-unsupervised, title = "Unsupervised stance detection for arguments from consequences", author = "Kobbe, Jonathan and Hulpu{\textcommabelow{s}}, Ioana and Stuckenschmidt, Heiner", booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2020.emnlp-main.4", doi = "10.18653/v1/2020.emnlp-main.4", pages = "50--60", abstract = "Social media platforms have become an essential venue for online deliberation where users discuss arguments, debate, and form opinions. In this paper, we propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. Most related work focuses on topic-specific supervised models that need to be trained for every emergent debate topic. To address this limitation, we propose a topic independent approach that focuses on a frequently encountered class of arguments, specifically, on arguments from consequences. We do this by extracting the effects that claims refer to, and proposing a means for inferring if the effect is a good or bad consequence. Our experiments provide promising results that are comparable to, and in particular regards even outperform BERT. Furthermore, we publish a novel dataset of arguments relating to consequences, annotated with Amazon Mechanical Turk.", } ```
tasksource/starcon
[ "task_categories:text-classification", "language:en", "license:unknown", "region:us" ]
2023-05-14T13:23:36+00:00
{"language": ["en"], "license": "unknown", "task_categories": ["text-classification"]}
2023-05-31T07:37:04+00:00
30b7e2cab8a735ba5a0386da37822e3c6ef9f858
# Dataset Card for "1-sentence-level-gutenberg-en_arxiv_pubmed_soda" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bjoernp/1-sentence-level-gutenberg-en_arxiv_pubmed_soda
[ "region:us" ]
2023-05-14T13:37:34+00:00
{"dataset_info": {"features": [{"name": "sentences", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 28929495509, "num_examples": 231591358}], "download_size": 16845472457, "dataset_size": 28929495509}}
2023-05-14T13:50:31+00:00
70e1d3ab17dba38d2da5d8b0e9ff2f5631e4c050
# Dataset Card for "noto-emoji-vector-512-svg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
darknoon/noto-emoji-vector-512-svg
[ "region:us" ]
2023-05-14T13:44:25+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "codepoints", "sequence": "int64"}, {"name": "name", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "svg_path", "dtype": "string"}, {"name": "svg_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 90176885.81, "num_examples": 2329}], "download_size": 74032133, "dataset_size": 90176885.81}}
2023-05-14T17:11:12+00:00
eed45d49906a7746fa4c6f17f0a938a6e1e3d85f
# Dataset Card for "RestMex2023_review-corpus_DataAugV1" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vg055/RestMex2023_review-corpus_DataAugV1
[ "region:us" ]
2023-05-14T13:51:40+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 121676941, "num_examples": 332823}], "download_size": 74199966, "dataset_size": 121676941}}
2023-05-14T13:51:43+00:00
3d9d883993a1c87cda39a6ec7bdf59d33e2ab15d
# Dataset Card for "CRC-VAL-HE-7K" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
polejowska/CRC-VAL-HE-7K
[ "region:us" ]
2023-05-14T13:52:14+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "ADI", "1": "BACK", "2": "DEB", "3": "LYM", "4": "MUC", "5": "MUS", "6": "NORM", "7": "STR", "8": "TUM"}}}}], "splits": [{"name": "train", "num_bytes": 52647542.52, "num_examples": 7180}], "download_size": 79889267, "dataset_size": 52647542.52}}
2023-05-14T13:54:39+00:00
aa4593f7bb0ae63e2fd6812ee9e821a26848b760
# Dataset Card for "BGDIA704_faces" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
JoffreyMa/BGDIA704_faces
[ "region:us" ]
2023-05-14T14:26:00+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": "int64"}, {"name": "genre", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 942521828.16, "num_examples": 192576}], "download_size": 900725876, "dataset_size": 942521828.16}}
2023-05-15T11:35:49+00:00
a56314ca01063a6964c53d9c08ac222df091654a
# Dataset Card for "code-search-net-ruby" ## Dataset Description - **Homepage:** None - **Repository:** https://huggingface.co/datasets/Nan-Do/code-search-net-go - **Paper:** None - **Leaderboard:** None - **Point of Contact:** [@Nan-Do](https://github.com/Nan-Do) ### Dataset Summary This dataset is the Ruby portion of the CodeSarchNet annotated with a summary column. The code-search-net dataset includes open source functions that include comments found at GitHub. The summary is a short description of what the function does. ### Languages The dataset's comments are in English and the functions are coded in Ruby ### Data Splits Train, test, validation labels are included in the dataset as a column. ## Dataset Creation May of 2023 ### Curation Rationale This dataset can be used to generate instructional (or many other interesting) datasets that are useful to train LLMs ### Source Data The CodeSearchNet dataset can be found at https://www.kaggle.com/datasets/omduggineni/codesearchnet ### Annotations This datasets include a summary column including a short description of the function. #### Annotation process The annotation procedure was done using [Salesforce](https://huggingface.co/Salesforce) T5 summarization models. A sample notebook of the process can be found at https://github.com/Nan-Do/OpenAssistantInstructionResponsePython The annontations have been cleaned to make sure there are no repetitions and/or meaningless summaries. (some may still be present in the dataset) ### Licensing Information Apache 2.0
Nan-Do/code-search-net-ruby
[ "task_categories:text-generation", "task_categories:text2text-generation", "task_categories:summarization", "language:en", "license:apache-2.0", "code", "ruby", "CodeSearchNet", "Summary", "region:us" ]
2023-05-14T14:40:05+00:00
{"language": ["en"], "license": "apache-2.0", "task_categories": ["text-generation", "text2text-generation", "summarization"], "pretty_name": "Ruby CodeSearchNet with Summaries", "dataset_info": {"features": [{"name": "repo", "dtype": "string"}, {"name": "path", "dtype": "string"}, {"name": "func_name", "dtype": "string"}, {"name": "original_string", "dtype": "string"}, {"name": "language", "dtype": "string"}, {"name": "code", "dtype": "string"}, {"name": "code_tokens", "sequence": "string"}, {"name": "docstring", "dtype": "string"}, {"name": "docstring_tokens", "sequence": "string"}, {"name": "sha", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "partition", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 125295534, "num_examples": 53228}], "download_size": 44007267, "dataset_size": 125295534}, "tags": ["code", "ruby", "CodeSearchNet", "Summary"]}
2023-05-14T23:59:38+00:00
77bd885a1dfcfcc240705544c171763a74868ebb
# zh-tw-llm-dev-sample-ta8k-d40d11-only_embeddings-tr_alp-69ed82-c2048 This dataset is a part of the `zh-tw-llm-dev` project. * Tokenizer: `zh-tw-llm-dev-tokenizer-a8k-d40d11` * Built with: `translations`, `alpaca` * Rows: `300` * Max length: `2048` * Full config: ```json {"build_with": ["translations", "alpaca"], "preview_length": 256, "translations_settings": {"source_dataset": "zetavg/coct-en-zh-tw-translations-twp-300k", "lang_1_key": "en", "lang_2_key": "ch", "templates": ["English: {lang_1}\nChinese: {lang_2}", "Chinese: {lang_2}\nEnglish: {lang_1}"], "rows_limit": 100}, "alpaca_settings": {"source_dataset": "zetavg/traditional-chinese-alpaca-en-align", "template": "short", "train_on_inputs": false, "rows_limit": 100}} ```
zh-tw-llm-dv-dv/zh-tw-llm-dev-sample-ta8k-d40d11-only_embeddings-tr_alp-69ed82-c2048
[ "region:us" ]
2023-05-14T14:40:28+00:00
{"dataset_info": {"dataset_size": 454603.0, "download_size": 175001, "features": [{"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}, {"name": "labels", "sequence": "int64"}, {"dtype": "string", "name": "preview"}], "splits": [{"name": "train", "num_bytes": 454603.0, "num_examples": 300}]}}
2023-05-14T14:44:02+00:00
290e46bcd592d8e8ec2f398456fc37d4d0efe116
Sybil-Vane/Test
[ "license:openrail", "region:us" ]
2023-05-14T14:41:01+00:00
{"license": "openrail"}
2023-08-17T20:56:30+00:00
2862e5657e7ce9446bffb39bdda26afdb337bb40
# Images of Parti Prompts for "sd-v2.1" Code that was used to get the results: ```py from diffusers import DiffusionPipeline, DDIMScheduler import torch import PIL pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16, safety_checker=None) pipe.to("cuda") pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) prompt = "" # a parti prompt generator = torch.Generator("cuda").manual_seed(0) image = pipe(prompt, generator=generator, num_inference_steps=100, guidance_scale=7.5).images[0] image = image.resize((256, 256), resample=PIL.Image.Resampling.LANCZOS) ```
diffusers-parti-prompts/sd-v2.1
[ "region:us" ]
2023-05-14T14:48:49+00:00
{"dataset_info": {"features": [{"name": "Prompt", "dtype": "string"}, {"name": "Category", "dtype": "string"}, {"name": "Challenge", "dtype": "string"}, {"name": "Note", "dtype": "string"}, {"name": "images", "dtype": "image"}, {"name": "model_name", "dtype": "string"}, {"name": "seed", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 191652463.0, "num_examples": 1632}], "download_size": 191500777, "dataset_size": 191652463.0}}
2023-05-17T15:51:54+00:00
0882ff3267d9ca675279e0971a9bede3b1057a4f
dembandoye/fakedocuments_version_1
[ "license:unknown", "region:us" ]
2023-05-14T15:01:40+00:00
{"license": "unknown"}
2023-05-14T15:02:31+00:00
a41133002b633d372a72b88f1b03232a50ab5314
# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** [Enhancement to Low Resource Text Classification via Sequential Transfer Learning](#) - **Leaderboard:** - **Point of Contact:** [Neil Riego](mailto:[email protected]) ### 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 A typical data point, comprises of a text and the corresponding label. An example from the YelpReviewFull test set looks as follows: ``` { 'label': pos, 'text': 'Huyyy ang gandaaaaaaaaaaa. Grabe sobrang ganda talaga wala ako masabi. Complete orders pa pinadala sa akin. Buti hindi nabasag kahit walang bubble wrap. Okay na lang din para save mother earth and at least hindi nabasag hehe. Oorder ulit ako ang ganda eh' } ``` ### Data Fields - 'text': The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). - 'label': Corresponds to the score associated with the review (between positive and negative). ### Data Splits The Shopee reviews tl binary dataset is constructed by randomly taking 14000 training samples and 3000 samples for testing and validation for each review star from neg and pos. In total there are 28000 training samples and 6000 each in validation and testing samples. ## 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]
