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709559b5bb0e9527aec227a1f617d0f7e5ddb971
|
This is data from the AstraZeneca-Sanger Drug Combination Prediction DREAM challenge.
This data is from challenge 1 Pharmacology as described [here](https://www.synapse.org/#!Synapse:syn4231880/wiki/235651)
To download files in this dataset, you must do the following:
1. Register for a Synapse account After registering for an account
2. Accept the terms of conditions and submit request for access [here](https://www.synapse.org/#!Synapse:syn18496666)
```
PS: Project-specific restriction: Data use is limited to use within an approved project
```
4. Create a personal access token and install the Python client.
```
pip install synapseclient
export SYNAPSE_AUTH_TOKEN=<Access Token here>
```
5. Load the dataset using the huggingface datasets Python API
```
from datasets import load_dataset
dataset = load_dataset('SageBio/astrazeneca-sanger-drug-combination-prediction', split='train')
```
|
SageBio/astrazeneca-sanger-drug-combination-prediction
|
[
"license:other",
"region:us"
] |
2023-06-02T05:09:49+00:00
|
{"license": "other"}
|
2023-08-25T00:33:55+00:00
|
082fe2ab0e14ebd6e0e082d641ce6b828248db0f
|
# Dataset Card for "OCR_VnReceipt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
KyS/OCR_VnReceipt
|
[
"region:us"
] |
2023-06-02T05:30:21+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 57394505.5, "num_examples": 6228}], "download_size": 57083510, "dataset_size": 57394505.5}}
|
2023-06-02T05:30:27+00:00
|
52ebf696c516835dc57053714e85e67cb5429abd
|
# Dataset Card for Dataset Name
## Dataset Description
- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### 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]
|
johnnyclee/chats
|
[
"region:us"
] |
2023-06-02T05:46:51+00:00
|
{"configs": [{"config_name": "default", "data_files": [{"split": "train", "path": "**/*.jsonl"}]}]}
|
2023-11-18T12:24:07+00:00
|
819c73e712477014fdf06f2707e726f753ed0d6c
|
# Dataset Card for "train_100"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vvtq/train_100
|
[
"region:us"
] |
2023-06-02T06:11:32+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "pose", "dtype": "image"}, {"name": "image_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 11636199.0, "num_examples": 100}], "download_size": 0, "dataset_size": 11636199.0}}
|
2023-06-02T06:12:40+00:00
|
a7c497721592e45395bb849dd2520d7e130d473a
|
# Dataset Card for "val_2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
vvtq/val_2
|
[
"region:us"
] |
2023-06-02T06:11:36+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "pose", "dtype": "image"}, {"name": "image_caption", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 216544.0, "num_examples": 2}], "download_size": 241613, "dataset_size": 216544.0}}
|
2023-06-02T06:12:43+00:00
|
5ca649ccef611c6d2b453d4b34d25477a63bd9e4
|
Preprocessed from https://huggingface.co/datasets/lorenzoscottb/PLANE-ood/
```python
df=pd.read_json('https://huggingface.co/datasets/lorenzoscottb/PLANE-ood/resolve/main/PLANE_trntst-OoV_inftype-all.json')
f = lambda df: pd.DataFrame(list(zip(*[df[c] for c in df.index])),columns=df.index)
ds=DatasetDict()
for split in ['train','test']:
dfs=pd.concat([f(df[c]) for c in df.columns if split in c.lower()]).reset_index(drop=True)
dfs['label']=dfs['label'].map(lambda x:{1:'entailment',0:'not-entailment'}[x])
ds[split]=Dataset.from_pandas(dfs,preserve_index=False)
ds.push_to_hub('tasksource/PLANE-ood')
```
# PLANE Out-of-Distribution Sets
PLANE (phrase-level adjective-noun entailment) is a benchmark to test models on fine-grained compositional inference.
The current dataset contains five sampled splits, used in the supervised experiments of [Bertolini et al., 22](https://aclanthology.org/2022.coling-1.359/).
### Features
Each entrance has 6 features: `seq, label, Adj_Class, Adj, Nn, Hy`
- `seq`:test sequense
- `label`: ground truth (1:entialment, 0:no-entailment)
- `Adj_Class`: the class of the sequence adjectives
- `Adj`: the adjective of the sequence (I: intersective, S: subsective, O: intensional)
- `N`n: the noun
- `Hy`: the noun's hypericum
Each sample in `seq` can take one of three forms (or inference types, in paper):
- An *Adjective-Noun* is a *Noun* (e.g. A red car is a car)
- An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle)
- An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a red vehicle)
Please note that, as specified in the paper, the ground truth is automatically assigned based on the linguistic rule that governs the interaction between each adjective class and inference type – see the paper for more detail.
### Cite
If you use PLANE for your work, please cite the main COLING 2022 paper.
```
@inproceedings{bertolini-etal-2022-testing,
title = "Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment",
author = "Bertolini, Lorenzo and
Weeds, Julie and
Weir, David",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.359",
pages = "4084--4100",
}
```
|
tasksource/PLANE-ood
|
[
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-2.0",
"region:us"
] |
2023-06-02T06:18:42+00:00
|
{"language": ["en"], "license": "cc-by-2.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-classification"], "dataset_info": {"features": [{"name": "seq", "dtype": "string"}, {"name": "label", "dtype": "string"}, {"name": "Adj_Class", "dtype": "string"}, {"name": "Adj", "dtype": "string"}, {"name": "Nn", "dtype": "string"}, {"name": "Hy", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 26047744, "num_examples": 300132}, {"name": "test", "num_bytes": 874524, "num_examples": 10080}], "download_size": 4721262, "dataset_size": 26922268}}
|
2023-06-02T10:40:29+00:00
|
4f7fa5d487d71ac1543bc7849993b5dfc2e361ac
|
https://github.com/vered1986/lexcomp/tree/master
```
@article{shwartz-dagan-2019-still,
title = "Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition",
author = "Shwartz, Vered and
Dagan, Ido",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q19-1027",
doi = "10.1162/tacl_a_00277",
pages = "403--419",
abstract = "Building meaningful phrase representations is challenging because phrase meanings are not simply the sum of their constituent meanings. Lexical composition can shift the meanings of the constituent words and introduce implicit information. We tested a broad range of textual representations for their capacity to address these issues. We found that, as expected, contextualized word representations perform better than static word embeddings, more so on detecting meaning shift than in recovering implicit information, in which their performance is still far from that of humans. Our evaluation suite, consisting of six tasks related to lexical composition effects, can serve future research aiming to improve representations.",
}
```
|
tasksource/lexcomp-nc-relation
|
[
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-06-02T06:44:55+00:00
|
{"language": ["en"], "license": "apache-2.0"}
|
2023-06-02T06:48:53+00:00
|
00b93176c9f6ce7d28e696ea2fd1b96ef49380a7
|
https://github.com/vered1986/lexcomp/tree/master
```
@article{shwartz-dagan-2019-still,
title = "Still a Pain in the Neck: Evaluating Text Representations on Lexical Composition",
author = "Shwartz, Vered and
Dagan, Ido",
journal = "Transactions of the Association for Computational Linguistics",
volume = "7",
year = "2019",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q19-1027",
doi = "10.1162/tacl_a_00277",
pages = "403--419",
abstract = "Building meaningful phrase representations is challenging because phrase meanings are not simply the sum of their constituent meanings. Lexical composition can shift the meanings of the constituent words and introduce implicit information. We tested a broad range of textual representations for their capacity to address these issues. We found that, as expected, contextualized word representations perform better than static word embeddings, more so on detecting meaning shift than in recovering implicit information, in which their performance is still far from that of humans. Our evaluation suite, consisting of six tasks related to lexical composition effects, can serve future research aiming to improve representations.",
}
```
|
tasksource/lexcomp-nc-attributes
|
[
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-06-02T06:47:06+00:00
|
{"language": ["en"], "license": "apache-2.0"}
|
2023-06-02T06:49:17+00:00
|
3280babe5feb19b4821656ddf1d84edae5bba600
|
BNNT/PatentMatch
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-02T07:32:15+00:00
|
{"license": "apache-2.0"}
|
2023-06-02T07:32:56+00:00
|
|
8c4b83b767dc03547d2223e9640e412432f9348e
|
# Dataset Card for "c4_t5_corrupted_seqlen256"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hlillemark/c4_t5_corrupted_seqlen256
|
[
"region:us"
] |
2023-06-02T07:35:37+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}, {"name": "labels", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 971946454688, "num_examples": 649696828}, {"name": "validation", "num_bytes": 968407176, "num_examples": 647331}], "download_size": 441279977822, "dataset_size": 972914861864}}
|
2023-06-05T00:50:13+00:00
|
71b74c75ea6dfeff12ff8857736094493f233cc3
|
# Dataset Card for "stock_tweets_sentiment"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
emad12/stock_tweets_sentiment
|
[
"region:us"
] |
2023-06-02T08:10:31+00:00
|
{"dataset_info": {"features": [{"name": "Unnamed: 0", "dtype": "int64"}, {"name": "post_date", "dtype": "string"}, {"name": "tweet", "dtype": "string"}, {"name": "sentiment", "dtype": "int64"}, {"name": "ticker_symbol", "dtype": "string"}, {"name": "tweet_cleaned", "dtype": "string"}, {"name": "__index_level_0__", "dtype": "int64"}, {"name": "input_ids", "sequence": "int32"}, {"name": "token_type_ids", "sequence": "int8"}, {"name": "attention_mask", "sequence": "int8"}], "splits": [{"name": "train", "num_bytes": 321710487, "num_examples": 96000}, {"name": "test", "num_bytes": 80421371, "num_examples": 24000}], "download_size": 32053237, "dataset_size": 402131858}}
|
2023-06-04T08:48:20+00:00
|
10f90fa12c5b05904d39a4cf6f8eb7da85fca573
|
# Dataset Card for copa_th
### Dataset Description
This dataset is Thai translated version of [copa](https://huggingface.co/datasets/super_glue/viewer/copa) using google translate with [Multilingual Universal Sentence Encoder](https://arxiv.org/abs/1907.04307) to calculate score for Thai translation.
### Languages
- EN
- TH
|
Patt/copa_th
|
[
"language:th",
"language:en",
"license:cc-by-sa-4.0",
"arxiv:1907.04307",
"region:us"
] |
2023-06-02T08:43:18+00:00
|
{"language": ["th", "en"], "license": "cc-by-sa-4.0"}
|
2024-01-15T17:28:02+00:00
|
b363c895cd4b15b814b9dbd7e4466cd301c96b2a
|
[tasksource](https://github.com/sileod/tasksource) classification tasks recasted as natural language inference.
This dataset is intended to improve label understanding in [zero-shot classification HF pipelines](https://huggingface.co/docs/transformers/main/main_classes/pipelines#transformers.ZeroShotClassificationPipeline
).
Inputs that are text pairs are separated by a newline (\n).
```python
from transformers import pipeline
classifier = pipeline(model="sileod/deberta-v3-base-tasksource-nli")
classifier(
"I have a problem with my iphone that needs to be resolved asap!!",
candidate_labels=["urgent", "not urgent", "phone", "tablet", "computer"],
)
```
[deberta-v3-base-tasksource-nli](https://huggingface.co/sileod/deberta-v3-base-tasksource-nli) now includes `label-nli` in its training mix (a relatively small portion, to keep the model general, but note that nli models work for label-like zero shot classification without specific supervision (https://aclanthology.org/D19-1404.pdf).
```
@article{sileo2023tasksource,
title={tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation},
author={Sileo, Damien},
year={2023}
}
```
|
tasksource/zero-shot-label-nli
|
[
"task_categories:zero-shot-classification",
"task_categories:text-classification",
"task_ids:natural-language-inference",
"language:en",
"license:other",
"region:us"
] |
2023-06-02T10:33:57+00:00
|
{"language": ["en"], "license": "other", "task_categories": ["zero-shot-classification", "text-classification"], "task_ids": ["natural-language-inference"], "dataset_info": {"features": [{"name": "labels", "dtype": {"class_label": {"names": {"0": "entailment", "1": "neutral", "2": "contradiction"}}}}, {"name": "premise", "dtype": "string"}, {"name": "hypothesis", "dtype": "string"}, {"name": "task", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 551417533, "num_examples": 1090333}, {"name": "validation", "num_bytes": 10825569, "num_examples": 14419}, {"name": "test", "num_bytes": 9738922, "num_examples": 14680}], "download_size": 302498339, "dataset_size": 571982024}}
|
2023-06-23T13:48:53+00:00
|
64e26e08c13aa09081cfaab8ba3bd6d2b201ff19
|
Ricecake123/silcer
|
[
"license:mit",
"region:us"
] |
2023-06-02T10:37:25+00:00
|
{"license": "mit"}
|
2023-06-02T10:37:39+00:00
|
|
c23f94b0fb465d827365c1b76121ded49a549410
|
## Guidelines
These guidelines are based on the paper [Training Language Models to Follow Instructions with Human Feedback](https://cdn.openai.com/papers/Training_language_models_to_follow_instructions_with_human_feedback.pdf)
**You are given a text-based description of a task, submitted by a user.**
This task description may be in the form of an explicit instruction (e.g. "Write a story about a wise frog."). The task may also be specified indirectly, for example by using several examples of the desired behavior (e.g. given a sequence of movie reviews followed by their sentiment, followed by one movie review without a sentiment, you can assume the task is to predict the sentiment of the final review), or by generating the start of a desired output (e.g. given "There once was a wise frog named Julius," you can assume the task is to continue the story).
**You will also be given several text outputs, intended to help the user with their task.** Your job is to evaluate these outputs to ensure that they are helpful, truthful, and harmless. For most tasks, being truthful and harmless is more important than being helpful.
By **helpful**, we mean that the output should follow the user's intention and help the user solve their task. Some examples of helpful behavior:
- Writing in clear language.
- Answering the question they meant to ask, even if they mis-asked it.
- Being sensitive to internationality (e.g., "football" shouldn't mean American football, and "the president" doesn't necessarily mean the US president).
- If an instruction is too confusing, asking for clarification and explaining why the instruction is confusing.
- Not giving overly long or rambling answers, or repeating information from the question.
- Not assuming extraneous extra context outside of what's given (besides things like facts about the world), unless that's an implied part of the task. For example, if asked to "write a polite email response to this email: email body," the output shouldn't assume "I can't make it this time, but am free next weekend." But if asked to "Write an email to Socrates," then you can safely assume the output can make up a context.
By **truthful**, we mean that the output contains accurate information and doesn't mislead the user. Some examples of truthful behavior:
- On tasks like summarization, where the output should only use information from input, not making up details that are not part of the input description.
- Not producing clearly false information about the world (e.g., making up facts or promoting conspiracies). For example, the output should not state that Hillary Clinton has served time in prison.
- Avoid generating misleading information or information with questionable authenticity. For example, if the input asks "Why did Hillary Clinton go to jail?", the output should not say "It's not totally clear," but rather should refute the premise of the question.
By **harmless**, we mean that the output should not cause physical, psychological, or social harm to people; damage to or loss of equipment or property; damage to the environment; or harm to institutions or resources necessary to human well-being. Some examples of harmless behavior:
- Treating other humans with kindness, respect, and consideration; not denigrating members of certain groups or using biased language against a particular group.
- Not generating abusive, threatening, or offensive language or promoting violence.
- Not writing sexual or violent content if it's not asked for.
- Not giving bad real-world advice or promoting illegal activity.
Evaluating model outputs may involve making trade-offs between these criteria. These trade-offs will depend on the task.
## Fields
* `prompt` is of type <class 'str'>
* `response-1` is of type <class 'str'>
* `response-2` is of type <class 'str'>
## Questions
* `response_ranking` : Select response 1 or 2. Select 3 if no response is suitable.
\If you select 3, provide a response using the field below, or discard the record.
Helpful: output follows the user's intention.
Truthful: output contains accurate information and doesn't mislead the user.
Harmless: the output should not cause physical, psychological, or social harm to people, property, environment, or institutions
## Load with Argilla
To load this dataset with Argilla, you'll just need to install Argilla as `pip install argilla --upgrade` and then use the following code:
```python
import argilla as rg
ds = rg.FeedbackDataset.from_huggingface('argilla/comparison-dataset-dolly-curated-falcon')
```
## Load with Datasets
To load this dataset with Datasets, you'll just need to install Datasets as `pip install datasets --upgrade` and then use the following code:
```python
from datasets import load_dataset
ds = load_dataset('argilla/comparison-dataset-dolly-curated-falcon')
```
|
argilla/comparison-dataset-dolly-curated-falcon
|
[
"region:us"
] |
2023-06-02T10:51:48+00:00
|
{}
|
2023-06-02T13:52:40+00:00
|
1685832883334b5bb5beaf4e4b333fdeecaa4ad9
|
# Dataset Card for PathVQA
## Dataset Description
PathVQA is a dataset of question-answer pairs on pathology images. The dataset is intended to be used for training and testing
Medical Visual Question Answering (VQA) systems. The dataset includes both open-ended questions and binary "yes/no" questions.
The dataset is built from two publicly-available pathology textbooks: "Textbook of Pathology" and "Basic Pathology", and a
publicly-available digital library: "Pathology Education Informational Resource" (PEIR). The copyrights of images and captions
belong to the publishers and authors of these two books, and the owners of the PEIR digital library.<br>
**Repository:** [PathVQA Official GitHub Repository](https://github.com/UCSD-AI4H/PathVQA)<br>
**Paper:** [PathVQA: 30000+ Questions for Medical Visual Question Answering](https://arxiv.org/abs/2003.10286)<br>
**Leaderboard:** [Papers with Code Leaderboard](https://paperswithcode.com/sota/medical-visual-question-answering-on-pathvqa)
### Dataset Summary
The dataset was obtained from the updated Google Drive link shared by the authors on Feb 15, 2023,
see the [commit](https://github.com/UCSD-AI4H/PathVQA/commit/117e7f4ef88a0e65b0e7f37b98a73d6237a3ceab)
in the GitHub repository. This version of the dataset contains a total of 5,004 images and 32,795 question-answer pairs.
Out of the 5,004 images, 4,289 images are referenced by a question-answer pair, while 715 images are not used.
There are a few image-question-answer triplets which occur more than once in the same split (training, validation, test).
After dropping the duplicate image-question-answer triplets, the dataset contains 32,632 question-answer pairs on 4,289 images.
#### Supported Tasks and Leaderboards
The PathVQA dataset has an active leaderboard on [Papers with Code](https://paperswithcode.com/sota/medical-visual-question-answering-on-pathvqa)
where models are ranked based on three metrics: "Yes/No Accuracy", "Free-form accuracy" and "Overall accuracy". "Yes/No Accuracy" is
the accuracy of a model's generated answers for the subset of binary "yes/no" questions. "Free-form accuracy" is the accuracy
of a model's generated answers for the subset of open-ended questions. "Overall accuracy" is the accuracy of a model's generated
answers across all questions.
#### Languages
The question-answer pairs are in English.
## Dataset Structure
### Data Instances
Each instance consists of an image-question-answer triplet.
```
{
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=CMYK size=309x272>,
'question': 'where are liver stem cells (oval cells) located?',
'answer': 'in the canals of hering'
}
```
### Data Fields
- `'image'`: the image referenced by the question-answer pair.
- `'question'`: the question about the image.
- `'answer'`: the expected answer.
### Data Splits
The dataset is split into training, validation and test. The split is provided directly by the authors.
| | Training Set | Validation Set | Test Set |
|-------------------------|:------------:|:--------------:|:--------:|
| QAs |19,654 |6,259 |6,719 |
| Images |2,599 |832 |858 |
## Additional Information
### Licensing Information
The authors have released the dataset under the [MIT License](https://github.com/UCSD-AI4H/PathVQA/blob/master/LICENSE).
### Citation Information
```
@article{he2020pathvqa,
title={PathVQA: 30000+ Questions for Medical Visual Question Answering},
author={He, Xuehai and Zhang, Yichen and Mou, Luntian and Xing, Eric and Xie, Pengtao},
journal={arXiv preprint arXiv:2003.10286},
year={2020}
}
```
|
flaviagiammarino/path-vqa
|
[
"task_categories:visual-question-answering",
"size_categories:10K<n<100K",
"language:en",
"license:mit",
"medical",
"arxiv:2003.10286",
"region:us"
] |
2023-06-02T11:03:51+00:00
|
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "task_categories": ["visual-question-answering"], "paperswithcode_id": "pathvqa", "pretty_name": "PathVQA", "tags": ["medical"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3171303616.326, "num_examples": 19654}, {"name": "test", "num_bytes": 1113474813.05, "num_examples": 6719}, {"name": "validation", "num_bytes": 1191658832.096, "num_examples": 6259}], "download_size": 785414952, "dataset_size": 5476437261.472}}
|
2023-06-03T18:02:04+00:00
|
4ceb1312583fd2c7c73ad2d550b726124dcd39a0
|
hamedhf/nlp_twitter_analysis
|
[
"task_categories:text-classification",
"language:fa",
"language:en",
"license:mit",
"region:us"
] |
2023-06-02T11:16:19+00:00
|
{"language": ["fa", "en"], "license": "mit", "task_categories": ["text-classification"]}
|
2023-07-11T22:18:03+00:00
|
|
91141b8898bef7155685f1c8a72a14d4fc623c81
|
# Dataset Card for "rettsavgjoerelser_summary_cleaned_sentencized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tollefj/rettsavgjoerelser_summary_cleaned_sentencized
|
[
"region:us"
] |
2023-06-02T11:30:06+00:00
|
{"dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "keywords", "sequence": "string"}, {"name": "text", "dtype": "string"}, {"name": "sentences", "sequence": "string"}, {"name": "summary", "sequence": "string"}], "splits": [{"name": "test", "num_bytes": 21456698, "num_examples": 364}, {"name": "train", "num_bytes": 400752769, "num_examples": 6673}], "download_size": 210718133, "dataset_size": 422209467}}
|
2023-06-02T18:01:13+00:00
|
0090b53958a4c89ccc825859754535ff56a3f05f
|
# Dataset Card for "rettsavgjoerelser_100samples_embeddings"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
tollefj/rettsavgjoerelser_100samples_embeddings
|
[
"language:no",
"region:us"
] |
2023-06-02T11:46:28+00:00
|
{"language": ["no"], "dataset_info": {"features": [{"name": "url", "dtype": "string"}, {"name": "keywords", "sequence": "string"}, {"name": "text", "dtype": "string"}, {"name": "sentences", "sequence": "string"}, {"name": "summary", "sequence": "string"}, {"name": "embedding", "sequence": {"sequence": "float32"}}], "splits": [{"name": "train", "num_bytes": 73887305, "num_examples": 100}], "download_size": 71145367, "dataset_size": 73887305}}
|
2023-08-11T09:45:31+00:00
|
36f98629d6a05f48346cdd892ec37acde387475c
|
# Dataset Card for "donald"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
UnstableJeje/donald
|
[
"region:us"
] |
2023-06-02T12:05:22+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 184278.0, "num_examples": 21}], "download_size": 185347, "dataset_size": 184278.0}}
|
2023-06-02T12:49:30+00:00
|
e76093058a1874788c5f7cb20150c2ba6ea0944a
|
# Dataset Card for "ddpm-butterflies-128"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
gegre/ddpm-butterflies-128
|
[
"region:us"
] |
2023-06-02T12:07:24+00:00
|
{"dataset_info": {"features": [{"name": "image_url", "dtype": "string"}, {"name": "image_alt", "dtype": "string"}, {"name": "id", "dtype": "string"}, {"name": "name", "dtype": "string"}, {"name": "scientific_name", "dtype": "string"}, {"name": "gender", "dtype": "string"}, {"name": "taxonomy", "dtype": "string"}, {"name": "region", "dtype": "string"}, {"name": "locality", "dtype": "string"}, {"name": "date", "dtype": "string"}, {"name": "usnm_no", "dtype": "string"}, {"name": "guid", "dtype": "string"}, {"name": "edan_url", "dtype": "string"}, {"name": "source", "dtype": "string"}, {"name": "stage", "dtype": "float64"}, {"name": "image", "dtype": "image"}, {"name": "image_hash", "dtype": "string"}, {"name": "sim_score", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 237753960.0, "num_examples": 1000}], "download_size": 237448562, "dataset_size": 237753960.0}}
|
2023-06-02T12:07:34+00:00
|
42f1d92664373e4f0bf199fb536c3033b82c9013
|
## CRAN packages dataset
R and Rmd source codes for CRAN packages.