scaredmeow/shopee-reviews-tl-binary
[ "task_categories:text-classification", "size_categories:10K<n<100K", "language:tl", "license:odc-by", "reviews", "shopee", "doi:10.57967/hf/0657", "region:us" ]
2023-05-14T16:14:40+00:00
{"language": ["tl"], "license": "odc-by", "size_categories": ["10K<n<100K"], "task_categories": ["text-classification"], "tags": ["reviews", "shopee"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "negative", "1": "positive"}}}}]}}
2023-05-19T18:44:57+00:00
8589b879e24991eaf51fc9a1875491b0dffb556b
# Images of Parti Prompts for "if-v-1.0" Code that was used to get the results: ```py from diffusers import DiffusionPipeline import torch pipe_low = DiffusionPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", safety_checker=None, watermarker=None, torch_dtype=torch.float16, variant="fp16") pipe_low.enable_model_cpu_offload() pipe_up = DiffusionPipeline.from_pretrained("DeepFloyd/IF-II-L-v1.0", safety_checker=None, watermarker=None, text_encoder=pipe_low.text_encoder, torch_dtype=torch.float16, variant="fp16") pipe_up.enable_model_cpu_offload() prompt = "" # a parti prompt generator = torch.Generator("cuda").manual_seed(0) prompt_embeds, negative_prompt_embeds = pipe_low.encode_prompt(prompt) images = pipe_low(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, num_inference_steps=100, generator=generator, output_type="pt").images images = pipe_up(prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, image=images, num_inference_steps=100, generator=generator).images[0] ```
diffusers-parti-prompts/if-v-1.0
[ "region:us" ]
2023-05-14T16:31:58+00:00
{"dataset_info": {"features": [{"name": "Prompt", "dtype": "string"}, {"name": "Category", "dtype": "string"}, {"name": "Challenge", "dtype": "string"}, {"name": "Note", "dtype": "string"}, {"name": "images", "dtype": "image"}, {"name": "model_name", "dtype": "string"}, {"name": "seed", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 166170790.0, "num_examples": 1632}], "download_size": 166034308, "dataset_size": 166170790.0}}
2023-05-17T15:51:08+00:00
b932998d5592d8166e1c2de3251037e2dac0567b
from datasets import load_dataset dataset = load_dataset("lex_glue", "case_hold")
akorson/AKINTHECHI
[ "license:openrail", "region:us" ]
2023-05-14T17:30:08+00:00
{"license": "openrail"}
2023-05-14T17:32:13+00:00
6ac7a081e11023c59e19bc09f5218236fa497a4d
# Dataset Card for "stamp-verification" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bilal01/stamp-verification
[ "region:us" ]
2023-05-14T17:41:03+00:00
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 1191542422.0, "num_examples": 60}], "download_size": 332235726, "dataset_size": 1191542422.0}}
2023-05-14T17:41:23+00:00
4c3329a4bac705fb08ac0de13ff64a7e92279a64
# Dataset Card for "rest23_sentiment_data_v3_oversampling" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
javilonso/rest23_sentiment_data_v3_oversampling
[ "doi:10.57967/hf/0675", "region:us" ]
2023-05-14T17:41:24+00:00
{"dataset_info": {"features": [{"name": "Title", "dtype": "string"}, {"name": "Review", "dtype": "string"}, {"name": "Polarity", "dtype": "int64"}, {"name": "Country", "dtype": "int64"}, {"name": "Type", "dtype": "int64"}, {"name": "Title_Review", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 246698863.02163294, "num_examples": 287936}, {"name": "test", "num_bytes": 27411079.978367075, "num_examples": 31993}], "download_size": 170968852, "dataset_size": 274109943.0}}
2023-05-14T17:43:10+00:00
fa10a88183df4805e0e74e4961cf4d9e4028de8b
clueless/asvspoof2021
[ "task_categories:feature-extraction", "task_categories:audio-classification", "task_categories:voice-activity-detection", "size_categories:100M<n<1B", "language:en", "audio deepfake", "audio spoof", "voice conversion", "deepfake detection", "spoof detection", "audio classification", "region:us" ]
2023-05-14T18:25:32+00:00
{"language": ["en"], "size_categories": ["100M<n<1B"], "task_categories": ["feature-extraction", "audio-classification", "voice-activity-detection"], "tags": ["audio deepfake", "audio spoof", "voice conversion", "deepfake detection", "spoof detection", "audio classification"]}
2023-05-14T18:30:39+00:00
fd376b0a8fadf1fd8e2a1752870a536cc371ed6f
# Dataset Card for "seq2seq-glue" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
carlosejimenez/seq2seq-glue
[ "region:us" ]
2023-05-14T18:53:58+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "orig_idx", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 190089393, "num_examples": 949098}, {"name": "validation_cola", "num_bytes": 87041, "num_examples": 1043}, {"name": "test_cola", "num_bytes": 86025, "num_examples": 1063}, {"name": "validation_mnli", "num_bytes": 2157948, "num_examples": 9815}, {"name": "validation_mnli_mm", "num_bytes": 2274020, "num_examples": 9832}, {"name": "test_mnli", "num_bytes": 2162126, "num_examples": 9796}, {"name": "test_mnli_mm", "num_bytes": 2265807, "num_examples": 9847}, {"name": "validation_mrpc", "num_bytes": 120267, "num_examples": 408}, {"name": "test_mrpc", "num_bytes": 499335, "num_examples": 1725}, {"name": "validation_qnli", "num_bytes": 1554164, "num_examples": 5463}, {"name": "test_qnli", "num_bytes": 1542446, "num_examples": 5463}, {"name": "validation_qqp", "num_bytes": 7049694, "num_examples": 40430}, {"name": "test_qqp", "num_bytes": 67681991, "num_examples": 390965}, {"name": "validation_rte", "num_bytes": 100393, "num_examples": 277}, {"name": "test_rte", "num_bytes": 1070053, "num_examples": 3000}, {"name": "validation_sst2", "num_bytes": 126308, "num_examples": 872}, {"name": "test_sst2", "num_bytes": 260344, "num_examples": 1821}, {"name": "validation_stsb", "num_bytes": 262564, "num_examples": 1500}, {"name": "test_stsb", "num_bytes": 220997, "num_examples": 1379}], "download_size": 0, "dataset_size": 279610916}}
2023-05-15T02:21:03+00:00
be1ae652c1133b8dc626fbc17a8d8652428b92fd
# Dataset Card for "coco_val2014_tiny" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
henryscheible/coco_val2014_tiny
[ "region:us" ]
2023-05-14T19:03:00+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "captions", "dtype": "string"}], "splits": [{"name": "validation", "num_bytes": 5874023.0, "num_examples": 40}], "download_size": 5861043, "dataset_size": 5874023.0}}
2023-05-14T19:04:08+00:00
279b190d44a6da063c084769a43f72fb0a4dfaa0
Serum/for_sd
[ "license:openrail", "region:us" ]
2023-05-14T19:04:32+00:00
{"license": "openrail"}
2023-09-08T09:39:28+00:00
f30753461b71be5bebe5f7548a53973e7d3d52d1
talanAI/resumesamples
[ "region:us" ]
2023-05-14T19:20:01+00:00
{}
2023-05-15T01:13:33+00:00
3f3c03254005124211302fccee5c5a17e372873d
jurnu/df
[ "language:es", "license:creativeml-openrail-m", "region:us" ]
2023-05-14T19:45:47+00:00
{"language": ["es"], "license": "creativeml-openrail-m"}
2023-05-14T19:46:32+00:00
c154dc84202845f7f9ded1c125d6ea5d8615efc8
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jurnu/f
[ "license:bigscience-openrail-m", "region:us" ]
2023-05-14T19:47:22+00:00
{"license": "bigscience-openrail-m"}
2023-05-14T19:47:41+00:00
c4f6f7a717658253508104cf310b71bb1913e1ef
jurnu/m
[ "language:es", "license:openrail", "region:us" ]
2023-05-14T19:48:11+00:00
{"language": ["es"], "license": "openrail"}
2023-05-14T19:50:35+00:00
03a5e8ae3c7660e6c002891e7eabb6215acfd4fd
jurnu/d
[ "language:es", "license:openrail", "region:us" ]
2023-05-14T19:48:29+00:00
{"language": ["es"], "license": "openrail"}
2023-05-14T19:49:00+00:00
75d1fc7102d6af9c3b84d9bbb4c8cfe7373af92d
# Dataset Card for "VQAv2_testdev_final" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VQAv2_testdev_final
[ "region:us" ]
2023-05-14T19:53:43+00:00
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}], "splits": [{"name": "testdev", "num_bytes": 22095364841.0, "num_examples": 107394}], "download_size": 11622249771, "dataset_size": 22095364841.0}}
2023-05-14T20:04:58+00:00
11e1f0b32cb2f0033a7e5329b08c38017eee7649
# Dataset Card for "index-constituents-sp500" ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Annotations](#annotations) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** https://edarchimbaud.substack.com - **Repository:** https://github.com/edarchimbaud - **Point of Contact:** [email protected] ### Dataset Summary The index-constituents-sp500 dataset provides information about the constituents of the S&P 500 index. It contains several features that describe each constituent company. ### Supported Tasks and Leaderboards [N/A] ### Languages [N/A] ## Dataset Structure ### Data Instances [N/A] ### Data Fields - symbol (string): A string representing the ticker symbol or abbreviation used to identify the company. - security (string): A string specifying the name or title of the security. - gics_sector (string): A string indicating the Global Industry Classification Standard (GICS) sector to which the company belongs. GICS is a widely used classification system for categorizing companies based on their primary business activities. - gics_sub_industry (string): A string specifying the GICS sub-industry of the company, which provides further granularity within the sector classification. - headquarters_location (string): A string representing the location of the company's headquarters. - date_added (string): A string indicating the date when the company was added to the S&P 500 index. - cik (string): A string representing the Central Index Key (CIK) assigned to the company by the United States Securities and Exchange Commission (SEC). The CIK is a unique identifier used for regulatory filings. - founded (string): A string indicating the year or date of the company's founding. ### Data Splits [N/A] ## Dataset Creation ### Curation Rationale The index-constituents-sp500 dataset was developed to support the development of low-frequency trading algorithms. ### Source Data #### Initial Data Collection and Normalization This data was sourced from the web, and aggregated. ### 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 [N/A] ### Other Known Limitations [N/A] ## Additional Information ### Dataset Curators The index-constituents-sp500 dataset was collected by https://edarchimbaud.substack.com. ### Licensing Information The index-constituents-sp500 dataset is licensed under the MIT License. ### Citation Information > https://edarchimbaud.substack.com, index-constituents-sp500 dataset, GitHub repository, https://github.com/edarchimbaud ### Contributions Thanks to [@edarchimbaud](https://github.com/edarchimbaud) for adding this dataset.
edarchimbaud/perimeter-sp500
[ "task_categories:tabular-classification", "language:en", "license:mit", "region:us" ]
2023-05-14T20:03:49+00:00
{"language": ["en"], "license": "mit", "task_categories": ["tabular-classification"], "dataset_info": {"features": [{"name": "symbol", "dtype": "string"}, {"name": "security", "dtype": "string"}, {"name": "gics_sector", "dtype": "string"}, {"name": "gics_sub_industry", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 35469, "num_examples": 503}], "download_size": 0, "dataset_size": 35469}, "configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "data/train-*"}]}]}
2023-11-21T15:00:04+00:00
a422b91869053e300347c4e90a675d975f51d026
# Dataset Card for "VQAv2_testdev" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VQAv2_testdev
[ "region:us" ]
2023-05-14T20:10:07+00:00
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers", "sequence": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "image", "dtype": "image"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "blip_caption_beam_5", "dtype": "string"}], "splits": [{"name": "testdev", "num_bytes": 22099136791.0, "num_examples": 107394}], "download_size": 11623275665, "dataset_size": 22099136791.0}}
2023-05-14T21:46:15+00:00
1a849cec2f31b62aa1f95130ab0f49212c3d429d
# Images of Parti Prompts for "karlo-v1" Code that was used to get the results: ```py from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.from_pretrained("kakaobrain/karlo-v1-alpha", torch_dtype=torch.float16) pipe.to("cuda") prompt = "" # a parti prompt generator = torch.Generator("cuda").manual_seed(0) image = pipe(prompt, prior_num_inference_steps=50, decoder_num_inference_steps=100, generator=generator).images[0] ```
diffusers-parti-prompts/karlo-v1
[ "region:us" ]
2023-05-14T21:06:00+00:00
{"dataset_info": {"features": [{"name": "Prompt", "dtype": "string"}, {"name": "Category", "dtype": "string"}, {"name": "Challenge", "dtype": "string"}, {"name": "Note", "dtype": "string"}, {"name": "images", "dtype": "image"}, {"name": "model_name", "dtype": "string"}, {"name": "seed", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 161180147.0, "num_examples": 1632}], "download_size": 161038543, "dataset_size": 161180147.0}}
2023-05-17T15:49:02+00:00
72dc423b06fe77ecac47c78fb1638ead79924cd8
Zhoucai/RobertFrost
[ "region:us" ]
2023-05-14T21:22:10+00:00
{}
2023-05-14T23:23:18+00:00
2e10e51096cc1e1f65e367c5c9adbdcc3012c5c3
# Dataset Card for "Eval_STELLAR" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Dampish/Eval_STELLAR
[ "region:us" ]
2023-05-14T21:42:37+00:00
{"dataset_info": {"features": [{"name": "instruction", "dtype": "string"}, {"name": "output", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 7175678, "num_examples": 500}], "download_size": 830963, "dataset_size": 7175678}}
2023-05-14T21:42:38+00:00
b63ee8292fff59a3dae4a0170fdeac6379cc3bf0
# Dataset Card for "VQAv2_testdev_no_image" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Multimodal-Fatima/VQAv2_testdev_no_image
[ "region:us" ]
2023-05-14T22:02:56+00:00
{"dataset_info": {"features": [{"name": "question_type", "dtype": "string"}, {"name": "multiple_choice_answer", "dtype": "string"}, {"name": "answers_original", "list": [{"name": "answer", "dtype": "string"}, {"name": "answer_confidence", "dtype": "string"}, {"name": "answer_id", "dtype": "int64"}]}, {"name": "id_image", "dtype": "int64"}, {"name": "answer_type", "dtype": "string"}, {"name": "question_id", "dtype": "int64"}, {"name": "question", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "clip_tags_ViT_L_14", "sequence": "string"}, {"name": "blip_caption", "dtype": "string"}, {"name": "LLM_Description_gpt3_downstream_tasks_visual_genome_ViT_L_14", "sequence": "string"}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float32"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float32"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random", "list": [{"name": "attribute", "dtype": "string"}, {"name": "box", "sequence": "float64"}, {"name": "captions_module", "sequence": "string"}, {"name": "captions_module_filter", "sequence": "string"}, {"name": "label", "dtype": "string"}, {"name": "location", "dtype": "string"}, {"name": "ratio", "dtype": "float64"}, {"name": "size", "dtype": "string"}, {"name": "tag", "dtype": "string"}]}, {"name": "Attributes_ViT_L_14_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full", "sequence": "string"}, {"name": "clip_tags_ViT_L_14_with_openai", "sequence": "string"}, {"name": "clip_tags_LAION_ViT_H_14_2B_with_openai", "sequence": "string"}, {"name": "blip_caption_beam_5", "dtype": "string"}, {"name": "answers", "sequence": "string"}], "splits": [{"name": "testdev", "num_bytes": 5172192426, "num_examples": 107394}], "download_size": 1906506882, "dataset_size": 5172192426}}
2023-05-14T22:17:09+00:00
662271f8371af4a3d0497713a428056310b2572f
# Dataset Card for "VQAv2_testdev_no_image_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2_testdev_no_image_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_1000
[ "region:us" ]
2023-05-14T22:33:45+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_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_", "num_bytes": 94637, "num_examples": 1000}], "download_size": 30344, "dataset_size": 94637}}
2023-05-14T22:33:46+00:00
f7e5502e29d48ce32227c81dae16ef48710d35c4
# Dataset Card for "self-critiquing-base-topic-embeddings" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dmayhem93/self-critiquing-base-topic-embeddings
[ "region:us" ]
2023-05-14T22:38:51+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "time", "dtype": "float64"}, {"name": "labeler", "dtype": "string"}, {"name": "is_topic_based_summarization", "dtype": "bool"}, {"name": "prompt", "dtype": "string"}, {"name": "responses", "sequence": "string"}, {"name": "embedding", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 59346595, "num_examples": 2758}], "download_size": 40171704, "dataset_size": 59346595}}
2023-05-14T22:39:20+00:00
06a22a8da473c0652fef6b88448eea2b497968eb
# Dataset Card for "EVAL_STELLAR2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Dampish/EVAL_STELLAR2
[ "region:us" ]
2023-05-14T22:39:00+00:00
{"dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "input_ids", "sequence": "int32"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 6075678, "num_examples": 500}], "download_size": 809954, "dataset_size": 6075678}}
2023-05-14T22:39:02+00:00
00f145874a0cf3f64ab93db95a807112dac2c1fc
Wizard-LM-Chinese是在MSRA的Wizard-LM数据集上,对指令进行翻译,然后再调用GPT获得答案的数据集 Wizard-LM包含了很多难度超过Alpaca的指令。 中文的问题翻译会有少量指令注入导致翻译失败的情况 中文回答是根据中文问题再进行问询得到的。 我们会陆续将更多数据集发布到hf,包括 - [ ] Coco Caption的中文翻译 - [ ] CoQA的中文翻译 - [ ] CNewSum的Embedding数据 - [ ] 增广的开放QA数据 - [x] WizardLM的中文翻译 如果你也在做这些数据集的筹备,欢迎来联系我们,避免重复花钱。 # 骆驼(Luotuo): 开源中文大语言模型 [https://github.com/LC1332/Luotuo-Chinese-LLM](https://github.com/LC1332/Luotuo-Chinese-LLM) 骆驼(Luotuo)项目是由[冷子昂](https://blairleng.github.io) @ 商汤科技, 陈启源 @ 华中师范大学 以及 李鲁鲁 @ 商汤科技 发起的中文大语言模型开源项目,包含了一系列语言模型。 ( 注意: [陈启源](https://qiyuan-chen.github.io/) 正在寻找2024推免导师,欢迎联系 ) 骆驼项目**不是**商汤科技的官方产品。 ## Citation Please cite the repo if you use the data or code in this repo. ``` @misc{alpaca, author={Ziang Leng, Qiyuan Chen and Cheng Li}, title = {Luotuo: An Instruction-following Chinese Language model, LoRA tuning on LLaMA}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/LC1332/Luotuo-Chinese-LLM}}, } ```
silk-road/Wizard-LM-Chinese-instruct-evol
[ "task_categories:text-generation", "task_categories:question-answering", "size_categories:10K<n<100K", "language:zh", "language:en", "license:cc-by-4.0", "region:us" ]
2023-05-14T23:04:30+00:00
{"language": ["zh", "en"], "license": "cc-by-4.0", "size_categories": ["10K<n<100K"], "task_categories": ["text-generation", "question-answering"]}
2023-05-14T23:13:52+00:00
f345ccd6bc344ad7d946e906770ffc68f3bc7607
# Dataset Card for "self-critiquing-base-selected-900" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
dmayhem93/self-critiquing-base-selected-900
[ "region:us" ]
2023-05-14T23:05:12+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "split", "dtype": "string"}, {"name": "time", "dtype": "float64"}, {"name": "labeler", "dtype": "string"}, {"name": "is_topic_based_summarization", "dtype": "bool"}, {"name": "prompt", "dtype": "string"}, {"name": "responses", "sequence": "string"}, {"name": "embedding", "sequence": "float64"}], "splits": [{"name": "train", "num_bytes": 18994180, "num_examples": 900}], "download_size": 13075326, "dataset_size": 18994180}}