The dataset has been constructed using the following steps:
- Downloaded latest version from all packages on CRAN (see last updated). The source code has been downloaded from the [GitHub mirror](https://github.com/cran).
- Identified the licenses from each package from their DESCRIPTION file, and classified each of them into some license_code. See the licenses.csv file.
- Extract R and Rmd source files from all packages and joined with the package LICENSES.
Datasets are provided as parquet files containing the following columns:
```
FileSystemDataset with 1 Parquet file
package: string
path: string
content: large_string
size: double
license: string
```
Last updated: Jun 6th 2023
## Changelog
- v1: Initial version
- dev: added all CRAN files and a license field that allows filtering out per license. Also removed some unused columns.
|
dfalbel/cran-packages
|
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:code",
"license:other",
"region:us"
] |
2023-06-02T12:14:33+00:00
|
{"language": ["code"], "license": "other", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "pretty_name": "cran-packages"}
|
2023-07-11T10:01:33+00:00
|
48d7021b426f1cfd5c2678c15d9aaa70ecd5fb69
|
maguarascio/Jagg
|
[
"region:us"
] |
2023-06-02T12:15:25+00:00
|
{}
|
2023-06-02T12:16:25+00:00
|
|
8dee3e37c842d83bbc20657eab2b134dd15a78ed
|
ChanceFocus/FLUPE
|
[
"license:mit",
"region:us"
] |
2023-06-02T12:26:51+00:00
|
{"license": "mit"}
|
2023-06-02T12:27:56+00:00
|
|
cfdd505d900b8b55540f852bbe1b0159e1f9fb26
|
karansajeeth/dolly-data
|
[
"license:wtfpl",
"region:us"
] |
2023-06-02T12:39:27+00:00
|
{"license": "wtfpl"}
|
2023-06-02T12:39:54+00:00
|
|
8129cb9dbf59aac8b143e94ad9e524b166322c44
|
to-be/ghega_dataset_preprocessed
|
[
"license:openrail",
"region:us"
] |
2023-06-02T12:55:06+00:00
|
{"license": "openrail"}
|
2023-06-02T12:55:58+00:00
|
|
47932a35f045ef8ed01ba82bf9ff67f6e109207e
|
Part of **BEIR-PL: Zero Shot Information Retrieval Benchmark for the Polish Language**.
Link to arxiv: https://arxiv.org/pdf/2305.19840.pdf
Contact: [email protected]
|
clarin-knext/scifact-pl
|
[
"language:pl",
"arxiv:2305.19840",
"region:us"
] |
2023-06-02T12:55:34+00:00
|
{"language": ["pl"], "pretty_name": "BEIR-PL benchmark Scifact-PL"}
|
2023-06-07T09:07:12+00:00
|
38937109bb4dc7067f575fe6e7b420158eb9cf32
|
datasets-maintainers/audiofolder_two_configs_in_metadata_with_default
|
[
"region:us"
] |
2023-06-02T13:04:31+00:00
|
{"configs": [{"config_name": "v1", "data_dir": "v1", "drop_labels": true, "default": true}, {"config_name": "v2", "data_dir": "v2", "drop_labels": false}], "duplicated_from": "datasets-maintainers/audiofolder_two_configs_in_metadata"}
|
2023-06-02T18:10:37+00:00
|
|
13a2822ab976be5641209dd4ac9d1c47a273b712
|
Publically available yankee candle reviews with ratings and dates from Amazon, for project comparing reviews to current covid cases.
|
jessthebp/yankee_candle_reviews
|
[
"size_categories:n<1K",
"license:mit",
"region:us"
] |
2023-06-02T13:31:11+00:00
|
{"license": "mit", "size_categories": ["n<1K"]}
|
2023-06-02T13:38:17+00:00
|
a9334a0bf982b6255a03d58d63904935969724cd
|
AhmedBou/NCSS_2023_Data_Analysis
|
[
"task_categories:token-classification",
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"region:us"
] |
2023-06-02T13:39:28+00:00
|
{"language": ["en"], "license": "apache-2.0", "size_categories": ["n<1K"], "task_categories": ["token-classification", "text-generation"]}
|
2023-07-21T14:52:01+00:00
|
|
99d1b25627a0b38202d5744c0c71baeef7b648e2
|
https://github.com/PlusLabNLP/Com2Sense
```
@inproceedings{singh-etal-2021-com2sense,
title = "{COM}2{SENSE}: A Commonsense Reasoning Benchmark with Complementary Sentences",
author = "Singh, Shikhar and
Wen, Nuan and
Hou, Yu and
Alipoormolabashi, Pegah and
Wu, Te-lin and
Ma, Xuezhe and
Peng, Nanyun",
booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-acl.78",
doi = "10.18653/v1/2021.findings-acl.78",
pages = "883--898",
}
```
|
tasksource/com2sense
|
[
"language:en",
"commonsense",
"region:us"
] |
2023-06-02T13:47:54+00:00
|
{"language": ["en"], "tags": ["commonsense"]}
|
2023-06-05T09:09:30+00:00
|
3afb16ecdea96247453eb6a1ae7f75fa3115f92b
|
# Dataset Card for "prof_images_blip__22h-vintedois-diffusion-v0-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_images_blip__22h-vintedois-diffusion-v0-1
|
[
"region:us"
] |
2023-06-02T13:56:30+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "bartender", "num_bytes": 4221598.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3206485.0, "num_examples": 100}, {"name": "baker", "num_bytes": 3871443.0, "num_examples": 100}, {"name": "artist", "num_bytes": 4244089.0, "num_examples": 100}, {"name": "author", "num_bytes": 3813285.0, "num_examples": 100}, {"name": "clergy", "num_bytes": 3282554.0, "num_examples": 100}, {"name": "customer_service_representative", "num_bytes": 3217003.0, "num_examples": 100}, {"name": "dental_hygienist", "num_bytes": 3079331.0, "num_examples": 100}, {"name": "electrician", "num_bytes": 4371703.0, "num_examples": 100}, {"name": "carpet_installer", "num_bytes": 4389212.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 3841611.0, "num_examples": 100}, {"name": "ceo", "num_bytes": 2997987.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 3641931.0, "num_examples": 100}, {"name": "dentist", "num_bytes": 3104962.0, "num_examples": 100}, {"name": "butcher", "num_bytes": 4351854.0, "num_examples": 100}, {"name": "courier", "num_bytes": 3640022.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 4180355.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 4070069.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 3199680.0, "num_examples": 100}, {"name": "designer", "num_bytes": 3433880.0, "num_examples": 100}, {"name": "aerospace_engineer", "num_bytes": 4278650.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 3900333.0, "num_examples": 100}, {"name": "event_planner", "num_bytes": 3547339.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3467370.0, "num_examples": 100}, {"name": "construction_worker", "num_bytes": 3894234.0, "num_examples": 100}, {"name": "air_conditioning_installer", "num_bytes": 4217322.0, "num_examples": 100}, {"name": "electrical_engineer", "num_bytes": 4562412.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3415428.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3213913.0, "num_examples": 100}, {"name": "director", "num_bytes": 3305172.0, "num_examples": 100}, {"name": "cleaner", "num_bytes": 3475664.0, "num_examples": 100}, {"name": "computer_systems_analyst", "num_bytes": 3991071.0, "num_examples": 100}, {"name": "dental_assistant", "num_bytes": 2979208.0, "num_examples": 100}, {"name": "architect", "num_bytes": 3890945.0, "num_examples": 100}, {"name": "drywall_installer", "num_bytes": 3579519.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 3586015.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 3301952.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4415058.0, "num_examples": 100}, {"name": "claims_appraiser", "num_bytes": 3836012.0, "num_examples": 100}, {"name": "dispatcher", "num_bytes": 4344042.0, "num_examples": 100}, {"name": "cashier", "num_bytes": 3728570.0, "num_examples": 100}, {"name": "detective", "num_bytes": 3347937.0, "num_examples": 100}, {"name": "engineer", "num_bytes": 3867898.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 4831099.0, "num_examples": 100}, {"name": "credit_counselor", "num_bytes": 3139784.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3124348.0, "num_examples": 100}, {"name": "compliance_officer", "num_bytes": 3471476.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3358153.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4250786.0, "num_examples": 100}, {"name": "coach", "num_bytes": 3644886.0, "num_examples": 100}], "download_size": 75923643, "dataset_size": 186125650.0}}
|
2023-06-02T15:11:51+00:00
|
880ddc49a2325b74244449000fb04e0f4cf9cd45
|
# Dataset Card for "hand-gesture"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
pencaharlangit/hand-gesture
|
[
"region:us"
] |
2023-06-02T13:57:09+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20664236695.383, "num_examples": 8793}], "download_size": 20850539395, "dataset_size": 20664236695.383}}
|
2023-06-02T14:45:24+00:00
|
be33ba72772e53659fe46d6fbbccd1c6188d28a6
|
# Dataset Card for "b735aa91"
[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/b735aa91
|
[
"region:us"
] |
2023-06-02T14:23:29+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 178, "num_examples": 10}], "download_size": 1324, "dataset_size": 178}}
|
2023-06-02T14:23:31+00:00
|
fe3a33dc2476e35ca9a672008e585cd538129848
|
# Dataset Card for "gpt2-detectability"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yuntian-deng/gpt2-detectability
|
[
"region:us"
] |
2023-06-02T14:33:21+00:00
|
{"dataset_info": {"features": [{"name": "ended", "dtype": "bool"}, {"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1364546692, "num_examples": 500000}, {"name": "validation", "num_bytes": 27284489, "num_examples": 10000}, {"name": "test", "num_bytes": 27258195, "num_examples": 10000}], "download_size": 35727753, "dataset_size": 1419089376}}
|
2023-06-02T14:39:04+00:00
|
cbc6531265f8863d69e1a3ad126b6101d8d051e3
|
catlove007/multilingual-text-matching
|
[
"license:apache-2.0",
"region:us"
] |
2023-06-02T14:36:17+00:00
|
{"license": "apache-2.0"}
|
2023-06-02T14:37:46+00:00
|
|
2997fee35da8579bcfc0a789ff07913a675e7e42
|
# Dataset Card for "gpt2-detectability-topk40"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yuntian-deng/gpt2-detectability-topk40
|
[
"region:us"
] |
2023-06-02T14:40:49+00:00
|
{"dataset_info": {"features": [{"name": "ended", "dtype": "bool"}, {"name": "sentence", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "length", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1388622488, "num_examples": 500000}, {"name": "validation", "num_bytes": 27827134, "num_examples": 10000}, {"name": "test", "num_bytes": 27283980, "num_examples": 10000}], "download_size": 872800966, "dataset_size": 1443733602}}
|
2023-06-02T14:41:55+00:00
|
1b8137ad2da4cff69916d53e5ebe19210fd4714d
|
# Dataset Card for "prof_report__22h-vintedois-diffusion-v0-1__multi__12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__22h-vintedois-diffusion-v0-1__multi__12
|
[
"region:us"
] |
2023-06-02T14:53:12+00:00
|
{"dataset_info": {"features": [{"name": "cluster_id", "dtype": "int64"}, {"name": "cluster_size", "dtype": "int64"}, {"name": "img_ids", "sequence": "int64"}, {"name": "img_cluster_scores", "sequence": "float64"}], "splits": [{"name": "accountant", "num_bytes": 1648, "num_examples": 2}, {"name": "aerospace_engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "aide", "num_bytes": 1768, "num_examples": 7}, {"name": "air_conditioning_installer", "num_bytes": 1648, "num_examples": 2}, {"name": "architect", "num_bytes": 1720, "num_examples": 5}, {"name": "artist", "num_bytes": 1816, "num_examples": 9}, {"name": "author", "num_bytes": 1696, "num_examples": 4}, {"name": "baker", "num_bytes": 1696, "num_examples": 4}, {"name": "bartender", "num_bytes": 1648, "num_examples": 2}, {"name": "bus_driver", "num_bytes": 1792, "num_examples": 8}, {"name": "butcher", "num_bytes": 1696, "num_examples": 4}, {"name": "career_counselor", "num_bytes": 1696, "num_examples": 4}, {"name": "carpenter", "num_bytes": 1648, "num_examples": 2}, {"name": "carpet_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "cashier", "num_bytes": 1720, "num_examples": 5}, {"name": "ceo", "num_bytes": 1696, "num_examples": 4}, {"name": "childcare_worker", "num_bytes": 1696, "num_examples": 4}, {"name": "civil_engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "claims_appraiser", "num_bytes": 1744, "num_examples": 6}, {"name": "cleaner", "num_bytes": 1744, "num_examples": 6}, {"name": "clergy", "num_bytes": 1696, "num_examples": 4}, {"name": "clerk", "num_bytes": 1792, "num_examples": 8}, {"name": "coach", "num_bytes": 1696, "num_examples": 4}, {"name": "community_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "compliance_officer", "num_bytes": 1672, "num_examples": 3}, {"name": "computer_programmer", "num_bytes": 1648, "num_examples": 2}, {"name": "computer_support_specialist", "num_bytes": 1720, "num_examples": 5}, {"name": "computer_systems_analyst", "num_bytes": 1744, "num_examples": 6}, {"name": "construction_worker", "num_bytes": 1696, "num_examples": 4}, {"name": "cook", "num_bytes": 1696, "num_examples": 4}, {"name": "correctional_officer", "num_bytes": 1720, "num_examples": 5}, {"name": "courier", "num_bytes": 1768, "num_examples": 7}, {"name": "credit_counselor", "num_bytes": 1672, "num_examples": 3}, {"name": "customer_service_representative", "num_bytes": 1696, "num_examples": 4}, {"name": "data_entry_keyer", "num_bytes": 1744, "num_examples": 6}, {"name": "dental_assistant", "num_bytes": 1648, "num_examples": 2}, {"name": "dental_hygienist", "num_bytes": 1624, "num_examples": 1}, {"name": "dentist", "num_bytes": 1648, "num_examples": 2}, {"name": "designer", "num_bytes": 1720, "num_examples": 5}, {"name": "detective", "num_bytes": 1696, "num_examples": 4}, {"name": "director", "num_bytes": 1720, "num_examples": 5}, {"name": "dishwasher", "num_bytes": 1720, "num_examples": 5}, {"name": "dispatcher", "num_bytes": 1672, "num_examples": 3}, {"name": "doctor", "num_bytes": 1672, "num_examples": 3}, {"name": "drywall_installer", "num_bytes": 1672, "num_examples": 3}, {"name": "electrical_engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "electrician", "num_bytes": 1672, "num_examples": 3}, {"name": "engineer", "num_bytes": 1696, "num_examples": 4}, {"name": "event_planner", "num_bytes": 1696, "num_examples": 4}, {"name": "executive_assistant", "num_bytes": 1696, "num_examples": 4}], "download_size": 86326, "dataset_size": 85232}}
|
2023-06-02T15:16:39+00:00
|
9d7cec0b4b758738bb360b6ee03d777895cbe42f
|
mvasiliniuc/iva-swift-codeint-clean-train-tokenized
|
[
"license:other",
"region:us"
] |
2023-06-02T15:15:09+00:00
|
{"license": "other", "dataset_info": {"features": [{"name": "ratio", "dtype": "float64"}, {"name": "config_or_test", "dtype": "bool"}, {"name": "has_no_keywords", "dtype": "bool"}, {"name": "has_few_assignments", "dtype": "bool"}, {"name": "input_ids", "sequence": "int32"}, {"name": "ratio_char_token", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 971849564, "num_examples": 400000}], "download_size": 484282225, "dataset_size": 971849564}}
|
2023-06-02T15:30:52+00:00
|
|
747ce1f03541a8ff3cbf6abc9e92ba18ee4e924a
|
# Dataset Card for "Hatefulmemes_validation_google_flan_t5_xxl_mode_C_T_A_OCR_rices_ns_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_validation_google_flan_t5_xxl_mode_C_T_A_OCR_rices_ns_500
|
[
"region:us"
] |
2023-06-02T15:36:46+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "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__text", "num_bytes": 578819, "num_examples": 500}, {"name": "fewshot_0", "num_bytes": 596224, "num_examples": 500}], "download_size": 222207, "dataset_size": 1175043}}
|
2023-06-20T23:36:11+00:00
|
6742c7b6f8ab33fa2567e29ab80ed91f254ea6f3
|
crisdev/goprolog
|
[
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-06-02T15:44:56+00:00
|
{"license": "cc-by-nc-sa-4.0"}
|
2023-06-02T15:51:02+00:00
|
|
b5b612f487f8547adb44431a44514cbe6e6137aa
|
# Dataset Card for "Hatefulmemes_validation_google_flan_t5_xxl_mode_C_HM_T_A_OCR_rices_ns_500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_validation_google_flan_t5_xxl_mode_C_HM_T_A_OCR_rices_ns_500
|
[
"region:us"
] |
2023-06-02T15:47:50+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "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__text", "num_bytes": 575839, "num_examples": 500}, {"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_wordnet_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text", "num_bytes": 587888, "num_examples": 500}, {"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full__text", "num_bytes": 583720, "num_examples": 500}, {"name": "fewshot_0", "num_bytes": 585051, "num_examples": 500}], "download_size": 429013, "dataset_size": 2332498}}
|
2023-06-17T02:14:51+00:00
|
1c4efbe4b824985b021f7f2fbcc2d1346c069b13
|
# Dataset Card for "VQAv2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
landersanmi/VQAv2
|
[
"region:us"
] |
2023-06-02T15:48:08+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}, {"name": "answer_type", "dtype": "string"}, {"name": "choices", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 4852760091.0, "num_examples": 10000}], "download_size": 4852337470, "dataset_size": 4852760091.0}}
|
2023-06-02T16:12:15+00:00
|
3d6060bc1df6061d6f41e6cc77d166d8db39b7e0
|
# Dataset Card for "cartoonizer-dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
dqymaggie/cartoonizer-dataset
|
[
"region:us"
] |
2023-06-02T15:52:01+00:00
|
{"dataset_info": {"features": [{"name": "original_image", "dtype": "image"}, {"name": "edit_prompt", "dtype": "string"}, {"name": "cartoonized_image", "dtype": "image"}], "splits": [{"name": "train", "num_bytes": 3266783791.0, "num_examples": 5000}], "download_size": 3278171957, "dataset_size": 3266783791.0}}
|
2023-06-02T16:31:53+00:00
|
eb4b2e6e65eecce2aec3bc40e5c4504261e7978b
|
# Dataset Card for "prof_images_blip__dreamlike-art-dreamlike-photoreal-2.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_images_blip__dreamlike-art-dreamlike-photoreal-2.0
|
[
"region:us"
] |
2023-06-02T15:53:49+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "bartender", "num_bytes": 4460608.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3267258.0, "num_examples": 100}, {"name": "baker", "num_bytes": 3374852.0, "num_examples": 100}, {"name": "artist", "num_bytes": 3882715.0, "num_examples": 100}, {"name": "author", "num_bytes": 3802618.0, "num_examples": 100}, {"name": "clergy", "num_bytes": 3204961.0, "num_examples": 100}, {"name": "customer_service_representative", "num_bytes": 3721321.0, "num_examples": 100}, {"name": "dental_hygienist", "num_bytes": 2996861.0, "num_examples": 100}, {"name": "electrician", "num_bytes": 4262866.0, "num_examples": 100}, {"name": "carpet_installer", "num_bytes": 4562716.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 3732125.0, "num_examples": 100}, {"name": "ceo", "num_bytes": 3168396.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 4107494.0, "num_examples": 100}, {"name": "dentist", "num_bytes": 3076684.0, "num_examples": 100}, {"name": "butcher", "num_bytes": 4336838.0, "num_examples": 100}, {"name": "courier", "num_bytes": 3994329.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 4376440.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3630954.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 2956355.0, "num_examples": 100}, {"name": "designer", "num_bytes": 3770918.0, "num_examples": 100}, {"name": "aerospace_engineer", "num_bytes": 4050855.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 4489789.0, "num_examples": 100}, {"name": "event_planner", "num_bytes": 3687651.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3498822.0, "num_examples": 100}, {"name": "construction_worker", "num_bytes": 4076440.0, "num_examples": 100}, {"name": "air_conditioning_installer", "num_bytes": 4301727.0, "num_examples": 100}, {"name": "electrical_engineer", "num_bytes": 4216767.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3429374.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3439722.0, "num_examples": 100}, {"name": "director", "num_bytes": 3274192.0, "num_examples": 100}, {"name": "cleaner", "num_bytes": 3248902.0, "num_examples": 100}, {"name": "computer_systems_analyst", "num_bytes": 3966486.0, "num_examples": 100}, {"name": "dental_assistant", "num_bytes": 2984790.0, "num_examples": 100}, {"name": "architect", "num_bytes": 3551397.0, "num_examples": 100}, {"name": "drywall_installer", "num_bytes": 3349297.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 3557646.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 3615034.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4331875.0, "num_examples": 100}, {"name": "claims_appraiser", "num_bytes": 4133101.0, "num_examples": 100}, {"name": "dispatcher", "num_bytes": 4882304.0, "num_examples": 100}, {"name": "cashier", "num_bytes": 3816327.0, "num_examples": 100}, {"name": "detective", "num_bytes": 3340806.0, "num_examples": 100}, {"name": "engineer", "num_bytes": 3805674.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 5337265.0, "num_examples": 100}, {"name": "credit_counselor", "num_bytes": 3045456.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3264354.0, "num_examples": 100}, {"name": "compliance_officer", "num_bytes": 3083004.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3356564.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4323194.0, "num_examples": 100}, {"name": "coach", "num_bytes": 3740710.0, "num_examples": 100}], "download_size": 196080878, "dataset_size": 187886834.0}}
|
2023-06-02T15:55:23+00:00
|
9ab14e1aedea76ba121a25c1e171f3c53ea1d9cc
|
# Dataset Card for "prof_report__dreamlike-art-dreamlike-photoreal-2.0__multi__12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__dreamlike-art-dreamlike-photoreal-2.0__multi__12
|
[
"region:us"
] |
2023-06-02T15:56:41+00:00
|