2023-05-14T23:16:15+00:00
b85de0e28274c946acf873daca90211bc444a8c6
# Dataset Card for "rp_1b_tokenized" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
andersonbcdefg/rp_1b_tokenized
[ "region:us" ]
2023-05-14T23:10:35+00:00
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "targets", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 14438953168, "num_examples": 2347034}], "download_size": 4914094094, "dataset_size": 14438953168}}
2023-05-14T23:17:48+00:00
da99929d96655e05b39b8a1964e8814b4ab17980
# Benchmarking Spatial Relationships in Text-to-Image Generation *Tejas Gokhale, Hamid Palangi, Besmira Nushi, Vibhav Vineet, Eric Horvitz, Ece Kamar, Chitta Baral, Yezhou Yang* - We introduce a large-scale challenge dataset SR<sub>2D</sub> that contains sentences describing two objects and the spatial relationship between them. - We introduce a metric called VISOR (short for **V**erify**I**ng **S**patial **O**bject **R**elationships) to quantify spatial reasoning performance. - VISOR and SR<sub>2D</sub> can be used off-the-shelf with any text-to-image model. ## SR<sub>2D</sub> Dataset Our dataset is hosted as [here](https://huggingface.co/datasets/tgokhale/sr2d_visor). This contains 1. The text prompt dataset in `.json` format (`text_spatial_rel_phrases.json`) 2. Images generated using 7 models (GLIDE, CogView2, DALLE-mini, Stable Diffusion, GLIDE + Stable Diffusion + CDM, and Stable Diffusion v2.1) Alternatively, the text prompt dataset can also accessed from [`text_spatial_rel_phrases.json`](https://github.com/microsoft/VISOR/blob/main/text_spatial_rel_phrases.json). It contains all examples from the current version of the dataset (31680 text prompts) accompanied by the corresponding metadata. This dataset can also be generated by running the script `python create_spatial_phrases.py` ## GitHub repository The GitHub repository for [VISOR](https://github.com/microsoft/VISOR/) contains code for generating images with prompts from the SR<sub>2D</sub> dataset and evaluating the generated images using VISOR. ## References Code for text-to-image generation: 1. GLIDE: https://github.com/openai/glide-text2im 2. DALLE-mini: https://github.com/borisdayma/dalle-mini 3. CogView2: https://github.com/THUDM/CogView2 4. Stable Diffusion: https://github.com/CompVis/stable-diffusion 5. Composable Diffusion Models: https://github.com/energy-based-model/Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch 6. OpenAI API for DALLE-2: https://openai.com/api/ ## Citation If you find SR<sub>2D</sub> or VISOR useful in your research, please use the following citation: ``` @article{gokhale2022benchmarking, title={Benchmarking Spatial Relationships in Text-to-Image Generation}, author={Gokhale, Tejas and Palangi, Hamid and Nushi, Besmira and Vineet, Vibhav and Horvitz, Eric and Kamar, Ece and Baral, Chitta and Yang, Yezhou}, journal={arXiv preprint arXiv:2212.10015}, year={2022} } ```
tgokhale/sr2d_visor
[ "license:cc-by-nc-nd-4.0", "region:us" ]
2023-05-15T00:11:31+00:00
{"license": "cc-by-nc-nd-4.0", "viewer": false}
2023-06-12T03:49:57+00:00
d58fd2112e78ce0e9a91924a218cb34fcb33a9b7
# Dataset Card for "VQAv2_testdev_no_image_google_flan_t5_xl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_1000" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2_testdev_no_image_google_flan_t5_xl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_1000
[ "region:us" ]
2023-05-15T01:00:03+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_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_", "num_bytes": 94166, "num_examples": 1000}], "download_size": 30018, "dataset_size": 94166}}
2023-05-15T01:00:05+00:00
9f253c564695f4f6a622c3ccf81711d4b25268f8
dop13/ass
[ "region:us" ]
2023-05-15T01:01:35+00:00
{}
2023-05-15T04:31:07+00:00
b2829b748bb127d9fd5ed6633f1e5e8277523007
# Dataset Card for "clts" [original link](https://github.com/lxj5957/CLTS-Dataset)
Gdot/clts
[ "task_categories:summarization", "language:zh", "region:us" ]
2023-05-15T01:02:26+00:00
{"language": ["zh"], "task_categories": ["summarization"], "dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "summary", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 706157853, "num_examples": 148317}, {"name": "valid", "num_bytes": 97794789, "num_examples": 20393}, {"name": "test", "num_bytes": 78816630, "num_examples": 16687}], "download_size": 593531838, "dataset_size": 882769272}}
2023-05-19T01:14:56+00:00
7a4442345ff735c09fffa21b51a2732c933d7c1c
# Dataset Card for "RestMex2023_review-corpus_DataAugV2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vg055/RestMex2023_review-corpus_DataAugV2
[ "region:us" ]
2023-05-15T01:47:27+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 110309565, "num_examples": 265723}, {"name": "test", "num_bytes": 10317131, "num_examples": 25171}], "download_size": 72437271, "dataset_size": 120626696}}
2023-05-15T01:47:30+00:00
fb51f49114d68435996698378a95474a75867ddf
# Dataset Card for "dominoes" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
adelavega/dominoes
[ "region:us" ]
2023-05-15T01:51:11+00:00
{"dataset_info": {"features": [{"name": "pixel_values", "dtype": "image"}, {"name": "label", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 641551770.0, "num_examples": 763}], "download_size": 58011153, "dataset_size": 641551770.0}}
2023-05-15T01:51:28+00:00
29679f71e3fe75124d85b5e2234f97e39ae63f25
# Dataset Card for "VQAv2_testdev_no_image_google_flan_t5_xl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_107394" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2_testdev_no_image_google_flan_t5_xl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_107394
[ "region:us" ]
2023-05-15T01:52:20+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_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_", "num_bytes": 10089059, "num_examples": 107394}, {"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_", "num_bytes": 10094037, "num_examples": 107394}], "download_size": 6093142, "dataset_size": 20183096}}
2023-05-15T02:42:46+00:00
d31d36e42f80bffc1bc7a0caac1cd271d93e4531
talanAI/jobpostingsamples
[ "region:us" ]
2023-05-15T01:55:35+00:00
{}
2023-05-15T02:00:25+00:00
a2116ebb9bf4f6d32b871311bb48773ae10cd350
# Dataset Card for "legedo-github-issues" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
chenyanjin/legedo-github-issues
[ "region:us" ]
2023-05-15T02:10:02+00:00
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "repository_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "comments_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "user", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "labels", "list": [{"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "color", "dtype": "string"}, {"name": "default", "dtype": "bool"}, {"name": "description", "dtype": "string"}]}, {"name": "state", "dtype": "string"}, {"name": "locked", "dtype": "bool"}, {"name": "assignee", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "assignees", "list": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "milestone", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "labels_url", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "number", "dtype": "int64"}, {"name": "title", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "creator", "struct": [{"name": "login", "dtype": "string"}, {"name": "id", "dtype": "int64"}, {"name": "node_id", "dtype": "string"}, {"name": "avatar_url", "dtype": "string"}, {"name": "gravatar_id", "dtype": "string"}, {"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "followers_url", "dtype": "string"}, {"name": "following_url", "dtype": "string"}, {"name": "gists_url", "dtype": "string"}, {"name": "starred_url", "dtype": "string"}, {"name": "subscriptions_url", "dtype": "string"}, {"name": "organizations_url", "dtype": "string"}, {"name": "repos_url", "dtype": "string"}, {"name": "events_url", "dtype": "string"}, {"name": "received_events_url", "dtype": "string"}, {"name": "type", "dtype": "string"}, {"name": "site_admin", "dtype": "bool"}]}, {"name": "open_issues", "dtype": "int64"}, {"name": "closed_issues", "dtype": "int64"}, {"name": "state", "dtype": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "due_on", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "null"}]}, {"name": "comments", "sequence": "string"}, {"name": "created_at", "dtype": "timestamp[s]"}, {"name": "updated_at", "dtype": "timestamp[s]"}, {"name": "closed_at", "dtype": "timestamp[s]"}, {"name": "author_association", "dtype": "string"}, {"name": "active_lock_reason", "dtype": "string"}, {"name": "body", "dtype": "string"}, {"name": "reactions", "struct": [{"name": "url", "dtype": "string"}, {"name": "total_count", "dtype": "int64"}, {"name": "+1", "dtype": "int64"}, {"name": "-1", "dtype": "int64"}, {"name": "laugh", "dtype": "int64"}, {"name": "hooray", "dtype": "int64"}, {"name": "confused", "dtype": "int64"}, {"name": "heart", "dtype": "int64"}, {"name": "rocket", "dtype": "int64"}, {"name": "eyes", "dtype": "int64"}]}, {"name": "timeline_url", "dtype": "string"}, {"name": "performed_via_github_app", "dtype": "null"}, {"name": "state_reason", "dtype": "string"}, {"name": "draft", "dtype": "bool"}, {"name": "pull_request", "struct": [{"name": "url", "dtype": "string"}, {"name": "html_url", "dtype": "string"}, {"name": "diff_url", "dtype": "string"}, {"name": "patch_url", "dtype": "string"}, {"name": "merged_at", "dtype": "timestamp[s]"}]}, {"name": "is_pull_request", "dtype": "bool"}], "splits": [{"name": "train", "num_bytes": 