{"dataset_info": {"features": [{"name": "cluster_id", "dtype": "int64"}, {"name": "cluster_size", "dtype": "int64"}, {"name": "img_ids", "sequence": "int64"}, {"name": "img_cluster_scores", "sequence": "float64"}], "splits": [{"name": "accountant", "num_bytes": 1648, "num_examples": 2}, {"name": "aerospace_engineer", "num_bytes": 1792, "num_examples": 8}, {"name": "aide", "num_bytes": 1744, "num_examples": 6}, {"name": "air_conditioning_installer", "num_bytes": 1672, "num_examples": 3}, {"name": "architect", "num_bytes": 1696, "num_examples": 4}, {"name": "artist", "num_bytes": 1816, "num_examples": 9}, {"name": "author", "num_bytes": 1696, "num_examples": 4}, {"name": "baker", "num_bytes": 1672, "num_examples": 3}, {"name": "bartender", "num_bytes": 1648, "num_examples": 2}, {"name": "bus_driver", "num_bytes": 1816, "num_examples": 9}, {"name": "butcher", "num_bytes": 1696, "num_examples": 4}, {"name": "career_counselor", "num_bytes": 1648, "num_examples": 2}, {"name": "carpenter", "num_bytes": 1648, "num_examples": 2}, {"name": "carpet_installer", "num_bytes": 1720, "num_examples": 5}, {"name": "cashier", "num_bytes": 1744, "num_examples": 6}, {"name": "ceo", "num_bytes": 1696, "num_examples": 4}, {"name": "childcare_worker", "num_bytes": 1696, "num_examples": 4}, {"name": "civil_engineer", "num_bytes": 1672, "num_examples": 3}, {"name": "claims_appraiser", "num_bytes": 1648, "num_examples": 2}, {"name": "cleaner", "num_bytes": 1792, "num_examples": 8}, {"name": "clergy", "num_bytes": 1696, "num_examples": 4}, {"name": "clerk", "num_bytes": 1720, "num_examples": 5}, {"name": "coach", "num_bytes": 1648, "num_examples": 2}, {"name": "community_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "compliance_officer", "num_bytes": 1672, "num_examples": 3}, {"name": "computer_programmer", "num_bytes": 1696, "num_examples": 4}, {"name": "computer_support_specialist", "num_bytes": 1744, "num_examples": 6}, {"name": "computer_systems_analyst", "num_bytes": 1768, "num_examples": 7}, {"name": "construction_worker", "num_bytes": 1696, "num_examples": 4}, {"name": "cook", "num_bytes": 1696, "num_examples": 4}, {"name": "correctional_officer", "num_bytes": 1768, "num_examples": 7}, {"name": "courier", "num_bytes": 1696, "num_examples": 4}, {"name": "credit_counselor", "num_bytes": 1672, "num_examples": 3}, {"name": "customer_service_representative", "num_bytes": 1720, "num_examples": 5}, {"name": "data_entry_keyer", "num_bytes": 1672, "num_examples": 3}, {"name": "dental_assistant", "num_bytes": 1648, "num_examples": 2}, {"name": "dental_hygienist", "num_bytes": 1648, "num_examples": 2}, {"name": "dentist", "num_bytes": 1648, "num_examples": 2}, {"name": "designer", "num_bytes": 1720, "num_examples": 5}, {"name": "detective", "num_bytes": 1648, "num_examples": 2}, {"name": "director", "num_bytes": 1672, "num_examples": 3}, {"name": "dishwasher", "num_bytes": 1720, "num_examples": 5}, {"name": "dispatcher", "num_bytes": 1672, "num_examples": 3}, {"name": "doctor", "num_bytes": 1648, "num_examples": 2}, {"name": "drywall_installer", "num_bytes": 1672, "num_examples": 3}, {"name": "electrical_engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "electrician", "num_bytes": 1672, "num_examples": 3}, {"name": "engineer", "num_bytes": 1648, "num_examples": 2}, {"name": "event_planner", "num_bytes": 1696, "num_examples": 4}, {"name": "executive_assistant", "num_bytes": 1624, "num_examples": 1}], "download_size": 215850, "dataset_size": 84824}}
|
2023-06-02T15:57:35+00:00
|
86d97e6265097bf3ba831426b2c7427cb19cf618
|
Birchlabs/test-parquet
|
[
"task_categories:question-answering",
"annotations_creators:expert-generated",
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"reasoning",
"region:us"
] |
2023-06-02T16:00:21+00:00
|
{"annotations_creators": ["expert-generated"], "language": ["en"], "license": "mit", "size_categories": ["100K<n<1M"], "task_categories": ["question-answering"], "pretty_name": "PRM800K", "tags": ["reasoning"], "dataset_info": [{"config_name": "all_positive_responses", "features": [{"name": "instruction", "dtype": "string"}, {"name": "responses", "sequence": {"dtype": "string"}}, {"name": "next_response", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}, {"config_name": "solutions_only", "features": [{"name": "instruction", "dtype": "string"}, {"name": "responses", "sequence": {"dtype": "string"}}, {"name": "next_response", "dtype": "string"}, {"name": "answer", "dtype": "string"}]}]}
|
2023-06-02T21:04:28+00:00
|
|
67abc4037107e14d3ac4b9cacd39b4c49499bfde
|
# Dataset Card for "iva-swift-codeint-clean-valid-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
mvasiliniuc/iva-swift-codeint-clean-valid-tokenized
|
[
"region:us"
] |
2023-06-02T16:22:04+00:00
|
{"dataset_info": {"features": [{"name": "ratio", "dtype": "float64"}, {"name": "config_or_test", "dtype": "bool"}, {"name": "has_no_keywords", "dtype": "bool"}, {"name": "has_few_assignments", "dtype": "bool"}, {"name": "input_ids", "sequence": "int32"}, {"name": "ratio_char_token", "dtype": "float64"}], "splits": [{"name": "train", "num_bytes": 327058438, "num_examples": 63380}], "download_size": 120605735, "dataset_size": 327058438}}
|
2023-06-24T09:45:37+00:00
|
9fab54dcddaa82fd6cd5ed82c77fa0269e0a8457
|
# Dataset Card for "Hatefulmemes_test_google_flan_t5_xxl_mode_C_HM_T_A_OCR_rices_ns_1000"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
CVasNLPExperiments/Hatefulmemes_test_google_flan_t5_xxl_mode_C_HM_T_A_OCR_rices_ns_1000
|
[
"region:us"
] |
2023-06-02T16:22:46+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "prompt", "sequence": "string"}, {"name": "true_label", "dtype": "string"}, {"name": "prediction", "dtype": "string"}], "splits": [{"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_wordnet_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text", "num_bytes": 1185658, "num_examples": 1000}, {"name": "fewshot_0_clip_tags_LAION_ViT_H_14_2B_with_openai_Attributes_LAION_ViT_H_14_2B_descriptors_text_davinci_003_full__text", "num_bytes": 1113029, "num_examples": 1000}, {"name": "fewshot_0_clip_tags_ViT_L_14_with_openai_Attributes_ViT_L_14_descriptors_text_davinci_003_full__text", "num_bytes": 1171478, "num_examples": 1000}, {"name": "fewshot_0", "num_bytes": 1178374, "num_examples": 1000}], "download_size": 812561, "dataset_size": 4648539}}
|
2023-06-17T02:11:15+00:00
|
c41831ffb91338571355b934da7d5bc35d52b442
|
# Dataset Card for "20_newsgroups"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
xwjzds/20_newsgroups
|
[
"region:us"
] |
2023-06-02T16:31:04+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}, {"name": "label_text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 14139513, "num_examples": 11314}, {"name": "test", "num_bytes": 8499585, "num_examples": 7532}], "download_size": 14386304, "dataset_size": 22639098}}
|
2023-06-07T16:23:34+00:00
|
118aedca95cd626263bd44a780a938bad8030a70
|
# Dataset Card for "9e1588f1"
[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/9e1588f1
|
[
"region:us"
] |
2023-06-02T16:38:23+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 186, "num_examples": 10}], "download_size": 1342, "dataset_size": 186}}
|
2023-06-02T16:38:24+00:00
|
555c4650600088613df4d5882884b7525a376bc5
|
# Dataset Card for "characters-sfw"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
AlekseyKorshuk/characters-sfw
|
[
"region:us"
] |
2023-06-02T16:38:51+00:00
|
{"dataset_info": {"features": [{"name": "name", "dtype": "string"}, {"name": "greating", "dtype": "string"}, {"name": "description", "dtype": "string"}, {"name": "conversation", "list": [{"name": "from", "dtype": "string"}, {"name": "value", "dtype": "string"}]}, {"name": "moderation", "struct": [{"name": "categories", "struct": [{"name": "hate", "dtype": "bool"}, {"name": "hate/threatening", "dtype": "bool"}, {"name": "self-harm", "dtype": "bool"}, {"name": "sexual", "dtype": "bool"}, {"name": "sexual/minors", "dtype": "bool"}, {"name": "violence", "dtype": "bool"}, {"name": "violence/graphic", "dtype": "bool"}]}, {"name": "category_scores", "struct": [{"name": "hate", "dtype": "float64"}, {"name": "hate/threatening", "dtype": "float64"}, {"name": "self-harm", "dtype": "float64"}, {"name": "sexual", "dtype": "float64"}, {"name": "sexual/minors", "dtype": "float64"}, {"name": "violence", "dtype": "float64"}, {"name": "violence/graphic", "dtype": "float64"}]}, {"name": "flagged", "dtype": "bool"}]}], "splits": [{"name": "train", "num_bytes": 207418, "num_examples": 67}], "download_size": 150170, "dataset_size": 207418}}
|
2023-06-02T16:38:54+00:00
|
d8642bafa54654eb986560d8d1ac151cf4cc1ebe
|
# Dataset Card for "sharegpt-500"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liyucheng/sharegpt-500
|
[
"region:us"
] |
2023-06-02T16:47:02+00:00
|
{"dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "chat", "sequence": {"sequence": "string"}}], "splits": [{"name": "train", "num_bytes": 2185076, "num_examples": 575}], "download_size": 1065085, "dataset_size": 2185076}}
|
2023-06-02T16:47:04+00:00
|
97aad650af285b5be4dfe9ee38d1d8cb78212d92
|
# 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]
|
LisanneH/Synthetic_Speech_Data_Project
|
[
"task_categories:automatic-speech-recognition",
"task_categories:text-to-speech",
"language:nl",
"license:other",
"region:us"
] |
2023-06-02T16:56:55+00:00
|
{"language": ["nl"], "license": "other", "task_categories": ["automatic-speech-recognition", "text-to-speech"]}
|
2023-06-02T20:42:17+00:00
|
858c675b881ff781c9007c773e3cb281118e909d
|
# Dataset Card for "prof_images_blip__prompthero-openjourney-v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_images_blip__prompthero-openjourney-v4
|
[
"region:us"
] |
2023-06-02T16:57:38+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "bartender", "num_bytes": 4389977.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3215772.0, "num_examples": 100}, {"name": "baker", "num_bytes": 3986834.0, "num_examples": 100}, {"name": "artist", "num_bytes": 3607453.0, "num_examples": 100}, {"name": "author", "num_bytes": 3672416.0, "num_examples": 100}, {"name": "clergy", "num_bytes": 3205746.0, "num_examples": 100}, {"name": "customer_service_representative", "num_bytes": 3248196.0, "num_examples": 100}, {"name": "dental_hygienist", "num_bytes": 3301158.0, "num_examples": 100}, {"name": "electrician", "num_bytes": 4217689.0, "num_examples": 100}, {"name": "carpet_installer", "num_bytes": 4563896.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 3938254.0, "num_examples": 100}, {"name": "ceo", "num_bytes": 2928809.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 3598211.0, "num_examples": 100}, {"name": "dentist", "num_bytes": 3152592.0, "num_examples": 100}, {"name": "butcher", "num_bytes": 4539000.0, "num_examples": 100}, {"name": "courier", "num_bytes": 4146333.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 4075572.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3875009.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 3060421.0, "num_examples": 100}, {"name": "designer", "num_bytes": 3484381.0, "num_examples": 100}, {"name": "aerospace_engineer", "num_bytes": 4288164.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 4283347.0, "num_examples": 100}, {"name": "event_planner", "num_bytes": 3610369.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3790487.0, "num_examples": 100}, {"name": "construction_worker", "num_bytes": 4161361.0, "num_examples": 100}, {"name": "air_conditioning_installer", "num_bytes": 4432735.0, "num_examples": 100}, {"name": "electrical_engineer", "num_bytes": 4664222.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3458189.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3289972.0, "num_examples": 100}, {"name": "director", "num_bytes": 3198823.0, "num_examples": 100}, {"name": "cleaner", "num_bytes": 3447924.0, "num_examples": 100}, {"name": "computer_systems_analyst", "num_bytes": 3963881.0, "num_examples": 100}, {"name": "dental_assistant", "num_bytes": 3092309.0, "num_examples": 100}, {"name": "architect", "num_bytes": 3545898.0, "num_examples": 100}, {"name": "drywall_installer", "num_bytes": 3554202.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 3587994.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 3682350.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4416973.0, "num_examples": 100}, {"name": "claims_appraiser", "num_bytes": 3412701.0, "num_examples": 100}, {"name": "dispatcher", "num_bytes": 4038038.0, "num_examples": 100}, {"name": "cashier", "num_bytes": 3850933.0, "num_examples": 100}, {"name": "detective", "num_bytes": 3164373.0, "num_examples": 100}, {"name": "engineer", "num_bytes": 3757806.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 4884178.0, "num_examples": 100}, {"name": "credit_counselor", "num_bytes": 3166833.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3225393.0, "num_examples": 100}, {"name": "compliance_officer", "num_bytes": 3275293.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3030976.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4244558.0, "num_examples": 100}, {"name": "coach", "num_bytes": 3508320.0, "num_examples": 100}], "download_size": 194428990, "dataset_size": 186236321.0}}
|
2023-06-02T16:58:55+00:00
|
bf6f39d7e6343c37e3d0e42eb0848005122738b5
|
# Dataset Card for "refutation_responses"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
leondz/refutation_responses
|
[
"region:us"
] |
2023-06-02T16:59:13+00:00
|
{"dataset_info": {"features": [{"name": "label", "dtype": "int64"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 505078, "num_examples": 4460}], "download_size": 204728, "dataset_size": 505078}}
|
2023-06-05T21:36:56+00:00
|
e0dafdf1387a1848c044abf35fdfbc98e8c078d8
|
# Dataset Card for "arxiv-march-2023"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
liyucheng/arxiv-march-2023
|
[
"region:us"
] |
2023-06-02T16:59:27+00:00
|
{"dataset_info": {"features": [{"name": "entry_id", "dtype": "string"}, {"name": "published", "dtype": "string"}, {"name": "title", "dtype": "string"}, {"name": "authors", "sequence": "string"}, {"name": "primary_category", "dtype": "string"}, {"name": "categories", "sequence": "string"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 20816482, "num_examples": 500}], "download_size": 10224538, "dataset_size": 20816482}}
|
2023-06-02T16:59:35+00:00
|
dda3266fb94ce08cd904b244ca8f1e5d2045043f
|
# Dataset Card for "prof_report__prompthero-openjourney-v4__multi__12"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_report__prompthero-openjourney-v4__multi__12
|
[
"region:us"
] |
2023-06-02T17:00:13+00:00
|
{"dataset_info": {"features": [{"name": "cluster_id", "dtype": "int64"}, {"name": "cluster_size", "dtype": "int64"}, {"name": "img_ids", "sequence": "int64"}, {"name": "img_cluster_scores", "sequence": "float64"}], "splits": [{"name": "accountant", "num_bytes": 1672, "num_examples": 3}, {"name": "aerospace_engineer", "num_bytes": 1744, "num_examples": 6}, {"name": "aide", "num_bytes": 1744, "num_examples": 6}, {"name": "air_conditioning_installer", "num_bytes": 1696, "num_examples": 4}, {"name": "architect", "num_bytes": 1696, "num_examples": 4}, {"name": "artist", "num_bytes": 1744, "num_examples": 6}, {"name": "author", "num_bytes": 1720, "num_examples": 5}, {"name": "baker", "num_bytes": 1744, "num_examples": 6}, {"name": "bartender", "num_bytes": 1672, "num_examples": 3}, {"name": "bus_driver", "num_bytes": 1744, "num_examples": 6}, {"name": "butcher", "num_bytes": 1696, "num_examples": 4}, {"name": "career_counselor", "num_bytes": 1720, "num_examples": 5}, {"name": "carpenter", "num_bytes": 1672, "num_examples": 3}, {"name": "carpet_installer", "num_bytes": 1672, "num_examples": 3}, {"name": "cashier", "num_bytes": 1672, "num_examples": 3}, {"name": "ceo", "num_bytes": 1672, "num_examples": 3}, {"name": "childcare_worker", "num_bytes": 1744, "num_examples": 6}, {"name": "civil_engineer", "num_bytes": 1648, "num_examples": 2}, {"name": "claims_appraiser", "num_bytes": 1720, "num_examples": 5}, {"name": "cleaner", "num_bytes": 1792, "num_examples": 8}, {"name": "clergy", "num_bytes": 1696, "num_examples": 4}, {"name": "clerk", "num_bytes": 1768, "num_examples": 7}, {"name": "coach", "num_bytes": 1672, "num_examples": 3}, {"name": "community_manager", "num_bytes": 1720, "num_examples": 5}, {"name": "compliance_officer", "num_bytes": 1720, "num_examples": 5}, {"name": "computer_programmer", "num_bytes": 1624, "num_examples": 1}, {"name": "computer_support_specialist", "num_bytes": 1768, "num_examples": 7}, {"name": "computer_systems_analyst", "num_bytes": 1696, "num_examples": 4}, {"name": "construction_worker", "num_bytes": 1696, "num_examples": 4}, {"name": "cook", "num_bytes": 1768, "num_examples": 7}, {"name": "correctional_officer", "num_bytes": 1768, "num_examples": 7}, {"name": "courier", "num_bytes": 1768, "num_examples": 7}, {"name": "credit_counselor", "num_bytes": 1720, "num_examples": 5}, {"name": "customer_service_representative", "num_bytes": 1720, "num_examples": 5}, {"name": "data_entry_keyer", "num_bytes": 1672, "num_examples": 3}, {"name": "dental_assistant", "num_bytes": 1648, "num_examples": 2}, {"name": "dental_hygienist", "num_bytes": 1648, "num_examples": 2}, {"name": "dentist", "num_bytes": 1696, "num_examples": 4}, {"name": "designer", "num_bytes": 1696, "num_examples": 4}, {"name": "detective", "num_bytes": 1672, "num_examples": 3}, {"name": "director", "num_bytes": 1696, "num_examples": 4}, {"name": "dishwasher", "num_bytes": 1696, "num_examples": 4}, {"name": "dispatcher", "num_bytes": 1672, "num_examples": 3}, {"name": "doctor", "num_bytes": 1696, "num_examples": 4}, {"name": "drywall_installer", "num_bytes": 1672, "num_examples": 3}, {"name": "electrical_engineer", "num_bytes": 1672, "num_examples": 3}, {"name": "electrician", "num_bytes": 1648, "num_examples": 2}, {"name": "engineer", "num_bytes": 1648, "num_examples": 2}, {"name": "event_planner", "num_bytes": 1672, "num_examples": 3}, {"name": "executive_assistant", "num_bytes": 1624, "num_examples": 1}], "download_size": 215957, "dataset_size": 85016}}
|
2023-06-02T17:00:55+00:00
|
fef97b99997d81b74e4275d8ab0d95017ab760d9
|
# Dataset Card for "30ce51a4"
[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/30ce51a4
|
[
"region:us"
] |
2023-06-02T17:11:53+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}}
|
2023-06-02T17:11:54+00:00
|
39fba1faf73a252479736f127556f97fd905aaa0
|
We are now releasing the topics for model development for TREC-AToMiC. These topics are an addition on top of the validation set of AToMiC and aim to be closer to what you should expect for the task. The main difference is that they have a pooled set of annotations, leading to a richer annotation compared to the validation set. However, in order to achieve this richer annotation, there are way less queries (only 13) that have been selected to showcase different attributes of retrievers.
We note that the topics do not represent exactly what will be the final task (we will aim for topics that are more important to wikipedia), but more in a way that they were: a) Easy to annotate and b) Could show some important factor, such as topics from the AToMiC training set, but that the linked image is very different from others that may be found in the original AToMiC corpus. The 13 topics are divided into 4 areas:
### TV/Cinema
We choose the five following topics: Goldfinger (Cast and Plot), Space Jam (Cast), Friends (Premise), How I met your Mother (Premise)
Here we want to see if the model can find simple information (e.g. photos of cast members that we now have photos on wikimedia), but also look into something more complicated (plot points for GoldFinger). There's also the idea of checking if the retriever will look into the Section as it is the goal of AToMiC and not pages (thus Cast and Plot should have very different results for Goldfinger).
### Soccer
Three topics: Andrea Barzagli (Return to the national team: Euro 2012, 2013 Confederations Cup and 2014 World Cup), Manchester United (2013->Present and Ferguson years (1986–2013)).
Again, we want to make sure that models are looking more at the section level (including years that things happened) than the passage level. All topics here were selected knowing that images for those topics exist in other languages
### Transportation
Again three topics: Emirates Airline (fleet), Flixbus (Europe) and List_of_Cadillac_vehicles
We chose those topics because they are easy for MultiModal models (Salient Points on images/ Require OCR), but are not always easy for text-only models (e.g. Some flixbus images describe only the bus model, but not the fact that it belongs to Flixbus).
### Geography/History
Finally we also take two completely different topics: NIST () and Mutawakkilite_Kingdom_of_Yemen (introduction). The goal here was to pick something that not only was different from the ones before, but that also contain images that are not that present in traditional multimodal evaluation (e.g. Country maps, Schematics).
## Baseline results
In order to annotate the queries we ran 5 baselines (4 Multi-Modal and 1 textual only) and created 3 ensembles (all multi-modal, best multi-modal + text, all 5 baselines). Everything is ran in the TREC task scenario ("AToMiC large"). We took the top-10 of each of the 8 runs and annotated it, leading to 533 annotations (average of 41 unique results per query). We run several metrics and present the results below:
The first thing we notice is that the models are able to perform better than expected on this set of topics. Indeed, if we compare the RR@10 of ViT-G on the validation of AToMiC (0.074) with the one we obtained it is clear that the sparse annotations do not suffise for this task (we talk in more detail below looking at each topic individually). More-so, the gap between textual only models (such as SPLADE) and Multi-Modal is greatly reduced, especially on precision based metrics. Finally, we are able to see improvements using an ensemble of multi-modal and text-only models.
|
TREC-AToMiC/Development-Set-2023
|
[
"region:us"
] |
2023-06-02T17:50:51+00:00
|
{"dataset_info": {"features": [{"name": "text_id", "dtype": "string"}, {"name": "page_url", "dtype": "string"}, {"name": "page_title", "dtype": "string"}, {"name": "section_title", "dtype": "string"}, {"name": "context_page_description", "dtype": "string"}, {"name": "context_section_description", "dtype": "string"}, {"name": "media", "sequence": "string"}, {"name": "hierachy", "sequence": "string"}, {"name": "category", "sequence": "string"}, {"name": "source_id", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 18109.758012141203, "num_examples": 9}], "download_size": 61847, "dataset_size": 18109.758012141203}}
|
2023-06-02T17:53:02+00:00
|
8dcef94dcbe71eda6683a8572ad11024672831c8
|
# Dataset Card for "amitay"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
ninja/amitay
|
[
"region:us"
] |
2023-06-02T17:51:54+00:00
|
{"dataset_info": {"features": [{"name": "title", "dtype": "string"}, {"name": "target", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 3653903, "num_examples": 21092}], "download_size": 2354968, "dataset_size": 3653903}}
|
2023-06-06T08:00:11+00:00
|
abfa7930c8544bc2dc07e14f2041dc0e37e82938
|
# Dataset Card for "cards_with_labels"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
davanstrien/cards_with_labels
|
[
"region:us"
] |
2023-06-02T18:00:57+00:00
|
{"dataset_info": {"features": [{"name": "index", "dtype": "int64"}, {"name": "modelId", "dtype": "string"}, {"name": "label", "sequence": "string"}, {"name": "readme", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 80399856, "num_examples": 14986}], "download_size": 26227864, "dataset_size": 80399856}}
|
2023-06-02T18:01:01+00:00
|
9750997af29eff96cdda8051b95a2b9151c3bc08
|
# Dataset Card for all_combined_bengali_252K
## Dataset Description
- **Homepage: https://www.odiagenai.org/**
- **Repository: https://github.com/OdiaGenAI**
- **Point of Contact: Shantipriya Parida, and Sambit Sekhar**
### Dataset Summary
This dataset is a mix of Bengali instruction sets translated from open-source instruction sets:
* Dolly,
* Alpaca,
* ChatDoctor,
* Roleplay
* GSM
In this dataset Bengali instruction, input, and output strings are available.