8768991, "num_examples": 2819}], "download_size": 2134021, "dataset_size": 8768991}}
2023-05-15T02:10:04+00:00
327d328179c407b7928fc14ee089703c352eb484
aquamansam/Shrekfest
[ "license:cc-by-4.0", "region:us" ]
2023-05-15T03:51:00+00:00
{"license": "cc-by-4.0"}
2023-05-15T04:17:51+00:00
a17b34ef5e1c4c3a0650fb8f36b556d18af90930
# Dataset Card for "RestMex2023_unsupervized-corpus_DataAugV2" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
vg055/RestMex2023_unsupervized-corpus_DataAugV2
[ "region:us" ]
2023-05-15T03:52:48+00:00
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 132060636, "num_examples": 354634}], "download_size": 79177356, "dataset_size": 132060636}}
2023-05-15T03:52:53+00:00
43e26923c4710d2242f97f68993679d9a606a2ad
# Dataset Card for Dataset Name ## Dataset Description - **Homepage:** - **Repository:** - **Paper:** - **Leaderboard:** - **Point of Contact:** ### Dataset Summary This dataset is aims for bittensor subnet 1 model. It contains around 3K record and it has group of 3 corresponding questions and answers in jsonl file format. Most of the unicode charecter is filtered out but some are there to add noise in the training data. ## Dataset Creation ### Source Data [https://huggingface.co/datasets/mrseeker87/bittensor_qa/] ## Contact [https://github.com/Kunj-2206]
Defalt-404/Bittensor_relative_QA
[ "task_categories:text-generation", "size_categories:1K<n<10K", "dataset", "bittensor", "gpt4", "prompt", "response", "region:us" ]
2023-05-15T04:54:56+00:00
{"size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "pretty_name": "Bittensor_netuid_1", "tags": ["dataset", "bittensor", "gpt4", "prompt", "response"]}
2023-05-15T05:07:40+00:00
dc34f86f5a397a30d9cc2100f53cfe3613dddd65
MMC4-130k是对MMC4中,抽样了130k左右 simliarty较高的图文pair得到的数据集 我们准备陆续翻译这个子集 我们会陆续将更多数据集发布到hf,包括 - [ ] Coco Caption的中文翻译 - [ ] CoQA的中文翻译 - [ ] CNewSum的Embedding数据 - [ ] 增广的开放QA数据 - [x] WizardLM的中文翻译 如果你也在做这些数据集的筹备,欢迎来联系我们,避免重复花钱。 # 骆驼(Luotuo): 开源中文大语言模型 [https://github.com/LC1332/Luotuo-Chinese-LLM](https://github.com/LC1332/Luotuo-Chinese-LLM) 骆驼(Luotuo)项目是由[冷子昂](https://blairleng.github.io) @ 商汤科技, 陈启源 @ 华中师范大学 以及 李鲁鲁 @ 商汤科技 发起的中文大语言模型开源项目,包含了一系列语言模型。 ( 注意: [陈启源](https://qiyuan-chen.github.io/) 正在寻找2024推免导师,欢迎联系 ) 骆驼项目**不是**商汤科技的官方产品。 ## Citation Please cite the repo if you use the data or code in this repo. ``` @misc{alpaca, author={Ziang Leng, Qiyuan Chen and Cheng Li}, title = {Luotuo: An Instruction-following Chinese Language model, LoRA tuning on LLaMA}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/LC1332/Luotuo-Chinese-LLM}}, } ```
silk-road/MMC4-130k-image-english
[ "task_categories:image-to-text", "task_categories:text-to-image", "size_categories:100K<n<1M", "language:en", "license:odc-by", "region:us" ]
2023-05-15T05:20:57+00:00
{"language": ["en"], "license": "odc-by", "size_categories": ["100K<n<1M"], "task_categories": ["image-to-text", "text-to-image"]}
2023-05-15T05:28:46+00:00
b4522b3bed42bdcfb32a773010d27f7e20334751
zzzzzy/zy
[ "license:openrail", "region:us" ]
2023-05-15T05:42:14+00:00
{"license": "openrail"}
2023-05-15T06:18:21+00:00
8ba5c7014d9fbea1a9205481f8349c7a8fc07393
# Dataset Card for luotuo-QA-A ## Dataset Description - **Homepage:** https://github.com/LC1332/Luotuo-Chinese-LLM - **Repository:** https://github.com/LC1332/Luotuo-QA - **Point of Contact:** [email protected] ### Dataset Summary CoQA(Conversational Question Answering)数据集是一个用于对话式问答任务的大规模数据集,包含超过127,000个问题及其对应的答案。这些文本来自七个不同领域的段落:儿童故事、文学作品、中学和高中英语考试、新闻、维基百科、Reddit和Science。 CoQA数据集经过简单清洗,共有7012个story,我们在此基础上将整个数据集翻译成了中文并进行了增广,其中每个story中包含5个左右的问题,每个问题进行了5次增广。 由于此数据集是我们Luotuo-QA项目的一部分,我们将它叫做luotuo-QA-A,旨在促进对话式问答在中文语境下的研究和应用。 您可以在这里查看Luotuo-QA项目:https://github.com/LC1332/Luotuo-QA 此数据集适用于训练和评估中文对话式问答模型。有益于推动中文自然语言处理领域的发展,同时也为研究人员和开发者提供了一个基准,用于比较不同模型的性能和探索新的方法。 我们希望这一工作能够促进全球范围内中文语境对话式问答任务的研究和进一步的创新。 The CoQA (Conversational Question Answering) dataset is a large-scale dataset for conversational question answering tasks, consisting of over 127,000 questions and their corresponding answers. These texts are derived from passages in seven different domains: children's stories, literature, middle and high school English exams, news, Wikipedia, Reddit, and Science. The CoQA dataset has undergone simple cleaning and consists of 7,012 stories. Building upon this dataset, we have translated the entire collection into Chinese and performed augmentation. Each story contains around 5 questions, and each question has been augmented 5 times. As this dataset is part of our Luotuo-QA project, we name this dataset as luotuo-QA-A. It aims to facilitate research and applications of conversational question answering in the Chinese language context. You can find our Luotuo-QA project here: https://github.com/LC1332/Luotuo-QA This dataset is suitable for training and evaluating Chinese conversational question answering models. It contributes to the advancement of Chinese natural language processing and provides researchers and developers with a benchmark to compare the performance of different models and explore new approaches. We hope that this work will foster research and further innovation in conversational question answering tasks in the Chinese language context on a global scale. ### Languages CHINESE ### Data Instances ``` 文本:长妈妈曾经讲给我一个故事听:先前,有一个读书人住在古庙里用功,晚间, 在院子里纳凉的时候,突然听到有人在叫他。答应着,四面看时,却见一个美女的 脸露在墙头上,向他一笑,隐去了。他很高兴;但竟给那走来夜谈的老和尚识破了 机关。说他脸上有些妖气,一定遇见“美女蛇”了;这是人首蛇身的怪物,能唤人 名,倘一答应,夜间便要来吃这人的肉的。他自然吓得要死,而那老和尚却道无妨 ,给他一个小盒子,说只要放在枕边,便可高枕而卧。他虽然照样办,却总是睡不 着,——当然睡不着的。到半夜,果然来了,沙沙沙!门外象是风雨声。他正抖作 一团时,却听得豁的一声,一道金光从枕边飞出,外面便什么声音也没有了,那金 光也就飞回来,敛在盒子里。后来呢?后来,老和尚说,这是飞蜈蚣,它能吸蛇的 脑髓,美女蛇就被它治死了。 原始问题为:谁遇到了美女蛇? 问题转义为:谁被美女蛇所困扰? 答案为:读书人 问题转义为:美女蛇袭击了谁? 答案为:读书人 原始问题为:谁杀了美女蛇 问题转义为:谁杀死了美女蛇 答案为:飞蜈蚣 ``` ### Licensing Information 我们的协议与CoQA数据集原始协议保持一致,请阅读以下内容。 CoQA数据集包含来自七个领域的段落。我们将其中五个领域的段落以以下许可证公开: 文学和维基百科段落遵循CC BY-SA 4.0许可证共享。 儿童故事选自MCTest,该数据集附带MSR-LA许可证。 中学/高中考试段落选自RACE,该数据集有自己的许可证。 新闻段落选自DeepMind CNN数据集,该数据集有Apache许可证。 Our licenses aligns with the original licenses of the CoQA dataset. Please refer to the following information. CoQA contains passages from seven domains. It make five of these public under the following licenses. We did translation and augmentation on the CoQA dataset. Therefore, the generated part of the data still complies with the original agreement of CoQA: Literature and Wikipedia passages are shared under CC BY-SA 4.0 license. Children's stories are collected from MCTest which comes with MSR-LA license. Middle/High school exam passages are collected from RACE which comes with its own license. News passages are collected from the DeepMind CNN dataset which comes with Apache license. ### Citation Information 如果您在项目中使用了我们的模型、代码或者数据,请引用我们。 Please cite us if you use the data or code in this repo. ```bibtex @misc{alpaca, author={Jianshen Liao, Ao Sun, Qinyu Luo, Hongsen Huang, Cheng Li}, title = {Luotuo-QA: Better Conversational Question Answering Model with Answer Completion}, year = {2023}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/LC1332/Luotuo-QA}}, } ``` ### Contributions Thanks to @XXX, @XXXXXX, @XXXX, @XXXXXX, @XXXXXX, @XXX for adding this dataset.