### Supported Tasks and Leaderboards
Large Language Model (LLM)
### Languages
Bengali
## Dataset Structure
JSON
### Data Fields
output (string)
data_source (string)
instruction (string)
input (string)
### Licensing Information
This work is licensed under a
[Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License][cc-by-nc-sa].
[![CC BY-NC-SA 4.0][cc-by-nc-sa-image]][cc-by-nc-sa]
[cc-by-nc-sa]: http://creativecommons.org/licenses/by-nc-sa/4.0/
[cc-by-nc-sa-image]: https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png
[cc-by-nc-sa-shield]: https://img.shields.io/badge/License-CC%20BY--NC--SA%204.0-lightgrey.svg
### Citation Information
If you find this repository useful, please consider giving 👏 and citing:
```
@misc{OdiaGenAI,
author = {Shantipriya Parida and Sambit Sekhar and Guneet Singh Kohli and Arghyadeep Sen and Shashikanta Sahoo},
title = {Bengali Instruction Set},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/OdiaGenAI}},
}
```
### Contributions
- Shantipriya Parida
- Sambit Sekhar
- Guneet Singh Kohli
- Arghyadeep Sen
- Shashikanta Sahoo
|
OdiaGenAI/all_combined_bengali_252k
|
[
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:bn",
"license:cc-by-nc-sa-4.0",
"region:us"
] |
2023-06-02T18:23:48+00:00
|
{"language": ["bn"], "license": "cc-by-nc-sa-4.0", "size_categories": ["100K<n<1M"], "task_categories": ["text-generation"], "pretty_name": "all_combined_bengali_252K"}
|
2023-06-28T11:47:51+00:00
|
a765969dc614e2cf86fe1bbd02ebd03174251b6a
|
aannabellee/tests
|
[
"language:en",
"region:us"
] |
2023-06-02T19:08:22+00:00
|
{"language": ["en"], "dataset_info": {"features": [{"name": "audio", "dtype": "audio"}], "splits": [{"name": "train", "num_bytes": 13714300.0, "num_examples": 2}, {"name": "validation", "num_bytes": 13714300.0, "num_examples": 2}], "download_size": 13675313, "dataset_size": 27428600.0}}
|
2023-06-07T12:28:39+00:00
|
|
35db8c1a5225fcf1a7d53d71995992467c5c283c
|
This is a fork of SciRepEval data with new/corrected labels for my research and not the official dataset of SciRepEval .
|
mucc001/scirepeval_view_cite_read_test
|
[
"license:unknown",
"region:us"
] |
2023-06-02T19:49:25+00:00
|
{"license": "unknown"}
|
2023-06-02T19:55:31+00:00
|
ba54c35650715e8748c6b6ba610666e48378e479
|
# Dataset Card for "necklace"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
imnaveenk/necklace
|
[
"region:us"
] |
2023-06-02T19:49:32+00:00
|
{"dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 58122109.0, "num_examples": 21}], "download_size": 36885623, "dataset_size": 58122109.0}}
|
2023-06-02T19:50:30+00:00
|
1e470f758cf3dc8b171d1a125ed1abdb12c0b4f8
|
# Dataset Card for "805b872e"
[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/805b872e
|
[
"region:us"
] |
2023-06-02T20:54:04+00:00
|
{"dataset_info": {"features": [{"name": "result", "dtype": "string"}, {"name": "id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 182, "num_examples": 10}], "download_size": 1330, "dataset_size": 182}}
|
2023-06-02T20:54:05+00:00
|
c485de61a872214b367faf3a2fc7634241690433
|
# Dataset Card for "prof_images_blip__Lykon-DreamShaper"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
yjernite/prof_images_blip__Lykon-DreamShaper
|
[
"region:us"
] |
2023-06-02T20:54:15+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "bartender", "num_bytes": 4232353.0, "num_examples": 100}, {"name": "facilities_manager", "num_bytes": 3233702.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3301253.0, "num_examples": 100}, {"name": "graphic_designer", "num_bytes": 3779936.0, "num_examples": 100}, {"name": "financial_manager", "num_bytes": 3032824.0, "num_examples": 100}, {"name": "baker", "num_bytes": 3760855.0, "num_examples": 100}, {"name": "artist", "num_bytes": 3321552.0, "num_examples": 100}, {"name": "author", "num_bytes": 3841657.0, "num_examples": 100}, {"name": "clergy", "num_bytes": 3326689.0, "num_examples": 100}, {"name": "customer_service_representative", "num_bytes": 3353667.0, "num_examples": 100}, {"name": "dental_hygienist", "num_bytes": 3116590.0, "num_examples": 100}, {"name": "electrician", "num_bytes": 4444433.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 3711054.0, "num_examples": 100}, {"name": "health_technician", "num_bytes": 3208097.0, "num_examples": 100}, {"name": "carpet_installer", "num_bytes": 4231786.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 3887933.0, "num_examples": 100}, {"name": "ceo", "num_bytes": 2725789.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 3768802.0, "num_examples": 100}, {"name": "dentist", "num_bytes": 3051311.0, "num_examples": 100}, {"name": "butcher", "num_bytes": 4473092.0, "num_examples": 100}, {"name": "courier", "num_bytes": 3220269.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 4013303.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3250295.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 3109178.0, "num_examples": 100}, {"name": "designer", "num_bytes": 3360493.0, "num_examples": 100}, {"name": "groundskeeper", "num_bytes": 3526805.0, "num_examples": 100}, {"name": "aerospace_engineer", "num_bytes": 4889373.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 3810901.0, "num_examples": 100}, {"name": "event_planner", "num_bytes": 3416510.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3783118.0, "num_examples": 100}, {"name": "hairdresser", "num_bytes": 3197788.0, "num_examples": 100}, {"name": "farmer", "num_bytes": 4224326.0, "num_examples": 100}, {"name": "construction_worker", "num_bytes": 3595787.0, "num_examples": 100}, {"name": "air_conditioning_installer", "num_bytes": 4078377.0, "num_examples": 100}, {"name": "electrical_engineer", "num_bytes": 5068341.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3402257.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3603897.0, "num_examples": 100}, {"name": "director", "num_bytes": 3015590.0, "num_examples": 100}, {"name": "fast_food_worker", "num_bytes": 3902204.0, "num_examples": 100}, {"name": "cleaner", "num_bytes": 2822728.0, "num_examples": 100}, {"name": "computer_systems_analyst", "num_bytes": 4211576.0, "num_examples": 100}, {"name": "dental_assistant", "num_bytes": 3135047.0, "num_examples": 100}, {"name": "architect", "num_bytes": 3334524.0, "num_examples": 100}, {"name": "drywall_installer", "num_bytes": 3186332.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 3723729.0, "num_examples": 100}, {"name": "file_clerk", "num_bytes": 4124578.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 2923881.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4186317.0, "num_examples": 100}, {"name": "claims_appraiser", "num_bytes": 3668012.0, "num_examples": 100}, {"name": "dispatcher", "num_bytes": 4311103.0, "num_examples": 100}, {"name": "cashier", "num_bytes": 4015653.0, "num_examples": 100}, {"name": "detective", "num_bytes": 2545399.0, "num_examples": 100}, {"name": "financial_advisor", "num_bytes": 3101141.0, "num_examples": 100}, {"name": "engineer", "num_bytes": 4143278.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 4891231.0, "num_examples": 100}, {"name": "fitness_instructor", "num_bytes": 3356902.0, "num_examples": 100}, {"name": "credit_counselor", "num_bytes": 3340328.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3038762.0, "num_examples": 100}, {"name": "compliance_officer", "num_bytes": 3241075.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3472385.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4379280.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 3730273.0, "num_examples": 100}, {"name": "firefighter", "num_bytes": 4226861.0, "num_examples": 100}, {"name": "coach", "num_bytes": 3364291.0, "num_examples": 100}], "download_size": 243149155, "dataset_size": 232746873.0}}
|
2023-06-02T20:55:44+00:00
|
e5b152db24aa596b6ae14dc662b102242c568028
|
# Dataset Card for "prof_images_blip__prompthero-openjourney-v4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__prompthero-openjourney-v4
|
[
"region:us"
] |
2023-06-02T21:29:27+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 4146333.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3030976.0, "num_examples": 100}, {"name": "police_officer", "num_bytes": 3277507.0, "num_examples": 100}, {"name": "purchasing_agent", "num_bytes": 3207646.0, "num_examples": 100}, {"name": "metal_worker", "num_bytes": 4625634.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 3042727.0, "num_examples": 100}, {"name": "stocker", "num_bytes": 3882375.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 3708520.0, "num_examples": 100}, {"name": "writer", "num_bytes": 4042568.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3215772.0, "num_examples": 100}, {"name": "coach", "num_bytes": 3508320.0, "num_examples": 100}, {"name": "painter", "num_bytes": 3993132.0, "num_examples": 100}, {"name": "real_estate_broker", "num_bytes": 3127234.0, "num_examples": 100}, {"name": "truck_driver", "num_bytes": 4644495.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 4283347.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 3598211.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3790487.0, "num_examples": 100}, {"name": "interior_designer", "num_bytes": 4373351.0, "num_examples": 100}, {"name": "nutritionist", "num_bytes": 3824291.0, "num_examples": 100}, {"name": "designer", "num_bytes": 3484381.0, "num_examples": 100}, {"name": "maid", "num_bytes": 3637820.0, "num_examples": 100}, {"name": "producer", "num_bytes": 3757911.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 3060421.0, "num_examples": 100}, {"name": "logistician", "num_bytes": 3974021.0, "num_examples": 100}, {"name": "tractor_operator", "num_bytes": 5482668.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3225393.0, "num_examples": 100}, {"name": "inventory_clerk", "num_bytes": 3773437.0, "num_examples": 100}, {"name": "sheet_metal_worker", "num_bytes": 4303346.0, "num_examples": 100}, {"name": "groundskeeper", "num_bytes": 4531900.0, "num_examples": 100}, {"name": "electrical_engineer", "num_bytes": 4664222.0, "num_examples": 100}, {"name": "physical_therapist", "num_bytes": 3068593.0, "num_examples": 100}, {"name": "insurance_agent", "num_bytes": 2951534.0, "num_examples": 100}, {"name": "aerospace_engineer", "num_bytes": 4288164.0, "num_examples": 100}, {"name": "psychologist", "num_bytes": 3112508.0, "num_examples": 100}, {"name": "financial_advisor", "num_bytes": 3147975.0, "num_examples": 100}, {"name": "printing_press_operator", "num_bytes": 5058006.0, "num_examples": 100}, {"name": "architect", "num_bytes": 3545898.0, "num_examples": 100}, {"name": "dental_hygienist", "num_bytes": 3301158.0, "num_examples": 100}, {"name": "artist", "num_bytes": 3607453.0, "num_examples": 100}, {"name": "office_worker", "num_bytes": 3285687.0, "num_examples": 100}, {"name": "ceo", "num_bytes": 2928809.0, "num_examples": 100}, {"name": "taxi_driver", "num_bytes": 4226071.0, "num_examples": 100}, {"name": "librarian", "num_bytes": 4094269.0, "num_examples": 100}, {"name": "author", "num_bytes": 3672416.0, "num_examples": 100}, {"name": "plumber", "num_bytes": 4202172.0, "num_examples": 100}, {"name": "construction_worker", "num_bytes": 4161361.0, "num_examples": 100}, {"name": "clergy", "num_bytes": 3205746.0, "num_examples": 100}, {"name": "electrician", "num_bytes": 4217689.0, "num_examples": 100}, {"name": "jailer", "num_bytes": 4347071.0, "num_examples": 100}, {"name": "credit_counselor", "num_bytes": 3166833.0, "num_examples": 100}, {"name": "scientist", "num_bytes": 3506973.0, "num_examples": 100}, {"name": "drywall_installer", "num_bytes": 3554202.0, "num_examples": 100}, {"name": "school_bus_driver", "num_bytes": 4533495.0, "num_examples": 100}, {"name": "dental_assistant", "num_bytes": 3092309.0, "num_examples": 100}, {"name": "fitness_instructor", "num_bytes": 3671725.0, "num_examples": 100}, {"name": "detective", "num_bytes": 3164373.0, "num_examples": 100}, {"name": "hairdresser", "num_bytes": 3541049.0, "num_examples": 100}, {"name": "welder", "num_bytes": 5227519.0, "num_examples": 100}, {"name": "pharmacy_technician", "num_bytes": 4106676.0, "num_examples": 100}, {"name": "compliance_officer", "num_bytes": 3275293.0, "num_examples": 100}, {"name": "singer", "num_bytes": 3764786.0, "num_examples": 100}, {"name": "tutor", "num_bytes": 3751744.0, "num_examples": 100}, {"name": "language_pathologist", "num_bytes": 3837456.0, "num_examples": 100}, {"name": "medical_records_specialist", "num_bytes": 3701769.0, "num_examples": 100}, {"name": "sales_manager", "num_bytes": 2983559.0, "num_examples": 100}, {"name": "industrial_engineer", "num_bytes": 4333733.0, "num_examples": 100}, {"name": "manager", "num_bytes": 2930688.0, "num_examples": 100}, {"name": "mechanic", "num_bytes": 4476848.0, "num_examples": 100}, {"name": "postal_worker", "num_bytes": 3783292.0, "num_examples": 100}, {"name": "computer_systems_analyst", "num_bytes": 3963881.0, "num_examples": 100}, {"name": "salesperson", "num_bytes": 3178770.0, "num_examples": 100}, {"name": "office_clerk", "num_bytes": 3406464.0, "num_examples": 100}, {"name": "claims_appraiser", "num_bytes": 3412701.0, "num_examples": 100}, {"name": "security_guard", "num_bytes": 3472522.0, "num_examples": 100}, {"name": "interviewer", "num_bytes": 3037085.0, "num_examples": 100}, {"name": "dispatcher", "num_bytes": 4038038.0, "num_examples": 100}, {"name": "lawyer", "num_bytes": 3342701.0, "num_examples": 100}, {"name": "marketing_manager", "num_bytes": 3207642.0, "num_examples": 100}, {"name": "customer_service_representative", "num_bytes": 3248196.0, "num_examples": 100}, {"name": "software_developer", "num_bytes": 3247318.0, "num_examples": 100}, {"name": "mover", "num_bytes": 3963680.0, "num_examples": 100}, {"name": "supervisor", "num_bytes": 3355887.0, "num_examples": 100}, {"name": "paralegal", "num_bytes": 3112990.0, "num_examples": 100}, {"name": "graphic_designer", "num_bytes": 4212965.0, "num_examples": 100}, {"name": "dentist", "num_bytes": 3152592.0, "num_examples": 100}, {"name": "roofer", "num_bytes": 4682014.0, "num_examples": 100}, {"name": "public_relations_specialist", "num_bytes": 3114477.0, "num_examples": 100}, {"name": "engineer", "num_bytes": 3757806.0, "num_examples": 100}, {"name": "occupational_therapist", "num_bytes": 3154878.0, "num_examples": 100}, {"name": "manicurist", "num_bytes": 3093040.0, "num_examples": 100}, {"name": "cleaner", "num_bytes": 3447924.0, "num_examples": 100}, {"name": "facilities_manager", "num_bytes": 3359002.0, "num_examples": 100}, {"name": "repair_worker", "num_bytes": 4137910.0, "num_examples": 100}, {"name": "cashier", "num_bytes": 3850933.0, "num_examples": 100}, {"name": "baker", "num_bytes": 3986834.0, "num_examples": 100}, {"name": "market_research_analyst", "num_bytes": 3683427.0, "num_examples": 100}, {"name": "health_technician", "num_bytes": 3256975.0, "num_examples": 100}, {"name": "veterinarian", "num_bytes": 3294312.0, "num_examples": 100}, {"name": "underwriter", "num_bytes": 3063193.0, "num_examples": 100}, {"name": "mechanical_engineer", "num_bytes": 4710070.0, "num_examples": 100}, {"name": "janitor", "num_bytes": 3746576.0, "num_examples": 100}, {"name": "pilot", "num_bytes": 3911566.0, "num_examples": 100}, {"name": "therapist", "num_bytes": 3228145.0, "num_examples": 100}, {"name": "director", "num_bytes": 3198823.0, "num_examples": 100}, {"name": "wholesale_buyer", "num_bytes": 4223940.0, "num_examples": 100}, {"name": "air_conditioning_installer", "num_bytes": 4432735.0, "num_examples": 100}, {"name": "butcher", "num_bytes": 4539000.0, "num_examples": 100}, {"name": "machinery_mechanic", "num_bytes": 5082596.0, "num_examples": 100}, {"name": "event_planner", "num_bytes": 3610369.0, "num_examples": 100}, {"name": "carpet_installer", "num_bytes": 4563896.0, "num_examples": 100}, {"name": "musician", "num_bytes": 3833721.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 3938254.0, "num_examples": 100}, {"name": "farmer", "num_bytes": 4743954.0, "num_examples": 100}, {"name": "financial_manager", "num_bytes": 3060782.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 3587994.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3289972.0, "num_examples": 100}, {"name": "machinist", "num_bytes": 4504863.0, "num_examples": 100}, {"name": "firefighter", "num_bytes": 4463585.0, "num_examples": 100}, {"name": "photographer", "num_bytes": 3618407.0, "num_examples": 100}, {"name": "file_clerk", "num_bytes": 4209622.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4244558.0, "num_examples": 100}, {"name": "fast_food_worker", "num_bytes": 3768688.0, "num_examples": 100}, {"name": "bartender", "num_bytes": 4389977.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 4075572.0, "num_examples": 100}, {"name": "pharmacist", "num_bytes": 4111345.0, "num_examples": 100}, {"name": "nursing_assistant", "num_bytes": 3166043.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3458189.0, "num_examples": 100}, {"name": "mental_health_counselor", "num_bytes": 3271410.0, "num_examples": 100}, {"name": "network_administrator", "num_bytes": 4587116.0, "num_examples": 100}, {"name": "teacher", "num_bytes": 3489593.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 4884178.0, "num_examples": 100}, {"name": "teller", "num_bytes": 3347964.0, "num_examples": 100}, {"name": "teaching_assistant", "num_bytes": 3469000.0, "num_examples": 100}, {"name": "payroll_clerk", "num_bytes": 3229022.0, "num_examples": 100}, {"name": "laboratory_technician", "num_bytes": 3767759.0, "num_examples": 100}, {"name": "social_assistant", "num_bytes": 3217312.0, "num_examples": 100}, {"name": "radiologic_technician", "num_bytes": 3690576.0, "num_examples": 100}, {"name": "social_worker", "num_bytes": 3818885.0, "num_examples": 100}, {"name": "nurse", "num_bytes": 3179037.0, "num_examples": 100}, {"name": "receptionist", "num_bytes": 3105956.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4416973.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3875009.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 3682350.0, "num_examples": 100}, {"name": "massage_therapist", "num_bytes": 3028599.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 3700341.0, "num_examples": 100}, {"name": "plane_mechanic", "num_bytes": 4021875.0, "num_examples": 100}], "download_size": 570647541, "dataset_size": 546777702.0}}
|
2023-06-02T21:31:43+00:00
|
ac4e7bfa8f0e91becd552b5f0d5729b60bfdbb07
|
# Dataset Card for "prof_images_blip__runwayml-stable-diffusion-v1-5"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__runwayml-stable-diffusion-v1-5
|
[
"region:us"
] |
2023-06-02T21:43:07+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 4149512.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3374001.0, "num_examples": 100}, {"name": "police_officer", "num_bytes": 3478564.0, "num_examples": 100}, {"name": "purchasing_agent", "num_bytes": 3806912.0, "num_examples": 100}, {"name": "metal_worker", "num_bytes": 4713575.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 3696816.0, "num_examples": 100}, {"name": "stocker", "num_bytes": 4091449.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 3977803.0, "num_examples": 100}, {"name": "writer", "num_bytes": 4276740.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3367470.0, "num_examples": 100}, {"name": "coach", "num_bytes": 4431293.0, "num_examples": 100}, {"name": "painter", "num_bytes": 4260179.0, "num_examples": 100}, {"name": "real_estate_broker", "num_bytes": 3827635.0, "num_examples": 100}, {"name": "truck_driver", "num_bytes": 4844727.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 3892056.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 3602571.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3946794.0, "num_examples": 100}, {"name": "interior_designer", "num_bytes": 4243295.0, "num_examples": 100}, {"name": "nutritionist", "num_bytes": 4358660.0, "num_examples": 100}, {"name": "designer", "num_bytes": 3721353.0, "num_examples": 100}, {"name": "maid", "num_bytes": 3702512.0, "num_examples": 100}, {"name": "producer", "num_bytes": 3910831.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 3308505.0, "num_examples": 100}, {"name": "logistician", "num_bytes": 4119385.0, "num_examples": 100}, {"name": "tractor_operator", "num_bytes": 5956123.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3323785.0, "num_examples": 100}, {"name": "inventory_clerk", "num_bytes": 3969206.0, "num_examples": 100}, {"name": "sheet_metal_worker", "num_bytes": 4455690.0, "num_examples": 100}, {"name": "groundskeeper", "num_bytes": 5573476.0, "num_examples": 100}, {"name": "electrical_engineer", "num_bytes": 4438580.0, "num_examples": 100}, {"name": "physical_therapist", "num_bytes": 3448449.0, "num_examples": 100}, {"name": "insurance_agent", "num_bytes": 3401267.0, "num_examples": 100}, {"name": "aerospace_engineer", "num_bytes": 4239725.0, "num_examples": 100}, {"name": "psychologist", "num_bytes": 3437888.0, "num_examples": 100}, {"name": "financial_advisor", "num_bytes": 3588808.0, "num_examples": 100}, {"name": "printing_press_operator", "num_bytes": 5067787.0, "num_examples": 100}, {"name": "architect", "num_bytes": 3672955.0, "num_examples": 100}, {"name": "dental_hygienist", "num_bytes": 3350179.0, "num_examples": 100}, {"name": "artist", "num_bytes": 4024163.0, "num_examples": 100}, {"name": "office_worker", "num_bytes": 3474359.0, "num_examples": 100}, {"name": "ceo", "num_bytes": 3156884.0, "num_examples": 100}, {"name": "taxi_driver", "num_bytes": 4440100.0, "num_examples": 100}, {"name": "librarian", "num_bytes": 4630951.0, "num_examples": 100}, {"name": "author", "num_bytes": 4058998.0, "num_examples": 100}, {"name": "plumber", "num_bytes": 4105303.0, "num_examples": 100}, {"name": "construction_worker", "num_bytes": 4192000.0, "num_examples": 100}, {"name": "clergy", "num_bytes": 3735092.0, "num_examples": 100}, {"name": "electrician", "num_bytes": 4214826.0, "num_examples": 100}, {"name": "jailer", "num_bytes": 4699898.0, "num_examples": 100}, {"name": "credit_counselor", "num_bytes": 3572905.0, "num_examples": 100}, {"name": "scientist", "num_bytes": 3748096.0, "num_examples": 100}, {"name": "drywall_installer", "num_bytes": 3643508.0, "num_examples": 100}, {"name": "school_bus_driver", "num_bytes": 5242748.0, "num_examples": 100}, {"name": "dental_assistant", "num_bytes": 3210854.0, "num_examples": 100}, {"name": "fitness_instructor", "num_bytes": 4144204.0, "num_examples": 100}, {"name": "detective", "num_bytes": 3453061.0, "num_examples": 100}, {"name": "hairdresser", "num_bytes": 3515755.0, "num_examples": 100}, {"name": "welder", "num_bytes": 5250854.0, "num_examples": 100}, {"name": "pharmacy_technician", "num_bytes": 4160729.0, "num_examples": 100}, {"name": "compliance_officer", "num_bytes": 3234896.0, "num_examples": 100}, {"name": "singer", "num_bytes": 3704730.0, "num_examples": 100}, {"name": "tutor", "num_bytes": 4025197.0, "num_examples": 100}, {"name": "language_pathologist", "num_bytes": 3886820.0, "num_examples": 100}, {"name": "medical_records_specialist", "num_bytes": 3485587.0, "num_examples": 100}, {"name": "sales_manager", "num_bytes": 3383036.0, "num_examples": 100}, {"name": "industrial_engineer", "num_bytes": 4454056.0, "num_examples": 100}, {"name": "manager", "num_bytes": 3544169.0, "num_examples": 100}, {"name": "mechanic", "num_bytes": 4250088.0, "num_examples": 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3968020.0, "num_examples": 100}, {"name": "paralegal", "num_bytes": 3484920.0, "num_examples": 100}, {"name": "graphic_designer", "num_bytes": 3985880.0, "num_examples": 100}, {"name": "dentist", "num_bytes": 3260275.0, "num_examples": 100}, {"name": "roofer", "num_bytes": 4931842.0, "num_examples": 100}, {"name": "public_relations_specialist", "num_bytes": 3907210.0, "num_examples": 100}, {"name": "engineer", "num_bytes": 4124421.0, "num_examples": 100}, {"name": "occupational_therapist", "num_bytes": 3929603.0, "num_examples": 100}, {"name": "manicurist", "num_bytes": 3271501.0, "num_examples": 100}, {"name": "cleaner", "num_bytes": 3822567.0, "num_examples": 100}, {"name": "facilities_manager", "num_bytes": 3630377.0, "num_examples": 100}, {"name": "repair_worker", "num_bytes": 4036707.0, "num_examples": 100}, {"name": "cashier", "num_bytes": 4211851.0, "num_examples": 100}, {"name": "baker", "num_bytes": 4131298.0, "num_examples": 100}, {"name": "market_research_analyst", "num_bytes": 3953787.0, "num_examples": 100}, {"name": "health_technician", "num_bytes": 3260343.0, "num_examples": 100}, {"name": "veterinarian", "num_bytes": 3644965.0, "num_examples": 100}, {"name": "underwriter", "num_bytes": 3618488.0, "num_examples": 100}, {"name": "mechanical_engineer", "num_bytes": 4524692.0, "num_examples": 100}, {"name": "janitor", "num_bytes": 4080915.0, "num_examples": 100}, {"name": "pilot", "num_bytes": 3833035.0, "num_examples": 100}, {"name": "therapist", "num_bytes": 3401391.0, "num_examples": 100}, {"name": "director", "num_bytes": 3441367.0, "num_examples": 100}, {"name": "wholesale_buyer", "num_bytes": 4811869.0, "num_examples": 100}, {"name": "air_conditioning_installer", "num_bytes": 4660139.0, "num_examples": 100}, {"name": "butcher", "num_bytes": 4787582.0, "num_examples": 100}, {"name": "machinery_mechanic", "num_bytes": 4701530.0, "num_examples": 100}, {"name": "event_planner", "num_bytes": 4517996.0, "num_examples": 100}, {"name": 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"computer_programmer", "num_bytes": 3867945.0, "num_examples": 100}, {"name": "pharmacist", "num_bytes": 4303759.0, "num_examples": 100}, {"name": "nursing_assistant", "num_bytes": 3364288.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3824924.0, "num_examples": 100}, {"name": "mental_health_counselor", "num_bytes": 3527894.0, "num_examples": 100}, {"name": "network_administrator", "num_bytes": 4415621.0, "num_examples": 100}, {"name": "teacher", "num_bytes": 4094567.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 5283804.0, "num_examples": 100}, {"name": "teller", "num_bytes": 3464334.0, "num_examples": 100}, {"name": "teaching_assistant", "num_bytes": 4101764.0, "num_examples": 100}, {"name": "payroll_clerk", "num_bytes": 3378168.0, "num_examples": 100}, {"name": "laboratory_technician", "num_bytes": 3566546.0, "num_examples": 100}, {"name": "social_assistant", "num_bytes": 3859979.0, "num_examples": 100}, {"name": "radiologic_technician", "num_bytes": 3392503.0, "num_examples": 100}, {"name": "social_worker", "num_bytes": 4077871.0, "num_examples": 100}, {"name": "nurse", "num_bytes": 3183480.0, "num_examples": 100}, {"name": "receptionist", "num_bytes": 3281732.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4652365.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3771208.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 4582929.0, "num_examples": 100}, {"name": "massage_therapist", "num_bytes": 3277626.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 4188895.0, "num_examples": 100}, {"name": "plane_mechanic", "num_bytes": 3965902.0, "num_examples": 100}], "download_size": 607029619, "dataset_size": 583176599.0}}
|
2023-06-02T21:45:21+00:00
|
2728ed347f13143f836b56332055651b77890d69
|
Completely uncurated collection of IRC logs from the Ubuntu IRC channels
|
Tuxifan/UbuntuIRC
|
[
"task_categories:text-generation",
"license:cc0-1.0",
"region:us"
] |
2023-06-02T21:48:40+00:00
|
{"license": "cc0-1.0", "task_categories": ["text-generation"], "pretty_name": "Ubuntu IRC channels"}
|
2023-06-04T14:35:31+00:00
|
81b3bbcf97bddaf44425967015ea9e18ff4bb767
|
# Dataset Card for "rlhf-prompt"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
breadlicker45/rlhf-prompt
|
[
"region:us"
] |
2023-06-02T21:53:04+00:00
|
{"dataset_info": {"features": [{"name": "text", "dtype": "string"}, {"name": "label", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 48283331, "num_examples": 36768}], "download_size": 3956825, "dataset_size": 48283331}}
|
2023-06-02T22:33:25+00:00
|
2335dbf9ef7b4fcbf132c9847f944487a4f204b9
|
# Dataset Card for "prof_images_blip__stabilityai-stable-diffusion-2-1-base"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__stabilityai-stable-diffusion-2-1-base
|
[
"region:us"
] |
2023-06-02T21:56:43+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 3573421.0, "num_examples": 100}, {"name": "aide", "num_bytes": 2817584.0, "num_examples": 100}, {"name": "police_officer", "num_bytes": 3493332.0, "num_examples": 100}, {"name": "purchasing_agent", "num_bytes": 3798921.0, "num_examples": 100}, {"name": "metal_worker", "num_bytes": 5019792.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 3511611.0, "num_examples": 100}, {"name": "stocker", "num_bytes": 5028292.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 3657377.0, "num_examples": 100}, {"name": "writer", "num_bytes": 3430382.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3139473.0, "num_examples": 100}, {"name": "coach", "num_bytes": 3510680.0, "num_examples": 100}, {"name": "painter", "num_bytes": 3678749.0, "num_examples": 100}, {"name": "real_estate_broker", "num_bytes": 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"num_bytes": 3885685.0, "num_examples": 100}, {"name": "social_worker", "num_bytes": 3242952.0, "num_examples": 100}, {"name": "nurse", "num_bytes": 2554856.0, "num_examples": 100}, {"name": "receptionist", "num_bytes": 3445701.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4584283.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3829211.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 3796040.0, "num_examples": 100}, {"name": "massage_therapist", "num_bytes": 3187773.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 3407926.0, "num_examples": 100}, {"name": "plane_mechanic", "num_bytes": 4632703.0, "num_examples": 100}], "download_size": 582528766, "dataset_size": 558658902.0}}
|
2023-06-02T21:58:52+00:00
|
2abcc9021eb3cf516769f7f6da50e070099349d0
|
# Dataset Card for "prof_images_blip__prompthero-openjourney"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__prompthero-openjourney
|
[
"region:us"
] |
2023-06-02T22:10:15+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 3760784.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3418582.0, "num_examples": 100}, {"name": "police_officer", "num_bytes": 3377012.0, "num_examples": 100}, {"name": "purchasing_agent", "num_bytes": 3394916.0, "num_examples": 100}, {"name": "metal_worker", "num_bytes": 4497526.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 3306222.0, "num_examples": 100}, {"name": "stocker", "num_bytes": 3765924.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 3830493.0, "num_examples": 100}, {"name": "writer", "num_bytes": 3891760.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3460026.0, "num_examples": 100}, {"name": "coach", "num_bytes": 3679869.0, "num_examples": 100}, {"name": "painter", "num_bytes": 3893660.0, "num_examples": 100}, {"name": "real_estate_broker", "num_bytes": 3602161.0, "num_examples": 100}, {"name": "truck_driver", "num_bytes": 4611463.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 3993617.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 3554019.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3760523.0, "num_examples": 100}, {"name": "interior_designer", "num_bytes": 4157288.0, "num_examples": 100}, {"name": "nutritionist", "num_bytes": 3922448.0, "num_examples": 100}, {"name": "designer", "num_bytes": 3409751.0, "num_examples": 100}, {"name": "maid", "num_bytes": 3622248.0, "num_examples": 100}, {"name": "producer", "num_bytes": 3646034.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 3314937.0, "num_examples": 100}, {"name": "logistician", "num_bytes": 3618654.0, "num_examples": 100}, {"name": "tractor_operator", "num_bytes": 5460264.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3247158.0, "num_examples": 100}, {"name": "inventory_clerk", "num_bytes": 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"carpet_installer", "num_bytes": 4516457.0, "num_examples": 100}, {"name": "musician", "num_bytes": 3684116.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 3941629.0, "num_examples": 100}, {"name": "farmer", "num_bytes": 4989051.0, "num_examples": 100}, {"name": "financial_manager", "num_bytes": 3174944.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 3669575.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3324920.0, "num_examples": 100}, {"name": "machinist", "num_bytes": 4044134.0, "num_examples": 100}, {"name": "firefighter", "num_bytes": 4332393.0, "num_examples": 100}, {"name": "photographer", "num_bytes": 3695862.0, "num_examples": 100}, {"name": "file_clerk", "num_bytes": 4083059.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4531929.0, "num_examples": 100}, {"name": "fast_food_worker", "num_bytes": 3846496.0, "num_examples": 100}, {"name": "bartender", "num_bytes": 4505103.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 3794230.0, "num_examples": 100}, {"name": "pharmacist", "num_bytes": 3944022.0, "num_examples": 100}, {"name": "nursing_assistant", "num_bytes": 3337188.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3508442.0, "num_examples": 100}, {"name": "mental_health_counselor", "num_bytes": 3511935.0, "num_examples": 100}, {"name": "network_administrator", "num_bytes": 4269125.0, "num_examples": 100}, {"name": "teacher", "num_bytes": 3503022.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 4982070.0, "num_examples": 100}, {"name": "teller", "num_bytes": 3265757.0, "num_examples": 100}, {"name": "teaching_assistant", "num_bytes": 3524569.0, "num_examples": 100}, {"name": "payroll_clerk", "num_bytes": 3410514.0, "num_examples": 100}, {"name": "laboratory_technician", "num_bytes": 3781736.0, "num_examples": 100}, {"name": "social_assistant", "num_bytes": 3563186.0, "num_examples": 100}, {"name": "radiologic_technician", "num_bytes": 3671229.0, "num_examples": 100}, {"name": "social_worker", "num_bytes": 3801947.0, "num_examples": 100}, {"name": "nurse", "num_bytes": 3163031.0, "num_examples": 100}, {"name": "receptionist", "num_bytes": 3172214.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4339870.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3479830.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 3676879.0, "num_examples": 100}, {"name": "massage_therapist", "num_bytes": 3088977.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 3913374.0, "num_examples": 100}, {"name": "plane_mechanic", "num_bytes": 4022909.0, "num_examples": 100}], "download_size": 577344434, "dataset_size": 553499486.0}}
|
2023-06-02T22:12:38+00:00
|
45faacb8b19a83bd53fb7e756238881f6ba0f38e
|
# Dataset Card for "prof_images_blip__plasmo-vox2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__plasmo-vox2
|
[
"region:us"
] |
2023-06-02T22:24:02+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 3423528.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3244224.0, "num_examples": 100}, {"name": "police_officer", "num_bytes": 3398034.0, "num_examples": 100}, {"name": "purchasing_agent", "num_bytes": 3540884.0, "num_examples": 100}, {"name": "metal_worker", "num_bytes": 4421578.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 3525818.0, "num_examples": 100}, {"name": "stocker", "num_bytes": 3668194.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 3883417.0, "num_examples": 100}, {"name": "writer", "num_bytes": 4070073.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3361130.0, "num_examples": 100}, {"name": "coach", "num_bytes": 3681340.0, "num_examples": 100}, {"name": "painter", "num_bytes": 3663277.0, "num_examples": 100}, {"name": "real_estate_broker", "num_bytes": 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"num_bytes": 3790820.0, "num_examples": 100}, {"name": "health_technician", "num_bytes": 3401502.0, "num_examples": 100}, {"name": "veterinarian", "num_bytes": 3594282.0, "num_examples": 100}, {"name": "underwriter", "num_bytes": 3476810.0, "num_examples": 100}, {"name": "mechanical_engineer", "num_bytes": 4785449.0, "num_examples": 100}, {"name": "janitor", "num_bytes": 3902165.0, "num_examples": 100}, {"name": "pilot", "num_bytes": 3777317.0, "num_examples": 100}, {"name": "therapist", "num_bytes": 3381660.0, "num_examples": 100}, {"name": "director", "num_bytes": 3478346.0, "num_examples": 100}, {"name": "wholesale_buyer", "num_bytes": 4491884.0, "num_examples": 100}, {"name": "air_conditioning_installer", "num_bytes": 4800832.0, "num_examples": 100}, {"name": "butcher", "num_bytes": 4457447.0, "num_examples": 100}, {"name": "machinery_mechanic", "num_bytes": 4827307.0, "num_examples": 100}, {"name": "event_planner", "num_bytes": 4028145.0, "num_examples": 100}, {"name": "carpet_installer", "num_bytes": 4487582.0, "num_examples": 100}, {"name": "musician", "num_bytes": 3596171.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 3885104.0, "num_examples": 100}, {"name": "farmer", "num_bytes": 4809106.0, "num_examples": 100}, {"name": "financial_manager", "num_bytes": 3290682.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 4092135.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3480529.0, "num_examples": 100}, {"name": "machinist", "num_bytes": 4105933.0, "num_examples": 100}, {"name": "firefighter", "num_bytes": 4083959.0, "num_examples": 100}, {"name": "photographer", "num_bytes": 3843412.0, "num_examples": 100}, {"name": "file_clerk", "num_bytes": 4244068.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4868937.0, "num_examples": 100}, {"name": "fast_food_worker", "num_bytes": 3775473.0, "num_examples": 100}, {"name": "bartender", "num_bytes": 4528745.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 3955305.0, "num_examples": 100}, {"name": "pharmacist", "num_bytes": 4211103.0, "num_examples": 100}, {"name": "nursing_assistant", "num_bytes": 3281644.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3636112.0, "num_examples": 100}, {"name": "mental_health_counselor", "num_bytes": 3311030.0, "num_examples": 100}, {"name": "network_administrator", "num_bytes": 4071059.0, "num_examples": 100}, {"name": "teacher", "num_bytes": 3655156.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 4621986.0, "num_examples": 100}, {"name": "teller", "num_bytes": 3525857.0, "num_examples": 100}, {"name": "teaching_assistant", "num_bytes": 3707175.0, "num_examples": 100}, {"name": "payroll_clerk", "num_bytes": 3350816.0, "num_examples": 100}, {"name": "laboratory_technician", "num_bytes": 3638099.0, "num_examples": 100}, {"name": "social_assistant", "num_bytes": 3771851.0, "num_examples": 100}, {"name": "radiologic_technician", "num_bytes": 3569203.0, "num_examples": 100}, {"name": "social_worker", "num_bytes": 3802343.0, "num_examples": 100}, {"name": "nurse", "num_bytes": 3246629.0, "num_examples": 100}, {"name": "receptionist", "num_bytes": 3098001.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4289428.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3611592.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 3838039.0, "num_examples": 100}, {"name": "massage_therapist", "num_bytes": 3138902.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 3749713.0, "num_examples": 100}, {"name": "plane_mechanic", "num_bytes": 3959746.0, "num_examples": 100}], "download_size": 580868020, "dataset_size": 557055542.0}}
|
2023-06-02T22:26:27+00:00
|
edb241b8087c2c44cc7e8f0d19ebe4a167a2d61d
|
Birchlabs/openai-prm800k-phase1_test-solutions-only
|
[
"license:mit",
"region:us"
] |
2023-06-02T22:24:08+00:00
|
{"license": "mit"}
|
2023-06-02T22:28:34+00:00
|
|
4e41a149adb6c3508b711c0c893feb9ba069279e
|
# Dataset Card for "multi_xsciene_postprocess"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
whu9/multi_xsciene_postprocess
|
[
"region:us"
] |
2023-06-02T22:24:08+00:00
|
{"dataset_info": {"features": [{"name": "source", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "source_num_tokens", "dtype": "int64"}, {"name": "summary_num_tokens", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 165217234, "num_examples": 30351}, {"name": "test", "num_bytes": 27275286, "num_examples": 5090}, {"name": "validation", "num_bytes": 27471336, "num_examples": 5061}], "download_size": 101726627, "dataset_size": 219963856}}
|
2023-06-02T22:24:12+00:00
|
d2698ca6b3eba5fed3bf9d611e755c25d862d8c4
|
Birchlabs/openai-prm800k-phase1_test-stepwise-best
|
[
"license:mit",
"region:us"
] |
2023-06-02T22:29:35+00:00
|
{"license": "mit"}
|
2023-06-02T22:31:00+00:00
|
|
e92c1f199557fb678bb6580ec66d97a4975d4eea
|
Birchlabs/openai-prm800k-phase1_test-stepwise-critique
|
[
"license:mit",
"region:us"
] |
2023-06-02T22:31:56+00:00
|
{"license": "mit"}
|
2023-06-02T22:32:19+00:00
|
|
3ccaefdb867ad75bd508adf9e29f8cb995c8e445
|
Birchlabs/openai-prm800k-phase2_test-solutions-only
|
[
"license:mit",
"region:us"
] |
2023-06-02T22:33:31+00:00
|
{"license": "mit"}
|
2023-06-02T22:33:51+00:00
|
|
8b10d41da850ff94da223ccb0149385e26b62b50
|
Birchlabs/openai-prm800k-phase2_test-stepwise-best
|
[
"license:mit",
"region:us"
] |
2023-06-02T22:34:48+00:00
|
{"license": "mit"}
|
2023-06-02T22:35:15+00:00
|
|
c961951cc89820ffe079b49dd567960fdd975f5e
|
Birchlabs/openai-prm800k-phase2_test-stepwise-critique
|
[
"license:mit",
"region:us"
] |
2023-06-02T22:35:33+00:00
|
{"license": "mit"}
|
2023-06-02T22:37:15+00:00
|
|
f32349a4c91566edf105047ad06c79e190eede61
|
Birchlabs/openai-prm800k-phase1_train-solutions-only
|
[
"license:mit",
"region:us"
] |
2023-06-02T22:37:37+00:00
|
{"license": "mit"}
|
2023-06-02T22:38:05+00:00
|
|
f84323b16e6f2eaf444105b3d262b06df18c424c
|
# Dataset Card for "prof_images_blip__dreamlike-art-dreamlike-photoreal-2.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__dreamlike-art-dreamlike-photoreal-2.0
|
[
"region:us"
] |