silk-road/Luotuo-QA-A-CoQA-Chinese
[ "task_categories:question-answering", "size_categories:10K<n<100K", "language:zh", "language:en", "license:other", "region:us" ]
2023-05-15T05:47:04+00:00
{"language": ["zh", "en"], "license": "other", "size_categories": ["10K<n<100K"], "task_categories": ["question-answering"], "extra_gated_prompt": "\u6211\u4eec\u7ffb\u8bd1\u4e86CoQA\u6570\u636e\u96c6\uff0c\u8bf7\u4ed4\u7ec6\u9605\u8bfbLicensing Information\u4e2d\u7684\u4fe1\u606f\u3002", "extra_gated_heading": "\u60a8\u9700\u8981\u63a5\u53d7\u534f\u8bae\u5e76\u63d0\u4ea4\u4fe1\u606f\u4ee5\u83b7\u53d6\u6b64\u6570\u636e\u96c6", "extra_gated_fields": {"\u59d3\u540d": "text", "\u90ae\u7bb1": "text", "\u6240\u5728\u7ec4\u7ec7": "text", "\u4f7f\u7528\u76ee\u7684": "text", "\u6211\u540c\u610f\u4ec5\u5c06\u6b64\u6570\u636e\u96c6\u7528\u4e8e\u975e\u5546\u4e1a\u7528\u9014": "checkbox"}, "extra_gated_button_content": "\u6211\u5df2\u9605\u8bfbLicensing Information\u4e2d\u7684\u4fe1\u606f\u5e76\u540c\u610f\u63d0\u4f9b\u76f8\u5173\u4fe1\u606f"}
2023-05-18T07:53:59+00:00
3bf00c25c9a0479baa59ccf537bb560923b7a0f5
Lxm001/001
[ "region:us" ]
2023-05-15T05:48:55+00:00
{}
2023-05-16T02:22:35+00:00
3303435acf878eee7f8931e6b125ae20d3e4a2ae
# Dataset Card for "VQAv2_testdev_no_image_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_107394" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
CVasNLPExperiments/VQAv2_testdev_no_image_google_flan_t5_xxl_mode_T_A_D_PNP_FILTER_C_Q_rices_ns_107394
[ "region:us" ]
2023-05-15T05:53:16+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_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_", "num_bytes": 10135347, "num_examples": 107394}, {"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full_DETA_detections_deta_swin_large_o365_coco_classes_caption_module_random_", "num_bytes": 10140696, "num_examples": 107394}], "download_size": 6149030, "dataset_size": 20276043}}
2023-05-15T13:22:31+00:00
065227569fce5caf32372047d7baaac83823ae38
xiazeyu/DT_SegNet
[ "license:mit", "region:us" ]
2023-05-15T06:05:29+00:00
{"license": "mit"}
2023-05-15T06:20:43+00:00
3ab3a4a46c8ecd6a37d89a935a6fa4331e655af5
# Dataset Card for "Covid23" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
Sid103/Covid23
[ "region:us" ]
2023-05-15T07:09:13+00:00
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "context", "dtype": "string"}, {"name": "answers", "struct": [{"name": "answer_start", "sequence": "int64"}, {"name": "text", "sequence": "string"}]}, {"name": "question", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 48653509, "num_examples": 1417}, {"name": "test", "num_bytes": 11608421, "num_examples": 375}, {"name": "valid", "num_bytes": 4314598, "num_examples": 203}], "download_size": 2241429, "dataset_size": 64576528}}
2023-05-15T07:09:22+00:00
5c71195e6a5ed9f7f33c9207988c1bf481422fa4
# Dataset Card for "ranking_data" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
hundredeuk2/ranking_data
[ "region:us" ]
2023-05-15T07:16:24+00:00
{"dataset_info": {"features": [{"name": "question", "dtype": "string"}, {"name": "response_j", "dtype": "string"}, {"name": "response_k", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 84003012, "num_examples": 67830}], "download_size": 9031121, "dataset_size": 84003012}}
2023-05-15T07:19:19+00:00
c74e1cd79ac7fdbf2cd725326555b72bdc2d1289
123
shangyou/1
[ "region:us" ]
2023-05-15T07:41:24+00:00
{}
2023-05-15T07:41:41+00:00
bf4f2685b3d82fe7d6842776b9783eaacf944559
# Dataset Card for "shp_with_features_20k_flan_t5_large" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
germank/shp_with_features_20k_flan_t5_large
[ "region:us" ]
2023-05-15T07:43:23+00:00
{"dataset_info": {"features": [{"name": "post_id", "dtype": "string"}, {"name": "domain", "dtype": "string"}, {"name": "upvote_ratio", "dtype": "float64"}, {"name": "history", "dtype": "string"}, {"name": "c_root_id_A", "dtype": "string"}, {"name": "c_root_id_B", "dtype": "string"}, {"name": "created_at_utc_A", "dtype": "int64"}, {"name": "created_at_utc_B", "dtype": "int64"}, {"name": "score_A", "dtype": "int64"}, {"name": "score_B", "dtype": "int64"}, {"name": "human_ref_A", "dtype": "string"}, {"name": "human_ref_B", "dtype": "string"}, {"name": "labels", "dtype": "int64"}, {"name": "seconds_difference", "dtype": "float64"}, {"name": "score_ratio", "dtype": "float64"}, {"name": "helpfulness_A", "dtype": "float64"}, {"name": "helpfulness_B", "dtype": "float64"}, {"name": "specificity_A", "dtype": "float64"}, {"name": "specificity_B", "dtype": "float64"}, {"name": "intent_A", "dtype": "float64"}, {"name": "intent_B", "dtype": "float64"}, {"name": "factuality_A", "dtype": "float64"}, {"name": "factuality_B", "dtype": "float64"}, {"name": "easy-to-understand_A", "dtype": "float64"}, {"name": "easy-to-understand_B", "dtype": "float64"}, {"name": "relevance_A", "dtype": "float64"}, {"name": "relevance_B", "dtype": "float64"}, {"name": "readability_A", "dtype": "float64"}, {"name": "readability_B", "dtype": "float64"}, {"name": "enough-detail_A", "dtype": "float64"}, {"name": "enough-detail_B", "dtype": "float64"}, {"name": "biased:_A", "dtype": "float64"}, {"name": "biased:_B", "dtype": "float64"}, {"name": "fail-to-consider-individual-preferences_A", "dtype": "float64"}, {"name": "fail-to-consider-individual-preferences_B", "dtype": "float64"}, {"name": "repetetive_A", "dtype": "float64"}, {"name": "repetetive_B", "dtype": "float64"}, {"name": "fail-to-consider-context_A", "dtype": "float64"}, {"name": "fail-to-consider-context_B", "dtype": "float64"}, {"name": "too-long_A", "dtype": "float64"}, {"name": "too-long_B", "dtype": "float64"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "log_score_A", "dtype": "float64"}, {"name": "log_score_B", "dtype": "float64"}], "splits": [{"name": "test", "num_bytes": 20659940, "num_examples": 9459}, {"name": "train", "num_bytes": 20707062, "num_examples": 9459}], "download_size": 23927350, "dataset_size": 41367002}}
2023-05-15T07:49:22+00:00
d6806a672d2a7b3774f8297e9daaed2ab67dfba0
# Dataset Card for "metabric" Metabric dataset from pycox package. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jarrydmartinx/metabric
[ "region:us" ]
2023-05-15T07:48:30+00:00
{"dataset_info": {"features": [{"name": "x0", "dtype": "float32"}, {"name": "x1", "dtype": "float32"}, {"name": "x2", "dtype": "float32"}, {"name": "x3", "dtype": "float32"}, {"name": "x4", "dtype": "float32"}, {"name": "x5", "dtype": "float32"}, {"name": "x6", "dtype": "float32"}, {"name": "x7", "dtype": "float32"}, {"name": "x8", "dtype": "float32"}, {"name": "event_time", "dtype": "float32"}, {"name": "event_indicator", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 83776, "num_examples": 1904}], "download_size": 68030, "dataset_size": 83776}}
2023-07-11T23:12:30+00:00
aba1def0bcc81d41e8a8c41c84fad927df8c57ee
# Dataset Card for "support" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jarrydmartinx/support
[ "region:us" ]
2023-05-15T07:53:52+00:00
{"dataset_info": {"features": [{"name": "x0", "dtype": "float32"}, {"name": "x1", "dtype": "float32"}, {"name": "x2", "dtype": "float32"}, {"name": "x3", "dtype": "float32"}, {"name": "x4", "dtype": "float32"}, {"name": "x5", "dtype": "float32"}, {"name": "x6", "dtype": "float32"}, {"name": "x7", "dtype": "float32"}, {"name": "x8", "dtype": "float32"}, {"name": "x9", "dtype": "float32"}, {"name": "x10", "dtype": "float32"}, {"name": "x11", "dtype": "float32"}, {"name": "x12", "dtype": "float32"}, {"name": "x13", "dtype": "float32"}, {"name": "event_time", "dtype": "float32"}, {"name": "event_indicator", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 567872, "num_examples": 8873}], "download_size": 212217, "dataset_size": 567872}}
2023-05-15T07:53:58+00:00
2dd2099abbac2af27f0d909a423d3b67ac257f1b
# Dataset Card for "deepsurv_gbsg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jarrydmartinx/deepsurv_gbsg
[ "region:us" ]
2023-05-15T07:56:39+00:00
{"dataset_info": {"features": [{"name": "x0", "dtype": "float32"}, {"name": "x1", "dtype": "float32"}, {"name": "x2", "dtype": "float32"}, {"name": "x3", "dtype": "float32"}, {"name": "x4", "dtype": "float32"}, {"name": "x5", "dtype": "float32"}, {"name": "x6", "dtype": "float32"}, {"name": "event_time", "dtype": "float32"}, {"name": "event_indicator", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 80352, "num_examples": 2232}], "download_size": 34711, "dataset_size": 80352}}
2023-05-15T07:56:42+00:00
408f4b90f188576b202a4594085a6ab5f0095c4c
Starcoder data subset pretokenized.