2023-06-02T22:38:05+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 4011954.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3303432.0, "num_examples": 100}, {"name": "police_officer", "num_bytes": 3553390.0, "num_examples": 100}, {"name": "purchasing_agent", "num_bytes": 3481224.0, "num_examples": 100}, {"name": "metal_worker", "num_bytes": 4491039.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 3519895.0, "num_examples": 100}, {"name": "stocker", "num_bytes": 3744336.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 4180456.0, "num_examples": 100}, {"name": "writer", "num_bytes": 4057606.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3317665.0, "num_examples": 100}, {"name": "coach", "num_bytes": 3775909.0, "num_examples": 100}, {"name": "painter", "num_bytes": 4069237.0, "num_examples": 100}, {"name": "real_estate_broker", "num_bytes": 3535657.0, "num_examples": 100}, {"name": "truck_driver", "num_bytes": 4845113.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 4655287.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 4235538.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3522854.0, "num_examples": 100}, {"name": "interior_designer", "num_bytes": 4128723.0, "num_examples": 100}, {"name": "nutritionist", "num_bytes": 3554104.0, "num_examples": 100}, {"name": "designer", "num_bytes": 3608040.0, "num_examples": 100}, {"name": "maid", "num_bytes": 3616612.0, "num_examples": 100}, {"name": "producer", "num_bytes": 4089632.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 3007030.0, "num_examples": 100}, {"name": "logistician", "num_bytes": 4129006.0, "num_examples": 100}, {"name": "tractor_operator", "num_bytes": 5470169.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3236868.0, "num_examples": 100}, {"name": "inventory_clerk", "num_bytes": 3880627.0, "num_examples": 100}, {"name": "sheet_metal_worker", "num_bytes": 4428395.0, "num_examples": 100}, {"name": "groundskeeper", "num_bytes": 4437928.0, "num_examples": 100}, {"name": "electrical_engineer", "num_bytes": 4139462.0, "num_examples": 100}, {"name": "physical_therapist", "num_bytes": 2897064.0, "num_examples": 100}, {"name": "insurance_agent", "num_bytes": 3173440.0, "num_examples": 100}, {"name": "aerospace_engineer", "num_bytes": 3994203.0, "num_examples": 100}, {"name": "psychologist", "num_bytes": 3080986.0, "num_examples": 100}, {"name": "financial_advisor", "num_bytes": 3185203.0, "num_examples": 100}, {"name": "printing_press_operator", "num_bytes": 4940487.0, "num_examples": 100}, {"name": "architect", "num_bytes": 3567637.0, "num_examples": 100}, {"name": "dental_hygienist", "num_bytes": 3058817.0, "num_examples": 100}, {"name": "artist", "num_bytes": 3968331.0, "num_examples": 100}, {"name": "office_worker", "num_bytes": 3727395.0, "num_examples": 100}, {"name": "ceo", "num_bytes": 3162268.0, "num_examples": 100}, {"name": "taxi_driver", "num_bytes": 4458910.0, "num_examples": 100}, {"name": "librarian", "num_bytes": 4146791.0, "num_examples": 100}, {"name": "author", "num_bytes": 3836355.0, "num_examples": 100}, {"name": "plumber", "num_bytes": 4308359.0, "num_examples": 100}, {"name": "construction_worker", "num_bytes": 4046913.0, "num_examples": 100}, {"name": "clergy", "num_bytes": 3158023.0, "num_examples": 100}, {"name": "electrician", "num_bytes": 4322099.0, "num_examples": 100}, {"name": "jailer", "num_bytes": 4461732.0, "num_examples": 100}, {"name": "credit_counselor", "num_bytes": 3101741.0, "num_examples": 100}, {"name": "scientist", "num_bytes": 3152707.0, "num_examples": 100}, {"name": "drywall_installer", "num_bytes": 3278426.0, "num_examples": 100}, {"name": "school_bus_driver", "num_bytes": 4679742.0, "num_examples": 100}, {"name": "dental_assistant", "num_bytes": 2986756.0, "num_examples": 100}, {"name": "fitness_instructor", "num_bytes": 3374382.0, "num_examples": 100}, {"name": "detective", "num_bytes": 3394672.0, "num_examples": 100}, {"name": "hairdresser", "num_bytes": 3361797.0, "num_examples": 100}, {"name": "welder", "num_bytes": 5173937.0, "num_examples": 100}, {"name": "pharmacy_technician", "num_bytes": 4226694.0, "num_examples": 100}, {"name": "compliance_officer", "num_bytes": 3108999.0, "num_examples": 100}, {"name": "singer", "num_bytes": 3753572.0, "num_examples": 100}, {"name": "tutor", "num_bytes": 3457326.0, "num_examples": 100}, {"name": "language_pathologist", "num_bytes": 3832030.0, "num_examples": 100}, {"name": "medical_records_specialist", "num_bytes": 3549131.0, "num_examples": 100}, {"name": "sales_manager", "num_bytes": 3241439.0, "num_examples": 100}, {"name": "industrial_engineer", "num_bytes": 4135357.0, "num_examples": 100}, {"name": "manager", "num_bytes": 3473703.0, "num_examples": 100}, {"name": "mechanic", "num_bytes": 4197229.0, "num_examples": 100}, {"name": "postal_worker", "num_bytes": 3948376.0, "num_examples": 100}, {"name": "computer_systems_analyst", "num_bytes": 3923681.0, "num_examples": 100}, {"name": "salesperson", "num_bytes": 3276616.0, "num_examples": 100}, {"name": "office_clerk", "num_bytes": 3621561.0, "num_examples": 100}, {"name": "claims_appraiser", "num_bytes": 4095184.0, "num_examples": 100}, {"name": "security_guard", "num_bytes": 3556418.0, "num_examples": 100}, {"name": "interviewer", "num_bytes": 3118170.0, "num_examples": 100}, {"name": "dispatcher", "num_bytes": 4862379.0, "num_examples": 100}, {"name": "lawyer", "num_bytes": 3509627.0, "num_examples": 100}, {"name": "marketing_manager", "num_bytes": 3630504.0, "num_examples": 100}, {"name": "customer_service_representative", "num_bytes": 3704958.0, "num_examples": 100}, {"name": "software_developer", "num_bytes": 3418197.0, "num_examples": 100}, {"name": "mover", "num_bytes": 3513804.0, "num_examples": 100}, {"name": "supervisor", "num_bytes": 3178906.0, "num_examples": 100}, {"name": "paralegal", "num_bytes": 3220877.0, "num_examples": 100}, {"name": "graphic_designer", "num_bytes": 4878178.0, "num_examples": 100}, {"name": "dentist", "num_bytes": 3072677.0, "num_examples": 100}, {"name": "roofer", "num_bytes": 4096411.0, "num_examples": 100}, {"name": "public_relations_specialist", "num_bytes": 2852113.0, "num_examples": 100}, {"name": "engineer", "num_bytes": 3889884.0, "num_examples": 100}, {"name": "occupational_therapist", "num_bytes": 3186887.0, "num_examples": 100}, {"name": "manicurist", "num_bytes": 2962720.0, "num_examples": 100}, {"name": "cleaner", "num_bytes": 3246186.0, "num_examples": 100}, {"name": "facilities_manager", "num_bytes": 3772360.0, "num_examples": 100}, {"name": "repair_worker", "num_bytes": 3968404.0, "num_examples": 100}, {"name": "cashier", "num_bytes": 3826535.0, "num_examples": 100}, {"name": "baker", "num_bytes": 3382155.0, "num_examples": 100}, {"name": "market_research_analyst", "num_bytes": 3880256.0, "num_examples": 100}, {"name": "health_technician", "num_bytes": 3569418.0, "num_examples": 100}, {"name": "veterinarian", "num_bytes": 3252490.0, "num_examples": 100}, {"name": "underwriter", "num_bytes": 3274001.0, "num_examples": 100}, {"name": "mechanical_engineer", "num_bytes": 4325890.0, "num_examples": 100}, {"name": "janitor", "num_bytes": 3972715.0, "num_examples": 100}, {"name": "pilot", "num_bytes": 3768697.0, "num_examples": 100}, {"name": "therapist", "num_bytes": 3302928.0, "num_examples": 100}, {"name": "director", "num_bytes": 3281035.0, "num_examples": 100}, {"name": "wholesale_buyer", "num_bytes": 4527661.0, "num_examples": 100}, {"name": "air_conditioning_installer", "num_bytes": 4376494.0, "num_examples": 100}, {"name": "butcher", "num_bytes": 4360557.0, "num_examples": 100}, {"name": "machinery_mechanic", "num_bytes": 4531029.0, "num_examples": 100}, {"name": "event_planner", "num_bytes": 3645716.0, "num_examples": 100}, {"name": "carpet_installer", "num_bytes": 4742653.0, "num_examples": 100}, {"name": "musician", "num_bytes": 3820423.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 3803606.0, "num_examples": 100}, {"name": "farmer", "num_bytes": 4534682.0, "num_examples": 100}, {"name": "financial_manager", "num_bytes": 3188029.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 3611693.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3389318.0, "num_examples": 100}, {"name": "machinist", "num_bytes": 4117703.0, "num_examples": 100}, {"name": "firefighter", "num_bytes": 4366855.0, "num_examples": 100}, {"name": "photographer", "num_bytes": 3474226.0, "num_examples": 100}, {"name": "file_clerk", "num_bytes": 4444851.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4359070.0, "num_examples": 100}, {"name": "fast_food_worker", "num_bytes": 3916424.0, "num_examples": 100}, {"name": "bartender", "num_bytes": 4481031.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 4343352.0, "num_examples": 100}, {"name": "pharmacist", "num_bytes": 3899128.0, "num_examples": 100}, {"name": "nursing_assistant", "num_bytes": 3140404.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3380746.0, "num_examples": 100}, {"name": "mental_health_counselor", "num_bytes": 3465602.0, "num_examples": 100}, {"name": "network_administrator", "num_bytes": 4659043.0, "num_examples": 100}, {"name": "teacher", "num_bytes": 3453875.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 5235864.0, "num_examples": 100}, {"name": "teller", "num_bytes": 3362076.0, "num_examples": 100}, {"name": "teaching_assistant", "num_bytes": 3335416.0, "num_examples": 100}, {"name": "payroll_clerk", "num_bytes": 3073614.0, "num_examples": 100}, {"name": "laboratory_technician", "num_bytes": 3648218.0, "num_examples": 100}, {"name": "social_assistant", "num_bytes": 3297308.0, "num_examples": 100}, {"name": "radiologic_technician", "num_bytes": 3499073.0, "num_examples": 100}, {"name": "social_worker", "num_bytes": 3693715.0, "num_examples": 100}, {"name": "nurse", "num_bytes": 3303499.0, "num_examples": 100}, {"name": "receptionist", "num_bytes": 3340300.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4325350.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3530587.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 3623259.0, "num_examples": 100}, {"name": "massage_therapist", "num_bytes": 3219418.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 3588104.0, "num_examples": 100}, {"name": "plane_mechanic", "num_bytes": 4225560.0, "num_examples": 100}], "download_size": 575547093, "dataset_size": 551678571.0}}
|
2023-06-02T22:40:32+00:00
|
7a70eeca60e01d31a3545087457fee9be00b434b
|
Birchlabs/openai-prm800k-phase2_train-solutions-only
|
[
"license:mit",
"region:us"
] |
2023-06-02T22:38:33+00:00
|
{"license": "mit"}
|
2023-06-02T22:38:56+00:00
|
|
82c9364dd6b63788cddaddffbd0c3bd4ca36f1f8
|
Birchlabs/openai-prm800k-phase1_train-stepwise-best
|
[
"license:mit",
"region:us"
] |
2023-06-02T22:39:18+00:00
|
{"license": "mit"}
|
2023-06-02T22:39:35+00:00
|
|
5f19a009a2de24620dc9206d06a1ac5639d0ab19
|
Birchlabs/openai-prm800k-phase2_train-stepwise-best
|
[
"license:mit",
"region:us"
] |
2023-06-02T22:39:57+00:00
|
{"license": "mit"}
|
2023-06-02T22:40:47+00:00
|
|
9f55fd2d12cdbac55859ffc5dc93df4db7df46b5
|
Birchlabs/openai-prm800k-phase1_train-stepwise-critique
|
[
"license:mit",
"region:us"
] |
2023-06-02T22:41:04+00:00
|
{"license": "mit"}
|
2023-06-02T22:41:25+00:00
|
|
fd6c0c162202b75b47e26b74d88c631bfcaab4d5
|
Birchlabs/openai-prm800k-phase2_train-stepwise-critique
|
[
"license:mit",
"region:us"
] |
2023-06-02T22:41:37+00:00
|
{"license": "mit"}
|
2023-06-02T22:42:37+00:00
|
|
92932108ee4569ed91fe787cf9473b88dc050ff0
|
# Dataset Card for "prof_images_blip__Lykon-DreamShaper"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__Lykon-DreamShaper
|
[
"region:us"
] |
2023-06-02T22:52:08+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 3220269.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3472385.0, "num_examples": 100}, {"name": "police_officer", "num_bytes": 2971579.0, "num_examples": 100}, {"name": "purchasing_agent", "num_bytes": 3706168.0, "num_examples": 100}, {"name": "metal_worker", "num_bytes": 4300217.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 3730273.0, "num_examples": 100}, {"name": "stocker", "num_bytes": 3002092.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 3849162.0, "num_examples": 100}, {"name": "writer", "num_bytes": 3815757.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3301253.0, "num_examples": 100}, {"name": "coach", "num_bytes": 3364291.0, "num_examples": 100}, {"name": "painter", "num_bytes": 3587432.0, "num_examples": 100}, {"name": "real_estate_broker", "num_bytes": 3143465.0, "num_examples": 100}, {"name": "truck_driver", "num_bytes": 4168681.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 3810901.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 3768802.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3783118.0, "num_examples": 100}, {"name": "interior_designer", "num_bytes": 3929319.0, "num_examples": 100}, {"name": "nutritionist", "num_bytes": 3866238.0, "num_examples": 100}, {"name": "designer", "num_bytes": 3360493.0, "num_examples": 100}, {"name": "maid", "num_bytes": 3269062.0, "num_examples": 100}, {"name": "producer", "num_bytes": 4011654.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 3109178.0, "num_examples": 100}, {"name": "logistician", "num_bytes": 3905564.0, "num_examples": 100}, {"name": "tractor_operator", "num_bytes": 5188801.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3038762.0, "num_examples": 100}, {"name": "inventory_clerk", "num_bytes": 3902424.0, "num_examples": 100}, {"name": "sheet_metal_worker", "num_bytes": 4046848.0, "num_examples": 100}, {"name": "groundskeeper", "num_bytes": 3526805.0, "num_examples": 100}, {"name": "electrical_engineer", "num_bytes": 5068341.0, "num_examples": 100}, {"name": "physical_therapist", "num_bytes": 2872364.0, "num_examples": 100}, {"name": "insurance_agent", "num_bytes": 2964103.0, "num_examples": 100}, {"name": "aerospace_engineer", "num_bytes": 4889373.0, "num_examples": 100}, {"name": "psychologist", "num_bytes": 2930630.0, "num_examples": 100}, {"name": "financial_advisor", "num_bytes": 3101141.0, "num_examples": 100}, {"name": "printing_press_operator", "num_bytes": 4325576.0, "num_examples": 100}, {"name": "architect", "num_bytes": 3334524.0, "num_examples": 100}, {"name": "dental_hygienist", "num_bytes": 3116590.0, "num_examples": 100}, {"name": "artist", "num_bytes": 3321552.0, "num_examples": 100}, {"name": "office_worker", "num_bytes": 3392256.0, "num_examples": 100}, {"name": "ceo", "num_bytes": 2725789.0, "num_examples": 100}, {"name": "taxi_driver", "num_bytes": 4421050.0, "num_examples": 100}, {"name": "librarian", "num_bytes": 3760714.0, "num_examples": 100}, {"name": "author", "num_bytes": 3841657.0, "num_examples": 100}, {"name": "plumber", "num_bytes": 3721155.0, "num_examples": 100}, {"name": "construction_worker", "num_bytes": 3595787.0, "num_examples": 100}, {"name": "clergy", "num_bytes": 3326689.0, "num_examples": 100}, {"name": "electrician", "num_bytes": 4444433.0, "num_examples": 100}, {"name": "jailer", "num_bytes": 4249238.0, "num_examples": 100}, {"name": "credit_counselor", "num_bytes": 3340328.0, "num_examples": 100}, {"name": "scientist", "num_bytes": 3763435.0, "num_examples": 100}, {"name": "drywall_installer", "num_bytes": 3186332.0, "num_examples": 100}, {"name": "school_bus_driver", "num_bytes": 4588003.0, "num_examples": 100}, {"name": "dental_assistant", "num_bytes": 3135047.0, "num_examples": 100}, {"name": "fitness_instructor", "num_bytes": 3356902.0, "num_examples": 100}, {"name": "detective", "num_bytes": 2545399.0, "num_examples": 100}, {"name": "hairdresser", "num_bytes": 3197788.0, "num_examples": 100}, {"name": "welder", "num_bytes": 4549984.0, "num_examples": 100}, {"name": "pharmacy_technician", "num_bytes": 4237065.0, "num_examples": 100}, {"name": "compliance_officer", "num_bytes": 3241075.0, "num_examples": 100}, {"name": "singer", "num_bytes": 3198810.0, "num_examples": 100}, {"name": "tutor", "num_bytes": 3442962.0, "num_examples": 100}, {"name": "language_pathologist", "num_bytes": 3238081.0, "num_examples": 100}, {"name": "medical_records_specialist", "num_bytes": 3478698.0, "num_examples": 100}, {"name": "sales_manager", "num_bytes": 2889842.0, "num_examples": 100}, {"name": "industrial_engineer", "num_bytes": 4524725.0, "num_examples": 100}, {"name": "manager", "num_bytes": 2976237.0, "num_examples": 100}, {"name": "mechanic", "num_bytes": 3973394.0, "num_examples": 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3256695.0, "num_examples": 100}, {"name": "paralegal", "num_bytes": 3144149.0, "num_examples": 100}, {"name": "graphic_designer", "num_bytes": 3779936.0, "num_examples": 100}, {"name": "dentist", "num_bytes": 3051311.0, "num_examples": 100}, {"name": "roofer", "num_bytes": 4510641.0, "num_examples": 100}, {"name": "public_relations_specialist", "num_bytes": 3018253.0, "num_examples": 100}, {"name": "engineer", "num_bytes": 4143278.0, "num_examples": 100}, {"name": "occupational_therapist", "num_bytes": 3172574.0, "num_examples": 100}, {"name": "manicurist", "num_bytes": 3014804.0, "num_examples": 100}, {"name": "cleaner", "num_bytes": 2822728.0, "num_examples": 100}, {"name": "facilities_manager", "num_bytes": 3233702.0, "num_examples": 100}, {"name": "repair_worker", "num_bytes": 3945550.0, "num_examples": 100}, {"name": "cashier", "num_bytes": 4015653.0, "num_examples": 100}, {"name": "baker", "num_bytes": 3760855.0, "num_examples": 100}, {"name": "market_research_analyst", "num_bytes": 3801266.0, "num_examples": 100}, {"name": "health_technician", "num_bytes": 3208097.0, "num_examples": 100}, {"name": "veterinarian", "num_bytes": 3218038.0, "num_examples": 100}, {"name": "underwriter", "num_bytes": 2965985.0, "num_examples": 100}, {"name": "mechanical_engineer", "num_bytes": 4864008.0, "num_examples": 100}, {"name": "janitor", "num_bytes": 3256354.0, "num_examples": 100}, {"name": "pilot", "num_bytes": 3849806.0, "num_examples": 100}, {"name": "therapist", "num_bytes": 2913566.0, "num_examples": 100}, {"name": "director", "num_bytes": 3015590.0, "num_examples": 100}, {"name": "wholesale_buyer", "num_bytes": 4007741.0, "num_examples": 100}, {"name": "air_conditioning_installer", "num_bytes": 4078377.0, "num_examples": 100}, {"name": "butcher", "num_bytes": 4473092.0, "num_examples": 100}, {"name": "machinery_mechanic", "num_bytes": 4410538.0, "num_examples": 100}, {"name": "event_planner", "num_bytes": 3416510.0, "num_examples": 100}, {"name": "carpet_installer", "num_bytes": 4231786.0, "num_examples": 100}, {"name": "musician", "num_bytes": 3496741.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 3887933.0, "num_examples": 100}, {"name": "farmer", "num_bytes": 4224326.0, "num_examples": 100}, {"name": "financial_manager", "num_bytes": 3032824.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 3723729.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3603897.0, "num_examples": 100}, {"name": "machinist", "num_bytes": 3776999.0, "num_examples": 100}, {"name": "firefighter", "num_bytes": 4226861.0, "num_examples": 100}, {"name": "photographer", "num_bytes": 3227910.0, "num_examples": 100}, {"name": "file_clerk", "num_bytes": 4124578.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4379280.0, "num_examples": 100}, {"name": "fast_food_worker", "num_bytes": 3902204.0, "num_examples": 100}, {"name": "bartender", "num_bytes": 4232353.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 4013303.0, "num_examples": 100}, {"name": "pharmacist", "num_bytes": 4163465.0, "num_examples": 100}, {"name": "nursing_assistant", "num_bytes": 3232853.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3402257.0, "num_examples": 100}, {"name": "mental_health_counselor", "num_bytes": 2864853.0, "num_examples": 100}, {"name": "network_administrator", "num_bytes": 4548591.0, "num_examples": 100}, {"name": "teacher", "num_bytes": 3003287.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 4891231.0, "num_examples": 100}, {"name": "teller", "num_bytes": 3044401.0, "num_examples": 100}, {"name": "teaching_assistant", "num_bytes": 2980715.0, "num_examples": 100}, {"name": "payroll_clerk", "num_bytes": 3659293.0, "num_examples": 100}, {"name": "laboratory_technician", "num_bytes": 3821994.0, "num_examples": 100}, {"name": "social_assistant", "num_bytes": 1642549.0, "num_examples": 100}, {"name": "radiologic_technician", "num_bytes": 3606317.0, "num_examples": 100}, {"name": "social_worker", "num_bytes": 3202655.0, "num_examples": 100}, {"name": "nurse", "num_bytes": 3163177.0, "num_examples": 100}, {"name": "receptionist", "num_bytes": 3232646.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4186317.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3250295.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 2923881.0, "num_examples": 100}, {"name": "massage_therapist", "num_bytes": 2775268.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 3711054.0, "num_examples": 100}, {"name": "plane_mechanic", "num_bytes": 4178003.0, "num_examples": 100}], "download_size": 547079713, "dataset_size": 524072204.0}}
|
2023-06-02T22:54:07+00:00
|
623d70fe075bedb2773366f4ddc1fd1afd113be7
|
mucc001/scirepeval_fos_test
|
[
"license:unknown",
"region:us"
] |
2023-06-02T22:58:45+00:00
|
{"license": "unknown", "dataset_info": {"features": [{"name": "paper_id", "dtype": "string"}, {"name": "label", "sequence": "int64"}], "splits": [{"name": "train", "num_bytes": 9377971, "num_examples": 53133}, {"name": "test", "num_bytes": 82664, "num_examples": 468}], "download_size": 703027, "dataset_size": 9460635}}
|
2023-06-08T16:24:45+00:00
|
|
7d4a7a325de6c64d0e3a171707a273873d3c3587
|
# Dataset Card for "prof_images_blip__andite-anything-v4.0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__andite-anything-v4.0
|
[
"region:us"
] |
2023-06-02T22:59:53+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 2531808.0, "num_examples": 100}, {"name": "aide", "num_bytes": 2679124.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 2813013.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 4639358.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 2896247.0, "num_examples": 100}, {"name": "coach", "num_bytes": 2882930.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 2818554.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 5378771.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3905031.0, "num_examples": 100}, {"name": "interior_designer", "num_bytes": 3331210.0, "num_examples": 100}, {"name": "designer", "num_bytes": 2642954.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 2258462.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 2518630.0, "num_examples": 100}, {"name": "inventory_clerk", "num_bytes": 3409472.0, "num_examples": 100}, {"name": "groundskeeper", "num_bytes": 2561060.0, "num_examples": 100}, {"name": "electrical_engineer", "num_bytes": 5135441.0, "num_examples": 100}, {"name": "insurance_agent", "num_bytes": 1620035.0, "num_examples": 100}, {"name": "aerospace_engineer", "num_bytes": 3775646.0, "num_examples": 100}, {"name": "financial_advisor", "num_bytes": 2101572.0, "num_examples": 100}, {"name": "architect", "num_bytes": 2260391.0, "num_examples": 100}, {"name": "dental_hygienist", "num_bytes": 2310714.0, "num_examples": 100}, {"name": "artist", "num_bytes": 2748513.0, "num_examples": 100}, {"name": "ceo", "num_bytes": 2000864.0, "num_examples": 100}, {"name": "author", "num_bytes": 3099107.0, "num_examples": 100}, {"name": "construction_worker", "num_bytes": 3129494.0, "num_examples": 100}, {"name": "clergy", "num_bytes": 1658981.0, "num_examples": 100}, {"name": "electrician", "num_bytes": 3902612.0, "num_examples": 100}, {"name": "jailer", "num_bytes": 2136768.0, "num_examples": 100}, {"name": "credit_counselor", "num_bytes": 2526314.0, "num_examples": 100}, {"name": "drywall_installer", "num_bytes": 2038117.0, "num_examples": 100}, {"name": "dental_assistant", "num_bytes": 2585938.0, "num_examples": 100}, {"name": "fitness_instructor", "num_bytes": 2716152.0, "num_examples": 100}, {"name": "detective", "num_bytes": 2062930.0, "num_examples": 100}, {"name": "hairdresser", "num_bytes": 2584556.0, "num_examples": 100}, {"name": "compliance_officer", "num_bytes": 2399239.0, "num_examples": 100}, {"name": "language_pathologist", "num_bytes": 1045161.0, "num_examples": 60}, {"name": "industrial_engineer", "num_bytes": 4357711.0, "num_examples": 100}, {"name": "computer_systems_analyst", "num_bytes": 4291635.0, "num_examples": 100}, {"name": "claims_appraiser", "num_bytes": 3193770.0, "num_examples": 100}, {"name": "interviewer", "num_bytes": 2338306.0, "num_examples": 100}, {"name": "dispatcher", "num_bytes": 3374529.0, "num_examples": 100}, {"name": "customer_service_representative", "num_bytes": 3173934.0, "num_examples": 100}, {"name": "graphic_designer", "num_bytes": 3311895.0, "num_examples": 100}, {"name": "dentist", "num_bytes": 1605601.0, "num_examples": 100}, {"name": "engineer", "num_bytes": 3633439.0, "num_examples": 100}, {"name": "cleaner", "num_bytes": 1870345.0, "num_examples": 100}, {"name": "facilities_manager", "num_bytes": 2759902.0, "num_examples": 100}, {"name": "cashier", "num_bytes": 3452619.0, "num_examples": 100}, {"name": "baker", "num_bytes": 2784630.0, "num_examples": 100}, {"name": "health_technician", "num_bytes": 2738076.0, "num_examples": 100}, {"name": "janitor", "num_bytes": 1169693.0, "num_examples": 100}, {"name": "director", "num_bytes": 2243652.0, "num_examples": 100}, {"name": "air_conditioning_installer", "num_bytes": 4242936.0, "num_examples": 100}, {"name": "butcher", "num_bytes": 1952460.0, "num_examples": 100}, {"name": "event_planner", "num_bytes": 3210506.0, "num_examples": 100}, {"name": "carpet_installer", "num_bytes": 3013415.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 3693552.0, "num_examples": 100}, {"name": "farmer", "num_bytes": 3084247.0, "num_examples": 100}, {"name": "financial_manager", "num_bytes": 2364896.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 1268385.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 2799459.0, "num_examples": 100}, {"name": "firefighter", "num_bytes": 3552905.0, "num_examples": 100}, {"name": "file_clerk", "num_bytes": 3642101.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 3449795.0, "num_examples": 100}, {"name": "fast_food_worker", "num_bytes": 3737822.0, "num_examples": 100}, {"name": "bartender", "num_bytes": 3623120.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 4341304.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 2783353.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 3136376.0, "num_examples": 100}, {"name": "laboratory_technician", "num_bytes": 3008169.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 2376679.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 2429145.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 3189906.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 3338009.0, "num_examples": 100}], "download_size": 214113890, "dataset_size": 215643446.0}}
|
2023-06-02T23:00:57+00:00
|
ba9c1d7bd6ced73cbfcb5a0447008044726920a7
|
# Dataset Card for "c4_llama_packed_seqlen256_tiny"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
hlillemark/c4_llama_packed_seqlen256_tiny
|
[
"region:us"
] |
2023-06-02T23:03:15+00:00
|
{"dataset_info": {"features": [{"name": "input_ids", "sequence": "int32"}], "splits": [{"name": "train", "num_bytes": 217480596, "num_examples": 211557}, {"name": "validation", "num_bytes": 21751452, "num_examples": 21159}], "download_size": 116858634, "dataset_size": 239232048}}
|
2023-06-02T23:03:28+00:00
|
30baacad7a3724e8d12cbc5c74b2600f9509af1f
|
# Dataset Card for "prof_images_blip__22h-vintedois-diffusion-v0-1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__22h-vintedois-diffusion-v0-1
|
[
"region:us"
] |
2023-06-02T23:12:33+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 3640022.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3358153.0, "num_examples": 100}, {"name": "police_officer", "num_bytes": 3522932.0, "num_examples": 100}, {"name": "purchasing_agent", "num_bytes": 3286344.0, "num_examples": 100}, {"name": "metal_worker", "num_bytes": 4410266.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 3727701.0, "num_examples": 100}, {"name": "stocker", "num_bytes": 3778322.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 4021431.0, "num_examples": 100}, {"name": "writer", "num_bytes": 4150377.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3206485.0, "num_examples": 100}, {"name": "coach", "num_bytes": 3644886.0, "num_examples": 100}, {"name": "painter", "num_bytes": 4259647.0, "num_examples": 100}, {"name": "real_estate_broker", "num_bytes": 3439406.0, "num_examples": 100}, {"name": "truck_driver", "num_bytes": 4438012.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 3900333.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 3641931.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3467370.0, "num_examples": 100}, {"name": "interior_designer", "num_bytes": 4011621.0, "num_examples": 100}, {"name": "nutritionist", "num_bytes": 3657524.0, "num_examples": 100}, {"name": "designer", "num_bytes": 3433880.0, "num_examples": 100}, {"name": "maid", "num_bytes": 3236767.0, "num_examples": 100}, {"name": "producer", "num_bytes": 3807892.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 3199680.0, "num_examples": 100}, {"name": "logistician", "num_bytes": 4051060.0, "num_examples": 100}, {"name": "tractor_operator", "num_bytes": 5097668.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3124348.0, "num_examples": 100}, {"name": "inventory_clerk", "num_bytes": 3830045.0, "num_examples": 100}, {"name": "sheet_metal_worker", "num_bytes": 4221678.0, "num_examples": 100}, {"name": "groundskeeper", "num_bytes": 4363064.0, "num_examples": 100}, {"name": "electrical_engineer", "num_bytes": 4562412.0, "num_examples": 100}, {"name": "physical_therapist", "num_bytes": 3189145.0, "num_examples": 100}, {"name": "insurance_agent", "num_bytes": 3040990.0, "num_examples": 100}, {"name": "aerospace_engineer", "num_bytes": 4278650.0, "num_examples": 100}, {"name": "psychologist", "num_bytes": 3143650.0, "num_examples": 100}, {"name": "financial_advisor", "num_bytes": 3196183.0, "num_examples": 100}, {"name": "printing_press_operator", "num_bytes": 4494714.0, "num_examples": 100}, {"name": "architect", "num_bytes": 3890945.0, "num_examples": 100}, {"name": "dental_hygienist", "num_bytes": 3079331.0, "num_examples": 100}, {"name": "artist", "num_bytes": 4244089.0, "num_examples": 100}, {"name": "office_worker", "num_bytes": 3462709.0, "num_examples": 100}, 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"fitness_instructor", "num_bytes": 3536687.0, "num_examples": 100}, {"name": "detective", "num_bytes": 3347937.0, "num_examples": 100}, {"name": "hairdresser", "num_bytes": 3080679.0, "num_examples": 100}, {"name": "welder", "num_bytes": 4766685.0, "num_examples": 100}, {"name": "pharmacy_technician", "num_bytes": 4070319.0, "num_examples": 100}, {"name": "compliance_officer", "num_bytes": 3471476.0, "num_examples": 100}, {"name": "singer", "num_bytes": 3452677.0, "num_examples": 100}, {"name": "tutor", "num_bytes": 3752207.0, "num_examples": 100}, {"name": "language_pathologist", "num_bytes": 3587772.0, "num_examples": 100}, {"name": "medical_records_specialist", "num_bytes": 3542194.0, "num_examples": 100}, {"name": "sales_manager", "num_bytes": 2974259.0, "num_examples": 100}, {"name": "industrial_engineer", "num_bytes": 4262086.0, "num_examples": 100}, {"name": "manager", "num_bytes": 3106718.0, "num_examples": 100}, {"name": "mechanic", "num_bytes": 4215182.0, "num_examples": 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3452055.0, "num_examples": 100}, {"name": "paralegal", "num_bytes": 3135751.0, "num_examples": 100}, {"name": "graphic_designer", "num_bytes": 4484133.0, "num_examples": 100}, {"name": "dentist", "num_bytes": 3104962.0, "num_examples": 100}, {"name": "roofer", "num_bytes": 4264565.0, "num_examples": 100}, {"name": "public_relations_specialist", "num_bytes": 3298954.0, "num_examples": 100}, {"name": "engineer", "num_bytes": 3867898.0, "num_examples": 100}, {"name": "occupational_therapist", "num_bytes": 3205012.0, "num_examples": 100}, {"name": "manicurist", "num_bytes": 3112145.0, "num_examples": 100}, {"name": "cleaner", "num_bytes": 3475664.0, "num_examples": 100}, {"name": "facilities_manager", "num_bytes": 3562539.0, "num_examples": 100}, {"name": "repair_worker", "num_bytes": 3906302.0, "num_examples": 100}, {"name": "cashier", "num_bytes": 3728570.0, "num_examples": 100}, {"name": "baker", "num_bytes": 3871443.0, "num_examples": 100}, {"name": "market_research_analyst", "num_bytes": 3889616.0, "num_examples": 100}, {"name": "health_technician", "num_bytes": 3356530.0, "num_examples": 100}, {"name": "veterinarian", "num_bytes": 3249094.0, "num_examples": 100}, {"name": "underwriter", "num_bytes": 3279381.0, "num_examples": 100}, {"name": "mechanical_engineer", "num_bytes": 4452331.0, "num_examples": 100}, {"name": "janitor", "num_bytes": 3733976.0, "num_examples": 100}, {"name": "pilot", "num_bytes": 3918334.0, "num_examples": 100}, {"name": "therapist", "num_bytes": 3077311.0, "num_examples": 100}, {"name": "director", "num_bytes": 3305172.0, "num_examples": 100}, {"name": "wholesale_buyer", "num_bytes": 3863590.0, "num_examples": 100}, {"name": "air_conditioning_installer", "num_bytes": 4217322.0, "num_examples": 100}, {"name": "butcher", "num_bytes": 4351854.0, "num_examples": 100}, {"name": "machinery_mechanic", "num_bytes": 4614179.0, "num_examples": 100}, {"name": "event_planner", "num_bytes": 3547339.0, "num_examples": 100}, {"name": "carpet_installer", "num_bytes": 4389212.0, "num_examples": 100}, {"name": "musician", "num_bytes": 3639823.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 3841611.0, "num_examples": 100}, {"name": "farmer", "num_bytes": 4438706.0, "num_examples": 100}, {"name": "financial_manager", "num_bytes": 3181723.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 3586015.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3213913.0, "num_examples": 100}, {"name": "machinist", "num_bytes": 4295487.0, "num_examples": 100}, {"name": "firefighter", "num_bytes": 4077232.0, "num_examples": 100}, {"name": "photographer", "num_bytes": 3606746.0, "num_examples": 100}, {"name": "file_clerk", "num_bytes": 4350476.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4250786.0, "num_examples": 100}, {"name": "fast_food_worker", "num_bytes": 3606432.0, "num_examples": 100}, {"name": "bartender", "num_bytes": 4221598.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 4180355.0, "num_examples": 100}, {"name": "pharmacist", "num_bytes": 3996786.0, "num_examples": 100}, {"name": "nursing_assistant", "num_bytes": 3158704.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3415428.0, "num_examples": 100}, {"name": "mental_health_counselor", "num_bytes": 3347482.0, "num_examples": 100}, {"name": "network_administrator", "num_bytes": 4600993.0, "num_examples": 100}, {"name": "teacher", "num_bytes": 3580451.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 4831099.0, "num_examples": 100}, {"name": "teller", "num_bytes": 3422422.0, "num_examples": 100}, {"name": "teaching_assistant", "num_bytes": 3551890.0, "num_examples": 100}, {"name": "payroll_clerk", "num_bytes": 3292390.0, "num_examples": 100}, {"name": "laboratory_technician", "num_bytes": 3898419.0, "num_examples": 100}, {"name": "social_assistant", "num_bytes": 3222358.0, "num_examples": 100}, {"name": "radiologic_technician", "num_bytes": 3937403.0, "num_examples": 100}, {"name": "social_worker", "num_bytes": 3582335.0, "num_examples": 100}, {"name": "nurse", "num_bytes": 3123385.0, "num_examples": 100}, {"name": "receptionist", "num_bytes": 3372519.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4415058.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 4070069.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 3301952.0, "num_examples": 100}, {"name": "massage_therapist", "num_bytes": 2954838.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 3612046.0, "num_examples": 100}, {"name": "plane_mechanic", "num_bytes": 3974652.0, "num_examples": 100}], "download_size": 567913139, "dataset_size": 544061331.0}}
|
2023-06-02T23:14:56+00:00
|
afcf0a26c604157456e34c337221fdb43933f8a5
|
# Dataset Card for "prof_images_blip__CompVis-stable-diffusion-v1-4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__CompVis-stable-diffusion-v1-4
|
[
"region:us"
] |
2023-06-02T23:26:33+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 4223382.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3433436.0, "num_examples": 100}, {"name": "police_officer", "num_bytes": 3525854.0, "num_examples": 100}, {"name": "purchasing_agent", "num_bytes": 3853558.0, "num_examples": 100}, {"name": "metal_worker", "num_bytes": 4799343.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 3427250.0, "num_examples": 100}, {"name": "stocker", "num_bytes": 4196338.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 3932067.0, "num_examples": 100}, {"name": "writer", "num_bytes": 4364139.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3582911.0, "num_examples": 100}, {"name": "coach", "num_bytes": 4085092.0, "num_examples": 100}, {"name": "painter", "num_bytes": 4180812.0, "num_examples": 100}, {"name": "real_estate_broker", "num_bytes": 3778436.0, "num_examples": 100}, {"name": "truck_driver", "num_bytes": 5016403.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 3879131.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 3626865.0, "num_examples": 100}, {"name": "cook", "num_bytes": 4055023.0, "num_examples": 100}, {"name": "interior_designer", "num_bytes": 4326260.0, "num_examples": 100}, {"name": "nutritionist", "num_bytes": 4123957.0, "num_examples": 100}, {"name": "designer", "num_bytes": 3688209.0, "num_examples": 100}, {"name": "maid", "num_bytes": 3518915.0, "num_examples": 100}, {"name": "producer", "num_bytes": 3857112.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 3321250.0, "num_examples": 100}, {"name": "logistician", "num_bytes": 4520499.0, "num_examples": 100}, {"name": "tractor_operator", "num_bytes": 6125566.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3440088.0, "num_examples": 100}, {"name": "inventory_clerk", "num_bytes": 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"carpet_installer", "num_bytes": 5150830.0, "num_examples": 100}, {"name": "musician", "num_bytes": 4124108.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 4513149.0, "num_examples": 100}, {"name": "farmer", "num_bytes": 5721441.0, "num_examples": 100}, {"name": "financial_manager", "num_bytes": 3374624.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 4097057.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3780094.0, "num_examples": 100}, {"name": "machinist", "num_bytes": 4583452.0, "num_examples": 100}, {"name": "firefighter", "num_bytes": 4457533.0, "num_examples": 100}, {"name": "photographer", "num_bytes": 4110260.0, "num_examples": 100}, {"name": "file_clerk", "num_bytes": 4401860.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4737548.0, "num_examples": 100}, {"name": "fast_food_worker", "num_bytes": 3939218.0, "num_examples": 100}, {"name": "bartender", "num_bytes": 4621090.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 3730954.0, "num_examples": 100}, {"name": "pharmacist", "num_bytes": 4305414.0, "num_examples": 100}, {"name": "nursing_assistant", "num_bytes": 3248194.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3656420.0, "num_examples": 100}, {"name": "mental_health_counselor", "num_bytes": 3548168.0, "num_examples": 100}, {"name": "network_administrator", "num_bytes": 4417771.0, "num_examples": 100}, {"name": "teacher", "num_bytes": 3958884.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 5511691.0, "num_examples": 100}, {"name": "teller", "num_bytes": 3582169.0, "num_examples": 100}, {"name": "teaching_assistant", "num_bytes": 4022541.0, "num_examples": 100}, {"name": "payroll_clerk", "num_bytes": 3401694.0, "num_examples": 100}, {"name": "laboratory_technician", "num_bytes": 3639997.0, "num_examples": 100}, {"name": "social_assistant", "num_bytes": 3970721.0, "num_examples": 100}, {"name": "radiologic_technician", "num_bytes": 3331186.0, "num_examples": 100}, {"name": "social_worker", "num_bytes": 4215871.0, "num_examples": 100}, {"name": "nurse", "num_bytes": 3397043.0, "num_examples": 100}, {"name": "receptionist", "num_bytes": 3274844.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4641218.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3715116.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 4068697.0, "num_examples": 100}, {"name": "massage_therapist", "num_bytes": 3257536.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 4083876.0, "num_examples": 100}, {"name": "plane_mechanic", "num_bytes": 4092378.0, "num_examples": 100}], "download_size": 609647639, "dataset_size": 585820373.0}}
|
2023-06-02T23:29:20+00:00
|
16abc10e261cd46d72013605c8ae725791066632
|
# Dataset Card for "prof_images_blip__andite-pastel-mix"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__andite-pastel-mix
|
[
"region:us"
] |
2023-06-02T23:41:08+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 3728137.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3533609.0, "num_examples": 100}, {"name": "police_officer", "num_bytes": 3764372.0, "num_examples": 100}, {"name": "purchasing_agent", "num_bytes": 3847745.0, "num_examples": 100}, {"name": "metal_worker", "num_bytes": 3704792.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 4179766.0, "num_examples": 100}, {"name": "stocker", "num_bytes": 3658002.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 4138564.0, "num_examples": 100}, {"name": "writer", "num_bytes": 3769188.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3913750.0, "num_examples": 100}, {"name": "coach", "num_bytes": 3601098.0, "num_examples": 100}, {"name": "painter", "num_bytes": 3841437.0, "num_examples": 100}, {"name": "real_estate_broker", "num_bytes": 3798206.0, "num_examples": 100}, {"name": "truck_driver", "num_bytes": 3486918.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 3998095.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 4032722.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3610019.0, "num_examples": 100}, {"name": "interior_designer", "num_bytes": 3551697.0, "num_examples": 100}, {"name": "nutritionist", "num_bytes": 3975024.0, "num_examples": 100}, {"name": "designer", "num_bytes": 4350345.0, "num_examples": 100}, {"name": "maid", "num_bytes": 3316931.0, "num_examples": 100}, {"name": "producer", "num_bytes": 4105705.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 3787341.0, "num_examples": 100}, {"name": "logistician", "num_bytes": 4158777.0, "num_examples": 100}, {"name": "tractor_operator", "num_bytes": 4228107.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3658142.0, "num_examples": 100}, {"name": "inventory_clerk", "num_bytes": 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3795396.0, "num_examples": 100}, {"name": "paralegal", "num_bytes": 3999180.0, "num_examples": 100}, {"name": "graphic_designer", "num_bytes": 4813106.0, "num_examples": 100}, {"name": "dentist", "num_bytes": 3295120.0, "num_examples": 100}, {"name": "roofer", "num_bytes": 3152196.0, "num_examples": 100}, {"name": "public_relations_specialist", "num_bytes": 3911685.0, "num_examples": 100}, {"name": "engineer", "num_bytes": 3960231.0, "num_examples": 100}, {"name": "occupational_therapist", "num_bytes": 3854486.0, "num_examples": 100}, {"name": "manicurist", "num_bytes": 3545009.0, "num_examples": 100}, {"name": "cleaner", "num_bytes": 3775026.0, "num_examples": 100}, {"name": "facilities_manager", "num_bytes": 3771581.0, "num_examples": 100}, {"name": "repair_worker", "num_bytes": 3571632.0, "num_examples": 100}, {"name": "cashier", "num_bytes": 3912805.0, "num_examples": 100}, {"name": "baker", "num_bytes": 3551856.0, "num_examples": 100}, {"name": "market_research_analyst", 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"carpet_installer", "num_bytes": 4047955.0, "num_examples": 100}, {"name": "musician", "num_bytes": 4083969.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 3600869.0, "num_examples": 100}, {"name": "farmer", "num_bytes": 3367641.0, "num_examples": 100}, {"name": "financial_manager", "num_bytes": 3853773.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 3791195.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3754385.0, "num_examples": 100}, {"name": "machinist", "num_bytes": 3924644.0, "num_examples": 100}, {"name": "firefighter", "num_bytes": 3828617.0, "num_examples": 100}, {"name": "photographer", "num_bytes": 3924231.0, "num_examples": 100}, {"name": "file_clerk", "num_bytes": 3875992.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 3846140.0, "num_examples": 100}, {"name": "fast_food_worker", "num_bytes": 3779034.0, "num_examples": 100}, {"name": "bartender", "num_bytes": 3557577.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 4026900.0, "num_examples": 100}, {"name": "pharmacist", "num_bytes": 3895948.0, "num_examples": 100}, {"name": "nursing_assistant", "num_bytes": 3506147.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3647153.0, "num_examples": 100}, {"name": "mental_health_counselor", "num_bytes": 3415399.0, "num_examples": 100}, {"name": "network_administrator", "num_bytes": 4927703.0, "num_examples": 100}, {"name": "teacher", "num_bytes": 3491741.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 3667556.0, "num_examples": 100}, {"name": "teller", "num_bytes": 3627718.0, "num_examples": 100}, {"name": "teaching_assistant", "num_bytes": 3617832.0, "num_examples": 100}, {"name": "payroll_clerk", "num_bytes": 4155239.0, "num_examples": 100}, {"name": "laboratory_technician", "num_bytes": 4161997.0, "num_examples": 100}, {"name": "social_assistant", "num_bytes": 3802528.0, "num_examples": 100}, {"name": "radiologic_technician", "num_bytes": 4181639.0, "num_examples": 100}, {"name": "social_worker", "num_bytes": 3684552.0, "num_examples": 100}, {"name": "nurse", "num_bytes": 3434064.0, "num_examples": 100}, {"name": "receptionist", "num_bytes": 3764476.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 3311541.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3300645.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 3962049.0, "num_examples": 100}, {"name": "massage_therapist", "num_bytes": 2992325.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 3548152.0, "num_examples": 100}, {"name": "plane_mechanic", "num_bytes": 3844177.0, "num_examples": 100}], "download_size": 577245338, "dataset_size": 553388728.0}}
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2023-06-02T23:43:53+00:00
|
fa66b59393b2b57ce4e8fc9a3797d27d495ed08f
|
# Dataset Card for "prof_images_blip__stabilityai-stable-diffusion-2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__stabilityai-stable-diffusion-2
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[
"region:us"
] |
2023-06-02T23:55:54+00:00
|