junliu44/code_subset
[ "license:cc-by-4.0", "region:us" ]
2023-05-15T08:01:22+00:00
{"license": "cc-by-4.0"}
2023-06-20T12:51:38+00:00
b30741a5626f8973d5ccbca34588e66f37d55b69
# Dataset Card for "nwtco" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
jarrydmartinx/nwtco
[ "region:us" ]
2023-05-15T08:16:13+00:00
{"dataset_info": {"features": [{"name": "stage", "dtype": "int64"}, {"name": "age", "dtype": "float32"}, {"name": "in.subcohort", "dtype": "float32"}, {"name": "instit_2", "dtype": "float32"}, {"name": "histol_2", "dtype": "float32"}, {"name": "study_4", "dtype": "float32"}, {"name": "event_time", "dtype": "float32"}, {"name": "event_indicator", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 161120, "num_examples": 4028}], "download_size": 41178, "dataset_size": 161120}}
2023-05-15T08:16:18+00:00
57f3b2187d901a1c8c395aa042b05ccf0b0eab84
banloada/ban
[ "license:other", "region:us" ]
2023-05-15T08:18:07+00:00
{"license": "other"}
2023-05-15T08:45:08+00:00
8d1d881db9984ec7e092e0248c7e05593efc4c8b
bene-ges/en_cmudict
[ "language:en", "license:bsd-3-clause", "region:us" ]
2023-05-15T08:38:03+00:00
{"language": ["en"], "license": "bsd-3-clause"}
2023-05-31T08:21:54+00:00
7dd9e7b91656ff458353085f8fcd200d66d49edf
catslovedata/testdata1
[ "task_categories:text-generation", "size_categories:1K<n<10K", "language:en", "license:bsd", "legal", "region:us" ]
2023-05-15T08:39:31+00:00
{"language": ["en"], "license": "bsd", "size_categories": ["1K<n<10K"], "task_categories": ["text-generation"], "tags": ["legal"]}
2023-05-15T08:40:17+00:00
2f385260710738968558410d16f6e3d3bdc97309
apsys/WCS
[ "license:openrail", "region:us" ]
2023-05-15T08:56:00+00:00
{"license": "openrail"}
2023-05-15T09:00:04+00:00
08afc54680613fec1d99b2434069f414f374e239
# Dataset Card for "gaps_fr" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
bjoernp/gaps_fr
[ "region:us" ]
2023-05-15T08:59:59+00:00
{"dataset_info": {"features": [{"name": "sentences", "dtype": "string"}, {"name": "sentences_fr", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 60066623080, "num_examples": 231339924}], "download_size": 34555416733, "dataset_size": 60066623080}}
2023-05-15T10:20:31+00:00
05111ddeb8bbae329e65c11ca71d3a8dcc386b2b
jm4n21/CR
[ "license:unknown", "region:us" ]
2023-05-15T09:20:26+00:00
{"license": "unknown"}
2023-05-15T10:33:32+00:00
c21f1bc12250910303f6720b6975de954ec7d414
dgblife/detection_clp
[ "license:other", "region:us" ]
2023-05-15T09:37:35+00:00
{"license": "other"}
2023-05-15T09:37:35+00:00
1e65e97a1c56930e9ce563e505d7d695b7b8cef6
# Dataset Card for "cards20230512" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
davanstrien/cards20230512
[ "region:us" ]
2023-05-15T09:43:50+00:00
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "card", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 264313043, "num_examples": 202911}], "download_size": 78441708, "dataset_size": 264313043}}
2023-05-15T09:43:56+00:00
963a536ef6e06860219d01eefb410a25225fb9ea
# Dataset Card for "metabric" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pytorch-survival/metabric
[ "region:us" ]
2023-05-15T09:49:57+00:00
{"dataset_info": {"features": [{"name": "x0", "dtype": "float32"}, {"name": "x1", "dtype": "float32"}, {"name": "x2", "dtype": "float32"}, {"name": "x3", "dtype": "float32"}, {"name": "x4", "dtype": "float32"}, {"name": "x5", "dtype": "float32"}, {"name": "x6", "dtype": "float32"}, {"name": "x7", "dtype": "float32"}, {"name": "x8", "dtype": "float32"}, {"name": "event_time", "dtype": "float32"}, {"name": "event_indicator", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 83776, "num_examples": 1904}], "download_size": 68030, "dataset_size": 83776}}
2023-05-15T09:50:01+00:00
544797a0f687467f801d046178af4478f60f138c
# Dataset Card for "flchain" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pytorch-survival/flchain
[ "region:us" ]
2023-05-15T09:53:54+00:00
{"dataset_info": {"features": [{"name": "age", "dtype": "float32"}, {"name": "sex", "dtype": "float32"}, {"name": "sample.yr", "dtype": "int64"}, {"name": "kappa", "dtype": "float32"}, {"name": "lambda", "dtype": "float32"}, {"name": "flc.grp", "dtype": "int64"}, {"name": "creatinine", "dtype": "float32"}, {"name": "mgus", "dtype": "float32"}, {"name": "event_time", "dtype": "float32"}, {"name": "event_indicator", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 339248, "num_examples": 6524}], "download_size": 98347, "dataset_size": 339248}}
2023-05-15T09:53:57+00:00
ae7cafd2bf409d3638b01309a505360865fae985
# Dataset Card for "support" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pytorch-survival/support
[ "region:us" ]
2023-05-15T09:54:06+00:00
{"dataset_info": {"features": [{"name": "x0", "dtype": "float32"}, {"name": "x1", "dtype": "float32"}, {"name": "x2", "dtype": "float32"}, {"name": "x3", "dtype": "float32"}, {"name": "x4", "dtype": "float32"}, {"name": "x5", "dtype": "float32"}, {"name": "x6", "dtype": "float32"}, {"name": "x7", "dtype": "float32"}, {"name": "x8", "dtype": "float32"}, {"name": "x9", "dtype": "float32"}, {"name": "x10", "dtype": "float32"}, {"name": "x11", "dtype": "float32"}, {"name": "x12", "dtype": "float32"}, {"name": "x13", "dtype": "float32"}, {"name": "event_time", "dtype": "float32"}, {"name": "event_indicator", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 567872, "num_examples": 8873}], "download_size": 212217, "dataset_size": 567872}}
2023-05-15T09:54:10+00:00
12c544f22295a2d0662b4cc26746525be04acebf
# Dataset Card for "rotterdam_gbsg" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pytorch-survival/rotterdam_gbsg
[ "region:us" ]
2023-05-15T09:54:34+00:00
{"dataset_info": {"features": [{"name": "x0", "dtype": "float32"}, {"name": "x1", "dtype": "float32"}, {"name": "x2", "dtype": "float32"}, {"name": "x3", "dtype": "float32"}, {"name": "x4", "dtype": "float32"}, {"name": "x5", "dtype": "float32"}, {"name": "x6", "dtype": "float32"}, {"name": "event_time", "dtype": "float32"}, {"name": "event_indicator", "dtype": "int32"}], "splits": [{"name": "train", "num_bytes": 80352, "num_examples": 2232}], "download_size": 34711, "dataset_size": 80352}}
2023-05-15T09:54:38+00:00
81c5dcad282eda2c2df6c304f1d9cb45fb7fe312
# Dataset Card for "nwtco" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
pytorch-survival/nwtco
[ "region:us" ]
2023-05-15T09:54:51+00:00
{"dataset_info": {"features": [{"name": "stage", "dtype": "int64"}, {"name": "age", "dtype": "float32"}, {"name": "in.subcohort", "dtype": "float32"}, {"name": "instit_2", "dtype": "float32"}, {"name": "histol_2", "dtype": "float32"}, {"name": "study_4", "dtype": "float32"}, {"name": "event_time", "dtype": "float32"}, {"name": "event_indicator", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 161120, "num_examples": 4028}], "download_size": 41178, "dataset_size": 161120}}
2023-05-15T09:54:55+00:00
16c3eac1f3d0ee5f13aaaef6309b92bbeba8bf8d
Ranjan22/Medium_Articles
[ "license:mit", "region:us" ]
2023-05-15T09:58:41+00:00
{"license": "mit"}
2023-05-15T09:58:41+00:00
d3f7dea5f5019b6a80260806876ca25f2ef4ac76
# Dataset Card for "thermal-dgan" [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
flagship/thermal-dgan
[ "region:us" ]
2023-05-15T10:32:20+00:00
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "label", "dtype": {"class_label": {"names": {"0": "blast", "1": "blb", "2": "healthy", "3": "hispa", "4": "leaf_spot"}}}}], "splits": [{"name": "train", "num_bytes": 33516167.68, "num_examples": 3960}, {"name": "test", "num_bytes": 7604736.0, "num_examples": 990}], "download_size": 37710904, "dataset_size": 41120903.68}}
2023-05-15T10:32:34+00:00
45d4c7436053ec5c48324bf2f55da16a3aa225d8
brycegreened/ye
[ "license:openrail", "region:us" ]
2023-05-15T10:41:52+00:00
{"license": "openrail"}
2023-05-15T10:41:52+00:00
a2df17edff8e3146fc65546a5792df3a5173313c
# Dataset Card for "c840440e" [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/c840440e
[ "region:us" ]
2023-05-15T10:55:10+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 36, "num_examples": 2}], "download_size": 1264, "dataset_size": 36}}
2023-05-15T10:55:13+00:00
262097793b8e584f9be3dc947b5da0b7f8ac7c8f
The dataset used for training the NSFW Detect classification model is divided into five categories: `drawing`, `hentai`, `neutral`, `porn`, and `sexy`, following the format mentioned in [GantMan/nsfw_model](https://github.com/GantMan/nsfw_model) and [yangbisheng2009/nsfw-resnet](https://github.com/yangbisheng2009/nsfw-resnet).