{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 4257204.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3491678.0, "num_examples": 100}, {"name": "police_officer", "num_bytes": 4252502.0, "num_examples": 100}, {"name": "purchasing_agent", "num_bytes": 5285492.0, "num_examples": 100}, {"name": "metal_worker", "num_bytes": 5097159.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 5261660.0, "num_examples": 100}, {"name": "stocker", "num_bytes": 5700830.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 5731031.0, "num_examples": 100}, {"name": "writer", "num_bytes": 4309299.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 4689435.0, "num_examples": 100}, {"name": "coach", "num_bytes": 4537870.0, "num_examples": 100}, {"name": "painter", "num_bytes": 3686294.0, "num_examples": 100}, {"name": "real_estate_broker", "num_bytes": 5241610.0, "num_examples": 100}, {"name": "truck_driver", "num_bytes": 4955237.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 6112746.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 5187781.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3905039.0, "num_examples": 100}, {"name": "interior_designer", "num_bytes": 4970044.0, "num_examples": 100}, {"name": "nutritionist", "num_bytes": 4904269.0, "num_examples": 100}, {"name": "designer", "num_bytes": 5618928.0, "num_examples": 100}, {"name": "maid", "num_bytes": 3653112.0, "num_examples": 100}, {"name": "producer", "num_bytes": 5532083.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 5181631.0, "num_examples": 100}, {"name": "logistician", "num_bytes": 5623819.0, "num_examples": 100}, {"name": "tractor_operator", "num_bytes": 5566239.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3922429.0, "num_examples": 100}, {"name": "inventory_clerk", "num_bytes": 5675119.0, "num_examples": 100}, {"name": "sheet_metal_worker", "num_bytes": 4932393.0, "num_examples": 100}, {"name": "groundskeeper", "num_bytes": 5624913.0, "num_examples": 100}, {"name": "electrical_engineer", "num_bytes": 5486843.0, "num_examples": 100}, {"name": "physical_therapist", "num_bytes": 4416383.0, "num_examples": 100}, {"name": "insurance_agent", "num_bytes": 4503029.0, "num_examples": 100}, {"name": "aerospace_engineer", "num_bytes": 5005814.0, "num_examples": 100}, {"name": "psychologist", "num_bytes": 4751138.0, "num_examples": 100}, {"name": "financial_advisor", "num_bytes": 4616805.0, "num_examples": 100}, {"name": "printing_press_operator", "num_bytes": 4885677.0, "num_examples": 100}, {"name": "architect", "num_bytes": 4694972.0, "num_examples": 100}, {"name": "dental_hygienist", "num_bytes": 4051984.0, "num_examples": 100}, {"name": "artist", "num_bytes": 4093686.0, "num_examples": 100}, {"name": "office_worker", "num_bytes": 4984173.0, "num_examples": 100}, {"name": "ceo", "num_bytes": 4753603.0, "num_examples": 100}, {"name": "taxi_driver", "num_bytes": 4839205.0, "num_examples": 100}, {"name": "librarian", "num_bytes": 5209270.0, "num_examples": 100}, {"name": "author", "num_bytes": 4326443.0, "num_examples": 100}, {"name": "plumber", "num_bytes": 5004142.0, "num_examples": 100}, {"name": "construction_worker", "num_bytes": 5173177.0, "num_examples": 100}, {"name": "clergy", "num_bytes": 3852512.0, "num_examples": 100}, {"name": "electrician", "num_bytes": 5239521.0, "num_examples": 100}, {"name": "jailer", "num_bytes": 5032189.0, "num_examples": 100}, {"name": "credit_counselor", "num_bytes": 4814481.0, "num_examples": 100}, {"name": "scientist", "num_bytes": 4363783.0, "num_examples": 100}, {"name": "drywall_installer", "num_bytes": 4174819.0, "num_examples": 100}, {"name": "school_bus_driver", "num_bytes": 4998022.0, "num_examples": 100}, {"name": "dental_assistant", "num_bytes": 4140296.0, "num_examples": 100}, {"name": "fitness_instructor", "num_bytes": 4416504.0, "num_examples": 100}, {"name": "detective", "num_bytes": 4583678.0, "num_examples": 100}, {"name": "hairdresser", "num_bytes": 4463307.0, "num_examples": 100}, {"name": "welder", "num_bytes": 4918374.0, "num_examples": 100}, {"name": "pharmacy_technician", "num_bytes": 4661790.0, "num_examples": 100}, {"name": "compliance_officer", "num_bytes": 4845349.0, "num_examples": 100}, {"name": "singer", "num_bytes": 4638247.0, "num_examples": 100}, {"name": "tutor", "num_bytes": 3678185.0, "num_examples": 100}, {"name": "language_pathologist", "num_bytes": 5254361.0, "num_examples": 100}, {"name": "medical_records_specialist", "num_bytes": 5634506.0, "num_examples": 100}, {"name": "sales_manager", "num_bytes": 5056132.0, "num_examples": 100}, {"name": "industrial_engineer", "num_bytes": 5172786.0, "num_examples": 100}, {"name": "manager", "num_bytes": 4931846.0, "num_examples": 100}, {"name": "mechanic", "num_bytes": 4613784.0, "num_examples": 100}, {"name": "postal_worker", "num_bytes": 4441299.0, "num_examples": 100}, {"name": "computer_systems_analyst", "num_bytes": 6079263.0, "num_examples": 100}, {"name": "salesperson", "num_bytes": 5024667.0, "num_examples": 100}, {"name": "office_clerk", "num_bytes": 4755914.0, "num_examples": 100}, {"name": "claims_appraiser", "num_bytes": 4478395.0, "num_examples": 100}, {"name": "security_guard", "num_bytes": 5230558.0, "num_examples": 100}, {"name": "interviewer", "num_bytes": 4656965.0, "num_examples": 100}, {"name": "dispatcher", "num_bytes": 5291584.0, "num_examples": 100}, {"name": "lawyer", "num_bytes": 4277664.0, "num_examples": 100}, {"name": "marketing_manager", "num_bytes": 5252505.0, "num_examples": 100}, {"name": "customer_service_representative", "num_bytes": 4896010.0, "num_examples": 100}, {"name": "software_developer", "num_bytes": 5547030.0, "num_examples": 100}, {"name": "mover", "num_bytes": 4568118.0, "num_examples": 100}, {"name": "supervisor", "num_bytes": 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"num_bytes": 5593811.0, "num_examples": 100}, {"name": "health_technician", "num_bytes": 4477836.0, "num_examples": 100}, {"name": "veterinarian", "num_bytes": 4197438.0, "num_examples": 100}, {"name": "underwriter", "num_bytes": 4662113.0, "num_examples": 100}, {"name": "mechanical_engineer", "num_bytes": 5722417.0, "num_examples": 100}, {"name": "janitor", "num_bytes": 5390408.0, "num_examples": 100}, {"name": "pilot", "num_bytes": 3829685.0, "num_examples": 100}, {"name": "therapist", "num_bytes": 3859109.0, "num_examples": 100}, {"name": "director", "num_bytes": 4096085.0, "num_examples": 100}, {"name": "wholesale_buyer", "num_bytes": 6300854.0, "num_examples": 100}, {"name": "air_conditioning_installer", "num_bytes": 5741602.0, "num_examples": 100}, {"name": "butcher", "num_bytes": 5332426.0, "num_examples": 100}, {"name": "machinery_mechanic", "num_bytes": 5057875.0, "num_examples": 100}, {"name": "event_planner", "num_bytes": 5039870.0, "num_examples": 100}, {"name": "carpet_installer", "num_bytes": 5903738.0, "num_examples": 100}, {"name": "musician", "num_bytes": 4431405.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 5463535.0, "num_examples": 100}, {"name": "farmer", "num_bytes": 4772114.0, "num_examples": 100}, {"name": "financial_manager", "num_bytes": 5187071.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 4326077.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 4268877.0, "num_examples": 100}, {"name": "machinist", "num_bytes": 4593820.0, "num_examples": 100}, {"name": "firefighter", "num_bytes": 4414229.0, "num_examples": 100}, {"name": "photographer", "num_bytes": 4056745.0, "num_examples": 100}, {"name": "file_clerk", "num_bytes": 5925494.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4714778.0, "num_examples": 100}, {"name": "fast_food_worker", "num_bytes": 4778503.0, "num_examples": 100}, {"name": "bartender", "num_bytes": 5407515.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 5961791.0, "num_examples": 100}, {"name": "pharmacist", "num_bytes": 5103862.0, "num_examples": 100}, {"name": "nursing_assistant", "num_bytes": 3969985.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 4578703.0, "num_examples": 100}, {"name": "mental_health_counselor", "num_bytes": 4915411.0, "num_examples": 100}, {"name": "network_administrator", "num_bytes": 6576986.0, "num_examples": 100}, {"name": "teacher", "num_bytes": 4112341.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 4582691.0, "num_examples": 100}, {"name": "teller", "num_bytes": 4580341.0, "num_examples": 100}, {"name": "teaching_assistant", "num_bytes": 4310141.0, "num_examples": 100}, {"name": "payroll_clerk", "num_bytes": 5104922.0, "num_examples": 100}, {"name": "laboratory_technician", "num_bytes": 4772940.0, "num_examples": 100}, {"name": "social_assistant", "num_bytes": 4975461.0, "num_examples": 100}, {"name": "radiologic_technician", "num_bytes": 4614401.0, "num_examples": 100}, {"name": "social_worker", "num_bytes": 4143912.0, "num_examples": 100}, {"name": "nurse", "num_bytes": 3251197.0, "num_examples": 100}, {"name": "receptionist", "num_bytes": 4962877.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4367834.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 5027428.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 5327391.0, "num_examples": 100}, {"name": "massage_therapist", "num_bytes": 4309573.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 4488723.0, "num_examples": 100}, {"name": "plane_mechanic", "num_bytes": 4650683.0, "num_examples": 100}], "download_size": 729196101, "dataset_size": 705285705.0}}
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2023-06-02T23:58:31+00:00
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8c6d26958f2410dc8808fed1fc0901470290315b
|
# Dataset Card for "prof_images_blip__SG161222-Realistic_Vision_V1.4"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__SG161222-Realistic_Vision_V1.4
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[
"region:us"
] |
2023-06-03T00:10:30+00:00
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{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 3764373.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3056396.0, "num_examples": 100}, {"name": "police_officer", "num_bytes": 3099176.0, "num_examples": 100}, {"name": "purchasing_agent", "num_bytes": 3251868.0, "num_examples": 100}, {"name": "metal_worker", "num_bytes": 4312082.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 3516982.0, "num_examples": 100}, {"name": "stocker", "num_bytes": 3403079.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 3708720.0, "num_examples": 100}, {"name": "writer", "num_bytes": 4048957.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 2823321.0, "num_examples": 100}, {"name": "coach", "num_bytes": 3398051.0, "num_examples": 100}, {"name": "painter", "num_bytes": 3788267.0, "num_examples": 100}, {"name": "real_estate_broker", "num_bytes": 3156797.0, "num_examples": 100}, {"name": "truck_driver", "num_bytes": 4403296.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 3623965.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 3520536.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3584001.0, "num_examples": 100}, {"name": "interior_designer", "num_bytes": 3730454.0, "num_examples": 100}, {"name": "nutritionist", "num_bytes": 3438909.0, "num_examples": 100}, {"name": "designer", "num_bytes": 3113883.0, "num_examples": 100}, {"name": "maid", "num_bytes": 3622458.0, "num_examples": 100}, {"name": "producer", "num_bytes": 3667845.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 2907430.0, "num_examples": 100}, {"name": "logistician", "num_bytes": 3823886.0, "num_examples": 100}, {"name": "tractor_operator", "num_bytes": 5132720.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 2957858.0, "num_examples": 100}, {"name": "inventory_clerk", "num_bytes": 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{"name": "ceo", "num_bytes": 2772737.0, "num_examples": 100}, {"name": "taxi_driver", "num_bytes": 4501534.0, "num_examples": 100}, {"name": "librarian", "num_bytes": 4050948.0, "num_examples": 100}, {"name": "author", "num_bytes": 3987201.0, "num_examples": 100}, {"name": "plumber", "num_bytes": 3750790.0, "num_examples": 100}, {"name": "construction_worker", "num_bytes": 3748678.0, "num_examples": 100}, {"name": "clergy", "num_bytes": 3187537.0, "num_examples": 100}, {"name": "electrician", "num_bytes": 4154537.0, "num_examples": 100}, {"name": "jailer", "num_bytes": 4463218.0, "num_examples": 100}, {"name": "credit_counselor", "num_bytes": 2903663.0, "num_examples": 100}, {"name": "scientist", "num_bytes": 3297142.0, "num_examples": 100}, {"name": "drywall_installer", "num_bytes": 2991628.0, "num_examples": 100}, {"name": "school_bus_driver", "num_bytes": 4487490.0, "num_examples": 100}, {"name": "dental_assistant", "num_bytes": 2860282.0, "num_examples": 100}, {"name": 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3114846.0, "num_examples": 100}, {"name": "paralegal", "num_bytes": 3053572.0, "num_examples": 100}, {"name": "graphic_designer", "num_bytes": 3804559.0, "num_examples": 100}, {"name": "dentist", "num_bytes": 2852736.0, "num_examples": 100}, {"name": "roofer", "num_bytes": 4525331.0, "num_examples": 100}, {"name": "public_relations_specialist", "num_bytes": 3024488.0, "num_examples": 100}, {"name": "engineer", "num_bytes": 3505002.0, "num_examples": 100}, {"name": "occupational_therapist", "num_bytes": 2997675.0, "num_examples": 100}, {"name": "manicurist", "num_bytes": 2875178.0, "num_examples": 100}, {"name": "cleaner", "num_bytes": 3026534.0, "num_examples": 100}, {"name": "facilities_manager", "num_bytes": 3251426.0, "num_examples": 100}, {"name": "repair_worker", "num_bytes": 3730984.0, "num_examples": 100}, {"name": "cashier", "num_bytes": 3702937.0, "num_examples": 100}, {"name": "baker", "num_bytes": 3482248.0, "num_examples": 100}, {"name": "market_research_analyst", 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"carpet_installer", "num_bytes": 4220710.0, "num_examples": 100}, {"name": "musician", "num_bytes": 3610371.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 3561364.0, "num_examples": 100}, {"name": "farmer", "num_bytes": 4330463.0, "num_examples": 100}, {"name": "financial_manager", "num_bytes": 2898424.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 3421546.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3215643.0, "num_examples": 100}, {"name": "machinist", "num_bytes": 4108935.0, "num_examples": 100}, {"name": "firefighter", "num_bytes": 4059114.0, "num_examples": 100}, {"name": "photographer", "num_bytes": 3206033.0, "num_examples": 100}, {"name": "file_clerk", "num_bytes": 3940552.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4138995.0, "num_examples": 100}, {"name": "fast_food_worker", "num_bytes": 3680999.0, "num_examples": 100}, {"name": "bartender", "num_bytes": 4143942.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 3666082.0, "num_examples": 100}, {"name": "pharmacist", "num_bytes": 3786996.0, "num_examples": 100}, {"name": "nursing_assistant", "num_bytes": 3004957.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3276618.0, "num_examples": 100}, {"name": "mental_health_counselor", "num_bytes": 3051254.0, "num_examples": 100}, {"name": "network_administrator", "num_bytes": 4227732.0, "num_examples": 100}, {"name": "teacher", "num_bytes": 3177864.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 4978622.0, "num_examples": 100}, {"name": "teller", "num_bytes": 3018467.0, "num_examples": 100}, {"name": "teaching_assistant", "num_bytes": 3144416.0, "num_examples": 100}, {"name": "payroll_clerk", "num_bytes": 3157765.0, "num_examples": 100}, {"name": "laboratory_technician", "num_bytes": 3673896.0, "num_examples": 100}, {"name": "social_assistant", "num_bytes": 3152726.0, "num_examples": 100}, {"name": "radiologic_technician", "num_bytes": 3559946.0, "num_examples": 100}, {"name": "social_worker", "num_bytes": 3433765.0, "num_examples": 100}, {"name": "nurse", "num_bytes": 2974989.0, "num_examples": 100}, {"name": "receptionist", "num_bytes": 2905913.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4171511.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3409309.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 3286300.0, "num_examples": 100}, {"name": "massage_therapist", "num_bytes": 2784826.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 3550315.0, "num_examples": 100}, {"name": "plane_mechanic", "num_bytes": 3976019.0, "num_examples": 100}], "download_size": 538604644, "dataset_size": 514762151.0}}
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2023-06-03T00:12:47+00:00
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f7a07c818533df75e2b91afb674c8f783332b5ce
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# Dataset Card for "prof_images_blip__wavymulder-Analog-Diffusion"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
sasha/prof_images_blip__wavymulder-Analog-Diffusion
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[
"region:us"
] |
2023-06-03T00:24:44+00:00
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{"dataset_info": {"features": [{"name": "images", "dtype": "image"}, {"name": "embeddings", "sequence": "float32"}], "splits": [{"name": "courier", "num_bytes": 3774069.0, "num_examples": 100}, {"name": "aide", "num_bytes": 3686691.0, "num_examples": 100}, {"name": "police_officer", "num_bytes": 3716514.0, "num_examples": 100}, {"name": "purchasing_agent", "num_bytes": 3374948.0, "num_examples": 100}, {"name": "metal_worker", "num_bytes": 4585929.0, "num_examples": 100}, {"name": "financial_analyst", "num_bytes": 3272085.0, "num_examples": 100}, {"name": "stocker", "num_bytes": 4284000.0, "num_examples": 100}, {"name": "it_specialist", "num_bytes": 3445262.0, "num_examples": 100}, {"name": "writer", "num_bytes": 4338105.0, "num_examples": 100}, {"name": "accountant", "num_bytes": 3273259.0, "num_examples": 100}, {"name": "coach", "num_bytes": 4333295.0, "num_examples": 100}, {"name": "painter", "num_bytes": 4207207.0, "num_examples": 100}, {"name": "real_estate_broker", "num_bytes": 3744904.0, "num_examples": 100}, {"name": "truck_driver", "num_bytes": 4744401.0, "num_examples": 100}, {"name": "data_entry_keyer", "num_bytes": 4750907.0, "num_examples": 100}, {"name": "computer_support_specialist", "num_bytes": 3220896.0, "num_examples": 100}, {"name": "cook", "num_bytes": 3507117.0, "num_examples": 100}, {"name": "interior_designer", "num_bytes": 3385993.0, "num_examples": 100}, {"name": "nutritionist", "num_bytes": 4499939.0, "num_examples": 100}, {"name": "designer", "num_bytes": 3262956.0, "num_examples": 100}, {"name": "maid", "num_bytes": 3688106.0, "num_examples": 100}, {"name": "producer", "num_bytes": 3855517.0, "num_examples": 100}, {"name": "executive_assistant", "num_bytes": 2956660.0, "num_examples": 100}, {"name": "logistician", "num_bytes": 3785521.0, "num_examples": 100}, {"name": "tractor_operator", "num_bytes": 6024318.0, "num_examples": 100}, {"name": "doctor", "num_bytes": 3241492.0, "num_examples": 100}, {"name": "inventory_clerk", "num_bytes": 3888705.0, "num_examples": 100}, {"name": "sheet_metal_worker", "num_bytes": 4317010.0, "num_examples": 100}, {"name": "groundskeeper", "num_bytes": 5131469.0, "num_examples": 100}, {"name": "electrical_engineer", "num_bytes": 4010184.0, "num_examples": 100}, {"name": "physical_therapist", "num_bytes": 3392181.0, "num_examples": 100}, {"name": "insurance_agent", "num_bytes": 3757883.0, "num_examples": 100}, {"name": "aerospace_engineer", "num_bytes": 3796254.0, "num_examples": 100}, {"name": "psychologist", "num_bytes": 3300681.0, "num_examples": 100}, {"name": "financial_advisor", "num_bytes": 3319034.0, "num_examples": 100}, {"name": "printing_press_operator", "num_bytes": 4371701.0, "num_examples": 100}, {"name": "architect", "num_bytes": 3624303.0, "num_examples": 100}, {"name": "dental_hygienist", "num_bytes": 3037225.0, "num_examples": 100}, {"name": "artist", "num_bytes": 4038195.0, "num_examples": 100}, {"name": "office_worker", "num_bytes": 3343369.0, "num_examples": 100}, 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"carpet_installer", "num_bytes": 4831490.0, "num_examples": 100}, {"name": "musician", "num_bytes": 3852103.0, "num_examples": 100}, {"name": "civil_engineer", "num_bytes": 4235637.0, "num_examples": 100}, {"name": "farmer", "num_bytes": 5696634.0, "num_examples": 100}, {"name": "financial_manager", "num_bytes": 2996790.0, "num_examples": 100}, {"name": "childcare_worker", "num_bytes": 4586909.0, "num_examples": 100}, {"name": "clerk", "num_bytes": 3629737.0, "num_examples": 100}, {"name": "machinist", "num_bytes": 3743309.0, "num_examples": 100}, {"name": "firefighter", "num_bytes": 4238724.0, "num_examples": 100}, {"name": "photographer", "num_bytes": 4709558.0, "num_examples": 100}, {"name": "file_clerk", "num_bytes": 4227425.0, "num_examples": 100}, {"name": "bus_driver", "num_bytes": 4414556.0, "num_examples": 100}, {"name": "fast_food_worker", "num_bytes": 3558196.0, "num_examples": 100}, {"name": "bartender", "num_bytes": 4220859.0, "num_examples": 100}, {"name": "computer_programmer", "num_bytes": 3728830.0, "num_examples": 100}, {"name": "pharmacist", "num_bytes": 3708399.0, "num_examples": 100}, {"name": "nursing_assistant", "num_bytes": 3099367.0, "num_examples": 100}, {"name": "career_counselor", "num_bytes": 3393143.0, "num_examples": 100}, {"name": "mental_health_counselor", "num_bytes": 3215113.0, "num_examples": 100}, {"name": "network_administrator", "num_bytes": 3856159.0, "num_examples": 100}, {"name": "teacher", "num_bytes": 4085339.0, "num_examples": 100}, {"name": "dishwasher", "num_bytes": 4261050.0, "num_examples": 100}, {"name": "teller", "num_bytes": 3322494.0, "num_examples": 100}, {"name": "teaching_assistant", "num_bytes": 3491372.0, "num_examples": 100}, {"name": "payroll_clerk", "num_bytes": 3263271.0, "num_examples": 100}, {"name": "laboratory_technician", "num_bytes": 3271155.0, "num_examples": 100}, {"name": "social_assistant", "num_bytes": 3774971.0, "num_examples": 100}, {"name": "radiologic_technician", "num_bytes": 3021348.0, "num_examples": 100}, {"name": "social_worker", "num_bytes": 4232132.0, "num_examples": 100}, {"name": "nurse", "num_bytes": 3272832.0, "num_examples": 100}, {"name": "receptionist", "num_bytes": 3134671.0, "num_examples": 100}, {"name": "carpenter", "num_bytes": 4402559.0, "num_examples": 100}, {"name": "correctional_officer", "num_bytes": 3789430.0, "num_examples": 100}, {"name": "community_manager", "num_bytes": 3756220.0, "num_examples": 100}, {"name": "massage_therapist", "num_bytes": 2980706.0, "num_examples": 100}, {"name": "head_cook", "num_bytes": 3919248.0, "num_examples": 100}, {"name": "plane_mechanic", "num_bytes": 3715118.0, "num_examples": 100}], "download_size": 581741855, "dataset_size": 557958672.0}}
|
2023-06-03T00:27:19+00:00
|
11e31215b82d1045da392573a5e08dd3d6859e4a
|
# Dataset Card for "Control-Face-data"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
PhilSad/Control-Face-data
|
[
"region:us"
] |
2023-06-03T00:26:39+00:00
|
{"dataset_info": {"features": [{"name": "gender", "dtype": "string"}, {"name": "conditionning_image", "dtype": "image"}, {"name": "objective_image", "dtype": "image"}, {"name": "caption", "dtype": "string"}, {"name": "pers_id", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 142522633.433, "num_examples": 10177}], "download_size": 138066980, "dataset_size": 142522633.433}}
|
2023-06-03T01:05:39+00:00
|
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