deepghs/nsfw_detect
[ "size_categories:10K<n<100K", "license:mit", "art", "region:us" ]
2023-05-15T10:57:46+00:00
{"license": "mit", "size_categories": ["10K<n<100K"], "tags": ["art"]}
2023-05-15T11:08:47+00:00
db6f6c4d12c578ab42d71ffdaabdd86377a56575
Leejk/121212
[ "license:openrail++", "region:us" ]
2023-05-15T11:09:38+00:00
{"license": "openrail++"}
2023-05-15T11:12:09+00:00
77d90cf574c0779b29e7c01ebf1c5f0fe90c1504
# 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]
Wanghaifeng68/earthquake
[ "region:us" ]
2023-05-15T11:10:27+00:00
{}
2023-05-15T11:24:00+00:00
5462127b089a33412e618819d46ddcdf377689eb
# Dataset Card for "1245832e" [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/1245832e
[ "region:us" ]
2023-05-15T11:15:43+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 36, "num_examples": 2}], "download_size": 1264, "dataset_size": 36}}
2023-05-15T11:15:44+00:00
da5143a073f47e09f2fdcaf3e81be29104872647
# Dataset Card for "d6b9c357" [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/d6b9c357
[ "region:us" ]
2023-05-15T11:19:36+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 38, "num_examples": 2}], "download_size": 1272, "dataset_size": 38}}
2023-05-15T11:19:38+00:00
536134cce29367c10c7db80d0ef68aab6afa7461
# Dataset Card for MDK_taxonomy ## Dataset Description / Summary This dataset was created as part of the [Bertelsmann Foundation](https://www.bertelsmann-stiftung.de/de/startseite) [Musterdatenkatalog]("https://www.bertelsmann-stiftung.de/de/unsere-projekte/smart-country/musterdatenkatalog") project. See the project on GitHub [here](https://github.com/bertelsmannstift/Musterdatenkatalog-V4). The MDK provides an overview of Open Data in municipalities in Germany. This data contains the taxonomy created by and-effect as part of the project. The taxonomy adheres to the SKOS standard and is available as RDF and JSON-LD. There are two levels to the taxonomy: 'Thema' (Topic, first level) and 'Bezeichnung' (Label, second level). The taxonomy contains 25 elements on the first level and 241 elements on the second level. ### Languages German, some information translated to English ## Dataset Structure ### Data Fields Contains information for each concept such as 'skos:prefLabel' (in german and english), 'skos:definition' in german and optionally matches to other concepts. ## Dataset Creation The RDF and JSON-LD file are created with the help of '2023-05-17_MDK_taxonomy_info.csv' which contains all the information about the taxonomy and its concepts. ## Additional Information ### Licensing Information CC0 ### Contributions The taxonomy was based on previous work by the Bertelsmann Stiftung and together with and-effect revised.
and-effect/MDK_taxonomy
[ "size_categories:n<1K", "language:de", "license:cc0-1.0", "region:us" ]
2023-05-15T11:24:02+00:00
{"language": ["de"], "license": "cc0-1.0", "size_categories": ["n<1K"]}
2023-06-07T11:37:44+00:00
f8f0c70e813b131dd8e5881dc30769171738b02d
VSDEV/QOUTES
[ "license:openrail", "region:us" ]
2023-05-15T11:35:52+00:00
{"license": "openrail"}
2023-05-15T11:35:52+00:00
c4a6acab0f72afbf174719337973716ffa306a42
liliy/123456
[ "license:openrail", "region:us" ]
2023-05-15T11:36:36+00:00
{"license": "openrail"}
2023-05-16T03:50:25+00:00
14cae65a23b2d304064f76cd5f810b8d841c5cd4
# Dataset Card for "f8ebda26" [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/f8ebda26
[ "region:us" ]
2023-05-15T11:39:02+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1336, "dataset_size": 178}}
2023-05-15T11:39:03+00:00
0a59302aab4768ffaa54def309dd25582d599764
# Dataset Card for "b5cf07b3" [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/b5cf07b3
[ "region:us" ]
2023-05-15T11:40:16+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1325, "dataset_size": 186}}
2023-05-15T11:40:17+00:00
ec1b3da8cf10cf739bd2ab31cc1a65b24f2675b0
# Dataset Card for "6c06c658" [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/6c06c658
[ "region:us" ]
2023-05-15T11:42:07+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1338, "dataset_size": 182}}
2023-05-15T11:42:08+00:00
db48933550b91a61da1e7efd1ab54845097a0397
# Dataset Card for "7254e21f" [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/7254e21f
[ "region:us" ]
2023-05-15T12:03:39+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 180, "num_examples": 10}], "download_size": 1339, "dataset_size": 180}}
2023-05-15T12:03:40+00:00
6bd1d809107a0ff2d015e66c79ee6657ada0c28b
# Dataset Card for "1085a5b6" [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/1085a5b6
[ "region:us" ]
2023-05-15T12:24:58+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 184, "num_examples": 10}], "download_size": 1336, "dataset_size": 184}}
2023-05-15T12:24:59+00:00
20a3c0a09a78d22bafbc887338d78e3fa89599d8
# Dataset Card for "0840a30b" [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/0840a30b
[ "region:us" ]
2023-05-15T12:25:42+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1342, "dataset_size": 178}}
2023-05-15T12:25:43+00:00
cdd96a396ef6728046330e313de5136d0bd00f78
This dataset is the LLaMini from MBZUAI/LaMini-instruction, removing instances of blatant alignment and removes duplicates. 2290278 instructions remain. i merged the parquets from original repo with parquet2json then ran clean_format_dedupe.py on the resulting jsonl credit to ehardford for his contains_unwanted_words function
ewof/lamini-instruct-unfiltered-deduped
[ "size_categories:1M<n<10M", "region:us" ]
2023-05-15T12:26:14+00:00
{"size_categories": ["1M<n<10M"], "pretty_name": "LaMini Instruct Unfiltered Deduped"}
2023-05-15T14:55:14+00:00
8d96a601235a8191f5363c5bf2ce576a8fe88b39
# データセット概要 手動で作成したDatabricksに関する質問と回答ペアの日本語データセットです。 - 件数:約1,300件 - 情報源:Databricks HPの日本語ブログやFAQなど、データブリック社員がポストしたQitta記事 https://github.com/yulan-yan/build-your-chat-bot-JP デモに利用したデータです。
yulanfmy/databricks-qa-ja
[ "task_categories:question-answering", "size_categories:1K<n<10K", "language:ja", "license:cc-by-sa-3.0", "region:us" ]
2023-05-15T12:27:23+00:00
{"language": ["ja"], "license": "cc-by-sa-3.0", "size_categories": ["1K<n<10K"], "task_categories": ["question-answering"]}
2023-05-15T13:55:06+00:00
b5646dd519083e88c9bf86fdf8d4050c9f4a58bd
# Dataset Card for "d690e2ac" [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/d690e2ac
[ "region:us" ]
2023-05-15T12:35:21+00:00
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 36, "num_examples": 2}], "download_size": 1264, "dataset_size": 36}}
2023-05-15T12:35:24+00:00